Developing State-Wide Modeling Guidance for the Use of
AERMOD – A Workgroup’s Experience
Paper #1302
Carolee Laffoon, Joey Rinaudo, Raghu Soule, and Ted Bowie
ENVIRON International Corporation, 8280 YMCA Plaza Dr., Bldg. 9, Baton Rouge, LA 70810
Chris Meyers
Trinity Consultants, 4000 S. Sherwood Forest Blvd., Suite 503, Baton Rouge, LA 70816
Richard L. Madura
RTP Environmental Associates, Inc., 285 W. Esplanade Ave., Suite 308, Kenner, LA 70065
Sirisak Patrick Pakunpanya
Louisiana Department of Environmental Quality, Office of Environmental Assessment, Air
Quality Assessment, P.O. Box 4314, Baton Rouge, LA 70821
ABSTRACT
For over two decades, the Industrial Source Complex (ISC) dispersion model has been the
primary model used to predict ambient air impacts from stationary sources, particularly in regard
to regulatory considerations when permitting new or modified sources. Recent advances in
dispersion modeling theory and computational power have led to major improvements over the
ISC model. These improvements resulted in the creation of a new type of modeling algorithm
referred to as the American Meteorological Society/U.S. Environmental Protection Agency
Regulatory Modeling System (AERMOD).
The U.S. Environmental Protection Agency (EPA) has proposed replacing ISC with the
AERMOD dispersion model for evaluating near-field impacts for regulatory purposes,
anticipating adopting it as the standard model by the end of 2004 and phasing out ISC in 2005.
This action will have numerous implications for industrial facilities and regulators. AERMOD
requires several additional geophysical meteorological input parameters that ISC does not utilize.
Much discussion has taken place as to how these parameters should be set, but little overall
guidance exists to assist modelers in determining their values. To address this lack of guidance,
Louisiana Department of Environmental Quality (LDEQ) formed a Modeling Workgroup to
evaluate issues with implementing AERMOD and to develop modeling guidelines for use with
AERMOD within the state of Louisiana.
Workgroup members performed several hypothetical case studies to (1) evaluate AERMOD’s
behavior in comparison with ISC, (2) compare use of on-site meteorological data to National
1
Weather Service (NWS) data, and (3) determine AERMOD’s sensitivity to changes in input
land-use parameters. The results of these analyses are being used to develop modeling
guidelines, including site-specific parameters required by AERMOD.
The paper summarizes the results of analyses performed by Workgroup members, as well as
recommendations made by the members. In addition, general differences between ISC and
AERMOD are discussed, including processing times, land-use parameters, meteorology inputs,
and treatment of terrain.
INTRODUCTION
Regulatory History
In the 1977 Clean Air Act (CAA), Congress mandated consistency in the application of air
quality models for regulatory purposes, fulfilling the needs of industry and control agencies and
encouraging the standardization of model applications. The Guideline on Air Quality Models (or
simply Guideline), found in 40 CFR 51 Appendix W,1 was first published in April 1978 and was
incorporated by reference in the regulations for the Prevention of Significant Deterioration (PSD)
of Air Quality in June 1978. The Guideline was revised in 1986, updated with supplements in
1987, revised further in July 1993 in concurrence with being published as appendix W to 40 CFR
Part 51, revised again in August 1995, and republished in August 1996. The Guideline is used
by EPA, States, and industry to prepare and review air quality analyses requiring regulatory
models such as new source review (NSR) permits and Sate Implementation Plan (SIP) revisions.
The Industrial Source Complex (ISC) model, in various versions (the most current version is
Industrial Source Complex Short Term Version 3 [ISCST3]), has served as the United States
Environmental Protection Agency’s (U.S. EPA’s) basic regulatory model over the last two
decades and has been extensively used to predict ambient air concentrations from industrial
sources. Due to the various limitations and inadequacies of the ISC model recognized during the
early years after its implementation, as described below, the American Meteorological Society
(AMS) and the EPA initiated a formal collaboration in 1991 with the designated goal of
introducing recent advances in handling boundary layer conditions.2 The AMS/EPA Regulatory
Model Improvement Committee (AERMIC) formulated AERMOD and EPA proposed several
changes to the Guideline, including (i) adopting AERMOD to replace ISCST3 as the regulatory
model, (ii) revising ISCST3 by incorporating a new building downwash algorithm incorporating
Plume Rise Model Enhancements (PRIME) and renaming the model ISC-PRIME, and (iii)
updating the Emissions and Dispersion Modeling System (EDMS 3.1) in appendix A of the
Guideline.3 In April 2003,4 EPA promulgated other previously proposed changes but deferred
all of the aforementioned actions in order to address several significant public comments in
response to the 2000 proposed changes. EPA later published a notice of additional information
in response to public comments.5 Upon promulgation, it is anticipated that the EPA will allow a
one-year transition period from the effective date, during which either of the two models
(ISCST3 or AERMOD) can be used for regulatory-driven air quality modeling analyses. After
the end of the transition period, AERMOD will be the recommended regulatory model to be used
for regulatory air quality analyses.
2
Technical Description of Model
Several shortcomings of the ISC model necessitated the need for developing a more
sophisticated model.6 ISCST3 is best described as a Gaussian steady-state plume dispersion
model with a minimum one-hour timestep, meaning the model calculates downwind
concentrations for each hour of the modeled period treating the meteorological conditions as
constant throughout the modeled domain and over time, ignoring the fact that the atmosphere is
continuously varying spatially and over time and neglecting boundary layer effects. Other
shortcomings of the ISCST3 model include poor characterization of building downwash and
terrain features, a lack of chemical transformation and physical removal processes, and a useful
range limit of approximately 40 to 50 kilometers. Simple terrain includes receptors with
elevations below the top of the stack and at elevations above or below the stack base.
Intermediate terrain includes receptors above stack top and below the plume centerline.
Complex terrain includes receptors with elevations above the stack top. AERMOD was
developed to overcome all of the aforementioned shortcomings. The AERMOD modeling
system has 3 components: AERMOD - the air dispersion model; AERMET - the meteorological
data preprocessor; and AERMAP - the terrain data preprocessor. EPA has described AERMOD
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: (i) dispersion in both the convective and stable boundary layers; (ii)
plume rise and buoyancy; (iii) plume penetration into elevated inversions; (iv) treatment of
elevated, near-surface, and surface level sources; (v) computation of vertical profiles of wind,
turbulence, and temperature; and (vi) the treatment of receptors on all types of terrain (from
surface up to and above the plume height). More recently, the PRIME algorithm was
implemented into AERMOD to use the AERMOD meteorological profiles and to use the
ambient turbulence intensities based on the AERMOD profiles.2
Reason for Workgroup
Several states, including Louisiana, have set up local workgroups to: (i) conduct, compare, and
discuss “case studies” to analyze the differences between ISCST3 and AERMOD including
varying scenarios, i.e., meteorological data, land-use, source types, data point size (no. of
sources), etc., (ii) assist the state in developing a strategy to implement the use of AERMOD in
the state’s modeling guidelines, (iii) discuss issues, news, and/or questions on the AERMOD
model, and iv) identify potential permitting and financial impacts on industry due to the
differences between ISCST3 and AERMOD. Unlike for ISCST3, pre-processed “model-ready”
meteorological data can not be used for AERMOD; data must first be processed with the
AERMET pre-processor prior to use in AERMOD. In comparison with ISCST3, AERMOD
requires several additional meteorological parameters, including albedo, Bowen ratio, and
surface roughness, which are site-specific and need to be determined on a case-by-case basis
based on a protocol recommended by the EPA. Since this can be confusing, the workgroup’s
specific objectives were to review the use of these parameters, test the sensitivity of these
parameters, and attempt to develop a state-specific protocol for determining the appropriate
values to use for specific geographic/topographic locations. Among other topics that were
reviewed by the workgroup was evaluation of the need to include digital elevation model (DEM)
terrain data, since most of Louisiana is near sea level with flat terrain.
3
Activities Conducted by the Workgroup
In order to address the goals of the Workgroup, the following analyses were performed by the
workgroup:

The first case-study compared concentrations predicted by ISCST3 with AERMOD-PRIME,
for an urban setting with flat terrain and using National Weather Service (NWS)
meteorological data, in order to compare concentration distribution, model run time, and
isopleths. The sensitivity of the meteorological variables, i.e., albedo, Bowen ratio, and
surface roughness was investigated. The model run-times were also compared.

The second case-study accomplished the same objectives as the first case-study albeit
comparing AERMOD-PRIME with ISC-PRIME for a rural setting with slight terrain. The
model run times were also compared.

The third case-study compared results from AERMOD-PRIME using NWS and on-site
meteorological data. Also, a comparison of ISC-PRIME results from NWS and on-site
meteorological data was performed. AERMOD-PRIME results were compared using no
terrain and importing terrain at a relatively flat site. Issues related to the use of onsite
meteorological data were identified. The model run times were also compared.

The fourth case study performed similar comparisons to the first two case studies, albeit at a
facility located in a marshy, rural setting and compared ISCST3 with AERMOD-PRIME.
The model run times were also compared.
Summary of Significant Findings
The following observations were made by the workgroup in the course of conducting the
aforementioned analyses: (i) AERMOD run-time is significantly greater than that of ISC models,
(ii) more complex meteorological variables are used by AERMOD compared to ISC models
making it difficult to predict the impact of variation in meteorological parameters on the
concentrations, (iii) use of AERMOD meteorological parameters may not be appropriate for all
averaging periods; it may be necessary to break runs up into seasons for shorter averaging
periods, and (iv) AERMOD modeling generates significantly greater number of files compared
to ISC models and is, in general, a significantly bulkier process than ISC.
Two of the case studies indicated that AERMOD consistently produced lower maximum
concentrations than ISC. The remaining two case studies, which used the same meteorological
data set, indicated that AERMOD consistently produced higher maximum concentrations than
ISC. The sensitivity analyses performed clearly indicated that surface roughness length is the
most sensitive AERMOD meteorological parameter. Also, a “break-even point” was observed
for this parameter, where the predicted concentrations were found to increase dramatically with
the decrease in surface roughness length below a certain threshold. Specifically, modeled
concentrations plotted against the surface roughness values indicated that there was a gradual
increase in modeled concentrations with a decrease in surface roughness values until the value
dropped below the 0.3 – 0.35 range, when the modeled concentrations increased by five to ten
orders of magnitude. The second case study did not agree with some of the literature findings.
4
One of the literature studies9 concluded that the ratio of AERMOD to ISCST3 concentrations
tends to increase as the length of the averaging period increases for sources in rural flat terrain.
The second case study did not necessarily agree with this observation although the modeled
terrain was moderately hilly (almost flat terrain). Another literature study10 concluded that for
area sources, the concentrations predicted by AERMOD are approximately equal to those
predicted by ISCST3 model. When the area sources were modeled as an independent group in
the second case study, the differences between AERMOD and ISC were not insignificant. In the
third case study, AERMOD predicted concentrations greater than ISC when utilizing on-site
meteorological data for the annual averaging period and for the 24-hr averaging period with just
one outlying meteorological year. The fourth case study concluded that the correlation between
run time and number of receptors was not linear and also when seasonal periods are analyzed,
meteorological pre-processing can be a time-intensive task. The results are covered in greater
detail in the Results and Discussion section of this paper.
LITERATURE REVIEW
Several studies and observations presented in literature were reviewed by the Workgroup for the
purpose of identifying previous similar analyses that have been performed and comparing to
Workgroup observations. EPA conducted several studies to compare the AERMOD versus ISC
analysis performed prior to the April 2000 Federal Register publication with the AERMODPRIME versus ISC analysis conducted after the Federal Register publication. In AERMOD:
Latest Features and Evaluation Results,2 comparisons of model estimates with measured air
quality concentrations for a variety of source types and locations were provided. An evaluation
of AERMOD (version 02222) versus ISCST3 was performed for sulfur dioxide (SO2) for ten
non-downwash databases in two phases: (i) the developmental evaluation, which was performed
concurrently with model development for five databases, and (ii) independent evaluation to
avoid any bias effects for five additional databases. Three short-term tracer studies and two
conventional long-term monitoring databases in the developmental evaluation phase and one
tracer study and four long-term monitoring databases in the independent evaluation phase were
employed, in a variety of settings, for the purpose of generating observed concentrations. The
databases included a variety of pollutant source types including, (i) flat terrain, moderately hilly
terrain, hilly terrain, and grassy field, (ii) rural and urban environments, and (iii) near-surface
non-buoyant and elevated buoyant releases. For the tracer databases, results for 1-hour averages
were reported with the exception of one database, where 10-minute measurements were used.
For the long term SO2 data sets, 3-hour, 24-hour, and annual results were reported. The EPA reran the AERMOD and ISCST3 models for all the aforementioned databases and generated the
“ratio of modeled-to-observed Robust Highest Concentrations (RHCs).” The RHC served as a
robust test statistic for assessing the difference between AERMOD and ISCST3 and represented
a smoothed estimate of the highest concentrations, based on a tail exponential fit to the upper end
of the concentration distribution. This procedure was used to reduce the effect of extreme values
on model comparison. It was concluded from this study that (i) the performance of the revised
version of AERMOD (02222) is slightly better than the April 2000 proposal and both versions of
AERMOD significantly outperform ISCST3 as compared to monitored observations, and (ii)
AERMOD (02222) with PRIME performs slightly better than ISC-PRIME for aerodynamic
downwash cases.
5
Another document compiled by the EPA, Comparison of Regulatory Design Concentrations,7
documents a consequence analysis of effects on design concentrations and provides comparisons
of design concentrations (on which emission control limits might be based) for a wide variety of
source configurations and settings. There were three parts to this study: (i) the flat and simple
terrain component; (ii) the building downwash component, and (iii) the complex terrain
component. The flat and simple terrain consequence analysis was based on comparative runs
made using a composite of standard data sets. These data sets include a range of point sources
with varying stack parameters, area and volume sources, and two point sources in simple terrain.
All source scenarios were evaluated with two meteorological data sets representing different
climatic regimes in the U.S. For building downwash, a series of point sources with varying stack
heights and different building configurations were included in the data sets. For the complex
terrain, the study included a number of stack heights, buoyancy regimes, distances from source
to hill, and hill types along with its own meteorological database. Observations from the “flat
and simple terrain” analysis included: (i) AERMOD predicted lower than ISCST3 for low level
stack in rural environments; (ii) AERMOD predicted higher than ISCST3 for taller stacks in
rural environments for long-term averaging periods; (iii) AERMOD predicted lower than
ISCST3 for urban short stacks and area sources for the short-term averaging period. In
summary, the analysis indicated that: (i) for non-downwash settings, the revised version of
AERMOD (02222), on average, tends to predict concentrations closer to ISCST3 with somewhat
smaller variations than the April 2000 proposal of AERMOD, (ii) where downwash is a
significant factor in the air dispersion analysis, the revised version of AERMOD predicts
maximum concentrations that are very similar to ISC-PRIME; (iii) for those source scenarios
where maximum 1-hour cavity concentrations are calculated, the average AERMOD predicted
cavity concentration tends to be about the same as the average ISC-PRIME cavity
concentrations; and (iv) in general, the consequences of using the revised AERMOD, instead of
the older model ISCST3, in complex terrain remained essentially unchanged, although they
varied in individual circumstances.
Literature observations related to the Sensitivity of AERMOD to land-use parameters8 concluded
that: (i) for surface sources, only surface roughness length affects the modeled concentrations
significantly. The albedo and Bowen ratio have little or no effect on the annual modeled
concentration. (The modeled short-term results for this study occurred at night when the albedo
and Bowen ratio have no effect), (ii) for elevated stacks (case evaluated: 35 meters), all three
land-use parameters affect modeled concentrations with albedo still having a relatively small
effect and surface roughness still having the largest effect, and (iii) for very tall stacks (case
evaluated: 100 meters), varying the land-use parameters had varied effect on the modeled
concentrations. The authors of this study concluded that the effects these parameters have on the
modeled design concentrations are sufficiently complex that it cannot be accurately anticipated
what effect any change in those values will have on design concentrations for a given source
configuration. The authors advise that reasonably accurate estimates of albedo, Bowen ratio, and
surface roughness lengths are necessary for AERMOD to provide accurate results.
A study6 related to the “run-time” issues and comparison of results to ISCST3 observed that the
enhanced algorithms of AERMOD require significantly more computer time to run. AERMAP
can, by itself, require a substantial amount of time to process and set up the modeling domain.
AERMOD was observed to typically yield lower concentrations than ISCST3 when nearby
complex terrain is present, but can yield higher concentrations in other terrain regimes.
6
Another published literature9 observed that AERMOD tends to predict lower concentrations than
ISCST3 for shorter stacks (less than 20 meters) in rural conditions and that the ratio of
AERMOD to ISCST3 concentrations tends to increase as the length of the averaging period
increases for sources in rural flat terrain.
Yet another study10 concluded that for point sources, the results varied depending on stack
height, urban/rural, and terrain configurations; and for area sources, AERMOD’s predictions
were almost identical to that of ISCST3.
EPA and the American Meteorological Society (AMS) performed an evaluation of AERMOD
with different met data sets in a 1998 study11 that showed the use of onsite data appeared to
overpredict results, often by a factor of two.
MATERIALS AND METHODS
All model runs were performed with ISCST and AERMOD. Building downwash was obtained
from either the Building Profile Input Program (BPIP) or the Building Profile Input Program
with the PRIME downwash algorithm (BPIP-PRIME). Table 1 identifies the model
specifications and hardware used in each test case.
Table 1. Model and Hardware Specifications.
Test
Case
Case
Study 1
ISC
02035 with BPIP
95086
Software
AERMOD
02222 with BPIPPRIME 95086
Case
Study 2
01228 with BPIPPRIME 95086
03273 with BPIPPRIME 95086
Case
Study 3
02035 with BPIPPRIME 95086
02222 with BPIPPRIME 95086
Case
Study 4
00101 with BPIP
95086
02222 with BPIPPRIME 95086
Computers Used
ISC - Mix 500, 900, and 1800
Mhz
AERMOD - 1.8 GHz Dual
Processor
ISC – Dell Inspiron, 650 MHz
CPU, 256Mb RAM
AERMOD – Compaq Evo
W6000 Workstation, Dual
Xenon 2.0 GHz CPUs, 512 Mb
RAM
ISC – HP Vectra XE310, 1.2
GHz Celeron CPU, 512 Mb
RAM
AERMOD – Dell Dimension
8300, 3.2 GHz Pentium 4 CPU,
2 Gb RAM
Dell Inspiron 8200, 2 GHz
Pentium 4 CPU, 256k RAM
Input meteorological and terrain data were obtained from a number of sources. The types and
sources of data are shown in Table 2.
7
Table 2. Meteorological and Terrain Data.
Test
Case
Case
Study 1
Meteorological Data
Type
Source
NWS
Surface Air – Baton Rouge,LA
Data
Upper Air – Lake Charles, LA
Case
Study 2
NWS
Data
Surface Air – Shreveport, LA
Upper Air – Longview, TX
Case
Study 3
NWS
Data
On-Site
Data
Surface Air – New Orleans, LA
Upper Air – Slidell, LA
River Region Environmental
Association Hahnville, LA Station
NWS
Data
Surface Air – New Orleans, LA
Upper Air – Slidell, LA
Case
Study 4
Terrain Data
Type
ISC - none
AERMOD none
ISC AERMOD –
7.5 x 7.5
min DEM
(1:24,000
scale)
ISC - none
AERMOD –
7.5 x 7.5
min DEM
(1:24,000
scale)
ISC - none
AERMOD –
7.5 x 7.5
min DEM
(1:24,000
scale)
Source
NA
NA
NA
Geo
Community
Web Site11
NA
Atlas Web
Site12
Site, Source and Input Information
Case Study 1
A hypothetical facility located in south Louisiana was modeled to determine the differences
between AERMOD and ISC modeling results. This facility included information from various
Louisiana companies and focused on PSD sources. Additionally, a sensitivity analysis was
performed to determine the effect of varying AERMOD model inputs.
The hypothetical facility center was located between Lafayette and Baton Rouge, Louisiana.
The facility includes 47 point sources that are representative of PSD sources including boilers
and flares. Stack heights for on-site sources range from 25 and 245 feet. 2916 receptors were
included in the modeling domain.
Meteorological data from 1997–2001 was used in both the AERMOD and ISC models. Surface
air data from the Automated Surface Observation Station (ASOS) located at the Baton Rouge
airport was used. Upper air data from the monitoring station in Lake Charles, Louisiana was
used. Missing data was filled with meteorological data from the Baker, Louisiana monitoring
site and the Capitol monitoring site located in Baton Rouge.
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A Land Use/Land Classification (LULC) analysis was performed and the meteorological data
was appropriately processed for use in the AERMOD model. The ISC modeling was performed
using urban land-use classification parameters. Additionally, a series of AERMOD model runs
were set up by varying albedo, Bowen ratio, and surface roughness values from their respective
minimum and maximum values to determine what effect, if any, these parameters would have on
AERMOD model results.
The BPIP-PRIME algorithm was used in the AERMOD model analysis. The standard BPIP
algorithm was used in the ISC model analysis. A hypothetical pollutant was modeled at 3-hour,
24-hour, and annual averaging periods. Maximum short-term emission rates were used for all
sources in the short-term analyses (and what about long-term averaging perid? - the annual
average emission rate..).
Case Study 2
A hypothetical facility located in Northern Louisiana was modeled to determine the differences
between AERMOD and ISC modeling results. The facility is located in a marshy valley between
a river and a bayou. Elevation changes of up to 60 feet are present in the surrounding area.
Additional model runs were setup to determine what effect, if any, varying the AERMOD
meteorological parameters would have, as well as the effect of running the AERMOD model
with and without terrain data.
The hypothetical facility includes five point sources, three area sources, and fifty-two volume
sources. Point sources included in this analysis are representative of two large boilers, two
cyclones, and a cogeneration facility. Stack heights for on-site sources range from 50 to 66 feet.
Area sources represent two log piles and a bark hog. Fugitive emissions from unpaved roads are
included as a series of volume sources. A regional emissions inventory including 20 point
sources representing miscellaneous combustion sources, cyclones, and fugitive emissions was
also included for both the AERMOD and ISC modeling analyses. 934 receptors were included
in the modeling domain (it is also important to state the interval/distance of separation between
rceptors)..
Meteorological data from 1990-1994 was used in both the AERMOD and ISC models. Surface
air data from the National Weather Service Station located at the Shreveport airport was used.
These data were collected prior to the installation of the ASOS. Upper air data from the
monitoring station in Longview, Texas was used. Surface air data was complete, but upper air
data was completed according to U.S. EPA guidelines using interpolation.
An LULC analysis was performed and the meteorological data was appropriately processed for
use in the AERMOD model. The ISC model was performed using rural land-use classification
parameters. U.S. Geological Survey (USGS) maps were used to perform the land-use analyses.
Additionally, a series of AERMOD model runs were setup by varying albedo, Bowen ratio, and
surface roughness from their respective minimum and maximum values to determine what effect,
if any, these parameters would have on AERMOD model results.
USGS 7.5 min x 7.5 min DEM Data (1:24,000 scale) was used in both the AERMOD and ISC
models. AERMAP was used to import terrain data for all model objects in the AERMOD-
9
PRIME model and to generate hill-height scale data that was used to drive advanced terrain
processing algorithms. Additionally, a second set of AERMOD models was set up without
importing the terrain data with AERMAP to determine whether or not the use of this data would
affect model results.
The BPIP-PRIME algorithm was used in both AERMOD and ISC modeling analyses. A
hypothetical pollutant was modeled at 3-hour, 24-hour, and annual averaging periods. Maximum
short-term emission rates were used for all sources in the analyses.
Case Study 3
A hypothetical facility was located in Southeastern Louisiana along the bank of the Mississippi
River near a meteorological data monitoring station that served as the source of the onsite
meteorological data used in some of the model runs for this case study. The main goals of this
effort were to determine the differences in utilization of AERMOD vs. ISC with varying
meteorological data and dispersion coefficients. NWS meteorological data and site-specific
meteorological data were used as the data sources in the meteorological data comparison. The
dispersion coefficient scenario compared rural dispersion coefficients and urban dispersion
coefficients in ISC to represent the “heat island” effect occasionally assumed for Louisiana
facilities. A third goal of this scenario was to determine the effects of running an AERMOD
model with the inclusion of terrain data for an area that is relatively flat, as opposed to excluding
the terrain data.
The hypothetical facility contained a total of forty-four point sources and three area sources.
These sources are representative of combustion devices, particulate control devises, storage
tanks, wastewater treatment equipment, and fugitive emissions. Hypothetical structure
downwash effects were also included with the scenario, as this is a common situation with most
facilities of this type. The BPIP-PRIME algorithm was used to estimate these effects in both the
ISC and AERMOD model runs.
The receptor grid for this scenario was laid out according to current LDEQ modeling guidelines
of 100 m receptor spacing along the facility fence line and a Cartesian grid with 100m receptor
spacing out 1km from the facility and 1km receptor spacing out 10km from the facility. For the
AERMOD model runs, terrain data was included such that the entire modeled area accounted for
its appropriate elevation. The terrain data for these analyses was obtained from USGS DEMs. It
should be noted that the terrain in Southeastern Louisiana is relatively flat; in fact the most
significant topographical feature within the receptor grid of these analyses is the levee system
that runs along the Mississippi River.
An LULC analysis of the area was performed utilizing the most recent aerial photographs
available along with a general knowledge of the topography of the area. This LULC analysis
provided a basis for estimation of the seasonal albedo, Bowen ratio, and surface roughness
coefficients. This data was then appropriately processed along with the different sets of
meteorological data for inclusion in the AERMOD models.
Meteorological data from 1998-2002 was used in both the AERMOD and ISC models. Surface
air data from the ASOS located at the New Orleans International Airport and upper air data from
10
the Slidell Weather Service Meteorological Observatory (WSMO) were used in the NWS
scenarios. The onsite met data used was collected from the River Regional Environmental
Association station located in Hahnville, Louisiana.
Ground level concentrations of a hypothetical pollutant were then estimated for the 3-hour, 24hour, and annual averaging periods. Maximum short-term emission rates were used for all
sources in the analyses, as appropriate.
Case Study 4
A hypothetical manufacturing facility with two nearby facilities located in open marshland south
of New Orleans was modeled to determine the differences between AERMOD and ISC modeling
results. The domain overlapped Universal Transverse Mercator (UTM) Zones 15 and 16. The
site is characterized as open, treeless marshland with relatively no elevation change and
alternating land and water. This location was chosen in order to evaluate the availability and
quality of data for a remote region, and to determine what effect, if any, including terrain data
would have on the AERMOD model results.
The hypothetical facility included ten point sources and two area sources. Sources typical in this
area of the state would include boilers, gas turbines, tanks, and other miscellaneous combustion
sources. Stack heights for on-site sources range from 4 to 70 feet. Area sources represent
fugitive emission sources. A regional emissions inventory including two point sources at an
adjacent facility to the southwest and six point sources at a facility located approximately two
kilometers to the southwest were also included for both the AERMOD and ISC modeling
analyses. 2.25 million receptors were included in the modeling domain, simulating a large PSD
modeling project.
Meteorological data from 1998-2002 was used in both the AERMOD and ISC models. Surface
air data from the ASOS located at the New Orleans International Airport was used. Upper air
data from the monitoring station in Slidell, Louisiana was used. Where necessary, all data was
completed according to U.S. EPA guidelines using interpolation.
An LULC analysis was performed and the meteorological data was appropriately processed for
use in the AERMOD model. The ISC model was performed using rural land-use classification
parameters. USGS maps were used to perform the LULC/Land-use analyses. Albedo, Bowen
ratio, and surface roughness were calculated seasonally based on values provided in the
AERMOD Manual for water and swamp.
USGS 7.5 min x 7.5 min DEM Data (1:24,000 scale) was obtained from the Atlas web site12 and
used in the AERMOD model. As the area is essentially flat, no terrain data was used in the ISC
model. AERMAP was used to import terrain data for all model objects in the AERMOD-PRIME
model and to generate hill-height scale data that was used to derive advanced terrain processing
algorithms.
The BPIP-PRIME algorithm was used in the AERMOD modeling analysis, and BPIP was used
in the ISC modeling analysis. A hypothetical pollutant was modeled at 3-hour, 24-hour, and
annual averaging periods. Maximum short-term emission rates were used for all sources in the
analyses.
11
RESULTS AND DISCUSSION
The sites modeled by the Workgroup were intended to provide a representative sampling of the
various terrain types located in Louisiana, which include marsh, swamp, coastal plains, coastal
savannah, river valleys, and rolling terrain. Prior to the performance of AERMOD, it was
necessary to run the AERMET preprocessor to obtain input files for the meteorological data. In
order to obtain the surface parameters, an LULC analysis must be performed using available
information relevant to the terrain, land use, population density, and other physical
characteristics. The LULC analysis can be performed by a number of means, including use of an
area-weighted average, a distance-weighted average, or other appropriate methodology. All case
studies performed by the Workgroup used an area-weighted approach to the LULC analysis.
Based on the LULC analyses performed for the individual sites, site-specific surface parameters
were developed for each site and used in the base case AERMOD runs for comparison to ISC.
In addition, several studies varied the surface parameters, generally from the minimum to
maximum values presented in the tables provided in the AERMOD user’s manual. Table 3
shows the annual average surface parameters calculated for the four test cases and the ranges of
surface parameters used in the studies.
Table 3. Surface Parameters used in AERMET.
Test
Case
Case
Study 1
Case
Study 2
Case
Study 3
Case
Study 4
Albedo
Bowen Ratio
Site: 0.21
Site: 2.24
Min/Max: 0.05-0.95 Min/Max: 0.1-10
Site: 0.2
Site: 0.62
Min/Max: 0.05-0.95 Min/Max: 0.1-10
Site: 0.15
Site: 0.65
Min/Max: 0.13-0.17 Min/Max: 0.2-1.26
Site: 0.16
Site: 0.175
Surface Roughness
Site: 0.74
Min/Max: 0.001-1.3
Site: 0.34
Min/Max: 0.001-1.3
Site: 0.44
Min/Max: 0.14-0.77
Site: 0.0813
In general, USGS topographic maps were used to develop the LULC. One observation made is
that USGS topographic maps for remote locations in Louisiana are not updated as frequently as
those containing developed areas, and some of the topographic maps referenced for coastal
Louisiana were out of date. Due to the significant coastal erosion occurring in Louisiana’s
coastal wetlands, it is likely that major differences exist between the information presented in the
topographic maps and the terrain currently present. Aerial photos of coastal Louisiana are
available from several sources, including LaCoast13 in conjuction with the USGS National
Wetlands Research Center. However, these aerial photos are updated infrequently and may not
be representative of conditions currently existing. In these cases, the most recent data available
was used for the LULC analyses.
DEM files for the AERMAP preprocessor were readily available from a number of sources, and
several sources were used, including the GeoCommunity web site11 and the Atlas web site12.
DEMs from the Atlas site are provided in the “new” format, and the CRLF program in the
AERMAP package (demfilz) was necessary to convert the files into the proper input format.
12
Some DEMs for the remote site south of New Orleans were not available from the Atlas site, and
AERMAP was performed without a DEM for this location.
In addition to the efforts required to prepare and perform the AERMET and AERMAP
preprocessors, a point of interest of the Workgroup was the computer resources necessary to run
AERMOD as compared to ISC. AERMOD was found to take four to thirteen times longer to run
than ISC, with run times ranging from 1.5 hours for small receptor grids to 22.9 hours for large
receptor grids. Considering the additional time required to prepare and perform the
preprocessors, overall preparation and modeling time can be expected to significantly increase
the time required to prepare and run AERMOD. Table 4 shows the typical run times for ISC and
AERMOD under the various test cases.
Table 4. Comparison of Run Times.
Test Case
Case Study 1
Case Study 2
Case Study 3
Case Study 4
*ISC-PRIME
3-hr
ISC
AERMOD
21 min
4.1 hr
22.1 min*
2.1 hr
20-30 min* 1.5 hr
3.5 hr
22.1 hr
Averaging Period
24-hr
ISC
AERMOD
21 min
4.1 hr
22.1 min*
2.1 hr
20-30 min* 1.5 hr
2.5 hr
22.9 hr
Annual
ISC
AERMOD
18 min
4.5 hr
22.1 min*
2.1 hr
20-30 min* 1.5 hr
1.67 hr
22.5 hr
All four cases performed model runs with both ISC and AERMOD using NWS meteorological
data, with Case Study 3 also utilizing local meteorlogical data. Table 5 shows maximum off-site
concentrations in g/m3 obtained in the model runs using the calculated annual average surface
parameters and NWS meteorological data. The test cases fell into two categories-sites where
ISC predicted greater concentrations than AERMOD and sites where AERMOD predicted
greater concentrations than ISC. Case Studies 1 and 2 both demonstrated lower maximum
concentrations from AERMOD than from ISC, with the exception of a single meteorological
year in Case Study 2. Case Studies 3 and 4 both demonstrated higher maximum concentrations
from AERMOD than from ISC, with the exception of one averaging period in a single
meteorological year in Case Study 4. Two possible causes have been identified explaining this
difference. First, Case Studies 3 and 4 both used New Orleans surface meteorological data and
Slidell upper air data. Both stations are relatively near the coastline, which appears to influence
the modeling results. Second, Case Study 4 incorporates an extremely low surface roughness
factor due to the prevalence of open water and marsh in the area. However, Case Study 3
incorporates a medium surface roughness factor, possibly indicating that meteorology in the
coastal area plays a greater role than surface factors in AERMOD. Interestingly, the isopleths in
Case Study 4 were generally smaller for the AERMOD runs than for the ISC runs for all
averaging periods, consistent with Case Studies 1 and 2, in spite of AERMOD predicting higher
maximum off-site concentrations.
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Table 5. Maximum Receptor Concentrations Using NWS Meteorological Data.
Test Case
Case Study 1
Case Study 2
Case Study 3
Case Study 4
Year
1997
1998
1999
2000
2001
1990
1991
1992
1993
1994
1998
1999
2000
2001
2002
1998
1999
2000
2001
2002
3-hr
ISC
243.2
229.4
256.9
220.73
225.2
664.7
520.2
476.3
485.3
410.6
128.33
114.10
142.54
139.40
125.06
1700.51
1921.68
5679.37
4486.71
3155.76
Averaging Period
24-hr
Annual
AERMOD ISC
AERMOD ISC
AERMOD
134.9
92.8
53.2
17.2
12.0
133.5
92.0
37.8
18.2
12.2
117.9
86.9
39.6
17.6
11.7
91.63
79.3
38.4
17.3
11.6
107.4
85.8
38.1
20.3
13.4
714.1
173.4
205.6
25.1
30.5
521.5
133.1
116.3
29.4
23.9
348
153.8
131.4
28.5
23.9
512.0
160.6
99.1
21.5
17.0
394.4
145.9
114.8
29.0
23.5
159.38
665.00
492.37
15.65
35.45
205.15
556.34
782.27
16.39
36.10
184.83
640.54
635.19
17.32
33.04
237.95
624.18
778.95
18.89
59.29
169.88
562.88
622.60
14.92
33.65
2494.82
408.50
641.64
18.68
25.43
3588.53
404.83
647.70
21.60
29.29
3274.34
750.91
514.28
19.17
26.25
644.79
20.59
31.71
406.14
20.52
-
Case Study 3 also performed ISC and AERMOD runs using on-site meteorological data obtained
from the River Regions Environmental Association meteorological station located in Hahnville,
LA, approximately 10 miles west-southwest of the New Orleans International Airport station.
Table 6 shows the difference between the ISC results when on-site meteorological data is used as
compared to NWS data for a single receptor. The on-site data produced concentrations ranging
from approximately 78-150 % greater than the NWS data. Table 7 shows the difference between
the AERMOD results when on-site meteorological data is used as compared to NWS data for a
single receptor. As with the ISC runs, the on-site data produced significantly greater
concentrations than the NWS data, ranging from approximately 245-480% greater. An earlier
EPA/AMS study11 showed that the use of on-site meteorological data overpredicted results by as
much as 200%. In this case, the station used was capable of collecting data in a single layer,
possibly leading to the higher concentrations.
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Table 6. Comparison of NWS vs. On-Site Meteorological Data in ISC.
Year
3-hr
1998
1999
2000
2001
2002
Average
Difference
NWS
347.95
340.16
304.45
300.43
318.52
On-Site
665.00
556.34
640.54
624.18
562.88
89.91%
Averaging Period
24-hr
NWS
On-Site
74.58
128.33
79.69
114.10
84.88
142.54
65.70
139.40
64.33
125.06
Annual
NWS
On-Site
7.24
15.65
6.64
16.39
6.77
17.32
6.00
18.89
7.28
14.92
77.95%
147.78%
Table 7. Comparison of NWS vs. On-Site Meteorological Data in AERMOD.
Year
3-hr
1998
1999
2000
2001
2002
Average
Difference
NWS
196.12
167.36
187.09
214.76
198.63
On-Site
492.37
782.27
635.19
778.95
622.60
246.83%
Averaging Period
24-hr
NWS
On-Site
67.57
159.38
53.49
205.15
48.48
184.83
41.21
237.95
59.68
169.88
Annual
NWS
On-Site
6.83
35.45
6.70
36.10
6.74
33.04
6.45
59.29
7.58
33.65
272.53%
482.23%
The Workgroup was tasked with evaluating impacts resulting from variation of the surface
parameters input to AERMET. Case Studies 1 and 2 performed these evaluations. Table 8
shows the results of the sensitivity analyses performed on the surface parameters in Case Study
1, and Table 9 shows the results of the sensitivity analyses performed on the surface parameters
in Case Study 2. In both cases, albedo and Bowen ratio were found to have little to no impact on
the maximum off-site concentrations. However, surface roughness was found to significantly
affect the results, with concentrations increasing from two to eight times for the 3-hr and 24-hr
averaging periods when comparing low surface roughness to high surface roughness.
Interestingly, while concentrations for the annual average period in Case Study 2 were consistent
with the other averaging periods, concentrations were lower for low surface roughness in Case
Study 1. It is possible that this anomaly can be attributed it to possible meteorological or sitespecific terrain effects. It is also possible that the high stacks in this case study may have
impacted this result.
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Table 8. Surface Parameter Sensitivity Analysis for Case Study 1.
Averaging Period
3-hr
24-hr
Annual
AERMOD Input Parameter
Albedo
Bowen Ratio
Surface Roughness
0.05
0.95
0.1
10
0.001
1.3
134.9
134.9 134.9
134.9
413.4
133.2
52.9
54.0
53.4
53.1
101.1
50.4
12.3
8.4
9.9
12.2
8.9
15.2
Table 9. Surface Parameter Sensitivity Analysis for Case Study 2.
Averaging Period
3-hr
24-hr
Annual
Albedo
0.05
0.95
714.1
714.1
204.4
231.1
30.1
36.4
AERMOD Input Parameter
Bowen Ratio
Surface Roughness
0.1
10
0.001
1.3
714.1
714.1 1729.0
213.4
207.4
205.8
507.5
105.5
30.6
30.0
56.5
19.5
Louisiana is comparatively flat, and it is common to not incorporate terrain data in ISC runs due
to the gradual, if any, variations in elevation. However, the Workgroup was interested in
evaluating the performance of AERMOD using the non-default FLAT terrain option as
compared to incorporating DEM data. Case Study 3 performed this evaluation, and Table 10
shows the effects of running AERMOD with terrain and with the non-default FLAT terrain
option. The FLAT terrain option was found to produce concentrations ranging from 20.9840.90% greater than runs incorporating DEM data. Case Study 2 also evaluated the effects of
running AERMOD with terrain and the non-default FLAT terrain. It was concluded from Case
Study 2 that the higher concentrations in the flat terrain case were found to be due to a small
difference in distance from receptor to centerline. There was a source-receptor elevation
difference of 1.35 meters for the max receptor in the model using the DEM data; whereas, the
elevation difference was only 1 meter in the flat terrain model since the terrain and receptors
were at equal heights.
Table 10. Comparison of FLAT vs. DEM Terrain Data.
Year
1998
1999
2000
2001
2002
Difference
3-hr
Flat
DEM
492.37
343.26
782.27
380.96
635.19
292.16
778.95
406.82
622.60
492.10
40.90%
Averaging Period
24-hr
Flat
DEM
159.38
119.57
205.15
99.56
184.83
91.66
237.95
126.73
169.88
126.13
39.87%
Annual
Flat
DEM
35.45
31.52
36.10
31.82
33.04
29.58
59.29
29.72
33.65
26.36
20.98%
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CONCLUSIONS AND RECOMMENDATIONS
Performance of AERMOD was found to be a bulkier and more time consuming process than the
performance of ISC. A significantly greater number of files are generated by the preprocessors
that must be properly managed in order to successfully complete a model run. Modeling time for
the cases presented was significantly greater using AERMOD, and, if seasonal periods are
analyzed, it is anticipated that preprocessing itself can become time and labor-intensive.
Terrain and meteorological data were readily available. However, DEM data is difficult to
obtain for remote areas of the Louisiana coastal area, and USGS topographic maps are
potentially inaccurate when considering coastal erosion. A review of the most up-to-date
aerial/satellite photos or other data may be necessary when performing the Land Use/Land
Classification Analysis.
In general, Case Studies 1 and 2 predicted concentrations from AERMOD lower than those
obtained from ISC. Case Studies 3 and 4 predicted higher concentrations from AERMOD than
those obtained from ISC. The major difference between the two groups was the meteorological
data sets used. Case Studies 3 and 4 used New Orleans surface data and Slidell, LA upper air
data. Compared to the stations used in the other two cases, these stations are near the coast,
which may possibly influence the results. It is possible that these results are meteorological data
set dependent. Likewise, on-site meteorological data used in Case Study 3 produced higher
concentrations than National Weather Service Data when running AERMOD. The quality and
representativeness of the on-site data should be evaluated prior to use in AERMOD runs.
Only the surface parameter of surface roughness was found to significantly impact the results of
an AERMOD run for the locations and meteorological data sets modeled. Albedo and Bowen
ratio had little to no impact on results. Great care must be taken when performing the Land
Use/Land Classification analysis to properly characterize the surrounding terrain and, in
particular, surface roughness in order to avoid over- or under-predicting concentrations.
As a result of this work, the Workgroup made the following recommendations for consideration
by regulatory agencies:

Develop a means of establishing agreement with regulatory agencies on meteorological
variables is essential before any modeling can begin

Establish guidance for on-site meteorological data, i.e., when is it appropriate to use, what
minimum data is required, etc.

Establish guidance on the characterization of areas for surface parameter land use analysis,
possibly including acceptable ranges for various locations in the state and differentiation
among rural, urban, and residential areas

Establish guidance on use of surface parameters in wet/dry/average conditions and
incorporation of seasonal variability
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
Establish guidance on the methodology of sector surface parameter calculation for
development of surface parameters (i.e., area-weighted, distance-weighted, etc.)

Establish guidance for terrain data inputs, including identification of acceptable data sources
and accounting for missing terrain files.
ACKNOWLEDGEMENTS
Patrick Pakunpanya with LDEQ assembled and led the Workgroup.
Ryan Clausen and Tom Petroski with URS performed the modeling analysis for Case Study 1.
Raghu Soule, formerly with Trinity Consultants (currently with ENVIRON International
Corporation), Chris Meyers with Trinity Consultants, and Lyn Tober formerly with Trinity
Consultants (currently with Providence Environmental Engineering and Environmental Group,
LLC) conducted the Case Study 2 modeling analysis.
Ted Bowie, Doug Daugherty, Carolee Laffoon, and Joey Rinaudo with ENVIRON performed
the modeling analysis for Case Study 3.
Rick Madura with RTP conducted the Case Study 4 modeling analysis.
Other Workgroup members included John Black with Enviro-One, Cade Borque with Shaw
Group, Kerry Brouillette with URS, Kevin Calhoun with CRA, Scott Dorris with ERM, Beth
Hughes with C-K and Associates, Keith Jordan and Tien Nguyen with LDEQ, Gerhard Pringer
with Tulane, and Yousheng Zeng with Providence.
REFERENCES
1. SCRAM Website. See http://www.epa.gov/scram001/guidance/guide/appw_03.pdf (accessed
July 2004).
2. AERMOD: Latest Features and Evaluation Results, U.S. EPA, Office of Air Quality Planning
and Standards, Emissions Monitoring and Analysis Division, EPA-454/R-03-003, June 2003.
3. 65 FR 21506, Requirements for preparation, Adoption, and Submittal of State Implementation
Plans (Guideline on Air Quality Models), 40 CFR Part 51, April 21, 2000.
4. 68 FR 18440, Revision to the Guideline on Air Quality Models: Adoption of a Preferred Long
Range Transport Model and Other Revisions, April 15, 2003.
5. 68 FR 52934, 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. September 8, 2003.
18
6. Reeves, Douglas, Understanding and Adapting to New Dispersion Models, Trinity
Consultants.
7. Comparison of Regulatory Design Concentrations, AERMOD vs ISCST3, CTDMPLUS, ISCPRIME, U.S. EPA, Office of Air Quality Planning and Standards, Emissions Monitoring and
Analysis Division, Research Triangle Park, NC 27711, EPA Report No. EPA-454/R-03-002,
July 2003.
8. Grosch, T.G. and Lee, R.F., Sensitivity of the AERMOD Air Quality Model to Selection of
Land Use Parameters.
9. PES Website, http://home.es.com/aerfaqs.htm (accessed 2004).
10. Peters, W.D., et al., Comparison of Regulatory Design Concentrations: AERMOD Versus
ISCST3 and CTDMPlus, DRAFT, April 1999.
11. Cimorelli, Alan J., et al, Minimum Meteorological Data Requirements for AERMOD – Study
and Recommendations, Draft Document, December 1998.
12. GeoCommunity Website. See http://data.geocomm.com/dem/demdownload.html (accessed
2004).
13. Atlas Website. See http://www. atlas.lsu.edu (accessed 2004).
14. LaCoast Website. See http://www.lacoast.gov (accessed 2004).
KEYWORDS
Dispersion modeling; AERMOD; ISC; PRIME; Louisiana
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