The paper summarizes the results several hypothetical case studies to evaluate AERMOD’s behavior in comparison with ISC. In addition, general differences between ISC and AERMOD are discussed, including processing times, land-use parameters, meteorology inputs, and treatment of terrain.

1. Environmental solutions delivered uncommonly well
Revising State Air Quality Modeling
Guidance for the Incorporation of
AERMOD - A Workgroup's Experience
Paper No. 476
Prepared By:
Raghu Soule - Principal Consultant
Chris Meyers - Consultant
Joey Rinaudo - ENVIRON International Corporation
Carolee Laffoon - ENVIRON International Corporation
Scott Dorris - ERM Information Solutions
Richard L. Madura - RTP Environmental Associates, Inc.
Lyn Tober - Providence Engineering & Environmental Group, LLC
Sirisak Patrick Pakunpanya - Louisiana Department of Environmental Quality
TRINITY CONSULTANTS
12700 Park Central Drive
Suite 2100
Dallas, TX 75251
+1 (972) 661-8881
trinityconsultants.com
June 20, 2006

2. 2
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. Recent advances in
dispersion modeling theory have 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. EPA has promulgated the approval of AERMOD dispersion model as the replacement
for ISC for evaluating near-field impacts for regulatory purposes. AERMOD requires several
additional geophysical meteorological input parameters that ISC does not utilize. To provide
guidance as well as to implement the use of AERMOD, the Louisiana Department of
Environmental Quality (LDEQ) formed a Modeling Workgroup to evaluate issues with
implementing AERMOD and to revise the existing Louisiana Air Quality Modeling Procedures
issued in October 1999 to incorporate guidance for using AERMOD .
Workgroup members performed several hypothetical case studies to evaluate AERMOD’s
behavior in comparison with ISC. The case studies involved source/geography configurations
typical to Louisiana industrial facilities. The results of these analyses, along with case studies
already put forth by EPA and other groups were used to develop updated modeling guidance,
including the development of site-specific parameters required by AERMOD for the various
geographic regions of the state.
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. Lastly, the updates to the Louisiana Air Quality Modeling Procedures
are discussed.
INTRODUCTION
Regulatory History and Update
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.

3. 3
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 the AERMIC dispersion MODel
(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 that includes 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, 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.4
EPA later
published a notice of additional information in response to public comments.5
Finally, after
more than five and half years since the initial proposal, the U.S. EPA Administrator signed
revisions to the federal Guideline on Air Quality Models (40 CFR 51, Appendix W) on October
21, 2005. These revisions were published in the Federal Register on November 9, 2005, and
recommended that AERMOD (EPA Version 04300), including the PRIME building downwash
algorithms, be used for dispersion modeling evaluations of criteria air pollutant and toxic air
pollutant emissions from typical industrial facilities. This new modeling system replaces
ISCST3, upon which previous dispersion modeling determinations were based. Potentially
important regulatory applications of AERMOD include New Source Review and Health Risk
Assessment for Maximum Achievable Control Technology (MACT) and Residual Risk
demonstrations.
The revised Guideline became effective on December 9, 2005 (30 days after Federal Register
publication), and a one-year transition period commenced with promulgation. During the
transition, state and local regulators will have to revise existing modeling guidance to require the
use of AERMOD and permit applicants may have the option of using either ISCST3 or
AERMOD for modeling analyses. However, most U.S. EPA regions and state and local agencies
are expected to emphasize the use of AERMOD during the transition period.
After the one-year period, beginning November 9, 2006, AERMOD is required to be used for
federal regulatory applications. The LDEQ is allowing the use of ISCST3, ISC-PRIME, or
AERMOD during the one-year transition period for federal Prevention of Significant
Deterioration (PSD) air quality analyses and state ambient air quality standard (AAQS) analyses.
Consistent with the federal guidance, the LDEQ will require applicants to use AERMOD only
for all PSD Air Quality analyses as well as for state NAAQS compliance demonstration purposes
after November 9, 2006. For toxic air pollutant modeling required by state air toxic compliance
rules, Louisiana will allow applicants to use ISCST3/ISC-PRIME or AERMOD.

4. 4
Technical Description of Model
EPA has described AERMOD as an advanced dispersion model that incorporates state-of-the-art
boundary layer parameterization techniques, convective dispersion, plume rise formulations, and
complex terrain/plume interactions. More recently, the PRIME algorithm, which is a building
wake/building downwash algorithm developed by the Electric Power Research Institute (EPRI),
was implemented into AERMOD to make use of the AERMOD meteorological profiles.2
However, its main purpose is to account for the relative locations of the stack and the building
causing downwash, which is not accounted for in the original ISC or early version of AERMOD.
The AERMOD modeling system has 3 components: AERMOD - the air dispersion model;
AERMET - the meteorological data preprocessor; and AERMAP - the terrain data preprocessor.
Several shortcomings of the ISC model necessitated the need for developing a more
sophisticated model. Relative to ISC3, AERMOD currently contains 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) computation of vertical profiles of
wind, turbulence, and temperature; 5) urban nighttime boundary layer effects; 6) treatment of
receptors on all types of terrain, from the surface up to and above the plume height; 7) treatment
of building wake effects; 8) an improved approach for characterizing the fundamental boundary
layer parameters; and 9) treatment of plume meander.6
With respect to the parameters affecting
the meteorological data, AERMOD is improved in comparison to ISC3 in its characterization of
the modeling domain surface characteristics. While the characterization in ISC3 is based on a
choice of rural or urban land-use classification, AERMOD uses a more rigorous approach with
the site-specific parameters of roughness length, albedo, and Bowen ratio based on direction and
season.
Workgroup Approach
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, iv) identify potential permitting and financial impacts on industry due to the differences
between ISCST3 and AERMOD, and v) update and revise the state modeling guidance
document.
Unlike ISCST3, pre-processed “model-ready” meteorological data can not be as easily prepared
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 the more rigorous processing required for AERMOD can be confusing, the workgroup’s
specific objectives were to (1) review the use of these parameters, (2) test the sensitivity of these

5. 5
parameters, and (3) attempt to develop a state-specific protocol for determining the appropriate
values to use for specific geographic/topographic locations.
Another model enhancement was a more rigorous treatment of terrain. Since most of Louisiana
is near sea level with flat terrain, the workgroup also evaluated the need to include digital
elevation model (DEM) terrain data.
Workgroup Activities
In order to address the goals of the Workgroup, the following activities were conducted by the
workgroup:
• Four “Louisiana-specific” case studies were conducted for the purposes of comparing
ISCST3 and AERMOD modeling analyses. Focusing on Louisiana industrial settings, the
parameters that were varied among the studies included topographical location/terrain,
meteorological data, and type and number of modeled emission sources.
• The LDEQ Modeling Guidelines were revised to implement AERMOD modeling
requirements based on federal guidelines as well as on the findings of the Workgroup.
Additionally, other details in the modeling guidelines, not necessarily related to AERMOD,
were revised and clarified as necessary.
LITERATURE REVIEW
Several studies and observations presented in the literature were reviewed by the Workgroup for
the purpose of identifying previous similar analyses that had been performed and comparing
them to Louisiana Workgroup observations. EPA conducted several studies to compare the
AERMOD versus ISC analysis prior to the April 2000 Federal Register publication with the
AERMOD-PRIME versus ISC analysis conducted after the Federal Register publication. In
AERMOD: Latest Features and Evaluation Results, comparisons of model estimates with
measured air quality concentrations for a variety of source types and locations were provided.2
EPA’s 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 re-ran 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

6. 6
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)
was slightly better than the April 2000 proposal and both versions of AERMOD significantly
outperformed ISCST3 as compared to monitored observations, and (ii) AERMOD (02222) with
PRIME performed slightly better than ISC-PRIME for aerodynamic downwash cases.
Another document compiled by the EPA, Comparison of Regulatory Design Concentrations,
documented a consequence analysis of effects on design concentrations and provided
comparisons of design concentrations (on which emission control limits might be based) for a
wide variety of source configurations and settings.9
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 included 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
complex terrain situations, 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 parameters concluded
that: (i) for surface sources, only surface roughness length affects the modeled concentrations
significantly.10
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

7. 7
what effect any change in those values will have on design concentrations for a given source
configuration. The authors advised that reasonably accurate estimates of albedo, Bowen ratio,
and surface roughness lengths are necessary for AERMOD to provide accurate results.
A study11
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.
Another published report 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.7
Yet another study 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 ISCST3.8
EPA and the American Meteorological Society (AMS) performed an evaluation of AERMOD
with different meteorological data sets in a 1998 study that showed the use of single level on-site
meteorological data overpredicted results, in some cases by as much as a factor of two.12
MATERIALS AND METHODS
Summary of Louisiana Workgroup Case Study Analysis
The Workgroup conducted four case studies; each focusing on different aspects of AERMOD,
such as meteorological data, land-use parameters, source types, and number of sources.7
Each
analysis was conducted in a different region of the state using sources indicative of a generic
facility type (e.g., refinery) and the results of AERMOD were compared to that of
ISCST3/ISC-PRIME. Two of the four 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. Two of the four case studies performed sensitivity analyses
on the land-use parameters (i.e., surface roughness length, Bowen ratio, and albedo) to determine
the most conservative land-use parameters to use as defaults in each region. Surface roughness
length can vary from <0.0001 over calm water to 1.5 m in forest and 3 m in some urban areas.
Mid-day values of Bowen ratio range from 0.1 (over water) to 10.0 (over desert). Albedo values
range from 0 to 1.0 (e.g., 0.1 for deciduous forests and 0.9 for fresh snow). The sensitivity
analyses performed clearly indicated that surface roughness length is the most sensitive
AERMOD site-specific 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

8. 8
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. One of the literature
studies 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.8
The second case study
did not necessarily agree with this observation although the modeled terrain was moderately hilly
(almost flat terrain). Another literature study concluded that for area sources, the concentrations
predicted by AERMOD are approximately equal to those predicted by ISCST3 model.9
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 that when seasonal periods were
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.
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 more complex process than using ISC.
Meteorological Data – Station Selection
In revising the Louisiana Modeling Guidelines to incorporate AERMOD modeling requirements,
the use of appropriate meteorological data was carefully reviewed since (1) the meteorological
data inputs for AERMOD differ significantly from that of ISCST3/ISC-PRIME, and (2) the
potential burden to the regulated community in processing the meteorological data could be
overwhelming.
Using the case study and literature review findings, it was decided to continue to allow modeling
analyses to utilize one of the four primary surface meteorological stations in Louisiana. In
addition, surrogate sites were chosen. The primary meteorological data sources suggested to be
used by applicant-facilities are specified in the revised modeling guidelines and are shown in
Table 1 below. These meteorological stations, based upon LDEQ regional offices, are to be used
for both ISCST3/ISC-PRIME as well as AERMOD modeling analyses. Occasionally, raw
meteorological data is missing information. Minor gaps in surface data (i.e., 4 consecutive hours
or less) may be filled by step-wise, linear interpolation. Major gaps in surface data (i.e., greater
than four consecutive hours) may be filled by surrogate data from a nearby station. Minor gaps
in mixing-height data (i.e., 1 missed observation) may be filled by reasonable interpolation from
sounding on the previous and succeeding day. Major gaps in mixing-height data may be filled

9. 9
by the seasonal average of morning or evening soundings. In addition, an applicant facility can
choose to use onsite meteorological data. The Louisiana Modeling Guidelines request submittal
of meteorological data and documentation of data filling performed.
Table 1. LDEQ Primary Meteorological Data Sources
Regional
Office
Primary Surface
Station
Surrogate
Surface Station
Surrogate Cloud
Cover Station Upper Air Station
Acadiana Case-by-case Case-by-case Case-by-case
Lake Charles
(NWS 03937)
Capital
Baton Rouge
(NWS 13970)
Baker
LDEQ site1
Lafayette
(NWS 13976)
Lake Charles
(NWS 03937)
Northeast
Shreveport
(NWS 13957)
Barksdale
(WBAN 12958)
Longview, TX
(WBAN 03901)
Shreveport
(NWS 13957)
Northwest
Shreveport
(NWS 13957)
Barksdale
(WBAN 12958)
Barksdale
(WBAN 12958)
Shreveport
(NWS 13957)
Southeast
New Orleans
(NWS 12916)
Belle Chase
(WABAN 12958)
New Orleans
(NWS 12942)
Slidell
(NWS 53813)
Southwest
Lake Charles
(NWS 03937)
NA
Port Arthur
(NWS 12917)
Lake Charles
(NWS 03937)
Footnotes:
1. LDEQ Baker ambient monitoring site collects meteorological data.
AERMOD Meteorological Variables
AERMET, the meteorological processor of AERMOD, requires the calculation of several
meteorological variables for a facility. Albedo, Bowen Ratio, and Surface Roughness are three
critical variables that need to be calculated on a case-by-case basis. Based on the workgroup
findings, it was decided that default values, shown in Table 2, could be used for the various
LDEQ regions.
The workgroup has proposed that these default values can used by an applicant without
additional negotiations. The updated guidance allows the user to develop alternate values for
these meteorological variables on a case-by-case basis.
Table 2. Default AERMOD Meteorological Variables
Regional
Office Albedo1
Bowen Ratio1
Rural Surface
Roughness2
Urban Surface
Roughness1
Acadiana 0.18 0.65 0.10 0.22
Capital 0.16 1.47 0.10 0.93
Northeast 0.18 2.04 0.10 0.86
Northwest 0.18 2.04 0.10 0.86
Southeast 0.16 1.58 0.10 0.87

10. 10
Regional
Office Albedo1
Bowen Ratio1
Rural Surface
Roughness2
Urban Surface
Roughness1
Southwest 0.18 0.65 0.10 0.22
1. Calculated based on primary surface station, except Acadiana (since Acadiana has two distinct geographic regions), which is based on the most
conservative value from all airports in Louisiana. The default variables calculated for the Northwest are assumed to be also representative of the Northeast
region as the land use land classifications for the two regions are similar.
2. EPA surrogate for surface roughness at NWS station (grassland / summer). EPA Human Health Risk Assessment Protocol (p.3-19).
If the applicant uses National Weather Service (NWS) surface meteorological data, the EPA
recommends setting the surface roughness height for the measurement site at 0.10 meters
(grassland, summer). If the applicant chooses to calculate “site-specific” meteorological
variables, the LDEQ requires that the applicant follow the procedure listed below.
1. Draw a 3 or 5-kilometer radius from the center of the meteorological station;
2. Divide the circle into a maximum of eight specific wind sectors for evaluation (please note
that the sectors do not need to be evenly distributed);
3. Use the USGS Land Use Classification to classify each sector’s land-use according to
AERMET categories (please note that aerial photographs and a site visit may be used to
update the maps since the USGS publication date); and
4. Estimate the seasons of the year based upon the classification in the AERMET User’s Guide
(the workgroup has proposed that LDEQ accept the following categories without additional
negotiation);
5. Calculate a surface roughness for each sector; and
6. Input surface roughness into AERMET for meteorological data processing.
Table 3. Default Louisiana Seasons for AERMOD Meteorological Variables
Season Months Fraction
Spring: 3 25%
Summer: 6 50%
Autumn: 3 25%
Winter: 0 0%
Sum: 12 100%
The values provided for use for the Winter season in the AERMET User’s Guide are
representative of continuous snow cover and are not representative of typical winters in
Louisiana. Therefore, spring or autumn values would be most appropriate.
Please note that LDEQ must approve the “site-specific” surface roughness in each sector prior to
submittal of any modeling results. On a “case-by-case” basis, LDEQ may accept alternative
methodologies to calculate other AERMOD surface parameters.

11. 11
Terrain Requirements
Although the EPA suggests the use of terrain data when using AERMOD for all terrain types, the
LDEQ, in the revised modeling guidelines, states that due to the predominantly flat terrain,
terrain data would not be required for Acadiana, Capital, Southeast, and Southwest regional
offices. The applicant has the option to use terrain in these southern regional areas.
RESULTS AND DISCUSSION
Using the results of Louisiana Workgroup findings, the AERMOD section of the revised
Louisiana Modeling Guidance provides a more streamlined approach to setting up and running
AERMOD. The user is allowed to use default parameters for some meteorological variables
which simplify the meteorological pre-processing effort. In addition, the user can elect for flat
terrain in the southern part of the state, which provides an additional simplification for the terrain
pre-processing effort. It should be noted that while these processing simplifications should not
materially affect the resulting modeled concentrations, they might negate some of the very model
enhancements that AERMOD allows.
Because of the increased runtime of AERMOD along with the pre-processing of the terrain and
meteorological data, the preparation time of modeling analyses to support permitting efforts will
increase, sometimes dramatically. It is anticipated that the agency review time will also increase
as the intricacies of the new model are understood. As a result, permit applicants should plan for
a longer preparation and review cycle for future permitting efforts.
CONCLUSIONS AND RECOMMENDATIONS
The LDEQ has revised the Louisiana modeling guidelines to implement the use of AERMOD
and has provided default meteorological data inputs that can be used.
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.
In summary, the LDEQ has established guidance for use of meteorological data for AERMOD
modeling. Guidance has been established for the characterization of areas for surface parameter
land-use analysis, including values for various locations in the state and differentiation between
rural and urban areas. Also, guidance has been established for terrain data inputs, including
identification of acceptable data sources.
ACKNOWLEDGEMENTS

12. 12
Patrick Pakunpanya, LDEQ
Raghu Soule and Chris Meyers, Trinity Consultants
Carolee Laffoon and Joey Rinaudo, ENVIRON International Corporation
Scott Dorris, ERM Information Solutions
Rick Madura, RTP Environmental Associates
Lyn Tober, Providence Environmental Engineering and Environmental Group, LLC
Other Workgroup members included Ryan Clausen and Tom Petroski with URS, John Black
with Enviro-One, Kerry Brouillette with URS, Kevin Calhoun with CRA, Beth Hughes with
C-K and Associates, Tien Nguyen with LDEQ, 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.
6. AERMOD: Description of Model Formulation, U.S. Environmental Protection Agency, EPA
454/R-02-002d, October 31, 2002, Available from U.S. EPA Website
http://www.epa.gov/ttn/scram/001/.
7. C. Laffoon, J. Rinaudo, J. Bowie, ENVIRON International Corporation; R. Soule and C.
Meyers, Trinity Consultants; R. Madura, RTP Environmental Associates, Inc.; S. Pakunpanya,
Louisiana Department of Environmental Quality (2005). Developing State-Wide Modeling
Guidelines for the Use of AERMOD – A Workgroup’s Experience, Proceedings of Air and Waste
Management Association’s (A&WMA’s) 98th
Annual Conference and Exhibition, Minneapolis,
Minnesota.
8. PES Website, http://home.es.com/aerfaqs.htm (accessed 2004).

13. 13
9. Peters, W.D., et al., Comparison of Regulatory Design Concentrations: AERMOD Versus
ISCST3 and CTDMPlus, DRAFT, April 1999.
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PRIME, U.S. EPA, Office of Air Quality Planning and Standards, Emissions Monitoring and
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July 2003.
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Selection of Land Use Parameters, Air Pollution VII, WIT Press, 1999.
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and Recommendations, Draft Document, December 1998.
14. WorldGeoData Website. See http://www.worldgeodata.com.
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17. LaCoast Website. See http://www.lacoast.gov (accessed 2004).
18. Federal Register, Vol. 70, No. 216, November 9, 2005 (68218 – 68261), Part III,
Environmental Protection Agency, 40 CFR Part 51, Revision to the Guideline on Air Quality
Models: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion
Model and Other Revisions; Final Rule.
KEYWORDS
Dispersion modeling; Modeling Guidance, AERMOD; ISC; PRIME; Louisiana