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DISCLAIMER
This report has been reviewed by the Office of Air Quality Planning and Standards, EPA. and
approved for publication. Mention of trade names or commercial products is not intended to
constitute endorsement or recommendation for use.
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FOREWORD
This draft document contains proposed revisions to the Guideline
on Air Quality Models. Until the regulatory process for revising the
document is completed, including public review, Agency policy on the
use of air quality models in regulatory programs will continue to be
represented by:
1. Guideline on Air Quality Models, EPA-450/2-78-027, April 1978;
2. Regional Workshops on Air Quality Modeling: A Summary Report
(with addenda), EPA-450/4-82-015, April 1981.
CAUTIONARY NOTE TO THE READER. The model summaries contained in Appendix
A reflect proposed changes to some models. Algorithms and parameters
dealing with wind speed profiles, stack tip downwash, urban dispersion
coefficients, etc. may be affected. These changes are in response to
earlier comments solicited by EPA and to recommendations from EPA's
research programs. As a result, the summaries do not precisely reflect
currently available versions of the models. Justifications for the
proposed changes are detailed in "Summary of Comments and Responses on
the October 1980 Proposed Revisions to the Guideline on Air Quality
Models," February 1984 (Docket A-80-46, Reference No. II-G-5). A draft
revision of the MPTER Model computer code'(rural options) has been
prepared to reflect the proposed changes and may be obtained for review
and comment from Docket A-80-46, Reference No. II-G-12.
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PREFACE
Industry and control agencies have long expressed a need for
consistency in the application of air quality models for regulatory
purposes. In the 1977 Clean Air Act, Congress mandated such consistency
and encouraged the standardization of model applications. The Guideline
on Air Quality Models was first published in April 1978 to satisfy these
requirements by specifying models and providing guidance for their use.
This guideline provides a common basis for estimating the air quality con-
centrations used in assessing control strategies and developing emission
limits.
The continuing development of new air quality models in response to
regulatory requirements and the expanded requirements for models to
cover even more complex problems have emphasized the need for periodic
review and update of guidance on these techniques. Four primary on-going
activities provide direct input to revisions of this modeling guideline.
The first is a series of annual internal EPA workshops attended primarily
by Regional Meteorologists and conducted for the purpose of ensuring con-
sistency and providing clarification in the application of models. The
second activity, directed toward the improvement of modeling procedures,
is the cooperative agreement that EPA has with the scientific community
represented by the American Meteorological Society. This agreement pro-
vides scientific assessment of procedures and proposed techniques and
sponsors workshops on key technical issues. The third activity is the
.solicitation and review of new models from the technical and user com-
munity. In the March 27, 1980 Federal Register, a procedure was outlined
for the submittal to EPA of privately developed models. After extensive
evaluation and scientific review, these models, as well as those made
available by EPA, are considered for recognition in this guideline. The
fourth activity is the extensive on-going research efforts by EPA and
others in air quality and meteorological modeling.
Based primarily on these four activities, this document embodies
revisions to the "Guideline on Air Quality Models." Although the text
has been revised from the 1978 guide, the present content and topics are
similar. As necessary, new sections and topics are included. A new
format has also been adopted in an attempt to lessen the time required to
incorporate changes. The looseleaf notebook format allows future changes
to be made on a page-by-page basis. Changes will not be scheduled, but
announcements of proposed changes will be made in the Federal Register as
needed. EPA believes that revisions to this guideline should be timely
and responsive to user needs and should involve public participation to
the greatest possible extent. Information on the current status of
modeling guidance can always be obtained from EPA's Regional Offices.
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TABLE OF CONTENTS
Page
FOREWORD i 1 i
PREFACE v
TABLE OF CONTENTS vil
LIST OF TABLES xi
1.0 INTRODUCTION 1-1
2.0 OVERVIEW OF MODEL USE 2-1
2.1 Suitability of Models 2-2
2.2 Classes of Models 2-4
2.3 Levels of Sophistication of Models 2-6
3.0 RECOMMENDED AIR QUALITY MODELS 3-1
3.1 Preferred Modeling Techniques 3-3
3.2 Use of Alternative Models 3-6
3.3 Availability of Supplementary Modeling Guidance 3-9
3.3.1 The Model Clearinghouse ; 3-10
3.3.2 Regional Meteorologists Workshops 3-12
4.0 SIMPLE-TERRAIN STATIONARY-SOURCE MODELS 4-1
\
4.1 Discussion 4-1
4.2 Recommendations 4-2
4.2.1 Screening Techniques 4-2
4.2.2 Refined Analytical Techniques 4-3
5.0 MODEL USE IN COMPLEX TERRAIN 5-1
5.1 Discussion 5-1
5.2 Recommendations 5-3
5.2.1 Screening Techniques 5-4
5.2.2 Refined Analytical Techniques 5-7
6.0 MODELS FOR OZONE, CARBON MONOXIDE AND NITROGEN DIOXIDE 6-1
6.1 Discussion 6-1
6.2 Recommendations 6-3
6.2.1 Models for Ozone 6-3
6.2.2 Models for Carbon Monoxide 6-4
6.2.3 Models for Nitrogen Dioxide (Annual Average) 6-5
vii
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7.0 OTHER MODEL REQUIREMENTS 7-1
7.1 Discussion 7-1
7.2 Recommendations 7-3
7.2.1 Fugitive dust/Fugitive emissions 7-3
7.2.2 Particulate Matter 7-4
7.2.3 Lead 7-5
7.2.4 Visibility 7-6
7.2.5 Good Engineering Practice Stack Height 7-7
7.2.6 Long Range Transport 7-8
7.2.7 Modeling Guidance for Other Governmental Programs. 7-9
8.0 GENERAL MODELING CONSIDERATIONS 8-1
8.1 Discussion 8-1
8.2 Recommendations 8-2
8.2.1 Design Concentrations 8-2
8.2.2 Critical Receptor Sites 8-4
8.2.3 Dispersion Coefficients 8-5
8.2.4 Stability Categories 8-6
8.2.5 Plume Rise 8-7
8.2.6 Chemical Transformation 8-8
8.2.7 Gravitational Settling and Deposition 8-9
8.2.8 Urban/Rural Classification 8-10
8.2.9 Fumigation 8-11
8.2.10 Stagnation 8-12
8.2.11 Calibration of Models 8-13
9.0 MODEL* INPUT DATA 9-1
9.1 Source Data 9-2
9.1.1 Discussion 9-2
9.1.2 Recommendations 9-3
9.2 Background Concentrations 9-5
9.2.1 Discussion 9-5
9.2.2 Recommendations (Isolated Single Source) 9-6
9.2.3 Recommendations (Multi-Source Areas) 9-6
9.3 Meteorological Input Data 9-8
9.3.1 Length of Record of Meteorological Data 9-9
9.3.2 National Weather Service Data 9-11
9.3.3 Site-Specific Data 9-13
9.3.4 Treatment of Calms 9-21
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Page
10.0 ACCURACY AND UNCERTAINTY OF MODELS 10-1
10.1 Discussion 10-1
10.1.1 Overview of Model Uncertainty 10-1
10.1.2 Studies of Model Accuracy 10-3
10.1.3 Use of Uncertainty in Decision-Making 10-4
10.1.4 Evaluation of Models 10-5
10.2 Recommendations 10-8
11.0 REGULATORY APPLICATION OF MODELS 11-1
11.1 Discussion 11-1
11.2 Recommendations 11-3
11.2.1 Analysis Requirements 11-3
11.2.2 Use of Measured Data in Lieu of Model Estimates 11-5
11.2.3 Emission Limits 11-7
12.0 REFERENCES 12-1
13.0 BIBLIOGRAPHY 13-1
14.0 GLOSSARY OF TERMS 14-1
APPENDIX A. SUMMARIES OF PREFERRED AIR QUALITY MODELS A-l
APPENDIX B. SUMMARIES OF ALTERNATIVE AIR QUALITY MODELS B-l
APPENDIX C. EXAMPLE AIR QUALITY ANALYSIS CHECKLIST C-l
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LIST OF TABLES
Table No. Title Page
4-1 Preferred Models for Selected Applications in Simple
Terrain 4-4
5-1 Preferred Options for the SHORTZ/LONGZ Computer Codes
When Used in a Screening Mode 5-6
9-1 Averaging Times for Site-Specific Wind and Turbulence
Measurements 9-18
9-2 Wind Fluctuation Criteria for Estimating Pa squill
Stability Categories 9-19
9-3 Nighttime P-G Stability Categories Based on a^
from Table 9-2 9-20
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1.0 INTRODUCTION
This guideline recommends air quality modeling techniques that should
be applied to State Implementation Plan (SIP)1 revisions for existing sources
and to new source reviews,2 including prevention of significant deteriora-
tion (PSD).3 It is intended for use by EPA Regional Offices in judging the
adequacy of modeling analyses performed by EPA, State and local agencies
and by industry. The guidance is appropriate for use by other Federal
agencies and by State agencies with air quality and land management respon-
sibilities. It serves to identify, for all interested parties, those tech-
niques and data bases EPA considers acceptable. The guide is not intended
to be a compendium of modeling techniques. Rather it provides air quality
managers with a common basis for acceptable technical analyses.
Due to limitations in the spatial coverage of air quality measure-
ments, monitoring data normally are not sufficient as the sole basis for
demonstrating the adequacy of emission limits for existing sources. Also,
the impacts of new sources that do not yet exist can only be determined
through modeling. Thus, models, while uniquely filling one program need,
have become a primary analytical tool in most air quality assessments.
Air quality measurements can be used, and should be used in a complemen-
tary manner, to assess the accuracy of model estimates. The use of air
quality measurements alone though would be suitable, as detailed in a
later section of this document, when models are found to be unacceptable
and monitoring data with sufficient spatial and temporal coverage are
available.
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It would be advantageous to categorize the various regulatory programs
and to apply a designated model to each proposed source needing analysis
under a given program. However, the diversity of the nation's topography
and climate, and variations in source configurations and operating charac-
teristics dictate against a strict modeling "cookbook." There is no one
model capable of properly addressing all conceivable situations even with-
in a broad category such as point sources. Meteorological phenomena
associated with threats to air quality standards are rarely amenable to a
single mathematical treatment; thus, case-by-case analysis and judgment
are frequently required. As modeling efforts become more complex, it is
increasingly important that they be directed by highly competent individuals
with a broad range of experience and knowledge in air quality meteorology.
Further, they should be coordinated closely with specialists in emissions
characteristics, air monitoring and data processing. The judgment of
experienced meteorologists and analysts is essential.
It is clear from the needs expressed by the States and EPA Regional
Offices, by many industries and trade associations, and also by the delib-
erations of Congress, that consistency in the use of models and data bases
should be sought even in case-by-case analyses. Such consistency ensures
that air quality control agencies and the general public have a common
basis for estimating pollutant concentrations, assessing control strate-
gies and specifying emission limits. This guide promotes that required
consistency.
Recommendations are made in this guide concerning air quality models,
data bases, requirements for concentration estimates, the use of measured
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data in lieu of model estimates, and model evaluation procedures. Models
are identified for some specific applications. The guidance provided here
should be followed in all air quality analyses relative to State Imple-
mentation Plans and in analyses required by EPA, State and local agency
air programs. The EPA Regional Administrator may approve the use of
another technique that can be demonstrated to be more appropriate than
those recommended in this guide. This is discussed at greater length in
Section 3.0. In all cases, the model applied to a given situation should
be the one that best simulates atmospheric transport and dispersion in
the area of interest. However, to ensure consistency, deviations from
this guide should be carefully documented and fully supported.
From time to time situations arise requiring clarification of the
intent of.the guidance on a specific topic. Periodic workshops are held
with the EPA Regional Meteorologists to ensure consistency in modeling
guidance. The workshops serve to provide further explanations of guide-
line requirements to the Regional Offices and workshop reports are issued
with this clarifying information. In addition, findings from on-going
research programs, new model submittals, or results from model evaluations
and applications are continuously evaluated. Based on this information
changes in the guidance may be indicated.
All changes to this guideline must follow rulemaking requirements
since the guideline has been incorporated by reference in the PSD regula-
tions. Changes will be proposed and noticed in the Federal Register.
Ample opportunity for public comment will be provided for each proposed
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change and public hearings scheduled if requested. Published, final changes
will be made available through the National Technical Information Service
(NTIS).
A wide range of topics on modeling and data bases are discussed in
the remainder of this guideline. Where specific recommendations are made,
the recommendations are typed in a single-spaced format. Chapter 2 gives
an overview of models and their appropriate use. Chapter 3 provides spe-
cific guidance on the use of "preferred" air quality models and on the
selection of alternative techniques. Chapters 4 through 7 provide recom-
mendations on modeling techniques for application to simple-terrain sta-
tionary source problems, complex terrain problems, and mobile source
problems. Specific modeling requirements for selected regulatory issues
are also addressed. Chapter 8 discusses issues common to many modeling
analyses, including acceptable model components. Chapter 9 makes recom-
mendations for data inputs to models including source, meteorological
and background air quality data. Chapter 10 covers the uncertainty in
model estimates and how that information can be useful to the regulatory
decision-maker. The last chapter summarizes how estimates and measure-
ments of air quality are used in assessing source impact and in evaluating
control strategies.
Appendix A contains summaries of refined air quality models that are
"preferred" for specific applications; both EPA models and models developed
by others are included. Appendix B contains summaries of other refined
models that may be considered with a case-specific justification. Appendix C
contains a checklist of requirements for an air quality analysis.
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2.0 OVERVIEW OF MODEL USE
Before attempting to implement the guidance contained in this document,
the reader should be aware of certain general information concerning air
quality models and their use. Such information is provided in this section.
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2.1 Suitability of Models
The extent to which a specific air quality model is suitable for
the evaluation of source impact depends upon several factors. These include:
(1) the meteorological and topographic complexities of the area; (2) the
level of detail and accuracy needed for the analysis; (3) the technical
competence of those undertaking such simulation modeling; (4) the resources
available; and (5) the detail and accuracy of the data base, i.e., emis-
sions inventory, meteorological data, and air quality data. Appropriate
data should be available before any attempt is made to apply a model. A
model that requires detailed, precise, input data should not be used when
such data are unavailable. However, assuming the data are adequate, the
greater the detail with which a model considers the spatial and temporal
variations in emissions and meteorological conditions, the greater the
ability to evaluate the source impact and to distinguish the effects of
various control strategies.
Air quality models are most successfully applied in areas with
relatively simple topography. Areas subject to major topographic influ-
ences experience meteorological complexities that are extremely difficult
to simulate. Although models are available for such circumstances, they
are frequently site specific and resource intensive. In the absence of
a model capable of-simulating such complexities, only a preliminary
approximation may be feasible until such time as better models and data
bases become available.
Models are highly specialized tools. Competent and experienced
personnel are an essential prerequisite to the successful application of
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simulation models. The need for specialists is critical when the more
sophisticated models are used or the area being investigated has compli-
cated meteorological or topographic features. A model applied improperly,
or with inappropriately chosen data, can lead to serious misjudgments
regarding the source impact or the effectiveness of a control strategy.
The resource demands generated by use of air quality models vary
widely depending on the specific application. The resources required
depend on the nature of the model and its complexity, the detail of the
data base, the difficulty of the application, and the amount and level of
expertise required. The costs of manpower and computational facilities
may also be important factors in the selection and use of a model for a
specific analysis. However, consideration of these factors should not
lead to selection of an inappropriate model.
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2.2 Classes of Models
The air quality modeling procedures discussed in this guide
can be categorized into four generic classes: Gaussian, numerical,
statistical or empirical, and physical. Within these classes, espe-
cially Gaussian and numerical models, a large number of individual
"computational algorithms" may exist, each with its own specific appli-
cations. While each of the algorithms may have the same generic basis,
e.g., Gaussian, it is accepted practice to refer to them individually as
models. For example, the CRSTER model and the RAM model are commonly
referred to as individual models. In fact, they are both variations of
a basic Gaussian model. In many cases the only real difference between
models within the different classes is the degree of detail considered
in the input or output data.
Gaussian models are the most widely used techniques for esti-
mating the impact of nonreactive pollutants. Numerical models may be
more appropriate than Gaussian models for area source urban applications
that involve reactive pollutants, but they require much more extensive
input data bases and resources and therefore are not as widely applied.
Statistical or empirical techniques are frequently employed in situations
where incomplete scientific understanding of the physical and chemical
processes or lack of the required data bases make the use of a Gaussian
or numerical model impractical. Various specific models in these three
generic types are discussed in this guideline.
Physical modeling, the fourth generic type, involves the use of
wind tunnel or other fluid modeling facilities. This class of modeling is
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a complex process requiring a high level of technical expertise, as well
as access to the necessary facilities. Nevertheless, physical modeling
may be very useful in evaluating the air quality impact of a source or
group of sources in a geographic area limited to a few square kilometers.
If physical modeling is available and its applicability demonstrated, it
may be the best technique. A discussion of physical modeling is beyond
the scope of this guide. The EPA publication "Guideline for Fluid Model-
ing of Atmospheric Diffusion,"4 provides information on fluid modeling
applications and the limitations of that method.
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2.3 Levels of Sophistication of Models
In addition to the various classes of models, there are two levels
of sophistication. The first level consists of general, relatively simple
estimation techniques that provide conservative estimates of the air quality
impact of a specific source, or source category. These are screening tech-
niques or screening models. The purpose of such techniques is to eliminate
from further consideration those sources that clearly will not cause or con-
tribute to ambient concentrations in excess of either the National Ambient
Air Quality Standards (NAAQS)5 or the allowable prevention of significant
deterioration (PSD) concentration increments.3 If a screening technique
indicates that the concentration contributed by the source exceeds the PSD
increment or the increment remaining to just meet the NAAQS, then the
second level of more sophisticated models should be applied.
The second level consists of those analytical techniques that pro-
vide more detailed treatment of physical and chemical atmospheric processes,
require more detailed and precise input data, and provide more specialized
concentration estimates. As a result they provide a more refined and, at
least theoretically, a more accurate estimate of source impact and the effec-
tiveness of control strategies. These are referred to as refined models.
The use of screening techniques followed by a more refined analysis
is always desirable, however there are situations where the screening tech-
niques are practically and technically the only viable option for estimating
source impact. In such cases, an attempt should be made to acquire or improve
the necessary data bases and to develop appropriate analytical techniques.
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3.0 RECOMMENDED AIR QUALITY MODELS
This section recommends refined modeling techniques that are preferred
for use in regulatory air quality programs. The status of models developed
by EPA, as well as those submitted to EPA for review and possible inclusion
in this guidance, is discussed. The section also addresses the selection
of models for individual cases and provides recommendations for situations
where the preferred models are not applicable. Two additional sources of
modeling guidance, the Model Clearinghouse^ and periodic Regional Meteor-
ologists' workshops, are also briefly discussed here.
In all regulatory analyses, especially if other than preferred
models are selected for use, early discussions among Regional Office
staff, State and local control agencies, industry representatives, and
where appropriate, the Federal Land Manager, are invaluable and are
encouraged. Agreement on the data base to be used, modeling techniques
to be applied and the overall technical approach, prior to the actual
analyses, helps avoid misunderstandings concerning the final results and
may reduce the later need for additional analyses. The use of an air
quality checklist, such as presented in Appendix C, and the preparation
of a written protocol help to keep misunderstandings at a minimum.
It should not be construed that the preferred models identified here
are to be permanently used to the exclusion of all others or that they are
the only models available for relating emissions to air quality. The model
that most accurately estimates concentrations in the area of interest is
always sought. However, designation of specific models is needed to promote
consistency in model selection and application.
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The 1980 solicitation of new or different models from the technical
community? and the program whereby these models are evaluated, estab-
lished a means by which new models are identified, reviewed and made
available in the guideline. There is a pressing need for the development
of models for a wide range of regulatory applications. Refined models
that more realistically simulate the physical and chemical process in the
atmosphere and that more reliably estimate pollutant concentrations are
required. Thus, the solicitation of models is considered to be continuous.
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3.1 Preferred Modeling Techniques
3.1.1 Discussion
EPA has developed approximately 10 models suitable for
regulatory application. More than 20 additional models were submitted by
private developers for possible inclusion in the guideline. These refined
models have all been organized into eight categories of use: rural, urban
industrial complex, reactive pollutants, mobile sources, complex terrain,
visibility, and long range transport. They are undergoing an intensive
evaluation by category. The evaluation exercises^»^>^ include statistical
measures of model performance suggested by the American Meteorological
Society11 and, where possible, peer scientific reviews.12'13 If a single
model is found to be superior to others in a given category, it is recom-
mended for application in that category as a preferred model and listed in
Appendix A. If no one model is found clearly superior through the evalu-
ation exercise, then the preferred model listed in Appendix A is selected
on the basis of other factors such as past use, public familiarityj 'cost
or resource requirements, and availability. The models not specifically
recommended for use in a particular category are summarized in Appendix B.
The solicitation of new refined models which are based
on sounder scientific principles and which more reliably estimate pollut-
ant concentrations is considered by EPA to be continuous. Models that
are submitted in accordance with the provisions outlined in the Federal
Register notice of March 1980 (45 FR 20157)7 will be evaluated as submitted.
The evaluation process will include a determination of technical merit,
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in accordance with the six items listed in the above-mentioned Federal
Register notice, including the practicality of the model for use in
ongoing regulatory programs. Each model will also be subjected to a
performance evaluation for an appropriate data base and to a peer scien-
tific review. Models found to be clearly superior for wide use (not just
an isolated case!) will be proposed for inclusion as preferred models
in a future guideline revision.
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3.1.2 Recommendations
Appendix A identifies refined models that are preferred
for use in regulatory applications. If a model is required for a partic-
ular application, the user should select a model from that appendix. These
models may be used without a formal demonstration of applicability as long
as they are used as indicated in each model summary of Appendix A. Further
recommendations for the application of these models to specific source
problems are found in subsequent Sections of this guide.
If changes are made to a preferred model without affect-
ing the concentration estimates, the preferred status of the model is
unchanged. Examples of modifications that do not affect concentrations
are those made to enable use of a different computer or those that affect
only the format or averaging time of the model results. However, when
any changes are made, the Regional Administrator should require a test
case example to demonstrate that the concentration estimates are not
affected.
A preferred model should be operated with the options
listed in Appendix A as "Recommendations for Regulatory Use." If other
options are exercised, the model is no longer "preferred." Any other
modification to a preferred model that would result in a change in the
concentration estimates likewise alters its status as a preferred model.
Use of the model must then be justified on a case-by-case basis.
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3.2 Use of Alternative Models
3.2.1 Discussion
Selection of the best techniques for each individual
air quality analysis is always encouraged, but the selection should be
done in a consistent manner. A simple listing of models in this guide
cannot alone achieve that consistency nor can it necessarily provide the
best model for all possible situations. An EPA document, "Interim Pro-
cedures for Evaluating Air Quality Models,"1^ has been prepared to assist
in developing a consistent approach when justifying the use of other than
the preferred modeling techniques recommended in this guide. These pro-
cedures provide a general framework for objective decision-making on the
acceptability of an alternative model for a given regulatory application.
The document contains procedures for conducting both the technical evalu-
ation of the model and the field test or performance evaluation. An
example problem that focuses on the design and execution of the protocol
for conducting a field performance evaluation is also included in that
document.
This section discusses the use of alternate modeling
techniques and defines three situations when alternative models may be
used.
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3.2.2 Recommendations
Determination of acceptability of a model is a Regional
Office responsibility. Where the Regional Administrator or reviewing
authority finds that an alternative model is more appropriate than a pre-
ferred model, that model may be used subject to the recommendations below.
This finding will normally result from a determination that (Da pre-
ferred air quality model is not appropriate for the particular applica-
tion; or (2) a more appropriate model or analytical procedure is available
and is applicable.
An alternative model should be evaluated from both a
theoretical and a performance perspective before it is selected for use.
There are three separate conditions under which such a model will normally
be approved for use: (1) if a demonstration can be made that the model pro-
duces concentration estimates equivalent to the estimates obtained using
a preferred model; (2) if a statistical performance evaluation has been
conducted using measured air quality data and the results of that evaluation
indicate the alternative model performs better for the application than a
comparable model in Appendix A; and (3) if there is no preferred model for
the specific application but a refined model is needed to satisfy regula-
tory requirements. Any one of these three separate conditions may warrant
use of an alternative model. Some alternative models known to be available
to the public that are applicable for selected situations are contained in
Appendix B. However, inclusion there does not infer any unique status rela-
tive to other alternative models that are being or will be developed for
the future.
Equivalency is established by demonstrating that the
maximum or highest, second highest concentrations are within two percent
of the estimates obtained from the preferred model. The option to show
equivalency is intended as a simple demonstration of acceptability for an
alternative model that is so nearly identical (or contains options that
can make it identical) to a preferred model that it can be treated for
practical purposes as the preferred model. Two percent was selected as the
basis for equivalency since it is a rough approximation of the fraction
that PSD Class I increments are of the NAAQS for S02, i. e., the difference
in concentrations that is judged to be significant. However, this demon-
stration is not intended to preclude the use of models that are not equiva-
valent. They may be used when one of two other conditions identified below
are satisfied.
The procedures and techniques for determining the
acceptability of a model for an individual case based on superior perform-
ance is contained in the document entitled "Interim Procedures for Evaluating
Air Quality Models,"l* and should be followed, as appropriate. Preparation
and implementation of an evaluation protocol which is acceptable to both
control agencies and regulated industry is an important element in such
an evaluation.
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When no Appendix A model is applicable to the modeling
problem, an alternative refined model may be used provided that:
1. the model can be demonstrated to be applicable to
the problem on a theoretical basis, and
2. the data bases which are necessary to perform the
analysis are available and adequate, and
3a. performance evaluations of the model in similar
circumstances have shown that the model is not biased toward underestimates,
or
3b. after consultation with the EPA Regional Office, a
second model is selected as a baseline or reference point for performance
and the interim procedures14 are then used to demonstrate that the proposed
model is superior.
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3.3 Availability of Supplementary Modeling Guidance
The Regional Administrator has the authority to select models
that are appropriate for use in a given situation. However, there is a
need for assistance and guidance in the selection process so that fairness
and consistency in modeling decisions is fostered among the various
Regional Offices and the States. To satisfy that need, EPA established
the Model Clearinghouse and also holds periodic workshops with headquarters
and Regional Office modeling representatives.
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3.3.1 The Model Clearinghouse
3.3.1.1 Discussion
The Model Clearinghouse is the single EPA focal
point for review of air quality simulation models proposed for use in spe-
cific regulatory applications. Details concerning the Clearinghouse and
its operation are found in the document, "Model Clearinghouse: Opera-
tional PI an."6 Three primary functions of the Clearinghouse are:
(1) Review of decisions proposed by EPA Regional
Offices on the use of modeling techniques and data bases.
(2) Periodic visits to Regional Offices to gather
information pertinent to regulatory model usage.
(3) Preparation of an annual report summarizing
activities of the Clearinghouse including specific determinations made
during the course of the year.
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3.3.1.2 Recommendations
The Regional Administrator may request assistance
from the Model Clearinghouse after an initial evaluation and decision has
been reached concerning the application of a model, analytical technique or
data base in a particular regulatory action. The Clearinghouse may also
consider and evaluate the use of modeling techniques submitted in support
of any regulatory action. Additional responsibilities are: (1) review
proposed action for consistency with agency policy; (2) determine techni-
cal adequacy; and (3) make recommendations concerning the technique or data
base.
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3.3.2 Regional Meteorologists Workshops
3.3.2.1 Discussion
EPA conducts an annual in-house workshop for
the purpose of mutual discussion and problem resolution among Regional
Office modeling specialists, EPA research modeling experts and EPA Head-
quarters modeling and regulatory staff. A summary of the issues resolved
at previous workshops was issued in 1981 as "Regional Workshops on Air
Quality Modeling: A Summary Report."15 That report clarified procedures
not specifically defined in the 1978 guideline and was issued to ensure
the consistent interpretation of model requirements from Region to Region.
Similar in-house workshops for the purpose of clarifying guideline pro-
cedures or providing detailed instructions for the use of those procedures
are anticipated in the future.
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3.3.2.2 Recommendations
The Regional Office should always be consulted
for information and guidance concerning modeling methods and interpretations
of modeling guidance, and to ensure that the air quality model user has
available the latest most up-to-date policy and procedures.
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4.0 SIMPLE-TERRAIN STATIONARY-SOURCE MODELS
4.1 Discussion
Simple terrain, as used here, is considered to be an area where
terrain features are all lower in elevation than the top of the stack of
the source(s) in question. The models recommended in this section are
most often used in the air quality impact analysis of stationary sources
of S02 and particulates. The averaging time of the concentration esti-
mates produced by these models ranges from 1 hour to an annual average.
Model evaluation exercises have been conducted to determine the
"best, most appropriate point source model" for use in simple terrain.8.12
However, no one model has been found to be clearly superior. Thus, based
on past use, public familiarity, and availability CRSTER remains the recom-
mended model for rural, simple terrain, single point source applications.
Similar determinations were made for the other refined models that are
identified in the following sections.
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4.2 Recommendations
4.2.1 Screening Techniques
The EPA document "Guidelines for Air Quality Maintenance
Planning and Analysis, Volume 10R: Procedures for Evaluating Air Quality
Impact of New Stationary Sources"^ contains screening procedures that
should be used if the source is in simple terrain. A computerized ver-
sion of the Volume 10R screening technique for use in rural simple terrain
is available in "UNAMAP"!? as PTPLU; PTCITY* should be used for urban
areas.
All screening procedures should be adjusted to the site
and problem at hand. Close attention should be paid to whether the area
should be classified urban or rural in accordance with Section 8.2.8.
The climatology of the area should be studied to help define the worst-case
meteorological conditions. Agreement should be reached between the model
user and the reviewing authority on the choice of the screening model for
each analysis, and on the input data as well as the ultimate use of the
results.
*PTCITY, which contains urban dispersion coefficients will be made
available in the next update of UNAMAP.
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4.2.2 Refined Analytical Techniques
Table 4-1 lists preferred models for selected applications,
These preferred models should be used for the sources, land use categories
and averaging times indicated in the table. A brief description of each of
these models is found in Appendix A. Also listed in that appendix are the
model input requirements, the standard options that should be selected when
running the program and output options.
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Table 4-1
Preferred Models for Selected Applications in Simple Terrain
Short Term (1-24 hours) Land Use Model*
Single Source Rural CRSTER
Urban RAM
Multiple Source Rural MPTER
Urban RAM
Complicated Sources** Rural/Urban ISCST
Buoyant Industrial Line Sources Rural BLP
Long Term (monthly, seasonal or annual)
Single Source Rural CRSTER
Urban RAM
Multiple Source Rural MPTER
Urban CDMQC*** or RAM****
Complicated Sources** Rural/Urban ISCLT
Buoyant Industrial Line Sources Rural BLP
*Several of these models contain options which allow them to be inter-
changed, e.g., ISCST can be substituted for CRSTER and equivalent, if not
identical, concentration estimates obtained. Where a substitution is convenient
to the user and equivalent estimates are assured, it may be made. The models
as listed here reflect the applications for which they were originally intended.
**Complicated sources are sources with special problems such as aerodynamic
downwash, particle deposition, volume and area sources, etc.
***A1though CDMQC contains a method to convert from long-term to short term
averages (Larsen's transform), this method is not acceptable in regulatory
appl ications.
****If only a few sources in an urban area are to be modeled, RAM should be
used.
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5.0 MODEL USE IN COMPLEX TERRAIN
5.1 Discussion
For the purpose of this guideline, complex terrain is defined as
terrain exceeding the height of the stack being modeled. Complex terrain
dispersion models are normally applied to stationary sources of pollutants
such as S02 and particulates.
Although the need for refined complex terrain dispersion models
has been acknowledged for several years, adequate refined models have not
been developed. The lack of detailed, descriptive data bases and basic
knowledge concerning the behavior of atmospheric variables in the vicinity
of complex terrain presents a considerable obstacle to the solution of the
problem and the development of refined models.
A workshop1^ of invited complex terrain experts was held by
the American Meteorological Society as a part of the AMS-EPA Cooperative
Agreement in May of 1983. Several major complex terrain problems were
identified at this workshop; among them were: (1) valley stagnation,
(2) valley fumigation, (3) downwash on the leeside of terrain obstacles
and (4) the identification of conditions under which plume impaction can
occur.
A first step toward the solution of two of these problems has
been taken in the multi-year EPA Complex Terrain Model Development pro-
ject. 19 >20,21 one product of this project is expected to be a model suit-
able for regulatory application to plume impaction problems in complex
terrain. In addition, insight into the leeside effects problem is also
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anticipated. Completion of the project is not expected before 1986. Pre-
liminary results have identified at least two concepts that have important
implications for the regulatory application of models in complex terrain
and will require further detailed study and evaluation. First, plume im-
paction resulting in high concentrations was observed to occur during the
field study as well as in supporting fluid modeling studies.19 Further,
the occurrence of impaction was linked to a "critical streamline" that sep-
arates flow around an obstacle from flow over an obstacle. Second, high
concentrations were also observed to occur in the lee of the obstacle and
were of sufficient magnitude to indicate that this phenomenon should be
considered, if appropriate, in the determination of source impacts.20
To date most projects have been designed to identify plume behav-
ior in complex terrain and to define the meteorological variables influ-
encing that behavior. Until such time as it is possible to develop and
evaluate a model based on the quantification of the meteorological and
plume parameters identified in these studies, existing algorithms adapted
to site-specific complex terrain situations are all that are available.
The methods discussed in this section should be considered screening, or
"refined" screening, techniques and not refined dispersion models.
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5.2 Recommendations
The following recommendations apply primarily to the situations
where the impaction of plumes on terrain at elevations equal to or greater
than the plume center!ine during stable atmospheric conditions are deter-
mined to be the problem. The evaluation of other concentrations should be
considered after consultation with the Regional Office. However, limited
guidance on calculation of concentrations between stack height and plume
center!ine is provided.
Models developed for specific uses in complex terrain will be
considered on a case-by-case basis after a suitable demonstration of their
technical merits and an evaluation using measured on-site data following
the procedures in "Interim Procedures for the Evaluation of Air Quality
Models."14 Since the location of plume centerline is as important a concern
in complex terrain as dispersion rates, it should be noted that the dis-
persion models combined with a wind field analysis model should be superior
to an assumption of straight-line plume travel. Such hybrid modeling tech-
niques are also acceptable, after the appropriate demonstration and evalu-
ation.
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5.2.1 Screening Techniques
In the absence of an approved case-specific, refined,
complex terrain model, three screening techniques are currently available
to aid in the evaluation of concentrations due to plume impaction during
stable conditions: the Valley Screening Technique as outlined in the
Valley Model's User's Guide," COMPLEX I,17 and SHORTZ/LONGZ.23 These
methods should be used only to calculate concentrations at receptors whose
elevations are greater than or equal to plume height. Receptors below
stack height should be modeled using a preferred simple terrain model
(see Chapter 4). Receptors between stack height and plume height should
be modeled with both complex terrain and simple terrain models and the
highest concentration used. (For the simple terrain models, terrain may
have to be "chopped-off" at stack height, since these models are frequently
limited to receptors no greater than stack height.)
5.2.1.1 Initial Screening Technique
The initial screen to determine 24-hour averages
is the Valley Screening Technique. This technique uses the Valley Model
with the following worst-case assumptions for rural areas: (1) P-G stability
"F"; (2) wind speed of 2.5 m/s; and (3) 6 hours of occurrence. For urban
areas the stability should be changed to "P-G stability E."
When using the Valley Screening Technique to obtain
24-hour average concentrations the following apply: (1) multiple sources
should be treated individually and the concentrations for each wind direc-
tion summed; (2) only one wind direction should be used (see User's Guide,22
page 2-15) even if individual runs are made for each source; (3) for buoy-
ant sources, the BID option may be used, and the option to use the 2.6 stable
plume rise factor should be selected; (4) if plume impaction is likely on any
elevated terrain closer to the source than the distance from the source to
the final plume rise, then the transitional (or gradual) plume rise option
for stable conditions should be selected.
The receptor grid found in the Valley Model User's
Guide may not be sufficient for all analyses if only one geographical scale
factor is used. The Valley Model is very sensitive to the ground-level
elevation at the receptor, and the use of the standard polar grid could miss
the worst-case receptor. If this situation occurs, the user should choose
an additional set of receptors at appropriate downwind distances whose ele-
vations are equal to plume height minus 10 meters.
5.2.1.2 Second-Level Screening Technique (Rural)
If a violation of any NAAQS or the controlling
increment is indicated by using the Valley Screening Technique, a second-
level screening technique may be used. A site-specific data base of at
least 1 full year of meteorological data is preferred for use with any
second-level screening technique. Meteorological data used in the analysis
should be reviewed for both spatial and temporal representativeness.
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If the area is rural, the suggested second-level
screening technique is COMPLEX I for all averaging times. COMPLEX lisa
modification of the MPTER model that incorporates the plume impaction
algorithm of the Valley Model. It is a multiple-source screening technique
that accepts hourly meteorological data as input. The output is the same
as the normal MPTER output.
be selected: (1) set terrain
dispersion IOPT (4) = 1; (3)
values to 0.5, 0.5, 0.5 0.5,
and (5) set Z MIN = 10.
When using COMPLEX I the following options should
adjustment IOPT(1) = 1; (2) set buoyancy induced
set IOPT (25) =1; (4) set the terrain adjustment
0.0, 0.0, (respectively for 6 stability classes);
If gradual plume rise is used to estimate concen-
trations at nearby elevated receptors, each of the concentrations listed
in the model output table of high values should be carefully examined prior
to regulatory application. The gradual plume rise option in COMPLEX I is
not specific to stable conditions and the high concentrations could be the
result of the larger dispersion coefficients assigned to unstable or neutral
conditions and plume touchdown on other than elevated terrain. Only those
concentrations specifically recorded at receptors above plume height should
be used.
5.2.1.3 Second-Level Screening Technique (Urban)
In the event the source(s) is located in an
urbanized (Section 8.2.8) complex terrain valley, then the screening tech-
nique of choice is SHORTZ for short term averages or LONGZ for long term
averages. (SHORTZ and LONGZ may be used as screening techniques in these
complex terrain applications without demonstration and evaluation. Appli-
cation of these models in other than urbanized valley situations will
require the same evaluation and demonstration procedures as are required
for all Appendix B models.)
One full year of site-specific meteorological data
is preferred when applying SHORTZ/LONGZ as a screening technique. If more
data are available, they should be used.
Both SHORTZ and LONGZ have a number of options.
When using these models as screening techniques for urbanized valley appli-
cations, the options listed in Table 5-1 should be selected.
5.2.1.4 Restrictions
For screening analyses using the Valley Screening
Technique or Complex I, a sector greater than 22-1/2° should not be allowed
and full ground reflection should always be used.
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Table 5-1
Preferred Options for the SHORTZ/LONGZ Computer Codes When Used
in a Screening Mode
Option
I Switch 9
I Switch 17
GAMMA 1
GAMMA 2
XRY
NS, VS, FRQ (SHORTZ)
VS, FRQ (LONGZ)
"(particle size,
etc.)
ALPHA
Selection
If using NWS data, set = 0
If using site-specific data, check
with the Regional Office
Set = 1 (urban option)
Use default values (0.6 entrainment
coefficient)
Always default to stable
Set = 0 (50 m rectilinear expansion
distance)
Do not use
(Applicable only in flat terrain)
Select 0.9
SIGEPUl
/(dispersion parameters)
SIGAPUJ
Use Cramer curves (default)
If site-specific turbulence
data are available, see the
Regional Office for advice.
P (wind profile)
Select default values given in
Table 2-2 of User's Instructions
If site-specific data are avail-
able, see the Regional Office
for advice.
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5.2.2 Refined Analytical Techniques
When the results of the screening analysis demonstrate
a possible violation of NAAQS or the controlling PSD increments, a more
refined analysis should be conducted. Since there are no refined tech-
niques currently recommended for complex terrain applications, any refined
model used should be applied in accordance with Section 3.2. In particu-
lar, use of the "Interim Procedures for Evaluating Air Quality Models"14
and a second model to serve as a baseline or reference point for the com-
parison should be used in a demonstration of applicability. Hybrid models
which incorporate an accurate wind field analysis may provide a superior
analysis tool.
In the absence of an appropriate refined model, screening
results may need to be used to determine air quality impact and/or emission
limits.
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6.0 MODELS FOR OZONE, CARBON MONOXIDE AND NITROGEN DIOXIDE
6.1 Discussion
Models discussed in this section are generally applicable to
mobile sources of pollutants. Those pollutants are typically ozone, car-
bon monoxide and nitrogen dioxide. Where stationary sources of those
pollutants are of concern, the reader is referred to Sections 4 and 5.
A control agency whose jurisdiction contains areas with sign-
ificant ozone problems and who has sufficient resources and data to use
a photochemical dispersion model is encouraged to do so. Experience with
and evaluations of the Urban Airshed Model show it to be an acceptable,
refined approach. Better data bases are becoming available that support
the more sophisticated analytical procedures. However, empirical models
(.e.g. EKMA) fill the gap between more sophisticated photochemical dis-
persion models and proportional (rollback) modeling techniques and may be
the only applicable procedure if the data bases available are insufficient
for refined dispersion modeling.
Carbon monoxide is generally considered to be a problem only
in specific areas with high numbers of vehicles or slow moving traffic.
For that reason, frequently only "hot spots" or project level analyses
are needed in SIP revisions.
Nitrogen oxides are reactive and also an important contribution
to the photochemical ozone problem. They are usually of most concern in
areas of high ozone concentrations. Unless suitable photochemical dis-
persion models are used, assumptions regarding the conversion of NO to
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NC>2 are required when modeling. Site-specific conversion factors may
be developed. If site-specific conversion factors are not available or
photochemical models are not used, N0£ modeling should be considered only
a screening procedure.
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6.2 Recommendations
6.2.1 Models for Ozone
The Urban Airshed Model24 is recommended for photochemical
or reactive pollutant modeling applications involving entire urban areas.
To ensure proper execution of this numerical model, users must satisfy the
extensive input data requirements for the model as listed in Appendix A and
the users guide. Users are also referred to the "Guideline for Applying
the Airshed Model to Urban Areas"25 for further information on data base
requirements, kinds of tasks involved in the model application, and the
overall level of resources required.
The empirical model, City-specific EKMA26,27,28,29,30 1s
an acceptable approach for urban ozone applications.
Appendix B contains some additional models that may be
applied on a case-by-case basis for photochemical or reactive pollutant
modeling. Other photochemical models, including multi-layered trajectory
models, that are available may be used if shown to be appropriate. Most
photochemical dispersion models require emission data on individual hydro-
carbon species and may require three dimensional meteorological information
on an hourly basis. Reasonably sophisticated computer facilities are also
often required. Because the input data are not universally available and
studies to collect such data are very resource intensive, there are only
limited evaluations of those models.
Proportional (rollback/forward) modeling is no longer
an acceptable procedure for evaluating ozone control strategies.
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6.2.2 Models for Carbon Monoxide
Carbon monoxide modeling for the development of SIP-
required control strategies should follow the guidance provided in the
"Carbon Monoxide Hot Spot Guidelines"31 or in Volume 9 of the "Guidelines
for Air Quality Maintenance Planning and Analysis."-^ These volumes pro-
vide screening techniques for locating and quantifying worst case carbon
monoxide concentrations, and for establishing background values; they
also provide methods for assessing areawide carbon monoxide concentrations.
If results from screening techniques or measured carbon monoxide levels in
an urban area are clearly well below the standards and expected to remain
below the standard, or it can be demonstrated that the Federal Motor
Vehicle Control Program will provide the needed CO reductions, then urban
area-wide strategies may be evaluated using a modified rollback or pro-
portional model approach.
Project analysis of mobile source emissions of carbon
monoxide should first include an analysis using the screening techniques
referenced above. If concentrations using these techniques exceed the
NAAQS, then refined techniques are needed to determine compliance with
the standards. CALINE3 (see Appendix A) is the preferred model for use
when refined analyses are required.
Situations that require the use of refined techniques on
an urban-wide basis should be considered on a case-by-case basis. If "a
suitable model is available and the data and technical competence required
for its use are available, then such a model should be considered.
Where point sources of CO are of concern, they should
be modeled using the screening and preferred techniques of Sections 4
or 5.
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6.2.3 Models for Nitrogen Dioxide (Annual Average)
A three-tiered screening approach is recommended to
obtain annual average estimates of N02 from point sources:
a. Initial screen: Use an appropriate Gaussian model
from Appendix A to estimate the maximum annual average concentration and
assume a total conversion of NO to N02- If the concentration exceeds the
NAAQS for N02, proceed to the 2nd level screen.
b. 2nd level screen: Apply the Ozone Limiting Method33
to the annual NOX estimate obtained in (a) above using a representative
average annual ozone concentration. If the result is still greater than
the NAAQS, the more refined Ozone Limiting Method in the 3rd level screen
should be applied.
c. 3rd level screen: Apply the Ozone Limiting Method
separately for each hour of the year or multi-year period. Use rep-
resentative hourly N02 background and ozone levels in the calculations.
In urban areas, a proportional model may be used as a
preliminary assessment to evaluate control strategies for multiple sources
(mobile and area) of NOX; concentrations resulting from major point sources
should be estimated separately as discussed above, then added to the impact
of area sources. An acceptable screening technique for urban complexes is
to assume that all NOX is emitted in the form of N02 and to use a model from
Appendix A for nonreactive pollutants to estimate N02 concentrations. A
more accurate estimate can be obtained by (1) calculating the annual average
concentrations of NOX with an urban model, and (2) converting these esti-
mates to N02 concentrations based on a spatially averaged N02/NOX annual
ratio determined from an existing air quality monitoring network.
Situations that require more refined techniques, such as
those where there are sufficient hydrocarbons available that the assumptions
implicit in the above procedure are no longer valid, should be considered
on a case-by-case basis and agreement with the reviewing authority should
be obtained. Such techniques should consider individual quantities of
NO and N02 emissions, atmospheric transport and dispersion, and atmospheric
transformation of NO to N02- Where it is available site-specific data on
the conversion of NO to N02 may be used. Photochemical dispersion models,
if used for other pollutants in the area, may also be applied to the NOX
problem.
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7.0 OTHER MODEL REQUIREMENTS
7.1 Discussion
This section covers those cases where specific techniques have
been developed for special regulatory programs. Most of the programs
have, or will have when fully developed, separate guidance documents that
cover the program and a discussion of the tools that are needed. The
following paragraphs reference those guidance documents, when they are
available. No attempt has been made to provide a comprehensive discus-
sion of each topic since the reference documents were designed to do
that. This section will undergo periodic revision as new programs are
added and new techniques are developed.
Other Federal agencies have also developed specific modeling
approaches for their own regulatory or other requirements. An example
of this is the three-volume manual issued by the U. S. Department of
Housing and Urban Development, "Air Quality Considerations in Residential
Planning."3* Although such regulatory requirements and manuals may have
come about because of EPA rules or standards, the implementation of such
regulations and the use of the modeling techniques is under the jurisdic-
diction of the agency issuing the manual or directive.
The need to estimate impacts at distances greater than 50 km
(the nominal distance to which EPA considers most Gaussian models appli-
cable) is an important one especially when considering the effects from
secondary pollutants. Unfortunately, models submitted to EPA have not
as yet undergone sufficient field evaluation to be recommended for general
use. Existing data bases from field studies at mesoscale and long range
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transport distances are limited in detail. This limitation is a result
of the expense to perform the field studies required to verify and improve
mesoscale and long range transport models. Particularly important and
sparse are meteorological data adequate for generating three dimensional
wind fields. Application of models to complicated terrain compounds the
difficulty.
A current EPA agreement with Argonne National Laboratory,
scheduled for completion in FY 1985, will result in the development of
evaluation procedures for long range transport models. Models submitted
to EPA will be tested for currently available data bases with these pro-
cedures. Similar research in this area is also being performed by others
in EPA and other organizations. For the time being, however, long range
and mesoscale transport models must be evaluated for regulatory use on a
case-by-case basis.
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7.2 Recommendations
7.2.1 Fugitive dust/Fugitive emissions
Fugitive dust usually refers to the dust put into the
atmosphere by the wind blowing over plowed fields, dirt roads or desert
or sandy areas with little or no vegetation. Reentrained dust is that
which is put into the air by reason of vehicles driving over dirt roads
(or dirty roads) and dusty areas. Such sources can be characterized as
line, area or volume sources. Emission rates may be based on site-
specific data or values from the general literature.
Fugitive emissions are usually defined as emissions
that come from an industrial source complex. They include the emissions
resulting from the industrial process that are not captured and vented
through a stack but may be released from various locations within the
complex. Where such fugitive emissions can be properly specified, the
ISC model, with consideration of gravitational settling and dry depo-
sition, is the recommended model. In some unique cases a model devel-
oped specifically for the situation may be needed.
Due to the difficult nature of characterizing and
modeling fugitive dust and fugitive emissions, it is recommended that
the proposed procedure be cleared by the appropriate Regional Office
for each specific situation before the modeling exercise is begun.
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7.2.2 Particulate Matter
Currently a proposed NAAQS for participate matter includes
both provisions for particles in the size range less than 10 micrometers and
for Total Suspended Particulates (TSP). State Implementation Plans will be
developed by States to attain and maintain this new standard when the stand-
ard is promulgated.
Screening techniques like those identified in Section 4
are also applicable to PM^o and to large particles (TSP). It is recom-
mended that subjectively determined values for "half-life" or pollutant
decay not be used as a surrogate for particle removal. Conservative
assumptions which do not allow removal or transformation are suggested for
screening. Proportional models(roll back/forward) may not be applied for
screening analysis, unless such techniques are used in conjunction with
receptor modeling.
Refined models such as those in Section 4 are recommended
for both ?MIQ and TSP. However, where possible, particle size, gas-to-
particle formation and their effect on ambient concentrations may be con-
sidered. For urban-wide refined analyses CDMQC or RAM should be used.
CRSTER and MPTER are recommended for point sources of small particles.
For.source-specific analyses of complicated sources, the ISC model is
preferred. No model recommended for general use at this time accounts
for secondary particulate formation or other transformations in a manner
suitable for SIP control strategy demonstrations. Where possible, the
use of receptor model$35,36 in conjunction with dispersion models is
encouraged to more precisely characterize the emissions inventory and to
validate source specific impacts calculated by the dispersion model.
For those cases where no recommended technique is avail-
able or applicable, modeling approaches should be approved by the appro-
priate Regional Office on a case-by-case basis. At this time analyses
involving model calculations for distances beyond 50 km should also be
justified on a case-by-case basis (see Section 7.2.6).
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7.2.3 Lead
The air quality analyses required for lead implementa-
tion plans are given in Sections 51.83, 51.84 and 51.85 of 40 CFR Part
51. Sections 51.83 and 51.85 require the use of a modified rollback model
as a minimum to demonstrate attainment of the lead air quality standard
but the use of a dispersion model is the preferred approach. Section
51.83 requires the analysis of an entire urban area if the measured lead
concentration in the urbanized area exceeds a quarterly (three month)
average of 4.0 vg/\n^. Section 51.84 requires the use of a dispersion
model to demonstrate attainment of the lead air quality standard around
specified lead point sources. For other areas reporting a violation of
the lead standard, Section 51.85 requires an analysis of the area in
the vicinity of the monitor reporting the violation. The NAAQS for
lead is a quarterly (three month) average, thus requiring the use of
modeling techniques that can provide long-term concentration estimates.
The SIP should contain an air quality analysis to deter-
mine the maximum quarterly lead concentration resulting from major lead
point sources, such as smelters, gasoline additive plants, etc. For these
applications the ISC model is preferred, since the model can account for
deposition of particles and the impact of fugitive emissions. If the
source is located in complicated terrain or is subject to unusual climatic
conditions, a case-specific review by the appropriate Regional Office may
be required.
In modeling the effect of traditional line sources (such
as a specific roadway or highway) on lead air quality, dispersion models
applied for other pollutants can be used. Dispersion models such as
CALINE3 and APRAC-3 have been widely used for modeling carbon monoxide emis-
sions from highways. However, none of these models accounts for deposi-
tion of particles. Where deposition is of concern, the line source treat-
ment in ISC may be used. Also, where there is a point source in the middle
of a substantial road network, the lead concentrations that result from the
road network should be treated as background (see Section 9.2); the point
source and any nearby major roadways should be modeled separately using the
ISC model.
To model an entire major urban area or to model areas
without significant sources of lead emissions, as a minimum a proportional
(rollback) model may be used for air quality analysis. The rollback phi-
losophy assumes that measured pollutant concentrations are proportional to
emissions. However, urban or other dispersion models are encouraged in
these circumstances where the use of such models is feasible.
For further information concerning the use of models in
the development of lead implementation plans, the documents "Supplementary
Guidelines for Lead Implementation Plans,"37 and "Updated Information on
Approval and Promulgation of Lead Implementation Plans,"38 should be
consulted.
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7.2.4 Visibility
The visibility regulations as promulgated in December
1980* require consideration of the effect of new sources on the visibility
values of Federal Class I areas. The state of scientific knowledge con-
cerning identifying, monitoring, modeling, and controlling visibility
impairment is contained in an EPA report "Protecting Visibility: An EPA
Report to Congress."39 /\t the present time, "although information derived
from modeling and monitoring can, in some cases, aid the States in devel-
opment and implementation of the visibility program,"* the States are not
currently required to establish monitoring networks or perform modeling
analyses. However, a monitoring strategy is required. As additional
knowledge is gained, guidance on "plume blight" and regional scale models
will be provided, as appropriate.
References 40, 41, and 42 may also be useful when visi-
bility evaluations are needed. Appendix B contains two models developed
for application to visibility problems.
*45 FR 80084.
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7.2.5 Good Engineering Practice Stack Height
The use of stack heights in excess of Good Engineering
Practice (GEP) stack height is prohibited in the development of emission
limitations by 40 CFR 51.12 and 40 CFR 51.18. The definition of GEP
stack height is contained in 40 CFR 51.1. Methods and procedures for
making the appropriate stack height calculations, determining stack height
credits and an example of applying those techniques are found in references
43, 44, and 45.
If stacks for new or existing major sources are found to
be less than GEP height, then air quality impacts associated with cavity
or wake effects due to the nearby building structures should be determined.
Detailed downwash screening procedures^ for both the cavity and wake
regions should be followed. If more refined concentration estimates are
required, the Industrial Source Complex (ISC) model contains algorithms
for building wake calculations and should be used. Fluid modeling may
also be used to evaluate and describe the cavity and wake effects.
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7.2.6 Long Range Transport (beyond 50 km)
Suspected significant impacts on PSD Class I areas (as
defined in the PSD Regulations) require that impact analyses be performed.
However, the useful distance to which most Gaussian models are considered
accurate for setting emission limits is 50 km. Since in many cases Class I
areas may be threatened at distances greater than 50 km from new sources,
some procedure is needed to (1) determine if a significant impact will
occur, and (2) identify the model to be used in setting an emission limit
if the Class I increments are threatened (models for this purpose should
be approved for use on a case-by-case basis as required in Section 3.2).
This procedure and the models selected for use should be determined in
consultation with the EPA Regional Office and the appropriate Federal Land
Manager (FLM). While the ultimate decision on whether a Class I area
is adversely affected is the responsibility of the permitting authority,
the FLM has an affirmative responsibility to protect air quality related
models that may be affected.
Models for use beyond 50 km and not for PSD purposes also
must be selected for use on a case-by-case basis. Normally, use of these
models will require an acceptable demonstration of applicability and an
evaluation of model performance if possible (see Section 3.2).
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7.2.7 Modeling Guidance for Other Governmental Programs
When using the models recommended or discussed in this
guideline in support of programmatic requirements not specifically
covered by EPA regulations, the model user should consult the appropri-
ate Federal or State agency to ensure the proper application and use of
that model. For modeling associated with PSD permit applications that
involve a Class I area, the appropriate Federal Land Manager should be
consulted on all modeling questions.
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8.0 GENERAL MODELING CONSIDERATIONS
8.1 Discussion
This section contains recommendations concerning a number of
different issues not explicitly covered in other sections of this guide,
The topics covered here are not specific to any one program or modeling
area but are common to nearly all modeling analyses.
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8.2 Recommendations
8.2.1 Design Concentrations
8.2.1.1 Design Concentrations for S02, Participate
Matter, Lead, and N02
If the air quality analyses are conducted using
the period of meteorological input data recommended in Section 9.3.1.2
(e.g., 5 years of NWS data or one year of site-specific data), then the
highest, second-highest short-term concentration should be used to determine
emission limitations to assess compliance with the NAAQS and to determine
PSD increments.
When sufficient and representative data exist
for less than a 5-year period from a nearby NWS site, or when on-site data
have been collected for less than a full continuous year, or when it has
been determined that the on-site data may not be temporally representative,
then the highest concentration estimate should be considered the design .
value. This is because the length of the data record may be too short to
assure that the conditions producing worst-case estimates have been ade-
quately sampled. The highest value is then a surrogate for the concentra-
tion that is not to be exceeded more than once per year (the wording of the
deterministic standards). Also, the highest concentration should be used
whenever selected worst-case conditions are input to a screening technique.
This specifically applies to the use of techniques such as outlined in
"Procedures for Evaluating Air Quality Impact of New Stationary Sources."16
If the design concentration is an annual average
value and multiple years of data (on-site or NWS) are used, then the design
value is the highest of the annual averages calculated for the individual
years. If the design concentration is a quarterly average, then the highest
individual quarterly period from a single year should be used.
As long a period of record as possible should be
used in making estimates to determine design values and PSD increments. If
more than one year of site-specific data is available, it should be used.
8.2.1.2 Design Concentrations for Criteria Pollutants
with Expected Exceedance Standards
Specific instructions for the determination of
design concentrations for criteria pollutants with expected exceedance
standards are contained in special guidance documents for the preparation
of State Implementation Plans for those pollutants. For all SIP revisions
the user should check with the Regional Office to obtain the most recent
guidance documents and policy memoranda concerning the pollutant in
question.
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8.2.1.3 Block Averaging Times
Concentration estimates should be based on
block averaging times rather than running average times unless running
averages are specifically included in the definition of the standard for
the pollutant being modeled. The times for the blocked periods should be
oriented to midnight unless specified otherwise in the standard. For
example, 24-hour averages should be midnight to midnight, while 3-hour
averages should be midnight-3, 3-6, 6-9, etc. Annual and quarterly
averages should be on a calendar year basis.
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8.2.2 Critical Receptor Sites
Receptor sites for refined modeling should be utilized
in sufficient detail to estimate the highest concentrations and possible
violations of a NAAQS or a PSD increment. For large sources (those
equivalent to a 500 MW power plant) and where violations of the NAAQS or
PSD increment are likely, the selection of receptor sites should be a
case-by-case determination taking into consideration the topography, the
climatology, and the results of the initial screening procedure. Usually
360 receptors for a polar coordinate grid system and 400 receptors for a
rectangular grid system, where the distance from the source to the far-
thest receptor is 10 km, are adequate to identify areas of high concen-
tration. Additional receptors may be needed in the high concentration
location if greater resolution is indicated by terrain or source factors.
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8.2.3 Dispersion Coefficients
Gaussian models used in most applications should employ
dispersion coefficients consistent with those contained in the preferred
models in Appendix A. Factors such as averaging time, urban/rural sur-
roundings, and type of source (point vs. line) may dictate the selection
of specific coefficients. Generally, coefficients used in Appendix A
models are identical to, or at least based on, Pasquill-Gifford coeffi-
cients46 in rural areas and McElroy-Pooler4? coefficients in urban areas.
Research is continuing toward the development of methods
to determine dispersion coefficients directly from measured or observed
variables.48 No method to date has proved to be widely applicable. Thus,
direct measurement, as well as other dispersion coefficients related to
distance and stability, may be used in Gaussian modeling only if a demon-
stration can be made that such parameters are more applicable and accurate
for the given situation than are algorithms contained in the preferred
models.
Buoyancy-induced dispersion (BID), as identified by
Pasquill,4^ is included in the preferred models and should be used where
buoyant sources, e.g., those involving fuel combustion, are involved.
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8.2.4 Stability Categories
The Pasquill approach to classifying stability is generally
required in all preferred models (Appendix A). The Pasquill method, as modi-
fied by Turner,50 Was developed for use with commonly observed meteorological
data from the National Weather Service and is based on cloud cover, insola-
tion and wind speed.
Procedures to determine Pasquill stability categories from
other than NWS data are found in subsection 9.3. Any other method to deter-
mine Pasquill stability categories must be justified on a case-by-case basis.
For a given model application where stability categories
are the basis for selecting dispersion coefficients, both oy and oz should
be determined from the same stability category. "Split sigmas" in that
instance are not recommended.
Sector averaging, which eliminates the oy term, is generally
acceptable only to determine long-term averages, such as seasonal or annual,
and when the meteorological input data are statistically summarized as in the
STAR summaries. Sector averaging is, however, commonly acceptable in complex
terrain screening methods.
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8.2.5 Plume Rise
The plume rise methods of Briggs51>52 are incorporated in
the preferred models and are recommended for use in all modeling applica-
tions. No provisions in these models are made for fumigation or multi-
stack plume rise enhancement or the handling of such special plumes as
flares; these problems should be considered on a case-by-case basis.
The algorithm for momentum plume rise, available in the
preferred models, should be used where high velocity or nonbuoyant plumes
are being considered.
Since there is insufficient information to identify and
quantify dispersion during the transitional plume rise period, gradual
plume rise is not generally recommended for use. There are two exceptions
where the use of gradual rise is appropriate: (1) in complex terrain
screening procedures to determine close-in impacts; (2) to identify the
existence of building wake problems. These are automatically provided
for in the complex terrain screening techniques and in the ISC building
wake algorithms.
Stack tip downwash generally occurs with poorly constructed
stacks and when the ratio of the stack exit velocity to wind speed is small.
An algorithm developed by Bjorkland and Bowers2^ is the recommended technique
for this-situation and is found in the point source preferred models.
Where aerodynamic downwash occurs due to the adverse in-
fluence of nearby structures, the algorithms included in ISC^3 should be
used.
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8.2.6 Chemical Transformation
The chemical transformation of S02 emitted from point
sources or single industrial plants in rural areas is generally assumed to
be relatively unimportant to the estimation of maximum concentrations when
travel time is limited to a few hours. However, in urban areas, where
synergistic effects among pollutants are of considerable consequence, chemi
cal transformation rates may be of concern. In urban area applications, a
half-life of 4 hours50 may be applied to the analysis of SO? emissions.
Calculations of transformation coefficients from site-specific studies can
be used to define a "half-life" to be used in a Gaussian model with any
travel time, or in any application, if appropriate documentation is pro-
vided. Such conversion factors for pollutant half-life should not be used
with screening analyses.
Complete conversion of NO to N0£ should be assumed for all
travel time when simple screening techniques are used to model point source
emissions of nitrogen oxides. If a Gaussian model is used, and data are
available on seasonal variations in maximum ozone concentrations, the Ozone
Limiting Method2^ is recommended. In refined analyses, case-by case con-
version rates based on technical studies appropriate to the site in ques-
tion may be used. The use of more sophisticated modeling techniques should
be justified for individual cases.
Use of models incorporating complex chemical mechanisms
should be considered only on a case-by-case basis with proper demonstration
of applicability. These are generally regional models not designed for
the evaluation of individual sources but used primarily for region-wide
evaluations. Visibility models also incorporate chemical transformation
mechanisms which are an integral part of the visibility model itself and
should be used in visibility assessments.
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8.2.7 Gravitational Settling and Deposition
An "infinite half-life" should be used for estimates of
total suspended particulate concentrations when Gaussian models contain-
ing only exponential decay terms for treating settling and deposition are
used.
Gravitational settling and deposition may be directly
included in a model if either is a significant factor. At least one
preferred model (ISC) contains settling and deposition algorithms and is
recommended for use when particulate matter sources can be quantified and
settling and deposition are problems.
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8.2.8 Urban/Rural Classification
The selection of either rural or urban dispersion
coefficients in a specific application should follow one of the procedures
suggested by Irwin5^ and briefly described below. These include a land
use classification procedure or a population based procedure to determine
whether the character of an area is primarily urban or rural.
Land Use Procedure: (1) Classify the land use within
the total area, A0, circumscribed by a 3 km radius circle about the source
using the meteorological land use typing scheme proposed by Auer^S;
(2) if land use types II, 12, Cl, R2, and R3 account for 50 percent or more
of A0, use urban dispersion coefficients; otherwise, use appropriate rural
dispersion coefficients.
Population Density Procedure: (1) Compute the average
population density, p per square kilometer with A0 as defined above;
(2) If p is greater than 750 people/km2, use urban dispersion coefficients;
otherwise use appropriate rural dispersion coefficients.
Of the two methods the land use procedure is considered
more definitive. Population density should be used with caution and
should not be applied to highly industrialized areas where the population
density may be low and thus a rural classification would be indicated,
but the area is sufficiently built-up so .that the urban land use criteria
would be satisfied. In this case, the classification should already be
"urban" and urban dispersion parameters should be used.
Sources located in an area defined as urban should be
modeled using urban dispersion parameters. Sources located in areas
defined as rural should be modeled using the rural dispersion parameters.
For analyses of whole urban complexes, the entire area should be modeled
as an urban region if most of the sources are located in areas classified
as urban.
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8.2.9 Fumigation
Fumigation occurs when a plume (or multiple plumes)
is emitted into a stable layer of air and that layer is subsequently
mixed to the ground either through convective transfer of heat from
the surface or because of advection to less stable surroundings. Fumi-
gation may cause excessively high concentrations but is usually rather
short-lived at a given receptor. There are no recommended refined
techniques to model this phenomenon. There are, however, screening
procedures (see "Guidelines for Air Quality Maintenance Planning and
Analysis Volume 10R: Procedures for Evaluating Air Quality Impact of
New Stationary Sources")^ that may be used to approximate the con-
centrations. Considerable care should be exercised in the use of the
results obtained from the screening techniques.
Fumigation is also an important phenomenon on and near
the shoreline of bodies of water. This can affect both individual
plumes and area-wide emissions. Although models have been developed
to address this problem, the evaluations so far do not permit the
recommendation of any specific technique.
The Regional Office should be contacted to determine
the appropriate model for applications where fumigation is of concern.
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8.2.10 Stagnation
Although both short and long term periods of very light
winds are important in the identification of worst-case conditions, the
models identified in this guideline cannot adequately simulate such con-
ditions. If stagnation conditions are determined to be important to the
analysis, then techniques specific to the situation and location must be
developed. Such techniques might include empirical models or box models.
Assistance from the appropriate Regional Office should be obtained prior
to embarking on the development of such a procedure.
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8.2.11 Calibration of Models
Calibration of long-term multi-source models has been
a widely used procedure even though the limitations imposed by statis-
tical theory on the reliability of the calibration process for long-term
estimates are well known.56 jn some cases, where a more accurate model
is not available, calibration may be the best alternative for improving
the accuracy of the estimated concentrations needed for control strategy
evaluations. When calibration is warranted, the procedures described in
the "Addendum to User's Guide for Climatological Dispersion Model"5?
should be followed.
Calibration of short-term models is not common prac-
tice and is subject to much greater error and misunderstanding. There
have been attempts by some to compare short-term estimates and measure-
ments on an event-by-event basis and then to calibrate a model with
results of that comparison. This approach is severely limited by un-
certainties in both source and meteorological data and therefore it is
difficult to precisely estimate the concentration at an exact location
for a specific increment of time. Such uncertainties make calibration
of short-term models of questionable benefit. Therefore, short-term
model calibration is unacceptable.
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9.0 MODEL INPUT DATA
Data bases and related procedures for estimating input parameters
are an integral part of the modeling procedure. The most appropriate
data available should always be selected for use in modeling analyses.
Concentrations can vary widely depending on the source data or meteor-
ological data used. Input data are a major source of inconsistencies
in any modeling analysis. This section attempts to minimize the uncer-
tainty associated with data base selection and use by identifying
requirements for data used in modeling. A checklist of input data
requirements for modeling analyses is included as Appendix C. Specific
data requirements and formats for individual models are described in
detail in the users' guide for each model.
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9.1 Source Data
9.1.1 Discussion
Sources of pollutants can be classified as point, line
and area/volume sources. Point sources are defined in terms of size and
may vary from program to program. The line sources most frequently con-
sidered are roadways and streets along which there are well-defined move-
ments of motor vehicles, but they may be lines of roof vents or stacks
such as in aluminum refineries. Area and volume sources are often
collections of a multitude of minor sources with individually small
emissions that are impractical to consider as separate point or line
sources. Large area sources are typically treated as a grid network of
square areas, with pollutant emissions distributed uniformly within
each grid square.
Emission factors are compiled in an EPA publication
commonly known as AP-42^8; an indication of the quality and amount of
data on which many of the factors are based is also provided. Other
information concerning emissions is available in EPA publications
relating to specific source categories. The Regional Office should be
consulted to determine appropriate source definitions and for guidance
concerning the determination of emissions from and techniques for
modeling the various source types.
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9.1.2 Recommendations
For point source applications the load or operating
condition that causes maximum ground-level concentrations should be
established. As a minimum, the source should be modeled using the design
capacity (100 percent load). If a source operates at greater than design
capacity for periods that could result in violations of the standards or
PSD increments, this load* should be modeled. Where the source operates
at substantially less than design capacity, and the changes in the stack
parameters associated with the operating conditions could lead to higher
ground level concentrations, loads such as 50 percent and 75 percent of
capacity should also be modeled. A range of operating conditions should
be considered in screening analyses; the load causing the highest concen-
tration, in addition to the design load, should be included in refined
modeling. The following example for a power plant is typical of the kind
of data on source characteristics and operating conditions that may be
needed. Generally, input data requirements for air quality models neces-
sitate the use of metric units; where English units are common for
engineering usage, a conversion to metric is required.
a. Plant layout. The connection scheme between boilers
and stacks, and the distance and direction between stacks, building para-
meters (length, width, height, location and orientation relative to stacks)
for plant structures which house boilers, control equipment, and surrounding
buildings within a distance of approximately five stack heights.
b. Stack 'parameters. For all stacks, the stack height
and diameter (meters), and the temperature (K) and volume flow rate
(actual cubic meters per second) or exit gas velocity (meters per second)
for operation at 100 percent, 75 percent and 50 percent load.
c. Boiler size. For all boilers, the associated
megawatts, 10^ BTU/hr, and pounds of steam per hour, and the design and/or
actual fuel consumption rate for 100 percent load for coal (tons/hour),
oil (barrels/hour), and natural gas (thousand cubic feet/hour).
d. Boiler parameters. For all boilers, the percent
excess air used, the boiler type (e.g., wet bottom, cyclone, etc.), and
the type of firing (e.g., pulverized coal, front firing, etc.).
e. Operating conditions. For all boilers, the type, amount
and pollutant contents of fuel, the total hours of boiler operation and the
boiler capacity factor during the year, and the percent load for peak conditions.
*Malfunctions which may result in excess emissions are not considered
to be a normal operating condition. They generally should not be considered
in determining allowable emissions. However, if the excess emissions are
the result of poor maintenance, careless operation, or other preventable
conditions, it may be necessary to consider them in determining source impact.
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f. Pollution control equipment parameters. For each
boiler served and each pollutant affected, the type of emission control
equipment, the year of its installation, its design efficiency and mass
emission rate, the date of the last test and the tested efficiency, the
number of hours of operation during the latest year, and the best
engineering estimate of its projected efficiency if used in conjunction
with coal combustion; data for any anticipated modifications or additions,
g. Data for new boilers or stacks. For all new
boilers and stacks under construction and for all planned modifications
to existing boilers or stacks, the scheduled date of completion, and
the data or best estimates available for items (a) through (f) above
following completion of construction or modification.
In multi-source applications for compliance with short
term ambient standards, control strategies for point sources should be
tested using allowable emission rates and design capacity (100 percent
load). For quarterly and annual standards, historical maximum operating
rates, based on the last 3 years of operation, can be used in the
analysis. In the case where the physical characteristics of the source
make it impossible to emit at the allowable rate, the achievable maximum
emission rate may be used as long as this rate is restricted by an
enforceable permit condition. Emissions from area sources should
generally be based on annual average conditions. The source input
information in each model user's guide should be carefully consulted
and the checklist in Appendix C should be consulted for other possible
emission data that could be helpful.
Line source modeling of streets and highways requires
data on the width of the roadway and the median strip, the types and
amounts (grams per second per meter) of pollutant emissions, the number
of lanes, the emissions from each lane and the height of emissions.
The location of the ends of the straight roadway segments should be
specified by appropriate grid coordinates. Detailed information and
data requirements for modeling mobile sources of pollution are provided
in the user's manuals for each of the models available for line source
modeling.
The impact of growth on emissions should be considered
in all modeling analyses covering existing sources. Increases^ in
emissions due to planned expansion or planned fuel switches should be
identified. Increases in emissions at individual sources that may be
associated with a general industrial/commercial/residential expansion
in multi-source urban areas should also be treated. For new sources the
impact of growth on emissions should generally be considered for the
period prior to the start-up date for the source. Such changes in
emissions should treat increased area source emissions, changes in
existing point source emissions which were not subject to preconstruc-
tion review, and emissions due to sources with permits to construct
that have not yet started operation.
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9.2 Background Concentrations
9.2.1 Discussion
Background concentrations are an essential part of the
total air quality concentration to be considered in determining source
impacts. Background air quality includes pollutant concentrations due
to: (1) natural sources; (2) nearby sources other than the one(s)
currently under consideration; and (3) unidentified sources.
Typically, air quality data should be used to establish
background concentrations in the vicinity of the source(s) under con-
sideration. The monitoring network used for background determinations
should conform to the same quality assurance and other requirements as
those networks established for PSD purposes.59 An appropriate data
validation procedure should be applied to the data prior to use.
If the source is not isolated, it may be necessary to
use a multi-source model to establish the impact of nearby sources.
Background concentrations should be determined for each critical (con-
centration) averaging time.
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9.2.2 Recommendations (Isolated Single Source)
Two options are available to determine background near
isolated sources.
Option One: Use air quality data collected in the
vicinity of the source to determine the background concentration for
the averaging times of concern.* Determine the mean background
concentration at each monitor by excluding values when the source in
question is impacting the monitor. The mean annual background is the
average of the annual concentrations so determined at each monitor. For
shorter averaging periods, the meteorological conditions accompanying
the concentrations of concern should be identified. Concentrations for
meteorological conditions of concern, at monitors not impacted by the
source in question, should be averaged for each separate averaging time
to determine the average background value. Monitoring sites inside a
90° sector downwind of the source may be used to determine the area
of impact. One hour concentrations may be added and averaged to
determine longer averaging periods.
Option Two: If there are no monitors located in the
vicinity of the source, a "regional site" may be used to determine
background. A "regional site" is one that is located away from the
area of interest but is impacted by similar natural and unidentified
sources.
9.2.3 Recommendations (Multi-Source Areas)
In multi-source areas two components of background
should be determined.
Nearby Sources: All sources expected to cause a
significant concentration gradient in the vicinity of the source or
sources under consideration should be explicitly modeled. For evaluation
against annual standards these sources under consideration should be
modeled at worst case actual emissions. For evaluation against short
term standards these sources should be modeled at maximum allowable
emissions. The nearby source inventory should be determined in consul-
tation with the local air pollution control agency. It is envisioned
that the nearby sources and the sources under consideration will be
evaluated together using an appropriate Appendix A model.
*For purposes of PSD, the location of monitors as well as data quality
assurance procedures must satisfy requirements listed in the PSD
Monitoring Guidelines.54
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The impact of the nearby sources should be examined
at locations where interactions between the plume of the point source
under consideration and those of nearby sources (plus natural background)
can occur. Significant locations include: (1) the area of maximum
impact of the point source; (2) the area of maximum impact of nearby
sources; and (3) the area where all sources combine to cause maximum
impact. These locations may be identified through trial and error
analyses.
Other Sources: That portion of the background
attributable to all other sources (e.g., natural sources, minor sources
and distant major sources) should be determined by the procedures found
in Section 9.2.2.
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9.3 Meteorological Input Data
The meteorological data used as input to a dispersion model
should be selected on the basis of spatial and climatological (temporal)
representativeness as well as the ability of the individual parameters
selected to characterize the transport and dispersion conditions in the
area of concern. The representativeness of the data is dependent on:
(1) the proximity of the meteorological monitoring site to the area
under consideration; (2) the complexity of the terrain; (3) the exposure
of the meteorological monitoring site; and (4) the period of time during
which data are collected. The spatial representativeness of the data
can be adversely affected by large distances between the source and
receptors of interest and the complex topographic characteristics of
the area. Temporal representativeness is a function of the year-to-year
variations in weather conditions.
Model input data are normally obtained either from the National
Weather Service or as part of an on-site measurement program. Some
recommendations for the use of each type of data are included in this
subsection.
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9.3.1 Length of Record of Meteorological Data
9.3.1.1 Discussion
The model user should acquire enough
meteorological data to ensure that worst-case meteorological conditions
are adequately represented in the model results. The trend toward
statistically based standards suggests a need for all meteorological
conditions to be adequately represented in the data set selected for
model input. The number of years of record needed to obtain a stable
distribution of conditions depends on the variable being measured and
has been estimated by Landsberg and Jacobs60 for various parameters.
Although that study indicates in excess of 10 years may be required to
achieve stability in the frequency distributions of some meteorological
variables, such long periods are not reasonable for model input data.
This is due in part to the fact that hourly data in model input format
are frequently not available for such periods and that hourly calculations
of concentration for long periods are prohibitively expensive. A recent
study6* compared various periods from a 17-year data set to determine the
minimum number of years data needed to approximate the concentrations
modeled with a 17-year period of meteorological data from one station.
This study indicated that the variability of model estimates due to the
meteorological data input was adequately reduced if a 5-year period of
record of meteorological input was used.
It should be noted that if less than 5 years of model
input data are used in a modeling analysis to obtain a high second-high
concentration, there is a degree of uncertainty that should be considered
when applying those model results to the establishment of emission limits.
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9.3.1.2 Recommendations
Five years of representative meteorological
data should be used when estimating concentrations with an air quality
model. Consecutive years from the most recent, readily available
5-year period are preferred. The meteorological data may be data
collected either onsite or at the nearest National Weather Service
(NWS) station. If the source is large, e.g., a 500 MW power plant, the
use of 5 years of NWS meteorological data or at least 1 year of site-
specific data is required. As many years, up to five, of site-specific
data as are available should be used; such data should have been
subjected to quality assurance procedures as described in Section
9.3.3.2.
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9.3.2 National Weather Service Data
9.3.2.1 Discussion
The National Weather Service (NWS) meteorological
data are routinely available and familiar to most model users. Although
the NWS does not provide direct measurements of all the needed dispersion
model input variables, methods have been developed and successfully used
to translate the basic NWS data to the needed model input. Direct meas-
urements of model input parameters have been made for limited model studies
and those methods and techniques are becoming more widely applied; however,
most model applications still rely heavily on the NWS data.
There are two standard formats of the NWS data
for use in air quality models. The short term models use the standard
hourly weather observations available from the National Climatic Data
Center (NCDC). 'These observations are then "preprocessed" before they
can be used in the models. "STAR" summaries are available from NCDC for
long term model use. These are joint frequency distributions of wind
speed, direction and P-G stability category. They are used as direct
input to models such as CDMQC.57
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9.3.2.2 Recommendations
The preferred short term models listed in
Appendix A all accept as input the NWS meteorological data preprocessed
into model compatible form. Long-term (monthly seasonal or annual) pre-
ferred models use NWS "STAR" summaries. Summarized concentration esti-
mates from the short-term models may also be used to develop long-term
averages; however, concentration estimates based on the two separate
input data sets may not necessarily agree.
Although most NWS measurements are made at a
standard height of 10 meters, the actual anemometer height should be
used as input to the preferred model.
National Weather Service wind directions are
reported to the nearest 10 degrees. A specific set of randomly generated
numbers has been developed for use with the preferred EPA models and should
be used to ensure a lack of bias in wind direction assignments within the
models;
Data from FAA and military stations may be
used if these data are equivalent in accuracy and detail to the NWS data.
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9.3.3 Site-Speci fie Data
9.3.3.1 Discussion
Spatial or geographical representativeness is
best achieved by collection of all of the needed model input data at
the actual site of the source(s). Site-specific measured data are
therefore preferred as model input, provided appropriate instrumentation
and quality assurance procedures are followed and that the data collected
are representative (free from undue local or "micro" influences) and
compatible with the input requirements of the model to be used. However,
direct measurements of all the needed model input parameters may not be
possible. This section discusses suggestions for the collection and
use of on-site data. Since the methods outlined in this section are
still being tested, comparison of the model parameters derived using
these site-specific data should be compared at least on a spot-check
basis, with parameters derived from more conventional observations. Of
particular concern are stability category determinations.
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9.3.3.2 Recommendations
Site-sped fie Data Collection
Guidance provided in the "Ambient Monitoring
Guidelines for Prevention of Significant Deterioration (PSD)"59 should
be used for the establishment of special monitoring networks for PSD and
other air quality modeling analyses. That guidance includes requirements
and specifications for both pollutant and meteorological monitoring.
Additional information is available in the EPA quality assurance hand-
books and site selection guidance documents published on a pollutant-
by-pollutant basis (see the Air Programs Report and Guidelines Index
EPA-450/2-82-016). Volume IV of the series of reports "Quality Assurance
Handbook for Air Pollution Measurement Systems"^ contains such information
for meteorological measurements. As a minimum, site-specific measurements
of ambient air temperature, transport wind speed and direction, and the
parameters to determine Pasquill-Gifford stability categories should be
available in meteorological data sets to be used in modeling. Care should
be taken to ensure that monitors are located to represent the area of con-
cern and that they are not influenced by very localized effects. Site-
specific data for model applications should cover as long a period of meas-
urement as is possible to ensure adequate representation of "worst-case"
meteorology. The Regional Office will determine the appropriateness of
the measurement locations.
All site-specific data should be reduced to
hourly averages. Table 9-1 lists the wind related parameters and the
averaging time requirements.
Temperature Measurements
Temperature measurements should be made at
standard shelter height in accordance with the guidance referenced above.
Wind Measurements
In addition to surface wind measurements, the
transport wind direction should be measured at an elevation as close as
possible to the plume height. To approximate this, if a source has a
stack below 100 m, select the stack top height as the transport wind
measurement height. For sources with stacks extending above 100 m, a
100 m tower is suggested unless the stack top is significantly above
100 meters (200 m or more). In cases with stacks 200 m or above, the
Regional Office should determine the appropriate measurement height on
a case-by-case basis. Remote sensing may be a feasible alternative.
The dilution wind speed used in determining plume rise and also used in
the Gaussian dispersion equation is, by convention, defined as the wind
speed at stack top.
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For routine tower measurements and surface.
measurements the wind speed should be measured using an anemometer and
the wind direction measured using a horizontal vane. Specifications
for wind measuring instruments and monitoring systems are contained in
the "Ambient Air Monitoring Guidelines for Prevention of Significant
Deterioration (PSD)"^9 and in the quality assurance handbook on meteoro-
logical measurements^^, irwin^S provides additional guidance for
processing wind data.
Stability Categories
The Pasquill-Gifford (P-G) stability categories,
as originally defined, couple near-surface measurements of wind speed
with subjectively determined insolation assessments based on hourly
cloud cover and ceiling observations. The wind speed measurements are
made at or near 10 m. The insolation rate is typically assessed using
the cloud cover and ceiling height criteria outlined by Turner^, often
the cloud cover data are not available in site-specific data sets. In
the absence of such observations, it is recommended that the P-G stability
category be estimated using Table 9-2. This table requires 0^, the
standard deviation of the vertical wind direction fluctuations. If
the surface roughness of the area surrounding the source is different
from the 15 cm roughness length upon which the table is based, an adjustment
may be made as.indicated in footnote 2 of Table 9-2. o£ is computed from
direct measurements of the elevation angle of the vertical wind directions.
available, OE may
If measurements
be determined using the
of elevation
transform:
angle are not
where: OE = the standard deviation of the vertical wind
direction fluctuations over a one-hour period.
ow = the standard deviation of the vertical wind speed
fluctuations over a one-hour period.
u = the average horizontal wind speed for a one-hour
period.
Since both ow and u are in meters per second, OE is in radians. To
use OE in Table 9-2, OE must be coverted to degrees. It is recommended
that a vertically mounted propeller anemometer be used to measure the
vertical wind speed fluctuations. The instrument should meet the
specifications given in the Ambient Monitoring Guidelines referenced
above. Compute ow directly each hour using at least 3600 values based
on a recommended readout interval of 1 second. If OE is computed using
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the output of the anemometer by other than direct application of the
formula for a variance, the method should be demonstrated to be equivalent
to direct computation. Both the vertical wind speed fluctuations and
the horizontal wind speed should be measured at the same level. Moreover,
these measurements should be made at a height of 10 m for use in estimating
the P-G stability category. Where trees or land use preclude measurements
as low as 10 m, measurements should be made at a height above the
obstructions.
If on-site measurements of either o£ or
ow are not available, stability categories may be determined using
the horizontal wind direction fluctuation, OA, as outlined by Irwin .
Irwin includes the Mitchell and Timbre^5 method that uses categories
of oA66 listed in Table 9-2, as an initial estimate of the P-G stability
category. This relationship is considered adequate for daytime use. During
the nighttime (one hour prior to sunset to one hour after sunrise), the
adjustments given in Table 9-3 should be applied to these categories.
As with OA an hourly average OA may be adjusted for surface roughness
by multiplying the table values of o/\ by a factor based on the average
surface routhness length determined within 1 to 3 km of the source.
The need for such adjustments should be determined on a case-by-case
basis.
Wind direction meander may, at times, lead to
an erroneous determination of P-G stability category based on o/\. To
minimize wind direction meander contributions, OA may be determined
for each of four 15-minute periods in an hour. To obtain the oy\
for stability determinations in these situations, take the square root
of one-quarter of the sum of the squares of the four 15-minute o/\'s,
as illustrated in the footnote to Table 9-1. While this approach is
acceptable for determining stability, o/^'s calculated in this manner
are not likely to be suitable for input to models under development
that are designed to accept on-site hourly o's based on 60-minute
periods.
There has not been a widespread use of OE and
OA to determine P-G categories. As mentioned in the footnotes to
Table 9-2, the techniques outlined have not been extensively tested.
The criteria listed in Table 9-2, are for OE and OA values at 10 m.
For best results, the oj: and OA values should be for heights near the
surface as close to 10 m as practicable. Obstacles and large roughness
elements may preclude measurements as low as 10 m. If circumstances
preclude measurements below 30 m, the Regional Meteorologist should be
consulted to determine the appropriate measurements to be taken on a
case-by-case basis. The criteria listed in Tables 9-2 and 9-3 result
from studies conducted in relatively flat terrain in rather ideal
circumstances. For routine applications where conditions are often
less than ideal, it is recommended that a temporary program be initiated
at each site to spot-check the stability class estimates. Irwin1s
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method using CE or o^ should be compare with P-6 stability class
estimates using on-site wind speed and subjective assessments of
the insolation based on ceiling height and cloud cover. The Regional
Meteorologist should be consulted when using the spot-check results to
refine and adjust the preliminary criteria outlined in Tables 9-2 and
9-3.
In summary, when on-site data sets are being
used, Pasquill-Gifford stability categories should be determined from
one of the following schemes listed in the order of preference:
(1) Turner's 1964 methodic using site-specific
data which include cloud cover, ceiling height and surface (~10 m)
wind speeds.
(2) c£ from site-specific measurements and
Table 9-2 ( c£ may be determined from elevation angle measurements
or may be estimated from measurements of QW according to the transform:
(see page 9-15)).
(3) o/\ from site-specific measurements and
Tables 9-2 and 9-3.
(4) Turner's 1964 method using site-specific
wind speed with cloud cover and ceiling height from a nearby NWS site.
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Table 9-1
Averaging Times for Site-Specific Wind and Turbulence Measurements
Parameter Averaging Time
Surface wind speed 1-hr
(for use in stability
determinations)
Transport direction 1-hr
Dilution wind speed 1-hr
Turbulence measurements 1-hr*
(o£ and OA) for use
in stability determinations
*To minimize meander effects in OA when wind conditions are light and/or
variable, determine the hourly average 0 from four 15-minute o1s according
to the following formula:
- hr =
°152 + °152 + °152 + °152
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Table 9-2
Wind Fluctuation Criteria For Estimating Pasquill Stability Categories*
Pasquill
Stability
Category
Standard Deviation of
the Horizontal Wind
Direction Fluctuations**, ***
(in degrees)
Standard Deviation of
the Vertical Wind
Direction Fluctuations**, ****
(in degrees)
A
B
C
D
E
F
Adapted from:
Greater than 22.5°
17.5° to 22.5°
12.5° to 17.5°
7.5°- to 12.5°
3.8° to 7.5°
Less than 3.8°
Irwin,-J., I96064.
Greater than 11.5°
10.0° to 11.5°
7.8° to 10.0°
5.0° to 7.8°
2.4° to 5.0°
Less than 2.4°
*These criteria are appropriate for steady-state conditions, a measurement
height of 10 m, for level terrain, and an aerodynamic surface roughness
length of 15 cm. Care should be taken that the wind sensor is responsive
enough for use in measuring wind direction fluctuations.59
**A surface roughness factor of (zQ/15 cm)^'2, where ZQ is the average
surface roughness in centimeters within a radius of T-3 km of the
source, may be applied to the table values. It should be noted that
this factor, while theoretically sound, has not been subjected to
rigorous testing and may not improve the estimates in all circumstances.
A table of z0 values that may be used as a guide to estimating surface
roughness is given in Smedman-Hogstrom and Hogstrom.67
***These criteria are from a NRC proposal.6** It would seem reasonable to
restrict the possible categories to A through D during daytime hours with
a restriction that for wind speeds above 6 m/s, conditions are neutral.
Likewise, during the nighttime hours, some restrictions, as in Table 9-3,
are needed to preclude occurrences of categories A through C.
****These criteria were adapted from those presented by Smith and Howard.68
It would seem reasonable to restrict the possible categories to A
through D during the daytime hours and to categories D through F
during the nighttime hours. During the night, conditions are neutral
for wind speeds equal to or greater than 5 m/s.
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Table 9-3
Nighttime* P-G Stability Categories Based on OA from Table 9-2
If the OA And the Wind Then the Pasquill
Stability Speed at 10 m is Stability Category
Category is m/s is
A <2.9 F
2.9 to 3.6 E
>3.6 D
B <2.4 F
2.4 to 3.0 E
^3.0- D
C <2.4. E
>2.4 D
D wind speed not considered D
E wind speed not considered** E
F wind speed not considered*** F
Adapted from Irwin, J. 198064.
*Nighttime "is considered to be from V hour prior to sunset to
1 hour after sunrise.
**The original Mitchell and Timbre^ table had no wind speed restrictions;
However, the original Pasquill criteria suggest that for wind speeds greater
than 5 m/s, neutral conditions would be appropriate.
***The original Mitchell and Timbre6^ table had no wind speed restrictions;
however, the original Pasquill criteria suggest that for wind speeds greater
than or equal to 5 m/s, the D category would be appropriate, and for
wind speeds between 3 m/s and 5 m/s, the E category would be appropriate.
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9.3.4 Treatment of Calms
9.3.4.1 Discussion
Treatment of calm or light and variable wind
poses a special problem in model applications since Gaussian models
assume that concentration is inversely proportional to wind speed. The
NWS generally lists all winds as calm when the wind speed indicator
drops to approximately 1 m/s or below. Concentrations become unrealis-
tically large when wind speeds less than 1 m/s are input to the model.
A procedure has been developed for use with NWS data to prevent the
occurrence of overly conservative concentration estimates during periods
of calms. This procedure acknowledges that a Gaussian plume model does
not apply during calm conditions and that our knowledge of plume behavior
and wind patterns during these conditions does not, at present, permit
the development of a better technique. Therefore, the procedure disregards
hours when the speed is recorded as less than 1 m/s and concentration
calculations using wind speed and direction cannot be made. The hour
is treated as missing and a convention for handling missing hours is
recommended.
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9.3.4.2 Recommendations
Hourly concentrations calculated with Gaussian
models using calms should not be considered valid; the wind and concentra-
tion estimates for these hours should be disregarded and considered to
be missing. Critical concentrations for 3, 8, and 24-hour averages
should be calculated by dividing the sum of the hourly concentration
for the period by the number of valid or nonmissing hours. If the
total number of valid hours is less than 18 for 24-hour averages, less
than 6 for 8-hour averages or less than 3 for 3-hour averages, the
total concentration should be divided by 18 for the 24-hour average, 6
for the 8-hour average and 3 for the 3-hour average. For annual averages,
the sum of all valid hourly concentrations is divided by the number of
non-calm hours during the year. A post-processor computer program
(CALMPRO) has been prepared following these instructions and is available
through any EPA Regional Office.
The recommendations above apply to the use of
calms for short-term averages and do not apply to the determination of
long-term averages using "STAR" data summaries. Calms should continue
to be included in the preparation of "STAR" summaries.
Stagnant conditions, including extended periods
of calms, often produce high concentrations over wide areas for relatively
long averaging periods. The standard short-term Gaussian models are
often not applicable to .such situations. When stagnation conditions
are of concern, other modeling techniques should be considered on a
case-by-case basis. (See also Section 8.2.10)
Measured on-site wind speeds of less than
1 m/s should be set equal to 1 m/s when used as input to Gaussian
models. Wind direction for these low wind speed hours may be determined
on a case-by-case basis from the available site-specific records. If the
wind is indeterminate with respect to speed or direction, it should be
treated as missing data and short-term averages should then be calculated
as above.
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10.0 ACCURACY AND UNCERTAINTY OF MODELS
10.1 Discussion
Increasing reliance has been placed on concentration estimates
from models as the primary basis for regulatory decisions concerning
source permits and emission control requirements. In many situations,
such as review of a proposed source, no practical alternative exists.
Therefore, there is an obvious need to know how accurate models really
are and how any uncertainty in the estimates affects regulatory decisions.
EPA recognizes the need for incorporating such information and has spon-
sored workshops11»69 on model accuracy, the possible ways to quantify
accuracy, and on considerations in the incorporation of model accuracy
and uncertainty in the regulatory process. The Second (EPA) Conference
on Air Quality Modeling, August 1982,70 was devoted to that subj'ect.
10.1.1 Overview of Model Uncertainty
Dispersion models generally attempt to estimate conc-
centrations at specific sites that really represent an ensemble average of
numerous repetitions of the same event. The event is characterized by
measured or "known" conditions that are input to the models, e.g., wind
speed, mixed layer height, surface heat flux, emission characteristics,
etc. However, in addition to the known conditions, there are unmeasured
or unknown variations in the conditions of this event, e.g., unresolved
details of the atmospheric flow such as the turbulent velocity field.
These unknown conditions, may vary among repetitions of the event. As
a result, deviations in observed concentrations from their ensemble
average, and from the concentrations estimated by the model, are likely
10-1
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to occur even though the known conditions are fixed. Even with a perfect
model that predicts the correct ensemble average, there are likely to be
deviations from the observed concentrations in individual repetitions of
the event, due to variations in the unknown conditions. The statistics
of these concentration residuals are termed "inherent" uncertainty.
Available evidence suggests that this source of uncertainty alone may be
responsible for a typical range of variation in concentrations of as much
as +50 percent.''1
Moreover, there is "reducible" uncertainty^ associated
with the model and its input conditions; neither models nor data bases are
perfect. Reducible uncertainties are caused by: (1) uncertainties in the
input values of the known conditions—emission characteristics and meteoro-
logical data; (2) errors in the measured concentrations which are used to
compute the concentration residuals; and (3) inadequate model physics and
formulation. The "reducible" uncertainties can be minimized through better
(more accurate and more representative) measurements and better model physics.
To use the terminology correctly, reference to model
accuracy should be limited to that portion of reducible uncertainty which
deals with the physics and the formulation of the model. The accuracy of
the model is normally determined by an evaluation procedure which involves
the comparison of model concentration estimates with measured air quality
data.73 The statement of accuracy is based on statistical tests or perform-
ance measures such as bias, noise, correlation, etc.1! However, information
that allows a distinction between contributions of the various elements of
inherent and reducible uncertainty is only now beginning to emerge. As a
10-2
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result most discussions of the accuracy of models make no quantitative
distinction between (1) limitations of the model versus (2) limitations
of the data base and of knowledge concerning atmospheric variability.
The reader should be aware that statements on model accuracy and uncer-
tainty may imply the need for improvements in model performance that
even the "perfect" model could not satisfy.
10.1 .2 Studies of Model Accuracy
A number of studies74'75 have been conducted to examine
model accuracy, particularly with respect to the reliability of short-term
concentrations required for ambient standard and increment evaluations. The -
results of these studies are not surprising. Basically, they confirm what
leading atmospheric scientists have said for some time: (1) models are more
reliable for estimating longer time-averaged concentrations than for esti-
mating short-term concentrations at specific locations; and (2) the models
are reasonably reliable in estimating the magnitude of highest concentrations
occurring sometime, somewhere within an area. For example, errors in highest
estimated concentrations of + 10 to 40 percent are found to be typical ,^6
i.e., certainly well within the often-quoted factor-of-two accuracy that has
long been recognized for these models. However, estimates of concentrations
that occur at a specific time and site, are poorly correlated with actually
observed concentrations and are much less reliable.
As noted above, poor correlations between paired concen-
trations at fixed stations may be due to "reducible" uncertainties in knowl-
edge of the precise plume location and to unquantified inherent uncertain-
ties. For example, Pasquill77 estimates that, apart from data input errors,
10-3
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maximum ground-level concentrations at a given hour for a point source in
flat terrain could be in error by 50 percent due to these uncertainties.
Uncertainty of five to 10 degrees in the measured wind direction, which
transports the plume, can result in concentration errors of 20 to 70 per-
cent for a particular time and location, depending on stability and sta-
tion location. Such uncertainties do not indicate that an estimated con-
centration does not occur, only that the precise time and locations are
in doubt.
10.1.3 Use of Uncertainty in Decision-Making
The accuracy of model estimates varies with the model
used, the type of application, and site-specific characteristics. Thus, it
is desirable to quantify the accuracy or uncertainty associated with concen-
tration estimates used in decision-making. Communications between modelers
and decision-makers must be fostered and further developed. Communications
concerning concentration estimates currently exist in most cases, but the
communications dealing with the accuracy of models and its meaning to the
decision-maker are limited by the lack of a technical basis for quantifying
and directly including uncertainty in decisions. Procedures for quantifying
and interpreting uncertainty in the practical application of such concepts
are only beginning to evolve; much study is still required.69>7°»72
In all applications of models an effort is encouraged
to identify the reliability of the model estimates for that particular area
and to determine the magnitude and sources of error associated with the use
of the model. The analyst is responsible for recognizing and quantifying
limitations in the accuracy, precision and sensitivity of the procedure.
10-4
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Information that might be useful to the decision-maker in recognizing the
seriousness of potential air quality violations includes such model accu-
racy estimates as accuracy of peak predictions, bias, noise, correlation,
frequency distribution, spatial extent of high concentration, etc. Both
space/time pairing of estimates and measurements and unpaired comparisons
are recommended. Emphasis should be on the highest concentrations and
the averaging times of the standards or increments of concern. Where
possible, confidence intervals about the statistical values should be
provided. However, while such information can be provided by the modeler
to the decision-maker, it is unclear how this information should be used to
make an air pollution control decision. Given a range of possible outcomes,
it is easiest and tends to ensure consistency if the decision-maker confines
his judgment to use of the "best estimate" provided by the modeler. This is
an indication of the practical limitations imposed by current abilities of
the technical community.
To improve the basis for decision-making, EPA has devel-
oped and is continuing to study procedures for determining the accuracy of
models, quantifying the uncertainty, and expressing confidence levels in
decisions that are made concerning emissions controls.78»?9 However, work
in this area involves "breaking new ground" with slow and sporadic progress
likely. As a result, it may be necessary to continue using the "best esti-
mate" until sufficient technical progress has been made to meaningfully
implement such concepts dealing with uncertainty.
10.1.4 Evaluation of Models
A number of actions are being taken to ensure that the
best model is used correctly for each regulatory application and that a
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model is not arbitrarily imposed. First, this guideline clearly recommends
that the most apropriate model be used in each case. Preferred models,
based on a number of factors, are identified for many uses. General guid-
ance on using alternatives to the preferred models is also provided. Second,
all the models in eight categories (i.e., rural, urban, industrial complex,
reactive pollutants, mobile source, complex terrain, visibility and long-
range transport) that are candidates for inclusion in this guideline are
being subjected to a systematic performance evaluation and a peer scientific
review.80 The same data bases are being used to evaluate all models within
each of eight categories. Statistical performance measures, including meas-
ures of difference (or residuals) such as bias, variance of difference and
gross variability of the difference, and measures of correlation such as
time, space and time and space combined as recommended by the AMS Woods
Hole Workshop,11 are being followed. The results of the scientific review
are being incorporated in this guideline and will be the basis for future
revision.12,13 Third, more specific information has been provided for
justifying the site specific use of alternative models in the document
"Interim Procedures for Evaluating Air Quality Models."14 This document
provides a method, following recommendations of the Woods Hole Workshop,
that allows a judgment to be made as to what models are most appropriate
for a specific application. For the present, performance and the theo-
retical evaluation of models are being used as an indirect means to quantify
one element of uncertainty in air pollution regulatory decisions.
In addition to performance evaluation of models,
sensitivity analyses are encouraged since they can provide additional
10-6
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information on the effect of inaccuracies in the data bases and on the
uncertainty in model estimates. Sensitivity analyses can aid in deter-
mining the effect of inaccuracies of variations or uncertainties in the
data bases on the range of likely concentrations. Such information may
be used to determine source impact and to evaluate control strategies.
Where possible, information from such sensitivity analyses should be made
available to the decision-maker with an appropriate interpretation of
the effect on the critical concentrations.
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10.2. Recommendations
No specific guidance on the consideration of model
uncertainty in decision-making is being given at this time. There is
incomplete technical information on measures of model uncertainty that
are most relevant to the decision-maker. It is not clear how a decision-
maker could use such information, particularly given limitations of the
Clean Air Act. As procedures for considering uncertainty develop and
become implementable, this guidance will be changed and expanded. For
the present, continued use of the "best estimate" is acceptable and is
consistent with CAA requirements.
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11.0 REGULATORY APPLICATION OF MODELS
11.1 Discussion
Procedures with respect to the review and analysis of air
quality modeling and data analyses in support of SIP revisions, PSD per-
mitting or other regulatory requirements need a certain amount of stand-
ardization to ensure consistency in the depth and comprehensiveness of
both the review and the analysis itself. This section recommends pro-
:
cedures that permit some degree of standardization while at the same time
allowing the flexibility needed to assure the technically best analysis
for each regulatory application.
Dispersion model estimates, especially with the support of
measured air quality data, are the preferred basis for air quality demon-
strations. Nevertheless, there are instances where the performance of
recommended dispersion modeling techniques, by comparison with observed
air quality data, may be shown to be less than acceptable. Also, there
may be no recommended modeling procedure suitable for the situation. In
these instances, emission limitations may be established solely on the
basis of observed air quality data. The same care should be given to the
analysis of the air quality data as would be applied to a modeling analysis.
The current NAAQS for S02, TSP, and CO are all stated in terms
of a concentration not to be exceeded more than once a year. There is
only an annual standard for N02« The ozone standard was revised in 1979
and that standard permits the exceedance of a concentration on an average
of not more than once a year, averaged over a 3-year period.5.81 This
11-1
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represents a change from a deterministic to a more statistical form of the
standard and permits some consideration to be given to unusual circumstances.
The NAAQS are subjected to extensive review and possible revision every 5
years.
This section discusses general requirements for concentration
estimates and identifies the relationship to emission limits. The follow-
ing recommendations apply to: (1) revisions of State Implementation Plans;
(2) the review of new sources and the prevention of significant deterioration
(PSD); and (3) analyses of the emissions trades ("bubbles").
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11.2 Recommendations
11.2.1 Analysis Requirements
Every effort should be made by the Regional Office to
meet with all parties involved in either a SIP revision or a PSD permit
application prior to the start of any work on such a project. During this
meeting, a protocol should be established between the preparing and review-
ing parties to define the procedures to be followed, the data to be col-
lected, the model to be used, and the analysis of the source and concentra-
tion data. An example of requirements for such an effort is contained in
the Air Quality Analysis Checklist included here as Appendix C. This check-
list suggests the level of detail required to assess the air quality result-
ing from the proposed action. Special cases may require additional data
collection or analysis and this should be determined and agreed upon at this
preapplication meeting. The protocol should be written and agreed upon by
the parties concerned, although a formal legal document is not intended.
Changes in such a protocol are often required as the data collection and anal-
ysis progresses. However, the protocol establishes a common understanding
of the requirements.
An air quality analysis should begin with a screening
model to determine the potential of the proposed source or control strategy
to violate the PSD increment or the NAAQS. It is recommended that the
screening techniques found in "Procedures for Evaluating Air Quality Impact
of New Stationary Sources"^ be used for point source analyses. Screen-
ing procedures for area source analysis are discussed in "Applying Atmos-
pheric Simulation Models to Air Quality Maintenance Areas'."°2
If the concentration estimates from screening techniques
indicate that the PSD increment or NAAQS may be approached or exceeded, then
a more refined modeling analysis is appropriate and the model user should
select a model according to recommendations in Sections 4, 5, 6 or 7. In
some instances, no refined technique may be specified in this guide for the
situation. The model user is then encouraged to submit a model developed
specifically for the case at hand. If that is not possible, a screening
technique may supply the needed results.
Regional Offices should require permit applicants to
incorporate the pollutant contributions of all sources into their analysis.
Where necessary this may include emissions associated with growth in the area
of impact of the new or modified source's impact. PSD air quality assessments
should consider the amount of the allowable air quality increment that has
already been granted to any other sources. The most recent source applicant
should be allowed the prerogative to remodel the existing or permitted
sources in addition to the one currently under consideration. This would
permit the use of newly acquired data or improved modeling techniques if
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such have become available since the last source was permitted. When
remodeling, the worst case used in the previous modeling analysis should
be one set of conditions modeled in the new analysis. All sources should
be modeled for each set of meteorological conditions selected and for all
receptor sites used in the previous applications as well as new sites
specific to the new source.
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11.2.2 Use of Measured Data in Lieu of Model Estimates
Modeling is the preferred method for determining emis-
sion limitations for both new and existing sources. When a preferred model
is available, model results alone (including background) are sufficient.
Monitoring will normally not be accepted as the sole basis for emission
limitation determination in flat terrain areas. In some instances when the
modeling technique available is only a screening technique, the addition of
air quality data to the analysis may lend credence to model results.
There are circumstances where there is no applicable
model, and measured data may need to be used. Examples of such situations
are: (1) complex terrain locations; (2) land/water interface areas; and
(3) urban locations with a large fraction of particulate emissions from
nontraditional sources. However, only in the case of an existing source
should monitoring data alone be a basis for emission limits. In addition,
the following items should be considered prior to the acceptance of the
measured data:
a. Does a monitoring network exist for the pollutants
and averaging times of concern;
b. Has the monitoring network been designed to locate
points of maximum concentration;
c. Do the monitoring network and the data reduction and
storage procedures meet EPA monitoring and quality assurance requirements;
d. Do the data set and the analysis allow impact of the
most important individual sources to be identified if more than one source
or emission point is involved;
e. Is at least one full year of valid ambient data
available; and
f. Can it be demonstrated through the comparison of
monitored data with model results that available models are not applicable?
The number of monitors required is a function of the problem being considered.
The source configuration, terrain configuration, and meteorological variations
all have an impact on number and placement of monitors. Decisions can only
be made on a case-by-case basis. The Interim Procedures for Evaluating Air
Quality Models*4 should be used in establishing criteria for demonstrating
that a model is not applicable.
Sources should obtain approval from the Regional Office
or reviewing authority for the monitoring network prior to the start of
monitoring. A monitoring protocol agreed to by all concerned parties is
highly desirable. The design of the network, the number, type and location
11-5
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of the monitors, the sampling period, averaging time as well as the need
for meteorological monitoring or the use of mobile sampling or plume track-
ing techniques, should all be specified in the protocol and agreed upon
prior to start-up of the network.
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11.2.3 Emission Limits
11.2.3.1 Design Concentrations
Emission limits should be based on concentra-
tion estimates for the averaging time that results in the most stringent
control requirements. The concentration used in specifying emission limits
is called the design value or design concentration and is a sum of the con-
centration contributed by the source and the background concentration.
To determine the averaging time for the design
value, the most restrictive National Ambient Air Quality Standard (NAAQS)
should be identified by calculating, for each averaging time, the ratio of
the estimated concentration (including background) to the applicable NAAQS.
The averaging time with the highest ratio of estimated concentration to
NAAQS identifies the most restrictive standard. If the annual average is
the most restrictive, the highest estimated annual average concentration
from one or a number of years of data is the design value. When short-
term standards are most restrictive, the frequency of occurrence of the
concentrations must be considered. For pollutants such as $03, the high-
est, second-highest concentration is the design value. For pollutants
with statistically based NAAQS, the design value is found by determining
the value that is not expected to be exceeded more than once per year over
the period specified in the standard.
When the highest, second-highest concentra-
tion is used in assessing potential violations of a short-term NAAQS,
criteria that are identified in "Guideline for Interpretation of Air Quality
Standards'^3 should be followed. This guideline specifies that a violation
of a short-term standard occurs at a site when the standard is exceeded a
second time. Thus, emission limits that protect standards for averaging
times of 24 hours or less are appropriately based on the highest, second-
highest estimated concentration plus a background concentration which can
reasonably be assumed to occur with the concentration.
11.2.3.2 Air Quality Standards
For new or modified sources to be located in
areas where the S02, TSP, lead, N02, or CO NAAQS are being attained, the
determination of whether or not the source will cause or contribute to an
air quality violation should be based on (1) the highest estimated annual
average concentration determined from annual averages of individual years
or (2) the the highest, second-highest estimated concentration for aver-
aging times of 24-hours or less. For lead, the highest estimated concen-
tration based on an individual calendar quarter averaging period should
be used. Background concentrations should be added to the estimated impact
of the source. The most restrictive standard should be used in all cases
to assess the threat of an air quality violation.
11-7
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11.2.3.3 PSD Air Quality Increments and Impacts
The allowable PSD increments for criteria
pollutants are established by regulation and cited in 40 CFR 51.24. These
maximum allowable increases in pollutant concentrations may be exceeded
once per year at each site, except for the annual increment that may not be
exceeded. The highest, second-highest increase in estimated concentrations
for the short-term averages as determined by a model should be less than or
equal to the permitted increment. The modeled annual averages should not
exceed the increment.
Screening techniques defined in Sections 4
and 5 can sometimes be used to estimate short-term incremental concen-
trations for the first new source that triggers the baseline in a given
area. However, when multiple increment-consuming sources are involved in
the calculation, the use of a refined model with at least one year of
on-site or five years of off-site NWS data is normally required. In such
cases, sequential modeling must demonstrate that the allowable increments
are not exceeded temporally and spatially, i.e., for all receptors for each
time period throughout the year(s) (time period means the appropriate PSD
averaging time, e.g., 3-hour, 24-hour, etc.).
The PSD regulations require an estimation
of the SC>2 and TSP impact on any Class I area. Normally, Gaussian models
should not be applied at distances greater than can be accommodated by the
steady state assumptions inherent in such models. The maximum distance for
refined Gaussian model application for regulatory purposes is generally con-
sidered to be 50 km. Beyond the 50 km range, screening techniques may be
used to determine if more refined modeling is needed. If refined models are
needed, long-range transport models should be considered in accordance with
Section 7.2.6. As previously noted in Sections 3 and 7, the need to involve
the Federal Land Manager in decisions on potential air quality impacts,
particularly in relation to PSD Class I areas, cannot be overemphasized.
11.2.3.4 Emissions Trading Policy (Bubbles)
EPA's Emissions Trading Policy, commonly
referred to as the "bubble policy," was proposed in the Federal Register
on April 7, 1982.84 Until a final policy is promulgated, principles con-
tained in the proposal should be used to evaluate trading activities which
become ripe for decision. Certain technical clarifications of the policy,
including procedures for modeling bubbles, were provided to the Regional
Offices in February, 1983.85
Emission increases and decreases within the
bubble should result in ambient air quality equivalence. Two levels of
analysis are defined for establishing this equivalence. In a Level I
analysis the source configuration and setting must meet certain limitations
(defined in the policy and clarification to the policy) that ensure ambient
11-8
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equivalence; no modeling is required. In a Level II analysis a modeling
demonstration of ambient equivalence is required but only the sources
involved in the emissions trade are modeled. The resulting ambient esti-
mates of net increases/decreases are compared to a set of significance
levels to determine if the bubble can be approved. A Level II analysis
requires the use of a refined model and one year of representative meteo-
reolgical data. Sequential modeling must demonstrate that the significance
levels are met temporally and spatially, i.e., for all receptors for each
time period throughout the year (time period means the appropriate NAAQS
averaging time, e.g., 3-hour, 24-hour, etc.)
For those bubbles that cannot meet the Level
I or Level II requirements, the Emissions Trading Policy allows for a Level
III analysis. A Level III analysis, from a modeling standpoint, is equiv-
alent to the requirements for a standard SIP revision where all sources
(and background) are considered and the estimates are compared to the NAAQS
as in Section 11.2.3.2.
The Emissions Trading Policy allows States
to adopt generic regulations for processing bubbles. The modeling proced-
ures recommended in this guideline apply to such generic regulations.
However, an added requirement is that the modeling procedures contained in-
any generic regulation must be replicable such that there is no doubt as to
how each individual bubble will be modeled. In general this means that the
models, the data bases and the procedures for applying the model must be
defined in the regulation. The consequences of the replicability require-
ment are that bubbles for sources located in complex terrain and certain
industrial sources where judgments must be made on source characterization
cannot be handled generically.
11-9
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12.0 REFERENCES*
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11. Fox, D. 6., 1981. Judging Air Quality Model Performance.
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Simulation Models to Air Quality Maintenance Areas. EPA Publication
No. EPA-450/4-74-013. U.S. Environmental Protection Agency, Research
Triangle Park, NC. (NTIS No. PB 237750).
12-8
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83. Environmental Protection Agency, 1977. Guidelines for Interpretation
of Air Quality Standards (Revised). OAQPS No. 1.2-008. U.S. Environ-
mental Protection Agency, Research Triangle Park, NC. (NTIS No.
PB 81-196420).
84. Environmental Protection Agency, 1982. Emissions Trading Policy State-
ment; General Principles for Creation, Banking, and Use of Emission
Reduction Credits. Federal Register, 47(67):15076-15086.
85. Meyer, S., 1983. Memorandum of February 17 to Regional Office Air
Management Division Directors, Emissions Trading Policy Technical
Clarifications. Office of Air, Noise and Radiation, U.S. Environmental
Protection Agency, Washington, DC. (Docket Reference No. II-B-34).
12-9
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13.0 BIBLIOGRAPHY*
American Meteorological Society, 1971-1983. Symposia on Turbulence,
Diffusion, and Air Pollution (1st - 6th). Boston, MA.
American Meteorological Society, 1977-1982. Joint Conferences on
Applications of Air Pollution Meteorology (1st - 3rd). Sponsored
by the American Meteorological Society and the Air Pollution Control
Association. Boston, MA.
American Meteorological Society, 1978. Accuracy of Dispersion Models.
Bulletin of the American Meteorological Society, 59(8) :1025-1026.
American Meteorological Society, 1981. Air Quality Modeling and the
Clean Air Act: Recommendations to EPA on Dispersion Modeling for
Regulatory Applications. Boston, MA.
Briggs, G. A., 1969. Plume Rise. U.S. Atomic Energy Commission Critical
Review Series, Oak Ridge National Labratory, Oak Ridge, TN.
Drake, R. L. and S. M. Barrager, 1979. Mathematical Models for Atmos-
pheric Pollutants. EPRI EA-1131. Electric Power Research Institute,
Palo Alto, CA.
Environmental Protection Agency, 1978. Workbook for Comparison of Air
Quality Models. EPA Publication No. £PA-450/2-78-028a and b. U.S.
Environmental Protection Agency, Research Triangle Park, NC.
Fox, D. G., and J. E. Fairobent, 1981. NCAQ Panel Examines Uses and Limi-
tations of Air Quality Models. Bulletin of the American Meteorological
Society, 62(2):218-221.
Gifford, F. A., 1976. Turbulent Diffusion Typing Schemes: A Review.
Nuclear Safety, 17(l):68-86.
Gudiksen, P. H., and M. H. Dickerson, Eds., 1983. Executive Summary:
Atmospheric Studies in Complex Terrain Technical Progress Report FY-1979
Through FY-1983. Lawrence Livermore National Laboratory, Livermore,
CA. (Docket Reference No. II-I-103).
Hales, J. M., 1976. Tall Stacks and the Atmospheric Environment. EPA
Publication No. EPA-450/3-76-007. U.S. Environmental Protection
Agency. Research Triangle Park, NC.
*The documents listed here are major sources of supplemental information
on the theory and application of mathematical air quality models.
13-1
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Hanna, S. R., G. A. Briggs, J. Deardorff, B. A. Egan, F. A. Glfford and
F. Pasquill, 1977. AMS Workshop on Stability Classification Schemes
and Sigma Curves—Summary of Recommendations. Bulletin of the
American Meteorological Society, 58(12)-.1305-1309.
Hanna, S. R., G. A. Briggs and R. P. Hosker, Jr., 1982. Handbook on
Atmospheric Diffusion. Technical Information Center, U.S. Department
of Energy, Washington, D.C.
Haugen, D. A., Workshop Coordinator, 1975. Lectures on Air Pollution and
Environmental Impact Analyses. Sponsored by the American Meteorological
Society, Boston, MA.
Hoffnagle, G. F., M. E. Smith, T. V. Crawford and T. J. Lockhart, 1981.
On-Site Meteorological Instrumentation Requirements to Characterize
Diffusion from Point Sources—A Workshop, 15-17 January 1980, Raleigh,
NC. Bulletin of the American Meteorological Society, 62(2) :255-261.
McMahon, R. A. and P. J. Denison, 1979. Empirical Atmospheric Deposition
Parameters—A Survey. Atmospheric Environment, 13:571-585.
McRae, G. J., J. A. Leone and J. H. Seinfeld, 1983. Evaluation of Chemical
Reaction Mechanisms for Photochemcal Smog. Part I: Mechanism
Descriptions and Documentation. EPA Publication No. EPA-600/3-83-086.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
Pasquill, F., 1974. Atmospheric Diffusion, 2nd Edition. John Wiley and
Sons, New York, NY, 479 pp.
Roberts, J. J., Ed., 1977. Report to U.S. EPA of the Specialists' Conference
on the EPA Modeling Guideline. U.S. Environmental Protection Agency,
Research Triangle Park, NC.
Slade, D. H., Ed., 1968. Meteorology and Atomic Energy 1968. USAEC Division
of Technical Information, Oak Ridge, TN.
Smith, M. E., Ed., 1973. Recommended Guide for the Prediction of the
Dispersion of Airborne Effluents. The American Society of Mechanical
Engineers, New York, NY.
Stern, A. C., Ed., 1976. Air Pollution, Third Edition, Volume I: Air
Pollutants, Their Transformation and Transport. Academic Press,
New York, NY.
Turner, D. B., 1979. Atmospheric Dispersion Modeling: A Critical Review.
Journal of the Air Pollution Control Association, 29(5):502-519.
Whiteman, C. D. and K. J. Allwine, 1982. Green River Ambient Model
Assessment Program FY-1982 Progress Report. PNL-4520. Pacific
Northwest Laboratory, Richland, WA. (Document Reference No. II-I-140).
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14.0 GLOSSARY OF TERMS
AIR QUALITY--Ambient pollutant concentrations and their temporal and
spatial distribution.
ALGORITHM—A specific mathematical calculation procedure. A model may
contain several algorithms.
BACKGROUND—Ambient pollutant concentrations due to (1) natural sources,
(2) distant, unidentified man-made sources, and (3) nearby sources other
than those currently under consideration.
CALIBRATE--An objective adjustment using measured air quality data (e.g.,
an adjustment based on least-squares linear regression).
CALM—For purposes of air quality modeling, calm is used to define the
situation when the wind is indeterminate with regard to speed or direction.
COMPLEX TERRAIN--Terrain exceeding the height of the stack being modeled.
COMPUTER CODE—A set of statements that comprise a computer program.
EVALUATE—To appraise the performance and accuracy of a model based on a
comparison of concentration estimates with observed air quality data.
MODEL--A quantitative or mathematical representation or simulation which
attempts to describe the characteristics or relationships of physical events.
PREFERRED MODEL--A refined model that is recommended for a specific type
of regulatory application.
RECEPTOR--A location at which ambient air quality is measured or estimated.
RECEPTOR MODELS—Procedures that examine an ambient monitor sample of
particulate matter and the conditions of its collection to infer the types
or relative mix of sources impacting on it during collection.
REFINED MODEL--An analytical technique that provides a detailed treatment of
physical and chemical atmospheric processes and requires detailed and precise
input data. Specialized estimates are calculated that are useful for eval-
uating source impact relative to air quality standards and allowable incre-
ments. The estimates are more accurate than those obtained from conservative
screening techniques.
ROLLBACK--A simple model that assumes that if emissions from each source
affecting a given receptor are decreased by the same percentage, ambient
air quality concentrations decrease proportionately.
SCREENING TECHNIQUE—A relatively simple analysis technique to determine
if a given source is likely to pose a threat to air quality. Concentration
estimates from screening techniques are conservative.
SIMPLE TERRAIN--An area were terrain features are all lower in elevation
than the top of the stack of the source.
14-1
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APPENDIX A
SUMMARIES
OF
PREFERRED AIR QUALITY MODELS
-------
APPENDIX A
Table of Contents
A.O INTRODUCTION A-1
A.I BUOYANT LINE AND POINT SOURCE DISPERSION MODEL (BLP) A-3
A.2 CALINE 3 A-7
A.3 CLIMATOLOGICAL DISPERSION MODEL (COM) A-11
A.4 GAUSSIAN-PLUME MULTIPLE SOURCE AIR QUALITY ALGORITHM
(RAM) A-15
A.5 INDUSTRIAL SOURCE COMPLEX MODEL (ISC) A-19
A.6 MULTIPLE POINT GAUSSIAN DISPERSION ALGORITHM WITH
TERRAIN ADJUSTMENT (MPTER) A-25
A.7 SINGLE SOURCE (CRSTER) MODEL A-29
A.8 URBAN AIRSHED MODEL (UAM) A-33
A.9 REFERENCES , A-39
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A.O INTRODUCTION
This appendix summarizes key features of refined air quality models
preferred for specific regulatory applications. For each model,
information is provided on availability*, costs, regulatory use, data
input, output format and options, simulation of atmospheric physics,
and accuracy. These models may be used without a formal demonstration
of applicability provided they satisfy the recommendations for regulatory
use; not all options in the models are necessarily recommended for
regulatory use. The models are listed by name in alphabetical order.
Each of these models has been subjected to a performance evaluation
using comparisons with observed air quality data. A summary of such
comparisons for all models contained in this appendix is included in "A
Survey of Statistical Measures of Model Performance and Accuracy for
Several Air Quality Models," EPA-450/4-83-Op1. Where possible, several
of the models contained herein have been subjected to rigorous evaluation
exercises, including (1) statistical performance tests recommended by
the American Meteorological Society and (2) peer scientific reviews.
The models in this appendix have been selected on the basis of the
results of the model evaluations, experience with previous use,
familiarity of the model to various air quality programs, and the costs
and resource requirements for use.
*Where reference to UNAMAP (Version 6) is made, the assumption is
implicit that the next update of UNAMAP will contain revisions of the
models proposed in these descriptions. UNAMAP (Version 5) contains the
current coding of these models.
A-l
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A.I BUOYANT LINE AND POINT SOURCE DISPERSION MODEL (BLP)
Reference: Schulman, Lloyd L., and Joseph S. Scire. "Buoyant Line and
Point Source (BLP) Dispersion Model User's Guide," Document
P-7304B. Environmental Research and Technology, Inc.,
Concord, Massachusetts, 1980.
Availability: This model is available as part of UNAMAP (Version 6). The
computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: BLP is a Gaussian plume dispersion model designed to handle
unique modeling problems associated with aluminum reduction
plants, and other industrial sources where plume rise and down-
wash effects from stationary line sources are important.
a. Recommendations for Regulatory Use
The BLP model is appropriate for the following applications:
aluminum reduction plants which contain buoyant, elevated line
sources;
rural areas;
transport distances less than 50 kilometers;
simple terrain; and
one hour to one year averaging times.
The following options should be selected for regulatory applications:
rural (IRU=1) mixing height option
default (no selection) for:
plume rise wind shear (LSHEAR), transitional point source
plume rise (LTRANS), vertical potential temperature gradient
(DTHTA), vertical wind speed power law profile exponents
(PEXP), maximum variation in number of stability classes per
hour (IDELS), pollutant decay (DECFAC), the constant in
Briggs' stable plume rise equation (CONST2), constant in
Briggs1 neutral plume rise equation (CONST3), convergence
A-3
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criterion for the line source calculations (CRIT), and maximum
iterations allowed for line source calculations (MAXIT).
terrain option (TERAN) set equal to 0., 0., 0., 0., 0., 0.
For other applications, BLP can be used if it can be demonstrated to give
the same estimates as a recommended model for the same application, and
will subsequently be executed in that mode.
BLP can be used on a case-by-case basis with specific options not available
in a recommended model if it can be demonstrated, using the criteria in
Section 3.2, that the model is more appropriate for a specific application.
b. Input Requirements
Source data requirements are: point sources require stack location,
elevation of stack base, physical stack height, stack inside diameter, stack
gas exit velocity, stack gas exit temperature, and pollutant emission
rate. Line sources require coordinates of the end points of the line,
release height, emission rate, average line source width, average, building
width, average spacing between buildings, and average line source buoyancy
parameter.
Meteorological data requirements are: hourly surface weather data
from the EPA meteorological preprocessor program or input from punched
cards (up to 24 hours). Preprocessor output includes hourly stability
class, wind direction, wind speed, temperature, and mixing height.
Wind speed profile exponents (one for each stability class) are
required if on-site data are input.
Receptor data requirements are: locations and elevations of receptors,
or location and size of receptor grid or request automatically gener-
ated receptor grid.
c. Output
Printed output (from a separate post-processor program) includes:
total concentration or, optionally, source contribution analysis;
monthly and annual frequency distributions for 1-, 3-, and 24-hour
average concentrations; tables of 1-, 3-, and 24-hour average
concentrations at each receptor; table of the annual (or length
of run) average concentrations at each receptor;
five highest 1-, 3-, and 24-hour average concentrations at each
receptor; and
fifty highest 1-, 3-, and 24-hour concentrations over the receptor
field.
d. Type of Model
BLP is a gaussian plume model.
A-4
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e. Pollutant Types
BLP may be used to model primary pollutants. This model does not treat
settling and deposition.
f. Source-Receptor Relationship
BLP treats up to 50 point sources, 10 parallel line sources, and 100
receptors arbitrarily located
User-input topographic elevation is applied for each stack and each receptor.
g. Plume Behavior
BLP uses plume rise formulas of Schulman and Scire (1980).
Vertical potential temperature gradients of .02 Kelvin per meter for E
stability and .035 Kelvin per meter are used for stable plume rise
calculations. An option for user input values is included.
Transitional rise is used for line sources.
Option to not use transitional plume rise for point sources is included.
The building downwash algorithn of Schulman and Scire (1980) is used.
h. Horizontal Minds
Constant, uniform (steady-state) wind is assumed for an hour.
Straight Vine plume transport is assumed to all downwind distances.
Wind speeds profile exponents of .10, .15, .20, .25, .30, and .30 are used
for stability classes A through F, respectively. An option for user-
defined values and an option to not use the wind speed profile feature
are included.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Rural dispersion coefficients are from Turner (1969), with no adjust-
ment made for variations in surface roughness or averaging time.
Six stability classes are used, with Turner class 7 treated as class 6.
k. Vertical Dispersion
Rural dispersion coefficients are from Turner (1969), with no adjustment
made for variations in surface roughness.
A-5
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Six stability are classes used, with Turner class 7 treated as class 6.
Mixing height is accounted for with multiple reflections until the
vertical plume size equals 1.6 times the mixing height; uniform mixing
is assumed beyond that point.
Perfect reflection at the ground is assumed.
1. Chemical Transformation
Chemical transformations are treated using linear decay. Decay rate
is input by the user.
m. Physical Removal
Physical removal is not explicitly treated.
n. Evaluation Studies
Schulman, L. L., and J. S. Scire. "Buoyant Line and Point Source
(BLP) Dispersion Model User's Guide," P-7304B, Environmental
Research and Technology, Inc., Concord, Massachusetts, 1980.
Scire, J. S., and L. L. Schulman. "Evaluation of the BLP and ISC Models
with SFg Tracer Data and S02 Measurements at Aluminum Reduction
Plants, APCA Speciality Conference on Dispersion Modeling for
Complex Sources, 1981.
A-6
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Availability:
Abstract:
A.2 CALINE3
Reference: Benson, Paul E. "CALINE3 - A Versatile Dispersion Model
for Predicting Air Pollutant Levels Near Highways and
Arterial Streets." Interim Report, Report Number
FHWA/CA/TL-79/23, Federal Highway Administration, 1979.
Messina, A. D., J. A. Bullin, J. P. Nelli, and R. D. Moe.
Estimates of Air Pollution. Near Signalized Intersections.
Report No. FHWA/TX-81/541-1, U.S. Department of Transportation,
FHUA, Washington, D. C., 1983.
The CALINE3 model is available from the California
Department of Transportation on an at-cost basis ($10 for
documentation, approximately $50 for the model).
Requests should be directed to:
Mr. Ebert Jung
Chief, Office of Computer Systems
California Department of Transportation
1120 N. Street
Sacramento, California 95814
CALINE3 can be used to estimate the concentrations of
non-reactive pollutants from highway traffic. This
steady-state Gaussian model can be applied to determine
air pollution concentrations at receptor locations
downwind of "at-grade," "fill," "bridge," and "cut sec-
tion" highways located in relatively uncomplicated ter-
rain. The model is applicable for any wind direction,
highway orientation, and receptor location. The model
has adjustments for averaging time and surface roughness,
and can handle up to 20 links and 20 receptors. It also
contains an algorithm for deposition and settling veloci-
ty so that particulate concentrations can be predicted.
Recommendations for Regulatory Use
CALINE-3 is appropriate for the following appalications:
highway (line) sources;
urban or rural areas;
simple terrain;
transport distances less than 50 kilometers; and
one hour to 24 hours averaging times.
a.
A-7
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b. Input Requirements
Source data requirements are: up to 20 highway links classed as
"at-grade," "fill," "bridge," or "depressed"; coordinates of link end
points; traffic volume; emission factor; source height; and mixing
zone width.
Meteorological data requirements are: wind speed, wind angle
(measured in degrees clockwise from the Y axis), stability class,
mixing height, ambient (background to the highway) concentration of
pollutant.
Receptor data requirements are: coordinates and height above ground
for each receptor.
c. Output
Printed output includes:
concentration at each receptor for the specified meteoro-
logical condition.
d. Type of Model
CALINE-3 is a Gaussian plume model.
e. Pollutant Types
CALINE-3 may be used to model primary pollutants.
f. Source-Receptor Relationship
Up to 20 highway links are treated.
CALINE-3 applies user input location and emission rate for each link.
User-input receptor locations are applied.
g. Plume Behavior
Plume rise is not treated.
hJ Horizontal Winds
User-input hourly wind speed and direction are applied.
Constant, uniform (steady-state) wind is assumed for an hour.
A-8
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i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Six stability classes are used.
Rural dispersion coefficients from Turner (1969) are used, with
adjustment for roughness length and averaging time.
Initial traffic-induced dispersion is handled implicitly by plume
size parameters.
k. Vertical Dispersion
Six stability classes are used.
Empirical dispersion coefficients from Benson (1979) are used including
an adjustment for roughness length. .
Initial traffic-induced dispersion is handled implicitly by plume
size parameters.
Adjustment for averaging time is included.
1. Chemical Transformation
Not treated.
m. Physical Removal
Optional deposition calculations are included.
n. Evaluation Studies
Bennis, G. R., et. al. "Air Pollution and Roadway Location, Design,
and Operation - Project Overview," FHWA-CA-TL-7080-77-25,
Federal Highway Administration, Washington, DC, 1977.
Cadle, S. H., et. al. "Results of the General Motors Sulfate
Dispersion Experiment," General Motors Research Laboratories,
GMR-2107, 1976.
Dabberdt, W. F. "Studies of Air Quality on and Near Highways,"
Project 2761, Stanford Research Institute, Menlo Park,
California, 1975.
Messina, A. D., J. A. Bull in, J. P. Nelli, and R. D. Moe, Estimates
of Air Pollution Near Signalized Intersections, Report No.
FHWA/TX-81/541-1, U. S. Department of Transportation, FHWA,
Washington, D. C, 1983.
A-9
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A.3 CLIMATOLOGICAL DISPERSION MODEL (COM)
References: Busse, A. D. and J. R. Zimmerman. "User's Guide for the
Climatological Dispersion Model." Publication No. EPA-R4-
73-024 (NTIS PB 227346/AS), Environmental Protection Agency,
Research Triangle Park, North Carolina 27711, 1973.
Brubaker, K. L., P. Brown, and R. R. Cirillo. "Addendum
to User's Guide for Climatological Dispersion Model."
Publication No. EPA-450/3-77-015 (NTIS PB 274-040),
Environmental Protection Agency, Research Triangle Park,
North Carolina 27711, 1977.
Availability: This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: COM is a Climatological steady-state Gaussian plume model
for determining long-term (seasonal or annual) arithmetic
average pollutant concentrations at any ground-level receptor
in an urban area. An expanded version (CDMQC) includes the
capability of producing a source contribution list and
using a statistical model based on Larsen (1971) to transform
the average concentration data from a limited number of
receptors into expected geometric mean and maximum concentra-
tion values for several different averaging times.
a. Recommendations for Regulatory Use
COM and CDMQC are appropriate for the following applications:
point and area sources;
urban areas;
flat terrain;
transport distances less than 50 kilometers;
long term averages over one month to one year or longer.
The following option should be selected for regulatory applications:
See Sections and 8.2.6 for 8.2.7 guidance on use of exponential
decay.
A-ll
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b. Input Requirements
Source data requirements are: location, average emissions rates
and heights of emissions for point and area sources. Point source
data requirements also include stack gas temperature, stack gas exit
velocity, and stack inside diameter for plume rise calculations for
point sources.
Meteorological data requirements are: stability wind rose (STAR deck
day/night version), average afternoon mixing height, average morning
mixing height, and average air temperature.
Receptor data requirements are: cartesian coordinates of each receptor.
If the Larsen transform option is to be used to estimate short averaging
time concentrations, measured standard geometric deviation of concentra-
tions is required.
c. Output
Printed output includes:
one month to one year average concentrations (arithnetic mean only)
at each-receptor, and
optional point and area concentration rose for each receptor.
. Printed output from the expanded version (CDMQC) includes:
optional arbitrary averaging time by Larsen (1971) procedure
(typically 1-24 hr.), and
optional individual point, area source culpability list for each
receptor.
d. Type of Model
COM is a climatological Gaussian plume model.
e. Pollutant Types
COM may be used to model primary pollutants. Settling and deposition
are not treated.
f. Source-Receptor Relationship
COM applies user-specified locations for all point sources and receptors.
Area sources are input as multiples of a user-defined unit area source
grid size.
User specified release heights are applied for individual point sources
and the area source grid.
A-12
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Actual separation between each source-receptor pair is used.
All receptors are assumed to be at ground level.
No terrain differences between source and receptor are treated.
g.- Plume Behavior
COM and CDMQC use Briggs (1971) neutral/unstable plume rise equations
for final rise. Optionally a plume rise-wind speed product may be
input for each point source.
Stack tip downwash equation from Bjorklund and Bowers (1982) is used.
No plume rise is calculated for area sources.
Does not treat fumigation or building downwash.
h. Horizontal Winds
Wind data are input as a stability wind rose (joint frequency distri-
bution of 16 wind directions, 6 wind classes, and 5 stability classes).
Wind speed profile exponents for the urban case (Irwin, 1979) are used.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Pollutants are assumed evenly distributed across a 22.5 degree sector.
Buoyancy-induced dispersion (Pasquill, 1976) is included.
k. Vertical Dispersion
Urban dispersion coefficients from McElroy and Pooler (1968), as
formulated by Briggs (1974) are used in CDM and CDMQC.
Mixing height has no effect until dispersion coefficient equals 0.8 times
the mixing height; uniform vertical mixing is assumed beyond that point.
Buoyancy-induced disperion (Pasquill, 1976) is included.
Perfect reflection is assumed at the ground.
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Half-life
is input by the user.
A-13
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m. Physical Removal
Physical removal is not explicitly treated.
n. Evaluation Studies
Londergan, R., D. Minott, D. Wachter and R. Fizz. "Evaluation of Urban
Air Quality Simulation Models," EPA 450/4-83-020, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711, 1983.
Turner, D. B., J. R. Zimmerman, and A. D. Busse. "An Evaluation of
Some Climatological Dispersion Models," Third Meeting on the NATO/CCMS
Panel on Modeling, Reproduced in Busse and Zimmerman, 1973.
Zimmerman, J. R. "Some Preliminary Results of Modeling from the Air
Pollution Study of Ankara, Turkey," Proceedings of the Second Meeting
of the Expert Panel on Air Pollution Modeling, NATO Committee on
the Challenges of Modern Society, Paris, France, 1971.
Zimmerman, J. R. "The NATO/CCMS Air Pollution Study of St. Louis,
Missouri." Presented at the Third Meeting of the Expert Panel on
Air Pollution Modeling, NATO Committee on the Challenges of Modern
Society, Paris, France, 1972.
A-14
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A.4 GAUSS IAN-PLUME MULTIPLE SOURCE AIR QUALITY ALGORITHM (RAM)
Reference: Turner, D. B., and J. H. Novak, "User's Guide for RAM."
EPA-600/8-78-016 Vol a, b. (NTIS PB 294791 and
PB 294792). Environmental Protection Agency, Research
Triangle Park, North Carolina 27711, 1978.
Availability: This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U. S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: RAM is a steady-state Gaussian plume model for estimating
concentrations of relatively stable pollutants for averaging
times from an hour to a day from point and area sources in
an urban setting. Level or gently rolling terrain is assumed.
Calculations are performed for each hour. A rural version
exists but is not recommended for regulatory applications.
a. Recommendations for Regulatory Use
RAM is appropriate for the following applications:
point and area sources;'
urban areas;
flat or rolling terrain;
transport distances less than 50 kilometers; and
one hour to one year averaging times.
The following option should be selected for regulatory applications:
see Sections 8.2.6 and 8.2.7 for guidance on use of exponen-
tial decay.
b. Input Requirements
Source data requirements are: location, emissions rate, physical
stack height are required for point sources. Area source data
requirements include location, size, emission rate, and height of
emissions.
A-15
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Meteorological data requirements are: hourly surface weather data
from the EPA meteorological preprocessor program. Preprocessor
output includes hourly stability class, wind direction, wind speed,
temperature, and mixing height. Actual anemometer height (a single
value) is also required. Wind speed profile exponents (one for
each stability class) are required if on-site data are input.
Receptor data requirements are: coordinates of each receptor.
Options for automatic placement of receptors near expected
concentration maxima, and a gridded receptor array are included.
c. Output
Printed output includes:
hourly and average (up to 24 hours) concentrations at
each receptor,
limited individual source contribution list, and
cumulative frequency distribution based on 24-hour
averages and up to one year of data at a limited number
of receptors.
d. Type of Model
RAM is a Gaussian plume model.
e. Pollutant Types
RAM may be used to model primary pollutants. Settling and deposi-
tion are not treated.
f. Source-Receptor Relationship
RAM applies user-specified locations for all point sources and
receptors.
Area sources are input as multiples of a user-defined unit area
source grid size. •
User specified release heights are applied for individual point
sources.
Up to 3 effective release heights may be specified for the area
sources. Area source release heights are assumed to be appropriate
for a 5 meter per second wind and to be inversely proportional to
wind speed.
Actual separation between each source-receptor pair is used.
A-16
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All receptors are assumed to be at the same height above ground level,
or at ground level.
No terrain differences between source and receptor are accounted for.
g. Plume Behavior
RAM uses Briggs (1969, 1971, 1972) plume rise equations for final rise.
For rolling terrain (terrain not above stack height), plume centerline
is horizontal at the height of final rise above the source.
Stack tip downwash equation from Bjorklund and Bowers (1982) is used.
No plume rise is calculated for area sources.
Fumigation and building downwash are not treated.
h. Horizontal Minds
Constant, uniform (steady state) wind is assumed for an hour.
Straight line plume transport is assumed to all downwind distances.
Wind s-peed profile exponents (Irwin, 1979) for the urban case are used.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Urban dispersion coefficients from McElroy and Pooler (1968), as
formulated by Briggs (1974) are used in RAM.
Buoyancy-induced dispersion (Pasquill, 1976) is included.
Six stability classes are used, with Turner class 7 treated as class 6.
k. Vertical Dispersion
Urban dispersion coefficients from McElroy and Pooler (1969), as
formulated by Briggs (1974) are used in RAM.
Buoyancy-induced dispersion (Pasquill, 1976) is included.
Six stability classes are used, with Turner class 7 treated as class 6.
Mixing height is accounted for with multiple reflections until
A-17
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the vertical plume size equals 1.6 times the mixing height; uniform
vertical mixing is assumed beyond that point.
Perfect reflection is assumed at the ground.
1. Chemical Transformation
Chemical transformations are treated using exponential decay.
Half-life is input by the user.
m. Physical Removal
Physical removal is not explicitly treated.
n. Evaluation Studies
Ellis, H., P. Lou, and G. Dalzell. "Comparison Study of Measured and
Predicted Concentrations with the RAM Model at Two Power Plants
Along Lake Erie," Second Joint Conference on Applications of Air
Pollution Meteorology, New Orleans, Louisiana, 1980.
Guldberg, P. H., and C. W. Kern. "A Comparison Validation of the RAM
and PTMTP Models for Short-Term Concentrations in Two Urban Areas,"
J. Air Poll. Control Assoc., Vol. 28, pp. 907-910, 1978.
Hqdanbosi, R. R., and L. K. Peters. "Evaluation of RAM Model for
Cleveland, Ohio," J. Air Poll. Control Assoc., Vol. 31, pp.
253-255, 1981.
Kummier, R. H., B. Cho, G. Roginski, R. Sinha and A. Greenburg. "A
Comparative Validation of the RAM and Modified SAI Models for Short-
Term S02 Concentrations in Detroit," J. Air Poll. Control Assoc.,
Vol. 29, pp. 720-723, 1979.
Londergan, R. J., N. E. Bowne, D. R. Murray, H. Borenstein, and
J. Mangano. "An Evaluation of Short-Term Air Quality Models Using
Tracer Study Data," 4333, American Petroleum Institute, Washington,
D. C. 20006, 1980.
Morgenstern, P., M. J. Geraghty, and A. McKnight. "A Comparative Study
of the RAM (Urban) and RAMR (Rural) Models for Short-term S02
Concentrations in Metropolitan Indianapolis," 79-2.2, 72nd Annual
Meeting of the Air Pollution Control Association, Cincinnati, Ohio,
1979.
Ruff, R. E. "Evaluation of the RAM Using the RAPS Data Base," Contract
68-02-2770, SRI International, Menlo Park, California, 1980.
Londergan, R., D. Minott, D. Wackter, and R. Fizz. "Evaluation of Urban
Air Quality Simulation Models," EPA 450/4-83-020, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina 27711, 1983.
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A.5 INDUSTRIAL SOURCE COMPLEX MODEL (ISC)
Reference: Bowers, J. R., J. R. Bjorklund and C. S. Cheney. "Indus-
trial Source Complex (ISC) Dispersion Model User's Guide,
Volumes 1 and 2." Publication Nos. EPA-450/4-79-030,
031 (NTIS Numbers: Volume 1, PB-80-133044; Volume 2, PB-
80-133051; Magnetic tape, PB-80-133036) Office of Air
Quality Planning and Standards, U. S. Environmental
Protection Agency, Research Triangle Park,
North Carolina 27711, 1979.
Availability: This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
Abstract: The ISC model is a steady-state Gaussian plume model
which can be used to access pollutant concentrations from
a wide variety of sources associated with an industrial
source complex. This model can account for settling and
dry deposition of particulates, downwash area, line and
volume sources, plume rise as a function of downwind
distance, separation of point sources, and limited ter-
rain adjustment. It operates in both long- and short-
term modes.
a. Recommendations for Regulatory Use
ISC is appropriate for the following applications:
industrial source complexes;
rural or urban areas;
flat or rolling terrain; and
transport distances less than 50 kilometers;
one hour to annual averaging times.
The following options should be selected for regulatory applications:
concentration option (ISW(1)=1);
elevated terrain option (ISW{4)=1) if terrain is to be
considered, or select flat terrain ISW(4)=0 if no terrain
is to be considered;
A-19
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rural option (ISW(20)=0) or urban option (ISW{20)=1) as
specified in Section 8.2.8;
user-provided wind speed profile exponents (ISW(21)=2),
or the recommended default exponents (ISW(21)=1) for the
rural case, or the following values (ISW(21)=2) for the
urban case:
.15, .15, .15, .15, .15, .15,
.15, .15, .15, .15, .15, .15,
.20, .20, .20, .20, .20, .20,
.25, .25, .25, .25, .25, .25,
.40, .40, .40, .40, .40, .40,
.60, .60, .60, .60, .60, .60;
default values for vertical potential temperature
gradient option (ISW(22)=1);
final plume rise for all calculations (ISW(24)=1), except
when stack height is less than GEP then set ISW(24)=2;
default value for decay coefficient following guidance in
Section 8.2.6. and 8.2.7 on use of exponential decay.
b. Input Requirements
Source data requirements are: location, emission rate, pollutant
decay coefficient, elevation of source, stack height, stack exit
velocity, stack inside diameter, stack exit temperature, particle
size distribution with corresponding settling velocities, surface
reflection coefficient, and dimensions of adjacent buildings.
Meteorological data requirements are: for short term modeling,
hourly surface weather data from the EPA meteorological preprocessor
program. Preprocessor output includes hourly stability class, wind
direction, wind speed, temperature, and mixing height. For long-term
modeling, stability wind rose (STAR deck), average afternoon mixing
height, average morning mixing height, and average air temperature.
Receptor data requirements are: coordinates of each receptor.
c. Output
Printed output options include:
A-20
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program control parameters, source data and receptor data;
tables of hourly meteorological data for each specified day;
"N"-day average concentration or total deposition calcu-
lated at each receptor for any desired combinations of
sources;
concentration or deposition values calculated for any
desired combinations of sources at all receptors for any
specified day or time period within the day;
tables of highest and second-highest concentration or
deposition values calculated at each receptor for each
receptor for each specified time period during an "N"-day
period for any desired combinations of sources; and
tables of the maximum 50 concentration or deposition
values calculated for any desired combinations of sources
for each specified time period.
d. Type of Model '
ISC is a Gaussian plume model.
e. Pollutant Types
ISC may be used to model primary pollutants. Settling and deposi-
tion are treated.
f. Source-Receptor Relationships
ISC applies user-specified locations for point, line, area and
volume sources, and user-specified receptor locations or receptor
rings.
Receptors are assumed to be at ground level, and must be at eleva-
tions not exceeding stack height.
Actual separation between each source-receptor pair is used.
g. Plume Behavior
ISC uses Briggs (1971, 1972) plume rise equations for final rise.
Stack tip downwash (Bjorklund and Bowers, 1982) and building downwash
(Huber and Snyder, 1976) are used.
For rolling terrain (terrain not above stack height), plume center!ine
is horizontal at height of final rise above source.
Fumigation is not treated.
A-21
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h. Horizontal Winds
Constant, uniform (steady-state) wind is assumed for an each hour.
Straight line plume transport is assumed to all downwind distances.
Separate wind speed profile exponents (Irwin, 1979) for both rural and
urban cases are used.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Rural dispersion coefficients from Turner (1969) are used, with no
adjustments for surface roughness or averaging time.
Urban dispersion coefficients from McElroy and Pooler (1968), as
formulated by Briggs (1974) are used.
Buoyancy induced dispersion (PasquilV, 1976) is included.
Six stability classes are used, with Turner class 7 treated as class 6.
k. Vertical Dispersion
Rural dispersion coefficients from Turner (1969) are used, with no
adjustments for surface roughness.
Urban dispersion coefficients from McElroy and Pooler (1969), as
formulated by Briggs (1974) are used.
Buoyancy induced dispersion (Pasquill, 1976) is included.
Six stability classes are used, with Turner class 7 treated as class 6.
Mixing height is accounted for with multiple reflections until the
vertical coefficient equals 1.6 times the mixing height; uniform
vertical mixing is assumed beyond that point.
Perfect reflection is assumed at the ground.
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Time
constant is input by the user.
A-22
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m. Physical Removal
Settling and dry deposition of participates are treated.
n. Evaluation Studies
Bowers, J. F., and A. J. Anderson. "An Evaluation Study for the
Industrial Source Complex (ISC) Dispersion Model," EPA-450/4-81-
002, U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina 27711, 1981.
Bowers, J. F., A. J. Anderson, and W. R. Hargraves. "Tests of
the Industrial Source Complex (ISC) Dispersion Model at the
Armco Middletown, Ohio Steel Mill," EPA-450/4-82-006 (NTIS PB
82-257-312), U. S. Environmental Protection Agency, Research
Triangle Park, North Carolina 27711, 1982.
Scire, J. S., and L. L. Schulman. "Evaluation of the BLP and ISC
Models with SF6 Tracer Data and S02 Measurements at Aluminum
Reduction Plants," APCA Specialty Conference on Dispersion
Modeling for Complex Sources, 1981.
A-23
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A.6 MULTIPLE POINT GAUSSIAN DISPERSION ALGORITHM WITH TERRAIN ADJUSTMENT
(MPTER)
Reference: Pierce, Thomas D. and D. Bruce Turner. "User's Guide for
MPTER." Publication No. EPA-600/8-80-016 (NTIS PB-80-197361).
Environmental Protection Agency, Research Triangle Park,
North Carolina 27711, 1980.
Availability: This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP Tape is PB
Abstract: MPTER is a Multiple Point Source Algorithm. This algorithm
is useful for estimating air quality concentrations of
relatively non-reactive pollutants. Hourly estimates
are made using the Gaussian steady state model.
a. Recommendations for Regulatory Use
MPTER is appropriate for the following applications:
point sources;
rural or urban areas;
flat or rolling terrain (no terrain above stack height);
transport distances less than 50 kilometers; and
one hour to one year averaging times.
The following options should be selected for regulatory applications:
rural or urban as specified in Section 8.2.8;
default wind speed profile exponents or appropriate on
site values; and
see Sections 8.2.6 and 8.2.7 for guidance on use of exponen-
tial decay.
A-25
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b. Input Requirements
Source data requirements are: location, emission rate, physical
stack height, stack gas exit velocity, stack inside diameter, stack
gas temperature, optional ground level elevation.
Meteorological data requirements are: hourly surface weather data
from the EPA meterological preprocessor program. Preprocessor output
includes hourly stability class, wind direction, wind speed, temperature,
and mixing height. Actual anemometer height (a single value) is also
required. Wind speed profile exponents (one for each stability
class) are required if on-site data are input.
Receptor data requirements are: coordinates and optional ground
elevation for each receptor.
c. Output
Printed output includes:
Hourly and average (up to 24-hours) concentrations at
each receptor;
Highest through fifth highest concentrations at each
receptor for period, with the highest and high, second-
high values flagged; and
Limited source contribution table.
d. Type of Model
MPTER is a Gaussian plume model.
e. Pollutant Types
MPTER may be used to model primary pollutants. Settling and deposi-
tion are not treated.
f. Source-Receptor Relationship
MPTER applies user-specified locations of point sources and receptors.
User input stack height and source characteristics for each source
are used.
User input topographic elevation for each receptor is used.
g. Plume Behavior
MPTER uses Briggs (1969, 1971, 1972) plume rise equations for final rise.
Stack tip downwash equation from Bjorklund and Bowers (1982) is used.
A-26
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For rolling terrain (terrain not above stack height), plume center-
line is horizontal at height of final rise above the source.
Fumigation and building downwash are not treated.
h. Horizontal Winds
Constant, uniform (steady-state) wind is assumed for an hour.
Straight line plume transport is assumed to all downwind distances.
Separate wind speed profile exponents (Irwin, 1979) for both rural
and urban cases are used.
i. . Vertical Wind Speed
Vertical speed is assumed equal to zero.
j. Horizontal Dispersion
Rural dispersion coefficients from Turner (1969) are used in MPTER,
with no adjustments made for variations in surface roughness or
averaging times.
Urban dispersion coefficients from McElroy and Pooler (1968), as
formulated by Briggs (1974), are used.
Buoyancy-induced dispersion (Pasquill, 1976) is included.
Six stability classes are used, with Turner class 7 treated as class 6.
k. Vertical Dispersion
Rural dispersion coefficients from Turner (1969) are used, with no
adjustments made for surface roughness.
Urban dispersion coefficients from McElroy and Pooler (1969), as
formulated by Briggs (1974), are used.
Buoyancy-induced dispersion (Pasquill, 1976) is included.
Six stability classes are used, with Turner class 7 treated as class.6.
Mixing height is accounted for with multiple reflections until the
vertical plume size equals 1.6 times the mixing height; uniform
vertical mixing is assumed beyond that point.
Perfect reflection is assumed at the ground.
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Half-
life is input by the user.
A-27
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m. Physical Removal
Physical removal is not explicitly treated.
n. Evaluation Studies
No specific studies for MPTER because regulatory editions of
CRSTER and MPTER are equivalent. Studies for CRSTER are relevant
to MPTER as well (See page A-32).
A-28
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A.7 SINGLE SOURCE (CRSTER) MODEL
Reference: Environmental Protection Agency. "User's Manual for
Single Source (CRSTER) Model." Publication No. EPA-
450/2-77-013 (NTIS PB 271360). Office of Air Quality
Planning and Standards, Research Triangle Park, North
Carolina 27711, 1977.
Availability: This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: CRSTER is a steady state, Gaussian dispersion model designed
to calculate concentrations from point sources at a single
location in either a rural or urban setting. Highest and
high-second high concentrations are calculated at each receptor
for 1-hour, 3-hour, 24-hour, and annual averaging time.
a. Recommendations for Regulatory Use
CRSTER is appropriate for the following applications:
single point sources;
rural or urban areas;
transport distances less than 50 kilometers; and
flat or rolling terrain (no terrain above stack height).
The following options should be selected for regulatory applications:
rural (IUR=1) or urban (IUR=2) options as specified in
Section 8.2.8; and
default wind speed profile exponents or appropriate on-
site values.
see Sections 8.2.6 and 8.2.7 for guidance on use of
exponential decay.
b. Input Requirements
Source data requirements are: emission rates, physical stack
height, stack gas exit velocity, stack inside diameter, stack gas
temperature.
A-29
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Meteorological data requirements are: hourly surface weather data
from the EPA meteorological preprocessor program. Preprocessor
output includes hourly stability class wind direction, wind speed,
temperature, and mixing height. Actual anemometer height (a single
value) is also required. Wind speed profile exponents (one for each
stability class) are required if on-site data are input.
Receptor data requirements are: distance of each of the five recep-
tor rings.
Output
Printed output includes:
highest and second highest concentrations for the year at
each receptor for averaging times of 1, 3, and 24-hours,
plus a user-selected averaging time which may be 2, 4, 6,
8, or 12 hours;
annual arithmetic average at each receptor;
for each day, the highest 1-hour and 24-hour concentra-
tions over the receptor field; and
option for source contributions to concentrations at
selected receptors.
Type of Model
CRSTER is a Gaussian plume model.
Pollutant Types
CRSTER may be used to model primary pollutants. Settling and depo-
sition are not treated.
Source-Receptor Relationship
CRSTER treats up to 19 point sources, no area sources.
All point sources are assumed collocated.
User input stack height is used for each source.
User input topographic elevation is used for each receptor, but must
be below top of stack or program will terminate execution.
Receptors are assumed at ground level.
Plume Behavior
CRSTER uses Briggs (1969, 1971, 1972) plume rise equations for final
rise.
A-30
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Stack tip downwash equation from Bjorklund and Bowers (1982) is used.
For rolling terrain (terrain not above stack height), plume center-
line is horizontal at height of final rise above the source.
Fumigation and building downwash are not treated.
h. Horizontal Winds
Constant, uniform (steady-state) wind is assumed for an hour.
Straight line plume transport is assumed to all downwind distances.
Separate set of wind speed profile exponents (Irwin, 1979) for both
rural and urban cases are used.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j . Horizontal Dispersion
Rural dispersion coefficients from Turner (1969) are used in CRSTER
with no adjustments made for variations in surface roughness or
averaging times.
Urban dispersion coefficients from McElroy and Pooler (1968), as
formulated by Briggs (1974), are used.
Buoyancy-induced dispersion (Pasquill, 1976) is included.
Six stability classes are used, with Turner class 7 treated as class 6
k. Vertical Dispersion
Rural dispersion coefficients from Turner (1969) are used with no
adjustments made for surface roughness.
Urban dispersion coefficients from McElroy and Pooler (1969), as
formulated by Briggs (1974), are used.
Buoyancy-induced dispersion (Pasquill, 1976) is included.
Six stability classes are used, with Turner class 7 treated as class 6.
Mixing height is accounted for with multiple reflections until the
vertical plume size equals 1.6 times the mixing height; uniform
mixing is assumed beyond that point.
Perfect reflection is assumed at the ground.
A-31
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1. Chemical Transformation
Chemical transformations are treated using exponential decay.
Half-life is input by the user.
m. Physical Removal
Physical removal is not explicitly treated.
n. Evaluation Studies
Klug, W. "Dispersion from Tall Stacks," 5th NATO/CCMS International
Technical Meeting on Air Pollution Modeling, Denmark, 1974.
Londergan, R. J., N. E. Bowne, D. R. Murray, H. Borenstein, and J.
Mangano. "An Evaluation of Short-Term Air Quality Models Using
Tracer Study Data," 4333, American Petroleum Institute, Washing-
ton DC, 20006, 1980.
Mills, M. T., R. Caiazza, D. D. Hergert, and D. A. Lynn. "Evalua-
tion of Point Source Dispersion Models," EPA-450/4-81-032, Envi-
ronmental Protection Agency, Research Triangle Park, North Carolina
27711, 1981.
Mills, M. T., and F. A. Record. "Comprehensive Analysis of Time-
Concentration Relationships and the Validation of a Single Source
Dispersion Model," EPA-450/3-75-083, Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, 1975.
Mills, M. T., and R. W. Stern. "Model Validation and Time-Concen-
tration Analysis of Three Power Plants," EPA-450/3-76-002, Envi-
ronmental Protection Agency, Research Triangle Park, North
Carolina 27711, 1975.
Londergan, R., D. Minott, D. Wackter, T. Kincaid, and B. Bonitata.
"Evaluation of Rural Air Quality Simulation Models," EPA-450/4-83-
033, (NTIS PB 83-182-758), Environmental Protection Agency,
Research Triangle Park, North Carolina 27711, 1983.
TRC-Environmental Consultants, Inc. "Overview, Results, and Conclu-
sions for the EPRI Plume Model Validation and Development Pro-
ject: Plains Site," EPRI EA-3074, Electric Power Research Insti-
tute, Palo Alto, California 94304, 1983.
A-32
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Availability:
Abstract:
A.8 URBAN AIRSHED MODEL (UAM)
References: Ames, J., T. C. Myers, L. E. Reid, D. C. Whitney, S. H. Golding,
S. R. Hayes, and S. D. Reynolds. "The User's Manual for the
SAI Urban Airshed Model," Document EM 78-89, Systems Applica-
tions, Inc., San Rafael, California, 1984.
Ames, J. S., R. Hayes, T. C. Myers, and D. C. Whitney.
"Systems Manual for the SAI Urban Airshed Model," Document
EM78-79R2, Systems Applications, Inc., San Rafael, California,
1984.
Environmental Protection Agency. "Guideline for Applying
the Airshed Model to Urban Areas." EPA 450/4-80-020 (NTIS PB 81-
200529). Environmental Protection Agency, Research Triangle
Park, North Carolina, 1980.
Systems Application, Inc.
101 Lucas Valley Road
San Rafael, California 94903
UAM is an urban scale, three dimensional, grid type, numerical
simulation model. The model incorporates a condensed photo-
chemical kinetics mechanism for urban atmospheres. The UAM is
designed for computing ozonetOs) concentrations under short-
term, episodic conditions lasting one or two days resulting
from emissions of oxides of nitrogen (NOX) and volatile organic
compounds (VOC). The model treats urban VOC emissions as their
carbon-bond surrogates.
a. Recommendations for Regulatory Use
UAM is appropriate for the following applications: single urban areas
having significant ozone attainment problems in the absence of interurban
emission transport; and one hour averaging times.
UAM has many options but no specific recommendations can be made at this
time on all options. The reviewing agency should be consulted on selection
of options to be used in regulatory applications. At the present time,
the following options should be selected for regulatory applications:
omit S0£ and AEROSOLS from the SPECIES packet for the CHEMPARAM
file;
set ROADWAY flag to FALSE in the SIMULATION packet for the SIM-
CONTROL file; and
set surface layer height to zero in the REGION packet for the
AIRQUALITY, BOUNDARY, DIFFBREAK, METSCALARS, PTSOURCE, REGIONTOP,
TEMPERATUR, TERRAIN, TOPCONC, and WIND files.
A-33
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b. Input Requirements
Source data requirements are: gridded, hourly emissions of PAR,
OLE, ETH, ARO, CARB, NO, and N0£ for low-level sources. CO is
optional. For major elevated point sources, hourly emissions, stack
height, stack diameter, exit velocity, and exit temperature.
Meteorological data requirements are: hourly, gridded, divergence
free, u and v wind components for each vertical level; hourly gridded
mixing heights; hourly gridded surface temperatures; hourly exposure
class; hourly vertical potential temperature gradient above and
below the mixing height; hourly surface atmospheric pressure;
hourly water mixing ratio; gridded surface roughness lengths.
Air quality data requirements are: concentration of 03, NO, N02,
PAR, OLE, ETH, ARO, CARB, PAN, and CO at the beginning of the simula-
tion for each grid cell; hourly concentrations of each pollutant
at each level along the inflow boundaries and top boundary of the
modeling region.
Other data requirements are: hourly mixed layer average,
photolysis rates; and ozone surface uptake resistance along with
associated gridded vegetation (scaling) factors.
c. Output
Printed output includes:
gridded instantaneous concentration fields at user-specified
time intervals for user-specified pollutants and grid levels;
gridded time average concentration fields for user- specified
time intervals, pollutants, and grid levels.
d. Type of Model
UAM is a three dimensional, numerical, photochemical grid model.
e. Pollutant Types
UAM may be used to model ozone (03) formation from oxides of
nitrogen (NOX) and volatile organic compound (VOC) emissions.
f. Source-Receptor Relationship
Low-level area and point source emissions are specified within
each surface grid cell.
Up to 500 major point sources are allowed.
Hourly average concentrations of each pollutant are calculated for
all grid cells at each vertical level.
A-34
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g. Plume Behavior
Plume rise is calculated for major point sources using relationships
recommended by Briggs (1971).
h. Horizontal Winds
See Input Requirements.
i. Vertical Wind Speed
Calculated at each vertical grid cell interface from the mass
continuity relationship using the input gridded horizontal wind
field.
j. Horizontal Dispersion
Horizontal eddy diffusivity is set to a user specified constant
value (nominally 50 m^/s).
k. Vertical Dispersion
Vertical eddy diffusivities for unstable and neutral conditions
calculated using relationships of Lamb et al. (1977); for stable
conditions, the relationship of Businger and Arya (1974) is
employed. Stability class, friction velocity, and Monin-Obukhov
length determined using procedure of Liu et al. (1976).
1. Chemical Transformation
UAM employs a simplified version of the Carbon-Bond II Mechanism
(CBM-II) developed by Whitten, Killus, and Hogo (1980) employing
various steady-state approximations. CBM-II is further simplified
during nightime hours to improve computational efficiency. CBM-II
utilizes five carbon-bond species (PAR-single bonded carbon atoms;
OLE-terminal double bonded carbon atoms; ETH-ethylene; ARO-alkylated
aromatic rings; and CARB-aldehydes, ketones, and surrogate carbonyls)
which serve as surrogates for the large variety of emitted organic
compounds in the urban atmosphere.
m. Physical Removal
Dry deposition of ozone and other pollutant species are calculated.
Vegetation (scaling) factors are applied to the reference surface
uptake resistance of each species depending on land use type.
n. Evaluation Studies
Builtjes, P. J. H., K. D. van der Hurt, and S. D. Reynolds.
"Evaluation of the Performance of a Photochemical Dispersion
Model in Practical Applications," 13th International Technical
Meeting on Air Pollution Modeling and Its Application," He
des Embiez, France, September 1982.
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Cole, H. S., D. E. Layland, G. K. Moss, and C. F. Newberry. "The
St. Louis Ozone Modeling Project," EPA 450/4-83-019 (NTIS
PB 84-236611), U. S. Environmental Protection Agency, Research
Triangle Park, North Carolina, 1983.
Dennis, R. L., M. W. Downton, and R. S. Keil. "Evaluation of Per-
formance Measures for an Urban Photochemical Model," EPA 450/4-
83-021 (NTIS PB 84-115062), U. S. Environmental Protection Agency,
Research Triangle Park, North Carolina ,1983.
Layland, D. E. and H. S. Cole. "A Review of Recent Applications of
the SAI Urban Airshed Model," EPA 450/4-84-004 (NTIS PB-185255),
U. S. Environmental Protection Agency, Research Triangle Park,
North Carolina, 1983.
Layland, D. E., S. D. Reynolds, H. Hogo and W. R. Oliver.
"Demonstration of Photochemical Grid Model Usage for Ozone Control
Assessment," Paper 83-31.6, 76th Annual Meeting of the Air Pollution
Control Association, Atlanta, Georgia, 1983.
Schere, K. L. and J. H. Shreffler. "Final Evaluation of Urban-
Scale Photochemical Air Quality Simulation Models," EPA 600/3-
82-094 (NTIS PB 83-147991), U. S. Environmental Protection Agency,
Research Triangle Park, North Carolina, 1982.
Seigneur C., T. W. Tesche, C. E. Reid, P. M. Roth, W. R. Oliver,
and J. C. Cassmassi. "The Sensitivity of Complex Photochemical
Model Estimates to Detail In Input Information, Appendix
A - A Compilation of Simulation Results," EPA 450/4-81-031b
(NTIS PB 82-234220), U. S. Environmental Protection Agency,
Research Triangle Park, North Carolina, 1981.
Stern, R. and B. Scherer. "Simulation of a Photochemical Smog
Episode in the Rhine-Ruhr Area with a Three Dimensional
Grid Model," 13th International Technical Meeting on Air
Pollution Modeling and Its Application," He des Embiez,
France, September 1982.
Tesche, T. W., C. Seigneur, L. E. Reid, P. M. Roth, W. R. Oliver,
and J. C. Cassmassi. "The Sensitivity of Complex Photochemical
Model Estimates to Detail in Input Information," EPA 450/4-81-031 a
(NTIS PB 82-234188), U. S. Environmental Protection Agency,
Research Triangle Park, North Carolina, 1981.
Tesche, T. W., W. R. Oliver, H. Hogo, P. Saxeena and J. L. Haney.
"Volume IV--Assessment of NOX Emission Control Requirements in
the South Coast Air Basin—Appendix A. Performance Evaluation
of the Systems Applications Airshed Model for the 26-27 June
1974 03 Episode in the South Coast Air Basin," SYSAPP 83/037,
Systems Applications, Inc., San Rafael, California, 1983.
A-36
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Tesche, T. W., W. R. Oliver, H. Hogo, P. Saxeena and J. L. Haney.
"Volume IV—Assessment of NOX Emission Control Requirements in
the South Coast Air Basin—Appendix B. Performance Evaluation
of the Systems Applications Airshed Model for the 7-8 November
1978 N02 Episode in the South Coast Air Basin," SYSAPP 83/038,
Systems Applications, Inc., San Rafael, California, 1983.
A-37
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A.9 REFERENCES
Benson, P. E., 1979. "CALINE3 - A Versatile Dispersion Model for Predict-
ing Air Pollution Levels Near Highways and Arterial Streets." Interim
Report, Report Number FHWA/CA/TL-79/23, Federal Highway Administration.
Briggs, 6. A., 1969. Plume Rise. U. S. Atomic Energy Commission Critical
Review Series, NTIS TID-25075 Oak Ridge National Laboratory, Oak Ridge, TN.
Briggs, G. A., 1971. Some Recent Analyses of Plume Rise Observations.
Proceedings of the Second International Clean Air Congress, edited by
H. M. Englund and W. T. Berry. Academic Press, New York, NY.
Briggs, 6. A., 1971. "Plume Rise: A Recent Critical Review," Nuclear
Safety, Vol. 12, pp. 15-24.
Briggs, G. A., 1972. Discussion on Chinney Plumes in Neutral and Stable
Surroundings. Atmos. Envir. 6: 507-510.
Briggs, G. A., 1974. Diffusion Estimation for Small Emissions, in ERL, ARL
USAEC Report ATDL-106. U. S. Atomic Energy Commission, Oak Ridge, TN.
Bjorklund, J. R., and J. F. Bowers, 1982. User's Instructions for the
SHORTZ and LONGZ Computer Programs. EPA-903/9-82-004a,b. U. S. Environ-
mental Protection Agency, Region III, Philadelphia, PA.
Businger, J. A., and S. P. Arya, 1974. Height of the Mixed Layer in the
Stably Stratified Planetary Boundary Layer. Advances in Geophysics, Vol.
ISA, F. N. Frankiel and R. E. Munn (Eds.) Academic Press, New York, NY.
Huber, A. H. and W. H. Snyder, 1976. Building Wake Effects on Short
Stack Effluents. Third Symposium on Atmospheric Turbulence, Diffusion
and Air Quality, American Meteorological Society, Boston, MA.
Irwin, J. S., 1979. A Theoretical Variation of the Wind Profile Power-Law
Exponent as a Function of Surface Roughness and Stability. Atmos. Envir.
13(1): 191-194.
Lamb, R. G., et al., 1977. Continued Research in Mesoscale Air Pollution
Simulation Modeling -- Vol. VI: Further Studies in the Modeling of Micro-
scale Phenomena. Report Number EF77-143. Systems Applications, Inc.,
San Rafael, CA.
Larsen, R. I., 1971. A Mathematical Model for Relating Air Quality
Measurements to Air Quality Standards. Office of Air Programs Publication
No. AP-89. U.S. Environmental Protection Agency, Research Triangle Park, NC.
Liu, M. K., et al., 1976. The Chemistry, Dispersion, and Transport of
Air Pollutants Emitted from Fossil Fuel Power Plants in California:
Data Analysis and Emission Impact Model. Systems Applications, .Inc.,
San Rafael, CA.
A-39
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McElroy, J. L. and F. Pooler, Jr., 1968. St. Louis Dispersion Study Volume
II-Analysis. NAPCA Publication No. AP-53. U.S. Environmental Protection
Agency, Research Triangle Park, NC.
Pasquill, F., 1976. Atmospheric Dispersion Parameters in Gaussian Plume
Modeling Part II. Possible Requirements for Change in the Turner Workbook
Values. EPA-600/4-76-030b. U.S. Environmental Protection Agency, Research
Triangle Park, NC.
Turner, D. B., 1969. Workbook of Atmospheric Dispersion Estimates. PHS
Publication No. 999-26. U.S. Environmental Protection Agency, Research
Triangle, Park, NC.
Whitten, 6. Z., J. P. Killus, and H. Hogo, 1980. Modeling of Simulated
Photochemical Smog with Kinetic Mechanisms. Volume 1. Final Report.
EPA-600/3-80-028a. U. S. Environmental Protection Agency, Research
Triangle Park, NC.
A-40
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APPENDIX B
SUMMARIES
OF
ALTERNATIVE AIR QUALITY MODELS
-------
APPENDIX B
TABLE OF CONTENTS
B.O INTRODUCTION B-1
B.I AIR QUALITY DISPLAY MODEL (AQDM) B-3
B.2 AIR RESOURCES REGIONAL POLLUTION ASSESSMENT (ARRPA) MODEL B-7
B.3 APRAC-3/MOBILE2 B-ll
B.4 COMPTER B-15
B.5 ERT AIR QUALITY MODEL (ERTAQ) B-19
B.6 ERT VISIBILITY MODEL B-23
B.7 HIWAY-2 B-27
B.8 INTEGRATED MODEL FOR PLUMES AND ATMOSPHERIC CHEMISTRY
IN COMPLEX TERRAIN (IMPACT)(Fabrick) B-31
8.9 INTEGRATED MODEL FOR PLUMES AND ATMOSPHERIC CHEMISTRY
'IN COMPLEX TERRAIN (IMPACT)(Skiarew) B-35
B.10 LONGZ B-39
B.ll MARYLAND POWER PLANT SITING PROGRAM (PPSP) MODEL B-43
B.12 MESOSCALE PLUME SEGMENT MODEL (MESOPLUME) B-47
B.13 MESOSCALE PUFF MODEL (MESOPUFF) B-51
B.14 MESOSCALE TRANSPORT DIFFUSION AND DEPOSITION MODEL FOR
INDUSTRIAL SOURCES (MTDDIS) .-.. B-55
B.15 MODELS 3141 and 4141 B-59
B.16 MULTIMAX B-63
B.17 MULTIPLE POINT SOURCE DIFFUSION MODEL (MPSDM) B-67
B.18 MULTI-SOURCE (SCSTER) MODEL B-71
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B.19 PACIFIC GAS AND ELECTRIC PLUMES MODEL B-75
B.20 PLMSTAR AIR QUALITY SIMULATION MODEL B-79
B.21 PLUME VISIBILITY MODEL (PLUVUE II) B-83
B.22 POINT, AREA, LINE SOURCE ALGORITHM (PAL) B-87
B.23 RANDOM WALK ADVECTION AND DISPERSION MODEL (RADM) B-91
B.24 REACTIVE PLUME MODEL (RPM-II) B-95
B.25 REGIONAL TRANSPORT MODEL (RTM-II) B-99
B.26 ROUGH TERRAIN DIFFUSION MODEL (RTDM) B-103
B.27 SHORTZ B-107
B.28 SIMPLE LINE-SOURCE MODEL (SLSM) B-lll
B.29 TEXAS CLIMATOLOGICAL MODEL (TCM) B-115
B.30 TEXAS EPISODIC MODEL (TEM) B-119
B.31 REFERENCES , B-123
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B.O INTRODUCTION
This appendix summarizes key features of refined air quality models
that may be considered on a case-by-case basis for individual regulatory
applications. For each model, information is provided on availability,*
regulatory use, data input, output format and options, simulation of atmos-
pheric physics and accuracy. The Models are listed by name in alphabetical
order.
There are three separate conditions under which these models will
normally be approved for use: first, if a demonstration can be made that
the model produces concentration estimates equivalent to the estimates
obtained using a preferred model (e.g. the maximum or highest second high-
est concentration is within 2% of the estimate using the comparable
preferred model); second, if a statistical performance evaluation has
been conducted using measured air quality data and the results of that
evaluation indicate the model in Appendix B performs better for the
application than a comparable model in Appendix A; and third, if there
is no preferred model for the specific application but a refined model is
needed to satisfy regulatory requirements. Any one of these three separate
conditions may warrant use of these models. See Section 3.2, Use of
Alternative Models, for additional details.
Many of these models have been subjected to a performance evaluation
by comparison with observed air quality data. A summary of such comparisons
for models contained in this appendix is included in "A Survey of Statistical
Measures of Model Performance and Accuracy for Several Air Quality Models,"
EPA-450/4-83-001. Where possible, several of the models contained herein
have been subjected to rigorous evaluation exercises, including (1)
statistical performance measures recommended by the American Meteorological
Society and (2) peer scientific reviews.
*Where reference is made to UNAMAP (Version 6), the assumption is implicit that
that update of UNAMAP will contain the model identified.
B-l
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B.I AIR QUALITY DISPLAY MODEL (AQDM)
Reference: TRW Systems Group. "Air Quality Display Model." Pre-
pared for National Air Pollution Control Administration,
DHEW, U.S. Public Health Service, Washington, D.C., 1969,
(NTIS PB 198194).
Availability: This model is available in the form of a punched card
deck from:
Library Services
MD-35
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
Attn: Ann Ingram
Abstract: AQDM is a climatological steady state Gaussian plume model
that estimates annual arithmetic average sulfur dioxide
and particulate concentrations at ground level in urban
areas. A statistical model based on Larsen (1971) is used
to transform the average concentration data from a limited
number of receptors into expected geometric mean and maximum
concentration values for several different averaging times.
a. Recommendations for Regulatory Use
AQDM can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. AQDM must be executed in the equivalent mode.
AQDM can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that
AQDM is more appropriate for the specific application. In this case
the model options/modes which are most appropriate for the application
should be used.
b. Input Requirements
Source data requirements are: average emissions rates and heights of
emissions for point and area sources; stack gas temperature, stack
gas exit velocity, and stack inside diameter for plume rise calculations
for point sources.
Meteorological data requirements are: stability wind rose (STAR
deck), average afternoon mixing height, average morning mixing
height, and average air temperature.
Receptor data requirements are: number and locations of receptors.
If the Larsen transform option is to be used to estimate short
averaging time concentrations, measured standard geometric deviation
B-3
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of concentrations is required.
c. Output
Printed output includes:
one month to one year average concentrations (arithnetic
mean only) at each receptor;
optional arbitrary averaging time by Larsen (1971) proce-
dure (typically 1-24 hr); and
optional individual point, area source culpability list
for each receptor.
d. Type of Model
AQDM is a Gaussian plume model.
e. Pollutant Types
AQDM may be used to model primary pollutants. Settling and deposi-
tion are not treated.
f. Source Receptor Relationship
AQDM applies user-specified locations stack height for each point
source.
AQDM uses any location and size for each area source.
Up to 225 receptors may be located on uniform rectangular grid.
Up to 12 user-specified receptor locations are permitted.
Unique release height is used for each point, area source.
Receptors are assumed to be at ground level.
No terrain differences between source and receptor are treated.
g. Plume Behavior
AQDM uses Briggs (1969) plume rise formulas.
No plume rise is calculated for area sources.
Fumigation and downwash are not treated.
Zero concentration is assumed when plume height is greater than
mixing height.
B-4
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h. Horizontal Minds
Wind data are input as stability wind rose (joint frequency distri-
bution) of 16 wind directions, six wind speed classes, and five
stabil ity classes.
No variation in wind speed with height is assumed.
Constant, uniform (steady-state) wind is assumed.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Pollutants are assumed evenly distributed across a 22.5 degree
sector.
Frequency of occurrence of a meteorological state is interpolated
between sector center lines.
Averaging times from 1 month to 1 year or longer are treated.
k. Vertical Dispersion
Rural dispersion coefficients from Turner (1969) are used.
Five stability classes are as defined by Turner (1964). Stability
classes E and F are combined, and assigned dispersion values equiva-
lent to stability class D.
Neutral stability is split internally into 60% day, 40% night, with
the two differing only in the treatment of mixing height.
Mixing height is a function of a single input afternoon mixing
height, a single input morning mixing height, modified by the
stability class.
1. Chemical Transformations
Not treated.
m. Physical Removal
Not treated.
n. Evaluation Studies
McNidar, R. R. (1977). "Variability Analysis of Long-term Dispersion
Models," Joint Conf. on Applications of Air Pollution Meteorolo-
gy, American Meteorology Society, 29 Nov.-2 Dec., 1977, Salt
Lake City, Utah.
B-5
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Turner, D. B., J. R. Zimmerman, and A. D. Busse. "An Evaluation of
Some Climatological Dispersion Models," in Appendix E. User's
Guide to the Climatological Dispersion Model, Environmental
Protection Agency, Research Triangle Park, North Carolina 27711,
1973.
Londergan, R. J., D. H. Minott, D. J. Wachter and R. R. Fizz. "Evalua-
tion of Urban Air Quality Simulation Models." EPA-450/4-83-020.
Environmental Protection Agency, Research Triangle Park, North
Carolina 27711, 1983.
B-6
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B.2 AIR RESOURCES REGIONAL POLLUTION ASSESSMENT (ARRPA) MODEL
Reference: Mueller, S. F., R. J. Valente, T. L. Crawford, A. L. Sparks,
and L. L. Gautney, Jr. "Description of the Air Resources
Regional Pollution Assessment (ARRPA) Model: September
1983." TVA/ONR/AQB-83/14. Tennessee Valley Authority,
Muscle Shoals, Alabama 35660, 1983.
Availability: The computer code and sample input for this model are
available on magnetic tape from:
Computer Services Development Branch
Office of Natural Resources and Economic Development
Tennessee Valley Authority
OSWHA
Muscle Shoals, Alabama 35660.
Phone (205) 386-2985
A hard copy of the model output corresponding with the
sample input is also available.
Abstract: The ARRPA model is a medium/long-range segmented-plume
model. It is designed to compute air concentrations and
surface dry mass deposition of sulfur dioxide and sulfate.
A unique feature of the model is its use of prognostic
meteorological output from the National Weather Service
Boundary Layer Model (BLM). Boundary layer conditions are
computed by the BLM on a grid with a spatial resolution of
80 km, and are archieved in intervals of 3 hours. BLM
output used by this model includes three dimensional wind
fiel'd components and potential -temperature at 10 height
levels from the surface through 2000 m above the surface.
a. Recommendations for Regulatory Use
There is no specific recommendation at this time. The ARRPA model may
be used when transport distances are greater than 50 km on a case-by-
case basis.
b. Input Requirements
Source data requirements: location (latitude and longitude), stack
height, stack diameter, stack gas exit velocity, stack gas temperature,
S02 emission rate, S0| emission rate, stack base elevation.
Meteorological data requirements: Hourly wind field components
(u,v,w), potential temperature (e), Pasquill-Gifford stability
class and mixing height. These data are obtained as output from the
B-7
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BLM output preprocessing program called MDPP [S.F. Mueller and R.
J. Valente. "Meteorological Data Preprocessing Manual for the Air
Resources Regional Pollution Assessment Model (Generic Version)."
TVA/ONR/AQB-83/13. Tennessee Valley Authority, September 1983.]
Required input to MDPP is BLM output (in three-hour intervals) of
u, v, w, and e, surface layer friction velocity (u*) and surface
layer values of the inverse Monin-Obukhov length (L~M.
Receptor data requirements: gridded receptor array coordinates
(x and y) and receptor heights (z) from a receptor preprocessing
program called HEIGHT. HEIGHT produces a user-designed array of
points which may be skewed up to +90 degrees relative to the model
x axis. The elevation of each receptor is adjusted to give height
above smoothed model terrain.
c. Output
Printed output includes:
listings of input parameters (except for meteorological data);
listing of hours processed and flags for missing data periods.
Disk output: -parameters for controlling analysis and printout options
in the postprocessing program called ANALYSIS; hourly S02 and S0|
air concentrations and dry deposition amounts at each receptor.
Optional printed output: two programs are available for displaying
model output - DISPLAY and ANALYSIS; DISPLAY prints out hourly
gridded concentration and/or deposition fields for a user-specified
time periods; ANALYSIS prints out (1) the five highest concentrations
of SOp and/or S0| at each receptor for 1-hour, 3-hour (optional)
and 24-hour (optional) averaging periods, (2) average S02 and/or
S0| concentrations at each receptor for the entire analysis period
and (3) gridded SOo and/or S0| dry deposition amounts for the day
having the greatest dry deposition and for the entire analysis period.
d. Type of Model
The ARRPA model is a Gaussian segmented-plume model.
e. Pollutant Types
S02 and S0| are treated.
f. Source-Receptor Relationship
One source is treated per model run, though results from several
sources may be superimposed.
B-8
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Emission rates are constant.
Receptors (up to 100) in gridded network may have different elevations.
Height of receptors above ground is variable.
g. Plume Behavior
Plume rise is computed in a piecewise-continuous manner through
discrete model layers (Mueller, et al., 1983).
Plume can be isolated from the ground (lofting).
Plume height varies in time and space.
h. Horizontal Winds
Hourly horizontal wind components, specified at 80-km intervals
across the model grid, are spatially interpolated and vertically
averaged through the plume depth to get plume transport vectors. A
model option is available that uses the wind vector near the vertical
plume center instead of computing a vertically-averaged vector.
i. Vertical Wind Speed
The mass-conserving BLM wind field used in this model provides
vertical wind components that vary horizontally and vertically, and
are used to adjust plume height.
j. Horizontal Dispersion
Plume half-width (ay) growth goes through four stages:
(1) growth follows Turner curves for 6000 m;
eddy diffusivity computed from Pasquill-Gifford stability class;
(4) growth approaches that described by Taylor's statistical theory
(limit of dynamical-statistical theory for time much larger than
the Lagrangian time correlation) for oy > 10000 m.
k. Vertical Dispersion
Plume half-depth (oz) growth is based on combination of Brook haven
curves for elevated plumes and Turner curves for near-ground plumes.
Vertical plume structure is Gaussian, with superimposed reflection
terms, until oz becomes sufficiently large that a vertically uniform
plume assumption is appropriate.
B-9
-------
Plume is assumed to be isolated above the ground when the plume
centerline height is more than a distance Oz above the mixing height.
Maximum depth of a plume is 2000 m.
1. Chemical Transformation
S02 oxidation to SO^ is treated using a first-order chemical
reaction rate constant which is parameterized to vary hourly following
diurnal and seasonal cycles.
m. Physical Removal
Dry deposition is computed using the source depletion equation.
Dry deposition velocities vary according to the stability of the
surface layer.
n. Evaluation Studies
Studies are in progress.
B-10
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B.3 APRAC-3/MOBILE2
Reference: Simmon, P. B., R. M. Patterson, F. L. Ludwig, and L. B. Jones.
"The APRAC-3/Mobile 1 Emissions and Diffusion Modeling
Package". EPA 909-9-81-002. Environmental Protection
Agency, Region IX, San Francisco, California 94104, 1981.
NTIS PB82-103763.
User's Guide to MOBILE2 (Mobile Source Emissions Model).
EPA-460/3-81-006, 1981.
Availability; This model is available as part of UNAMAP (Version 6). The
computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U. S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: APRAC-3 is a model which computes hourly average carbon monoxide
concentrations for any urban location. The model calculates
contributions from dispersion on various scales: extraurban,
mainly from sources upwind of the city of interest; intraurban,
from freeway, arterial, and feeder street sources; and local,
from dispersion within a street canyon. APRAC-3 requires an
extensive traffic inventory for the city of interest. An
emissions module incorporating Mobile 2 is used to compute
emission factors. The APRAC-3 documentation now contains a
revised Table 9, Inputs for Preprocessor Program, for accommo-
dating additional MOBILE2 inputs.
a. Recommendations for Regulatory Use
APRAC-3 can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given application.
APRAC-3 must be executed in the equivalent mode.
APRAC-3 can be used in a case-by-case basis in lieu of a preferred model if
it can be demonstrated using the criteria in Section 3.2, that APRAC-3 is
more appropriate for the specific application. In this case the model options/
mode which are most appropriate for the application should be used.
b. Input Requirements
Source data requirements are: line source (traffic link) end points, road
type and daily traffic volume.
B-ll
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Meteorological data requirements are: hourly wind direction (nearest 10
degrees), hourly wind speed, and hourly cloud cover for stability calculations.
Receptor data requirements are: coordinates for up to 10 receptors for any
single day and up to 8 receptors for the intersection submodel.
Output
Printed ouput includes:
hourly calculations at each receptor.
Type of Model
APRAC-3 is a Gaussian plume model.
Pollutant Types
APRAC-3 may be used to model primary pollutants.
Source-Receptor Relationship
The traffic links may have arbitrary length and orientation off-link
traffic allocated to two-mile square grid. Link traffic emissions
are aggregated into a receptor oriented area source array.
The boundaries of the area sources actually treated are (1) arcs at
radial distances from the receptor which increase in geometric
progression, (2) the sides of a 22.5° sector oriented upwind for
distances greater than 1000 m, and (3) the sides of a 45° sector
oriented upwind for distances less than 1000 m.
A similar area source array is established for each receptor.
Sources are assumed to be at ground level.
Up to 10 receptors are accepted for any single day.
Up to 625 receptors are accepted for a single-hour.
Up to 8 receptors are accepted for the intersection submodel.
Receptors are at ground level.
Receptor locations are arbitrary.
Four internally defined receptor locations on each user-designated
street are used in a special street canyon sub-model.
A box model is used to estimate contribution from upwind sources beyond
32 km based on wind speed, mixing height, annual fuel consumption.
B-12
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In street canyon sub-model, contribution from other streets is included
in background.
g. Plume Behavior
Plume rise is not treated.
Fumigation and downwash are not treated except in street canyon sub-model.
In street canyon sub-model, a helical circulation pattern is assumed.
h. Horizontal Winds
User input hourly wind speed and direction in tens of degrees are used.
No variation of wind speed or direction with height is assumed.
Constant, uniform (steady-state) wind is assumed within each hour.
The model can interpolate winds at receptors if more than one
wind is provided. •
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero except in street canyon sub-model.
Helical circulation assumed by street canyon sub-model.
j. Horizontal Dispersion
Sector averaging is used with uniform distribution within sectors. Sector
size is 22.5 degrees beyond 1 km and 45.0 degrees within 1 km.
k. Vertical Dispersion
Six stability classes are used. Stability class is determined internally
from user-supplied meteorological data modified from Turner (1964).
Dispersion coefficients are adapted from McElroy and Pooler (1968).
No adjustments are made for variations in surface roughness
Downwind distance variation of oz is assumed to be ax^ for purposes of
doing analytical integration.
In street canyon sub-model, an empirical function of wind speed and street
width and direction is used
Perfect reflection at the surface is assumed.
B-13
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Mixing height is ignored until concentration equals that calculated using box
model. A box model (uniform vertical distribution) is used beyond that distance.
1. Chemical Transformation
Not treated.
m. Physical Removal
Not treated.
n. Evaluation Studies
None for APRAC-3/MOBILE2.
B-14
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B.4 COMPTER
Reference:
State of Alabama. "COMPTER Model
Air Pollution Control Commission,
36130-1701.
Users Guide," Alabama
Montgomery, Alabama
Availability:
Abstract:
This model is available to users for tape reproduction
charges. Send tape and desired format and specifications to:
Alabama Air Pollution Control Commission
465 South McDonough
Montgomery, Alabama 36130
COMPTER is based on the Gaussian steady-state technique
applicable to both urban and rural areas. The model
contains the following attributes: (a) determines maximum
24-hour, 3-hour, 1-hour and variable hour concentrations for
both block and running averages; (b) elevated terrain
considered with the standard plume-chopping technique or
stability dependent plume path trajectory; (c) uses annual
hourly meteorological data in the CRSTER preprocessor
format; (d) uses Pasquill-Gifford stability curves; (e)
allows for stability class substitution in the stable
categories. Typical model use is in rural areas with
moderate to low terrain features.
a. Recommendations for Regulatory Use
COMPTER can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. COMPTER must be executed in the equivalent mode.
COMPTER can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that COMPTER
is more appropriate for the specific application. In this case the model
options/modes which are most appropriate for the application should be
used.
b. Input Requirements
Source data requirements are: annual or hourly values of emission rate,
exit velocity, stack gas temperature, stack height, and stack diameter.
Meteorological data requirements are: hourly surface weather data
from the EPA meteorological preprocessor program. Preprocessor
output includes hourly stability class wind direction, wind speed,
temperature, and mixing height. Actual anemometer height (a single
value) is optional.
Receptor data requirements are: individual receptor coordinates;
or a location, dimension, and spacing of a rectangular grid; or
B-15
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a location and distance from the center of five rings of receptors;
or a combination of individual receptors and either the rectangular
grid or the rings of receptors. Elevations of all receptors may be
input.
c. Output
Printed output includes:
Highest and second highest concentrations for the year at
each receptor for averaging times of 1, 3 and 24-hours, a
user-selected averaging time which may be 2-12 hours, and
a 50 high table for 1, 3, and 24-hours;
Annual arithmetic average at each receptor; and the
highest 1-hour and 24-hour concentrations over the recep-
tor field for each day considered.
Computer readable output includes:
Hourly, 3-hourly, variable hourly, and 24-hourly concen-
trations for each receptor on magnetic storage device.
d. Type of Model
COMPTER is a Gaussian plume model.
e. Pollutant Types
COMPTER may be used to model primary pollutants. Settling and
deposition are not treated.
f. Source-Receptor Relationship
A maximum 50 sources and 200 receptors are treated.
COMPTER applies user-specified locations of sources and recetors.
user input stack height and source characteristics for each source
are applied.
User input topographic elevation for each receptor is applied.
Receptors are assumed to be at ground level.
g. Plume Behavior
Briggs1 (1969, 1971, 1972) plume rise equations with limited mixing
are used.
Plume height is adjustable according to stability with use of plume
path coefficient.
B-16
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h. Horizontal Winds
Constant, uniform (steady-state) wind is assumed for an hour.
Straight line plume transport is assumed to all downwind distances.
Power law wind profile exponents used are .10, .15, .20, .25, .30,
and .30, for stability classes A through F, respectively. Anemome-
ter height is assumed to be 10 meters.
i. Vertical Wind Speed
Vertical wind speeds assumed equal to zero.
j. Horizontal Dispersion
Dispersion coefficients are from Turner (1969), with no further
adjustments made for variations in surface roughness or averaging
time.
Optionally, stability class 7 may be treated as Class 6.
Other options for stable class substitution include changing
stabilities F and G to E, and reducing E, F, and G to D, E, and F,
respectively.
k. Vertical Dispersion
Dispersion coefficients are from Turner (1969), with no further
adjustments made for variations in surface roughness.
Optionally, stability class 7 may be treated as class 6.
Other options for stable class substitution include changing
stabilities F and G to E; and reducing E, F, and G to D, E, and F,
respectively.
1. Chemical Transformation
Not treated.
m. Physical Removal
Not treated.
n. Evaluation Studies
Londergan, R., D. Minott, D. Wachter, T. Kincaid and D. Bonitata.
"Evaluation of Rural Air Quality Simulation Models." EPA-450/4-83-
003, Environmental Protection Agency, Research Triangle Park, North
Carolina, 1983.
B-17
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B.5 ERT AIR QUALITY MODEL (ERTAQ)
Reference: Environmental Research & Technology, Inc., ERTAQ Users'
Guide. ERT Document No. M-0186-001E. Environmental
Research & Technology, Inc., Concord, Massachusetts, 1980.
Availability: The ERTAQ model is available from:
Environmental Research & Technology, Inc.
ATTN: Mr. Joseph A. Curreri
Air Quality Center
696 Virginia Road
Concord, Massachusetts 01742
No cost has been specified.
Abstract: ERTAQ is a multiple point, line and area source disper-
sion model which utilizes the univariate Gaussian formula
with multiple reflections.
With the fugitive dust option, entrainment of particu-
lates from ground-level sources and subsequent deposition
are accountable. The model offers an urban/rural option,
and calculates long-term or worst-case concentrations due
to arbitrarily located sources for arbitrarily located
receptors above or at ground level. Background concen-
trations and calibration factors at each receptor can be
user specified. Unique flexibility is afforded by
postprocessing storage and manipulation capability.
a. Recommendations for Regulatory Use
ERTAQ can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. ERTAQ must be executed in the equivalent mode.
ERTAQ can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that ERTAQ
is more appropriate for the specific application. In this case the model
options/modes which are most appropriate for the application should be used.
b. Input Requirements
Source data requirements are: up to six pollutants may be specified,
citing quantity and calibration factor for each (and particle size, if
appropriate); heat rate and height of emissions per source for
determining plume height.
Meteorological data requirements are: stability wind rose, plus
annual average ambient air temperature and mixing height.
Receptor data requirements are: cartesian coordinates for each receptor.
B-19
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c. Output
Printed output includes:
Mean concentrations at designated receptors for long-term
mode. In worst-case mode, concentrations for user-
specified meteorological conditions.
d. Type of Model
ERTAQ is a climatological Gaussian plume model.
e. Pollutant Types
ERTAQ treats primary pollutants with or without significant settling
velocities.
f. Source-Receptor Relationship
Up to 501 user-specified locations for point, area, and line
sources, and up to 128 arbitrarily located receptors are permitted.
User-specified release heights are applied for all sources.
Simple terrain relief is treated.
Receptors may be at or above ground level.
g. Plume Behavior
Briggs (1975) plume rise formulas final rise only, are used.
Briggs calm formula is used when wind speed is less than 1.37 meters
per second.
Plume rise may be calculated for point and area sources.
Top of mixed layer is perfect reflector (full or no plume penetration).
Fumigation and downwash are not treated.
Buoyancy-induced dispersion is not treated.
h. Horizontal Winds
Steady state and homogeneous winds are assumed.
Sixteen wind directions and six speed classes are treated.
Exponential vertical profile extrapolates observed wind to release
height for plume rise and to plume height for downwind dilution.
B-20
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The exponents used are .10, .15, .20, .25, and .30 for stability
classes A through E, respectively.
i. Vertical Wind Speed
Vertical wind speed is assumed to be zero.
j. Horizontal Dispersion
Uniform distribution in 22.5 degree sector, or triangular distribu-
tion in 45 degree sector (user specified).
k. Vertical Dispersion
Gaussian plume with initial mixing specification is assumed.
Five stability categories are treated (converts all stability class
F to class E).
Rural dispersion coefficients from Turner (1969) are used with no
adjustments made for surface roughness.
Urban case is treated by shifting the stability one class toward
unstable.
Top of mixed layer is perfect reflector (full or no plume penetra-
tion).
Ground surface is total reflector.
Surface deposition reduces entire plume concentration using a source
depletion factor.
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Half-life
is input by the user.
m. Physical Removal
Particle deposition for ground-level sources is treated.
n. Evaluation Studies
Londergan, R. J., D. H. Minott, D. J. Wachter and R. R. Fizz. "Evaluation
of Urban Air Quality Simulation Models." EPA-450/4-83-020. Environ-
mental Protection Agency, Research Triangle Park, North Carolina 27711,
1983.
B-21
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B.6 ERT VISIBILITY MODEL
Reference: Drivas, Peter 0., Savithri Machiraju, and David W. Heinold.
"ERT Visibility Model: Version 3; Technical Description
and User's Guide." Document M2020-001. Environmental
Research & Technology, Inc., Concord, Massachusetts,
1980.
Availability: Anyone wishing to review the Visibility model should
contact Environmental Research & Technology, Inc. (ERT).
At present no cost has been identified for the user manual
or the model. Requests should be directed to:
Mr. Joseph A. Curreri
Air Quality Studies Division
Environmental Research & Technology, Inc.
696 Virginia Road
Concord, Massachusetts 01742
Abstract: The ERT Visibility model is a Gaussian dispersion model
designed to estimate visibility impairment for arbitrary
lines of sight due to isolated point source emissions by
simulating gase-to-particle conversion, dry deposition, NO
to N02 conversion and linear radiative transfer.
a. Recommendations for Regulatory Use
There is no specific recommendation at the present time. The ERT
visibility model may be used on a case by case basis.
b. Input Requirements
Source data requirements are: stack height, stack temperature,
emissions of SOg, NOX, TSP, fraction of NOX as N02, fraction of TSP
which are carbonaceous, exit velocity, and exit radius.
Meteorological data requirements are: hourly ambient temperature,
mixing depth, wind speed at stack height, stability class, potential
temperature gradient, and wind direction.
Receptor data requirements are: observer coordinates with respect to
source, latitude, longitude, time zone, date, time of day, elevation,
relative humidity, background visual range, line-of-sight azimuth and
elevation angle, inclination angle of the observed object, distance
from observer to object, object reflectivity, surface reflectivity,
number and spacing of integral receptor points along line-of-sight.
Other data requirements are: ambient concentrations of 03 and NOX,
deposition velocity of TSP, sulfate, nitrate, S0£ and NOX, first-order
transformation rate for sulfate and nitrate.
B-23
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c. Output
Printed output includes both summary and detailed results as follows:
Summary output: page 1 - site, observer and object parameters;
page 2 - optical pollutants and associated extinction coefficients;
page 3 - plume model input parameters; page 4 - total calculated visual
range reduction, and each pollutant's contribution; page 5 - calculated
plume contrast, object contrast and object contrast degradation at the
550 nm wavelength; page 6 - calculated blue/red ratio and A£ (U*V*W*)
values for both sky and object discoloration.
Detailed output: phase functions for each pollutant in four wavelengths
(400, 450, 550, 650 nm), concentrations for each pollutant along sight
path, solar geometry, contrast parameters at all wavelengths, intensities,
tristimulus values and chromaticity coordinates for views of the object,
sun, background sky and plume.
d. Type of Model
ERT Visibility is a Gaussian plume model for estimating visibility
impairment.
e. Pollutant Types
Optical activity of sulfate, nitrate, (derived from $03 and NOX emissions)
primary TSP and N02 is simulated.
f. Source Receptor Relationship
Single source and hour is simulated. Unlimited number of lines-of-sight
(receptors) is permitted per model run.
g. Plume Behavior
Briggs (1971) plume rise equations for final rise are used.
h. Horizontal Wind Field
A single wind speed and direction is specified for each case study.
The wind is assumed to be spatially uniform.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Rural dispersion coefficients from Turner (1969) are used.
B-24
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k. Vertical Dispersion
Rural dispersion coefficients from Turner (1969) are used. Mixing
height is accounted for with multiple reflection handled by summation
of series near the source, and Fourier representation farther downwind.
1. Chemical Transformation
First order transformations of sulfates and nitrates are used.
m. Physical Removal
Dry deposition is treated by the source depletion method.
n. Evaluation Studies
White, Warren H. et al. "An Intercomparison of Plume Visibility
Models with VISTTA Observations at the Navajo Generating Station,"
1983.
Seigneur, C., R. W. Bergstrom, and A. B. Hudischewskyj. "Evaluation
of the EPA PLUVUE Model and the ERT Visibility Model Based on the
1979 VISTTA Data Base" EPA-450/4-82-008, U. S. Environmental
Protection Agency, Research Triangle Park, North Carolina, 1982.
B-25
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Availability:
Abstract:
B.7 HIWAY-2
Reference: Petersen, W. B. User's Guide for HIWAY-2. Publication No.
EPA-600/8-80-018 (NTIS PB 80-227-556) Environmental Protec-
tion Agency, ESRL, Research Triangle Park, North Carolina
27711, 1980.
This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
HlWAY-2 can be used to estimate the concentrations of
non-reactive pollutants from highway traffic. This
steady-state Gaussian model can be applied to determine
air pollution concentrations at receptor locations
downwind of "at-grade" and "cut section" highways located
in relatively uncomplicated terrain. The model is appli-
cable for any wind direction, highway orientation, and
receptor location. The model was developed for situa-
tions where horizontal wind flow dominates. The model
cannot consider complex terrain or large obstructions to
the flow such as buildings or large trees.
a. Recommendations for Regulatory Use
HIWAY-2 can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. HIWAY-2 must be executed in the equivalent mode.
HIWAY-2 can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that HIWAY-2
is more appropriate for the specific application. In this case the model
options/modes which are most appropriate for the application should be used.
b. Input Requirements
Source data requirements are: a uniform emission rate by lane,
roadway end points; height of emission; length, width, and number of
lanes; and width of center strip.
B-27
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Meteorological data requirements are: one set at a time of hourly
averages of wind speed, wind direction, and mixing height and the
Pasquill-Gifford stability class. Wind speed and direction are
preferred to be at 2 meters above ground.
Receptor data requirements are: coordinates of each receptor.
c. Output
Printed output includes:
one hourly average concentration at each specified recep-
tor location.
d. Type of Model
HIWAY-2 is a Gaussian plume model.
e. Pollutant Types
HIWAY-2 may be used to model primary pollutants. Settling and
deposition are not treated.
f. Source-Receptor Relationship
HIWAY-2 applies user-specified end points for a single rpadway
segment, and user-specified receptor locations.
Plume impact on receptor is calculated by finite difference integra-
tion of a point source along each lane of the roadway.
g. Plume Behavior
HIWAY-2 does not treat plume rise.
h. Horizontal Winds
Constant, uniform (steady-state) wind is assumed for an hour.
Straight line plume transport is assumed to all downwind distances.
An aerodynamic drag factor is applied when winds are parallel to
the roadway and speeds are less than 2 m/sec.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
The total horizontal dispersion is that due to ambient turbulence plus
the turbulence generated by the vehicles on the roadway.
B-28
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Beyond 300 m downwind total turbulence is considered to be dominated
by atmospheric turbulence, with plume dispersion as described by
Turner (1969).
Three stability classes are considered: unstable, neutral and
stable.
k. Vertical Dispersion
The total horizontal dispersion is that due to ambient turbulence
plus the turbulence generated by the vehicles on the roadway.
Beyond 300 m downwind total turbulence is considered to be dominated
by atmospheric turbulence, with plume dispersion as described by
Turner (1969).
Mixing height is accounted for with multiple reflections until the
vertical plume size equals 1.6 times the mixing height; uniform
vertical mixing is assumed beyond that point.
Three stability classes are considered: unstable, neutral and
stable.
1. Chemical Transformation
Not treated.
m. Physical Removal
Not treated.
n. Evaluation Studies
Rao, S. T., and J. R. Visalli. "On the Comparative Assessment of
the Performance of Air Quality Models," J. Air Poll. Control
Assoc., Vol. 31, pp. 851-860, 1981.
B-29
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B.8 INTEGRATED MODEL FOR PLUMES AND ATMOSPHERIC CHEMISTRY IN COMPLEX
TERRAIN (IMPACT) (Fabrick)
Reference: Fabrick, Allan J. and Peter J. Haas. "User Guide to IMPACT:
An Integrated Model for Plumes and Atmospheric Chemistry in
Complex Terrain." DCN 80-241-403-01. Radian Corporation,
8501 Mo-Pac Blvd., Austin, Texas 78766, 1980.
Availability: Both a magnetic tape containing the IMPACT model and a set of
test data and a copy of the IMPACT User's Guide may be obtained
directly from Radian Corporation. The total cost is $500.
Requests should be sent to:
Radian Corporation
Atmospheric Science Division
8501 Mo-Pac Blvd.
Austin, Texas 78766
Abstract: IMPACT is an Eulerian, three-dimensional, finite difference
grid model designed to calculate the impact of pollutants,
either inert or reactive, in simple or complex terrain,
emitted from either point or area sources. It automatically
treats single or multiple point or area sources, the effects
of vertical temperature stratifications on the wind and dif-
fusion fields, shear flows caused by the atmospheric boundary
layer or by terrain effects, and chemical transformations.
a. Recommendations for Regulatory Use
IMPACT can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. IMPACT must be executed in the equivalent mode.
IMPACT can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that IMPACT
is more appropriate for the specific application. In this case the model
options/modes which are most appropriate for the application should be used.
There is no specific recommendation concerning the use of IMPACT for
photochemical applications. IMPACT may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: for point sources--!ocation {I, J), stack
height, exit temperature, volume flow rate or stack diameter and exit
velocity, hourly emission rates for all pollutants for area sources
location, of corners, and hourly emission rates for each pollutant.
Meteorological data requirements are: Hourly wind speed and direction,
surface and elevated, from meteorological stations within and surrounding
B-31
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the modeling area, temperature, pressure, humidity and insolation (the
three last variables are optional).
Receptor data requirements are: none since concentrations are
output for cells in the computational grid.
Air quality data (optional): One or more vertical concentration
profiles for each pollutant.
Other data: 2-D array of terrain heights, 2-D array of surface
roughness values (optional).
c. Output
Printed output options include:
surface and elevated horizontal cross sections of pollutant
concentrations (instantaneous, or averages over N hours where
N=l, 2,3,. . .);
horizontal cross sections of diffusivities and wind veloci-
ties; and
arbitrary vertical and horizontal cross sections of pollutant
concentrations and diffusivities, and CALCOMP wind field
vector plots are generated by the POST post-processor program.
Computer readable output includes:
Concentration, wind field and diffusivity data for each hour.
d. Type of Model
IMPACT is an Eulerian finite difference model.
e. Pollutant Types
IMPACT may be used to model any inert pollutant.
IMPACT may be used to model SOo, SO^, NOX, N02, 03, hydrocarbons (depends
upon chemistry mechanism selected).
f. Source-Receptor Relationship
Up to 20 point sources and 20 area sources may be treated (greater number
of sources may be treated by increasing common block storage allocation).
Concentrations are calculated at the center of each cell in the grid.
B-32
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g. Plume Behavior
Briggs (1975) formulation for plume rise is used.
Elevated inversions are considered.
h. Horizontal Winds
A three dimensional stability and terrain dependent nondivergent wind
field is interpolated from single or multiple wind data measurements
using a Poisson technique.
i. Vertical Wind Speed
Vertical wind speed is treated at each wind site, user specified or
extrapolated from surface data. Interpolated is accomplished as
part of the three dimensional wind field interpolation.
j. Horizontal Dispersion
A three dimensional diffusivity field is calculated using either
the technique of Myrup/Ranzieri or the DEPICT method (see User Guide,
Fabrick and Haas, 1980).
k. Vertical Dispersion
A three dimensional diffusivity field is calculated using either
the technique of Myrup/Ranzieri or the DEPICT method (see User Guide,
Fabrick and Haas, 1980).
1. Chemical Transformation
Either 3, 6, 8 or 15-species mechanisms are currently available (see
User Guide). Calculations are also performed for inert pollutants.
m. Physical Removal
Physical removal is treated using exponential decay. Half-life is
input by the user.
n. Evaluation Studies
Fabrick, A. J., R. Sklarew, and J. Wilson. Point Source Model Evaluation
and Development Study, report prepared for the California Air Resources
Board, 19//.
Sklarew, R., and V. Mirabel la. "Experience in IMPACT Modeling of Complex
Terrain," Fourth Symposium on Turbulence Diffusion and Air Pollution,
Reno, Nevada, 1979.
B-33
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Sklarew, R., J. Wilson, A. J. Patrick and V. Mirabella. "Rough Terrain
Modeling," presented at Geothermal Environmental Seminar '76, Clear
Lake California, 1976.
Sklarew, R., and K. Iran. "The NEWEST Wind Field Model with Applications
to Thermally Driven Drainage Wind in Mountainous Terrain," presentd
at the AMS Meeting, Lake Tahoe, 1978.
Fabrick, A. J., and P. J. Haas. "Analysis of Dispersion Models used
for Complex Terrain Simulation," presented at the Symposium on
Intermediate Range Transport Processes and Technology Assessment,
Gatlinburg, Tennessee, 1980.
B-34
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B.9 INTEGRATED MODEL FOR PLUMES AND ATMOSPHERIC CHEMISTRY IN COMPLEX
TERRAIN (IMPACT)(Skiarew)
Reference: User Guide to Impact: An Integrated Model for Plumes and
Atmospheric Chemistry in Complex Terrain, by Khanh T. Tran
and Ralph C. Skiarew, Form and Substance, Inc., Westlake
Village, California, 1979.
Availability: Available at a cost of $1720 (1981).
Form & Substance, Inc.
875 Westlake Boulevard
Westlake Village, California
91361
Abstract:
IMPACT is a three-dimensional numerical grid model. Ter-
rain is represented by blockages in the grid. IMPACT
solves the conservation of mass equation for each pollutant
by time splitting for each dimension and by one-dimensional
finite differencing. The three dimensional wind field is
developed from sparse inputs by an iterative three-dimensional
poisson solver that matches inputs, terrain and stability
effects on the wi.nd in response to terrain as well as
thermally generated downslope drainage winds. Diffusion
is simulated using k- theory with diffusivities computed
consistent with the computed wind field. IMPACT also can
perform chemical reaction simulation for each cell by a
predictor corrector solution of kinetics rate equations
and contains chemical mechanisms for photochemical 03/N02
and for
a. Recommendations for Regulatory Use
IMPACT can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. IMPACT must be executed in the equivalent mode.
IMPACT can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that IMPACT
is more appropriate for the specific application. In this case the model
options/modes which are most appropriate for the application should be used.
There is no specific recommendation concerning the use of IMPACT for
photochemical applications. IMPACT may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: all standard point, line and area
source parameters.
Meteorological data requirements are: hour by hour data for wind
speed and direction and stability or temperature at one or more
locations at ground level or vertical profile. (The more data the
B-35
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better but IMPACT can run with the same minimal data used in a
Gaussian model.)
Receptor data requirements are: none. Concentrations are calculated for
cells in the computational grid.
c. Output
Printed output includes:
One or multiple hour-averaged or instantaneous, concen-
tration for each grid cell. Summary edits for ground
level, specfied receptors and contour plots.
d. Type of Model
IMPACT is an Eulerian, finite difference model.
e. Pollutant Types
IMPACT treats inert and reactives species such as CO, 03, NO, N02,
S02, S0|, etc.
f. Source-Receptor Relationship
Concentrations are calculated at the center of each grid cell.
Up to 50 point sources and 50 area sources are printed.
g. Plume Behavior
Briggs (1975) plume rise is used. Optionally, Briggs (1969) plume
rise can be used. Fumigation and plume height above mixing height
are treated.
h. Horizontal Winds
Horizontal wind fields calculated self consistently from input data
and terrain.
i. Vertical Uind Field
Vertical wind fields calculated self consistently from input data
and terrain.
j. Horizontal Dispersion
A three dimensional diffusivity field is calculated using either the
technique of Myrup and Ranzieri, or the DEPICT method as described in
the User's Guide.
B-36
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k. Vertical Dispersion
A three dimensional diffusivity field is calculated using either the
technique of Myrup and Ranzieri, or the DEPICT method as described
in the User's Guide.
1. Chemical Transformation
Two mechanisms are included for photochemical 03/N02: the five
species GRC mechanism (Eschenroeder, 1972) and the 14 species
Hecht-Seinfeld-Dodge mechanism (Hecht and Seinfeld, 1974).
One mechanism is included for S(WS07, a simplified method described
by Sklarew, et al. (1979).
m. Physical Removal
Fallout and surface absorption is included.
n. Evaluation Studies
Fabrick, A. J., R. Sklarew and J. Wilson. Point Source Model Evaluation
and Development Study, report prepared for the California Air
Resources Board, 1977.
Sklarew, R., and V. Mirabella. "Experience in IMPACT Modeling of
Complex Terrain," Fourth Symposium on Turbulence Diffusion and
Air Pollution, Reno, Nevada, 1979.
Sklarew, R., J. Wilson, A. J. Fabrick, and V. Mirabella. "Rough
Terrain Modeling," presented at Geothermal Environmental Siminar
'76, Clear Lake, California, 1976.
Sklarew, R., and K. Tran. "The NEWEST Wind Field Model with Appli-
cations to Thermally Driven Drainage Wind in Mountainous Ter-
rain," presented at the AMS Meeting, Lake Tahoe, 1978.
Fabrick, A. J, and P. J. Haas. "Analysis of Dispersion Models used
for Complex Terrain Simulation," presented at the Symposium on
Intermediate Range Transport Processes and Technology
Assessment, Gatlinburg, Tennessee, 1980.
B-37
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B.10 LONGZ
Availability:
Reference: Bjorklund, J. R., and J. F. Bowers. "User's Instructions
for the SHORTZ and LONGZ Computer Programs. Volumes 1
and 2," EPA 903/9-82-004, U.S. Environmental Protection
Agency, Region III, Philadelphia, Pennsylvania 19106, 1982.
The model is available as part of UNAMAP (Version 6). The
computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: LONGZ utilizes the steady-state univariate Gaussian plume
formulation for both urban and rural areas in flat or
complex terrain to calculate long-term (seasonal and/or
annual) ground-level ambient air concentratins attribut-
able to emissions from up to 14,000 arbitrarily placed
sources (stacks, buildings and area sources). The output
consists of the total concentration at each receptor due
to emissions from each user-specified source or group of-
sources, including all sources. An option which considers
losses due to deposition (see the description of SHORTZ) is
deemed inappropriate by the authors for complex terrain,
and is not discussed here.
a. Recommendations for Regulatory Use
LONGZ can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. LONGZ must be executed in the equivalent mode.
LONGZ can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that
LONGZ is more appropriate for the specific application. In this case
the model options/modes which are most appropriate for the application
should be used.
b. Input Requirements
Source data requirements are: for point, building or area, sources,
location, elevation, total emission rate (optionally classified by
gravitational settling velocity) and decay coefficient; for stack
soruces, stack height, effluent temperature, effluent exit velocity,
stack radius (inner), emission rate, and ground elevation (optional);
B-39
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for building sources, height, length and width, and orientation; for
area sources, characteristic vertical dimension, and length, width
and orientation.
Meteorological data requirements are: wind speed and measurement
height, wind profile exponents, wind direction standard deviations
(turbulent intensities), mixing height, air temperature, vertical
potential temperature gradient.
Receptor data requirements are: coordinates, ground elevation.
c. Output
Printed output includes:
Total concentration due to emissions from user-specified
source groups, including the combined emissions from all
sources (with optional allowance for depletion by deposi-
tion) .
d. Type of Model
LONGZ is a climatological Gaussian plume model.
e. Pollutant Types
LONGZ may be used to model primary pollutants. Settling and deposition
are treated.
f. Source-Receptor Relationships
LONGZ applies user specified locations for sources and receptors.
Receptors are assumed to be at ground level.
g. Plume Behavior
Plume rise equations of Bjorklund and Bowers (1982) are used..
Stack tip downwash (Bjorklund and Bowers, 1982) is included.
All plumes move horizontally and will fully intercept elevated
terrain.
Plumes above mixing height are ignored.
Perfect reflection at mixing height is assumed for plumes below the
mixing height.
Plume rise is limited when the mean wind at stack height approaches
or exceeds stack exit velocity.
B-40
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Perfect reflection at ground is assumed for pollutants with no
settling velocity.
Zero reflection at ground is assumed for pollutants with finite
settling velocity.
LONGZ does not simulate fumigation.
Tilted plume is used for pollutants with settling velocity speci-
fied.
Buoyancy-induced dispersion is treated (Briggs, 1972).
h. Horizontal Winds
Wind field is homogeneous and steady-state.
Wind speed profile exponents are functions of both stability class
and wind speed. Default values are specified in Bjorklund and
Bowers (1982).
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
J• Horizontal Dispersion
Pollutants are initially uniformly distributed within each wind
direction sector. A smoothing function is then used to remove
discontinuities at sector boundaries.
k. Vertical Dispersion
Vertical dispersion is derived from input vertical turbulent inten-
sities using adjustments to plume height and rate of plume growth
with downwind distance specified in Bjorklund and Bowers (1982).
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Time
constant is input by the user.
m. Physical Removal
Gravitational settling and dry deposition of particulates are treated.
n. Evaluation Studies
Bjorklund, J. R., and 0. F. Bowers. "User's Instructions for the
SHORTZ and LONGZ Computer Programs," EPA-903/9-82-004, Environ-
mental Protection Agency, Region III, Philadelphia, Pennsylvania
19106, 1982.
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B.ll MARYLAND POWER PLANT SITING PROGRAM (PPSP) MODEL
Availability:
Abstract:
References: Brower, R. The Maryland Power Plant Siting Program (PPSP)
Air Quality Model User's Guide. Prepared by Environmental
Center, Martin Marietta Corporation, Baltimore, Maryland for
Maryland Department of Natural Resources, 1982. Ref.
No. PPSP-MP-38 (NTIS No. PB82-238387).
Weil, J. C. and R. P. Brower. The Maryland PPSP Dispersion
Model for Tall Stacks. Prepared by Environmental Center,
Martin Marietta Corporation, Baltimore, Maryland, for
Maryland Department of Natural Resources, 1982. Ref. No.
PPSP-MP-36 (NTIS No. PB82-219155).
Two reports referenced above are available from NTIS.
Tape of source code and test data not currently available.
PPSP is a Gaussian dispersion model applicable to tall
stacks in either rural or urban areas, but in terrain that
is essentially flat (on a scale large compared to the
ground roughness elements). The PPSP model follows the
same general formulation and computer coding as CRSTER,
also a Gaussian model, but it differs in four major ways.
The differences are in the scientific formulation of specific
ingredients or "sub-models" to the Gaussian model, and are
based on recent theoretical improvements as well as supporting
experimental data. The differences are: (1) stability
during daytime is based on convective scaling instead of the
Turner criteria; (2) Briggs1 dispersion curves for elevated
sources are used; (3) Briggs plume rise formulas for convec-
tive conditions are included; and (4) plume penetration of
elevated stable layers is given by Briggs' (1982) model.
a. Recommendations for Regulatory Use
PPSP can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. PPSP must be executed in the equivalent mode.
PPSP can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that PPSP
is more appropriate for the specific application. In this case the model
options/modes which are most appropriate for the application should be used.
b. Input Requirements
Source data requirements are: emission rate (monthly rates optional),
physical stack height, stack gas exit velocity, stack inside diameter,
stack gas temperature.
Meteorological data requirements are: hourly surface weather data
from the EPA meteorological preprocessor program. Preprocessor
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output includes hourly stability class wind direction, wind speed,
temperature, and mixing height. Actual anemometer height (a single
value) is also required. Wind speed profile exponents (one for each
stability class) are required if on-site data are input.
Receptor data requirements are: distance of each of the five recep-
tor rings.
c. Output
Printed output includes:
highest and second highest concentrations for the year at
each receptor for averaging times of 1, 3, and 24-hours,
plus a user-selected averaging time which may be 2, 4, 6,
8, or 12 hours;
annual arithmetic average at each receptor; and
for each day, the highest 1-hour and 24-hour concentra-
tions over the receptor field.
d. Type of Model
PPSP is a Gaussian plume model.
e. Pollutant Types
PPSP may be used to model primary pollutants. Settling and deposition
are not treated.
f. Source-Receptor Relationship
Up to 19 point sources are treated.
All point sources are assumed at the same location.
Unique stack height and stack exit conditions are applied for each source.
Receptor are locations restricted to 36 azimuths (every 10 degrees)
and five user-specified radial distances.
g. Plume Behavior
Briggs (1975) final rise formulas for buoyant plumes are used.
Momentum rise is not considered.
Transitional or distance-dependent plume rise is not modeled.
Penetration (complete, partial, or zero) of elevated inversions is
treated with Briggs (1983) model; ground-level concentrations are
dependent on degree of plume penetration.
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h. Horizontal Winds
Wind speeds are corrected for release height based on power law
variation, with different exponents for different stability classes
and variable reference height (7 meters is default). Wind speed
power law exponents are .10, .15, .20, .25, .30, and .30 for stabi-
lity classes A through F, respectively.
Constant, uniform (steady-state) wind assumed within each hour.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Rural dispersion parameters are Briggs (Gifford, 1975), with stabi-
lity class defined by u/w* during daytime, and by the method of
Turner (1964) at night.
Urban dispersion is treated by changing all stable cases to stabili-
ty class D.
Buoyancy-induced dispersion (Pasquill, 1976) is included
(using AH/3.5).
k. Vertical Dispersion
Rural dispersion parameters are Briggs (Gifford, 1975), with stability
class defined by u/w* during daytime, and by the method of Turner (1964).
Urban dispersion treated by changing all stable cases to stability class D.
Buoyancy-induced dispersion (Pasquill, 1976) is included (using AH/3.5).
1. Chemical Transformation
Not treated.
m. Physical Removal
Not treated.
n. Evaluation Studies
Weil, J. C. and R. P. Brower. The Maryland PPSP dispersion model for
tall stacks. Prepared by Environmental Center, Martin Marietta
Corporation, Baltimore, Maryland, for Maryland Department of Natural
Resources, 1982. Ref. No. PPSP MP-36 (NTIS No. PB 82-219155).
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Availability:
B.12 MESOSCALE PLUME SEGMENT MODEL (MESOPLUME)
Reference: Benkley, C. W. and A. Bass. "Development of Mesoscale Air
Quality Models: Volume 2. User's Guide to MESOPLUME (Meso-
scale Plume Segment) Model." EPA-600/7-80-057. U. S. Envi-
ronmental Protection Agency, Research Triangle Park, North
Carolina 27711, 1980.
This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: MESOPLUME is a mesoscale plume segment (or "bent plume")
model designed to calculate concentrations of S02 and
$04 over large distances. Plume growth is calculated by
finite difference methods with plume growth parameters
fitted to Turner's plume size (sigma) curves.
The MESOPLUME
a. Recommendations for Regulatory Use
There is no specific recommendation at the present time.
Model may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: location (x and y coordinates), stack
height, emission rate for SO^, emission rate for S0|, buoyancy
flux for plume rise, multipliers, by hour of the day, for the
emission rate and for the buoyancy flux; each for up to 10 sources.
Meteorological data requirements are: spatially variable, gridded
fields of horizontal (u,v) wind components, mixing height, and
Pasquill stability class. These data are normally, though not
necessarily, obtained from the output of the MESOPAC program (Volume i
EPA 600/7-80-061). MESOPAC requires, as input, radiosonde
observations from one or more stations, plus the wind components at
the most relevant level.
Receptor data requirements are: gridded array plus up to 10 optional
arbitrary receptors.
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Output
Optional printed output includes:
a table that lists all input parameters used in run;
optional arrays of ground-level concentrations of SOg and
S04= for user-specified intervals;
tables as above for specified receptors only;
arrays of maximum grid point concentration values of the
period of the run;
arrays of concentration values averaged over entire run span; and
table listing of the time when the first plume segment
from each source reached the edge of the computational grid.
Optional Disk Output:
the concentrations array may be output to disk for each
time step; and
the optional MESOFILE postprocessing program (Volume 5,
EPA 600/7-80-060) line printer plots and calcomp plots
are available.
d. Type of Model
MESOPLUME is a Gaussian plume segment model.
e. Pollutant Types
S02 and S0| are treated.
f . Source-Receptor Relationship
Up to 10 sources are permitted.
MESOPLUME uses optional 24-hour cycle of emission rate multipliers.
MESOPLUME uses optional 24-hour cycle of buoyancy flux multipliers.
Calculations are made over a gridded network of receptors.
Up to 10 arbitrary receptors are permitted.
g. Plume Behavior
Briggs (1975) plume rise equations are used, with buoyancy flux, F,
input to the model .
B-48
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Fumigation is treated.
h. Horizontal Winds
Derived gridded wind field specified for each grid square. MESOPAC
derives the values by interpolation between stations and hours.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Incremental puff growth is calculated over discrete time steps with
puff growth parameters reform chosen to approximate ay curves of
Turner out to 100 km. At distances greater than 100 Km, plume
growth parameters after Heffter (1965) are used.
Plume growth is a function of stability class.
Alternate plume growth coefficients may be used.
k. Vertical Dispersion
If plume element centerline is below the mixing height, the pollutant
is uniformly mixed through the mixing depth with the mixing lid and
ground (unless dry deposition is used) acting as perfect reflectors.
If plume element centerline is above the mixing height, ho mixing to
the ground is assumed.
Optional: oz is approximated in manner similar to oy (see item j).
With this option, no mixing lid is allowed.
Alternate plume growth coefficients may be used.
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Half-life
is input by the user.
m. Physical Removal
Dry deposition is treated.
n. Evaluation Studies
Bass, A., C. W. Benkley, J. S. Scire, and C. S. Morris. "Development
of Mesoscale Air Quality Simulation Models. Volume 1: Compara-
tive Studies of Puff, Plume, and Grid Models for Long Distance
Dispersion," EPA 600/7-80-056, Environmental Protection Agency,
Research Triangle Park, North Carolina 1979.
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Availability:
B.13 MESOSCALE PUFF MODEL (MESOPUFF)
Reference: Benkley, C. W. and A. Bass. "Development of Mesoscale Air
Quality Simulation Models, Volume 3, User's Guide to
MESOPUFF (Mesoscale Puff) Model." EPA 600/7-80-058. U. S.
Environmental Protection Agency, Research Triangle Park,
North Carolina 27711, 1980.
This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: MESOPUFF is a mesoscale puffjnodel designed to calculate
concentrations of S02 and S0| over long distances.
Plume growth is calculated by finite difference techniques
with plume growth parameters fitted to Turner's plume size
(sigma) curves.
The MESOPUFF
a. Recommendations for Regulatory Use
There is no specific recommendation at the present time.
Model may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: location (x and y coordinates), stack
height, emission rate for SOo, emission rate for S0|, buoyancy flux
for plume rise; optional multipliers, by hour of the day, for the
emission rate and for the buoyancy flux; each for up to 10 sources.
Meteorological data requirements: spatially variable, gridded
fields of horizontal (u,v) wind components, mixing height, and
Pasquill stability class. These data are normally, though not
necessarily, obtained from the output of the MESOPAC program (Volume
6, EPA-600/7-80-061). MESOPAC requires, as input, radiosonde
observations from one or more stations, plus the wind components at
the most relevant level.
Receptor data requirements: up to a 40 x 40 grid.
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c. Output
Printed output includes:
all input parameters;
optionally, arrays of ground-level concentrations of S02
and SO^ for user-specified averaging times at user-
specified intervals;
line printer plots and calcomp plots through the MESOFILE
postprocessing program (Volume 5, EPA 600/7-80-060).
Computer readable output includes:
The concentrations array may be output to disk for each
time step.
d. Type of Model
MESOPUFF is a Gaussian puff model.
e. Pollutant types
S02 and S0| are treated.
f. Source-Receptor Relationship
Up to 10 point sources are permitted.
Calculations are made over a gridded network of receptors.
g. Plume Behavior
Briggs (1975) plume rise equations are used, including plume
penetration, with buoyancy flux, F, input to the model.
Fumigation is included.
Fumigation may produce immediate mixing or multiple reflection
calculations at user's option.
h. Horizontal Winds
Derived gridded wind field is specified for each grid square. A
preprocessor program called MESOPAC derives the values by interpola-
tion between stations and hours.
B-52
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i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Incremental puff growth is calculated over discrete time steps with
puff growth parameters chosen to approximate oy curves of Turner
out to 100 kilometers. At distances greater than 100 km, growth is
based on plume growth rates given by Heffter (1965).
Plume growth is a function of stability class.
Optionally, alternate plume growth coefficients may be used.
k. Vertical Dispersion
For a puff element emitted at an effective stack height which is
less than the mixing height, uniform mixing of the pollutant within
the mixed layer is assumed.
For a puff element emitted above the mixing height, no effect of
the puff is felt at the grouind.
MESOPUFF includes an option to ignore mixing height.
Optionally, puff growth in the vertical (o,) may be simulated in
a manner similar to horizontal dispersion (see item j). Multiple
reflection effects are accounted for with this option.
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Half-life
is input by the user.
m. Physical Removal
Dry deposition is treated.
n. Evaluation Studies
Bass, A., C. W. Benkley, J. S. Scire, and C. S. Morris. "Develop-
ment of Mesoscale Air Quality Simulation Models. Volume 1:
Comparative Studies of Puff, Plume, and Grid Models for Long
Distance Dispersion," EPA 600/7-80-056, Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, 1979.
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Availability:
B.14 MESOSCALE TRANSPORT DIFFUSION AND DEPOSITION MODEL FOR INDUSTRIAL
SOURCES (MTDDIS)
Reference: Wang, I. T. and T. L. Waldron. "User's Guide for MTDDIS
Mesoscale Transport, Diffusion, and Deposition Model for
Industrial Sources." EMSC6062.1UR(R2). Rockwell Inter-
national, Environmental Monitoring & Services Center,
Newbury Park, California 91320, 1980.
Contact I. T. Wang or T. L. Waldron at:
Rockwell International
Environmental Monitoring and Services Center
242 West Hi 11 crest Drive
Newberry Park, California 19320
Abstract: MTDDIS is a variable-trajectory Gaussian puff model
applicable to long-range transport of point source emis-
sions over level or rolling terrain. It can be used to
determine 3-hour maximum and 24-hour average concentra-
tions of relatively nonreactive pollutants from up to 10
separate stacks.
a. Recommendations for Regulatory Use
There is no specific recommendation at the present time. The MTDDIS
Model may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: emission rate, physical stack height,
stack gas exit velocity, stack inside diameter, stack gas tempera-
ture, and location.
Meteorological data requirements are: hourly surface weather data,
from up to 10 stations, including cloud ceiling, wind direction,
wind speed; temperature, opaque cloud cover and precipitation. For
long-range applications, user-analyzed daily mixing heights are
recommended. If these are not available, the NWS daily mixing
heights will be used by the program. A single upper air sounding
station for the region is assumed. For each model run, air trajec-
tories are generated for a 48-hour period, and therefore, the after-
noon mixing height of the day before and the mixing heights of the
day after are also required by the model as input, in order to
generate hourly mixing heights for the modeled period.
Receptor data requirements are: up to three user-specified
rectangular grids.
c. Output
Printed output includes:
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tabulations of hourly meteorological parameters include
both input surface observations and calculated hourly
stability classes and mixing heights for each station;
printed air trajectories for the two consecutive 24-hour
periods for air parcels generated 4 hours apart starting
at 0000 LSI; and
3-hour maximum and 24-hour average grid concentrations
over user-specified rectangular grids are output for the
second 24-hour period.
d. Type of Model
MTDDIS is a Gaussian puff model.
e. Pollutant Types
MTDDIS can be used to model primary pollutants. Dry deposition is treated.
Exponential decay can account for some reactions.
f. Source-Receptor Relationship
MTDDIS treats up to 10 point sources.
Up to three user-specified rectangular receptor grids may be
specified by the user.
g. Plume Behavior
Briggs (1971, 1972) plume rise formulas are used.
If plume height exceeds mixing height, ground level concentration is
assumed zero.
Fumigation and downwash are not treated.
h. Horizontal Winds
Wind speeds and wind directions at each station are first corrected
for release height. Speed conversions are based on power law varia-
tion and direction conversions are based on linear height dependence
as recommended by Irwin (1979).
Converted wind speeds and wind directions are then weighted
according to the algorithms of Heffter (1980) to calculate the
effective transport wind speed and direction.
i. Vertical Wind Field
Vertical wind speed is assumed equal to zero.
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j. Horizontal Dispersion
Transport-time-dependent dispersion coefficients from Heffter (1980)
are used.
k. Vertical Dispersion
Transport-time-dependent dispersion coefficients from Heffter (1980)
are used.
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Half-life
is input by the user.
m. Physical Removal
Dry deposition is treated. User input deposition velocity is
required.
Wet deposition is treated. User input hourly precipitation rate and
precipitation layer depth or cloud ceiling height are required.
n. Evaluation Studies
None cited.
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B.15 MODELS 3141 and 4141
Reference: Enviroplan, Inc. "User's Manual for Enviroplan's Model
3141 and Model 4141." Enviroplan, Inc., West Orange,
New Jersey, 1981.
Availability: Requests should be directed to:
Environplan, Inc.
59 Main Street
West Orange, New Jersey 07052
Abstract: Models 3141 and 4141 are modifications of CRSTER (UNAMAP
VERSION 3) and are applicable to complex terrain par-
ticularly where receptor elevation approximately equals
or exceeds the stack top elevation. The model utilizes
intermediate ground displacement procedures and dis-
persion enhancements developed from an aerial tracer
study and ground level concentrations measured for a
power plant located in complex terrain.
a. Recommendations for Regulatory Use
3141 and 4141 can be used if it can be demonstrated to estimate concen-
trations equivalent to those provided by the preferred model for a given
application. 3141 and 4141 must be executed in the equivalent mode.
3141 and 4141 can be used on a case-by-case basis in lieu of a preferred
model if it can be demonstrated, using the criteria in Section 3.2, that
3141 and 4141 is more appropriate for the specific application. In this
case the model options/modes which are most appropriate for the application
should be used.
b. Input Requirements
Source data requirements are: emission rate, physical stack height,
stack gas exit velocity, stack inside diameter, stack gas exit
temperature.
Meteorological data requirements are: hourly surface weather data
from the EPA meteorological preprocessor program. Preprocessor
output includes hourly stability class, wind direction, wind speed,
temperature, and mixing height. Actual anemometer height (a single
value) is also required. Wind speed profile exponents (one for each
stability class) are required if on-site data are input.
Receptor data requirements are: distance of each of five receptor rings.
c. Output
Printed output includes:
B-59
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Highest and second highest concentrations for the year at
each receptor for averaging times of 1, 3, and 24-hours,
plus a user-selected averaging time which may be 2, 4, 6,
8, or 12 hours.
Annual arithmetic average at each receptor.
For each day, the highest 1-hour and 24-hour concentra-
tions over the receptor field.
d. Type of Model
3141 and 4141 are Gaussian plume models.
e. Pollutant Types
3141 and 4141 may be used to model primary pollutants. Settling and
deposition are not treated.
f. Source-Receptor Relationship
Up to 19 point sources are treated.
No area sources are treated.
All point-sources are assumed to be collocated.
Unique stack height is used for each source.
Receptor locations are restricted to 36 azimuths (every 10 degrees)
and 5 user-specified radial distances.
Unique topographic elevation is used for each receptor.
g. Plume Behavior
Briggs (1969, 1971, 1972) final plume rise formulas are used.
If plume height exceeds mixing height at a receptor location after
terrain adjustment, concentration is assumed equal to zero.
h. Horizontal Winds
Wind speeds are corrected for release height based on power law
variation exponents from DeMarrais (1959), different exponents for
different stability classes, reference height = 7 meters. Exponents
used are .10, .15, .20, .25, .30, and .30 for stability classes A
through F, respectively.
Constant, uniform (steady-state) wind is assumed within each hour.
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i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Dispersion coefficients are Pasquill-Gifford coefficients from
Turner (1969).
Dispersion is adjusted to 60 minute averaging time by one-fifth
power rule (Gifford, 1975)
Buoyancy-induced dispersion (Briggs, 1975) is included.
k. Vertical Dispersion
Dispersion coefficients are Pasquill-Gifford coefficients from
Turner (1969).
Buoyancy-induced dispersion (Briggs, 1975) is included.
1. Chemical Transformation
Not treated.
m.. Physical Removal
Not treated.
n. Evaluation Studies
Ellis, H. M., P. C. Liu, and C. Runyon. "Comparison of Predicted and
Measured Concentrations for 54 Alternative Models of Plume
Transport in Complex Terrain," Presented in APCA Annual
Conference, June 26-28, 1979, Cincinnati, Ohio.
Ellis, H. M., P. C. Liu, and C. Runyon. "Comparison of Predicted and
Measured Concentations for 58 Alternative Models of Plume Transport
in Complex Terrain," APCA Journal, Vol. 30, No. 6, 1980.
Londergan, R., D. Minott, D. Wachter, T. Kincaid and D. Bonitata.
"Evaluation of Rural Air Quality Simulation Models." EPA-450/4-
83-003, Environmental Protection Agency, Research Triangle Park,
North Carolina, 1983.
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Availability:
Abstract:
B.16 MULTIMAX
Reference: Moser, J. H. "MULTIMAX: An Air Dispersion Modeling
Program for Multiple Sources, Receptors, and Concentra-
tion Averages." Shell Development Company, Westhollow
Research Center, P. 0. Box 1380, Houston. Texas 77001,
1979.
The NTIS accession number for "MULTIMAX: An Air Disper-
sion Modeling Program for Multiple Sources, Receptors,
and Concentration Averages" is PB80-170178, and is
available at a cost of $12.50. The accession number for
the computer tape for MULTIMAX is PB80-170160, and the
cost is $300.00.
Requests should be sent to:
Computer Products
National Technical Information Service
U. S. Department of Commerce
5825 Port Royal Road
Springfield, Virginia 22161
MULTIMAX is a Gaussian plume model applicable to both
urban and rural areas. It can be used to calculate
highest and second-highest concentrations, for each of
several averaging times due to up to 100 sources
arbitrarily located.
a. Recommendations for Regulatory Use
MULTIMAX can be used if it can be demonstrated to estimate concentrations
equivalent to those prov.ided by the preferred model for a given appli-
cation. MULTIMAX must be executed in the equivalent mode.
MULTIMAX can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Secition 3.2, that
MULTIMAX is more appropriate for the specific application. In this case
the model options/modes which are most appropriate for the application
should be used.
b. Input Requirements
Source data requirements are: emission rate, physical stack height,
stack gas exit velocity, stack inside diameter, and stack gas
temperature.
Meteorological data requirements are: hourly surface weather data
from the EPA meteorological preprocessor program. Preprocessor
output includes hourly stability class, wind direction, wind speed,
temperature, and mixing height. Actual enemometer height (a single
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value) is also required. Wind speed profile exponents (one for
each stability class) are required if on-site data are input.
Receptor requirements are: individual receptor points, circles of
receptors, or lines of receptors may be input, with receptor point
locations, receptor line end points, and receptor circle center and
radius defined in either cartesian or polar coordinates.
c. Output
Printed output includes:
highest and second-highest concentrations for the year at
each receptor for averaging time of 1, 3, and 24 hours.
annual arithmetic average at each receptor.
Computer readable output includes:
input data and results.
d. Type of Model
MULTIMAX is a Gaussian plume model.
e. Pollutant Types
MULTIMAX may be used to model primary pollutants. Settling and
deposition are not treated.
f. Source-Receptor Relationship
Up to 100 point sources may be input.
Area sources are not treated.
Point sources may be at any location.
Unique stack height is used for each source.
Unique topographic elevation is used for each receptor; must be below
top of stack.
Receptors can be described individually, as lines, or as arcs.
g. Plume Behavior
MULTIMAX uses Briggs (1969, 1971, 1972) final plume rise formulas.
If plume height exceeds mixing height, concentrations downwind are
assumed equal to zero.
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h. Horizontal Winds
Wind speeds are corrected for release height based on power law
variation exponents from DeMarrais (1959), different exponents for
different stability classes, reference height = 10 meters. The
exponents are .10, .15, .20, .25, .30, and .30 for stability classes
A through F, respectively.
Constant, uniform (steady-state) wind is assumed within each hour.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Rural dispersion coefficients from Turner (1969) are used in MULTIMAX
with no adjustments made for variations in surface roughness.
Six stability classes are used, with Turner class 7 treated as Class 6.
Averaging time adjustment is optional.
k. Vertical Dispersion
Rural dispersion coefficients from Turner (1969) are used in MULTIMAX
with no adjustments made for variations in surface roughness.
Six stability classes are used, with Turner class 7 treated as Class 6.
Perfect reflection at the ground is assumed.
Mixing height is accounted for with multiple reflections until the
vertical plume size equals 1.6 times the mixing height; uniform
mixing is assumed beyond that point.
1. Chemical Transformation
Not treated.
m. Physical Removal
Not treated.
n. Evaluation Studies
Londergan, R., D. Minott, D. Wachter, T. Kincaid and D. Bonitata.
"Evaluation of Rural Air Quality Simulation Models." EPA-450/4-
83-003, Environmental Protection Agency, Research Triangle Park,
North Carolina, 1983.
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B.17 MULTIPLE POINT SOURCE DIFFUSION MODEL (MPSDM)
Reference: Environmental Research & Technology, Inc. User's Guide
to MPSDM. ERT Document No. M-186-001-630. Environmental
Research & Technology, Inc., Concord, Massachusetts, 1980.
Availability: Available only from:
Environmental Research & Technology, Inc.
ATTN: Mr. Joseph A. Curreri
Air Quality Center
696 Virginia Road
Concord, Massachusetts 01742
No cost has been specified.
Abstract: MPSDM is a steady-state Gaussian dispersion model designed
to calculate, in sequential model or in "case-by-case"
mode, concentrations of nonreactive pollutants resulting
from single or multiple source emissions. The MPSDM model
may be used for sources located in flat or complex terrain,
in a univariate (oz) or bivariate (oy, oz) mode.
Sufficient flexibility is allowed in the specification of
model parameters to enable the MPSDM user to duplicate
results that would be obtained from many other Gaussian
point-source models. A number of features are incorporated
to facilitate site-specific model validation studies.
a. Recommendations for Regulatory Use
MPSDM can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. MPSDM must be executed in the equivalent mode.
MPSDM can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that
MPSDM is more appropriate for the specific application. In this case
the model options/modes which are most appropriate for the application
should be used.
b. Input Requirements
Source data requirements are: Hourly or constant emission rate, stack
gas temperature, exit velocity, and stack inside diameter.
Meteorological data requirements are: Hourly wind speed, wind direction,
air temperature and mixing height; and vertical temperature difference
or stability class.
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Receptor data requirements are: Northing, easting, and ground level
elevation of each receptor.
Air quality data requirements are: Observed concentrations at any
monitor for any or all hours ("case-by-case" mode only) will be
compared with estimates, or (sequential mode only) will be used to
determine background levels. Background is calculated as the
average of those monitors more than ±i radians from the plume
centerline defined in the model. Default for i is the equivalent of
60°. User input for i is optional.
c. Output
Printed output includes:
"Case-by-case" mode: Printed output includes hourly
centerline, off centerline, sector averaged and observed
concentrations at all monitors; downwind profiles of
centerline concentrations; and a statistical summary of
all cases addressed.
Sequential mode: Printed output limited to ratio of
predicted maximum concentration to maximum concentration
measured at each monitor. Primary output is a file
output containing hourly averaged concentrations.
A post-processing program, ANALYSIS, is used to produce
averages for longer periods. For a user-specified ave-
rage period a ranked order of peak concentrations, the
cumulative frequency of occurrence of user specified
concentration levels or a summary of hourly meteorologi-
cal characteristics and concentrations contributing to
levels above a user-specified value can also be obtained
with the ANALYSIS post-processor.
d. Type of Model
MPSDM is a Gaussian plume model.
e. Pollutant Types
• MPSDM may be used to model primary pollutants. Settling and deposition
are not treated.
f. Source-Receptor Relationship
Arbitrary locations for sources and receptors are used.
Actual terrain elevations may be specified and accounted for by
plume-height adjustments.
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Actual separation between each source receptor pair is used.
Receptors at assumed to be at ground level.
Unique stack height is used for each source.
g. Plume Behavior
Briggs (1969, 1973, 1975) plume rise equations are used.
Partial (or total) penetration of plume into elevated
inversions (Briggs, 1975) is included.
Stack tip downwash (Briggs, 1975) is treated.
Fumigation (Turner, 1969) is treated.
h. Horizontal Winds
User-supplied hourly wind speed and direction are assumed to specify
horizontally homogeneous, steady-state conditions.
Wind speeds vary with height according to user-designated profiles
for each stability.
Wind direction is specifiable in whole degrees from 1° to 360°.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
ASME (Brookhaven) diffusion coefficients. (ASME, 1968) are used.
Options are Pasquill-Gifford coefficients or user input horizontal
plume with coefficients of the form ax&, or sector average with
user-input sector width.
Hourly stability (five classes—very unstable through slightly
stable) is determined internally from input vertical temperature
gradient and mean wind speed or stability classes.
A buoyancy-induced dispersion algorithm (Pasquill, 1976) is optional.
k. Vertical Dispersion
ASME (Brookhaven) diffusion coefficients (ASME, 1968) are used.
Options are Pasquill-Gifford coefficients or user input horizontal
plume with coefficients of the form ax&.
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Hourly stability (five classes—very unstable through slightly
stable) are determined internally from input vertical temperature
gradient and mean wind speed or stability classes.
A buoyancy-induced dispersion algorithm (Pasquill, 1976) is
optional.
Perfect reflection at ground is assumed.
Perfect reflection is assumed at the mixing height of pollutant
above or below top of mixing layer (except for partial plume
penetration).
1. Chemical Transformation
Not treated.
m. Physical Removal
Not treated.
n. Evaluation Studies
Lavery, T. F., and L. L. Schulman. "The Validity of a Gaussian .
Plume Point Source Diffusion Model for Predicting Short-Term S02
Levels in the Vicinity of. Electric Generating Plants in New York
State," Joint Conference on Applications of Air Pollution
Meteorology, AMS/APCA, November 29 - December 2, 1977, Salt Lake
City, Utah.
Londergan, R., D. Minott, D. Wachter, T. Kincaid and D. Bonitata.
"Evaluation of Rural Air Quality Simulation Models." EPA-450/4-
83-003, Environmental Protection Agency, Research Triangle Park,
North Carolina, 1983.
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B.18 MULTI-SOURCE (SCSTER) MODEL
Reference: Program Documentation EN7408SS Southern Company Services,
Inc., Technical Engineering Systems, 64 Perimeter Center
East, Atlanta, Georgia 30346.
Availability: The SCSTER model and user's manual are available at no
charge to a limited number of persons through Southern
Company Services. A magnetic tape must be provided to
Southern Company Services by those desiring the model.
Requests should be directed to:
Mr. Bryan Baldwin
Research Specialist
Southern Company Services
Post Office Box 2625
Birmingham, Alabama 35202
Abstract: SCSTER is a modified version of the EPA CRSTER model.
The primary distinctions of SCSTER are its capability to
consider multiple sources that are not necessarily collo-
cated, its enhanced receptor specifications, its variable
plume height terrain adjustment procedures and plume
distortion from directional wind shear.
a. Recommendations for Regulatory Use
SCSTER can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. SCSTER must be executed in the equivalent mode.
SCSTER can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that
SCSTER is more appropriate for the specific application. In this case
the model options/modes which are most appropriate for the application
should be used.
b. Input Requirements
Source data requirements are: emission rate, stack gas exit velocity,
stack gas temperature, stack exit diameter, physical stack height,
elevation of stack base, and coordinates of stack location. The
variable emission data can be monthly or annual averages.
Meteorological data requirements are: hourly surface weather data
from the EPA meteorological preprocessor program. Preprocessor
output includes hourly stability class wind direction, wind speed,
temperature, and mixing height. Actual anemometer height (a single
value) is optional. Wind speed profile exponents (one for each
stability class) are optional.
B-71
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Receptor data requirements are: cartesian coordinates and elevations
of individual receptors; distances of receptor rings, with elevation
of each receptor; receptor grid networks, with elevation of each
receptor.
c. Output
Printed output includes:
tables are given for each averaging time and the highest
50 concentrations or source contributions of individual
point sources at up to 20 receptor locations for each
averaging period;
listing of daily maximum 1-hour and 24-hour concentra-
tions; and
tables of both highest and second-highest concentrations.
Optional magnetic tape output includes:
all 1-hour concentrations.
d. Type of Model
SCSTER is a Gaussian plume, model.
e. Pollutant Types
SCSTER may be used to model primary pollutants. Settling and deposi-
tion are not treated.
f. Source-Receptor Relationship
SCSTER can handle up to 60 separate stacks at varying locations and
15 receptor rings.
SCSTER provides four terrain adjustments including the CRSTER full
terrain height adjustment and a half-height for receptors above
stack height.
g. Plume Behavior
SCSTER uses Briggs (1969, 1971, 1972) final plume rise formulas.
Transitional plume rise is optional.
SCSTER contains options to incorporate wind shear with a method
described in Appendix A of the User's Guide.
SCSTER provides four terrain adjustments including the CRSTER full
terrain height adjustment and a half-height for receptors above
stack height.
B-72
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Provides for Transitional plume rise at receptors close to source.
h. Horizontal Winds
Wind speeds are corrected for release height based on power law
exponents from DeMarrais (1959), different exponents for different
stability classes; reference height of 7 m. Exponents are .10, .15,
.20, .25, .30, and .30 for stability classes A through F, respectively.
Steady-state wind is assumed within a given hour.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
Rural dispersion coefficients from Turner (1969) are used.
Six stability classes are used, with Turner class seven treated as
class six.
k. Vertical Dispersion
Rural dispersion coefficients from Turner (1969) are used.
Six stability clashes are used, with Turner Class seven treated as
class six.
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Half-life
is input by the user.
m. Physical Removal
Physical removal is treated using exponential decay. Half-life is
input by the user.
n. Evaluation Studies
Londergan, R., D. Minott, D. Wachter, T. Kincaid and D. Bonitata.
"Evaluation of Rural Air Quality Simulation Models," EPA 450/4-83-003,
Environmental Protection Agency, Research Triangle Park, North
Carolina, 1983.
B-73
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B.19 PACIFIC GAS AND ELECTRIC PLUMES MODEL
Reference: User's Manual for Pacific Gas and Electric PLUMES Model,
March 1, 1981, Pacific Gas and Electric, San Francisco,
California.
Availability: Contact:
Pacific Gas and Electric Company
245 Market Street
San Francisco, California 94106
Abstract: PLUMES is a steady-state Gaussian plume model applicable
to both rural and urban areas in uneven terrain. Pol-
lutant concentrations at 500 receptors from up to 10
sources with up to 15 stacks each can be calculated using
up to 5 meteorological inputs. The model in its "basic"
mode is similar to CRSTER and MPTER. Several options are
available that allow better simulation of atmospheric con-
ditions and improved model outputs. These options allow
plume rise into or through a stable layer and crosswind
spread of the plume by wind directional shear with height,
initial plume expansion, mean (advective) wind speed,
terrain considerations, and chemical transformation of
pollutants.
Differences that exist between PLUMES and CRSTER are in
the following areas: Stability class determination,
hourly mixing height schemes, hourly stable layer data,
randomization of wind direction, extent of data set re-
quired for preprocessing, meteorological data inputs.
a. Recommendations for Regulatory Use
PLUMES can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. PLUMES must be executed in the equivalent mode.
PLUMES can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that PLUME5
is more appropriate for the specific application. In this case the model
options/modes which are most appropriate for the application should be
used.
b. Input Requirements
Source data requirements are:
each source with stack height,
velocity for each stack.
cartesian or polar coordinates of
diameter, gas temperature, and exit
Meteorological data requirements are: Surface data—hourly meteoro-
logical data including wind direction, wind speed, temperature, and
either ceiling height and total sky cover or sigma A or Delta T
depending on how stability is computed; stable layer data—either
NCC data or site specific user supplied data.
B-75
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Receptor data requirements are: cartesian or polar coordinates of
each receptor.
Output
Printed output includes:
highest and second highest concentrations for the year
printed out at each receptor for averaging times of 1, 3,
and 24-hours, plus a user-selected averaging time which
may be 2, 4, 6, 8, or 12 hours.
annual arithmetic average at each receptor.
for each day, the highest 1-hour and 24-hour concentra-
tions over the receptor field is printed.
hourly effective stack height and effective stack height
distributions.
vertical profiles of maximum pollutant concentrations
above a designated height (Z0) for the data period processed.
cumulative number of exceedances of 1 hour and 24-hour
specified values for all receptors during the entire
meteorological data period. These specified values will
normally be National and State Ambient Air Quality Standards.
Computer readable output includes:
hourly concentrations for each receptor on magnetic tape.
computer file for input to plotting routine. The file
stores the highest 1-hour (or other specified time period)
concentration at each receptor for the entire meteorological
data period for input into a user supplied plotting routine.
Type of Model
PLUMES is a Gaussian plume model.
Pollutant Types
PLUMES may be used to model primary pollutants.
Chemical transformations of pollutants are treated by exponential decay
and/or ozone limiting procedures.
Source-Receptor Relationship
Can input up to 10 separate sources with up to 15 stacks per source.
B-76
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Unique stack height for each source. Rectangular or circular
receptor locations (up to 500) can be either model generated or user input.
Terrain Considerations:
When plume rise, H, is above the stable layer top concentration
estimates will only be calculated for receptors at or above the stable
layer top. If the receptor is below the stable layer top, then the
concentration is zero.
When plume rise falls within the stable layer, concentration estimates
will be only calculated for receptors located within this region. If
the receptor height is above or below the stable top, then the
concentration is zero.
When plume rise falls below the stable layer and the receptor height is
above the stable layer base, then the concentration is zero. If the
receptor height is below the stable layer base, the receptor height
is redefined.
Plume Behavior
PLUMES uses Briggs (1975) final plume rise formulas.
Expansion of plumes within and above a stable layer is treated.
Horizontal Winds
User-supplied hourly wind directions are read to nearest 1, 5, 10,
and 22.5 degrees. (The 5, 10 and 22.5 degree values are randomly
modified to nearest whole degree within the intervals).
PLUME5 employs the extrapolated mean wind speed at stack height
when the effective stack height is equal to or less than the height
of the inversion base above ground. If the plume rises into a stable
layer, a separate algorithm is used.
Constant, uniform (steady state) wind assumed within each hour.
Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
Horizontal Dispersion
Six stability classes are defined by either radiation index and wind
speed (STAR), wind direction fluctuation, or temperature lapse rate.
Nighttime stability class is based on wind direction fluctuations or
temperature lapse rate and may be modified according to the method of
Mitchell and Timbre (1979).
B-77
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Dispersion curves are from Turner (1969).
k. Vertical Dispersion
Six stability classes are defined by either radiation index and wind
speed (STAR), wind direction fluctuations, or temperature lapse rate.
Nighttime stability class is based on wind direction fluctuations or
temperature laspe rate and modified according to the method of Mitchell-
Timbre (1979).
Dispersion curves are from Turner (1969).
1. Chemical Transformation
Chemical transformations are treated using exponential decay
and/or ozone limiting procedures.
m. Physical Removal
Physical removal is treated using exponential decay. Half-life is
input by the user.
n. Evaluation Studies
Londergan, R., D. Minott, D. Wachter, T. Kincaid and B. Bonitata.
"Evaluation of Rural Air Quality Simulation Models," EPA-450/4-83-033,
(NTIS PB 83-182-758), Environmental Protection Agency, Research
Triangle Park, North Carolina 27711, 1983.
B-78
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B.20 PLUMSTAR AIR QUALITY SIMULATION MODEL
Reference: Lurmann, F. W. and D. A. Godden 1983. "User's Guide to
the PLMSTAR Air Quality Simulation Model." ERT Document
No. M-2206, Environmental Research & Technology, Inc.,
Westlake Village, California 91361.
Abailiability: The model is not currently in the public domain; however,
requests for the model are considered on an individual
basis. Requests for the model should be sent to:
Mr. Frederick W. Lurmann
Environmental Research & Technology, Inc.
2625 Townsgate Road
Westlake Village, California 91361
Abstract: PLMSTAR (pronounced plume star) is a photochemical tra-
jectory model designed for urban scale and mesoscale
applications involving the impact of point and area source
reactive hydrocarbon (RHC), NOX, and SOX emissions on downwind
short-term concentrations of 03, N02, HN03, PAN, S02, and
SO^. The model's Lagrangian air parcel is subdivided into a 5
layer/9 column domain of computational cells. The approach
allows for realistic simulation of the combined effects of
atmospheric chemical reactions and pollutant dispersion in
the horizontal and vertical directions. Other key features.
of the model include: the capability for generation of
trajectories at any level of a three-dimensional, divergence-
free wind field; the capability for calculating the utilizing
time and space varying surface deposition of pollutants; an
up-to-date 03/RHC/NOX/SOX chemical mechanism that utilizes
eight classes of reactive hydrocarbons; and the capability for
simultaneously handling both point and area source emissions.
The PLUMSTAR
a. Recommendation for Regulatory Use
There is no specific recommendation at the present time.
Model may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: emssion rates, stack parameters,
diurnal emission profiles, and RHC, NOX, and SOX partitioning profiles.
Meteorological data requirements are: station location, grid geometry,
surface winds, surface roughness, surface temperature, temperature
profiles, mixing heights (optional), cloud cover, solar radiation,
and winds aloft.
Receptor data requirements are: receptor locations and topography.
B-79
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c. Output
Printed output includes:
computed concentrations at specified times and receptors
along the trajectory.
d. Type of Model
PLMSTAR is a photochemical trajectory model.
e. Pollutant Types
The key chemical species included in the model are 03, NO, NOg, HN03,
PAN, S02, S0£, CO, and eight classes of reactive hydrocarbons.
Twenty additional intermediate species are included in the chemical
mechanism.
f. Source-Receptor Relationships
Source-receptor relationships for individual sources are calculated
using a differencing technique. That is, simulations are made with
and without an individual source (or group of collocated sources) in
addition to the RHC/NOX/SOX emissions from all other sources in the
region. The emission processors allow for up to 250 point sources
and an unlimited number of area sources{ allocated to a grid of 36 x
36 squares) to be included in the simulation.
g. Plume Behavior
Plume rise calculations are based on Briggs (1975).
h. Horizontal Winds
Gridded hourly multi-level horizontal wind fields are generated
using techniques similar to those reported by Goodin et al. (1979).
These involve wind data interpolation, divergence minimization, and
terrain adjustment. Trajectory path segments are then generated by
interpolation from the gridded horizontal wind fields in 15 minute
steps at the user selected vertical level. Either source or
receptor oriented trajectory may be generated.
i. Vertical Wind speed
Vertical speed is produced by WINDMOD, but is not utilized in the
trajectory calculation or the pollutant advection algorithm.
j. Vertical Dispersion
Vertical eddy diffusivities (Kz) are calculated as a function of wind
speed, stability, surface roughness, and boundary layer height.
B-80
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The effects of vertical dispersion on pollutant concentrations are
calculated by numerically integrating finite difference approximations
to the diffusion equation.
Mixing heights can be internally calculated or externally specified.
k. Horizontal Dispersion
Horizontal eddy diffusivities (Ky) are calculated either as a
function of Kz and stability class or as a function of ay. Urban
or rural oy's may be selected. The effects of horizontal dispersion
on pollutant concentrations are calculated by numerically
integrating finite difference approximations to the diffusion
equation.
1. Chemical Transformation
PLMSTAR incorporates a slightly condensed version of the Atkinson
et al. (1982) photochemical mechanism for 03/RHC/NOx/SOx/air
mixtures. The mechanism contains 62 reactions involving 38 species,
including 8 classes of organic precursors. The effects of chemical
transformations on pollutant concentrations are computed by
numerically integrating the nonlinear kinetic rate equations.
m. Physical Removal
Dry deposition of 03, N02, HN03, PAN, S02, and 504 is based on the
model of Wesely and Hicks (1977).
n. Evaluation Studies
Lurmann, F. W., D. A. Godden and A. C. Lloyd. "The Development
and Selected Sensitivity, Tests of the PLMSTAR Reactive Plume
Model," presented at the Third Joint Conference on Applications
of Air Pollution Meteorology, San Antonio, Texas, 1982.
Godden, D. and F. Lurmann. Development of the PLMSTAR Model
and its Application to Ozone Episode Conditions in the South
Coast Air Basin, ERT Document No. P-A702-200, Environmental
Research & Technology, Inc., Westlake Village, California, 1983.
B-81
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Availability:
B.21 PLUME VISIBILITY MODEL (PLUVUE II)
Reference: Seigneur, C., C. D. Johnson, D. A. Latimer, R. W. Bergstrom
and H. Hogo. "User's Manual for the Plume Visibility Model
(PLUVUE II)." SYSAPP-83/097, Systems Applications, Inc.
San Rafael, California, 1983.
This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: The Plume Visibility Model (PLUVUE II) is a computerized
model used for estimating visual range reduction and
atmospheric discoloration caused by plumes resulting from
the emissions of particles, nitrogen oxides and sulfur oxides
from a single emission source. PLUVUE II predicts the
transport, dispersion, chemical reactions, optical effects
and surface deposition of point or area source emissions.
a. Recommendations for Regulatory Use
There is no specific recommendation at the present time. The Plume
Visibility Model (PLUVUE II) may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: location and elevation; emission rates
of S02, NOX, and particulates; flue gas flow rate, exit velocity, and
exit temperature; flue gas oxygen content; properties (including
density, mass median and standard geometric deviation of radius) of
the emitted aerosols in the accumulation (0.1-1.0 ym) and coarse
(1.0-10.0 um) size modes; and deposition velocities for S02, NOX,
coarse mode aerosol, and accumulations mode aerosol.
Meteorological data requirements are: stability class, wind direction
(for an observer-based run) wind speed, lapse rate, air temperature,
relative humidity, and mixing height.
Other data requirements are: ambient background concentrations of NOX,
NOg, 03, and S02, background visual range or sulfate and nitrate
concentrations.
B-83
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Receptor (observer) data requirements are: location, elevation, terrain
which will be observed through the plume (for observer based run with
white, gray, and black viewing backgrounds).
c. Output
Printed output includes:
Plume concentrations and visual effects at specified downwind
distances for calculated or specified lines of sight.
d. Type of Model
PLUVUE is a Gaussian plume model.
e. Pollutant Types
PLUVUE II treats NO, N02, S02, H2S04, HN03, 03, primary and secondary
particles to calculate effects on visibility.
f. Source Receptor Relationship
PLUVUE treats a single point or area source.
Predicted concentrations and visual effects are obtained
at user specified downwind distances.
g. Plume Behavior
PLUVUE uses Briggs (1969, 1971, 1972) final plume rise equations.
h. Horizontal Hinds
User-specified wind speed (and direction for an observer-based run)
are assumed constant for the calculation.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Horizontal Dispersion
User specified plume widths, or widths computed from either Pasquill-
Gifford-Turner curves (Turner, 1969) or TVA curves (Carpenter, et al.,
1971) are used in PLUVUE.
k. Vertical Dispersion
User specified plume depths, or computer from Pasquill-Gifford-Turner curves
(Turner, 1969) or TVA curves (Carpenter, et al., 1971) are used in PLUVUE.
B-84
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1. Chemical Transformation
PLUVUE II treats the chemistry of NO, NO?, 0,, OH, O^D), S02, HN03,
and H2S04, by means of nine reactions. Steady state approximations
are used for radicals and for the NO/N02/03 reactions.
m. Physical Removal
Dry deposition of gaseous and particulate pollutants is treated using
deposition velocities.
n. Evaluation Studies
Bergstrom, R. W., C. Seigneur, B. L. Babson, H. Y. Holman and M. A.
Wojcik. "Comparison of the Observed and Predicted Visual Effects
Caused by Power Plant Plumes," Atmospheric Environment, Vol. 15,
pp. 2135-2150, 1981.
Bergstrom, R. W., Seigneur, C. D. Johnson, and L. W. Richards. "Measure-
ments and Simulations of the Visual Effects of Particulate Plumes,"
Systems Applications, Inc. San Rafael, California.
Seigneur, C., R. W. Bergstrom, and A. B. Hudischewskyj. "Evaluation
of the EPA PLUVUE Model and the ERT Visibility Model Based on the
1979 VISTTA Data Base," EPA-450/4-82-008, U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina, 1982.
B-85
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B.22 POINT, AREA, LINE SOURCE ALGORITHM (PAL)
Reference: Petersen, W. B. "User's Guide for PAL - A Gaussian-Plume
Algorithm for Point, Area, and Line Sources." Publication
No. EPA-600/4-78-013 (NTIS PB 281306). Office of Research
and Development, Research Triangle Park, North Carolina
27711, 1978.
Availability: This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: PAL is an acronym for this point, area, and line source
algorithm. PAL is a method of estimating short-term dis-
persion using Gaussian-plume steady-state assumptions.
The algorithm can be used for estimating concentrations of
non-reactive pollutants at 99 receptors for averaging
times of from 1 to 24 hours, and for a limited number of
point, area, and line sources (99 of each type). This
algorithm is not intended for application to entire urban
areas but is intended, rather, to assess the impact on air
quality, on scales of tens to hundreds of meters, of
portions of urban areas such as shopping centers, large
parking areas, and airports. Level terrain is assumed.
The Gaussian point source equation estimates concentrations
from point sources after determining the effective height
of emission and the upwind and crosswind distance of the
source from the receptor. Numerical integration of the
Gaussian point source equation is used to determine
conce ntrations from the four types of line sources.
Subroutines are included that estimate concentrations for
multiple lane line and curved path sources, special line
sources (line sources with endpoints at different heights
above ground), and special curved path sources. Integra-
tion over the area source, which includes edge effects
from the source region, is done by considering finite line
sources perpendicular to the wind at intervals upwind from
the receptor. The crosswind integration is done analytic-
ally; integration upwind is done numerically by successive
approximations.
B-87
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a. Recommendations for Regulatory Use
PAL can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. PAL must be executed in the equivalent mode.
PAL can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that
PAL is more appropriate for the specific application. In this case
the model options/modes which are most appropriate for the application
should be used.
b. Input Requirements
Source data requirements are: point-sources—emission rate, physical
stack height, stack gas temperature, stack gas velocity, stack
diameter, stack gas volume flow, coordinates of stack, initial oy
and oz; area sources--source strength, size of area source, coordinates
of S.W. corner, and height of area source; and line sources—source
strength, number of lanes, height of source, coordinates of end
points, initial oy and oz, width of line source, and width of
median. Diurnal variations in emissions are permitted.
Meteorological data: wind profile exponents, anemometer height, wind
direction and speed, stability class, mixing height, air temperature,
and hourly variations in emission rate.
Receptor data requirements are: receptor coordinates.
c. Output
Printed output includes:
Hourly concentration for each source type at each receptor; and
Average concentration for up to 24 hrs for each source type
at each receptor.
d. Type of Model
PAL is a Gaussian plume model.
e. Pollutant Types
PAL may be used to model primary pollutants. Settling and deposition
are not treated.
f. Source-Receptor Relationships
Up to 99 sources of each of 6 source types: point, area, and
4 types of line sources.
B-88
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Source and receptor coordinates are uniquely defined.
Unique stack height for each source.
Coordinates of receptor locations are user defined.
g. Plume Behavior
Briggs final plume rise equations.
Does not treat fumigation or downwash.
If plume height exceeds mixing height, concentrations assumed
equal to zero.
Surface concentrations are set to zero when the plume centerline
exceeds mixing height.
h. Horizontal Winds
Uses user-supplied hourly wind data.
Constant, uniform (steady-state) wind assumed within each hour.
Wind increase with height, diurnal variation of emissions.
i. Vertical Wind Speeds
Assumed equal to zero.
j. Horizontal Dispersion
Rural dispersion coefficients from Turner (1969) are used with no
adjustments made for surface roughness,
6 stability classes are used.
Dispersion coefficients (Pasquill-Gifford) based on 3 cm roughness -
height.
k. Vertical Dispersion
6 stability classes used.
Rural dispersion coefficients from Turner (1969); no further adjustments
made for variation in surface roughness, transport or averaging time.
Multiple reflection handled by summation of series until oz = 1.6
times mixing height. Uniform vertical distribution thereafter.
B-89
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1. Chemical Transformation
Not treated.
m. Physical Removal
Not treated.
n. Evaluation Studies
None.
B-90
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B.23 RANDOM-WALK ADVECTION AND DISPERSION MODEL (RADM)
References:
Availability:
Abstract:
Austin, D. I., A. W. Bealer, and W. R. Goodin. Random-
Walk Advection and Dispersion Model (RADM), User's Manual.
Dames & Moore, Los Angeles, California, 1981.
Runchal, A. K., W. R. Goodin, A. W. Bealer, D. I. Austin.
Technical Description of the Random-Walk Advection
and Dispersion Model (RADM). Dames & Moore, Los Angeles,
California, 1981.
A. W. Bealer
Dames and Moore
222 East Anapamu Street
Santa Barbara, California 93101
RADM is a Lagrangian dispersion model which uses the random-
walk method to simulate atmospheric dispersion. The
technical procedure involves tracking tracer particles
having a given mass through advection by the mean wind and
diffusion by the random motions of atmospheric turbulence.
Turbulent movement is calculated by determining the probabi-
lity distribution of particle movement for a user-defined
time step. A random number between 0 and 1 is then computed
to determine the distance of particle movement according
to the probability distribution. A large number of particles
is used to statistically represent the distribution of
pollutant mass. Concentrations are calculated by summing
the mass in a volume around the receptor of interest and
dividing the total mass by the volume. Concentrations can
be calculated for any averaging time. RADM is applicable
to point and area sources.
The RADM model
a. Recommendations for Regulatory Use
There is no specific recommendation at the present time.
may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: emission rate, physical stack height,
stack gas exit velocity, stack inside diameter, stack gas temperature.
Hourly rates may be specified.
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Meteorological data requirements are: Gridded wind field including
wind speed, wind direction, stability class, temperature and mixing
height.
Receptor data requirements are: coordinates, ground elevation, and
receptor cell dimensions.
c. Output
Printed output includes:
average concentration by receptor for user-specified averaging
time (concentrations are printed for each block of n hours).
average concentrations for the entire period of the run.
d. Type of Model
RADM is a random-walk Lagrangian dispersion model.
e. Pollutant Types
RADM may be used to model inert gases and particles, and pollutants
with exponential decay or formation rates.
f. Source-Receptor Relationship
Multiple point and area sources may be specified at independent locations.
Unique stack characteristics are used for each source.
No restriction is placed on receptor locations.
Perfect reflection at the surface is assumed for the portion not
removed by dry deposition.
Particles leaving the gridded area are removed from simulation.
g. Plume Behavior
Briggs (1975) final plume rise equations are used.
Inversion penetration by the plume is allowed.
Fumigation may occur as mixing height rises above a plume which has
penetrated an inversion.
h. Horizontal Winds
Wind speed, wind direction, stability class, temperature and mixing
height are supplied on a gridded array.
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Any wind field may be used as long as output is in correct format for
RADM input.
Wind field is updated at user-specified intervals, which may be less
than one hour if data are available.
Vertical wind speed profile is used based on surface roughness and
stability using Monin-Obukhov length.
i. Vertical Wind Speed
Assumed equal to zero.
j. Horizontal Dispersion
Dispersion is based on diffusivity values calculated from surface rough-
ness, stability class and Monin-Obukhov length.
Diffusivity is a function of height.
k. Vertical Dispersion
Dispersion is based on diffusivity values calculated from surface rough-
ness, stability class and Monin-Obkhov length.
Diffusivity is a function of height.
1. Chemical Transformations
Simple exponential decay or formation is used.
m. Physical Removal
Dry deposition is treated.
n. Evaluation Studies
Runchal, A. K., A. W. Bealer, and G. S. Segal. A completely Lagrangian
Random-Walk Model for Atmospheric Dispersion. Proceedings of the
Thirteenth International Colloquim on Atmospheric Pollution,
National Institute for Applications of Chemical Research, Paris,
pp. 137-142, 1978.
Goodin, W. R., A. K. Runchal and G. Y. Lou. Evaluation and Application
of the Random-Walk Advection and Dispersion Model (RADM). Symposium
on Intermediate Range Atmospheric Transport Processes and Technology
Assessment, DOE/NOAA/ORNL, Gatlinburg, Tennessee, 1980.
Goodin, W. R., D. I. Austin and A. K. Runchal. A Model Verification
and Prediction Study of SOg/SO^ Concentrations in the San Francisco
Bay Area. Second Joint Conference on Applications of Air Pollution
Meteorology, AMS/APCA, New Orleans, Louisiana, 1980.
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B.24 REACTIVE PLUME MODEL (RPM-II)
Reference: "User's Guide to the Reactive Plume Model--RPM-II," D.
Stewart, M. Yocke, and M-K Liu, 1980. SAI No. EF80-75.
Availability: Contact:
Systems Applications, Inc.
101 Lucas Valley Road
San Rafael, California 94903
Abstract: The Reactive Plume Model, RPM-II, is a computerized model
used for estimating short-term concentrations of primary
and secondary pollutants resulting from point or area
source emissions. The model is capable of simulating the
complex interaction of plume dispersion and non-linear
photochemistry. Two main features of the model are: (1)
the horizontal resolution within the plume, which offers
a more realistic treatment of the entrainment process,
and (2) its flexibility with regards to choices of
chemical kinetic mechanisms.
a. Recommendations for Regulatory Use
There is no specific recommendation at the present time. The RPM-II
Model may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: emission rate, name, and molecular
weight of each species of pollutant emitted; ambient pressure,
ambient temperature, stack height, stack diameter, stack exit
velocity, stack gas temperature, and location.
Meteorological data requirements are: wind speeds, plume widths or
stability classes, photolysis rate constants, and plume depths or
stability classes.
Receptor data requirements are: downwind distances or travel times
at which calculations are to be made.
c. Output
Short-term concentrations of primary and secondary pollutants at either
user specified time increments, or user specified downwind distances.
d. Type of Model
Reactive plume model.
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e. Pollutant Types
Currently, using the Carbon Bond Mechanism (CBM-II), 35 species are
simulated (68 reactions), including NO, N02, 03, S02, S0|, five categories
of reactive hydrocarbons, secondary nitrogen compounds, organic aerosols,
and radical species.
f. Source-Receptor Relationships
Single point source.
Single area or volume source.
Multiple sources can be simulated if they are lined up along the wind
trajectory.
Predicted concentrations are obtained at a user specified time increment,
or at user specified downwind distances.
g. Plume Behavior
Briggs (1971) plume rise equations are used.
h. Horizontal Winds
User specifies wind speeds as a function of time.
i. Vertical Wind Speed
Not treated.
j. Horizontal Dispersion
User specified plume widths, or user may. specify stability and widths
will be computed using Turner (1969).
k. Vertical Dispersion
User specified plume depths, or user may specify stability in which
case depths will be calculated using Turner (1969). Note that vertical
uniformity in plume concentration is assumed.
1. Chemical Transformation
The RPM-II has the flexibility of using any user input chemical
kinetic mechanism. Currently it is run using the chemistry of the
Carbon Bond Mechanism, CBM-II (Whitten, Killus, and Hogo, 1980.
"Modeling of Simulated Photochemical Smog with Kinetic Mechanisms:
Volume 1. Final Report," Systems Applications Inc.). The CBM-II, as
incorporated in the RPM-II, contains 35 species and 68 reactions
focusing primarily on hydrocarbon-nitrogen oxides-ozone photochemistry.
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m. Physical Removal
Not treated.
n. Evaluation Studies
Stewart, D. A. and M-K Liu, "Development and Application of a Reactive
Plume Model," Atmospheric Environment, Vol. 15, 2377-2393, 1981.
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B.25 REGIONAL TRANSPORT MODEL (RTM-II)
Reference: Morris, R. E., D. A. Stewart, and M-K Liu. "Revised
User's Guide to the Regional Transport Model—Version II"
(RTM-II), Publication No. SYSAPP-83/022, Systems Applica-
tions Inc., San Rafael, California, 1982.
Availability: Contact:
Systems Applications, Inc.
101 Lucas Valley Road
San Raphael, California 94903
Abstract: The Regional Transport Model (RTM-II) is a computer based
air quality grid model whose primary use is estimating the
distribution of air pollution from multiple point sources
at large distances (on the scale of several hundred to a
thousand kilometers). It may also be applied to a limited
number of area sources. RTM-II offers significant advan-
tages over other long-range transport models because it is
a quasi-three dimensional hybrid (grid plus Lagrangian
puff) approach to the solution of the advection-diffusion
equation. Furthermore, its formulation allows the treatment
of spatially and temporally varying wind, mixing depths,
diffusivity, and transformation rate fields. It is also
capable of treating spatially varying surface depletion
processes. While the modeling concept is capable of
predicting concentration .distributions of many pollutant
species (e.g., NOX, CO, TSP, etc.), the most notable
applications of the model to date focus on the long-range
transport and transformation of S02 and sulfates.
a. Recommendations for Regulatory Use
There is no specific recommendation at the present time. The RTM
Model may be used on a case-by-case basis.
b. Input Requirements
Source data requirements are: major point source S02 and primary
sulfate emissions, including stack height, diameter, exit velocity,
exit temperature, and hourly emission factors; area source of S0£
and primary $04 emissions in gridded format with hourly emission
factors.
Meteorological data requirements are: gridded u, v wind fields at
3-hour intervals (model configured for separate wind fields in each
layer), derived from twice daily radiosonde data, time variation
linear between a maximum convectively driven boundary layer and a
minimum mechanically driven boundary layer, spatial interpolation by
an inverse distance weighted objective scheme; gridded hourly
precipitation fields determined either by averaging precipitation
rate of all stations in grid (if high density), or by inverse
B-99
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distance weighted interpolation (if low density).
Other data requirements are: parameter file, containing region
definition, starting time, output and averaging time intervals,
region top specifications, and various operational flags; horizontal
diffusivity fields calculated from wind fields; land use type file;
deposition velocities and roughness length determined internally
from tabulated values associated with land use types; initial
conditions and boundary conditions for both layers (boundary
conditions may be time varying).
c. Output
Printed output includes:
Diagnostic information.
Instantaneous S02 and sulfate concentration fields for
lower and upper layers at pre-specified time intervals.
Average S02 and sulfate concentration fields for upper
and lower layer, over pre-specified time intervals.
Accumulated dry and wet deposition for each species over
pre-selected time intervals.
d. Type of Model .
RTM-II is a hybrid Eulerian grid and Lagrangian puff model.
e. Pollutant Types
RTM-II is configured for S02 and sulfate only. Primary sulfate
emissions may be included.
f. Source Receptor Relationships
Area sources and minor point sources are specified at each grid within
the modeling domain.
Up to 500 major point sources (modeled with the Gaussian puff submodel)
are allowed.
Grid average concentration and deposition totals are provided at each
grid within the modeling domain (deposition for lower layer grid
only). All lower grid average concentration values are assumed to be
representative of ground-level receptors.
g. Plume Behavior
Plume rise (Briggs, 1971) is calculated for all major point sources
regardless of whether they are treated in the Gaussian puff submodel.
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h. Horizontal Winds
Gridded u, v wind fields are used at 3-hour interval for each layer.
Gaussian puff submodel tracks puff centroids horizontally at user
specified time intervals.
i. Vertical Wind Speed
Considered implicitly if convergent or divergent winds are provided.
j. Horizontal Dispersion
Plume dispersion is based on oy differentials derived from a
power law fit to Turner (1969) dispersion curves. Variable
stabilities within adjacent cells are considered.
Horizontal eddy diffusivities are proportional to the wind field
deformation and are calculated from the gridded wind fields as
ancilliary input. Maximum and minimum constraints are imposed on
the magnitude of the diffusivities.
k. Vertical Dispersion
Plume dispersion is based on oz differentials derived from a
power law fit to Turner (1969) dispersion curves. Variable
stabilities within adjacent cells are considered.
Vertical dispersion across the mixed layer-surface layer interface
is considered when calculating pollutant deposition.
1. Chemical Transformation
Linear S02 oxidation is treated. Rate constant is diurnally and
latitudinally variable. A minimum oxidation rate constant is
specified to account for heterogeneous oxidation during the
nighttime.
m. Physical Removal
Dry deposition of S02 and sulfate is treated.
Precipitation scavenging of S02 (reversible) and sulfate
(irreversible) is treated.
n. Evaluation Studies
Stewart, D. A., R. E. Morris, M-K Liu, and D. Henderson, "Evaluation
of an Episodic Regional Transport Model for a Multiple Day Episode,"
Atmos. Environ., 17, 1225-1252, 1983.
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B.26 ROUGH TERRAIN DIFFUSION MODEL (RTDM)
Reference: Environmental Research & Technology, Inc., Users' Guide
for to the Rough Terrain Diffusion Model (RTDM, Rev.
3.00). ERT Report No. M2209-585. Environmental Research
& Technology, Inc., 696 Virginia Road, Concord, Massachusetts
01742, 1982.
Availability: The RTDM model is available from:
Environmental Research & Technology, Inc.
ATTN: Mr. Joseph A. Curreri
696 Virginia Road
Concord, Massachusetts 01742
Abstract: The Rough Terrain Diffusion Model (RTDM) version 3.00 is a
sequential Gaussian plume model designed to estimate ground
level concentrations in rough (or flat terrain in the
vicinity of one or more co-located point sources. It is
specifically designed for applications involving chemically
stable atmospheric pollutants and is best suited for
evaluation of buoyant plume behavior within about 15 km
from the source(s). Model results for receptors beyond 15
km can be used with caution to 50 km, and RTDM can be used as
a screening model for distances beyond 50 km. RTDM has special
algorithms to deal with plume behavior in complex terrain,
and is especially suited for rough terrain applications.
While RTDM version 3.00 is specifically designed for use
with with sequential data sets, it can also be run in a
case-study mode. Various optional features of the model
make it useful for either research/sensitivity applications
or routine evaluations of source compliance. RTDM has the
ability to use hourly on-site measurements of turbulence
intensity, vertical temperature difference, horizontal
wind shear, and wind speed profile exponents. However,
RTDM version 3.00 retains sufficient flexibility in the
specification of model inputs to enable the user to obtain
results similar to many other Gaussian point source models
(including RTDM.WC). The ability of RTDM to read hourly
emissions data makes it useful for sitespecific model
evaluation studies.
a. Recommendations for Regulatory Use
RTDM can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. RTDM must be executed in the equivalent mode.
RTDM can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that RTDM
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is more appropriate for the specific application. In this case the model
options/modes which are most appropriate for the application should be used.
b. Input Requirements
Source data requirements are: physical stack height, stack inner
radius, stack-gas temperature and exit velocity, and pollutant
emission rate; a single ground elevation can be entered since all
sources are colocated.
Meteorological data requirements are: Range of atmospheric
stability classes (maximum of six); range of wind directions
(maximum of 16); range of wind speed classes (maximum of six); wind
speed for each speed class; mean ambient temperature; angular plume
width for stable (optional for nonstable).
Receptors data requirements are: downwind distances; terrain
elevations.
c. Output
Printed output includes:
a list of input parameters;
if the case-study mode is used, a detailed output of
plume orientation and size, and concentrations associated
with each source-receptor pair.
a concentration file is written to disk or tape for all
RTDM runs. This output file is used as input to the
ANALYSIS postprocessor, which gives information
concerning the highest concentrations for each receptor,
input meteorology for peak concentration events, and
cumulative frequency distributions.
d. Type of Model
RTDM is a Gaussian plume model.
e. Pollutant Types
RTDM may be used to model primary pollutants. Settling and deposition
are not treated.
f. Source-Receptor Relationship
All sources are collocated.
Up to 400 receptors may be placed arbitrarily with respect to the
source.
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Receptors are assumed to be at ground level.
Plume Behavior
Under all conditions, a partial reflection factor can be calculated;
thus, full doubling of ground-level concentrations is not necessarily
permitted if a plume impinges upon terrain. A stagnation streamline
height, Hcrjt, is calculated in stable conditions. Plumes below Hcrjt
are allowed to impinge upon terrain, otherwise, plumes pass over terrain
obstacles with a relative height computed from plume path coefficients.
Plume path coefficients (user specified) determines plume-terrain
interaction (from full lift to direct impingement), as a function of
stability.
Briggs (1975) formulas are used; calm formula is used for wind
speeds < 1.37 meters per second.
Fumigation and building downwash are not treated.
Stack-tip downwash is treated (ERT, 1982).
Horizontal Winds
Primary hourly wind speed is used to calculate plume rise and
possibly plume dilution.
A second wind speed may be used to compute plume dilution.
Hourly or stability-dependent wind speed profile exponents may be
specified. The default values are .09, .11, .12, .14, .20, and .30
for stability classes A through F, respectively.
The base elevation of the wind speed profile is adjustable.
Vertical Wind speed
Vertical wind speed is assumed equal to zero.
Horizontal Dispersion
Default is 22.5 degree sector averaging. Optionally, other sector
widths may be specified by the user.
Other options are: Gaussian distribution for all stabilities; and
Gaussian distribution for stabilities A through D combined with
sector averaging for stable cases.
If Gaussian distribution is selected the default °y is fit to the
ASME(1979) curves using the form axb in three distance ranges.
B-105
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Other options for Gaussian distribution selection are
P-G ov values, and user input values of the form axb i
a fit to the
I %»** J V* I l%« VI W%* I i I IkS W* V> * Vft I Wl ^ W N* I V>I I W I V/ I III V*^\ ^ f' VW vl I I C t»
distance ranges.
Another option is to use hourly measured turbulence intensity (Iy).
Plume growth equation parallels Briggs (1974).
k. Vertical Dispersion
Gaussian distribution is assumed.
Default oz values are a fit to ASME (1979).
Optionally, oz is fit to Pasquill-Gifford curves (Turner, 1969),
or user may input curves of the form axb in up to three distance
ranges.
Another option is to use hourly measured turbulence intensity (Iz).
Plume growth equation parallels Briggs (1974).
1. Chemical Transformation
Not treated.
m. Physical Removal
Not treated.
n. Evaluation Studies
Egan, B. A., R. D'Errico, C. Vaudo. "Estimating Air Quality Levels
in Regions of High Terrain Under Stable Atmospheric Conditions,"
presented at the Fourth Symposium on Turbulence, Diffusion, and
Air Pollution at Reno, Nevada, American Meteorological Society,
Boston, Massachusetts, 1979.
Corkum, D. A., and J. W. Bradstreet. "A Sulfur Dioxide Modeling
Comparison Study in the Rough Terrain of Nothern New Hampshire
Near a Paper Mill," presented at the 73rd Annual Meeting of the
Air Pollution Control Association at Montreal, Quebec, Canada,
1980.
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Availability:
Abstract:
B.27 SHORTZ
Reference: Bjorklund, J. R., and J. F. Bowers. "User's Instructions
for the SHORTZ and LON6Z Computer Programs, Volumes 1 and
2," EPA 903/9-82-004, U.S. Environmental Protection Agency,
Region III, Philadelphia, Pennsylvania 19106, 1982.
This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
SHORTZ utilizes the steady state bivariate Gaussian plume
formulation for both urban and rural areas in flat or
complex terrain to calculate ground-level ambient air
concentrations. It can calculate 1-hour, 2-hour, 3-hour
etc. average concentrations due to emissions from stacks,
buildings and area sources for up to 300 arbitrarily
placed sources. The output consists of total concentra-
tion at each receptor due to emissions from each user-
specified source or group of sources, including all sour-
ces. If the option for gravitational settling is in-
voked, analysis cannot be accomplished in complex terrain
without violating mass continuity.
a. Recommendations for Regulatory Use
SHORTZ can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. SHORTZ must be executed in the equivalent mode.
SHORTZ can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that SHORTZ
is more appropriate for the specific application. In this case the model
options/modes which are most appropriate for the application should be
used.
b. Input Requirements
Source data requirements are: for point, building or area sources,
location, elevation, total emission rate (optionally classified by
gravitational settling velocity) and decay coefficient; for stack
sources, stack height, effluent temperature, effluent exit velocity,
B-107
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stack radius (inner), actual volumetric flow rate, and ground
elevation (optional); for building sources, height, length and
width, and orientation; for area sources, characteristic vertical
dimension, and length, width and orientation.
Meteorological data requirements are: wind speed and measurement
height, wind profile exponents, wind direction, standard deviations
of vertical and horizontal wind directions, (i.e., vertical and
lateral turbulent intensities), mixing height, air temperature, and
vertical potential temperature gradient.
Receptor data requirements are: coordinates, ground elevation.
c. Output
Printed output includes:
Total concentration due to emissions from user-specified
source groups, including the combined emissions from all
sources (with optional allowance for depletion by deposi-
tion).
d. Type of Model
SHORTZ is a Gaussian plume model.
e. Pollutant Types
SHORTZ may be used to model primary pollutants. Settling and deposition
of particulates are treated.
f. Source-Receptor Relationships
User specified locations for sources and receptors are used.
Receptors are assumed to be at ground level.
g. Plume Behavior
Plume rise equations of Bjorklund and Bowers (1982) are used.
Stack tip downwash (Bjorklund and Bowers, 1982) is included.
All plumes move horizontally and will fully intercept elevated terrain.
Plumes above mixing height are ignored.
Perfect reflection at mixing height is assumed for plumes below the
mixing height.
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Plume rise is limited when the mean wind at stack height approaches
or exceeds stack exit velocity.
Perfect reflection at ground is assumed for pollutants with no settling
velocity.
Zero reflection at ground is assumed for pollutants with finite
settling velocity.
Tilted plume is used for pollutants with settling velocity specified.
Buoyancy-induced dispersion (Briggs, 1972) is included.
h. Horizontal Winds
Winds are assumed homogeneous and steady-state.
Wind speed profile exponents are functions of both stability class and
wind speed. Default values are specified in Bjorklund and Bowers (1982).
i. Vertical Wind Speed
Vertical winds are assumed equal to zero.
j. Horizontal Dispersion
Horizontal plume size is derived from input lateral turbulent inten-
sities using adjustments to plume height, and rate of plume growth
with downwind distance specified in Bjorklund and Bowers (1982).
k. Vertical Dispersion
Vertical plume size is derived from input vertical turbulent inten-
sities using adjustments to plume height and rate of plume growth
with downwind distance specified in Bjorklund and Bowers (1982).
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Time
constant is input by the user.
m. Physical Removal
Settling and deposition of particulates are treated.
n. Evaluation Studies
Bjorklund, J. R., and J. F. Bowers. "User's Instructions for the
SHORTZ and LONGZ Computer Programs," EPA-903/9-82-004, Environ-
mental Protection Agency, Region III, Philadelphia, Pennsylvania
19106, 1982.
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B.28 SIMPLE LINE-SOURCE MODEL (SLSM)
Reference: Chock, D. P. "User's Guide for the Simple Line-Source Model
for Vehicle Exhaust Dispersion Near a Road," Environmental
Science Department, General Motors Research Laboratories,
Warren, Michigan 48090, 1980.
Availability: Copies of the above reference are available from:
Dr. D. P. Chock
Environmental Sciences Department
General Motors Research Laboratories
General Motors Technical Center
Warren, Michigan 48090
No information available on the cost or availability of a
tape or other direct computer input.
Abstract: SLSM is a simple steady-state Gaussian plume model which
can be used to determine hourly (or half-hourly) averages
of exhaust concentrations within 100m from a roadway on a
relatively flat terrain. .The model allows for plume rise
due to the heated exhaust, which can be important when the
crossroad wind is very low. It also utilizes a new set of
vertical dispersion parameters which reflects the influence
of traffic-induced turbulence.
a. Recommendations for Regulatory Use
SLSM can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. SLSM must be executed in the equivalent mode.
SLSM can be used on a casd-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that SLSM
is more appropriate fo the specific application. In this case the model
options/modes which are most appropriate for the application should be
used.
b. Input Requirements
Source data requirements are: emission rate per unit length per
lane, the number of lanes on the road, distances from lane centers
to the receptor, source and receptor heights.
Meteorological data requirements are: buoyancy flux, ambient stability
condition, ambient wind and its direction relative to the road.
Receptor data requirements are: distance and height above ground.
B-lll
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c. Output
Printed output includes:
Hourly or (half-hourly) concentrations at the receptor
due to exhaust emission from a road (or a system of roads
by summing the results from repeated model applications).
d. Type of Model
SLSM is a Gaussian plume model.
e. Pollutant Types
SLSM can be used to model primary pollutants. Settling and deposition
are not treated.
f. Source-Receptor Relationship
SLSM treats arbitrary location of line sources and receptors.
g. Plume Behavior •
Plume-rise formula adequate for a heated line source is used.
h. Horizontal Winds
SLSM uses user-supplied hourly or (or half-hourly) ambient wind speed
and direction. The wind measurements are from a height of 5 to 10 m.
i. Vertical Wind Speed
Vertical wind speed is assumed equal to zero.
j. Dispersion Parameters
Horizontal dispersion parameter is not used.
k. Vertical Dispersion
A vertical dispersion parameter is used which is a function of stability
and wind-road angle. Three stability classes are used: unstable,
neutral and stable. The parameters take into account the effect of
traffic-generated turbulence (Chock, 1980).
1. Chemical Transformation
Not treated.
m. Physical Removal
Not treated.
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n. Evaluation Studies
Chock, D. P. "A Study of Pollutant Dispersion Near Highways,"
Atmospheric Environment 12, 823-829, 1978.
Sistla, G., P. Samson, M. Keenan, and S. T. Ras. "A Study of
Pollutant Dispersion Near Highways," Atmospheric Environment 13,
669-685, 1979.
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Availability:
B.29 TEXAS CLIMATOLOGICAL MODEL (TCM-2)
Reference: Staff of the Texas Air Control Board, Users' Guide to the
TEXAS CLIMATOLOGICAL MODEL (TCM). Texas Air Control
Board, Permits Section, 6330 Highway 290 East, Austin,
Texas 78723.
The TCM-2 model is available from the Texas Air Control
Board at the following cost:
User's Manual only $ 20.00
User's Manual and Model (Magnetic Tape) $ 80.00
Requests should be directed to:
Data Processing Division
Texas Air Control Board
6330 Highway 290 East
Austin, Texas 78723
This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
Abstract: TCM is a climatological steady-state Gaussian plume model
for determining long-term (seasonal or annual arithmetic)
average pollutant concentrations of non-reactive
pollutants.
a. Recommendations for Regulatory Use
TCM can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. TCM must be executed in the equivalent mode.
TCM can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that TCM
is more appropriate for the specific application. In this case the
model options/modes which are most appropriate for the appliction should
be used.
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b. Input Requirements
Source data requirements are: point source coordinates emission
rates (by pollutant), stack height, stack diameter, stack gas exit
velocity, stack gas temperature; area source coordinates (southwest
corner), size, emission rate.
Meteorological data requirements are: stability wind rose and
average temperature.
Receptor data requirements are: size and spacing of the rectangular
receptor grid.
c. Output
Printed output includes:
period average concentrations listed, displayed in map
format, or punched on cards at the user's option.
culpability list option provides the contributions of the
five highest contributors at each receptor.
maximum concentration option provides the maximum
concentration for each scenario (run).
d. Type of Model
TCM is a Gaussian plume model.
e. Pollutant Types
TCM may be used to model primary pollutants. Settling and deposition
are not treated.
f. Source-Receptor Relationship
Arbitrary location of point sources and area sources are treated.
Arbitrary location and spacing of rectangular grid of receptors are
used. (Area source grid is best defined in terms of the receptor
grid, so that the receptors fall in the center of the area source).
Receptors located in simple terrain may be modeled.
g. Plume Behavior
Briggs (1975) plume rise equations, including momentum rise, are used
for point sources.
Two-thirds power law is used when transitional rise option is selected.
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Flares are treated.
Building downwash is treated using the Huber-Snyder (1976) technique.
h. Horizontal Winds
Characteristic wind speed is calculated for each direction-stability
class combination.
This characteristic speed is the inverse of the average inverse
speed for the stability-wind direction combination.
Wind speed is adjusted to stack height by a power law using exponents of
.10, .15, .20, .25, .30, and .30 for stabilities A through F, respectively.
i. Vertical Wind Speed
Vertical wind speed is assumed to be zero.
j." Horizontal Dispersion
Uniform distribution within each 22.5 degree sector is assumed.
k. Vertical Dispersion
Dispersion parameters for point sources are fit to Turner (1969); for
area sources in the urban mode the fit is to Gifford and Hanna (1970).
Seven stability classes are used.
Pasquill A through F are treated, with daytime "D" and nighttime "D"
given separately.
In the urban mode, E and F stability classes are treated as D-night.
Perfect reflection at the ground is assumed.
1. Chemical Transformation
Chemical transformations are treated using exponential decay. Half-
life is input by the user.
m. Physical Removal
Physical removal is treated using exponential decay. Half-life is
input by the user.
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n. Evaluation Studies
Londergan, R. J., D. H. Minott, D. J. Wachter and R. R. Fizz. "Evalua-
tion of Urban Air Quality Simulation Models." EPA-450/4-83-020.
Environmental Protection Agency, Research Triangle Park, North
Carolina 27711, 1983.
Durrenberger, C. S., B. A. Braberg, and K. Zimmermann. "Development
of a Protocol to be Used for Dispersion Model Comparison Studies,"
presented at the 76th Annual Meetiog of the Air Pollution Control
Association at Atlanta, Georgia, 1983.
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B.30 TEXAS EPISODIC MODEL (TEM-8)
Reference:
Availibillty:
Abstract:
Staff of the Texas Air Control Board. User's Guide to
the TEXAS EPISODIC MODEL. Texas Air Control Board, Permits
Section, 6330 Highway 290 East, Austin, Texas 78723.
The TEM -8 model is available from the Texas Air Control
Board at the following costs:
User's Manual only $ 20.00
User's Manual and Model (Magnetic Tape) $ 80.00
Requests should be directed to:
Data Processing Division
Texas Air Control Board
6330 Highway 290 East
Austin, Texas 78723
This model is available as part of UNAMAP (Version 6).
The computer code is available on magnetic tape from:
Computer Products
National Technical Information Service
U.S. Department of Commerce
Springfield, Virginia 22161
Phone (703) 487-4763
The accession number of the UNAMAP tape is PB
TEM is a short-term, steady-state Gaussian plume model for
determining short-term concentrations of non-reactive
pollutants.
a. Recommendations for Regulatory Use
TEM can be used if it can be demonstrated to estimate concentrations
equivalent to those provided by the preferred model for a given appli-
cation. TEM must be executed in the equivalent mode.
TEM can be used on a case-by-case basis in lieu of a preferred model
if it can be demonstrated, using the criteria in Section 3.2, that
TEM is more appropriate for the specific application. In this case
the model options/modes which are most appropriate for the application
should be used.
b. Input Requirements
Source data requirements are:
heights of emissions for both
locations, average emission rates and
point and area sources; stack gas
B-119
-------
temperature, stack gas exit velocity, and stack inside diameter for
point sources for plume rise calculations.
Any combination of hourly meteorological data up to 24 hours may be
used, (e.g. 1, 3, 5, 8, 24 hours).
Meteorological data requirements are: hourly surface weather data from
the EPA meteorological preprocessor program. Preprocessor output includes
hourly stability class, wind direction, wind speed, temperature, and mixing
height.
Receptor requirements are: size, spacing and location of rectangular
grid of receptors.
c. Output
Printed output includes:
concentration list;
spatial array (concentrations displayed as on a map);
punched cards of the concentration list;
culpability list (percent contributions) of the five
highest contributors to each receptor;
maximum concentration; and
point source list.
d. Type of Model
TEM is a Gaussian plume model.
e. Pollutant Types
TEM can be used to model primary pollutants. Settling and deposition
are not treated.
f. Source-Receptor Relationship
Arbitrary locations of point sources and area sources are treated.
Arbitrary location and spacing of rectangular grid of receptors is
treated. Area source grid is best defined in terms of the receptor
grid so that the receptors fall in the centers of the area sources.
Receptors located in simple terrain may be modeled.
g. Plume Behavior
Briggs (1975) plume rise equations are used, including momentum rise,
for point sources.
B-120
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Transitional rise is calculated.
Stack-tip downwash and building downwash (Huber and Snyder, 1976) may
be evaluated.
h. Horizontal Winds
Wind speeds are adjusted to release height by power law formula,
using exponents of .10, .15, .20, .25, .30 and .30 for stabilities A
through F respectively.
Steady-state wind is assumed.
i. Vertical Wind Speed
Vertical wind is assumed equal to zero.
j. Horizontal Dispersion
Gaussian plume coefficients are fitted to Turner (1969).
In the urban mode, stable cases are shifted to neutral nighttime (D-night)
conditions and urban mixing heights are used.
k. Vertical Dispersion
Dispersion parameters for point sources are fit to Turner (1969); for area
sources, in the urban mode, the fit is to Gifford and Hanna (1970).
Total reflection of the plume at the ground is assumed.
In the urban mode, coefficients are shifted from E and F to D-nighttime
coefficients.
1. Chemical Transformation
Chemical transformation is treated using exponential decay. Half-
life is input by the user.
m. Physical Removal
Physical removal is treated using exponential decay. Half-life is
input by the user.
n. Evaluation Studies
Londergan, R., D. Minott, D. Wachter, T. Kincaid and D. Bonitata.
"Evaluation of Rural Air Quality Simulation Models." EPA-450/4-
83-003, Environmental Protection Agency, Research Triangle Park,
North Carolina, 1983.
B-121
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Durrenberger, C. J., B. A. Broberg, and K. Zimmermann. "Development
of a Protocol to be Used for Dispersion Model Comparison Studies,"
presented at the 76th Annual Meeting of the Air Pollution Control
Association at Atlanta, Georgia, 1983.
B-122
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B.31 REFERENCES
American Society of Mechanical Engineers, 1968. Recommended Guide for
the Prediction of Airborne Effluents. American Society of Mechanical
Engineers, New York, NY.
American Society of Mechanical Engineers, 1979. Recommended Guide for
the Prediction of Airborne Effluents, Third Edition. American Society
of Mechanical Engineers, New York, NY.
Atkinson, R., A. C. Lloyd, and L. Winges, 1982. An Updated Chemical
Mechanism for Hydrocarbon/N0x/S0x Photooxidation Suitable for Inclusion
in Atmospheric Simulation Models. Atmos. Envir. 16(6):1341-1355.
Bjorklund, J. R., and J. F. Bowers, 1982. User's Instructions for the
SHORTZ and LONGZ Computer Programs. EPA-903/9-82-004a,b. U. S. Environ-
mental Protection Agency, Region III, Philadelphia, PA.
Briggs, G. A., 1969. Plume Rise. U. S. Atomic Energy Commission
Critical Review Series, Oak Ridge National Laboratory, Oak Ridge, TN.
NTIS TIO-25075.
Briggs, G. A., 1971. Some Recent Analyses of Plume Rise Observations.
Proceedings of the Second International Clean Air Congress, edited by
H. M. Englund and W. T. Berry. Academic Press, New York, NY.
Briggs, G. A., 1972. Discussion on Chinney Plumes in Neutral and Stable
Surroundings. Atmos. Envir. 6:507-510.
Briggs, G. A., 1974. Diffusion Estimation for Small Emissions. ERL,
ARL, USAEC Report ATDL-106. U. S. Atomic Energy Commission, Oak Ridge, TN.
Briggs, G. A., 1975. Plume Rise Predictions, in Lectures on Air Pollu-
tion and Environmental Impact Analyses. American Meteorological Society,
Boston, MA.
Briggs, G. A., 1983. Plume Rise and Buoyancy Effects. Atmospheric
Science and Power Production, Darryl Randerson (Ed.). DOE Report
DOE/TIC-27601, in press.
Carpenter, S. B., T. L. Montgomery, J. M. Leavitt, W. C. Colbaugh, and
F. W. Thomas, 1971. Principal Plume Dispersion Models: TVA Power Plants.
JAPCA 21(8):491-495.
Chock, D. P., 1980. User's Guide for the Simple Line-Source Model for
Vehicle Exhaust Dispersion Near a Road. Environmental Science Department,
General Motors Research Laboratories, Warren, MI.
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Cramer, H. E., H. V. Geary, andJ. F. Bowers, 1975. Diffusion-Model
Calculation of Long-term and Short-term Ground-level S02 Concentrations
in Alleghaney County, Pennsylvania. EPA 903/9-75-018. U. S. Environ-
mental Protection Agency, Region III, Philadelphia, PA. NTIS PB-245262/AS.
DeMarrais, G. A., 1959. Wind Speed Profiles at Brookhaven National
Laboratory. Journal of Appi ied Meteorology, 16:181-189.
Durbin, P. A., T. A. Hecht, and G. Z. Whitten, 1975. Mathematical Model-
ing of Simulated Photochemical Smog. EPA-600/4-75-026. U. S. Environmental
Protection Agency, Research Triangle Park, NC.
Eschenroeder, A. Q., 1972. Evaluation of a Diffusion Model for Photo-
chemical Smog Simulation. EPA-R4-73-012. U. S. Environmental Protection
Agency, Research Triangle Park, NC.
Environmental Research and Technology, Inc., 1980. User's Guide for
RTDM.WC A "Worst Case" Version of the ERT Rough Terrain Model. ERT
Document M-0186000R. Environmental Research and Technology, 696 Virginia
Road, Concord, MA.
Gifford, F. A., 1975. Atmospheric Dispersion Models for Environmental
Pollution Applications. Lectures on Air Pollution and Environmental
Impact Analyses. American Meteorological Society, Boston, MA.
Goodin, W. R., G. J. McRae, and J. H. Seinfeld, 1980. An Objective
Analysis Technique-for Constructing Three-Dimensional Urban-Scale Wind
Fields. Journal of Applied Meteorology, Volume 19:98-108.
Hecht, T. A., and J. H. Seinfeld, 1974. Further Development of Genera-
lized Kinetic Mechanism for Photochemical Smog. Environmental Science
and Technology, 84(327).
Heffter, J. L., 1965. The Variations of Horizontal Diffusion Parameters
with Time for Travel Periods of One Hour of Longer. Journal of Applied
Meteorology, 4:153-156.
Heffter, J. L., 1980. Air Resources Laboratories Atmospheric Transport
and Dispersion Model (ARL-ATAD). NOAA Technical Memorandum ERL ARL-81.
Air Resources Laboratories, Silver Spring, MD.
Irwin, J. S., 1979. Estimating Plume Dispersion - A Recommended Genera-
lized Scheme. Fourth Symposium on Turbulence, Diffusion and Air Pollution,
Reno, NV, pages 62-69. American Meteorological Society, Boston, MA.
Larsen, R. I., 1971. A Mathematical Model for Relating Air Quality
Measurements to Air Quality Standards. Office of Air Programs Publi-
cation No. AP-89. U.S. Environmental Protection Agency, Research Triangle
Park, NC. NTIS PB 205277.
B-124
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Mueller, S. F., R. J. Valente, T. L. Crawford, A. L. Sparks, and L. L.
Gautney, Jr., 1983. "Description of the Air Resources Regional Pollution
Assessment (ARRPA) Model: September 1983." TVA/ONR/AQB-83/14. Tennessee
Valley Authority, Muscle Shoals, Alabama 35660.
Myers, T. C., and J. E. Langstaff, 1981. Application of Meteorological
and Air Quality Modeling to the Las Vegas and Tampa Bay Areas. SAI
Number 101-81EF81-108. Systems Applications, Inc., San Rafael, CA.
Pasquill, F., 1976. Atmospheric Dispersion Parameters in Gaussian Plume
Modeling, Part II. EPA 600/4-76-030b. U. S. Environmental Protection
Agency, Research Triangle Park, NC.
Seigneur, C., et al., 1983. On the Treatment of Point Source Emissions
in Urban Air Quality Modeling. Atmospheric Environment (in Press).
Shere, K. L., and K. R. Demerjian, 1977. Calculation of Selected Photo-
lytic Rate Constants over a Diurnal Range. EPA-600/477-015. U. S.
Environmental Protection Agency, Research Triangle Park, NC.
Turner, D. B., 1964. A Diffusion Model of An Urban Area. Journal of
Applied Meteorology, 3:83-91.
Turner, D. B., 1969. Workbook of Atmospheric Dispersion Estimates.
PHS Publication No. 999-AP-26. U. S. Environmental Protection Agency,
Research Triangle Park, NC. NTIS PB 191482.
Wesely, M. L., and B. B. Hicks, 1977. Some Factors That Affect the
Deposition Rates of Sulfur Dioxide and Similar Gases on Vegetation.
Journal of the Air Pollution Control Association, 27:1110-1116.
B-125
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APPENDIX C
EXAMPLE AIR QUALITY ANALYSIS CHECKLIST
-------
C.O INTRODUCTION
This checklist recommends a standardized set of data and a standard
basic level of analysis needed for PSD applications and SIP revisions.
The checklist implies a level of detail required to assess both PSD
increments and the NAAQS. Individual cases may require more or less
information and the Regional Meteorologist should be consulted at an
early stage in the development of a data bases for a modeling analysis.
At pre-application meetings between source owner and reviewing
authority, this checklist should prove useful in developing a concensus
on the data base, modeling techniques and overall technical approach
prior to the actual analyses. Such agreement will help avoid misunder-
standings concerning the final results and may reduce the later need
for additional analyses.
C-l
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EXAMPLE AIR QUALITY ANALYSIS CHECKLIST*
1. Source location map(s) showing location with respect to:
0 Urban areas**
0 PSD Class I areas
0 Nonattairment areas**
0 Topographic features (terrain, lakes, river valleys, etc.)**
0 Other major existing sources**
0 Other major sources subject to PSD requirements
0 NWS meteorological observations (surface and upper
air)
0 On-site/local meteorological observations (surface and upper
ai r)
0 State/1 ocal/on-site air quality monitoring locations**
0 Plant layout on a topographic map covering a 1-km radius of
the source with information sufficient to determine GEP .
stack heights
2. Information on urban/rural characteristics:
0 Land use within 3 km of source classified according to
Auer, A. H. (1978): Correlation of land use and cover with
meteorological anomalies, J. of Applied Meteorology, 17:636-643.
0 Population
- total
- density
0 Based on current guidance determination of whether the area
should be addressed using urban or rural modeling methodology
*The "Guidelines for Air Quality Maintenance and Analysis," Volume 10R,
EPA-450/4-77-001, October 1977 should be used as a screening tool to
determine whether modeling analyses are required. Screening procedures
should be refined by the user to be site/problem specific.
**Within 50 km or distance to which source has a significant impact,
whichever is 1 ess.
C-3
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3. Emission inventory and operating/design parameters for major
sources within region of significant impact of proposed site (same as
required for applicant)
0 Actual and allowable annual emission rates (g/s) and opera-
ting rates*
0 Maximum design load short-term emission rate (g/s)*
0 Associated emissions/stack characteristics as a function of
load for maximum, average, and nominal operating conditions
if stack height is less than GEP or located in complex
terrain. Screening analyses as footnoted on page 1 or
detailed analyses, if necessary, must be employed to deter-
mine the constraining load condition (e. g., 50%, 75%, or
100% load) to be relied upon in the short-term modeling
analysis.
- location (UTM's)
- height of stack (m) and grade level above MSL
- stack exit diameter (m)
- exit velocity (m/s)
- exit temperature (°K)
0 Area source emissions (rates, size of area, height of area
source)*
0 Location and dimensions of buildings (plant layout drawing)
- to determine GEP stack height
- to determine potential building downwash considerations
for stack heights less than GEP
0 Associated parameters
- boiler size (megawatts, pounds/hr. steam, fuel consump-
tion, etc.)
- boiler parameters (% excess air, boiler type, type of
firing, etc.)
- operating conditions (pollutant content in fuel, hours of
operation, capacity factor, % load for winter, summer,
etc.)
- pollutant control equipment parameters (design efficiency,
operation record, e.g., can it be bypassed?, etc.)
0 Anticipated growth changes
*Particulate emissions should be specified as a function of particulate
diameter and density ranges.
C-4
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4. Air quality monitoring data:
0 Summary of existing observations for latest five years
(including any additional quality assured measured data
which can be obtained from any state or local agency or
company)*
0 Comparison with standards
0 Discussion of background due to uninventoried sources and
contributions from outside the inventoried area and descrip-
tion of the method used for determination of background
(should be consistent with the Guideline on Air Quality
Models)
5. Meteorological data:
0 Five consecutive years of the most recent representative
sequential hourly National Weather Service (NWS) data, or
one or more years of hourly sequential on-site data
0 Discussion of meteorological conditions observed (as applied
or modified for the site-specific area, i.e., identify
possible variations due to difference between the monitoring
site and the specific site of the source)
0 Discussion of topographic/land use influences
6. Air quality modeling analyses:
0 Model each individual year for which data are available with
a recommended model or model demonstrated to be acceptabl e
on a case-by-case basis
- urban dispersion coefficients for urban areas
- rural dispersion coefficients for rural areas
0 Evaluate downwash if stack height is less than GEP
0 Define worst case meteorology
0 Determine background and document method
- long-term
- short-term
*See **on page 1 of checklist.
C-5
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0 Provide topographic map(s) of receptor network with respect
to location of all sources
0 Follow current guidance on selection of receptor sites for
refined analyses
0 Include receptor terrain heights (if applicable) used in
analyses
0 Compare model estimates with measurements considering the
upper ends of the frequency distribution
0 Determine extent of significant impact—provide maps
0 Define areas of maximum and highest, second-highest impacts
due to applicant source (refer to format suggested in Air
Quality Summary Tables)
- long-term
- short-term
7. Comparison with acceptable air quality levels:
0 NAAQS
0 PSD increments
0 Emission offset impacts if nonattainment
8. Documentation and guidelines for modeling methodology:
0 Follow guidance documents
- Guideline on Air Quality Models, EPA-450/2-78-027R,
- Workbook for Comparison of Air Quality Models, EPA-450/2-
78-028a,b, May 1978
- Guidelines for AQMPA, Vol. 10R, EPA-450/4-77-001, October
1977
- Guideline for Determination of Good Engineering Practice
Stack Height (Technical Support Document for the Stack
Height Regulations), EPA-450/4-80-023, July 1981
- Ambient Air Monitoring Guidelines for PSD, EPA-450/2-78-
019, November 1980
- "Requirements for Preparation, Adoption and Submittal of
Implementation Plans; Approval and Promulgation of Implemen-
tation Plans," Federal Register, Vol. 43, No. 119,
pp. 52676-52748, August 1980 (Prevention of Significant
Deterioration)
C-6
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AIR QUALITY SUMMARY
For All New Sources
** **
Pollutant
HighestHighestHighestHighest Annual
2nd High 2nd High
Concentration Due to
Modeled Source (pg/m3)
Background Concentration
Total Concentration (yg/m3)
Receptor Distance (Km)
(or UTM Easting)
Receptor Direction (°)
(or UTM Northing)
Receptor Elevation (m)
Wind Speed (m/s)
Wind Direction (°)
Mixing Depth (m)
Temperature (°K)
Stability
Day/Month/Year
of Occurrence
*Use separate sheet for each pollutant (S02, TSP, CO, NOX, HC, Pb,
Hg, Asbestos, etc.)
**List all appropriate averaging periods (1-hr, 3-hr, 8-hr, 24-hr,
30-day, 90-day, etc.) for which an air quality standard exists
Surface Air Data From Surface Station Elevation (m)
Anemometer Height Above Local Ground Level (m)
Upper Air Data From
Period of Record Analyzed
Model Used
Recommended Model
C-7
-------
AIR QUALITY SUMMARY
For All Sources
** **
Pollutant
HighestHighestHighestHighest Annual
2nd High 2nd High
Concentration Due to
Modeled Source (yg/m3)
Background Concentration
(yg/m3)
Total Concentration (yg/m3)
Receptor Distance (Km)
(or UTM Easting)
Receptor Direction (°) .
(or UTM Northing)
Receptor Elevation (m)
Wind Speed (m/s)
Wind Direction (°)
Mixing Depth (m)
Temperature (°K)
Stability
Day/Month/Year
of Occurrence
*Use separate sheet for each pollutant (SC-2, TSP, CO, NOX, HC, Pb,
Hg, Asbestos, etc.)
**List all appropriate averaging periods (1-hr, 3-hr, 8-hr, 24-hr,
30-day, 90-day, etc.) for which an air quality standard exists
Surface Air Data From Surface Station Elevation (m)
Anemometer Height Above Local Ground Level (m)
Upper Air Data From
Period of Record Analyzed
Model Used
Recommended Model
C-8
-------
AIR QUALITY SUMMARY
For All Sources
** **
Pollutant
Highest Highest Highest Highest Annual
2nd High 2nd High
Concentration Due to
Model ed Source (pg/m3)
Background Concentration
Total Concentration ( pg/m3)
Receptor Distance (Km)
(or DIM Easting)
Receptor Direction (°)
(or UTM Northing)
Receptor El evation (m)
Wi nd Speed (m/s)
Wind Direction (°)
Mixi ng Depth (m)
Temperature (°K)
Stability
Day/Month/Year
of Occurrence
*Use separate sheet for each pollutant (SOg, TSP, CO, NOX, HC, Pb,
Hg, Asbestos, etc.)
**List all appropriate averagi ng periods (1-hr, 3-hr, 8-hr, 24-hr,
30-day, 90-day, etc.) for which an air quality standard exists
Surface Air Data From Surface Station El evation (m)
Anenometer Height Above Local Ground Level (m)
Upper Air Data From ^
Period of Record Analyzed
Model Used
Recommended Model
C-9
-------
STACK PARAMETERS FOR ANNUAL MODELING
Stack Serving Emission Rate Stack Exit Stack Exit Stack Exit Physical Stack GEP Stack Base Building Dimensions (m)
No. for each Diameter Velocity Temperature Height (m) Stack Elevation Height Width Length
Pollutant (g/s) (m) (m/s) ("K) Height (m) (m)
o
i
-------
STACK PAR ALTERS FOR SHORT-TERM MODELING*
Stack Serving Emission Rate Stack Exit Stack Exit Stack Exit Physical Stack GEP Stack Base Building Dimensions (m)
No. for each Diameter Velocity Temperature Height (m) Stack Elevation Height Width Length
Pollutant (g/s) (m) (m/s) (°K) Height (m) (m)
o
i
*Separate tables for 50*, 75%, 100* of full load operating condition (and any other operating conditions as
determined by screening or detailed modeling analyses to represent constraining operating conditions) should
be provided.
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