5779
OOOR77100
GUIDELINE SERIES
OAQPS NO. 1,2-080
OCTOBER 1977
INTERIM
GUIDELINE ON AIR QUALITY MODELS
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina
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INTERIM
GUIDELINE ON AIR QUALITY MODELS
OCTOBER 1977
F10NITORING AND DATA ANALYSIS DIVISION
OFFICE OF AIR QUALITY PLANNING AND STANDARDS
U,S, ENVIRONMENTAL PROTECTION AGENCY
RESEARCH TRIANGLE PARK, NORTH CAROLINA
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INTERIM
GUIDELINE ON AIR QUALITY MODELS
OCTOBER 1977
MONITORING AND DATA ANALYSIS DIVISION
OFFICE OF AIR QUALITY PLANNING AND STANDARDS
U.S. ENVIRONMENTAL PROTECTION AGENCY
RESEARCH TRIANGLE PARK, NORTH CAROLINA
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ACKNOWLEDGEMENT
This guideline was primarily prepared by Joseph Tikvart and
Herschel Slater of the Monitoring and Data Analysis Division, Office
of Air Quality Planning and Standards, Environmental Protection Agency,
Significant contributions were also made by James Dicke and Laurence
Budney of the same office. However, the many comments and suggestions
provided by those participating in the Specialists' Conference and
the five public meetings on these guidelines must also be recognized.
The manuscript was prepared with great care by Ann Asbill and Barbara
Stroud.
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Preface
In late 1976 it became clear from needs expressed by the States
and EPA Regional Offices, by many industries and trade associations
and by deliberations of Congress that greater consistency in the use
of air quality models is needed. Consistency is required so that air
pollution control agencies, industry and the general public have a
common basis for estimating pollutant concentrations, assessing control
strategies and specifying emission limits.
To meet this need, EPA undertook a series of steps that would lead
to a vn'dely reviewed guide on the use of air quality models. After
initial opinions from EPA's Regional Offices were received, the Office
of Air Quality Planning and Standards prepared a draft guide. This
guide was submitted for critical review to a conference of specialists.*
The individual conferees were widely recognized experts in the develop-
ment and use of air quality models. Based on the judgments and sugges-
tions of the conferees, the guide was revised and presented for public
comment at meetings** in Atlanta, Chicago, Denver, Mew York and San
Francisco. These meetings were attended by approximately 500 repre-
sentatives of control agencies, industry, environmental groups and the
scientific community. These attendees submitted extensive oral and
written comments which were evaluated and considered in the preparation
of this guide.
During the development of the guide the Clean Air Act Amendments
of 1977 were signed into law. These amendments require the promulgation
of regulations which specify models to be used in analyses pertinent to
prevention of significant deterioration. They also require EPA to con-
duct a conference on air quality modeling. This guide is published for
interim use, pending development of the regulations required by the Clean
Air Act. The guide will be used as a point of departure for the Modeling
Conference and the subsequent development of regulations.
Due to the continuing development of a wide variety of air quality
models and numerous gaps in our ability to simulate the atmospheric
dispersion process, EPA plans to review and update this document
periodically.
*Roberts, J. J., Ed. "Report to the U. S. EPA of the Specialists'
Conference on the EPA Modeling Guidelines." Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, February 1977.
**Slater, H. H., Chairman, "Comments and Recommendations Concerning
the Draft Guidelines on Air Quality Models." Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, May/June 1977.
m
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TABLE OF CONTENTS
Page
Acknowledgement ii
Preface iii
1.0 INTRODUCTION 1
2.0 OVERVIEW 5
3.0 REQUIREMENTS FOR CONCENTRATION ESTIMATES 7
3.1 Control Strategy Evaluations 7
3.? Hew Source Reviews 10
3.2.1 Meeting Air Quality Standards 10
3.2.2 Prevention of Significant Deterioration 12
4.0 AIR QUALITY MODELS 13
4.1 Suitability of Models 14
4.? Classes of Models 16
4.3 Recommended Models 18
4.3.1 Point Source Models for Sulfur Dioxide and
Particulate Matter (All Averaging Times) .... 18
4.3.2 Multi-Source Models for Sulfur Dioxide and
Particulate Matter (Annual Average) 20
4.3.3 Multi-Source Models for Sulfur Dioxide and
Particulate Matter (Short-Term Averages) .... 21
-4.3.4 Models for Carbon Monoxide 22
4.3.5 Models for Nitrogen Dioxide 23
4.4 Special Situations 24
5.0 DATA REQUIREMENTS 27
5.1 Source Data 27
5.2 Meteorological Data 31
5.3 Receptor Sites 33
5.4 background Air Quality 34
6.0 MODEL VALIDATION/CALIBRATION 39
7.0 REFERENCES 43
Glossary of Terms 48
Appendix A. Significant Air Quality Increments for Non-
attainment Areas A
Appendix C. Summaries of Recommended Air Quality Models B
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Page
B.I Air Quality Display Model (AQDM) B-l
B.2 APRAC-1A B-5
B.3 Climatological Dispersion Model (COM) B-10
B.4 Real-Time Air Quality Simulation Model (RAM) B-15
B.5 Single Source (CRSTER) Model B-20
B.6 Texas Climatological Model (TCM). B-24
B.7 Texas Episodic Model (TEM) B-29
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1.0 INTRODUCTION
The purpose of this guide is to recommend air quality modeling
techniques that may be applied to air pollution control strategy eval-
uations and to new source reviews, including prevention of significant
deterioration. It is intended for use by EPA Regional Offices in
judging the adequacy of modeling analyses performed by EPA, by State
and local agencies and by industry and its consultants. Similarly,
it serves to identify for all interested parties those techniques and
data bases that EPA considers acceptable. The guide is not intended
to be a compendium of modeling techniques. Rather it should serve as
a basis by which air quality managers, supported by sound scientific
judgment, have a common measure of acceptable technical analyses.
This guide makes specific recommendations concerning (1) air
quality models, (2) data bases and (3) general requirements for concen-
tration estimates. It should be followed in all evaluations relative
to State Implementation Plans (SIPs). However, it may be found that
(1) the recommended air quality model is not appropriate for a particu-
lar application, (2) the required data base is unavailable, or (3) a
better model or analytical procedure is available and applicable. In
such cases, alternatives indicated in this guide or other data, models
and techniques deemed appropriate by the Regional Administrator may be
used. Thus, even though specific recommendations are made, they should
not be considered rigid requirements. The preferred model is that which
best simulates atmospheric transport and dispersion in the area of
interest. However, deviations from this guide should be fully supported
arid documented.
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The contents of this guide are summarized in Figure 1. The basic
steps in applying an air quality model to a practical situation, and
the necessary data bases and information, are shown. The numbers in
parentheses refer to specific sections of the guide.
As indicated in Figure 1, it is generally advisable to first i-poly
a model requiring a minimum expenditure of resources (i.e., a prelimi-
nary screening technique). The purpose of a screenings technique is to
single out, with minimum effort, tho.,e sources that clearly will not
cause or contribute to ambient concentrations in excess of the National
Ambient Air Quality Standards (NAAQS) or allovjable concentration incre-
ments. In doinq so, unwarranted expenditure of resources (a refined
analysis when a simple approach would suffice) can be avoided. Another
advantage of first applying a relatively simple model is to obtain con-
centration estimates or receptor information that can be helpful in a
more refined analysis.
If the screening analysis indicates that the source may pose an
air quality problem, application of a relatively sophisticated model
is then warranted for obtaining more refined concentration estimates.
The selection of an appropriate model should be based upon all the
factors indicated in Figure 1. Of particular ihportance are the source
and meteorological data used in the application.
Given the selection of a refined air quality model, an appropriate
receptor field must be designated. The model can then be applied giving
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(3.0)
(4.3)
(44)
(5.1)
(5.2)
(4.2)
(4.1)
(5.3)
INPUT
INFORMATION
POLLUTANT OF CONCERN
REQUIREMENTS FOR CONCENTRATION
ESTIMATES
SOURCE TYPE (POINT. MULT I, ETC )
SPECIAL SITUATIONS/PROBLEMS
SOURCE DATA AVAILABLE
METEOROLOGICAL DATA AVAILABLE
ACCURACY OF CONCENTRATION
ESTIMATES
MODELING CAPABILITIES/RESOURCES
RECEPTOR LOCATIONS
(4.3,5.0)
AVAILABILITY OF
ADEQUATE MODEL
AND DATA BASES
(30)
i
DEFINE REQUIREMENTS FOR
CONCENTRATION ESTIMATES
(GO)
MODEL VALIDATION/
CALIBRATION
MODELING
STEPS
SIMPLE
SCREENING
PROCEDURE
YES
MODEL
SELECTION
ANO
APPLICATION
(4.2,4.3)
ISA
REFINED
ANALYSIS
REQUIRED
7
CONSIDERATION
OF BACKGROUND
AND GROWTH
(3.0,4.0)
(5.4)
(3.0)
Fiqure 1 Selection and application of air quality models and data bases. (Applicable
sections of the guideline are indicated m parentheses.)
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appropriate consideration to background concentrations and future
growth. The resulting concentration estimates can be used to analyze
source impact as required by the particular application. However, any
analytical technique may have deficiencies that cause estimated concen-
trations to be in error. Therefore, information on the accuracy of the
model should be available prior to evaluation of control strategies and
determination of allowable emissions.
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2.0 OVERVIEW
Air quality models have been widely used to identify potential
violations of the National Ambient Air Quality Standards (NAAQS) and
to determine emission limits. The need for air quality models in
the development and revision of SIP-related control strategies was
identified very early. However, due to the initial demands of the
Clean Air Act (1970) on available resources, it has not generally
been possible to use air quality models to the extent desired. Thus,
many SIPs are based on an example region concept and a simple emissions
rollback model. In recent years, however, air quality models have
been more widely used. As these models arid associated data bases
increase in sophistication, they allow more precision in estimating
concentrations and in assessing the adequacy of control strategies.
In addition to their use in development and revision of control
strategies, air quality models are also required in the New Source
Review program to insure attainment and maintenance of NAAQS, and
to prevent significant air quality deterioration. Judgments must be
made concerning allowable emission rates and I,he placement of new
sources that may cause specific air quality levels to be exceeded or
that may contribute significantly to existing violations.
It would be advantageous to categorize the various control
programs and to apply a designated model to each proposed source
which comes under a given program, llov/ever, the diversity of the
nation's topography and climate, and variations in source
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configurations and operating characteristics dictate against a routine
"cookbook" analysis. There is no single model capable of properly
addressing all conceivable situations. Meteorological phenomena
associated v/ith threats to air quality (standards are rarely amenable
to simple mathematical treatment. Any modeling effort should be
directed by highly competent individuals with a broad range of experience
and knowlege in air pollution meteorology and coordinated closely with
specialists in emissions characteristics and data processing. The
judgment of well-trained professional analysts is essential.
Nevertheless, it is clear from the needs expressed by the States
and EPA Regional Offices, by many industries and trade associations and
2
by the deliberations of Congress that greater consistency in the use
of models and data bases is in order. Consistency is required so that
air pollution 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 the required
consistency.
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3.0 REQUIREMENTS FOR CONCENTRATION ESTIMATES
Specific air quality standards and increments of pollutant con-
centrations must be considered for control strategy evaluations and
for new source reviev/s, including prevention of significant deteriora-
tion. This section specifies general requirements for concentration
estimates and identifies the relationship between emission limits and
air quality standards/increments for these applications.
3.1 Control Strategy Evaluations
SIP-related emission limits should be based on concentration
estimates for the averaging time which results in the most stringent
control requirements. In all cases these concentration estimates are
assumed to be a sum of the concentration contributed by the source and
an appropriate background concentration (see pp. 34.37).
If the annual average air quality standard is exceeded by a
greater degree (percentage) than standards for other averaging times,
the annual average is considered the restrictive standard. In this
case the sum of the highest estimated annual average concentration and
the annual average background provides the concentration which should
be used to specify emission limits. However, if a short-term standard
is exceeded by a greater degree and is thus identified as the restric-
tive standard, other considerations are required because the frequency
of occurrence must also be taken into account.
Historically, when dispersion model estimates are used to assist
in judging whether short-term NAAQS will be met, and ultimately in
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specifying appropriate emission limits, one of three types of
concentration estimates is used: (1) The highest of all estimated
concentrations, (2) the second-highest of all estimated concentra-
tions, or (3) the highest of second-highest concentrations estimated
for a field of receptor sites. The highest of second-highest concen-
trations for a field of receptors is obtained as follows:
(1) Frequency distributions of short-term concentrations are esti-
mated for each site in a field of receptors; (2) the highest estimated
concentration at each receptor is discarded; (3) the highest of the
remaining concentration estimates from the field of receptor sites
is identified. Throughout this guideline that concentration estimate
is referred to as the "highest, second-highest" concentration.
The first two types of estimates have been applied most often
in specifying emission limits. However, they may be unnecessarily
restrictive in many situations. The third type of estimate is more
consistent with the criteria for determining violations of the NAAQS,
which are identified in "Guidelines for Interpretation of Air Quality
3
Standards." That 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 which are to be based on an averag-
ing time of 24-hours or less should be based on the highest, second-
highest estimated concentration plus a background concentration
which can reasonably be assumed to occur with that concentration.
(See the section on background air quality for a discussion of the
factors and variety of situations that should be considered.)
8
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An estimate of the highest, second-highest concentration which is
based on many well-chosen receptor site? may well reveal previously
unidentified "hot spots." Such en estimate may provide a more conserva-
tive cind realistic indication of
and of the appropriate emission
a few monitoring sites. However
are limited to a short period, or
the potential fur MAAQS violations
imits than do actual measurements at
if the data available for modeling
source data are generalized, the
estimated highest, second-highest concentration is unlikely to provide
a true indication of the threat to air quality standards. Thus it is
essential that an adequate data base be available (see Section 5.0).
Data for a time period of sufficient length should be considered so
that there is reasonable certainty that meteorological conditions
associated with the greatest impacts on air quality are identified.
Similarly, detailed source data are required so that the air quality
impact can be assessed for the source conditions likely to result in
the greatest impact.
There are two exceptions to the above requirement to use the
highest, second-highest estimated concentrations. The first situation
occurs where monitored air quality data from specific sites indicate
that concentrations greater than those estimated can occur with little
or no impact from the source(s) in question. For the purpose of
specifying emission limits, these measured concentrations should be
ranked ahead of the estimated concentrations in the frequency distri-
bution of concentrations at that specific monitoring (receptor) site.
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The second situation occurs where the Regional Administrator
identifies inadequacies in the data base or the models for a particular
application. As a result of these inadequacies he may determine that
there is a lack of confidence in an emission limit based on the highest,
second-highest concentration or that this concentration simply cannot
be estimated. In this case, until such time as the necessary data
bases are acquired or analytical techniques are improved, the use of
the highest estimated concentration to determine source impact and to
evaluate control strategies may be justified.
3.2 New Source Reviews
Reviews for new sources that require an air quality impact analysis
should determine if the source will (1) cause or exacerbate violations
of a NAAQS or (2) cause air quality deterioration which is greater
than allowable increments. The following subsections identify require-
ments for concentration estimates associated with air quality standards
and with prevention of significant deterioration.
3.2.1 Meeting Air Quality Standards
For each new major source* or major modification of a source, an
air quality analysis should be performed to determine if the source
will cause or exacerbate a violation of a NAAQS. For a new major source
located in an attainment area, the concentration estimates should meet
the same requirements that are applicable to control strategy evaluations
*As defined in section 302(j) of the Clean Air Act, a major source
is any stationary facility which directly emits, or has the potential to
emit, 100 tons or more per year of any air pollutant.
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The determination of whether or not * ,ie source will cause an air quality
violation should be based on (1) the highest estimated concentration
for annual averages and (2) the highest, second-highest estimated concen-
tration for averaging times of 24-hours or less. The most restrictive
standard should be used in all cases to establish the potential for an
air quality violation. Background concentrations should be added in
assessing the source's impact. The two exceptions to the shorter-term
averaging times which were noted in the preceding section also apply
here; i.e., monitored data with higher concentrations and inadequacies
in data bases or model.
In some cases a new major source of sulfur dioxide, particulate
matter, nitrogen oxides or carbon monoxide may be (1) located in a non-
attainment area or (2) may be in an attainment area but is expected to
exacerbate air quality violations known to occur in a nearby non-
attainment area. In such situations, the expected incremental increase
in pollutant concentrations should be estimated for meteorological
conditions which accompany the existing violations. Incremental
increases in pollutant concentrations that may be considered significant
(for purposes of determining whether an existing violation is exacer-
bated) are discussed in Appendix A.* For all averaging times, the
highest estimated concentration increments are used. The second highest
*A source wTtlTrelatively low stack height, e.g., 30 meters, that
has actual emissions greater than 15 tons per year of sulfur dioxide or
particulate matter may have a significant impact as defined in Appendix
A. If the impact is significant, the source is subject to emissions
offsets as discussed in EPA's Interpretive Ruling.
11
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is not used in the case of short-term concentrations since the incre-
mental increase is added to a concentration which is already based on
the highest, second-highest value.
3.2.2 Prevention of Significant Deterioration
Air quality models should be used in all significant deterioration
evaluations. Allowable increments for sulfur dioxide and particulate
2
matter are set forth in the Clean Air Act Amendments of 1977. " These
maximum allowable increases in pollutant concentrations may be exceeded
once per year, except for the annual increment. Thus, in significant
deterioration evaluations for short-term periods the highest, second-
highest increase in estimated concentrations should be less than or
equal to the permitted increment.
Where an exemption to the Class I increments is requested and
approved pursuant to section 165(d)(2)(D) of the Clean Air Act, the
source may cause the Class I increments to be exceeded on a total of
18 days during any annual period. In this case it is necessary to
select the highest estimated concentration in the field of receptors
for each of the 365 days. These 365 values are then ranked and the 19th
highest is used to determine emission limits. However, the highest,
second-highest concentration may not exceed a somewhat higher increment
specified in section 165(d)(2)(D)(iii).
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4.0 AIR QUALITY MODELS
This Section recommends air quality models* for a wide variety of
specific applications. Factors are discussed that determine the suit-
ability of models for individual situations, classes and sub-classes
of models are identified, and special modeling problems are addressed.
Air quality models recommended in this section are state-of-the-
art analytical techniques that make it possible to perform control
strategy evaluations and new source reviews, including prevention of
significant deterioration. However, the responsible Regional Adminis-
trator may find that (1) the recommended air quality model is not appro-
priate for the particular application, (2) the required data base is
unavailable, or (3) a better model or analytical procedure is available
and applicable. In such cases, alternatives indicated in this guide
or other models deemed appropriate by the Regional Administrator may be
used. However, all deviations from this guide should be fully supported
and documented.
It must not be construed that the models recommended in this guide
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.
Similar models that are available from other governmental agencies and
private consultants have been summarized and discussed by Lamb, et al.,
7 8
Moses, Stern and others.
*A discussion of each specific model or refined analytical technique
is presented in Appendix B. Some of the models recommended here are also
applicable to the development and use of Supplementary Control Systems
(SCS). However, such control systems are not considered in the context.of
this guideline and the reader is referred to other publications on SCS.
13
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In all cases, and particularly when models and data bases other
than those recommended in this guide are being proposed, early discus-
sions among the Regional Office staff, the control agencies and industry
representatives are encouraged. Concurrence on the technical approach,
prior to the actual analyses, will help avoid disagreements concerning
the final results. The Office of A1r Quality Planning and Standards is
routinely available to the Regional Offices for consultation on partic-
ularly difficult or complex problems.
It should be noted that models applicable to photochemical oxidants
are not discussed in this guide. These models are undergoing a critical
review. Requirements for such models and associated data bases will be
specified at a later time.
4.1 Suitability of Models
The extent to which a specific air quality model is suitable for
the evaluation of source Impact and control strategies depends upon
several factors that should be judged by the responsible Regional Adminis-
trator. These Include (1) the detail and accuracy of the data base,
I.e., emission Inventory, meteorological data, air quality data; (2) the
meteorological and topographic complexities of the area; (3) the tech-
nical competence of those undertaking such simulation modeling; and
(4) the resources available. These factors, as well as others deemed
appropriate by the responsible Regional Administrator, should be con-
sidered in determining the suitability of a particular model application.
The data base required for air quality models includes source
data, meteorological data and air quality data (see Section 5.0).
14
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Appropriate data should be available before any attempt is made to apply
a model. A model which requires detailed, precise input data should not
be applied 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 1n emissions and meteorological condi-
tions, the greater the ability to evaluate the source impact and to
distingush the effects of various control strategies.
Most air quality models that describe atmospheric transport and
dispersion apply to areas with relatively simple topography. However,
areas subject to major topographic or marine influence experience
meteorological complexities that are extremely difficult to simulate.
In the absence of a model capable of simulating such complexities, only
a preliminary approximation may be feasible until such time that 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 simulation models. Whenever a model is applied, the services of
knowledgeable, well-trained air pollution engineers, meteorologists and
air quality analysts should be engaged. The need for specialists is
particularly critical when the more sophisticated models are used or
the area being investigated has complicated meteorological or topo-
graphic 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.
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The resource demands generated by use of air quality models vary
widely depending on the specific application. Resources required are
dependent 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 are also important factors.
4.2 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 some of these classes a large number
of individual "computational algorithms" exist, each with its own specific
applications. Hhile each of these algorithms may have the same generic
basis, e.g., Gaussian, it is accepted practice to refer to them individ-
ually as models. For example, the Climatological Dispersion Model, the
Air Quality Display Model and the Texas Climatological Model are commonly
referred to as individual models. In fact, they are all variations of a
basic Gaussian model. In many cases the only real difference between
models is the degree of detail considered in the input or output data.
Gaussian models are generally considered to be state-of-the-art
techniques for estimating the impact of nonreactive pollutants. Numerical
models are more appropriate than Gaussian models for multi-source appli-
cations which involve reactive pollutants. However, they frequently
require more extensive resources and are not as widely applied. Statis-
tical or empirical techniques are frequently employed in situations
where incomplete scientific understanding of the physical and chemical
16
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processes make the use of a Gaussian or numerical model impractical.
Various specific models of these three generic types are recommended
in this guideline.
Physical modeling, the fourth generic type, involves the use of
wind tunnel or other fluid modeling facilities. This type of 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.
Where physical modeling is available and applicable, it is recommended.
However, physical modeling is a complex process which requires a high
level of technical expertise and is beyond the scope of this guide.
In addition to the various classes of models, this guide considers
two levels of sophistication. The first level consists of general, rela-
tively simple estimation techniques that provide conservative estimates
of the air quality impact of a specific source, or source category. The
purpose of such techniques is to eliminate from further consideration
those sources that clearly will not cause or contribute to ambient con-
centrations in excess of NAAQS or allowable concentration increments.
Conversely, these techniques can be used to identify those control
strategies that have the potential to meet NAAQS and allowable incre-
ments. The second level consists of those analytical techniques which
provide 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 effectiveness of control strategies.
17
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In some cases, the first level of models may be equated with
screening techniques to determine if a second or more refined analysis
is required. However, while the use of screening techniques followed
by a more refined analysis is desirable, there are situations where the
screening techniques are practically and technically the only viable
option for estimating source impact and evaluating control strategies.
4.3 Recommended Models
To meet the need for consistency identified in Section 2, selected
point source and multi-source models applicable to specific pollutants
and averaging times are recommended in this subsection. Ideally, air
quality models that are recommended should meet prescribed standards
of performance for particular applications and should be subjected to
specific validation procedures. However, there are no generally accepted
standards of performance and validation procedures (see p. 39). The
models recommended in this guideline are simply those which are (1) rep-
resentative of the state-of-the-art for atmospheric simulation models
and (2) those most readily available to air pollution control agencies.
4.3.1 Point Source Models for Sulfur Dioxide and Particulate
Matter (All Averaging Times)
Gaussian models are considered to be state-of-the-art techniques
for estimating concentrations of sulfur dioxide and particulate matter.
They are the best choice for most point source evaluations. For all
point sources two levels of sophistication in the use of models are
suggested. The first level is composed of models which can provide a
preliminary estimate of concentrations. It is recommended that such a
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screening technique be applied to all major sources. If it is found from
the screening technique that the source will cause a concentration that
is more than one-half of an allowable air quality increment, then that
source should be subjected to a more refined analysis.
For flat terrain situations that have no significant meteorological
9-11
complexities, there are several standard publications and computer-
12 13
ized models that can be used for screening. In addition Pooler and
14
Carpenter et al. have discussed simplified techniques for estimating
15
concentrations during inversion-breakup fumigation. Lyons has summar-
ized information and techniques applicable to lake/sea breezes. Huber and
Snyder ' and Briggs have presented various techniques applicable to
19-22
aerodynamic downwash. Several authors have outlined techniques that
are useful for situations where long-range transport (greater than 50
23
kilometers) is important. The Valley Model is applicable to some
24
complex terrain situations; Egan has summarized information on other
applicable techniques. Volume 10 of the Guidelines for Air Quality Main-
25
tenance Planning and Analysis, "Procedures for Evaluating Air Quality
Impact of New Stationary Sources" has summarized, in a format useful
for screening, techniques applicable to both flat terrain arid more com-
plex situations; those techniques are recommended for use.
In those cases where a more refined analysis is required and there
are no significant meteorological or terrain complexities, the Single
or
Source (CRSTER) Model is recommended for use. If meteorological or
terrain complexities cause substantial uncertainties, then a model that
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is more detailed or more suitable than the Single Source (CRSTER) Model
should be applied. No refined, widely available models applicable to
complex situations are identified. It is recommended that each complex
situation be treated on a case-by-case basis with the assistance of
expert advice.
If the data bases required to applj the Single Source (CRSTER)
Model are unavailable or if other refined models applicable to a complex
rituation do not exist, then it may be necessary to bese estimates of
source impact and the evaluation of control strategies on only the esti-
mates provided by the screening techniques. In such cases, an attempt
should be made to acquire or improve the necessary data bases and to
develop appropriate analytical techniques.
Models specified here and in the following subsection are also applv
cable to stationary sources of lead pollutants, provided the pollutants
can be assumed to behave as a gas.
4.3.2 Multi-Source flodels for Sulfur Dioxide and Particulate
Matter (Annual Average)
Due to the complexity of most multi-source situations and the wide
acceptability of several models, a screening process is not generally
conducted. If a preliminary assessment of the adequacy of a control
27
strategy is desired, the Rollback Model may be used. However, in most
cases such a screening does not constitute an adequate control strategy
demonstration.
20
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The Clitnatological Dispersion Model (CDM), »"'" the Air Quality
on oi
Display Model (AQDM)JU and the Texas Climatological Model (TCMr1 are
recommended for evaluating the long-term impact of urban multi-source
complexes. In regions with major meteorological or topographic complex-
ities, more detailed or suitable models may be used. If the meteorolog-
ical or topographic complexities are such that the use of any available
air quality model is precluded, an attempt should be made to acquire or
improve the necessary data bases and to develop appropriate analytical
techniques.
4.3.3 Multi-Source Models for Sulfur Dioxide and Particulate
Matter (Short-Term Averages)
As noted in the preceding subsection, a Rollback Model may be used
for the preliminary assessment of a control strategy. The Real-Time Air-
32
Quality Simulation Model (RAM) is recommended for evaluating the impact
of multi-source complexes on air quality averaged over short-term periods.
It is applicable to both urban and rural situations. The Texas Episodic
33
Model (TEM) may be used if the data bases required to apply RAM are
unavailable. Also, if the resources required to operate RAM or TEM are
not available, then CDM, AQDM or TCM may be used to estimate short-term
concentrations of SOp and particulate matter. CDM and AQDM incorporate
procedures, such as that discussed by Larsen, to convert 3-hour and 24-
hour average concentrations from annual average concentration estimates.
Such statistical techniques are valid only in urban, multi-source areas
and should not be used in situations dominated by large point sources.
21
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In regions with major meteorological or topographic complexities,
more detailed or suitable models may be used. If the meteorological
or topographic complexities are such that the use of any available air
quality model is precluded, an attempt should be made to acquire or
improve the necessary data bases and to develop appropriate analytical
techniques.
4.3.4 Models for Carbon Monoxide
The recommendations for point source screening procedures and
models are also applicable to evaluate point sources of carbon monoxide
(CO). The models, procedures and requirements described in Volume 9 of
or;
the Guidelines for Air Quality Maintenance^ Planning and Analysis,
"Guidelines for Review of the Impact of Indirect Sources on Ambient Air
Quality," are recommended for screening all sources of CO which fulfill
the definition of an indirect source. The indirect source guideline is
12 Ifi
based on the use of HIWAY ' and other simple dispersion techniques.
It is acceptable to apply these latter techniques, e.g. HIWAY, indepen-
dently of the indirect source guideline if it is found that the guideline
does not adequately consider a wide enough set of circumstances. If a
preliminary assessment of the adequacy of a control strategy applicable
to an urban area is desired, the Rollback Model may be used.
Specific refined modeling techniques are not recommended here.
Situations that require more refined techniques should be considered on
a case-by-case basis with the use of expert consultation. If a suitable
model is available and the data and technical competence required for
22
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this model are available, it may be used. An example of such a refined
1237
technique is APRAC-1A. * However, if a region-wide analysis is
necessary and the complexities are such that the use of any available
air quality model is precluded, an attempt should be made to acquire or
improve the necessary data bases and to develop appropriate analytical
techniques.
4.3.5 Models for Nitrogen Dioxide
The recommendations for point source screening techniques and models
are also applicable to evaluate point sources of nitrogen oxides (NO )
A
under limited circumstances. The circumstances require an assumption
that all NO is emitted in the form of NO,, or is converted to N02 by the
time it reaches the ground and that NOp is a nonreactive pollutant.
For sources located where atmospheric photochemical reactions are
significant, a Rollback Model may be used as a preliminary assessment
to evaluate the control strategies for multiple sources (mobile and
stationary) of NO . Another acceptable screening technique for multiple
J\
sources is to make an assumption similar to that required for point
sources and then to use a model for nonreactive pollutants, such as COM.
Specific refined modeling techniques are not recommended here.
Situations that require more refined techniques should be considered on
a case-by-case basis with the use of expert consultation. If a suitable
model is available and the data and technical competence required for
this model are available, it may be used to estimate average concentra-
tions of NOp. However, if a region-wide analysis is necessary and the
23
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complexities are such that the use of any available air quality model
is precluded, an attempt should be made to acquire or improve the
necessary data bases and to develop appropriate analytical techniques.
4.4 Special Situations
Models with a wide applicability are not generally available for
dealing with long-range transport, deposition, wind-blown particulate
matter and unique topographic or meteorological circumstances, e.g.,
complex terrain, aerodynamic downwash. Thus with proper support and
documentation, the Regional Administrator may determine that a particular
model, not specifically recommended here, is appropriate for a special
situation. Examples of these situations are discussed for clarification.
The administration of the national prevention of significant deter-
ioration policy may require that the air quality impact of a source be
estimated for great distances downwind. It is uncertain, however, what
the impact of sources at such great distances is. Knowledge of the
dispersion coefficients for air quality models* becomes increasingly
tenuous with downwind distance. Plume transport beyond about 50 kilo-
meters usually requires substantial travel time. As travel time
increases, diurnal variations in meteorological conditions and movement
of weather systems are more likely to alter plume trajectories and
*Vertical dispersion in these situations is more appropriately
treated with numerical models. There are also inherent difficulties
with Gaussian models in cases where plume depletion through chemical
and physical removal processes is significant. Plume depletion would
normally be significant at distances beyond about 50 kilometers for
tall stacks under conditions of appreciable vertical mixing, and at
considerably shorter distances for near-ground sources.
24
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dispersion characteristics. Even though the impact at greater than 50-
100 kilometers may be relatively small, the impact can still be signifi-
cant for large sources and for situations where the merging of plumes
occurs. Techniques are available to examine these impacts, but only
limited experience in their use is currently available. If it appears
that a large source (for example, a 2000-MW coal-fired power plant meet-
ing new source performance standards) may constitute a threat to ambient
air quality standards or prevention of significant deterioration increments
at large distances, that source should be considered on a case-by-case
19-22
basis with available techniques.
The models presented in this guide for estimating ambient concen-
trations of suspended particulate matter assume that the particles
disperse as a gas and emanate from well-defined sources. Unfortunately,
In many areas, particularly where the air quality standards are not being
attained, these assumptions may not hold. Windblown dust, re-entrained
street dust, dry-land farming, and raw-material handling operations, all
of which are often referred to as fugitive dust sources, can be signifl-
38
cant sources of particulate matter. EPA has several on-going studies
concerned with fugitive sources of dust; however, the rate and distribu-
tion of particulate emissions from these sources is not yet fully known.
As a result, a widely applicable model for routinely estimating particulate
39
concentrations attributable to fugitive sources is not available.
Terrain dominated flows and wakes that develop in the vicinity of
1fi—1R ?^ ?4
pollutant sources are involved in many situations. ~ ' * The basic
25
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theoretical principles of these flows are generally understood. However,
the variety of terrain features is so great and the spectrum of atmos-
pheric circumstances so broad that no generally applicable model is
available that can adequately deal with the range of conditions encoun-
tered.
EPA will provide guidance on data bases and assessment procedures
to deal with special situations as the results of on-going field invest!'
gations and research on these matters become available.
26
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5.0 DATA REQUIREMENTS
It is essential that appropriate source and meteorological data
be used with any recommended model. Such data, and related procedures
for estimating these data, constitute an integral part of the model.
It is often overlooked that few of the variables input to a model are
directly measured or routinely available. Submodels must appropriately
convert the available source and meteorological data to a form that the
air quality model can accept. It is also important that a variety of
load/emissions conditions, and that a wide range of meteorological
conditions based on several years of data, be considered in evaluating
control strategies and in determining source impact for new source
reviews, including prevention of significant deterioration. In addi-
tion, there is a need to judiciously choose receptor sites and to
specify background air quality. This section identifies requirements
for these data bases.
5.1 Source Data
Sources of pollutants generally can be classified as point, line
and area sources. Major point sources are defined as those that emit,
or have the potential to emit, 100 tons or more per year of any air
pollutant. Line sources are generally confined to roadways and streets
along which there are well-defined movements of motor vehicles. Area
sources include the multitude of minor sources with individually small
emissions that are impractical to consider as separate point or line
sources. Area sources are typically treated as a grid network of
square areas, with pollutant emissions distributed uniformly within
27
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each grid square. Descriptions of individual models should be ref-
erenced for specific emissions inventory requirements.
For situations involving one or a few point sources the following
are minimum requirements for new source review and control strategy
evaluations. Design process rate or design load conditions must be
considered in determining pollutant emissions. Other operating con-
ditions that may result in high pollutant concentrations should also
be identified. A range of operating conditions, emission rates, and
physical plant characteristics based on the most recently available
data, should be used with the multiple years of meteorological data
(see Section 5.2) to estimate the source impact. The following
example (power plant) typifies the kind of data on source character-
istics and operating conditions that are required:
1. Plant layout. The connection scheme between boilers and
stacks, and the distance and direction between stacks, building param-
eters (length, width, height, location and orientation relative to
stacks) for plant structures which house boilers, control equipment,
etc.
2. 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.
3. Boiler size. For all boilers, the associated megawatts
and pounds of stean per hour, and the design and/or actual fuel con-
sumption rate for 100 percent load for coal (tons/hour), oil (barrels/
hour), and natural gas (thousand cubic feet/hour).
4. 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.).
28
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5. 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
winter and summer peaks.
6. Pollution control equipment parameters. For each boiler
served and each pollutant affected, the type of emission control equip-
ment, 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 conjunc-
tion with coal combustion; data for any anticipated modifications or
additions.
7. 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 1 through 6 above following com-
pletion of construction or modification.
Typically for line sources, such as streets and highways, data are
required on the width of the roadway and its center 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 must be speci-
fied in appropriate grid coordinates. More detailed information and
data requirements for modeling mobile sources of pollution are provided
35
in the guideline on indirect sources.
For multi-source urban situations, detailed source data are
often impractical to obtain. In these cases, source data should be
based on annual average conditions. Area source information required
are types and amounts of pollutant emissions, the physical size of the
area over which emissions are prorated, representative stack height
for the area, the location of the centroid or the southwest corner of
29
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the source in appropriate grid coordinates. If the model accepts data
on area-wide diurnal variations in emissions, such as those estimated
by emissions models which are based on urban activity levels and other
factors, those data should be used.
In cases where the required source data are not available and cannot
be obtained, the data limitation should be identified. Due to the
uncertainties associated with such a limitation the use of the highest
estimated concentration to determine source impact and to evaluate control
strategies may be justified until such time that a better data base
becomes available.
For control strategy evaluations the impact of growth on emissions
should be considered for the next 10-20 year period. Increases in
emissions due to planned expansion of the sources considered or planned
fuel switches should be identified. Increases in emissions at each
source which may be associated with general industrial/commercial/
residential expansion in multi-source urban areas should also be con-
sidered. Such information should be used to estimate the air quality
impact of those sources in future years. However, for new source
reviews, the impact of growth on emissions should only be considered
for the period prior to the start-up date for the source. Such changes
in emissions should consider increased area source emissions, changes
in existing point source emissions which would not be subject to
preconstruction review, and emissions due to sources with permits to
construct.
30
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5.2 Meteorological Data
For a dispersion model to provide useful and valid results, the
meteorological data used in the model must be representative of the
transport and dispersion conditions in the vicinity of the source that
the model is attempting to simulate. 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
in the area, (3) the exposure of the meteorological monitoring site and
(4) the period of time during which the data are collected. The repre-
sentativeness of the data can be adversely affected by large distances
between the source and receptors of interest and valley-mountain, land-
water, and urban-rural characteristics of the area.
For new source review and control strategy evaluation, the meteoro-
logical data required as a minimum to describe transport and dispersion
in the atmosphere are wind direction, wind speed, atmospheric stability,
mixing height or related indicators of atmospheric turbulence and mixing,
Site-specific data are preferable to data collected off-site. The avail-
ability of such meso- and micro-meteorological data collections permits
more detailed meteorological analyses and subsequent improvement of
model estimates. Local universities, industry, pollution control
agencies and consultants may be sources of such data. The parameters
typically required can also be derived from routine measurements by
National Weather Service stations. The data are available as individual
observations and in summarized form from the National Climatic Center,
As-heville, N. C. Descriptions of individual models should be referred
31
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to for specific meteorological data requirements. Many models require
either hourly meteorological data or annual stability wind roses.
It is preferable for the meteorological data base used with the
air quality models to include several years of data. Such a multi-year
data base allows the consideration of variations in meteorological con-
ditions that occur from year to year. The exact number of years needed
to account for such variations in meteorological conditions is uncertain
and depends on the climatic extremes in a given area. Generally five
40
years l yields an adequate meteorological data base.* For compatibility
of model estimates with the NAAQS, the single year with the highest,
second-highest short-term concentration estimate (or the highest annual
estimate) should then be used in evaluating source impact. However, if
long-term data records are not available, it may be necessary to limit
the modeling and subsequent analyses to a single year of meteorological
data. The use of one year of data might also be justified if the
climatological representativeness of that data can be demonstrated. A
longer record from a nearby National Weather Service site could be used
to check for representativeness.
The number of National Weather Service stations for which multiple
years of hourly weather data are available is increasing significantly.
*
An alternative approach is to use a shorter meteorological data
base and to use statistical techniques to identify the occurrence of
a rare event (highest, second-highest concentration that exceeds the
NAAQS) over a longer period of record. This is equivalent to applying
the "100-year flood" concept to air pollution control. EPA is studying
this concept but for the present recommends using a meteorological data
base of at least one year.
32
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Several EPA offices have ordered such data for a large number of stations,
It is clear that more detailed analyses than previously considered for
SIP evaluations and new source review are necessary. Thus, for areas
where meteorological conditions are adequately represented by weather
stations, the use of multiple years of meteorological data appears to
be viable and justified.
Where representative meteorological observations are not available,
the concentration estimates may be limited to consideration of worst
case conditions. An analysis of worst case conditions should be based
on reasonable interpretations of climatological data and should consider
such critical plume characteristics as looping, coning, limited mixing,
fumigation, aerodynamic downwash and plume impaction on terrain. Due
to the uncertainties of this approach, the use of the highest estimated
concentration (as opposed to the highest, second-highest concentration)
to determine source impact and to evaluate control strategies may be
justified until such time that a better data base becomes available.
5.3 Receptor Sites
A receptor site is a location for which an air pollution concentra-
tion is estimated. The choice of locations for receptor sites signifi-
cantly affects the evaluation of source impact and control strategy
effectiveness. It is most important to identify the location where the
maximum concentrations occur, both short- and long-term. The receptor
grid must allow sufficient spatial detail and resolution so that the
location of the maximum or highest, second-highest concentration is
identified.
33
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The receptor sites in the vicinity of large point sources at which
maximum concentrations are likely to occur can be identified by
(1) estimating concentrations for a sufficiently dense array of recep-
tors to identify concentration gradients and (2) subsequently refining
the location of the maximum by estimating concentrations for a finer
array of receptors in the general areas of maximum concentrations.
12
Another technique is to use a model such as PTMAX in combination with
joint frequency distributions of wind speed, wind direction and stability
to identify the downwind distance and direction at which the highest
concentrations are most likely to occur. However, other areas around
the source(s) should not be ignored, particularly if they are on
elevated terrain. In addition, a receptor should be specified at any
site where a monitor is located.
5.4 Background Air Qua1ity
To adequately assess the significance of the air quality impact
of a source, background concentrations must be considered. Background
air quality relevant to a given source includes those pollutant concen-
trations due to natural sources and distant, unidentified man-made
sources. For example, it is commonly assumed that the annual mean back-
ground concentration of particulate matter is 30-40 yg/m3 over much of
41
the Eastern United States. Typically, air quality data are used to
establish background concentrations in the vicinity of the source under
consideration. However, where the source is not isolated, it may be
necessary to use a multi-source model to establish the impact of all
other nearby sources during dispersion conditions conducive to high
concentrations.
34
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If the point source is truly isolated and not affected by other
readily identified man-made sources, two options for determining back-
ground concentrations from air quality data are available. The prefer-
able option it to use air quality data collected in the vicinity of the
source to determine mean background concentrations for the averaging
times of interest when the point source itself is not impacting on the
monitor. The second option applies when no monitors are located in the
vicinity of the source. In that case, average measured concentrations
from a "regional" site can be used to establish a background concentra-
tion.
For the first option it is a relatively straightforv/ard effort to
identify an annual average background from available air quality data.
For shorter averaging times, background concentrations are determined
by the following procedure. First, meteorological conditions are iden-
tified for the day and similar days when the highest, second-highest
estimated concentration due to the source occurs. Then the average
background concentration on days with similar meteorological conditions
is determined from air quality measurements. The background for each
hour is assumed to be an average of hourly concentrations measured at
sites outside of a 90° sector downwind of the source. The 1-hour con-
centrations are then averaged to obtain the background concentration
for the averaging time of concern.
If air quality data from a local monitoring network are not avail-
able, then monitored data from a "regional" site may be used for the
35
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second option. Such a site should characterize air quality across a
broad area, including that in which the source is located. The technique
of characterizing meteorological conditions and determining associated
background concentrations can then be employed.
If a small number of other identifiable sources are located nearby,
the impact of these sources should be specifically determined. The back-
ground concentration due to natural or distant sources can be determined
using procedures already described. The impact of the nearby sources
must be summed for locations where interactions between the effluents of
the point source under consideration and those of nearby sources 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. It may be
necessary to identify these locations through a trial and error analysis.
If the point source is located in or near an urban multi-source
area, there are several possibilities for estimating the impact of all
other sources. If a comprehensive air monitoring network is available,
it may be possible to rely entirely on the measured data. It is neces-
sary that the network include monitors judiciously located so as to
measure air quality at the locations of the point source's maximum impact
and locations of the highest concentrations in the area. If the point
source is not yet operating, its calculated impact can be added to these
measured concentrations. If the source already exists arid is contri-
buting to the measured concentrations, its calculated contribution
36
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should be subtracted from the measured values to estimate the concen-
tration caused by other man-made sources and by background.
If the monitored data are inadequate for such an analysis, then
multi-source models can be used to establish the impact of all other
sources. These models should be used for appropriate pollutants and
averaging times to identify concentrations at the times and locations
of maximum point source impact. The times and locations of maximum
impact due to all other sources must also be identified. If a model
is not available for the appropriate averaging times, statistical
techniques can be used with an appropriate model to extrapolate from
one averaging time to another. All statements in this guide regarding
the data requirements and validity of air quality models are applicable
to analyses of this type.
For control strategy evaluations, the impact of growth on area-wide
emissions and on concentrations caused by nearby sources should also be
considered for the next 10-20 year period. To determine concentrations
in future years, existing air quality should be proportionately adjusted
by the anticipated percent change in emissions in the vicinity of
individual monitoring sites. However, for new source reviews, changes
in existing air quality should only be considered for the period prior
to the start-up date of the source (see Section 5.1).
37
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38
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6.0 MODEL VALIDATION/CALIBRATION
Any application of an air quality model may have deficiencies
which cause estimated concentrations to be in error. When practical
to obtain a measure of confidence in the estimates, they should be
compared v/ith observed air quality data and their validity determined.
The model validation process* consists of a series of analytical
steps: (1) Comparing estimated concentrations with observed values,
(2) determining the cause of discrepancies, (3) correcting and improving
data bases, (4) modifying the model (if necessary) in a manner that
provides a better mathematical representation of physical reality, and
(5) documenting, for others, the accuracy of the estimates. Statis-
tical methods available for validation of models include skill scores,
contingency tables, correlation analyses, time series and spatial
analyses, and others. If evaluation by one or more statistical tech-
niques indicates that the concentration estimates are not a satisfactory
representation of observed concentrations, then it is likely that one
or both of the following problems exist: the source, meteorological
or air quality data are not appropriate, reliable and complete; or
the model itself is inadequate for the area under consideration.
The availability and accuracy of the input data significantly
influence the accuracy of the model estimates. The source factors
*There is a clear need for specific and uniform validation pro-
cedures and for standards of performance. The feasibility of specifying
such procedures and standards for air quality models is being studied
by EPA. However, for the present time, the generalized recommendations
presented in this section are suggested for use.
39
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that have the greatest impact on the accuracy of the estimates are the
accuracy and completeness of the (1) emissions data, (2) physical plant
parameters, and (3) site coordinates of the sources. Often the valida-
tion will reveal deficiencies in the emissions inventory, which can be
corrected to improve the accuracy of the model estimates. The accuracy
of the concentration estimates is also affected by the location and
exposure of the instrumentation used for obtaining the meteorological
data and the overall representativeness and completeness of those data.
Similarly, the validation of the dispersion model is affected by the
location, exposure and representativeness of the air quality sampling
sites and by the accuracy and completeness of the air quality data itself.
These data should be available for the same averaging times as the con-
centration estimates and should describe the spatial variation of
pollutant concentrations across the area. If the air quality data are
in any way unsuitable or incorrect, the accuracy of the dispersion model
estimates cannot be determined.
The following factors most frequently cause a model to be considered
inadequate or inappropriate for a given area: (1) The model is applied
to an area with complex or unique terrain or meteorological conditions;
(2) the source emissions vary markedly or irregularly with time; (3) the
pollutant is subject to major or highly variable atmospheric chemical
reactions or removal processes; (4) the model is applied to pollutants
with characteristics other than those considered in its development. If
any of these circumstances are encountered, it may be necessary to select
a more appropriate model or appropriately modify the model being used.
40
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When any analytical technique i<- employed, the analyst is
responsible for recognizing and quantifying limitations in the accuracy,
precision and sensitivity of the procedure. Thus, in all applications
of models an effort should be made to identify the reliability of the
model estimates for that particular area or similar areas and to deter-
mine the magnitude and sources of error associated with the use of the
model. In addition, sensitivity analyses are useful for determining
the effect of variations or uncertainties in the data bases on the range
of likely concentrations. Such information may be very useful in
determining source impact and evaluating control strategies. Where
possible, information on sensitivity should be made available by the
modeler.
Due to limitations of the data base, lack of scientific knowledge
or limitations on time and resources, it may not always be possible to
perform a thorough and complete model validation. Thus, in some situa-
tions, it has been necessary to revert to calibration of the model.
Calibration of a model is the process of identifying systematic errors
and applying a correction factor. In many cases this involves the
application of regression analysis or other statistical techniques to
adjust model estimates in order to increase agreement with measured
data.
Calibration of long-term multi-source models is a widely used
procedure. It is acceptable provided that reasonable resources have
been expended to validate the model, e.g., the five steps listed at
the beginning of this section. Limitations imposed by statistical
41
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theory on the reliability of the calibration process for long-term
42
estimates have been identified. In some cases, though,, calibration
may be the only alternative for improving the accuracy of estimated
concentrations and the control strategy evaluation. However, if the
model accounts for less than 50 percent of the variation of measured
concentrations, it is doubtful that there is justification for using
the model.
Calibration of short-term models has not been widely performed
and is subject to a greater amount of error and misunderstanding.
There have been attempts by some to compare short-term estimates
and measurements on an event-by-event basis and then to calibrate
the model with results of the comparison. This approach is severely
limited by uncertainties in source and meteorological data and thus
one's ability to precisely estimate the concentration at an exact
location for a specific increment of time. These uncertainties make
attempts to calibrate a short-term model questionable. As a result,
it appears that the most reliable direct comparison between estimated
and measured short-term concentrations involves the upper percentiles
of the respective frequency distributions. Even here, considerable
variation may be found from site-to-site and plant-to-plant. In such
43 44
comparisons ' for one basic Gaussian point source model it was found
that short-term estimates of highest concentrations are generally accu-
rate within a factor of two. This accuracy is consistent with the
empirical basis ' for these models. However, in general, estimates
which are both too high and too low may be expected.
42
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7.0 REFERENCES*
1. National Air Pollution Control Administration. "Guidelines for
the Development of Air Quality Standards and Implementation Plans."
DHEW, Public Health Service, Washington, D. C., May 1969.
2. U. S. Congress. "Clean Air Act Amendments of 1977." Public Law 95-95,
Government Printing Office, Washington, D. C., August 1977.
3. Environmental Protection Agency. "Guidelines for Interpretation of
Air Quality Standards." Office of Air Quality Planning and Standards,
Research Triangle Park, North Carolina 27711, September 1976.
4. Environmental Protection Agency. "Guidelines for Evaluating Supple-
mentary Control Systems." Publication No. EPA-450/2-76-OQ3,
Environmental Protection Agency, Research Triangle Park, North
Carolina 27711, February 1976.
5. Environmental Protection Agency. "Technique for Supplementary Control
System Reliability Analysis and Upgrading." Publication No. EPA-450/
2-76-015, Environmental Protection Agency, Research Triangle Park,
North Carolina 27711, March 1976.
6. Lamb, D. V., F. I. Eadgley, and A. T. Rossano. "A Critical Review
of Mathematical Diffusion Modeling Techniques for Air Quality with
Relation to Motor Vehicle Transportation." A study prepared for the
Washington State Highway Commission, Department of Highways, Univer-
sity of Washington, Seattle, Washington, June 1973.
7. Moses, H. "Mathematical Urban Air Pollution Model." Report No.
ANL/ES-RPY-001, Argonne National Laboratory, Argonne, Illinois
April 1969.
8. Stern, A. C. "Proceedings of Symposium of Multiple-Source Urban
Diffusion Models." Air Pollution Control Office Publication Mo.
AP-86_ (NTIS PB 198400), Environmental Protection Agency, Research
Triangle Park, North Carolina 27711, 1970.
9. Slade, D. H., Ed. Meteorology and Atomic Energy 1968. USAEC.
Division of Technical Information Extension, Oak Ridge, Tennessee,
July 1968.
10. Smith, M. E., Ed. "Recommended Guide for the Prediction of the
Dispersion of Airborne Effluents." The American Society of
Mechanical Engineers, United Engineering Center, 345 East 47th
Street, New York, New York, 1973. (Revised)
*A11 references with a "PB" number are available from the National
Technical Information Service, Springfield, Virginia 22151.
43
-------
11. Turner, D. B. "Workbook of Atmcspheric Dispersion Estimates." PHS_
Publication No. 999-AP-26 (NTIS PB 191482), Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, 1969.
12. Environmental Protection Agency. "User's Network for Applied
Modeling of Air Pollution (UNAMAP)." (Computer Programs on Tape
for Point Source Models, HIWAY, Climatological Dispersion Model
and APRAC-1A), NTIS PB 229771, National Technical Information Service,
Springfield, Virginia, 1974.
13. Pooler, F. "Potential Dispersion of Plumes from Large Power Plants."
PUS Publication No. 999-AP-16 (NTIS PB 168790). Superintendent of
Documents, Government Printing Office, Washington, D. C., 1965.
14. Carpenter, S. B., et al. "Principle Plume Dispersion Models: TVA
Power Plants." J. Air Poll. Control Assn., Vol. 22, No. 8, pp. 491-
495, 1971.
15. Lyons, W. A. "Turbulent Diffusion and Pollutant Transport in Shore-
line Environments." Lectures on Air Pollution and Environmental Im-
pact Analyses, American Meteorological Society, Boston, Massachusetts,
September 1975.
16. Huber, A. H., and W. H. Snyder. "Stack Placement in the Lee of a
Mountain Ridge: A Wind Tunnel Study." Publication No. EPA-600/4-76-
047 (NTIS PB 259877), Environmental Protection Agency, Research
TrTangle Park, North Carolina 27711, September 1976.
17. Huber, A. H., and W. H. Snyder. "Building Wake Effects on Short
Stack Effluents." Third Symposium on Atmospheric Turbulence, Dif-
fusion, and Air Quality, American Meteorological Society, Boston,
Massachusetts, September 1976.
18. Briggs, G. A. "Diffusion Estimation for Small Emissions." ATDL
Contribution File No. (Draft) 79, Air Resources Atmospheric Turbu-
lence and Diffusion Laboratory, Oak Ridge, Tennessee, May 1973.
19. Heffter, J. L. and G. A. Ferber. "A Regional-Continental Scale
Transport Diffusion and Deposition Model." NOAA Technical Memoran-
dum ERL ARL-50. Air Resources Laboratories, NOAA, Silver Spring,
Maryland, June 1975.
20. Rao, K. S., J. S. Lague, and B. A. Egan. "An Air Trajectory Model
for Regional Transport of Atmospheric Sulfates." Third Symposium
on Atmospheric Turbulence, Diffusion and Air Quality, American
Meteorological Society, Boston, Massachusetts, September 1976.
21. Scriven, R. A., and B. E. A. Fisher. "The Long Range Transport of
Airborne Material and Its Removal by Deposition and Washout. Atmos-
p he ri c Environment, Vol. 9, pp. 49-68, 1975.
44
-------
22. Hales, J. M., D. C. Powell, and T. D. Fox. "STRAM - An Air Pollution
Model Incorporating Nonlinear Chemistry, Variable Trajectories, and
Plume Segment Diffusion." Publication No. EPA 450/3-77-012 (NTIS
PB 270778/AS). Environmental Protection Agency, Research Triangle
Park, North Carolina 27711, April 1977.
23. Burt, E. "Valley Model User's Guide."
24.
25.
26.
27,
28.
29.
30.
31.
Publication No. EPA-450/2-77_j
North"
018, Environmental Protection Agency, Research Triangle Par
Carolina 27711, September 1977.
Egan, B. A. "Turbulent Diffusion in Complex Terrain" Lectures on
Air Pollution and Environmental Impact Analyses, American Meteoro-
logical Society, Boston, Massachusetts, September 1975.
Budney, L. J. "Procedures for Evaluating Air Quality Impact of New
Stationary Sources." Guidelines for Air Quality Maintenance Planning
and Analysis, Volume 10 (OAQPS No. 1.2-029R), Environmental Protec-
tion Agency, Research Triangle Park, North Carolina 27711, October
1977.
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, July 1977.
and J. R. Morris. "Rollback Modeling: Basic and
Air Pollution Control Assn., Vol. 25, No. 9, pp. 943-
de Nevers, N.,
Modified," J_.
947, 1975.
Busse, A. D., and J. R. Zimmerman. "User's Guide for the Climato-
logical Dispersion Model." Publication Mo. EPA-RA-73-024 (NTIS PB
227346/AS), Environmental Protection Agency, Research Triangle Park,
North Carolina 27711, December 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. Environmental Protection Agency, Research Triangle"
Park, North Carolina 27711, May 1977.
TRW Systems Group. Air Quality Display Model." Prepared for
National Air Pollution Control Administration under Contract No.
PH-22-68-60 (NTIS PB 189194), DHEW, U. S. Public Health Service,
Washington, D. C., November 1969.
Christiansen, J. H., and R. A. Porter. "User's Guide to the Texas
Climatological Model." Texas Air Control Board, Austin, Texas,
May 1976.
45
-------
32. Turner, D. B., and J. H. Novak. "User's Guide for RAM." Environ-
mental Protection Agency, Research Triangle Park, North Carolina
27711, 1977.
33. Christiansen, J. H. "Users Guide to the Texas Episodic Model."
Texas Air Control Board, Austin, Texas, May 1976.
34. Larsen, R. I. "A Mathematical Model for Relating Air Quality Measure-
ments to Air Quality Standards." Office of Air Programs Publication
No. AP-89 (NTIS PB 205277), Environmental Protection Agency, Research
Triangle Park, North Carolina 27711, November 1971.
35. Environmental Protection Agency. "Guidelines for the Review of
the Impact of Indirect Sources on Ambient Air Quality," Guidelines
for Air Quality Maintenance Planning and Analysis, Volume 9,
OAQPS No. 1.2-028, Environmental Protection Agency, Research Triangle
Park, North Carolina 27711, 1975.
36. Zimmerman, J. R., and R. S. Thompson. "User's Guide for HIWAY: A
Highway Air Pollution Model." Pub!ication No. EPA-650/4-74-008
(NTIS PB 239944/AS), Environmental Protection Agency, ResearcTT
Triangle Park, North Carolina 27711, February 1975.
37. Mancuso, R. L., and F. L. Ludwig. "User's Manual for the APRAC-1A
Urban Diffusion Model Computer Program." Publication No. EPA-65Q/
3-73-001 (NTIS PB 213091), Environmental Protection Agency, Research
Triangle Park, North Carolina 27711, September 1972.
38. Environmental Protection Agency. "Control Strategy Preparation
Manual for Particulate Matter." OAQPS No. 1.2-049, Environmental
Protection Agency, Research Triangle Park, North Carolina 27711,
September 1977.
39. Richard, G., J. Avery, and T. Baboolal. "An Implementation Plan for
Suspended Particulate Matter in the Phoenix Area: Volume III Model
Simulation of Total Suspended Particulate Levels." Publication No.
EPA-450/3-77-021c, Environmental Protection Agency, Research Triangle
Park, North Carolina 27711, August 1977.
40. Doty, S. R., B. L. Wallace, and G. C. Holzworth. "A Climatological
Analysis of Pasquill Stability Categories Based on 'STAR' Summaries."
National Climatic Center, National Oceanic and Atmospheric Adminis-
tration, Asheville, North Carolina 28801, April 1976.
41. McCormick, R. A. "Air Pollution Climatology." In Air Pollution
Volume 1, edited by A. Stern, Academic Press, New York, New York,
1968.
46
-------
42. Brier, G, W. "Validity of thf- / ir Quality Display Model Calibration
Procedure." Publication No. CPA-R4-73-017, Environmental Protection
Agency, Research Triangle PaTk, Nortfi Carolina 27711, January 1973.
43. Mills, M. T., and F. A. Record. "Comprehensive Analysis of Time-
Concentration Relationships and Validation of a Single-Source Dis-
persion Model." Publication .No. EPA-450/3-75-083 (NTIS PB 250814/
AS), Environmental Protection Agency, ResearcFTFiangle Park, North
Carolina 27711, March 1975.
44. Mills, M. T., and R. W. Stern. "Model Validation and Time-
Concentration Analysis of Three Power Plants." Publication No. EPA-
450/3-76-002 (NTIS PB 250685/AS), Environmental Protection Agency,
Research Triangle Park, North Carolina 27711, December 1975.
45. Pasquill, F. Atmospheric Diffusion, 2nd ed., John Wiley and Sons,
New York, New York, 1974.
46. Weber, A. H. "Atmospheric Dispersion Parameters in Gaussian Plume
Modeling: Part I. Review of Current Systems and Possible Future
Developments." Publication No. EPA-600/4-76-030a, Environmental
Protection Agency, Research Triangle Park, NortFi Carolina 27711,
July 1976.
47
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Glossary of Selected Terms
Air Quality - Ambient pollutant concentrations and their temporal
and spatial distributions.
Algorithm - A specific mathematical calculation procedure.
Background - Ambient pollutant concentrations due to natural sources
and distant, unidentified man-made sources.
Calibration - An adjustment applied to concentration estimates, based
on a comparison with measured air quality data, in order to improve the
accuracy of the model.
Computer code - A set of statements that comprise a computer program.
Model - A quantitative or mathematical representation or simulation
which attempts to describe the characteristics or relationships of
physical events.
Receptor - A location at which ambient air quality is measured or
estimated.
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 Procedure - A relatively simple analysis technique to determine
if a given source is likely to pose a threat to air quality.
Validation - Determination of the reliability of a model by comparing
the model estimates with measured air quality data.
48
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Appendix A
Significant Air Quality Increment
for Non-Attainment Areas
-------
Appendix A
Significant Air Quality Increments
for Non-Attainment Areas
A new major source of sulfur dioxide (SO-), particulate matter (PM),
nitrogen oxides (N02*) or carbon monoxide (CO) located in an attainment
area may cause or exacerbate a known existing air quality violation in
a nearby nonattainment area. In this case it is necessary to determine
if the air quality impact of the source is significant. The incremental
increase in concentration at the location of a violation may be con-
sidered significant if it is greater than the following concentrations:
Pollutant Averaging Time
Annual 24-Hour 8-Hour 3-Hour 1-Hour
S02 1 yg/m3 5 yg/m3 25 yg/m3
PM 1 yg/m3 5 yg/m3
N02 1 yg/m3
CO 0.5 mg/m3 2 mg/m3
These incremental concentrations of S02, PM and N02 are partially based
on allowable S02 increments for Class I areas. However, the annual
concentration increment is reduced to 1 yg/m3 since this value may
be considered significant for a point source in an area which exceeds
the NAAQS. The increments for CO are based on concentrations which are
*
For simplicity, all emissions of nitrogen oxides are treated as
if they are nitrogen dioxide (N02); see Section 4.3.5.
A-l
-------
5 percent of the CO NAAQS. All of these increments apply to the highest
estimated concentration for all averaging times. The second highest is
not used since the incremental increase in concentration is added to a
concentration which is already based on the highest, second-highest
concentration.
A-2
-------
Appendix B
Summaries of Recommended
Air Quality Models
Summaries presented in this appendix are largely based on similar
information summarized by J. J. Roberts (Ed.) in "Report to the U.S.
EPA of the Specialists'Conference on the EPA Modeling Guidelines,"
Environmental Protection Agency, Research Triangle Park, North Caroline,
27711, February 1977.
-------
B.I AIR QUALITY DISPLAY MODEL (AQDIi)
Reference:
Abstract:
Equations
TRW Systems Group. "Air Quality Display Model." Prepared
for National Air Pollution Control Administration, DHEW,
U.S. Public Health Service, Washington, D.C., November
1969, (NTIS PB 189194).
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 Larsen1 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.
For both point and area sources;
X =
16 6 5
I I I
k=l £=1 m=l
where:
Ji
2irx
2Q_
for x £ x.
16
2irx
Q /c-y.
u.L v c '
for x >_ 2x,
linear interpolation for x. < x < 2x,
x, defined by az^xi ^ = °-4^L
y = crosswind distance between receptor and sector k
centerline
c = sector width at receptor location
o (x) = ax + c; a,b,c = functions of stability class
a,b,c for neutral conditions split into
x > 1000m case and x < 1000m case.
B-l
-------
Q = emission rate (g/s)
H = plume height (m)
u = wind speed (m/s)
= relative frequency of occurrence from stability
wind rose
o^ = vertical standard deviation of plume concentrations (m)
x = downwind distance (m)
a. Source-Receptor Relationship
Arbitrary location and stack height for each point source
Arbitrary location and size for each area source
Up to 225 receptors located on uniform rectangular grid
Up to 12 user-specified receptor locations
Unique release height for each point, area source
Unique separation for each source-receptor pair
Receptors at ground level
No terrain differences between source and receptor
b. Emission Rate
Point sources: single rate for each source
Area sources: single rate for each source
Each source treated by effective single point
source approximation
No temporal variation allowed |
c. Chemical Composition '
Treats one or two inert pollutants simultaneously
d. Plume Behavior
Holland2 formula for point sources, with adjustment for
stability
Calculations based on single arbitrary values of stack
diameter, stack gas exit velocity and stack gas temperature
for each point source
No plume rise calculated for area sources
Does not treat fumigation or downwash
If stack height plus plume rise is greater than mixing height,
ground level concentration assumed equal to zero
B-2
-------
e. Horizontal _WInd Field
Climatologi'.a'i approach
16 wind directions
6 wind speed classes
No variation in wind speed with -.eight
Constant, uniform (steady-stace) wind assumed
f. Vertical Wind Speed
Assumed equal to zero
g. Horizontal Dispersion
Climatological approach
Uniform 22.5° wide plume assumed
Frequency of occurrence interpolated between sector centerlines
Averaging times from 1 month to 1 year or longer
h. Vertical Dispersion
Semi-empirical/Gaussian plume
5 stability classes as defined by Turner3
Neutral stability split internally into 60% diy, 40% night
Dispersion coefficients from Pasquill and Gifford
Neutral dispersion coefficients used for stable class
No provision for variations in surface roughness
i. Chemistry/Reaction Mechanism
No provision for treatment
j. Physical Removal
No provision for treatment
k. Background
Input single constant background value for each pollutant
1. Boundary Conditions
Lower boundary (ground): perfect reflection
Upper boundary (mixing height): no effect until a >_ 0.47L
(this occurs at x = x, ) For x. < x < 2x. , a is linearly
interpolated between Tts value at x. and its value at 2x.
B-3
-------
m. Emission and Meteorological Correlation
Wind speed, direction, stability correlated via wind rose
Emission rate - not correlated with any other factor
Non-sequential (climatological) limited correlation
Mixing height adjusted according to stability class:
Class A - 1.5 times the afternoon climatological value
Classes B, C, and D(day) - equal to the afternoon cli-
matological value
Class E - 100 meters
n. Validation/Calibration
Calibration option available
Substantial experience but limited documentation
o. Output
1 month to 1 year averaging time simulated (arithmetic
mean only)
Arbitrary averaging time by Larsen procedure
(typically 1 - 24 hours)
Assumes
(1) lognormal concentration distribution,
(2) power law dependence of median and maximum concen-
trations on averaging time
Up to 225 gridded receptor locations, 12 arbitrary locations
Individual point, area source culpability list for each receptor
p. Computer Requirements
Digital computer required
Core requirements are moderate
q. Limitations
Useable for urban areas only
B-4
-------
B.2 APRAC-1A
Reference:
Abstract:
Equations
Mancuso, R. L. and F. L. Ludwig. "User's Manual for the
APRAC-1A Urban Diffusion Model Computer Program."
Publication No. EPA-650/3-73-001 (NTIS PB 213091),
Environmental Protection Agency, Research Triangle Park,
North Carolina 27711, September 1972.
APRAC 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 requires an
extensive traffic inventory for the city of interest.
10'"F
Extraurban - xe -
F = annual fuel consumption within 22.5° sector extending
from 32 km to 1000 km upwind of receptor.
0.8Qi
x
xi
Intraurban - x,-.: =
Until this expression equals the "box model value"
'-"id
Qi
UL(xi+l
Thereafter the box model formula is used.
i = upwind area segment label
j = stability class label b. .
a., and b . . from (a ) = a., x 1J for x within segment i
J J 1J
Street Canyon - Lee side
KQ
(u+0.5)[(x')N. L
KQ.(H-z)
Windward side
-------
Intermediate wind direction(less than +_ 30° from street
(direction) -.
\ = 2 (xL + xw}
where:
x = horizontal distance from traffic lane (m)
z = height above pavement (m)
K = constant = 7
L = vehicle size = 2m
u = rooftop wind speed (m/s)
Q = CO emission rate (g/s-m)
W = Street width (m)
H = average building height = 38.8 m
a. Source-Receptor Relationship
User specifies set of traffic links (line sources) by pro-
viding link end points, road type, daily traffic volume
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 assumed at ground level
Up to 10 receptors
Receptors 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
B-6
-------
b. Emission Rate
Daily traffic volume for each link and off-link grid square
is input and modified by various factors to produce hour-
by-hour emissions from each link
Link emissions aggregated as described above: sector area
source contributions obtained analytically
Off-link traffic emissions on the two mile square grid are
added into sector area sources
In street canyon sub-model, a separate hourly emission rate
is provided by user for the link in question
c. Chemical Composition
Treats one inert pollutant
d. Plume Behavior
Does not treat plume rise
Does not treat fumigation or downwash except in street
canyon sub-model
In street canyon sub-model, a helical circulation pattern
is assumed
e. Horizontal Mind Field
Hourly wind speed and direction in tens of degrees are input
No variation of wind speed or direction with height
Constant, uniform (steady-state) wind assumed within each hour
f. Vertical Wind Speed
Assumed equal to zero except in street canyon sub-model
Helical circulation assumed by street canyon sub-model
g. Horizontal Dispersion
Sector averaging uniform distribution within sectors
22.5° sectors beyond 1 km
45.0° sectors within 1 km
B-7
-------
h. Vertical Dispersion
Semi-empirical/Gaussian plume
6 stability classes; stability class determined internally
from user-supplied meteorological data [modified from
Turner3]
Dispersion coefficients from McElroy and Pooler'*, modified
using information in Leighton and Ditmar5
No adjustments made for variations in surface roughness
Downwind distance variation of a assumed to be ax for
purposes of doing analytic integration
In street canyon sub-model, empirical function of wind speed
and street width and direction is used
i. Chemistry/Reaction Mechanism
Single inert pollutant
j. Physical Removal
Not treated
k. Background
Box model used to estimate contribution from upwind sources
beyond 32 km based on wind speed, mixing height, annual
fuel consumption
In street canyon sub-model, contribution from other streets
is included in background
1. Boundary Conditions
Lower boundary: perfect reflection
Upper boundary: perfect reflection; ignores effect until
concentration equals that calculated using box model;
uses box model (uniform vertical distribution) thereafter
Mixing height determined from morning radiosonde data as
follows:
midnight to dawn: constant at pre-dawn value obtained
using minimum urban temperature
dawn to sunset: afternoon maximum temperature used to
obtain maximum height; hourly values obtained from surface
temperature variations
sunset to midnight: linear interpolation with time
m. Emission and Meteorological Correlation
Emissions a function of hour of the day and day of the week
Meteorological parameters are functions of hour of the day
B-8
-------
n. Val idation/Calibration
No calibration option provided
Some documented validation experience available
o. Output
Hourly concentration values at each receptor
Frequency distribution based on hourly values can be obtained
p. Computer Requirements
Digital computer required
Core requirements are moderate
q. Limitations
Limited to urban areas
No means for including point sources
B-9
-------
B.3 CLIMATOLOGICAL DISPERSION MODEL (COM)
References:
Abstract:
Equations:
Busse, A. D. and J. R. Zimmerman.
Climatological Dispersion Model."
"User's Guide for the
Publication No. EPA-RA-
73-024 (NTIS PB 227346/AS), Environmental Protection Agency,
Research Triangle Park, North Carolina 27711, December 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, Environmental Protection
Agency, Research Triangle Park, North Carolina 27711,
May 1977.
CDM 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 a statistical model based on Larsen1 to transform
the average concentration data from a limited number of
receptors into expected geometric mean and maximum concen-
tration values for several different averaging times.
(point
6 6
I I
1=1 m=l
(pn)/Pn
16
'area
'16
I
qjp)
6 6
I I
i=l m=l
dp
with q,(P) = / Q(p,e)de
sector k
exp -
exp
0.692 p]
" u£ Tl/2j
for a, <_0.8L
L,
B-10
-------
for az > 0.8L
a = ap ; a,b = functions of stability class (m) and
downwind distance (p) - three ranges of
distance used: 100-500 m, 500-5000 m,
and 5000-50,000 m
kn = wind sector appropriate to the n point source
Q = emission rate of the n point source (g/s)
P = distance from the receptor to the n point source (m)
q, = emission rate of the area source per unit area and
unit time (g/s-m2)
p = distance from the receptor to an infinitesimal urea
source (m)
9 = angle relative to polar coordinates centered on the
receptor
£ = index identifying the wind speed class
m = index, identifying the class of the Pasquill stability
category
(k,£,m) = joint frequency function
z = height of receptor above ground level (m)
Un = representative wind speed (m/s)
h = effective stack height of source distribution, i.e.,
the average height of area source emissions in the
k wind direction sector at radial distance p from
the receptor (m)
L = the afternoon mixing height (m)
= assumed na^f ^ife °f pollutant hours (s)
B-ll
-------
a. Source-Receptor Relationship
Arbitrary location for each point source
Area sources equal uniform grid squares
Receptor location arbitrary
Arbitrary release heights for point and area sources
Unique separation for each source-receptor pair
Receptors are at ground level
No terrain differences between source/receptor
b. Emission Rate
Point sources: single rate for each source
Area sources: single rate for each source
area integrations are done numerically one
22.5° sector at a time; sampling at discrete
points defined by specific radial and angular
intervals on a polar grid centered on the
receptor
Day/night variations in emissions, same variation assumed
for all sources
c. Chemical Composition
Treats one or two inert pollutants simultaneously
d. Plume Behavior
Only Briggs neutral/unstable formula used for point sources
If stack height plus plume rise is greater than mixing height,
ground level concentrations assumed equal to zero
Alternative to Briggs - input value of plume rise times wind
speed for each point source
No plume rise calculated for area sources
Does not treat fumigation or downwash
e. Horizontal Wind Field
Climatological approach
16 wind directions
6 wind speed classes
Wind speed corrected for release height based on power law
variation exponents from DeMarrais°
Constant, uniform (steady-state) wind assumed
f. Vertical Wind Speed
Assumed equal to zero
B-12
-------
g. Horizontal Dispersion
Climatological approach
Uniform distribution within each of 16 sectors
Averaging time = 1 month to 1 year or longer
h. Vertical Dispersion
Semi-empirical/Gaussian plume
5 stability classes as defined by Turner3
Neutral stability split into day/night cases on input
Dispersion coefficients taken from Turner7
Area sources - stability class is decreased by 1 category
from input values (to account for urban effects)
Neutral dispersion coefficients are used for stable classes
No further adjustments made for variations in surface roughness
i. Chemistry/Reaction Mechanism
Exponential decay, user-input half life
j. Physical Removal
Exponential decay, user-input half life
Always applies the same rate constant
k. Background
Input single constant background value for each pollutant
1. Boundary Conditions
Lower boundary (ground): assumes perfect reflection
Upper boundary (mixing height): no effect until dispersion
coefficient equals 0.8 of the mixintg height, uniform
vertical mixing assumed beyond this point
m. Emission and Meteorological Correlation
Wind speed, direction, stability correlated via wind rose
Mixing height is adjusted according to stability class:
Class A - 1.5 times afternoon climatological value
Classes B, C, and D(day) - equal to the afternoon clima-
tological value
Class D(night) - average of morning and afternoon clima-
tological value
Class E - morning climatological value
Emission rates: day-night allowed; all sources vary by
same factor
Non-sequential (climatological) limited correlation
B-13
-------
n. Validation/Calibration
Limited validation experience
Calibration option available (CDMQC)
o. Output
One month to one-year averaging time simulated (arithmetic
mean only)
Arbitrary averaging time by Larsen1 procedure
(typically 1 - 24 hr.) (CDMQC)
Assumes
(1) lognormal concentration distribution,
(2) power law dependence of median and maximum concen-
trations on averaging time
Arbitrary number and location of receptors
Individual point, area source culpability list for each
receptor (CDMQC)
Point, area concentration rose for each receptor
p. Computer Requirements
Digital computer required
Core requirements are moderate
q. Limitations
Useable for urban areas only
Area source emission densities must not vary rapidly from
one area source to the next
B-14
-------
B.4 REAL-TIME AIR QUALITY SIMULATION MODEL (RAM)
Reference:
Abstract:
Equations:
Turner, D, B., and J, H. Novak. "User's Guide for RAM,"
Environmental Protection Agency, Research Triangle Park
North Carolina 27711 , 1977.
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.
Level or gently rolling terrain is assumed. Calculations
are performed for each hour. Both rural and urban versions
are available.
Contribution from single upwind area source
Xn = jj- / f dx integral evaluated numerically
xl
, x~ = points of intersection of ray from receptor
through area source in question
q = emission rate per unit area of the area source(g/s-m2)
u = mean wind speed (m/s)
For stable conditions: f = —
92
xpoint 2TTU a az 9192
For neutral or unstable conditions, a
^
•p =
-
xoint ~ 2iru a a 9193
y
B-15
-------
For neutral or unstable conditions, a > 1.6L
f =
(point
2TT uL a.
In which
91 = exP - 2
g2 = exp
[-1
rZ-H,
+ exo I- 1 (z^
" I ? a '
exp |- 1 (£^)' | + exp
a. Source-Receptor Relationship
Arbitrary location for point sources
Receptors may be
(1) arbitrarily located
(2) internally located near individual source maxima
(3) on a program-generated hexagonal grid to give good
coverage to a user-specified portion of the region of
interest
Receptors all at same height above (or at) ground
Flat terrain assumed
Unique stack height for each point source
User may specify up to three effective release heights for
area sources, each assumed appropriate for a 5 m/sec wind
speed. Value used for any given area source must be one
of these three
Unique separation for each source-receptor pair
B-16
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b. Emission Rate
Unique, constant emission rate for each point, area source
Area source treatment-
Narrow plume approximation
Area source used as input; not subdivided into uniform
elements
Arbitrary emission heights input by user
Areas must be squares; side lengths = integer multiples
of a basic unit
Effective emission height = that appropriate for 5 m/s wind
Area source contributions obtained by numerical integration
along upwind distance of narrow-plume approximation formulae
for contribution from area source with given effective
release height
c. Chemical Composition
Treats a single inert pollutant
d. Plume Behavior
Briggs8'9'10 plume rise formulas
Does not treat fumigations or downwash
If plume height exceeds mixing height, ground level concen-
tration is assumed zero
e. Horizontal Wind Field
Uses user-supplied hourly wind speeds
Uses user-supplied hourly wind directions (nearest 10°),
internally modified by addition of a random integer value
between -4° and +5°
Wind speeds corrected for release height based on power law
variation, exponents from DeMarrais*; different exponents
for different stability classes, reference height = 10 meters
Constant, uniform (steady-state) wind assumed within each hour
f. Vertical Wind Speed
Assumed equal to zero
g. Horizontal Dispersion
Semi-empirical/Gaussian plume
Hourly stability class determined internally by Turner3
procedure, six classes used
Dispersion coefficients from McElroy and Pooler4 (urban) or
Turner7 (rural). No further adjustments made for variations
in surface roughness or transport time
B-17
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h. Vertical Dispersion
Semi-empirical/Gaussian plume
Hourly stability class determined internally
Dispersion coefficients from McElroy and Pooler4 (urban) or
Turner7 (rural). No further adjustments made for variations
in surface roughness
i. Chemistry/Reaction Mechanism
Exponential decay, user-input half-life
j. Physical Removal
Exponential decay, user-input half-life
k. Background
Not treated
1. Boundary Conditions
Lower boundary: perfect reflection
Upper boundary: perfect reflection
Neutral and unstable conditions
Multiple reflections numerically accounted for by summation
of series until a = 1.6 times mixing height
Uniform mixing assumed in vertical thereafter
Stable conditions: ignore effect of upper boundary
Mixing height for a given hour is obtained by suitable
interpolation using data from soundings taken twice a day
Interpolation technique dependent on mode of operation (urban
or rural) and calculated stability class for the hour in
question as well as the stability class for the hour
just preceding sunrise
m. Emission and Meteorological Correlation
User supplies hourly values of wind speed, wind direction,
mixing height and other meteorological variables required
for determination of stability class and plume rise
n. Validation/Calibration
No calibration option provided
No documented validation or comparison with observational data
B-18
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o. Output
Hourly and average (up to 24 hours) concentrations at each
receptor
Limited individual source contribution list
Cumulative frequency distribution based on 24-hour averages
and up to 1 year of data at a limited number of receptors
p. Computer Requirements
Digital computer required
Core requirements are moderate
q. Limitations
Flat or gently rolling terrain
B-19
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B.5 SINGLE SOURCE (CRSTER) MODEL
Reference:
Abstract:
Equations:
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, July 1977.
CRSTER is a steady state Gaussian plume technique applicable
to both rural and urban areas in uneven terrain. The purpose
of the technique is: (1) to determine the maximum concen-
trations,for certain averaging times between 1-hour and
24-hours, over a one year period due to a single point source
of up to 19 stacks, (2) to determine the meteorological
conditions which cause the maximum concentrations, and
(3) to store concentration information useful in calculating
frequency distributions for various averaging times. The
concentration for each hour of the year is calculated and
midnight - to - midnight averages are determined for each
24-hour period.
gl
for a <_
1.61.
X =
/2T uL a.
for a > 1.61.
x = 0 (stability class 7)
L = mixing height (m)
H = (stack height + plume rise)-(difference in elevation
between receptor and base of stack) (m)
9, - exp
- \
-------
a. Source-Receptor Relationship
Up to 19 point sources, no area sources
All point sources assumed at the same location
Unique stack height for each source
Receptor locations restricted to 36 azimuths (every 10°)
and 5 user-specified radial distances
Unique topographic elevation for each receptor; must be
below top of stack
b. Emission Rate
Unique average emission rate for each source
Monthly variation in emission rate allowed
c. Chemical Composition
Treats a single inert pollutant
d. Plume Behavior
Briggs8'9'10 final plume rise formulas
Does not treat fumigation or downwash
If plume height exceeds mixing height, concentrations further
downwind assumed equal to zero
e. Horizontal Hind Field
Uses user-supplied hourly wind speeds
Uses user-supplied hourly wind directions (nearest 10°),
internally modified by addition of a random integer value
between -4° and +5°
Wind speeds corrected for release height based on power law
variation, exponents from DeMarrais6; different exponents
for different stability classes, reference height = 10
meters
Constant, uniform (steady-state) wind assumed within each
hour
f. Vertical Wind Speed
Assumed equal to zero
g. Horizontal Dispersion
Semi-empirical/Gaussian plume
7 stability classes used; Turner Class 7: extremely stable,
elevated plume assumed not to touch the ground
Dispersion coefficients from Turner; no further adjustments
made for variations in surface roughness, transport or
averaging time
B-21
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h. Vertical Dispersion
Semi-empirical/Gaussian plume
7 stability classes
Dispersion coefficients from Turner; no further adjustments
made
i. Chemistry/Reaction Mechanism
Not treated
j. Physical Removal
Not treated
k. Background
Not treated
1. Boundary Conditions
Lower boundary: perfect reflection at the same height as
the receptor
Upper boundary: perfect reflection
Multiple reflections handled by summation of series until
a = 1.6 x mixing height
Uniform vertical distribution thereafter
Mixing height is constant and follows topographic variations:
Taken from base of stack for determining whether plume
punches through
Taken from receptor elevation for determining vertical
concentration distribution
Mixing height for a given hour is obtained by suitable
interpolation using data from soundings taken twice a day.
Interpolation technique dependent on mode of operation
(urban or rural) and calculated stability class for the
hour in question as well as the stability class for the
hour just preceding sunrise.
m. Emission and Meteorological Correlation
User supplies hourly values of wind speed, direction, mixing
height and other meteorological variables required for
determination of stability class and plume rise
Monthly emission variation allows limited emission -
meteorology correlation
n. Validation/Calibration
No calibration option provided
Comparison with observations around at least 5 separate
power plants have been made
B-22
-------
o. Output
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 concentrations
over the receptor field
Hourly concentrations for each receptor on magnetic tape
p. Computer Requirements
Digital computer required
Core requirements are moderate
q. Limitations
Not applicable to area and line sources
Use care when applying to low-level sources
B-23
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B.6 TEXAS CLIMATOLOGICAL MODEL
References:
Abstract:
Equations:
Porter, R. A. and Christiansen, J. H.. "Two Efficient
Gaussian Plume Models Developed at the Texas Air Control
Board." Proceedings, of the 7th NATO/CCMS International
Technical Meeting on Air Pollution Modeling, Airlie House
Va., September, 1976.
Christiansen, J. H. and Porter, R. A.. Users Guide to the
Texas Climatological Model, Texas Air Control Board, Austin
Texas, May, 1976.
The TCM is a climatological model that predicts long-term
arithmetic mean concentrations of nonreactive pollutants
from point sources and area sources.
Area sources are handled by an algorithm proposed by Gifford
and Hanna11. The concentration due to area sources is
given by
XA = FQ/U
where XA = concentration (yg/m3)
Q = area source emission rate in the vicinity of
the receptor (yg/s-m2)
U = mean ground-level wind speed (m/s)
F = a dimension!ess constant
Gifford and Hanna have suggested a value of F = 50 for S02
and 225 for total suspended particulate (TSP). The area
emission rate, Q, is determined by averaging the emissions
in the area source square containing the receptor and in
the neighboring squares. The extent of the region around
each receptor to use for emission rate averaging is an input
parameter.
B-24
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The TCM uses steady-state Gaussian plume point source
logic, with the crosswind distribution averaged across
22.5° azimuthal sectors. The only meteorological input
required for area source calculations is the mean wind
speed, but the point source calculations require a mete-
orological joint frequency function with sixteen 22'.5°
wind sectors, six wind speed classes (0-3, 4-6, 7-10,
11-16, 17-21, and > 21 knots), and six stability classes
(Turner classes A, B, C, D (day), D (night), and E plus F)
The basic equation is:
6
m=l
(K(x,H,mMk,m)/ll). (decay tenn)
where K(x,H,m) = (32xl06/[(21f)3/2x QZ]) exp (-H2/2az2)
(K is precalculated for 20 distances, 9 effective source
heights, and six stability classes)
S = (2/[/2TUm oz]) exp (-H/2a
exp (-.692x/[UmT1/2])
U is a wind speed characteristic of an entire stability
class, and is computed in the model by the equation:
16
I
k=l
I 4>(k,£,m)
16
I
k=l
6
I
-1
with x = concentration, yg/m3
k = the wind sector index appropriate to source i
at the receptor
£ = wind speed class index
m = stability class index
<{> = meteorological joint frequency function
B-25
-------
Q. = emission rate of source i (g/s)
H. = effective height of source i (mi)
a = standard deviation of vertical Gaussian concentra-
z tion distribution (m)
T,/2 = half-life for first-order pollutand decay (s)
LL = central wind speed of class i (m/s)
x = downwind distance (m)
a. Source-Receptor Relationship
Arbitrary location for each point source
Unlimited number of sources
Arbitrary location and square grid width for each area source
The model will allocate area sources into a uniform square grid
Receptor location is arbitrary grid (max. 50 x 50)
Release heights for point sources
The area source algorithm (Gifford-Hanna) does not consider
height of release
Receptors are at ground level
No terrain difference between sources and receptors
b. Emission Rate
All sources have a single average emission rate for the
averaging time period (i.e., month, season, year)
c. Chemical Composition
One, two, or three inert pollutants are treated simultaneously
d. Plume Behavior
Plume rise calculated according to Briggs9 neutral/unstable
equation
Effective stack heights less than 10 meters are considered
10 meters
Effective stack heights greater than 300 meters are considered
300 meters
No plume rise for area sources
Downward and fumigation not considered
B-26
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e. Horizontal Wind Field
Climatological approach
16 wind directions
Mean wind speed calculated for each stability clas- from
the joint frequency function of stability, wind direction,
and wind speed
Wind speed corrected for physical stack height (same as COM)
f. Vertical Hind Speed
Assumed equal to zero
g. Horizontal Dispersion
Assumed to be uniform within each 22.5 degree sector (same
as COM)
h. Vertical Dispersion
Gaussian plume
6 stability classes (Pasquill-Gifford-Turner) A, B, C, D-Day,
D-Night, E and F
No provision for variation in surface roughness
i. Chemistry/Reaction Mechanism
Exponential decay according to user input half-life (same
as COM)
j. Physical Removal
Same as i above
k. Background
Background may be entered by calibration coefficient for
each pollutant
1. Boundary Conditions
Perfect reflection assumed at ground
Mixing height not considered
m. Emission and Meteorological Correlation
Emissions not varied
B-27
-------
n . Validation/Correlation
Model is self-calibrating with input of field receptor
observations
High correlation achieved of observed to calculated values
for Houston TSP 1975, Houston S02 1972, Dallas TSP 1972
Arithmetic mean concentration for the averaging time of the
climatological input and emission data (one month to one
year
Any combination of the following outputs are available:
(1) Listing of concentration for an arbitrarily spaced
square grid of up to 50 by 50 elements
(2) A print plot of the grid concentrations
(3) Punched card output for isopleth maping (same as COM)
(4) A listing of the five high contributors to the concen-
concentration (by % concentration) at each grid point
p. Computer Requirements
Digital computer required
Core requirements are moderate
q. Limitations
Flat terrain, relatively constant emissions
B-28
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B.7 TEXAS EPISODIC MODEL ("I EM)
References:
Abstract:
Equations:
Porter, R. A. and Christiansen, J. H. "Two Efficient
Gaussian Plume Models Developed at the Texas Air Control
Board." Proceedings of the 7th NATO/CCMS International
Technical Meeting »n Air Pol 1ution Model ing, Airlie House,
Va., September, i()/f).
Christiansen, J. H. Users Guide to the Texas Episodic Model,
Texas Air Control Board, May, i'^76.
The Texas Episodic Model TEM is a short-term (10 minute
to 24 hour averaging time) Gaussian Plume Model for pre-
diction of concentrations of nonreactive pollutants due to
up to 300 elevated poinc sources and up to 200 area sources.
Concentrations are calculated for 1 to 24 scenarios of
meteorological conditions, averaging time, and mixing height
The area source algorithm is due to Gifford and Hannai1
Each area source square is affected by its own diffuse
emissions and those in the N area source squares directly
upwind of it:
x -
x
+ I Q, [(2i+l)1-b - (2i-l)1-bj
with Ax = length of side of area source grid squares (m)
U - surface wind speed (m/s)
QQ - area emission rate of square containing the
receptor (ug/s-m2)
Q.J = area emission rates of the upwind area sources
a,b = stability and downwind distance-dependent
parameters from the equation o = ax
The TEM employs steady-state bivariate Gaussian plume
point source logic. The concentration due to an elevated
point source is given by
B-29
-------
x = ^-^jjexp (-y2/2o2) exp (-H2/2a^) exp (-.692x/UT1/2)
y z
where x = concentration (yg/m3)
Q = emission rate (g/s)
U = wind speed at physical source height (m/s)
H = effective source height (m)
x = downwind distance (m)
y = crosswind distance (m)
pollutant decay half-life (s)
a , a - the standard deviations of the plume concen
tration distribution,
a = ax
with stability and downwind distance-dependent coeffi-
cients a, b, c, and d from Busse and Zimmerman12 and
Turner7. The wind speed U is the surface wind speed
adjusted to the physical source height. Let K and K
be defined by ^
exp (-y2/2ay2),
KZ = exp (.
Then, x = yz (decay term)
B-30
-------
K was calculated for each of the 1120 combinations of
twenty downwind distances from 2 to 60 km, eight crosswind
angles (Tan^y/x) from 0° to 7°, with 6 varying from 1° to
5° depending on stability, and seven stability classes.
For total vertical mixing below a mixing height of L meters,
x = -^ exp (-yV2o ")
/2T a LU y
KVK2Q
This can be represented in the equation x = (decay term)
by setting KZ * 398L
a. Source-Receptor Relationship
Up to 300 arbitrarily located point sources
Up to 200 arbitrarily located area sources
A uniform square receptor grid of arbitrary spacing with up
to 50 by 50 rows or columns
Terrain assumed flat
Unique release height for each source
All receptors at ground level
b. Emission Rate
Unique emission rate for each source
c. Chemical Composition
One, two, or three inert pollutants treat simultaneously
d. Plume Behavior
Plume rise according to one of six equations from Briggs
selected according to stability and distance from source.
Effective stack heights less than 10 meters are considered
10 meters. Effective stack heights greater than 2000
meters are considered 2000 m.
Mixing height penetration factor (P) is a user input. If
effective source height (h) is greater than P times the
mixing height the plume escapes. Otherwise the .47L mixing
scheme from Turner-7 is used.
Does not treat downwash or fumigation
B-31
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e. Horizontal Wind Fijld
User supplied stability, wind speed, and direction for the
averaging time period (10 minutes to 3 hours) or for each
3 hour period to build a 24-hour day.
Power law variation of wind speed with release height (same
as COM).
Steady state wind for each scenario
f. Vertical Wind Speed
Equal to zero
g. Horizontal Dispersion
Semi-empirical Gaussian plume
User supplied stability class for each scenario (Pasquill-
Gifford-Turner)
Turner7 dispersion coefficients
No adjustment for surface roughness
h. Vertical Dispersion
Semi-empirical Gaussian plume
User supplied stability classes (Pasquill-Gifford-Turner)
for each scenario
Turner7 dispersion coefficients
No adjustment for surface roughness
i. Chemistry/Reaction Mechanism
Exponential decay with user supplied half-life
j. Physical Removal
Same as i above
k. Background
May be input with calibration factor
1. Boundary Conditions
Lower boundary: perfect reflection
Upper boundary: reflection from top of mixed layer by the
.47L scheme of Turner7 except as described in d above
B-32
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m. Emission/Meteorological Correlation
User supplied values of wind speed, wind direction, stability
class, mixing height, ambient temperature for each scenario
up to 24 scenarios
n. Validation/Calibration
Limited validation with observed vinyl chloride observations
Calibration by user supplied coefficients (A, B) so that
xcal = A + Bxpredicted
o. Output
Concentration for each receptor grid point for averaging
times of:
10 minutes
30 minutes
1 hour
3 hours
24 hours (based on eight 3-hour scenarios)
Output is available for from 1 to 24 scenarios in the fol-
lowing formats:
listing
print plot
punched cards for isopleth maps
culpability list of the high five contributors to the
concentration at each receptor grid point
p. Computer Requirements
Digital computer required
Core requirements are moderate
q. Limitations
Relatively uncomplicated terrain
B-33
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References
1. Larsen, R. I. "A Mathematical Model for Relating Air Quality
Measurements to Air Quality Standards." Office of Air Programs
Publication No. AP-89 (NTIS PB 205277), Environmental Protection
Agency, Research Triangle Park, North Carolina 27711, November
1971.
2. Holland, J. Z. "A Meteorological Survey of the Oak Ridge Area."
Atomic Energy Commission Report ORO-99, Oak Ridge National
Laboratory, Oak Ridge, Tennessee, 1953.
3. Turner, D. B. "A Diffusion Model of an Urban Area." Journal of
Applied Meteorology, American Meteorological Society, Boston,
Massachusetts, February 1964.
4. McElroy, J. L. and F. Pooler. St. Louis Pispersion Study,_ Voltime
II - Analyses. AP-53. National Air Pollution Control Administra-
tion, Arlington, Virginia 22203, December 1968.
5. Leighton, P. A. and R. B. Dittmar. "Behavior of Aerosol Clouds
within Cities." Joint Quarterly Progress Reports Nos. 2, 4, 5, 6
(2 vols.), Contracts DA-18-064-CMC-1856 and DA-18-064-CMC-2282.
Stanford University and Ralph M. Parsons Co. DDC Nos. AD 7261,
AD 31509, AD 31508, AD 31507, AD 31510, AD 31511, Respectively,
1952.
6. DeMarrais, G. A. "Wind Speed Profiles at Brookhaven National
Laboratory." Journal of Meteorology, Americal Meteorological
Society, Boston, Massachusetts, 1959.
7. Turner, D. B. "Workbook of Atmospheric Dispersion Estimates."
PHS Publication No. 999-AP-26 (NTIS PB 191482), Environmental
Protection Agency, Research Triangle Park, North Carolina 27711,
1969.
8. Briggs. Gary A. PJ[ume Rise. U.S. Atomic Energy Commission
Critical Review Series, (NTIS TID-25075) Oak Ridge National
Laboratory, Oak Ridge, Tennessee, 1969.
9. Briggs, Gary A. "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, 1971.
10. Briggs, Gary A. Discussion on Chimney Plumes in Neutral and
Stable Surroundings. Atmospheric Environment, July 1972.
B-34
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11. Gifford, F. A. and S. R. Hanna. "Modeling Urban Air Pollution."
Atmospheric Environment. January 1973.
12. Busse, A. D. and J. R. Zimmerman. "User's Guide for the Cli-
matological Dispersion Model." Publication No. EPA-RA-73-024
(NTIS PB 227346/AS), Environmental Protection Agency, Research
Triangle Park, North Carolina 27711, December 1973.
B-35
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