&EPA
united States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park NC 27711
EPA-450/2-78-027
OAQPS No 1 2-080
April 1978
Air
OAQPS Guideline
Series
Guideline on Air
Quality Models
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EPA-450/2-78-027
OAQPS No.1 .2-080
Guideline on Air Quality Models
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Waste Management
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
April 1978
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OAQPS GUIDELINE SERIES
The gUideline series of reports IS being issued by the Office of Air Quality Planning and Standards (OAQPS) to
provide informatIon to state and local air pollution control agencies; for example, to provide guidance on the
acquIsition and processing of aIr quality data and on the planning and analysis requisite for the maintenance of air
quality Reports published In this series will be available as supplies permit - from the Library Services Office
(MD-35), U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, or, for a nominal
fee, from the National Technical Information Service, 5285 Port Royal Road, Springfield, Virginia 22161.
Publication No. EPA-450/2-78-027
(OAQPS Guideline No. 1.2-080)
i i
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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, Laurence Budney
and Russell Lee of the same office.
The many comments and suggestions
provided by those participating in the Specialists' Conference, the
Conference on Air Quality Models and the five public meetings on these
guidelines were a valuable resource.
The manuscript was prepared with
great care by Ann Asbill and Barbara Stroud.
i i i
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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 widely reviewed guide on the use of air quality models. After
initial opinions from EPAls 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 specia1ists.*
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, New.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 required EPA to conduct
a Conference on Air Quality Modeling*** with special attention given to
models applicable to prevention of significant deterioration. That
Conference was held in Washington, D. C. on December 14-15, 1977, with
the "Interim Guideline on Air Quality Models" (October, 1977) serving as
the focal point. The Conference was attended by over 300 individuals
with similar backgrounds and interests to those who participated in the
five previous meetings. A verbatim transcript of the proceedings was
maintained and written comments were accepted. Those comments and
suggestions were considered in this revision to the guide.
*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.
***Environmenta1 Protection Agency. "Conference on Air Quality Modeling."
Acme Reporting Company, Washington, D. C. 20005, December, 1977.
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The Clean Air Act Amendments also required the promulgation of
regulations which specify air quality models to be used in analyses
pertinent to prevention of significant deterioration. The guide is
included by reference in 40 CFR 52.21 regulations concerni~g significant
deterioration. The models discussed here are those recommended for use
in PSD analyses.
Due to the continuing development of a wide variety of air quality
models and numerous gaps in our ability to simulate atmospheric disper-
sion processes, EPA plans to review and update this guide periodically.
EPA is also act)vely pursuing mechanisms by which (1) the technical
community can take an active role in such reviews and updates and (2) a
wider range of models, including those developed by groups other than
EPA, can be incorporated. EPA and other groups within the technical
community have ongo~nJ programs in the areas of complex terrain, long
range transport, fugitive dust, turbulence characterization, and model
validation/improvement. It is anticipated that within about 18 months
outputs WhlCh have practical applications will have resulted from these
various programs. Thus EPA expects that further revision to this guide
will he appropriate at that time. In the future, it appears that reviews
and updates of this guide at 18-24 month intervals are also appropriate.
These revisions will be synchronized with Conferences on Air Quality
Modeling which EPA is required to conduct at least every three years.
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TABLE OF CONTENTS
Acknowledgement. . . . . . . . . . .
. . . . . . . .
. . . . . .
Preface. . . . . . . . . . . .
. . . .
. . . . .
. . . .
. . . .
1.0 INTRODUCTION. .
2.0 OVERVIEW. .
. . . . . . .
. . . . . . .
. . . .
. . . .
........
. . . . . .
..........
3.0 REQUIREMENTS FOR CONCENTRATION ESTIMATES
..........
3.1 Control Strategy Evaluations. . . . . . . . . .
3.2 New Source Reviews. . . . . . . . . . . . . . .
3.2.1 Meeting Air Quality Standards. . . . . .
3.2.2 Prevention of Significant Deterioration.
. . . .
. . . .
. . . .
. . . .
4.0 AIR QUALITY MODELS. . . . . . .
..............
4.1 Suitability of Models .................
4.2 Classes of Models. . . . . . . . . . . . . . . . . . .
4.3 Recommended Models. . . . . . . . . . . . . . . . . . .
4.3.1 Point Source Models for Sulfur Dioxide and
Particulate Matter (All Averaging Times) . . . .
4.3.2 Multi-Source Models for Sulfur Dioxide and
Particulate Matter (Annual Average). . . . . . .
4.3.3 Multi-Source Models for Sulfur Dioxide and
Particulate Matter (Short-Term Averages) . . . .
4.3.4 Models for Carbon Monoxide. . . . . . . . . . .
4.3.5 Models for Nitrogen Dioxide (Annual Average) . .
4.4 Special Situations. . . . . . . . . . . . . . . . . . .
5.0 DATA REQUIREMENTS. . . . . . . .
. . . . .
. . . . . . . . .
5. 1 Sou rce Da ta . . . . . . . . . . . . . . . . . . . . . .
5.2 Meteorological Data. . . . . . . . . . . . . . . . . .
5.3 Receptor Sites. . . . . . . . . . . . . . . . . . . . .
5.4 Background Air Quality. . . . . . . . . . . . . . . . .
6.0 MODEL VALIDATION/CALIBRATION. . . . . . . . . . . . . . . .
7.0 REFERENCES.
................... .....
Glossary of Terms. . . .
....................
Appendix A. Summaries of Recommended Air Quality Models.
A.l Air Quality Display Model (AQDM) . . . . . . . . . .
. . . .
. . . .
A.2 APRAC-l A . . . . . . . . . . . . . . . . . . . .
. . . . . .
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Page
iii
iv
1
5
7
7
10
10
11
13
14
16
18
19
21
21
22
23
24
27
27
31
33
34
39
43
48
A-1
A-3
A-7
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Page
A.3 Climatological Dispersion Model (COM) . . . . . . . . . . . . A~12
A.4 Gaussian-Plume Multiple Source Air Quality Algorithm (RAM). . A-17
A.5 Single Source (CRSTER) Model. . .
. . . . .
. . . . . . .
. . A-2l
A.6 Texas Climatological Model (TCM). . . . . . . .
A.7 Texas Episodic Model (rEM). . . . . . .
. . . .
. . . A-25
. . . . . . .
. . . . A-3D
vii
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1.0 INTRODUCTION
The purpose of this guide is to recommend air quality modeling
techniques that may i)t: 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 analys2s performed by EPA, by State and local
agencies and by ~~dustry and lts consultants.
It is appropriate for use
by other Fedci-'il agencies with ef,yironmental impact statement and lGnd
management res pons i bil iti es.
Similarly. it serves to identify for all
intere~ted parties thos~ techniques and data bases that EPA considers
techniqL;es.
The guide is not intended to be a compendium of modeling
Rather it should serve as a basis by which air quality
dcceptable.
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 concentration
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 particular applica-
tion, (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 con-
sidered rigid requirements.
The preferred model is that which best
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simulates atmospheric transport and dispersion in the area of interest.
However, deviations from this guide should be fully supported and documented.
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 paren-
theses refer to specific sections of the guide.
As indicated in Figure 1, it is generally advisable to first apply
a model requiring a minimum expenditure of resources (i.e., a pre1imi-
nary screening technique).
The purpose of a screening technique is to
single out, with minimum effort, those sources that clearly will not
cause or contribute to ambient concentrations in excess of the National
Ambient Air Quality Standards (NAAQS) or allowable concentration incre-
ments.
In doing 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 importance 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
2
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MODELING
INPUT STEPS
INFORMATION ~
~
I POLLUTANT OF CONCERN I SIMPL E
SCREENING
PROCEDURE (4.2,4.31
(3.0) REQUIREMENTS FOR CONCENTRATION
ESTIMATES
(4.3) I SOURCE TYPE (POINT, MUl n, ETC.) I
\4.4) I SPECIAL SI TUATIDNS/PRDGLEMS J
(5.1) I SOURCE DATA AVAILABLE I NO END
I OF
METEOROLOGICAL DATA AVAILA~ ANAL YSIS
(5.2) I
(4.2) ACCURACY OF CONCENTRATION
ESTIMATES YES
(4.1) MODELING CAPABILITIES/RESOURCES
(5.3) I RECEPTOR LOCATIONS
MODEL
SELECTION (3.0,4.0)
ANO
APPLICATION
(4.3,5.0)
AVAILABILITY OF
ADEQUATF. MODEL
AND DATA BASES
CONSIDERATION
OF BACKGROUND
AND GROWTH
(5.4)
(3.01
DEFINE REQU:REMENTS FOR
CONCENTRATION ESTIMATES
(6.0)
MODEL VALIDATION/
CALIBRATION
ANALYZE
RESULTS
(3.0)
(43)
MEASURED AIR DUALITY
Figure 1. Selection and application of air quality models and data bases. (Applicable
sectior~s of the guideline are indicated in parentheses.)
3
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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.
Any analytical technique may have deficiencies that cause estimated
concentrations to be in error.
Therefore, whenever possible, measured
air quality data should be used to determine the accuracy of the model
estimates. 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 develop-
ment and revision of SIP-related control strategies was identified very
1
ea r 1 y .
However, due to the initial demands of the Clean Air Act (1970)
on available re~ources, it has not generally been possible to use air
quality models to the extent desired.
Thus, many SIPs are baserl on an
example region concept, a simple emissions rollback model and available
measurEments of air quality.
In recent years, however, air quality
models have been more widely used.
As these models and associated data
bases increase in sophistication, they allow more precision in estimating
concentrations and in assessing tne adequacy of control strategies.
in add~tio~ 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 ana maintenance of NAAQS, and to
prevent significant air quality deterioration.
Judgments must be made
concerning allowable emission rates and the placement of new sources
that may cause specific air quality levels to be exceeded or that may
contribute significantly to existing violations.
It must be noted that EPA has never encouraged the use of air
'1uality models in place of nieilslJred data.
In fact, EPA encourages the
use of measured data in evaluating the effectiveness of control strategies
and in determining emission limits.
The two should be u~ed in
2 com::.!e-
ment~ry manner whenever possible.
The air quality d~}tl ...,~r; ce esp~cio.lly
useful in validating air quality models and thus have a direct impact on
5
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the air quality assessment. However, in some cases, the source does not
yet exist or there are insufficient air quality data to evaluate the
spatial distribution of concentrations and the impact of control strategies.
Thus, the use of models as the primary analytical tool is unavoidaele.
In the use of models it would be advantageous to categorize the
various control programs and to apply a designated model to each pro-
posed source which comes under a given program.
However, the diversity
of the nation's topography and climate, and variations in source configu-
rations and operating characteristics dictate against a routine "cookbook"
analysis.
There is no single model capable of properly addressing all
conceivable situations.
Meteorological phenomena associated with 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 charac-
teristics and data processing.
The judgment of well-trained profes~ional
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
by the deliberations of Congress2 that greater consistency in the use of
models and data bases is in order.
Consistency is required so that air
pollution control agencies and tile general public have a common basis
for estimating pollutant concentrations, assessing control strategies
and specifying emission limits.
This guide promotes the required con-
sistency.
6
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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 reviews, including prevention of significant deterioration.
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 esti-
mates 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. 36-39).
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 restrictive standard, other
considerations are required because the frequency of occurrence must
~lso 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 specifying
7
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appropriate emission limits, one of three types of concentration estimates
is used:
(l) the highest of all estimated concentrations, (2) the
second-highest of all estimated concentrations, or (3) the highest of
second-highest concentrations estimated for a field of receptor sites.
The highest of second-highest concentrations for a field of receptors is
obtained as follows: (l) frequency distributions of short-term concen-
trations are estimated 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 concen-
tration 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 restric-
tive 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 Standards."3
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 averaging time of 24-hours
or less should be based on the highest, second-highest estimated concen-
tration 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 sites may well reveal previously
unidentified "hot spots."
Such an ~stimate may provide a more conserva-
tive and realistic indication of the potential for NAAQS violations and
of the appropriate emission limits than do actual measurements at a few
monitoring sites.
However, if the data available for modeling are
limited to a short period, or 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 avai1able (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 speci-
fying emission limits, these measured concentrations should be ranked
ahead of the estimated concentrations in the frequency distribution of
concentrations at that specific monitoring (receptor) site.
The second situation occurs where the Regional AdministratGr
identifies inadequacies in the data base or the models for a p~rticula~
9
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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 eval-
uate 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.
For reviews relative to both the NAAQS and preven-
tion of significant deterioration (PSD), the air quality impact analysis
should generally be limited to the area where the impact exceeds IIsignifi-
cant concentration increments." Such significant increments are defined
in EPA's PSD regulations (40 CFR 52.21) and in EPA's Emission Offset
Ruling (40 CFR Part 51, Appendix $).
In addition, due to the uncer-
tainties of estimates for large downwind distances, the air quality
impact analysis should generally be limited to a downwind distance of 50
kilometers from the source, regardless of the above mentioned signifi-
cant increments.
The following subsections further 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 source or major modification of a source which would
increase allowable emissions by 50 tons per year, 500 pounds per day, or
10
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100 pounds per hour, an air quality analysis should be performed to
determine if the source will cause or exacerbate a violation of a NAAQS.
For such new sources located in an attainment area, the concentration
estimates should meet the same requirements that are applicable to
control strategy evaluations. The determination of whether or not the
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 concentration 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 concen-
trations 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.
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
matter are set forth in the Clean Air Act Amendments of 1977.2 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.
Since the Clean Air Act Amendments express special concern for
Class I PSD areas, any expected impacts for these areas must be consid-
ered.
Thus, the distance limitation of 50 kilometers and the significant
11
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concentration increments discussed in the introduction to Section 3.2 do
not apply.
In addition, 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 concentratiun may not exceed a somewhat higher increment
specified in section 165(d)(2)(D)(iii).
12
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4.0 AIR QUALITY MODELS
This Section recommends air quality models* for a wide variety of
specific applications.
It identifies factors that determine the suit-
ability of models for individual situations, presents classes and sub-
classes of models and addresses special modeling problems.
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 Administrator may find
that (1) the recommended air quality model is not appropriate for the
partiuJlar 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 availabie for relating emissions to air quality.
Similar models that are available from other governmertal agencies and
private consultants have been summarized and discussed by Lamb, et al.,6
7 8
Moses, Stern and others.
*A discussion of each specific model or refined analjtical technique
is presented in Appendix A. 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 sonsidered in the
context4oS this guideline and the reader is referred to other publications
on SCS. '
13
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In ~ll cases, and particularly when models and data bases other
than those recommended in this guide are proposed, early discussions
among the Regional Office staff, appropriate federal land managers, the
control agencies and industry representatives can be invaluable and are
encouraged.
Concurrence on the data base, modeling techniques and
overall technical approach, prior to the actual analyses, will help
avoid disagreements concerning the final results and may reduce the
later need for additional analyses.
The Office of Air Quality Planning
and Standards is routinely available to the Regional Offices for consulta-
tion on particularly 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 suitablt for
the evaluation of source impact and control strategies depends up un
several factors that should be judged by the responsible Regional Admin-
istrator.
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 modeltng; 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.
! !
14
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The data base required for air quality models includes source data,
meteorological data and air quality data (see Section 5.0).
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 tile detail with which a model considers the spatial and
temporal variations in emissions and meteorological conditions, the
greater the ability to evaluate the source impact and to distinguish the
effects of various control strategies.
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 knowledge-
able, well-trained air pollution engineers, meteorologists and air
quality analysts should be engaged.
The need for specialists is partic-
ularly critical when the more sophisticated models are used or the area
being investigated has complicated meteorological or topographic features.
A model applied improperly or with inappropriately chosen data can lead
to serious misjudgments regarding the source impact or the effectiveness
of a control strategy.
1 5
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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 Ilcomputa ti ona 1 algorithms" exist, each with i ts own specific
applications. While each of these algorithms may have the same generic
basis, e.g., Gaussian, it is accepted practice to refer to them individ-
ua lly as mode 1 s .
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 multl-source appli-
cations which involve reactive pollutants.
However, they frequently
require more extensive resources and are not as widely applied.
S ta ti s -
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,
relatively 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 ln excess of NAAQS or allowable concentration increments.
Conversely, these techniques can be used to identify those control
strategies that hdve the potential to meet NAAQS and allJwable 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, a~d provide
more specialized concentration estimates.
As a result tr.ey ~rovide a
more refined and, at least theoreti.::al1y, a mN:.,' accur.ltE: eS:~'1!a~e 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.
In such cases, an attempt should be made to acquire or improve the
necessary data bases and to develop appropriate analytical techniques.
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 pp. 41-43).
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) most readily available to air pollution control agencies.
Nevertheless, the need for information on the accuracy of these
models exists.
Limited information on the accuracy of recommended
models is presented in Appendix A.
More detailed information is referred
to in some of the individual user's guides.
However, additional studies
which encompass a wider range of sources and climatic regimes are needed
since there is a general lack of reported information on the accuracy of
the models.
This is true of all classes of models, including widely
18
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applied Gaussian models.
Whenever possible, the user is encouraged to
further validate models recommended in this guide, or other models that
,
may be used.
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.
If it is estimated by the
screening technique that a source may cause a concentration that is an
unacceptable portion 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
complexities, there are several standard publications9-11 and computer-
ized models12 that can be used for screening. In addition Poo1er13 and
Carpenter et a1.14 have discussed simplified techniques for estimating
concentrations during inversion-breakup fumigation. LYOns15 has summar-
ized information and techniques applicable to lake/sea breezes. Huber
and Snyder16,17 and Briggs18 have presented various techniques applicable
to aerodynamic downwash. Several authors19-22 have outlined techniques
that are useful for situations where long-range transport (greater than
50 ki1cmeters) is important. The Valley Model12,23 is applicable to
some complex terrain situations; Egan24 has summarized information on
other applicable techniques. Volume 10 of the Guidelines for Air Quality
19
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Maintenance Planning and Ana1ysis,25 "procedures for Evaluating Air
Quality Impact of New Stationary Sources" has summari,zed, in a format
useful for screening, techniques applicable to both flat terrain and
more complex 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
Source (CRSTER) MOde112,26 is recommended for use. In some cases specia1-
ized data outputs from models comparable to the Single Source (CRSTER)
Model may be needed.
Where these mode1s* estimate essentially the same
concentration as the Single Source (CRSTER) Model, their modified computer
codes which provide more useab1e outputs are acceptable.
If meteorological or terrain complexities cause substantial uncertain-
ties, then a model that 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 apply the Single Source (CRSTER)
Model are unavailable, or if other refined models applicable to a complex
situation do not exist, then it may be necessary to base estimates of
source impact and the evaluation of control strategies on only the
estimates provided by the screening techniques.
In such cases, an
*One example of such a model is MUL TlMAX . See Moser, J. H. "MUL TlMAX:
An Air Dispersion Modeling Program for Multiple Sources, Receptors,
and Concentration Averages. II Shell Development Company. Houston,
Texas 77001, December, 1977.
20
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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
applicable to stationary sources of lead pollutants, provided the pollu-
tants can be assumed to behave as a gas.
4.3.2 Multi-Source Models 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
strategy is desired, the Rollback Mode127 may be used. However, in most
cases such a screening does not constitute an adequate control strategy
.demonstration.
The Climatological Dispersion Model (CDM/CDMQC},12,28,29 the Air
Quality Display Model (AQDM}30 and the Texas Climatological Model (TCM}31
are recommended for evaluating the long-term impact of urban multi-
source complexes.
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.
4.3.3 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 Gaussian-Plume
21
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Multiple-Source Air Quality Algorithm lRAM}12,32 is recommended for
evaluating the impact of multi-source complexes on air quality averaged
over short-term periods. Versions of this model are applicable to both
urban and rural situations. The Texas Episodic Model (TEM}33 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
CDMQC or AQDM may be used to estimate short-term concentrations of S02
and particulate matter. CDMQC and AQDM incorporate procedures, such as
that discussed by Larsen,34 to estimate 3-hour and 24-hour average
concentrations from annual average concentration estimates. Such statis-
tical techniques are valid only in urban, multi-source areas and should
not be used in situations dominated by large point sources.
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
the Guidelines for Air Quality Maintenance Planning and Analysis,35
"Guidelines for Review of the Impact of Indirect Sources on Ambient Air
Quality," are recommended for screening all sources of co which fulfill
22
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the definition of an indirect source. The indirect source guideline is
based on the use of HIWAy12,36 and other simple dispersion techniques.
It is acceptable to apply these techniques independent 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
this model are available, it may be used. Examples of such refined
techniques are APRAC-1A12,37 and its revision, APRAC 2.38 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 (Annual Average)
The recommendations for point source screening techniques and
models are also applicable to evaluate point sources of nitrogen oxides
(NO) under limited circumstances. The circumstances require an assumption
x
that all NOx is emitted in the form of N02 or is converted to N02 by
the time it reaches the ground and that N02 is a nonreactive pollutant.
For sources located where atmospheric photochemical reactions are
significant, a Rollback Model may be used as a preliminary assessment to
23
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evaluate the control strategies for multiple sources lmobile and stationary)
of NOx' Another acceptable screening technique for multiple sources is
to make an assumption similar to that required for point sources and
then to use a model for nonreactive pollutants, such as CDM.
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 N02.
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.
4.4 Special Situations
Models with a wide applicability are not generally available for
dealing with long-range transport, deposition, wind-blown particulate
matter, unique topographic or meteorological circumstances, and nontypical
sources such as open burning.39 Thus, with proper support and documenta-
tion, 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
24
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dispersion coefficients for air quality mode1s* becomes increasingly
tenuous with downwind distance.
Plume transport beyond aQout 50 ki1o-
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 dispersion
characteristics.
Even though the impact at greater than 50-100 kilometers
may be relatively small, the impact can still be significant 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 meeting 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 basis with
available techniques.19-22,25
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
*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.
25
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operations, all of which are often referred to as fugitive dust sources,
can be significant sources of particulate matter.40 EPA has several on-
going studies concerned with fugitive sources of dust; however, the rate
and distribution of par.ticulate emissions from these sources is not yet
fully known. As a result, a widely applicable model for routinely
estimating particulate concentrations attributable to fugitive sources
is not available.4l
Terrain dominated flows and wakes that develop in the vicinity of
pollutant sources are involved in many situations.16-l8,23,24 The basic
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 investi-
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 ai~ 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 addition, there is a need to judiciously
choose receptor sites and to specify background air quality.
identifies requirements for these data bases.
This section
.5.1
Source Data
Sources of pollutants generally can be classified as point, line and
area sources.
Point sources are generally considered to be those that
emit a substantial amount of an air pollutant, e.g., 50 tons per year,
from a stack or group of stacks.
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 indi-
vidual1y 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 each grid square.
Descriptions of individual models should be
referenced for specific emissions inventory requirements.
27
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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 deter-
mining pollutant emissions.
Other operating conditions 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 in the model
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 characteristics and operating conditions that
are required:
-- ---
1. Plant layout. The connection scheme between boilers and
stacks, and the distance and direction between stacks, building parameters
(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 steam per hour, and the design and/or actual fuel consumption
rate for 100 percent load for coal (tons/hour), oil (bar~els/ 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.).
*Malfunctions which may result in excess emissions are not considered to
be a normal operating condition. They generally should not be considered
in determining allowable emissions. However, if the excess emissions are
the result of poor maintenance, careless operation, or other preventable
condition, it may be necessary to consider them in determining source
ilnpact.
28
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5. Operating conditions. For all
pollutant contents of fuel, the total hours
boiler capacity factor during the year, and
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 emis-
sion rate, the date of the last test and the tested efficiency, the number
of hours of operation during the latest year, and the best engineering
estimate of its projected efficiency if used in conjunction with coal
combustion; data for any anticipated modifications or additions.
boilers, the type, amount and
of boiler operation and the
the percent load for winter
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 completion 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 specified in
appropriate grid coordinates.
More detailed information and data require-
ments for modeling mobile sources of pollution are provided in the guide-
line35 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 the source in appropri-
ate grid coordinates.
If the model accepts data on area-wide diurnal
variations in emissions, such as those estimated by emissions models which
29
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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 uncer-
tainties 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
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 considered.
However, for new
source reviews, the impact of growth on emissions should generally be
considered for the period prior to the start-up date for the source.*
Such changes in emissions should consider increased area source emissions,
changes in existing point source emissions which were not subject to
preconstruction review, and emissions due to sources with permits to
construct, but have not yet started operation.
*A new source may result in specific and well defined secondary emissions
which can be accurately quantified. Secondary emissions are those resulting
from operation of the source, but not directly emitted by the source
e.g., emissions from shipping at a port terminal. The reviewing authority
should consider such secondary emissions in determining whether the
source would cause or contribute to a violation of the NAAQS. However,
since EPAls authority to perform indirect source review relating to
parking-type facilities has been restricted by statute, consideration
of parking-type secondary impacts is not required.
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 trans-
port 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 representativeness
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 me~o- 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 sum-
marized form from the National Climatic Center, Asheville, N. C.
Descrip-
tions of individual models should be referred to for specific meteorological
31
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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 in~lude several years of data.
Such a multi-year data
base allows the consideration of variations in meteoro19gical conditions
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 years42 yields
an adequate meteorological data base.
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.
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 condttions should be based on
32
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reasonable interpretations of climatological data and should consider such
critical plume characteristics as looping, coning, limited mixing, fumiga-
tion, aerodynamic downwash and plume impaction on terrain.
Due to the
uncertainties of this approach, the use of the highest estimated concentra-
tion 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 receptors
selected must allow sufficient spatial detail and resolution so that the
location of the maximum or highest, second-highest concentration is identi-
fied.
The receptor sites in the vicinity of large point sources at which
maximum concentrations are likely to occur can be identified by (1) esti-
mating concentrations for a sufficiently dense array of receptors 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. Another technique is to
use a simple model such as PTMAX12 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
33
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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 Quality
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 concentrations
due to natural sources and distant, unidentified man-made sources.
For
example, it is commonly assumed that the annual mean background concentra-
tion of particulate matter is 30-40 ~g/m3 over much of the Eastern United
States.43 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.
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 preferable
option is 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 concentration.
For the ftrst (;:J~Ic.n i-,: is a relatil',=ly straightforward effort to
identify an annua~ average b~cksrnl)t1c from available ::-;" quality data.
For
34
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shorter averaging times, background concentrations are determined by the
following procedure.
First, meteorological conditions are identified for
the day and similar days when the highest, second-highest estimated concen-
tration 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 l-hour concentrations 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
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 back-
ground 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 (J) the area of maximum impact of the point
source, (~) the area of maximum impact of nearby sources, and l3) the area
where all sources combine to cause maximum impact.
It may be necessary to
identify these locations through a trial and error analysis.
35
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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 a1r monitoring network 1s available, it may
be possible to rely entirely on the measured data.
It 1s necessary 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 concentra-
tions.
If the source already exists and is contributing to the measured
concentrations, its calculated contribution should be subtracted from the
measured values to estimate the concentration 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 avail-
able 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 evaluationss 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
36
-------
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, ch~nges in existing
air quality should generally 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 with
observed air quality data and their validity determined.
The model validation process* consists of a series of analytical
steps:
(1) Comparing estimated concentrations with measured air quality
data, (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 physi~al 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-
niq~es 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
that have the greatest impact on the accuracy of the estimates are the
*There is a clear need for specific and uniform validation procedures
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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accuracy and completeness of the (l) emissions data, (2) physical plant
parameters, and (3) site coordinates of the sources. Often the va11da-
tion will reveal deficiencies 1n 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 concentration estimates and should describe the spatial variation of
pollutant concentrations across the area.
If the air quality data are
1n 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:
(l) 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 1S 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.
any of these circumstances are encountered, it may be necessary to
If
select a more appropriate model or appropriately modify the model being
used.
40
-------
When any analytical technique is employed, the an~ly~t is respon-
sible 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 determin-
ing 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 theory on
the reliability of the calibration process for long-term estimates
41
-------
have been identified.44 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 measure-
ments on an event-by-event basis and then to calibrate the model with
results of the comparison.
This approach is severely limited by uncer-
tainties 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 comparisons45,46 for one basic
Gaussian point source model it was found that short-term estimates of
highest concentrations are generally accurate within a factor of two.
This accuracy is consistent with the empirical basis47.48 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.
~~ationa1 Air Pollution Control Administration. "Guidelines for
the Development of Air Quality Standards and Implementation Plans."
DHEW, Public Health Service, Washington, D. C., May 1969.
U. S. Congress. "Clean Air Act Amendments of 1977." Public Law 95-95,
Government Printing Office, Washington, D. C., August 1977.
2.
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.
Environmental Protection Agency. "Guidelines for Evaluating Supple-
mentary Control Systems." Publication No. EPA-450/2-76-003,
Environmental Protection Agency, Research Triangle Park, North
Carolina 27711, February 1976.
4.
5.
Environmental Protection Agency. "Technique for Supplementary Control
System Reliability Analysis and Upgrading.1I Publication No. EPA-450/
2-76-015, Environmental Protection Agency, ~esearch Triangle Park,
North Carolina 27711, March 1976.
6.
Lamb, D. V., F. 1. Badg1 ey, and A. 1. Rossano. IIA Cri ti ca 1 Revi ew
of Mathematical Diffusion Modeling Techniques for Air Quality with
Relation to r'10tor Vehicle Transportation.1I A study prepared for the
Washington State Highway Commission, Department of Highways, Univer-
sity of Washington, Seattle, Washington, June 1973.
7. Moses, H. IIMathematical Urban Air Pollution Model.1I Report No.
ANL/ES-RPY-001, Argonne National Laboratory, Argonne, Illinois
April 1969.
8.
Stern, A. C. IIproceedings of Symposium of Multiple-Source Urban
Diffusion Mode1s.11 Air Pollution Control Office Publication No.
AP-86 (NTIS pa 1984001. Environmental Protection Agency, Research
Triangle Park, North Carolina 27711,1970.
Slade, D. H., Ed. Meteorology and Atomic Energy 1968. USAEC.
Division of Technical .Information Extension, Oak Ridge, Tennessee,
July 1968.
9.
10. Smith, M. E., Ed. IIRecommended 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)
*Al1 references with a "PBII number are available from the National
Technical Information Service, Springfield, Virginia 22151.
43
-------
11. Turner, D. B. "\~orkbook of Atmospheric Dispersion Estimates." PHS
Publication ~o. 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),
NTIS PB 277193 National Technical Information Service, Springfield,
Vi rg i n i a, 19 7 4 .
Pooler, F. "Potential Dispersion of Plumes from Large Power Plants."
PHS Publication No. 999-AP-16 (NTIS PB 168790). Superintendent of
Documents, Government Printing Office, Washington, D. C., 1965.
13.
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 \~ind Tunnel Study." Publication No. EPA-600/4-76-
047 (NTIS PB 259877), Environmental Protection Agency, Research
Triangle 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. Sriggs, 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 Su1fates.1I Third Symposium
on Atmospheric Turbulence, Diffusion and Air Quality, American
Meteorological Society, Boston, Massachusetts, September 1976.
*UNAMAP has now been expanded to include 11 commonly used air quality
models. The 11 computer codes include PTMAX, PTDIS, PTMTP, HIWAY, PAL,
APRAC-1A, CDM, CDMQC, CRSTER, VALLEY and RAM.
44
-------
21.
Scriven, R. A., and B. E. A. Fisher. "The Long Range Transport of
Airborne Material and Its Removal by ~eposition and Washout. Atmos-
--
pheric Environment, Vol. 9, pp. 4968, 1975.
Hales, J. M., O. C. Powell, and T. D. Fox. "STRAM - An Air Pollution
Model Incorporating Nonlinear Chemistry, Variable Trajectories, and
Plume Segment Diffusion.'1 Publication No. EPA 450/3-77-012 (NTIS
PB 270778/AS). Environmental Protection Agency, Research Triangle
Park, North Carolina 27711, April 1977.
22.
23.
Burt, E. "Valley Model User's Guide." Publication ~o. EPA-450/2-77-
018, Environmental Protection Agency, Research Triangle Park, North
Carolina 27711, September 1977.
24.
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 ualit Haintenance Plannin
and Analysis, Volume 10 OAQPS No. 1.2-029R , Environmental Protec-
tion Agency, Research Triangle Park, North Carolina 27711, October
1977 -
25.
26.
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 P~rk,
North Carolina 27711, July 1977.
27. de !\levers, N., and J. R. r.1orris. "Rollback Modeling: Basic and
Modified," J. Air Pollution Control Assn., Vol. 25, No.9, pp. 943-
947, 1975.
28.
Busse, A. D., and J. R. Zimmerman. "User's Guide for the Climato-
logical Dispersion ModeL" 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.
30. TRl~ Systems Group. Air Quality Display Model'" Prepared for
National Air Pollution Control Administration under Contract ~o.
PH-22-68-60 (NTIS PB 189194), DHEW, U. S. Public Health Service,
Washington, D. C., November 1969.
29.
31.
Christiansen, J. H., and R. A. Porter. "User's Guide to the Texas
Climatological Model." Texas Air Control Board, Austin, Texas,
May 1976.
45
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32. Turner, D. B., and J. H. Novak. "User's Guide for RAM." Environ-
mental Protection Agency. Research Triangle Park, North Carolina
27711, 1978.
33. Christiansen, J. H. "Users Guide to the Texas Episodic ~1ode1."
Texas Air Control Board, Austin, Texas. May 1976.
34. Larsen. R. 1. "A Mathematical r.'odel for Relating Air Quality ~1easure-
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.
r:nviromnental Protection Agency. "Guidelines for the Review of
the Impact of Indirect Sources on Ambient Pdr Quali+y." Guidelines
for Air Quality ~,1aintenance Planning and Analysis, Volume 9,
OAQPS No. 1.2-028, Environmental Protection Agency. Research Triangle
Park, North Carolina 27711, 1975.
36. ZilTlnerman, J. R.. and R. S. Thompson. "User's Guide for HH1AY: A
Highway Air Pollution Model." Publication No. EPA-650/4-74-008
(NTIS PB 239944/AS), Environmental Protection Agency, Research
Triangle Park. North Carolina 27711. February 1975.
37 - Mancuso. R. L., and F. L. Ludwi g. "User I s ~1anua 1 for the APRAC-l A
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.
38.
Ludwig, F. L.. et al. "User's Manual for the APRAC-2 Emisssions and
Diffusion Model." Prepared for the Environmental Protection Agency
under Contract No. 68-01-3807. Stanford Research Institute. Menlo
Park, California 94025. June 1977-
39. Department of Agriculture. "Southern Forestry Smoke Management
Guidebook." USDA Forest Service General Technical Report SE-10,
Southeastern Forest Experiment Station. Asheville, North Carolina.
December 1976.
40. Environmental Protection Agency. "Control
~1anua1 for Particulate Matter." QM1PS No.
Protection Agency, Research Triangle Park,
September 1977.
41. Richard, G.. J. Avery. and T. Baboo1a1. "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, ~orth Carolina 27711. August 1977.
Strategy Preparation
1.2-049, Environmental
North Carolina 27711,
46
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42.
Doty, S. R., B. L. ~Jallace, and G. C. Holzworth. "A Climatoloqical
Analysis of Pasquill Stability Categories Based on 'STAR' Summaries."
National Climatic Center, ~ational Oceanic and Atmospheric Adminis-
tration, Asheville, ~orth Carolina 28801, April 1976.
43. ~1cCormick, R. A. "Air Pollution Climatology." In i\ir Pollution
Volume 1, edited by A. Stern, Academic Press, New York, ~jew York,
1968.
44.
Brier, G. H. "Validity of the Air Quality Display ~lodel Calibration
Procedure. II Publication No. EPA-R4-73-017, Environmental Protection
Agency, Research Triangle Park, tJorth Carolina 27711, January 1973.
Mills, M. T., and F. l'\. Record. "Comprehensive Analysi"s of Time-
Concentration Relationships and Validation of a Single-Source Dis-
persion Mode1." Publication No. EPA-450/3-75-083 (NTIS PB 250814/
AS), Environmental Protection Agency, Research Triangle Park, North
Carolina 27711, March 1975.
45.
46. r~ills, M. T., and R. W. Stern. "Model Validation and Time-
Concentration Analysis of Three Power P1ants." Publication No. EPA-
450/3-76-002 (NTIS PB 250685/AS), Environmental Protection Agency,
Research Triangle Park, North Carolina 27711, December 1975.
Pasqui11, F. Atmospheric Diffusion, 2nd ed., John Wiley and Sons,
New York, New York, 1974.
47.
48. Weber, A. H. "Atmospheric Dispersion Parameters in Gaussian Plume
Modeling: Part I. Review of Current Systems and Possible Future
Developments. II Publication No. EPA-600/4-76-030a, Environmental
Protection Agency. Research Triangle Park, North 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 quar.titative 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
-5urrr.tar; es-*---of-Reconritended
Air Quality Models
*Summaries presented in this appendix are largely based on similar information
summarized by J. J. Roberts (Ed.) in IIReport to the U.S. EPA of the
Specialists' Conference on the EPA Modeling Guidelines,1I Environmental
Protection Agency, Research Triangle Park, North Carolina 27711, February
1977 .
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A-2
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A.l AIR QUALITY DISPLAY r10DEL (AQD~1)
Reference:
Abstract:
Equations:
TRW Systems Group. "Air Quality Display Model. II 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 Larsenl is used to transform the average concentra-
tion 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:
16 6 5
x = L L L ~klm xklm
k=l l=l m=l
where:
x~ . ~ . zg (~) exp1 t (:z)~X ~ xL
/2; 0z ul ' J IU'
16 . rt. (tl)
xlc.tJn = 2.x . u:r c
l
for x ~ 2xL
linear interpolation for
xl < x < 2xL
xL defined by
0z(xL) = O.47L
y = crosswind distance between receptor and sector k centerline
c = sector width at receptor location
cr (x) = axb + c; a,b,c = functions of stability class
z a,b,c for neutral conditions split into
x > lOOOm case and x < lOOOm case.
A-3
-------
Q = emission rate (g/s)
H = plume height (m)
u = wind speed (m/s)
~ = relative frequency of occurrence from stability wind rose
0z = 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
c.
Point sources: single rate for each source
Area sources: single rate for each source
Each source treated by effective
source approximatiQn
No temporal variati9n allowed
Chemical Composition
single point
d.
Treats one or two inert pollutants simuJtane~us1y
Plume Behavior
e.
Use of the Briggs plume rise formulation is recommended
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
Horizontal Wind Field
Climatological approach
16 wind directions
6 wind speed classes
A-4
-------
f.
No variation in wind speed with height
Constant, uniform (steady-state) wind assumed
Vertical Wind Speed
Assumed equal to zero
g.
Horizontal Dispersion
h.
Climatological approach
Uniform 22.5° wide plume assumed
Frequency of occurrence int~rpolated between sector
Averaging times from 1 mo~th to 1 year or longer
Vertical Dispersion
centerlines
Semi-empirical/Gaussian plume
5 stability classes as defined by Turner2
Neutral stability split internally into 60% day, 40% night
Dis~ersion 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
Upper boundary
(this occurs
interpolated
(ground): perfect reflection
(mixing height): no effect until cr > 0.47L
at x = x) For xL < x < 2xL' cr iszlTnearly
between ~ts value at xL and itsZvalue at 2xL
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 climatological
value
Class E - 100 meters
A-5
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n.
Validation/Calibration
Calibration option available
Valldation is routinely done in most applications. Thus a large
number of validations have been carried out, although few appear
in the literature. This model, with the Briggs plume rise formulas,
typically estimates concentrations ranging from nearly correct
to a factor of two high
Output
o.
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
Computer Requirements
p.
Digital computer required
Core requirements are moderate
q.
Limitations
Useable for urban areas only
In general, CDM/CDMQC is preferred over AQDM
A-6
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A.2 APRAC-1A
Reference:
Abstract:
~ations:
Mancuso, R. L. and F. :.... Ludwig. "User's Manual for the
APRAC-1A Urban Diffusion Model Computer Program."
Publication No. EPA-65013-73-001 (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 frp.eway, arterial, and feeder street sources; and local,
from dispersion within a street canyon. APRAC requires an
extensive traffic inventory for the city of interest.
5.15 x 10-llF
Extraurban - xe = uL
F = annual fuel consumption within 22.5° sector extending
from 32 km to 1000 km upwind of receptor.
0.8Qi
Intraurban - x.. =
1 J U a..
1J
l-b. .
1J
xi+l - Xi
l-b. .
1J
l-b. .
1J
Until this expression equals the "box model valuell
Q.
urxi+ 1 -xi)
Thereafter the box model formula is used.
i = upwind area segment label
j = s ta b i 1 ity c 1 ass 1 a be 1 b. .
a.. and b.. from (cr) = a.. x 1J for x within segment i
1J 1J Z ij 1J
Street Canyon - Lee side
KQS
x =
L (u+0.5)[(x2+z2)1/2+ Lo]
KQS(H-z)
Windward side Xw = (u+0.5) WH
A-7
-------
Intermediate wind direction(less than ~ 30° from street
(direction) 1
xL = 2 (xL + Xw)
where:
x = horizontal distance from traffic lane (m)
z = height above pavement (m)
K = constant ~ 7
Lo = vehicle size ~ 2m
u = rooftop wind speed (m/s)
Qs = 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
A-8
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b.
Emission Rate
c.
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
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 Wind 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
h.
Vertical Dispersion
Semi-empirical/Gaussian plume
6 stability classes; stability class determined internally from user-
supplied meteorological data [modified from Turner2J
Dispersion coefficients from McElroy and pooler3, modified using
information in Leighton and Ditmar4
No adjustments made for variations in surface rougBness
Downwind distance variation of a assumed to be ax for purposes of
doing analytic integration Z
In street canyon sub-model, empirical function of wind speed and street
width and direction is used
A-9
-------
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 untii
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 s~rtace
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
n.
Validation/Calibration
No calibration option provided
One validation study documented by authors of the model
o.
Output
Hourly concentration values at each receptor
Frequency distribution based on hourly values can be obtained
A-10
-------
p.
Computer Requirements
Digital computer required
Core requirements are moderate
q.
Limitations
Limited to urban areas
No means for including point sources
A-ll
-------
A.3 CLIMATOLOGICAL DISPERSION MODEL (CDM)
References:
Abstract:
Equations:
Busse, A. D. and J. R. Zinmerman. "User's Guide for the
Climatological Dispersion ModeL" 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 1 977 .
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 larsenl 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.
16 N
Xpoint = 2'IT I
n=l
6
I
£.=1
6
I Qn 8kn.tm S.tm (Pn)/Pn
m=l
l6~16
Xarea = 2'IT I qk(P)
k=l
6 6 ]
£.~l m~l ~k.tm S.tm(p)
dp
with qk(P) = J Q(p,e)de
sector k
S.tm(p) = 2 exp [- l (-.l!) 2] exp [- 0.692 pJ
12; crz u£. 2 crz u£. Tl/2
for cr < 0.8l
z -
A-12
-------
So~(p) = --L exp [- 0.692 p] for (J > 0.8l
~II Ut l Ut Tl/2 z
b
(J = ap. a b =
z "
functions of stability class (m) and
downwind distance (p) - three ranges of
distanr.e used: 100-500 m, 500-5000 m,
and 5000-50,000 m
th . t
k = wind sector appropriate to the n pOln source
n
Q = emission rate of the nth point source (g/s)
n
p = distance from the receptor to the nth point source (m)
n
qk = emission rate of the area source per unit area and
unit time (g/s-m2)
p ~ distance from the receptor to an infinitesimal area
source (m)
e = angle relative to polar coordinates centered on the
receptor
t = index identifying the wind speed class
m = index identifying the class of the Pasquill stability
category
~(k,t,m) = joint frequency function
z = height of receptor above ground level (m)
Ut = representative wind speed (m/s)
h = effective stack height of source distribution, i.e.,
t~~ 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)
Tl/2 = assumed half life of pollutant hours (s)
A-13
-------
a.
Source-Receptor Relationship
Arbitrary location for each point source
Arbitrary location for each receptor
Area sources input as multiples of a user-defined unit size
Arbitrary re~ease heights for point and area sources
Actual s~paration between each source-receptor pair used
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
exponents from DeMarra;ss
Constant, uniform (steady-state) wind assumed
on power law variation
f.
Vertical Wind Speed
Assumed equal to zero
g.
Horizontal Dispersion
Climatological approach
Uniform distribution within each of 16 sectors
Averaging time = 1 month to 1 year or longer
A-14
-------
h.
Vertical Dispersion
Semi-empirical/Gaussian plume
5 stability classes as defined by Turner2
Day and night cases of neutral stability input separately
Dispersion coefficients taken from Turner5
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 mixing 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 climatological
value
Class D(night) - average of morning and afternoon climatological
value
Class E - morning climatological value
Emission rates: day-night allowed; all sources vary by same factor
Non-sequential (climatological) limited correlation
n.
Validation/Calibration
Calibration option available (CDMQC)
Two validation studies documented in the user's guide
Some unpublished validation experience
A-15
-------
o.
Output
One month to one-year averaging time simulated (arithmetic mean only)
Arbitrary averaging time by Larsen! procedure (typically 1 - 24 hr.)
(CDMQC)
Assumes
(1) lognormal concentration distribution,
(2) power law dependence of median and maximum concentrations 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
Useab1e for urban areas only
Area source emission densities must not vary rapidly from one area
source to the next
.A.-16
-------
A.4 GAUSSIAN-PLUME MULTIPLE SOURCE AIR QUALITY ALGORITHM (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, 1978.
RAM is a steady state Gvu5sian plume model for estimating
concentrations of relativ~ly stable pollutants for averaging
times from an hour to a day from point and area sources.
Level or gently r"l1ing ten'aill is assumed. Calculations are
performed for each hour. aoth rural and urban versions are
available.
The area source contributions are determined using the narrow-
plume hypothesis cimilar to applications by Gifford and Hanna7.
CJntribution. Contribution from a single upwind area source
x2
xA = ~ f f dx
xl
integral evaluated numerically
Xl' x2 = 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 =
1
t[; a
Z
92
Q
xpoint = 2TIU a a 9192
y z
For neutral or unstable conditions, 0z ~ 1.6L:
1
93
t[; a
Z
f =
Q
Xpoint = 2TIU a a 9,93
y z
A-17
-------
For neutral or unstable conditions, cr > 1.6L:
z
f = 1
L
Q
xpoi nt = /2; uL cr
y
gl
In which [ 2]
gl = exp -} (~)
92 = exp [- ~ (~:H) 2] + exp t } (~:H) 2]
I r 1 z-H+2nL ] t 1 (z+H+2nL) 2]
93 = n~oo exp [ 2 ( 0z )2 + exp 2 0z
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 ar~a
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.
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
A-18
-------
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 contri-
bution from area source with given effective release height
c.
Chemical Composition
Treats a single inert pollutant
d.
Plume Behavior
BriggsS'9'lO plume rise formulas
Does not treat fumigations or downwash
If plume height exceeds mixing height,
is assumed zero
ground level concentration
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 varia-
tion, 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 Turner2 procedure,
six classes used
Dispersion coefficients from McElroy and Pooler3 (urban) or Turner6
(rural). No further adjustments made for variations in surface
roughness or transport time
h.
Vertical Dispersion
Semi-empirical/Gaussian plume
Hourly stability class determined internally
Dispersion coefficients from McElroy and Pooler3 (urban) or
Turner6 (rural). No further adjustments made for variations
in surface roughness
i.
Chemistry/Reaction Mechanism
Exponential decay, user-input half-life
A-19
-------
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 0z = 1.6 times mixing height
Uniform mixing assumed in vertical thereafter
Stable conditions: there is no 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
limited experience with validation or comparison with observed data
o.
Output
Hourly and average (up to 24 hours) concentrations
limited individual source contribution list
Cumulative frequency distribution based on 24-hour
1 year of data at a limited number of receptors
averages and up to
at each receptor
p.
Computer Requirements
Digital computer required
Core requirements are moderate
q.
Limitations
Flat or gently rolling terrain
A-20
-------
A.5 SINGLE SOURCE (CRSTER) MODEL
Reference:
Abstract:
Equations:
Environmental Protection Agency. "User's Manual for Single
Source (CRSTER) Model." Publication No. EPA-450j2-77-013
(NTIS PB 271360). Office of Air Quality Planning and standards,
Research Triangle Park, North Carolina 27711, July 1977.
CRSTER is a steady state Gauss~an plume technique applicable to
both rural and urban areas in uneven terrain. The purpose of the
techn~que is: (1) to determine the maximum concentrations, for
certain averaging times between I-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 concentra+i0ns, and (3) to store concentration informa-
tion useful in ca;culating frequency distributions for various
averaging ti8es. The concentration for each hour of the year is
calculated and midnight - to - midnight averages are determined
for each 24-hour period.
. - Q
'C - 21TU cry crz 91 g3
for a < 1.6L
z -
x =
Q
v'2:,- U L a
y
gl
for cr > 1.6L
z
x = 0 (stability class 7)
L = mixing height (m)
H = (stack height + plume rise)-(difference in elevation
between receptor and base of stack)
91 = exp H- (~l]
93 =
n=:~ exp f ~ 2:~-H 2] + exp f ~ 2:~+H 2]1
A-2l
-------
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
c.
Unique average emission rate for each source
Monthly variation in emission rate allowed
Chemical Composition
d.
Treats a single inert pollutant
Plume Behavior
BriggsB. 9. 10 final plume rise formulas
Does not treat fumigation or downwash
If plume height exceeds mixing height. concentrations
assumed ~qual to zero
further downwind
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 DeMarrais5. 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
h.
Vertical Dispersion
Semi-empirical/Gaussian plume
7 stability classes
Dispersion coefficients from Turner; no further adjustments made
A-22
-------
i. Chemistry/Reaction Mechanism
Not treated
j. Physical Removal
Not treated
k. Background
Not tf'r::ated
1. Boundary Conditions
m.
Lower boundary: perfect reflection at the same height as the receptor
Upper boundary: perfect reflection
Multiple reflections handled by summation of series until
cr = 1.6 x mixing height
Uhiform 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 concentra-
tion 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.
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
11.
Validation/Calibration
No calibration option provided
Comparison with observations around at least 5 separate power
plants have been made
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 l-hour and 24-hour concentrations over
the receptor field
Hourly concentrations for each receptor on magnetic tape
A-23
-------
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
A-24
-------
A.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."
Proce~dings of the 7th _~ATG!CCM5 International Technical Meeting
on Air Poilutic1 ModeUiS. Air1ie House, Va.) September, 1976.
Christiansen, J. ~. and Porter. R. A.. Users Guide to the
Texas Climatol:gical MOQEJL, 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 Doin~ sources and area sources.
Area sources are handled by an algorithm proposed by Gifford
and Hanna? The concentration due to area sources is
given by
XA = FQ/U
where xA = concentration (~g/m3)
Q = area source emission rate in the vicinity of
the receptor (~g/s-m2)
U = mean ground-level wind speed (m/s)
F = a dimensionless constant
Gifford and Hanna have suggested a value of F = 50 for 502
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.
Jl.-25
-------
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 calcu-
lations require a meteorological joint frequency function with
sixteen 22.5° wind sectors, six wind speed classes (0-3, 4-6, 7-10,
11-16, 1:-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
x = Q. L
1 m=l
(K(x,H.m)~(k,m)/U ).(decay term)
m
where K(x,H,m) = (32xl06/[(2TI)3/2x Gz]) exp (-H~/2Gz2)
(K is precalculated for 20 distances. 9 effective source
heights, and six stability classes)
U is a wind speed characteristic of an entire stability class,
aWd is computed in the model by the equation:
[16
Urn = I
k=l
I ~(k'i,m)]
i=l
[16 6
k~l i~l
] -1
~ (k,i,m)/Ui
with x = concentration, ~g/m3
k = the wind sector index appropriate to source i at the
receptor
i = wind speed class index
m = stability class index
~ = meteorological joint frequency function
Qi = emission rate of source i, g/s
Hi ~ effective height of source i, m
Gz = standard deviation of vertical Gaussian concentration
distribution, m
Tl/2 = half-life for first-order pollutand decay. s
Ui = central wind speed of class i, m/s
x = downwind distance, m
A-26
-------
a.
Source-Receptor Relationshi~
Arbitrary location for each point source
Unlimited number of sources
Arbitrary location and square grid w;~:h for each area source
The model will allocate area sources into a unitOl"ll1 ;.:;~~re 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
Recertors 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
Down-wash and fumigation not considered
e.
Horizontal Wind Field
f.
Climatological approach
16 wind directions
Mean wind speed calculated for each stability class from the joint
frequency function of stability, wind direction, and wind speed
Wind speed corrected for physical stack height (same as CDM)
Vertical Wind Speed
g.
Assumed equal to zero
Horizontal Dispersion
Assumed to be uniform within each 22.5 degree sector (same as CDM)
A-27
-------
h. Vertical Dispersion
Gaussian plume
6 stability classes (Pasquill-Glfford-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
n. Validation/Correlation
Model is self-calibrating with input of field receptor observations
High correlation achieved for areas dominated by point sources
Poorer correlations have been shown where area sources are dominant
o. Output
Arithmetic mean concentration for the averaging time of the clima-
tological 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 concentration
(by % concentration) at each grid point
A-28
-------
p.
Computer Requirements
Digital computer required
Core requirements are moderate
q.
Limitations
Flat terrain, relatively cor.stant emissions
Use not recommended where area sources dominate
A-29
-------
A.7 TEXAS EPISODIC MODEL (TEM)
References:
Abstract:
Equations:
?orter, R. A. and Christbnsen, J. H. "Two Efficient
Ga~5s;an Plu~e Models Developed at the Texas Air Control
Board. II Proceedings of the 7th NATO/CCMS International
T~chnical Meeting on Air Pollution Modeling, Airlie House,
Va., September, 1976.
Christiansen, J. H. Users Guide to the Texas Episodic Model,
Texas Air Control Board, May, 1976.
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
~p to 300 elevated point 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 Hanna.7
Each area source square is affected by its own diffuse
emissions and those in the N area source squares directly
upwind of it: -
x = (~)l/2 ~M~N~~~ { Qo + I Q. [(2i+1)1-b - (2i-l)1-bJ}
o i=l 1
with ~x = length of side of area source grid squares (m)
Uo = surface wind speed (m/s)
Q = area emission rate of square containing the
o receptor (~g/s-m2)
Qj = area emission rates of the upwind area sources
(ug/s-m2)
a,b = stability and downwind distance-depegdent
parameters from the equation 0z = ax
The TEM employs steady-state bivariate Gaussian plume
point source logic. The concentration due to an elevated
point source is given by
A-3D
-------
x = 106 Q u exp (-y2/2a2) exp (-H2/2a2) exp (-.692x/UT1/2)
way az y Z
where
x = concentration (~g!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)
Tl/2 = first-order pollutant decay half-life (s)
a . a ~ the standard deviations of the plume concen-
t~atiOn distribution,
b
a = ax
z
d
a = CX
Y
with stability and downwind distance-dependent coeffi-
cients a, b, c, and d from Busse and Zimmermanll and
Turne~. The wind speed U is the surface wind speed
adjusted to the physical source height. Let K and K
be defined by y Z
K = 1000 exp (-y2/2ay2),
y ay
K = 1000 exp (-H2/2a 2)
Z waz Z
Then,
- KyKzQ
x - U (decay term)
A-3l
-------
K was calculated for each of the 1120 combinations of
t~enty down~ind distances from 2 to 60 km, eight crosswind
angles (Tan ly/x) from 0° to 7°, with 0 varying from 1° to
5° depending on stability, and seven stability classes.
For total vertical mixing below a mixing height of L meters,
X = 106Q exp ( 2 2)
Iln 0 LU -y /20y
y
This can be represented in the equation x
KyKZQ
(decay term)
u
=
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
to 50 by 50 rows or columns
Terrain assumed flat
Unique release height for each source
All receptors at ground level
spacing with up
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 si~ 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 Turnerb ;s used.
Does not treat downwash or fumigation
A-32
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e.
Horizontal Wind Field
User supplied stability. wind speed, and direction for the
averaging time period (10 min~tes to 3 hours) or for each
3 hour period to build a 24-nour day.
Power law variation
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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.
Vali~Jtion/Ca1ibration
Calibration by user supplied coefficients (A, B) so that
xca1 = A + BXpredicted
Limited experience with validation or comparison with observed data
o.
Output
Concentration mean for eacn 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
Reliability is not established for cases where area sources are
dominant
A-34
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A.8 References
1.
Larsen, R. 1. "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.
Turner, D. B. "A Diffusion Model of an Urban Area." Journal of
~eplied Meteorology, American Meteorological Society, Boston,
Massachusetts, February 1964.
McElroy, J. L. and F. Pooler. St. Louis Dispersion Study, Volume
II - Analyses. AP-53. National Air Pollution Control Administra-
tion, Arlington, Virginia 22203, December 1968.
3.
4.
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. DOC Nos. AD 7261,
AD 31509, AD 31508, AD 31507, AD 31510, AD 31511, Respectively,
1952.
5.
DeMarrais, G. A. "Wind Speed Profiles at Brookhaven National
Laboratory." Journal of Meteorology, Americal Meteorological
Society, Boston, Massachusetts, 1959.
6.
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,
1970.
7.
Gifford, F. A. and S. R. Hanna. "Modeling Urban Air Pollution."
Atmospheric Environment, January 1973.
Briggs. Gary A. Plume Rise. U.S. Atomic Energy Commission
Critical Review Series, (NTIS TID-25075) Oak Ridge National
Laboratory, Oak Ridge, Tennessee, 1969.
8.
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.
Busse, A. D. and J. R. Zimmerman. "User's Guide for the Climatological
Dispersion Model.1I Publication No. EPA-RA-73-024 (NTIS PB 227346/AS),
Environmental Protection Agency, Research Triangle Park, North
Carolina 27711, December 1973.
11.
A-35
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- - -- --
-ECHNICAL REPORT DATA
P!t'a~i(' , '0/ J..1.JJ7/( fIul:S Oil tile JTl er\t ,I--, ,'( i '''/ll!l'lctillg)
1. ~~AO:4500/2-78-027 ------ _t~AQ£Lti~_~1.2-080 3. RECIPIENT'S ACCESSIO~NO.
4. TITLE AND SUBTITLE 5. REPORT DATE
Guideline on Air Qua 1 ity Mode 1 s Apri 1, 1978
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S) 8. PERFORMING ORGANIZATION REPORT NO.
OAQPS No. 1.2-080
9. PERFORMING ORGANIZATION NAM~ AND ADDRESS 10. PROGRAM ELEMENT NO.
U.S. Environmental Protection Agency
Office of Air and Waste Management 11. CONTRACT/GRANT NO.
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711 .
12. SPONSORING AGENCY NAME AND ADDRESS 13. TYPE OF REPORT AND PERIOD COVERED
EPA/OAOPS Guideline
14. SPONSORING AGENCY CODE
200/04
15. SUPPLEMENTARY NOTES
This is a revision of the Interim Guideline on Air Quality Models, published in
October, 1977.
16. ABSTRACT
The guideline recommends air quality modeling techniques that may be applied to
air pollution control strategy evaluations and 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. It also identifies modeling
techniques and data bases that EPA considers acceptable. The guideline makes specific
recommendations concerning air quality models, data bases, and general requirements
for concentration estimates.
17. KEY WORDS AND DOCUMENT ANAL YSIS
.
3, DESCRIPTORS b.IDENTIFIERS/OPEN ENDED TERMS c. COSATI Field/Group
Air Pollution Implementation Air 138
Atmospheric Models Pollution Planning
Atmospheric Diffusion Diffusion Modeling
Meteorology Gaussian Plume Models
Air Pollution Abatement Clean Air Act
1'3. DISTRIBUTIO". ~ ,..',!::oMFNT 19. SECURITY CLASS (' I/is RepVrl) 21. NO. OF PAGES
Release Unlimited NonE> 84
, 20, SECURITY CLASS (This page) 22.PRICE
I None
EPA Form 2220-1 (9-73)
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