5779
OOOR77100
                 GUIDELINE SERIES
                           OAQPS NO.  1,2-080
                              OCTOBER 1977
                               INTERIM
                       GUIDELINE ON AIR QUALITY MODELS
                    U.S. ENVIRONMENTAL PROTECTION AGENCY
                      Office of Air Quality Planning and Standards

                       Research Triangle Park, North Carolina

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                   INTERIM
       GUIDELINE ON AIR QUALITY MODELS
                OCTOBER 1977
    F10NITORING AND DATA ANALYSIS DIVISION
OFFICE OF AIR QUALITY PLANNING AND STANDARDS
    U,S, ENVIRONMENTAL PROTECTION AGENCY
   RESEARCH TRIANGLE PARK, NORTH CAROLINA

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                   INTERIM
       GUIDELINE ON AIR QUALITY MODELS
                OCTOBER 1977
    MONITORING AND DATA ANALYSIS DIVISION
OFFICE OF AIR QUALITY PLANNING AND STANDARDS
    U.S. ENVIRONMENTAL PROTECTION AGENCY
   RESEARCH TRIANGLE PARK, NORTH CAROLINA

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                        ACKNOWLEDGEMENT





     This guideline was primarily prepared by Joseph Tikvart and



Herschel Slater of the Monitoring and Data Analysis Division, Office



of Air Quality Planning and Standards, Environmental Protection Agency,



Significant contributions were also made by James Dicke and Laurence



Budney of the same office.  However, the many comments and suggestions



provided by those participating in the Specialists' Conference and



the five public meetings on these guidelines must also be recognized.



The manuscript was prepared with great care by Ann Asbill and Barbara



Stroud.

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                            Preface

     In late 1976 it became clear from needs expressed by the States
and EPA Regional Offices, by many industries and trade associations
and by deliberations of Congress that greater consistency in the use
of air quality models is needed.  Consistency is required so that air
pollution control agencies, industry and the general  public have a
common basis for estimating pollutant concentrations, assessing control
strategies and specifying emission limits.

     To meet this need, EPA undertook a series of steps that would lead
to a vn'dely reviewed guide on the use of air quality models.  After
initial opinions from EPA's Regional Offices were received, the Office
of Air Quality Planning and Standards prepared a draft guide.  This
guide was submitted for critical review to a conference of specialists.*
The individual conferees were widely recognized experts in the develop-
ment and use of air quality models.  Based on the judgments and sugges-
tions of the conferees, the guide was revised and presented for public
comment at meetings** in Atlanta, Chicago, Denver, Mew York and San
Francisco.  These meetings were attended by approximately 500 repre-
sentatives of control agencies, industry, environmental groups and the
scientific community.  These attendees submitted extensive oral and
written comments which were evaluated and considered in the preparation
of this guide.

     During the development of the guide the Clean Air Act Amendments
of 1977 were signed into law.  These amendments require the promulgation
of regulations which specify models to be used in analyses pertinent to
prevention of significant deterioration.  They also require EPA to con-
duct a conference on air quality modeling.  This guide is published for
interim use, pending development of the regulations required by the Clean
Air Act.  The guide will be used as a point of departure for the Modeling
Conference and the subsequent development of regulations.

     Due to the continuing development of a wide variety of air quality
models and numerous gaps in our ability to simulate the atmospheric
dispersion process, EPA plans to review and update this document
periodically.
      *Roberts, J. J.,  Ed.   "Report to the U. S. EPA of the Specialists'
 Conference  on the EPA  Modeling Guidelines."  Environmental Protection
 Agency,  Research Triangle  Park,  North Carolina  27711, February  1977.

     **Slater, H. H., Chairman, "Comments and Recommendations Concerning
 the  Draft Guidelines on  Air Quality Models."  Environmental Protection
 Agency,  Research Triangle  Park,  North Carolina  27711, May/June  1977.
                                m

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                         TABLE OF CONTENTS

                                                                   Page

Acknowledgement 	    ii

Preface	   iii

1.0  INTRODUCTION 	     1

2.0  OVERVIEW	     5

3.0  REQUIREMENTS FOR CONCENTRATION ESTIMATES 	     7

     3.1  Control Strategy Evaluations	     7
     3.?  Hew Source Reviews	    10
          3.2.1  Meeting Air Quality Standards	    10
          3.2.2  Prevention of Significant Deterioration	    12

4.0  AIR QUALITY MODELS	    13

     4.1  Suitability of Models	    14
     4.?  Classes of Models	    16
     4.3  Recommended Models	    18
          4.3.1  Point Source Models for Sulfur Dioxide and
                 Particulate Matter (All Averaging Times)  ....    18
          4.3.2  Multi-Source Models for Sulfur Dioxide and
                 Particulate Matter (Annual Average)	    20
          4.3.3  Multi-Source Models for Sulfur Dioxide and
                 Particulate Matter (Short-Term Averages)  ....    21
          -4.3.4  Models  for Carbon Monoxide	    22
          4.3.5  Models  for Nitrogen Dioxide	    23
     4.4  Special Situations	    24

5.0  DATA REQUIREMENTS	    27

     5.1  Source Data	    27
     5.2  Meteorological Data	    31
     5.3  Receptor Sites	    33
     5.4  background Air Quality	    34

6.0  MODEL VALIDATION/CALIBRATION  	    39

7.0  REFERENCES	    43

Glossary of Terms	    48

Appendix A.  Significant Air Quality Increments for  Non-
             attainment  Areas	     A

Appendix C.  Summaries of  Recommended Air  Quality Models	     B

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                                                                 Page
B.I  Air Quality Display Model (AQDM)	    B-l
B.2  APRAC-1A	    B-5
B.3  Climatological Dispersion Model (COM)	   B-10
B.4  Real-Time Air Quality Simulation Model  (RAM)	   B-15
B.5  Single Source (CRSTER) Model	   B-20
B.6  Texas Climatological Model  (TCM).	   B-24
B.7  Texas Episodic Model (TEM)	   B-29

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1.0  INTRODUCTION
     The purpose of this guide is to recommend air quality modeling
techniques that may be applied to air pollution control  strategy eval-
uations and to new source reviews, including prevention  of significant
deterioration.  It is intended for use by EPA Regional  Offices in
judging the adequacy of modeling analyses performed by  EPA, by State
and local agencies and by industry and its consultants.   Similarly,
it serves to identify for all interested parties those  techniques and
data bases that EPA considers acceptable.  The guide is  not intended
to be a compendium of modeling techniques.  Rather it should serve as
a basis by which air quality managers, supported by sound scientific
judgment, have a common measure of acceptable technical  analyses.

     This guide makes specific recommendations concerning (1) air
quality models, (2) data bases and (3) general requirements for concen-
tration estimates.  It should be followed in all evaluations relative
to State Implementation Plans (SIPs).  However, it may be found that
(1) the recommended air quality model is not appropriate for a particu-
lar application, (2) the required data base is unavailable, or (3) a
better model or analytical procedure is available and applicable.  In
such cases, alternatives indicated in this guide or other data, models
and techniques deemed appropriate by the Regional Administrator may be
used.  Thus, even though specific recommendations are made, they should
not be considered rigid requirements.  The preferred model is that which
best simulates atmospheric transport and dispersion in the area of
interest.  However, deviations from this guide should be fully supported
arid documented.

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     The contents of this guide are summarized  in  Figure  1.   The  basic
steps in applying an air quality model  to a  practical  situation,  and
the necessary data bases and information, are shown.   The numbers in
parentheses refer to specific sections  of the guide.

     As indicated in Figure 1, it is generally  advisable  to  first i-poly
a model requiring a minimum expenditure of resources  (i.e.,  a prelimi-
nary screening technique).  The purpose of a screenings technique  is to
single out, with minimum effort, tho.,e  sources  that clearly  will  not
cause or contribute to ambient concentrations in excess of the National
Ambient Air Quality Standards (NAAQS) or allovjable concentration  incre-
ments.  In doinq so, unwarranted expenditure of resources (a refined
analysis when a simple approach would suffice)  can be avoided.  Another
advantage of first applying a relatively simple model is  to  obtain con-
centration estimates or receptor information that can be  helpful  in a
more refined analysis.

     If the screening analysis indicates that the source may pose an
air quality problem, application of a relatively sophisticated model
is then warranted for obtaining more refined concentration estimates.
The selection of an appropriate model should be based upon all the
factors indicated in Figure 1.  Of particular ihportance are the source
and meteorological data used  in the application.

     Given the selection  of a refined air quality model, an appropriate
receptor field must be  designated.  The model can then be applied  giving

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(3.0)
(4.3)
(44)
 (5.1)
(5.2)
(4.2)
 (4.1)
 (5.3)
                     INPUT
                  INFORMATION
       POLLUTANT OF CONCERN
REQUIREMENTS FOR CONCENTRATION
ESTIMATES
SOURCE TYPE (POINT. MULT I, ETC )
       SPECIAL SITUATIONS/PROBLEMS
       SOURCE DATA AVAILABLE
METEOROLOGICAL DATA AVAILABLE
ACCURACY OF CONCENTRATION
ESTIMATES
MODELING CAPABILITIES/RESOURCES
       RECEPTOR LOCATIONS
 (4.3,5.0)
             AVAILABILITY OF
             ADEQUATE MODEL
             AND DATA BASES
 (30)
                           i
          DEFINE REQUIREMENTS FOR
          CONCENTRATION ESTIMATES
 (GO)
            MODEL VALIDATION/
                CALIBRATION
                                                              MODELING
                                                                STEPS
                                                                       SIMPLE
                                                                     SCREENING
                                                                     PROCEDURE
                                                                           YES
                                                                        MODEL
                                                                      SELECTION
                                                                         ANO
                                                                     APPLICATION
                                                                                 (4.2,4.3)
                                                                  ISA
                                                                REFINED
                                                               ANALYSIS
                                                               REQUIRED
                                                                  7
CONSIDERATION
OF BACKGROUND
 AND GROWTH
                                                                                  (3.0,4.0)
(5.4)
                                                                                         (3.0)
              Fiqure 1   Selection and application of air quality models and data bases.  (Applicable
              sections of the guideline are indicated m parentheses.)

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appropriate consideration to background concentrations and future
growth.  The resulting concentration estimates can be used to analyze
source impact as required by the particular application.  However, any
analytical technique may have deficiencies that cause estimated concen-
trations to be in error.  Therefore, information on the accuracy of the
model should be available prior to evaluation of control strategies and
determination of allowable emissions.

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2.0  OVERVIEW



     Air quality models have been widely used to identify potential



violations of the National Ambient Air Quality Standards (NAAQS) and



to determine emission limits.  The need for air quality models in



the development and revision of SIP-related control  strategies was



identified very early.   However, due to the initial demands of the



Clean Air Act (1970) on available resources, it has  not generally



been possible to use air quality models to the extent desired.  Thus,



many SIPs are based on an example region concept and a simple emissions



rollback model.  In recent years, however, air quality models have



been more widely used.  As these models arid associated data bases



increase in sophistication, they allow more precision in estimating



concentrations and in assessing the adequacy of control strategies.





     In addition to their use in development and revision of control



strategies, air quality models are also required in the New Source



Review program to insure attainment and maintenance of NAAQS, and



to prevent significant air quality deterioration.  Judgments must be



made concerning allowable emission rates and I,he placement of new



sources that may cause specific air quality  levels to be exceeded or



that may contribute significantly to existing violations.





     It would be advantageous to categorize the various control



programs and to apply a designated model to each proposed source



which comes under a given program,  llov/ever, the diversity of the



nation's topography and climate, and variations in  source

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configurations and operating characteristics dictate against a routine
"cookbook" analysis.  There is no single model  capable of properly
addressing all conceivable situations.  Meteorological phenomena
associated v/ith threats to air quality (standards are rarely amenable
to simple mathematical treatment.  Any modeling effort should be
directed by highly competent individuals with a broad range of experience
and knowlege in air pollution meteorology and coordinated closely with
specialists in emissions characteristics and data processing.  The
judgment of well-trained professional analysts is essential.

     Nevertheless, it is clear from the needs expressed by the States
and EPA Regional Offices, by many industries and trade associations and
                                2
by the deliberations of Congress  that greater consistency in the use
of models and data bases is in order.  Consistency is required so that
air pollution control agencies and the general  public have a common
basis for estimating pollutant concentrations, assessing control strate-
gies and specifying emission limits.  This guide promotes the required
consistency.

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3.0  REQUIREMENTS FOR CONCENTRATION ESTIMATES
     Specific air quality standards and increments of pollutant con-
centrations must be considered for control  strategy evaluations and
for new source reviev/s, including prevention of significant deteriora-
tion.  This section specifies general requirements for concentration
estimates and identifies the relationship between emission limits and
air quality standards/increments for these applications.

3.1  Control Strategy Evaluations
     SIP-related emission limits should be based on concentration
estimates for the averaging time which results in the most stringent
control requirements.  In all cases these concentration estimates are
assumed to be a sum of the concentration contributed by the source and
an appropriate background concentration (see pp. 34.37).

     If the annual average air quality standard is exceeded by a
greater degree (percentage) than standards for other averaging times,
the annual average is considered the restrictive standard.  In this
case the sum of the highest estimated annual average concentration and
the annual average background provides the concentration which should
be used to specify emission limits.  However, if a short-term  standard
is exceeded by a greater degree and  is thus  identified as the  restric-
tive standard, other considerations  are required because the frequency
of occurrence must also be taken into account.

     Historically, when dispersion model estimates are  used to assist
in judging whether short-term NAAQS  will be  met, and ultimately  in

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specifying appropriate emission limits,  one  of  three  types  of
concentration estimates is used:   (1)  The highest  of all estimated
concentrations, (2) the second-highest of all estimated  concentra-
tions, or (3) the highest of second-highest  concentrations  estimated
for a field of receptor sites.   The highest  of  second-highest  concen-
trations for a field of receptors is obtained as  follows:
(1)  Frequency distributions of short-term concentrations are  esti-
mated for each site in a field  of receptors; (2)  the  highest estimated
concentration at each receptor  is discarded; (3)  the  highest of the
remaining concentration estimates from  the field  of receptor sites
is identified.  Throughout this guideline that  concentration estimate
is referred to as the "highest, second-highest" concentration.
     The first two types of estimates have been applied  most often
in specifying emission limits.   However, they may be  unnecessarily
restrictive in many situations.  The third type of estimate is more
consistent with the criteria for determining violations  of  the NAAQS,
which are identified in "Guidelines for Interpretation of Air  Quality
           3
Standards."   That guideline specifies  that  a violation  of  a  short-
term standard occurs at a site when the standard is exceeded  a
second time.  Thus, emission limits which are to be based on  an averag-
ing time of 24-hours or less should be based on the highest,  second-
highest estimated concentration plus a  background concentration
which can reasonably be assumed to occur with that concentration.
(See the section on background air quality  for a discussion of the
factors and variety of situations that should be considered.)
                                 8

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     An estimate of the highest,  second-highest concentration which  is
based on many well-chosen receptor site?   may well  reveal  previously
unidentified "hot spots."  Such en estimate may provide a  more conserva-
tive cind realistic indication of
and of the appropriate emission
a few monitoring sites.  However
are limited to a short period, or
the potential  fur MAAQS violations
imits than do  actual  measurements at
 if the data available for modeling
 source data are generalized, the
estimated highest, second-highest concentration is unlikely to provide
a true indication of the threat to air quality standards.  Thus it is
essential that an adequate data base be available (see Section 5.0).
Data for a time period of sufficient length should be considered so
that there is reasonable certainty that meteorological conditions
associated with the greatest impacts on air quality are identified.
Similarly, detailed source data are required so that the air quality
impact can be assessed for the source conditions likely to result in
the greatest impact.

     There are two exceptions to the above requirement to use the
highest, second-highest estimated concentrations.  The first situation
occurs where monitored air quality data from specific sites indicate
that concentrations greater than those estimated can occur with little
or no impact from the source(s) in question.  For the purpose of
specifying emission limits, these measured concentrations should be
ranked ahead of the estimated concentrations in the frequency distri-
bution of concentrations at that specific monitoring  (receptor) site.

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     The second situation occurs  where  the  Regional Administrator
identifies inadequacies in the  data  base  or the models  for a  particular
application.   As a result of these  inadequacies he may  determine that
there is a lack of confidence in  an  emission limit based  on the highest,
second-highest concentration or that this concentration simply cannot
be estimated.  In this case, until  such time as the necessary data
bases are acquired or analytical  techniques are improved, the use of
the highest estimated concentration  to  determine  source impact and  to
evaluate control strategies may be  justified.
3.2  New Source Reviews
     Reviews for new sources that require an air  quality  impact analysis
should determine if the source will  (1) cause or  exacerbate violations
of a NAAQS or (2) cause air quality deterioration which is greater
than allowable increments.  The following subsections identify  require-
ments for concentration estimates associated with air quality standards
and with prevention of significant  deterioration.
     3.2.1  Meeting Air Quality Standards
     For each new major source* or  major modification of  a source,  an
air quality analysis should be performed to determine if  the  source
will cause or exacerbate a violation of a NAAQS.   For a new major source
located in an attainment area, the  concentration  estimates should meet
the same requirements  that are applicable to control  strategy evaluations
     *As defined in section 302(j) of the Clean Air Act, a major source
 is any stationary facility which directly emits, or has the potential to
 emit, 100 tons or more per year of any air pollutant.
                                  10

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The determination of whether or not * ,ie source will  cause an air quality
violation should be based on (1) the highest estimated concentration
for annual averages and (2) the highest, second-highest estimated concen-
tration for averaging times of 24-hours or less.   The most restrictive
standard should be used in all cases to establish the potential  for an
air quality violation.  Background concentrations should be added in
assessing the source's impact.  The two exceptions to the shorter-term
averaging times which were noted in the preceding section also apply
here; i.e., monitored data with higher concentrations and inadequacies
in data bases or model.

     In some cases a new major source of sulfur dioxide, particulate
matter, nitrogen oxides or carbon monoxide may be (1) located in a non-
attainment area or (2) may be in an attainment area but is expected to
exacerbate air quality violations known to occur in a nearby non-
attainment area.  In such situations, the expected incremental increase
in pollutant concentrations should be estimated for meteorological
conditions which accompany the existing violations.   Incremental
increases in pollutant concentrations that may be considered significant
(for purposes of determining whether an existing violation is exacer-
bated) are discussed  in Appendix A.*  For all averaging times, the
highest estimated concentration increments are used.  The second highest
     *A source wTtlTrelatively low stack height, e.g., 30 meters, that
has actual emissions greater than 15 tons per year of sulfur dioxide or
particulate matter may have a significant impact as defined in Appendix
A.  If the impact is significant, the source is subject to emissions
offsets as discussed in  EPA's Interpretive Ruling.
                                    11

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is not used in the case of short-term concentrations  since the incre-
mental increase is added to a concentration which  is  already based  on
the highest, second-highest value.
     3.2.2  Prevention of Significant Deterioration
     Air quality models should be used in all  significant deterioration
evaluations.  Allowable increments  for sulfur dioxide and particulate
                                                             2
matter are set forth in the Clean Air Act Amendments  of 1977. "  These
maximum allowable increases in pollutant concentrations may be exceeded
once per year, except for the annual increment.  Thus, in significant
deterioration evaluations for short-term periods the  highest, second-
highest increase in estimated concentrations should be less than or
equal to the permitted increment.

     Where an exemption to the Class I increments is  requested and
approved pursuant to section 165(d)(2)(D) of the Clean Air Act, the
source may cause the Class I increments to be exceeded on a total of
18 days during any annual period.   In this case it is necessary to
select the highest estimated concentration in the field of receptors
for each of the 365 days.  These 365 values are then ranked and the  19th
highest is used to determine emission limits.  However, the highest,
second-highest concentration may not exceed a somewhat higher increment
specified  in  section  165(d)(2)(D)(iii).
                                   12

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4.0  AIR QUALITY MODELS
     This Section recommends air quality models* for a wide variety of
specific applications.  Factors are discussed that determine the suit-
ability of models for individual situations, classes and sub-classes
of models are identified, and special modeling problems are addressed.
     Air quality models recommended in this section are state-of-the-
art analytical techniques that make it possible to perform control
strategy evaluations and new source reviews, including prevention of
significant deterioration.  However, the responsible Regional Adminis-
trator may find that (1) the recommended air quality model is not appro-
priate for the particular application, (2) the required data base is
unavailable, or (3) a better model or analytical procedure is available
and applicable.  In such cases, alternatives indicated in this guide
or other models deemed appropriate by the Regional Administrator may be
used.  However, all deviations from this guide should be fully supported
and documented.
     It must not be construed that the models recommended in this guide
are to be permanently used to the exclusion of all others or that they
are the only models available for relating emissions to air quality.
Similar models that are available from other governmental agencies and
private consultants have been summarized and discussed by Lamb, et al.,
      7      8
Moses,  Stern  and others.
     *A discussion of each specific model or refined analytical technique
 is presented in Appendix B.  Some of the models recommended here are also
 applicable to the development and use of Supplementary Control Systems
 (SCS).  However, such control systems are not considered in the context.of
 this guideline and the reader is referred to other publications on SCS.
                                 13

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     In all cases, and particularly when models and data bases other
than those recommended in this guide are being proposed, early discus-
sions among the Regional  Office staff, the control  agencies and industry
representatives are encouraged.  Concurrence on the technical  approach,
prior to the actual analyses, will  help avoid disagreements concerning
the final results.  The Office of A1r Quality Planning and Standards is
routinely available to the Regional Offices for consultation on partic-
ularly difficult or complex problems.
     It should be noted that models applicable to photochemical oxidants
are not discussed in this guide.  These models are undergoing a critical
review.  Requirements for such models and associated data bases will be
specified at a later time.
4.1  Suitability of Models
     The extent to which a specific air quality model is suitable for
the evaluation of source Impact and control strategies depends upon
several factors that should be judged by the responsible Regional Adminis-
trator.  These Include (1) the detail and accuracy of the data base,
I.e., emission Inventory, meteorological data, air quality data; (2) the
meteorological and topographic complexities of the area; (3) the tech-
nical competence of those undertaking such simulation modeling; and
(4) the resources available.  These factors, as well as others deemed
appropriate by the responsible Regional Administrator, should be con-
sidered  in determining the suitability of a particular model application.
     The data base required for air quality models includes source
data, meteorological data and  air  quality data  (see Section 5.0).

                                 14

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Appropriate data should be available before any attempt is made to apply
a model.  A model which requires detailed, precise input data should not
be applied when such data are unavailable.  However, assuming the data
are adequate, the greater the detail with which a model considers the
spatial and temporal variations 1n emissions and meteorological condi-
tions, the greater the ability to evaluate the source impact and to
distingush the effects of various control strategies.

     Most air quality models that describe atmospheric transport and
dispersion apply to areas with relatively simple topography.  However,
areas subject to major topographic or marine influence experience
meteorological complexities that are extremely difficult to simulate.
In the absence of a model capable of simulating such complexities, only
a preliminary approximation may be feasible until such time that better
models and data bases become available.

     Models are highly specialized tools.  Competent and experienced
personnel are an essential prerequisite to the successful application
of simulation models.  Whenever a model is applied, the services of
knowledgeable, well-trained air pollution engineers, meteorologists and
air quality analysts should be engaged.  The need for specialists is
particularly critical when the more sophisticated models are used or
the area being investigated has complicated meteorological or topo-
graphic features.  A model applied Improperly or with inappropriately
chosen data can  lead to serious misjudgments regarding the source impact
or the effectiveness of a control strategy.
                                  15

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     The resource demands generated by use of air quality models vary
widely depending on the specific application.  Resources required are
dependent on the nature of the model and its complexity, the detail  of
the data base, the difficulty of the application, and the amount and
level of expertise required.  The costs of manpower and computational
facilities are also important factors.
4.2  Classes of Models
     The air quality modeling procedures discussed in this guide can be
categorized into four generic classes:  Gaussian, numerical, statistical
or empirical, and physical.  Within some of these classes a large number
of individual "computational algorithms" exist, each with its own specific
applications.  Hhile each of these algorithms may have the same generic
basis, e.g., Gaussian, it is accepted practice to refer to them individ-
ually as models.  For example, the Climatological Dispersion Model, the
Air Quality Display Model and the Texas Climatological Model are commonly
referred to as individual models.   In fact, they are all variations of a
basic Gaussian model.  In many cases the only real difference between
models is the degree of detail considered in the input or output data.
     Gaussian models are generally considered to be state-of-the-art
techniques for estimating the impact of nonreactive pollutants.  Numerical
models are more  appropriate than Gaussian models for multi-source appli-
cations which involve reactive pollutants.  However, they frequently
require more  extensive resources and are not as widely  applied.  Statis-
tical or empirical  techniques are  frequently employed  in situations
where incomplete scientific understanding of the physical and chemical
                                 16

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processes make the use of a Gaussian or numerical model impractical.
Various specific models of these three generic types are recommended
in this guideline.
     Physical modeling, the fourth generic type, involves the use of
wind tunnel or other fluid modeling facilities.  This type of modeling
may be very useful in evaluating the air quality impact of a source or
group of sources in a geographic area limited to a few square kilometers.
Where physical modeling is available and applicable, it is recommended.
However, physical modeling is a complex process which requires a high
level of technical expertise and is beyond the scope of this guide.

     In addition to the various classes of models, this guide considers
two levels of sophistication.  The first level consists of general, rela-
tively simple estimation techniques that provide conservative estimates
of the air quality impact of a specific source, or source category.  The
purpose of such techniques is to eliminate from further consideration
those sources that clearly will not cause or contribute to ambient con-
centrations in excess of NAAQS or allowable concentration increments.
Conversely, these techniques can be used to identify those control
strategies that have the potential to meet NAAQS and allowable incre-
ments.  The second level consists of those analytical techniques which
provide more detailed treatment of physical and chemical atmospheric
processes, require more detailed and precise input data, and provide more
specialized concentration estimates.  As a result they provide a more
refined and, at  least theoretically, a more accurate estimate of source
impact and the effectiveness of control strategies.
                                 17

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     In some cases, the first level  of models may be equated with
screening techniques to determine if a second or more refined analysis
is required.  However, while the use of screening techniques followed
by a more refined analysis is desirable, there are situations where the
screening techniques are practically and technically the only viable
option for estimating source impact  and evaluating control  strategies.

4.3  Recommended Models
     To meet the need for consistency identified in Section 2, selected
point source and multi-source models applicable to specific pollutants
and averaging times are recommended  in this subsection.   Ideally, air
quality models that are recommended  should meet prescribed  standards
of performance for particular applications and should be subjected to
specific validation procedures.  However, there are no generally accepted
standards of performance and validation procedures (see p.  39).  The
models recommended in this guideline are simply those which are (1) rep-
resentative of the state-of-the-art for atmospheric simulation models
and (2) those most readily available to air pollution control agencies.
     4.3.1  Point Source Models for Sulfur Dioxide and Particulate
            Matter (All Averaging Times)
     Gaussian models are considered to be state-of-the-art techniques
for estimating concentrations of sulfur dioxide and particulate matter.
They are the best choice for most point source evaluations.  For all
point sources two levels of sophistication in the use of models are
suggested.  The  first  level is composed of models which can provide a
preliminary estimate of concentrations.   It is recommended that such  a
                                 18

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screening technique be applied to all major sources.  If it is found from



the screening technique that the source will  cause a concentration that



is more than one-half of an allowable air quality increment, then that



source should be subjected to a more refined  analysis.





     For flat terrain situations that have no significant meteorological


                                                     9-11
complexities, there are several standard publications     and computer-


           12                                                    13
ized models   that can be used for screening.  In addition Pooler   and


                14
Carpenter et al.   have discussed simplified  techniques for estimating


                                                          15
concentrations during inversion-breakup fumigation.  Lyons   has summar-



ized information and techniques applicable to lake/sea breezes.  Huber and



Snyder   '   and Briggs   have presented various techniques applicable to


                                      19-22
aerodynamic downwash.  Several authors      have outlined techniques that



are useful for situations where long-range transport  (greater than 50


                                           23
kilometers) is important.  The Valley Model   is applicable to some


                                24
complex terrain situations; Egan   has summarized information on other



applicable techniques.  Volume 10 of the Guidelines for Air Quality Main-


                              25
tenance  Planning and Analysis,   "Procedures for Evaluating Air Quality



Impact of New Stationary Sources" has summarized, in a format useful



for screening, techniques applicable to both flat terrain arid more com-



plex situations; those techniques are recommended for use.





     In  those cases where a more refined analysis is  required and there
are no significant meteorological or terrain complexities, the Single

                     or

Source (CRSTER) Model   is recommended for use.  If meteorological or



terrain complexities cause substantial uncertainties, then a model that
                                   19

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is more detailed or more suitable than the Single Source (CRSTER)  Model
should be applied.  No refined, widely available models applicable to
complex situations are identified.  It is recommended that each complex
situation be treated on a case-by-case basis with the assistance of
expert advice.

         If the data bases required to applj  the Single Source (CRSTER)
Model are unavailable  or if other refined models applicable to a complex
rituation do not exist, then it may be necessary to bese estimates of
source impact and the evaluation of control strategies on only the esti-
mates provided by the screening techniques.  In such cases, an attempt
should be made to acquire or improve the necessary data bases and to
develop appropriate analytical techniques.

     Models specified here and in the following subsection are also applv
cable to stationary sources of lead pollutants, provided the pollutants
can be assumed to behave as a gas.
     4.3.2  Multi-Source flodels for Sulfur Dioxide and Particulate
            Matter  (Annual Average)
     Due to the complexity of most multi-source situations and the wide
acceptability of several models, a screening process is not generally
conducted.  If a preliminary assessment of the adequacy of a control
                                       27
strategy is desired,  the Rollback Model   may be used.  However,  in most
cases such a  screening does not constitute an adequate control strategy
demonstration.
                                 20

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     The Clitnatological Dispersion Model  (CDM),  »"'" the Air Quality
                    on                                         oi
Display Model (AQDM)JU and the Texas Climatological Model  (TCMr1  are
recommended for evaluating the long-term impact of urban multi-source
complexes.  In regions with major meteorological  or topographic complex-
ities, more detailed or suitable models may be used.  If the meteorolog-
ical or topographic complexities are such that the use of any available
air quality model is precluded, an attempt should be made to acquire or
improve the necessary data bases and to develop appropriate analytical
techniques.
     4.3.3  Multi-Source Models for Sulfur Dioxide and Particulate
            Matter (Short-Term Averages)
     As noted in the preceding subsection, a Rollback Model may be used
for the preliminary assessment of a control strategy.  The Real-Time Air-
                              32
Quality Simulation Model (RAM)   is recommended for evaluating the impact
of multi-source complexes on air quality averaged over short-term periods.
It is applicable to both urban and rural situations.  The Texas Episodic
           33
Model (TEM)   may be used if the data bases required to apply RAM are
unavailable.  Also, if the resources required to operate RAM or TEM are
not available, then CDM, AQDM or TCM may be used to estimate short-term
concentrations of SOp  and particulate matter.  CDM and AQDM incorporate
procedures,  such as that discussed by Larsen,   to convert 3-hour and 24-
hour average concentrations from annual average concentration estimates.
Such statistical techniques are valid only in urban, multi-source areas
and should not be used in situations dominated by  large point sources.
                                  21

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     In regions with major meteorological  or topographic complexities,
more detailed or suitable models may be used.   If the meteorological
or topographic complexities are such that  the use of any available air
quality model is precluded, an attempt should be made to acquire or
improve the necessary data bases and to develop appropriate analytical
techniques.

     4.3.4  Models for Carbon Monoxide
     The recommendations for point source  screening procedures and
models are also applicable to evaluate point sources of carbon monoxide
(CO).  The models, procedures and requirements described in Volume 9 of
                                                                   or;
the Guidelines for Air Quality Maintenance^   Planning and Analysis,
"Guidelines for Review of the Impact of Indirect Sources on Ambient Air
Quality," are recommended for screening all sources of CO which fulfill
the definition of an indirect source.  The indirect source guideline  is
                         12 Ifi
based on the use of HIWAY  '   and other simple dispersion techniques.
It is acceptable to apply these latter techniques, e.g. HIWAY, indepen-
dently of the indirect source guideline if it is found that the guideline
does not adequately consider a wide enough set of circumstances.  If a
preliminary assessment of the adequacy of a control strategy applicable
to an urban area is desired, the Rollback Model may be used.
     Specific refined modeling techniques are not recommended here.
Situations that require more refined  techniques should be  considered  on
a case-by-case basis with the use of  expert consultation.   If a suitable
model is available  and the data and technical competence required for
                                 22

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this model are available, it may be used.  An example of such a refined
                      1237
technique is APRAC-1A.  *    However, if a region-wide analysis is
necessary and the complexities are such that the use of any available
air quality model is precluded, an attempt should be made to acquire or
improve the necessary data bases and to develop appropriate analytical
techniques.

     4.3.5  Models for Nitrogen Dioxide
     The recommendations for point source screening techniques and models
are also applicable to evaluate point sources of nitrogen oxides (NO )
                                                                    A
under limited circumstances.  The circumstances require an assumption
that all NO  is emitted in the form of NO,, or is converted to N02 by the
time it reaches the ground and that NOp is a nonreactive pollutant.

     For sources located where atmospheric photochemical reactions are
significant, a Rollback Model may be used as a preliminary assessment
to evaluate the control strategies for multiple sources (mobile and
stationary) of NO .  Another acceptable screening technique for multiple
                 J\
sources is to make an assumption similar to that required for point
sources and then to use a model for nonreactive pollutants, such as COM.

     Specific refined modeling techniques are not recommended here.
Situations that require more refined techniques should be considered on
a case-by-case basis with the use of expert consultation.  If a suitable
model is available and the data and technical competence required for
this model are available, it may be used to estimate average concentra-
tions of NOp.  However, if a region-wide analysis is necessary and the
                                 23

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complexities are such that the use of any available  air  quality  model
is precluded, an attempt should be made to acquire or  improve  the
necessary data bases and to develop appropriate analytical  techniques.

4.4  Special Situations
     Models with a wide applicability are not generally  available for
dealing with long-range transport, deposition, wind-blown particulate
matter and unique topographic or meteorological circumstances, e.g.,
complex terrain, aerodynamic downwash.  Thus with proper support and
documentation, the Regional Administrator may determine  that a particular
model, not specifically recommended here, is appropriate for a special
situation.  Examples of these situations are discussed for clarification.
     The administration of the national prevention of significant deter-
ioration policy may require that the air quality impact  of a source be
estimated for great distances downwind.  It is uncertain, however, what
the impact of sources at such great distances is.  Knowledge of the
dispersion coefficients for air quality models* becomes  increasingly
tenuous with downwind distance.  Plume transport beyond  about 50 kilo-
meters usually requires substantial travel time.  As travel time
increases, diurnal variations in meteorological conditions and movement
of weather systems are more likely to alter plume trajectories and
     *Vertical dispersion in these situations is more appropriately
 treated with numerical models.  There are also inherent difficulties
 with Gaussian models  in cases where plume depletion through chemical
 and physical removal  processes is significant.  Plume depletion would
 normally be significant at distances beyond about 50 kilometers for
 tall stacks under  conditions of appreciable vertical mixing, and at
 considerably shorter  distances for near-ground sources.
                                 24

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dispersion characteristics.  Even though the impact at greater than 50-
100 kilometers may be relatively small, the impact can still  be signifi-
cant for large sources and for situations where the merging of plumes
occurs.  Techniques are available to examine these impacts, but only
limited experience in their use is currently available.  If it appears
that a large source (for example, a 2000-MW coal-fired power plant meet-
ing new source performance standards) may constitute a threat to ambient
air quality standards or prevention of significant deterioration increments
at large distances, that source should be considered on a case-by-case
                                19-22
basis with available techniques.

     The models presented in this guide for estimating ambient concen-
trations of suspended particulate matter assume that the particles
disperse as a gas and emanate from well-defined sources.  Unfortunately,
In many areas, particularly where the air quality standards are not being
attained, these assumptions may not hold.  Windblown dust, re-entrained
street dust, dry-land farming, and raw-material handling operations, all
of which are often referred to as fugitive dust sources, can be signifl-
                                   38
cant sources of particulate matter.    EPA has several on-going studies
concerned with fugitive sources of dust; however, the rate and distribu-
tion of particulate emissions from these sources is not yet fully known.
As a result, a widely applicable model for routinely estimating particulate
                                                                 39
concentrations attributable to fugitive sources is not available.
     Terrain dominated flows and wakes that develop in the vicinity of
                                                  1fi—1R ?^ ?4
pollutant sources are involved in many situations.  ~   '   *    The basic

                                   25

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theoretical principles of these flows are generally understood.  However,
the variety of terrain features is so great and the spectrum of atmos-
pheric circumstances so broad that no generally applicable model is
available that can adequately deal with the range of conditions encoun-
tered.

     EPA will provide guidance on data bases and assessment procedures
to deal with special situations as the results of on-going field invest!'
gations and research on these matters become available.
                                  26

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5.0  DATA REQUIREMENTS
     It is essential that appropriate source and meteorological  data
be used with any recommended model.  Such data, and related procedures
for estimating these data, constitute an integral part of the model.
It is often overlooked that few of the variables input to a model  are
directly measured or routinely available.  Submodels must appropriately
convert the available source and meteorological data to a form that the
air quality model can accept.  It is also important that a variety of
load/emissions conditions, and that a wide range of meteorological
conditions based on several years of data, be considered in evaluating
control strategies and in determining source impact for new source
reviews, including prevention of significant deterioration.  In addi-
tion, there is a need to judiciously choose receptor sites and to
specify background air quality.  This section identifies requirements
for these data bases.

5.1  Source Data
     Sources of pollutants generally can be classified as point, line
and area sources.  Major point sources are defined as those that emit,
or have the potential to emit, 100 tons or more per year of any air
pollutant.  Line sources are generally confined to roadways and streets
along which there are well-defined movements of motor vehicles.  Area
sources include the multitude of minor sources with individually small
emissions that are impractical to  consider as separate point or line
sources.  Area sources are typically treated as a grid network of
square areas, with pollutant emissions distributed uniformly within
                                 27

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each grid square.   Descriptions of individual  models  should  be ref-

erenced for specific emissions inventory requirements.


     For situations involving one or a few point sources  the following

are minimum requirements for new source review and control  strategy

evaluations.  Design process rate or design load conditions  must be

considered in determining pollutant emissions.  Other operating con-

ditions that may result in high pollutant concentrations  should also

be identified.  A range of operating conditions, emission rates, and

physical plant characteristics based on the most recently available

data, should be used with the multiple years of meteorological data

(see Section 5.2) to estimate the source impact.  The following

example (power plant) typifies the kind of data on source character-

istics and operating conditions that are required:
         1.  Plant layout.  The connection scheme between boilers and
stacks, and the distance and direction between stacks, building param-
eters (length, width, height, location and orientation relative to
stacks) for plant structures which house boilers, control equipment,
etc.

         2.  Stack parameters.  For all stacks, the stack height and
diameter (meters), and the temperature (K) and volume flow rate
(actual cubic meters per second) or exit gas velocity (meters per
second) for operation at 100 percent, 75 percent and 50 percent load.

         3.  Boiler size.  For all boilers, the associated megawatts
and pounds of stean per hour, and the design and/or actual fuel con-
sumption rate for 100 percent load for coal (tons/hour), oil (barrels/
hour), and natural gas (thousand cubic feet/hour).

         4.  Boiler parameters.  For all boilers, the percent excess
air used, the boiler type  (e.g., wet bottom, cyclone, etc.), and the
type of firing  (e.g., pulverized coal, front firing, etc.).
                                 28

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         5.  Operating conditions.   For all  boilers, the type,  amount
and pollutant contents of fuel, the total  hours of boiler operation and
the boiler capacity factor during the year,  and the percent load for
winter and summer peaks.

         6.  Pollution control equipment parameters.  For each  boiler
served and each pollutant affected, the type of emission control equip-
ment, the year of its installation, its design efficiency and mass
emission rate, the date of the last test and the tested efficiency,
the number of hours of operation during the  latest year, and the best
engineering estimate of its projected efficiency if used in conjunc-
tion with coal combustion; data for any anticipated modifications or
additions.

         7.  Data for new boilers or stacks.  For all new boilers and
stacks under construction and for all planned modifications to  existing
boilers or stacks, the scheduled date of completion, and the data or
best estimates available for items 1 through 6 above following  com-
pletion of construction or modification.
     Typically for line sources, such as streets and highways, data are

required on the width of the roadway and its center strip,  the types and

amounts (grams per second per meter) of pollutant emissions, the number

of lanes, the emissions from each lane and the height of emissions.

The location of the ends of the straight roadway segments must be speci-

fied in appropriate grid coordinates.  More detailed information and

data requirements for modeling mobile sources of pollution are provided
                35
in the guideline   on indirect sources.


     For multi-source urban situations, detailed source data are

often impractical to obtain.  In these cases, source data should be

based on annual average conditions.  Area source information required

are types and amounts of pollutant emissions, the physical  size of the

area over which emissions are prorated, representative stack height

for the area, the location of the centroid or the southwest corner of
                                 29

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the source in appropriate grid coordinates.   If the model  accepts data
on area-wide diurnal  variations in emissions, such as those estimated
by emissions models which are based on urban activity levels and other
factors, those data should be used.
     In cases where the required source data are not available and cannot
be obtained, the data limitation should be identified.  Due to the
uncertainties associated with such a limitation the use of the highest
estimated concentration to determine source impact and to evaluate control
strategies may be justified until such time that a better data base
becomes available.

     For control strategy evaluations the impact of growth on emissions
should be considered for the next 10-20 year period.  Increases in
emissions due to planned expansion of the sources considered or planned
fuel switches should be identified.  Increases in emissions at each
source which may be associated with general industrial/commercial/
residential expansion in multi-source urban areas should also be con-
sidered.  Such information should be used to estimate the air quality
impact of those sources in future years.  However, for new source
reviews, the impact of growth on emissions should only be considered
for the period prior to the start-up date for the source.  Such changes
in emissions should consider increased area source emissions, changes
in existing point  source emissions which would not be subject to
preconstruction review, and emissions due to sources with permits to
construct.
                                 30

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5.2  Meteorological Data
     For a dispersion model to provide useful  and valid results, the
meteorological data used in the model  must be  representative of the
transport and dispersion conditions in the vicinity of the source that
the model is attempting to simulate.  The representativeness of the
data is dependent on (1) the proximity of the  meteorological monitoring
site to the area under consideration, (2) the  complexity of the terrain
in the area, (3) the exposure of the meteorological monitoring site and
(4) the period of time during which the data are collected.  The repre-
sentativeness of the data can be adversely affected by large distances
between the source and receptors of interest and valley-mountain, land-
water, and urban-rural characteristics of the  area.

     For new source review and control strategy evaluation, the meteoro-
logical data required as a minimum to describe transport and dispersion
in the atmosphere are wind direction, wind speed, atmospheric stability,
mixing height or related indicators of atmospheric turbulence and mixing,
Site-specific data are preferable to data collected off-site.  The avail-
ability of such meso- and micro-meteorological data collections permits
more detailed meteorological analyses and subsequent improvement of
model estimates.  Local universities, industry, pollution control
agencies and consultants may be sources of such data.  The  parameters
typically required can also be derived from routine measurements by
National Weather Service stations.  The data are available  as individual
observations and in summarized form from the National  Climatic  Center,
As-heville, N. C.  Descriptions of individual models should  be referred
                                 31

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to for specific meteorological  data requirements.   Many models  require
either hourly meteorological  data or annual  stability wind roses.

     It is preferable for the meteorological data  base used with the
air quality models to include several years  of data.   Such a multi-year
data base allows the consideration of variations in meteorological  con-
ditions that occur from year to year.  The exact number of years needed
to account for such variations in meteorological conditions is  uncertain
and depends on the climatic extremes in a given area.  Generally five
     40
years l  yields an adequate meteorological data base.*  For compatibility
of model estimates with the NAAQS, the single year with the highest,
second-highest short-term concentration estimate (or the highest annual
estimate) should then be used in evaluating  source impact.  However, if
long-term data records are not available, it may be necessary to limit
the modeling and subsequent analyses to a single year of meteorological
data.  The use of one year of data might also be justified if the
climatological representativeness of that data can be demonstrated.  A
longer record  from a nearby National Weather Service site could be used
to check for representativeness.
     The number of National Weather Service stations for which multiple
years of hourly weather data are available is increasing significantly.
     *
      An alternative approach is to use a shorter meteorological data
base and to use statistical techniques to identify the occurrence of
a rare event  (highest, second-highest concentration that exceeds the
NAAQS) over a longer period of record.  This is equivalent to applying
the "100-year flood" concept to air pollution control.  EPA is studying
this concept  but for the present recommends using a meteorological data
base of at least one year.
                                 32

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Several EPA offices have ordered such data for a large number of stations,
It is clear that more detailed analyses than previously considered for
SIP evaluations and new source review are necessary.  Thus, for areas
where meteorological conditions are adequately represented by weather
stations, the use of multiple years of meteorological data appears to
be viable and justified.

     Where representative meteorological observations are not available,
the concentration estimates may be limited to consideration of worst
case conditions.  An analysis of worst case conditions should be based
on reasonable interpretations of climatological data and should consider
such critical plume characteristics as looping, coning, limited mixing,
fumigation, aerodynamic downwash and plume impaction on terrain.  Due
to the uncertainties of this approach, the use of the highest estimated
concentration (as opposed to the highest, second-highest concentration)
to determine source impact and to evaluate control strategies may be
justified until such time that a better data base becomes available.
5.3  Receptor Sites
     A receptor site is a location for which an air pollution concentra-
tion is estimated.  The choice of locations for receptor sites signifi-
cantly affects the evaluation of source impact and control strategy
effectiveness.  It is most important to identify the location where the
maximum concentrations occur, both short- and long-term.  The receptor
grid must allow sufficient spatial detail and resolution so that the
location of the maximum or highest, second-highest concentration is
identified.
                                 33

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     The receptor sites in the vicinity of large point sources  at which
maximum concentrations are likely to occur can be identified by
(1) estimating concentrations for a sufficiently dense array of recep-
tors to identify concentration gradients and (2) subsequently refining
the location of the maximum by estimating concentrations for a  finer
array of receptors in the general areas of maximum concentrations.
                                                 12
Another technique is to use a model such as PTMAX   in combination with
joint frequency distributions of wind speed, wind direction and stability
to identify the downwind distance and direction at which the highest
concentrations are most likely to occur.  However, other areas  around
the source(s) should not be ignored, particularly if they are on
elevated terrain.  In addition, a receptor should be specified  at any
site where a monitor is located.
5.4  Background Air Qua1ity
     To adequately assess the significance of the air quality impact
of a source, background concentrations must be considered.  Background
air quality relevant to a given source includes those pollutant concen-
trations due to natural sources and distant, unidentified man-made
sources.  For example, it is commonly assumed that the annual mean back-
ground concentration of particulate matter is 30-40 yg/m3 over much of
                          41
the Eastern United States.    Typically, air quality data are used to
establish background concentrations in the vicinity of the source under
consideration.  However, where the source is not isolated, it may be
necessary to use  a multi-source model to establish the impact of  all
other  nearby sources during dispersion conditions  conducive to high
concentrations.
                                 34

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     If the point source is truly isolated and not affected by other
readily identified man-made sources, two options for determining back-
ground concentrations from air quality data are available.   The prefer-
able option it to use air quality data collected in the vicinity of the
source to determine mean background concentrations for the  averaging
times of interest when the point source itself is not impacting on the
monitor.  The second option applies when no monitors are located in the
vicinity of the source.  In that case, average measured concentrations
from a "regional" site can be used to establish a background concentra-
tion.
     For the first option it is a relatively straightforv/ard effort to
identify an annual average background from available air quality data.
For shorter averaging times, background concentrations are  determined
by the following procedure.  First, meteorological conditions are iden-
tified for the day and similar days when the highest, second-highest
estimated concentration due to the source occurs.  Then the average
background concentration on days with similar meteorological conditions
is determined from air quality measurements.  The background for each
hour is assumed to be an average of hourly concentrations measured at
sites outside of a 90° sector downwind of the source.  The  1-hour con-
centrations are then averaged to obtain the background concentration
for the averaging time of concern.
     If air quality data from a local monitoring network are not avail-
able, then monitored data from a "regional" site may be used for the
                                 35

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second option.  Such a site should characterize air quality across  a
broad area, including that in which the source is  located.   The technique
of characterizing meteorological  conditions  and determining associated
background concentrations can then be employed.

     If a small number of other identifiable sources are  located nearby,
the impact of these sources should be specifically determined.   The back-
ground concentration due to natural or distant sources can  be determined
using procedures already described.  The impact of the nearby sources
must be summed for locations where interactions between the effluents of
the point source under consideration and those of nearby  sources can occur.
Significant locations include (1) the area of maximum impact of the point
source, (2) the area of maximum impact of nearby sources., and (3) the
area where all sources combine to cause maximum impact.  It may be
necessary to  identify these locations through a trial and error analysis.

     If the point source is located in or near an urban multi-source
area, there are several possibilities for estimating the  impact of all
other sources.  If a comprehensive air monitoring network is available,
it may be possible to rely entirely on the measured data.  It is neces-
sary that the network include monitors judiciously located so as to
measure air quality at the locations of the point source's maximum impact
and  locations of the highest concentrations in the area.   If the point
source is not yet operating, its calculated impact can be added to these
measured concentrations.   If the source already exists arid is contri-
buting to the measured concentrations, its  calculated contribution
                                 36

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should be subtracted from the measured values to estimate the concen-
tration caused by other man-made sources and by background.
     If the monitored data are inadequate for such an analysis, then
multi-source models can be used to establish the impact of all other
sources.  These models should be used for appropriate pollutants and
averaging times to identify concentrations at the times and locations
of maximum point source impact.  The times and locations of maximum
impact due to all other sources must also be identified.  If a model
is not available for the appropriate averaging times, statistical
techniques can be used with an appropriate model to extrapolate from
one averaging time to another.  All statements in this guide regarding
the data requirements and validity of air quality models are applicable
to analyses of this type.
     For control strategy evaluations, the impact of growth on area-wide
emissions and on concentrations caused by nearby sources should also be
considered for the next 10-20 year period.  To determine concentrations
in future years, existing air quality should be proportionately adjusted
by the anticipated percent change in emissions in the vicinity of
individual monitoring sites.  However, for new source reviews, changes
in existing air quality should only be considered for the period prior
to the start-up date of the source (see Section 5.1).
                                 37

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38

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6.0  MODEL VALIDATION/CALIBRATION
     Any application of an air quality model  may have deficiencies
which cause estimated concentrations to be in error.   When practical
to obtain a measure of confidence in the estimates, they should be
compared v/ith observed air quality data and their validity determined.

     The model validation process* consists of a series of analytical
steps:  (1)  Comparing estimated concentrations with observed values,
(2) determining the cause of discrepancies, (3) correcting and improving
data bases, (4) modifying the model (if necessary) in a manner that
provides a better mathematical representation of physical reality, and
(5) documenting, for others, the accuracy of the estimates.  Statis-
tical methods available for validation of models include skill scores,
contingency tables, correlation analyses, time series and spatial
analyses, and others.  If evaluation by one or more statistical tech-
niques indicates that the concentration estimates are not a satisfactory
representation of observed concentrations, then it is likely that one
or both of the following problems exist:  the source, meteorological
or air quality data are not appropriate, reliable and complete; or
the model itself is inadequate for the area under consideration.
     The availability and accuracy of the input data significantly
influence the accuracy of the model estimates.  The source factors
     *There is a clear need for specific and uniform validation pro-
cedures and for standards of performance.  The feasibility of specifying
such procedures and standards for air quality models is being studied
by EPA.  However, for the present time, the generalized recommendations
presented in this section are suggested for use.
                                 39

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that have the greatest impact on the accuracy of the estimates  are the
accuracy and completeness of the (1) emissions data, (2)  physical  plant
parameters, and (3) site coordinates of the sources.  Often the valida-
tion will reveal deficiencies in the emissions inventory, which can be
corrected to improve the accuracy of the model estimates.  The  accuracy
of the concentration estimates is also affected by the location and
exposure of the instrumentation used for obtaining the meteorological
data and the overall representativeness and completeness  of those data.
Similarly, the validation of the dispersion model is affected by the
location, exposure and representativeness of the air quality sampling
sites and by the accuracy and completeness of the air quality data itself.
These data should be available for the same averaging times as  the con-
centration estimates and should describe the spatial variation  of
pollutant concentrations across the area.  If the air quality data are
in any way unsuitable or incorrect, the accuracy of the dispersion model
estimates cannot be determined.
     The following factors most frequently cause a model  to be considered
inadequate or inappropriate for a given area:  (1)  The model is applied
to an area with complex or unique terrain or meteorological conditions;
(2)  the  source  emissions vary markedly or irregularly with time;  (3) the
pollutant is subject to major or highly variable atmospheric chemical
reactions or removal processes;  (4) the model is applied to pollutants
with characteristics other than those considered in its  development.   If
any  of these circumstances are encountered,  it may  be necessary to select
a more appropriate model or  appropriately modify the model being  used.
                                 40

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     When any analytical technique i<- employed, the analyst is
responsible for recognizing and quantifying limitations in the accuracy,
precision and sensitivity of the procedure.  Thus, in all  applications
of models an effort should be made to identify the reliability of the
model estimates for that particular area or similar areas  and to deter-
mine the magnitude and sources of error associated with the use of the
model.  In addition, sensitivity analyses are useful  for determining
the effect of variations or uncertainties in the data bases on the range
of likely concentrations.  Such information may be very useful in
determining source impact and evaluating control strategies.  Where
possible, information on sensitivity should be made available by the
modeler.

     Due to limitations of the data base, lack of scientific knowledge
or limitations on time and resources, it may not always be possible to
perform a thorough and complete model validation.  Thus, in some situa-
tions, it has been necessary to revert to calibration of the model.
Calibration of a model is the process of identifying systematic errors
and applying a correction factor.  In many cases this involves the
application of regression analysis or other statistical techniques to
adjust model estimates in order to increase agreement with measured
data.
     Calibration of long-term multi-source models is a widely used
procedure.  It is acceptable provided that reasonable resources have
been expended to validate the model, e.g., the five steps listed at
the beginning of this section.  Limitations imposed by statistical
                                41

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theory on the reliability of the calibration process  for long-term
                               42
estimates have been identified.    In some cases,  though,, calibration
may be the only alternative for improving the accuracy of estimated
concentrations and the control  strategy evaluation.   However,  if the
model accounts for less than 50 percent of the variation of measured
concentrations, it is doubtful  that there is justification for using
the model.

     Calibration of short-term models has not been widely performed
and is subject to a greater amount of error and misunderstanding.
There have been attempts by some to compare short-term estimates
and measurements on an event-by-event basis and then to calibrate
the model with results of the comparison.  This approach is severely
limited by uncertainties in source and meteorological data and thus
one's ability to precisely estimate the concentration at an exact
location for a specific increment of time.  These uncertainties make
attempts to calibrate a short-term model questionable.  As a result,
it appears that the most reliable direct comparison between estimated
and measured short-term concentrations involves the upper percentiles
of the respective frequency distributions.  Even here, considerable
variation may be found from site-to-site and plant-to-plant.  In such
           43 44
comparisons   '   for one basic Gaussian point source model it was found
that short-term estimates of highest concentrations are  generally accu-
rate within a factor of two.  This accuracy is consistent with the
empirical basis   '   for these models.  However, in general, estimates
which are both too high and too  low may be  expected.
                                 42

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7.0  REFERENCES*

1.  National  Air Pollution Control  Administration.   "Guidelines  for
    the Development of Air Quality  Standards  and  Implementation  Plans."
    DHEW, Public Health Service, Washington,  D.  C.,  May 1969.

2.  U. S. Congress.  "Clean Air Act Amendments  of 1977." Public  Law  95-95,
    Government Printing Office, Washington,  D.  C., August 1977.

3.  Environmental  Protection Agency.   "Guidelines for Interpretation of
    Air Quality Standards."  Office of Air Quality Planning  and  Standards,
    Research  Triangle Park, North Carolina 27711, September  1976.

4.  Environmental  Protection Agency.   "Guidelines for Evaluating Supple-
    mentary Control Systems."  Publication No.  EPA-450/2-76-OQ3,
    Environmental  Protection Agency, Research Triangle Park, North
    Carolina  27711, February 1976.

5.  Environmental  Protection Agency.   "Technique  for Supplementary Control
    System Reliability Analysis and Upgrading."  Publication No. EPA-450/
    2-76-015, Environmental Protection Agency,  Research Triangle Park,
    North Carolina 27711, March 1976.

6.  Lamb, D.  V., F. I. Eadgley, and A. T.  Rossano.   "A Critical  Review
    of Mathematical Diffusion Modeling Techniques for Air Quality with
    Relation  to Motor Vehicle Transportation."   A study prepared for the
    Washington State Highway Commission, Department  of Highways, Univer-
    sity of Washington, Seattle, Washington,  June 1973.

7.  Moses, H.  "Mathematical Urban  Air Pollution  Model."  Report No.
    ANL/ES-RPY-001, Argonne National  Laboratory,  Argonne, Illinois
    April 1969.

8.  Stern, A. C.  "Proceedings of Symposium of  Multiple-Source Urban
    Diffusion Models."  Air Pollution  Control Office Publication Mo.
    AP-86_ (NTIS PB 198400), Environmental  Protection Agency, Research
    Triangle  Park, North Carolina  27711,  1970.

9.  Slade, D. H., Ed.  Meteorology  and Atomic Energy 1968.  USAEC.
    Division  of Technical Information  Extension,  Oak Ridge,  Tennessee,
    July 1968.

10. Smith, M. E., Ed.  "Recommended Guide for the Prediction of the
    Dispersion of Airborne Effluents."  The American Society of
    Mechanical Engineers, United Engineering Center, 345 East 47th
    Street, New York, New York, 1973.   (Revised)
    *A11 references with a "PB" number are available from the National
Technical Information Service, Springfield, Virginia  22151.
                                 43

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11.   Turner,  D.  B.   "Workbook  of  Atmcspheric Dispersion Estimates."  PHS_
     Publication No.  999-AP-26 (NTIS  PB  191482),  Environmental Protection
     Agency,  Research Triangle Park,  North  Carolina 27711, 1969.

12.   Environmental  Protection  Agency.   "User's  Network for Applied
     Modeling of Air  Pollution (UNAMAP)."   (Computer Programs on Tape
     for Point Source Models,  HIWAY,  Climatological Dispersion Model
     and APRAC-1A), NTIS  PB  229771, National Technical Information Service,
     Springfield, Virginia,  1974.

13.   Pooler,  F.   "Potential  Dispersion  of Plumes  from Large  Power Plants."
     PUS Publication  No.  999-AP-16  (NTIS PB 168790).  Superintendent of
     Documents,  Government Printing Office, Washington, D. C., 1965.

14.   Carpenter,  S.  B., et al.   "Principle Plume Dispersion Models:  TVA
     Power Plants."  J. Air  Poll.  Control Assn.,  Vol. 22, No. 8, pp. 491-
     495, 1971.

15.   Lyons, W. A.  "Turbulent  Diffusion and Pollutant Transport  in Shore-
     line Environments."   Lectures  on Air Pollution and Environmental  Im-
     pact Analyses, American Meteorological Society, Boston, Massachusetts,
     September 1975.

16.   Huber, A. H., and W. H. Snyder.   "Stack  Placement in the Lee of a
     Mountain Ridge:   A Wind Tunnel Study."  Publication No. EPA-600/4-76-
     047 (NTIS PB 259877), Environmental Protection Agency,  Research
     TrTangle Park, North Carolina  27711,  September 1976.

17.   Huber, A. H., and W. H. Snyder.   "Building Wake Effects on  Short
     Stack Effluents."  Third  Symposium on  Atmospheric Turbulence, Dif-
     fusion, and Air Quality,  American  Meteorological Society, Boston,
     Massachusetts, September  1976.

18.   Briggs, G.  A.  "Diffusion Estimation for Small Emissions."  ATDL
     Contribution File No. (Draft)  79,  Air  Resources Atmospheric Turbu-
     lence and Diffusion Laboratory,  Oak Ridge, Tennessee, May  1973.

19.   Heffter, J. L. and G. A.  Ferber.  "A Regional-Continental Scale
     Transport Diffusion and Deposition Model."  NOAA Technical  Memoran-
     dum ERL ARL-50. Air Resources Laboratories,  NOAA, Silver Spring,
     Maryland, June 1975.

20.   Rao, K. S., J. S. Lague,  and B.  A. Egan.   "An Air Trajectory  Model
     for Regional Transport of Atmospheric  Sulfates."  Third Symposium
     on Atmospheric Turbulence, Diffusion  and Air Quality,  American
     Meteorological Society, Boston,  Massachusetts, September  1976.

21.   Scriven, R. A., and B. E. A. Fisher.   "The Long  Range  Transport  of
     Airborne Material and  Its Removal  by  Deposition  and  Washout.   Atmos-
     p he ri c  Environment, Vol.  9, pp.  49-68, 1975.
                                 44

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22.  Hales, J.  M.,  D.  C.  Powell,  and  T.  D.  Fox.   "STRAM  -  An  Air  Pollution
     Model Incorporating  Nonlinear Chemistry,  Variable Trajectories,  and
     Plume Segment  Diffusion."  Publication No.  EPA 450/3-77-012  (NTIS
     PB 270778/AS).   Environmental Protection  Agency, Research  Triangle
     Park, North Carolina  27711, April  1977.
23.  Burt, E.  "Valley Model  User's Guide."
24.
25.
26.
27,
28.
29.
30.
31.
                          Publication  No.  EPA-450/2-77_j
                                                North"
018, Environmental  Protection Agency, Research Triangle  Par
Carolina  27711, September 1977.

Egan, B. A.  "Turbulent Diffusion in Complex Terrain"   Lectures  on
Air Pollution and Environmental  Impact Analyses, American Meteoro-
logical Society, Boston, Massachusetts, September 1975.

Budney, L. J. "Procedures for Evaluating Air Quality Impact  of New
Stationary Sources." Guidelines  for Air Quality Maintenance  Planning
and Analysis, Volume 10 (OAQPS No. 1.2-029R), Environmental  Protec-
tion Agency, Research Triangle Park, North Carolina  27711,  October
1977.

Environmental Protection Agency.   "User's Manual for Single  Source
(CRSTER) Model."  Publication No. EPA-450/2-77-013 (NTIS PB  271360)
Office of Air Quality Planning and Standards, Research Triangle Park,
North Carolina  27711, July 1977.
 and J. R. Morris.  "Rollback Modeling:  Basic and
Air Pollution Control  Assn., Vol.  25, No.  9, pp.  943-
de Nevers, N.,
Modified," J_.
947, 1975.

Busse, A. D., and J. R. Zimmerman.  "User's Guide for the Climato-
logical Dispersion Model."  Publication Mo. EPA-RA-73-024 (NTIS PB
227346/AS), Environmental Protection Agency, Research Triangle Park,
North Carolina  27711, December 1973.

Brubaker, K. L., P. Brown, and R. R. Cirillo.  "Addendum to User's
Guide for Climatological Dispersion Model."  Publication No. EPA-
450/3-77-015.  Environmental Protection Agency, Research Triangle"
Park, North Carolina  27711, May 1977.

TRW Systems Group.  Air Quality Display Model."  Prepared for
National Air Pollution Control Administration under Contract No.
PH-22-68-60 (NTIS PB 189194), DHEW, U. S. Public Health Service,
Washington, D. C., November 1969.

Christiansen, J. H., and R. A. Porter.  "User's Guide to the Texas
Climatological Model."  Texas Air Control Board, Austin, Texas,
May 1976.
                                 45

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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, 1977.

33.  Christiansen, J.  H.  "Users Guide to the  Texas  Episodic Model."
     Texas Air Control Board,  Austin, Texas, May  1976.

34.  Larsen, R.  I.  "A Mathematical  Model for  Relating Air  Quality  Measure-
     ments to Air Quality Standards." Office  of  Air Programs  Publication
     No. AP-89 (NTIS PB 205277), Environmental  Protection Agency, Research
     Triangle Park, North Carolina 27711, November 1971.

35.  Environmental Protection  Agency.  "Guidelines for the  Review of
     the Impact of Indirect Sources on Ambient Air Quality," Guidelines
     for Air Quality Maintenance Planning and  Analysis,  Volume 9,
     OAQPS No. 1.2-028, Environmental Protection  Agency, Research Triangle
     Park, North Carolina 27711, 1975.

36.  Zimmerman, J. R., and R.  S. Thompson.  "User's Guide for  HIWAY:  A
     Highway Air Pollution Model."  Pub!ication No.  EPA-650/4-74-008
     (NTIS PB 239944/AS), Environmental  Protection Agency,  ResearcTT
     Triangle Park, North Carolina 27711, February 1975.

37.  Mancuso, R. L., and F. L. Ludwig.  "User's Manual  for the APRAC-1A
     Urban Diffusion Model Computer Program."   Publication No. EPA-65Q/
     3-73-001 (NTIS PB 213091), Environmental  Protection Agency, Research
     Triangle Park, North Carolina  27711, September 1972.

38.  Environmental Protection Agency.   "Control Strategy Preparation
     Manual for Particulate Matter."  OAQPS No. 1.2-049, Environmental
     Protection Agency, Research Triangle Park, North Carolina  27711,
     September 1977.

39.  Richard, G., J. Avery, and T. Baboolal.   "An Implementation Plan for
     Suspended Particulate Matter in the Phoenix Area:   Volume III  Model
     Simulation of Total Suspended Particulate Levels."   Publication  No.
     EPA-450/3-77-021c,  Environmental  Protection Agency, Research  Triangle
     Park, North  Carolina  27711, August 1977.

40.  Doty, S. R., B. L. Wallace, and G.  C. Holzworth.  "A Climatological
     Analysis of  Pasquill Stability  Categories Based on 'STAR' Summaries."
     National Climatic  Center,  National  Oceanic and Atmospheric Adminis-
     tration, Asheville, North  Carolina  28801, April 1976.

41.  McCormick, R. A.   "Air Pollution Climatology."  In Air Pollution
     Volume  1, edited  by A. Stern, Academic Press,  New York, New York,
     1968.
                                 46

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42.  Brier, G, W.   "Validity of thf- / ir Quality Display  Model  Calibration
     Procedure."  Publication No.  CPA-R4-73-017, Environmental  Protection
     Agency, Research Triangle PaTk, Nortfi Carolina   27711,  January  1973.

43.  Mills, M. T., and F.  A. Record.  "Comprehensive  Analysis  of  Time-
     Concentration Relationships and Validation of a  Single-Source Dis-
     persion Model."  Publication  .No.  EPA-450/3-75-083 (NTIS PB 250814/
     AS), Environmental Protection Agency, ResearcFTFiangle Park, North
     Carolina  27711, March 1975.

44.  Mills, M. T., and R.  W. Stern.  "Model  Validation and Time-
     Concentration Analysis of Three Power Plants."   Publication  No.  EPA-
     450/3-76-002  (NTIS PB 250685/AS), Environmental  Protection Agency,
     Research Triangle Park, North Carolina   27711,  December 1975.

45.  Pasquill, F.   Atmospheric Diffusion, 2nd ed., John  Wiley  and Sons,
     New York, New York, 1974.

46.  Weber, A. H.   "Atmospheric Dispersion Parameters in Gaussian Plume
     Modeling:  Part I.  Review of Current Systems and Possible Future
     Developments."  Publication No. EPA-600/4-76-030a,  Environmental
     Protection Agency, Research Triangle Park, NortFi Carolina 27711,
     July 1976.
                                 47

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                    Glossary of Selected Terms
Air Quality - Ambient pollutant concentrations and  their temporal
and spatial distributions.

Algorithm - A specific mathematical  calculation procedure.

Background - Ambient pollutant concentrations due to natural  sources
and distant, unidentified man-made sources.

Calibration - An adjustment applied  to concentration estimates, based
on a comparison with measured air quality data, in  order to improve the
accuracy of the model.

Computer code - A set of statements  that comprise a computer program.

Model - A quantitative or mathematical representation or simulation
which attempts to describe the characteristics or relationships of
physical events.

Receptor - A location at which ambient air quality is measured or
estimated.

Rollback - A simple model that assumes that if emissions from each
source affecting a given receptor are decreased by the same percentage,
ambient air quality concentrations decrease proportionately.

Screening Procedure - A relatively simple analysis technique to determine
if a given source is likely to pose a threat to air quality.

Validation - Determination of the reliability of a model by comparing
the model estimates with measured air quality data.
                                  48

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           Appendix A







Significant Air Quality Increment



    for Non-Attainment Areas

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                            Appendix A
                Significant Air Quality Increments
                     for Non-Attainment Areas

     A new major source of sulfur dioxide (SO-), particulate matter (PM),
nitrogen oxides (N02*) or carbon monoxide (CO)  located in an attainment
area may cause or exacerbate a known existing air quality violation in
a nearby nonattainment area.  In this case it is necessary to determine
if the air quality impact of the source is significant.   The incremental
increase in concentration at the location of a violation may be con-
sidered significant if it is greater than the following concentrations:

Pollutant                       Averaging Time
              Annual      24-Hour      8-Hour      3-Hour      1-Hour
  S02         1 yg/m3     5 yg/m3                 25 yg/m3
  PM          1 yg/m3     5 yg/m3
  N02         1 yg/m3
  CO                                 0.5 mg/m3                2 mg/m3
These incremental concentrations of S02, PM and N02 are partially based
on allowable S02 increments for Class I areas.  However, the annual
concentration increment is reduced to 1 yg/m3 since this value may
be considered significant for a point source in an area which exceeds
the NAAQS.  The increments for CO are based on concentrations which are
     *
      For simplicity, all emissions of nitrogen oxides are treated as
if they are nitrogen dioxide (N02); see Section 4.3.5.
                                   A-l

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5 percent of the CO NAAQS.  All  of these increments apply to the highest
estimated concentration for all  averaging times.   The second highest is
not used since the incremental  increase in concentration is added to a
concentration which is already based on the highest, second-highest
concentration.
                                 A-2

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                            Appendix B
                     Summaries  of Recommended
                        Air Quality Models
      Summaries presented in this appendix are largely based on similar
information summarized by J. J. Roberts (Ed.) in "Report to the U.S.
EPA of the Specialists'Conference on the EPA Modeling Guidelines,"
Environmental Protection Agency, Research Triangle Park, North Caroline,
27711, February 1977.

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B.I  AIR QUALITY DISPLAY MODEL (AQDIi)
Reference:
Abstract:
Equations
TRW Systems Group.  "Air Quality Display Model."  Prepared
for National Air Pollution Control Administration, DHEW,
U.S. Public Health Service, Washington, D.C., November
1969, (NTIS PB 189194).

AQDM is a climatological steady state Gaussian plume model
that estimates annual arithmetic average sulfur dioxide
and particulate concentrations at ground level in urban
areas.  A statistical model based on Larsen1  is used to
transform the average concentration data from a limited
number of receptors into expected geometric mean and maximum
concentration values for several different averaging times.
            For both point and area sources;
X =
                 16  6   5
                 I   I   I
                k=l £=1 m=l
            where:
                   Ji
                   2irx
                 2Q_
                                              for x £ x.
        16
       2irx
                          Q   /c-y.
                         u.L  v c  '
for x >_ 2x,
             linear interpolation for   x. < x < 2x,


            x,  defined by     az^xi  ^ = °-4^L

            y = crosswind distance  between receptor and  sector  k
                centerline

            c = sector width at receptor  location

            o (x) = ax  + c;   a,b,c = functions of stability class
                               a,b,c for  neutral conditions  split  into
                               x >  1000m  case and  x <  1000m  case.
                              B-l

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       Q = emission rate (g/s)

       H = plume height (m)

       u = wind speed (m/s)

        = relative frequency of occurrence from stability
           wind rose

      o^ = vertical standard deviation of plume  concentrations  (m)

       x = downwind distance (m)


a.  Source-Receptor Relationship

     Arbitrary location and  stack height for each point source
     Arbitrary location and  size for each area source
     Up to 225 receptors located on uniform rectangular grid
     Up to 12 user-specified receptor locations
     Unique release height for each point, area source
     Unique separation for each source-receptor pair
     Receptors at ground level
     No terrain differences  between source and receptor

b.  Emission Rate

     Point sources:  single rate for each source
     Area sources:  single rate for each source
                    Each source treated by effective single point
                    source approximation
     No temporal variation allowed                                          |

c.  Chemical Composition                                                    '

     Treats one or two inert pollutants simultaneously

d.  Plume Behavior

     Holland2 formula  for point sources, with adjustment for
        stability
     Calculations  based on single arbitrary values of stack
        diameter, stack gas exit velocity and stack gas temperature
        for each point  source
     No plume rise calculated for area sources
     Does not treat fumigation or downwash
     If stack height plus plume rise  is greater than mixing height,
        ground level concentration assumed equal to zero
                          B-2

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e.   Horizontal _WInd Field

     Climatologi'.a'i approach
     16 wind directions
     6 wind speed classes
     No variation in wind speed with -.eight
     Constant, uniform (steady-stace) wind assumed

f.   Vertical Wind Speed

     Assumed equal to zero

g.   Horizontal Dispersion

     Climatological approach
     Uniform 22.5° wide plume assumed
     Frequency of occurrence interpolated between sector centerlines
     Averaging times from 1 month to 1 year or longer

h.   Vertical Dispersion

     Semi-empirical/Gaussian plume
     5 stability classes as defined by Turner3
     Neutral stability split internally into 60% diy, 40% night
     Dispersion coefficients from Pasquill and Gifford
     Neutral dispersion coefficients used for stable class
     No provision for variations in surface roughness

i.   Chemistry/Reaction Mechanism

     No provision for treatment

j.  Physical Removal

     No provision for treatment

k.  Background

     Input  single constant  background value for each pollutant

1.  Boundary Conditions

     Lower  boundary  (ground):  perfect reflection
     Upper  boundary  (mixing height):  no effect until a  >_  0.47L
       (this occurs  at x =  x, )  For x. < x < 2x. , a  is  linearly
       interpolated  between Tts value at x. and its value at  2x.
                          B-3

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m.  Emission and Meteorological Correlation

     Wind speed, direction, stability correlated via wind rose
     Emission rate - not correlated with any other factor
     Non-sequential (climatological) limited correlation
     Mixing height adjusted according to stability class:
       Class A - 1.5 times the afternoon climatological value
       Classes B, C, and D(day) - equal to the afternoon cli-
         matological value
       Class E - 100 meters

n.  Validation/Calibration

     Calibration option available
     Substantial experience but limited documentation

o.  Output

     1 month to 1 year averaging time simulated (arithmetic
       mean only)
     Arbitrary averaging time by Larsen procedure
       (typically 1 - 24 hours)
       Assumes
       (1)  lognormal concentration distribution,
       (2)  power law dependence of median and maximum concen-
            trations on averaging time
     Up  to 225 gridded receptor locations, 12 arbitrary  locations
     Individual point, area source culpability list for  each receptor

p.  Computer Requirements

     Digital computer required
     Core requirements are moderate

q.  Limitations

     Useable for urban areas only
                          B-4

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B.2  APRAC-1A
Reference:
Abstract:
Equations
Mancuso, R. L. and F. L. Ludwig.  "User's Manual  for the
APRAC-1A Urban Diffusion Model Computer Program."
Publication No. EPA-650/3-73-001 (NTIS PB 213091),
Environmental Protection Agency, Research Triangle Park,
North Carolina  27711, September 1972.

APRAC is a model which computes hourly average carbon monoxide
concentrations for any urban location.  The model calculates
contributions from dispersion on various scales:   extraurban,
mainly from sources upwind of the city of interest; intraurban,
from freeway, arterial, and feeder street sources; and local,
from dispersion within a street canyon.  APRAC requires an
extensive traffic inventory for the city of interest.
                                     10'"F
            Extraurban - xe -
            F = annual fuel consumption within 22.5° sector extending
                from 32 km to 1000 km upwind of receptor.
                               0.8Qi
                                   x
                                   xi
Intraurban - x,-.: =


Until this expression equals the "box model value"
                                            '-"id
                    Qi
                    UL(xi+l
            Thereafter the box model formula is used.
            i = upwind area segment label
            j = stability class label       b. .
            a., and b . . from (a )   = a., x  1J for x within segment i
              J       J                1J
            Street Canyon - Lee side
                                                       KQ
                                            (u+0.5)[(x')N. L
                                            KQ.(H-z)
                         Windward side
                                        
-------
       Intermediate  wind  direction(less  than +_ 30°  from  street
         (direction)          -.

                         \  =  2  (xL  +  xw}


       where:

       x =  horizontal  distance from  traffic lane  (m)

       z =  height above  pavement (m)

       K =  constant  =  7

      L  =  vehicle size  = 2m

       u =  rooftop wind  speed  (m/s)

      Q  =  CO emission rate  (g/s-m)

       W =  Street width  (m)

       H =  average building  height = 38.8 m


a.  Source-Receptor Relationship

     User specifies set of traffic links (line sources)  by pro-
       viding link end points, road  type, daily traffic  volume
     The traffic links may have  arbitrary length and orientation
     Off-link traffic  allocated  to two mile  square grid
     Link traffic emissions  are  aggregated into a receptor oriented
       area source array
     The boundaries of the area  sources actually treated are (1)
       arcs at radial  distances  from the receptor which  increase in
       geometric progression,  (2) the sides  of a 22.5° sector
       oriented upwind for distances greater than 1000 m, and (3)
       the sides of a  45° sector oriented upwind for distances less
       than 1000 m.
     A similar area source array is  established for each receptor
     Sources assumed at ground level
     Up to 10 receptors
     Receptors at ground level
     Receptor locations are arbitrary
     Four internally defined receptor locations on each user-
       designated street are used in a special street canyon
       sub-model
                         B-6

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b.  Emission Rate

     Daily traffic volume for each link and off-link grid square
       is input and modified by various factors to produce hour-
       by-hour emissions from each link
     Link emissions aggregated as described above:  sector area
       source contributions obtained analytically
     Off-link traffic emissions on the two mile square grid are
       added into sector area sources
     In street canyon sub-model, a separate hourly emission rate
       is provided by user for the link in question

c.  Chemical Composition

     Treats one inert pollutant

d.  Plume Behavior

     Does not treat plume rise
     Does not treat fumigation or downwash except in street
       canyon sub-model
     In street canyon sub-model, a helical circulation pattern
       is assumed

e.  Horizontal Mind Field

     Hourly wind speed and direction in tens of degrees are input
     No variation of wind speed or direction with height
     Constant, uniform (steady-state) wind assumed within each hour

f.  Vertical Wind Speed

     Assumed equal to zero except in street canyon sub-model
     Helical circulation assumed by street canyon sub-model

g.  Horizontal Dispersion

     Sector averaging uniform distribution within sectors
       22.5° sectors beyond  1 km
       45.0° sectors within  1 km
                         B-7

-------
h.   Vertical  Dispersion

     Semi-empirical/Gaussian plume
     6 stability classes; stability class determined internally
       from user-supplied meteorological  data [modified from
       Turner3]
     Dispersion coefficients from McElroy and Pooler'*, modified
       using information in Leighton and  Ditmar5
     No adjustments made for variations in surface roughness
     Downwind distance variation of a  assumed to be ax  for
       purposes of doing analytic integration
     In street canyon sub-model, empirical function of wind speed
       and street width and direction is  used

i.   Chemistry/Reaction Mechanism

     Single inert pollutant

j.   Physical Removal

     Not treated

k.   Background

     Box model used to estimate contribution from upwind sources
       beyond 32 km based on wind speed, mixing height, annual
       fuel consumption
     In street canyon sub-model, contribution from other streets
       is included in background

1.   Boundary Conditions

     Lower boundary:  perfect reflection
     Upper boundary:  perfect reflection; ignores effect until
       concentration equals that calculated  using box model;
       uses box model  (uniform vertical distribution) thereafter
     Mixing height determined from morning radiosonde data  as
       follows:
       midnight to dawn:  constant at pre-dawn  value  obtained
       using minimum urban  temperature
       dawn to sunset:   afternoon maximum temperature used  to
       obtain maximum  height; hourly values  obtained  from surface
       temperature variations
       sunset  to midnight:  linear  interpolation with time

m.  Emission and Meteorological  Correlation

      Emissions a function of hour of the  day and day  of the week
     Meteorological parameters  are  functions of hour  of the day
                         B-8

-------
n.  Val idation/Calibration

     No calibration option provided
     Some documented validation experience available

o.  Output

     Hourly concentration values at each receptor
     Frequency distribution based on hourly values can be obtained

p.  Computer Requirements

     Digital computer required
     Core requirements are moderate

q.  Limitations

     Limited to urban areas
     No means for including point sources
                         B-9

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B.3  CLIMATOLOGICAL DISPERSION MODEL (COM)
References:
Abstract:
Equations:
Busse, A.  D. and J.  R.  Zimmerman.
Climatological  Dispersion Model."
                   "User's Guide  for  the
                   Publication  No.  EPA-RA-
73-024 (NTIS PB 227346/AS),  Environmental  Protection Agency,
Research Triangle Park, North Carolina  27711,  December 1973.

Brubaker, K. L., P. Brown, and R.  R.  Cirillo.   "Addendum
to User's Guide for Climatological Dispersion  Model."
Publication No. EPA-450/3-77-015,  Environmental Protection
Agency, Research Triangle Park, North Carolina  27711,
May 1977.

CDM is a Climatological steady-state Gaussian  plume model
for determining long-term (seasonal or annual) arithmetic
average pollutant concentrations at any ground-level
receptor in an urban area.  An expanded version (CDMQC)
includes a statistical model based on Larsen1  to transform
the average concentration data from a limited  number of
receptors into expected geometric mean and maximum concen-
tration values for several different averaging times.
              (point
   6    6
   I    I
  1=1   m=l
                                        (pn)/Pn
                     16
              'area
            '16
             I
qjp)
 6    6
 I    I
i=l  m=l
                                                            dp
                 with  q,(P) =   /     Q(p,e)de
                              sector k
                                 exp   -
                                    exp
                           0.692 p]
                         " u£ Tl/2j
                                          for  a,  <_0.8L
                                               L,
                               B-10

-------
                                     for az  >  0.8L
      a  = ap ;   a,b = functions  of stability class  (m)  and
                       downwind distance (p)  - three ranges  of
                       distance used:  100-500 m,  500-5000 m,
                       and 5000-50,000 m

      kn = wind  sector appropriate to  the n   point  source

      Q  = emission rate of the n   point source  (g/s)

      P  = distance from the receptor  to the n   point  source (m)

      q,  = emission rate of the area source per unit area and
           unit  time (g/s-m2)

       p = distance from the receptor  to an infinitesimal urea
           source (m)

       9 = angle relative to polar coordinates centered on the
           receptor

       £ = index identifying the wind  speed class

       m = index, identifying the class of the Pasquill  stability
           category

(k,£,m) = joint frequency function

       z = height of receptor above ground level (m)

      Un = representative wind speed  (m/s)

       h = effective stack height of source distribution, i.e.,
           the average height of area  source emissions  in the
           k   wind direction sector at radial distance p from
           the receptor (m)

       L = the afternoon mixing height (m)

         = assumed na^f ^ife °f pollutant hours (s)
                       B-ll

-------
a.  Source-Receptor Relationship

     Arbitrary location for each point source
     Area sources equal uniform grid squares
     Receptor location arbitrary
     Arbitrary release heights for point and area sources
     Unique separation for each source-receptor pair
     Receptors are at ground level
     No terrain differences between source/receptor

b.  Emission Rate

     Point sources:  single rate for each source
     Area sources:  single rate for each source
                    area integrations are done numerically one
                    22.5° sector at a time; sampling at discrete
                    points defined by specific radial and angular
                    intervals on a polar grid centered on the
                    receptor
     Day/night variations in emissions, same variation assumed
       for all sources

c.  Chemical Composition

     Treats one or two inert pollutants simultaneously

d.  Plume Behavior

     Only Briggs neutral/unstable formula used for point sources
     If stack height plus plume rise is greater than mixing height,
       ground level concentrations assumed equal to zero
     Alternative to Briggs - input value of plume rise times wind
       speed for each  point source
     No plume rise calculated for area sources
     Does not treat fumigation or downwash

e.  Horizontal Wind Field
     Climatological approach
     16 wind directions
     6 wind speed classes
     Wind speed corrected for release height  based on power  law
       variation exponents from  DeMarrais°
     Constant, uniform  (steady-state) wind assumed

 f.   Vertical Wind Speed

     Assumed equal  to zero
                         B-12

-------
g.  Horizontal  Dispersion

     Climatological  approach
     Uniform distribution within each of 16 sectors
     Averaging  time = 1  month to 1  year or longer

h.  Vertical Dispersion

     Semi-empirical/Gaussian plume
     5 stability classes as defined by Turner3
     Neutral stability split into day/night cases on input
     Dispersion coefficients taken from Turner7
     Area sources - stability class is decreased by 1  category
       from input values (to account for urban effects)
     Neutral dispersion  coefficients are used for stable classes
     No further adjustments made for variations in surface roughness

i.  Chemistry/Reaction Mechanism

     Exponential decay,  user-input half life

j.  Physical Removal

     Exponential decay,  user-input half life
     Always applies the  same rate constant

k.  Background

     Input single constant background value for each pollutant

1.  Boundary Conditions

     Lower boundary (ground):  assumes perfect reflection
     Upper boundary (mixing height):  no effect until  dispersion
       coefficient equals 0.8 of the mixintg height, uniform
       vertical mixing assumed beyond this point

m.  Emission and Meteorological Correlation

     Wind speed, direction, stability correlated via wind rose
     Mixing height is adjusted according to stability class:
       Class A - 1.5 times afternoon climatological value
       Classes  B, C, and D(day) - equal to the afternoon clima-
       tological value
       Class D(night) -  average of morning and afternoon clima-
       tological value
       Class E - morning climatological value
     Emission rates:  day-night allowed; all sources vary by
       same factor
     Non-sequential (climatological) limited correlation
                        B-13

-------
n.  Validation/Calibration

     Limited validation experience
     Calibration option available (CDMQC)

o.  Output

     One month to one-year averaging time simulated (arithmetic
       mean only)
     Arbitrary averaging time by Larsen1 procedure
       (typically 1 - 24 hr.) (CDMQC)
       Assumes
         (1)  lognormal concentration distribution,
         (2)  power law dependence of median and maximum concen-
              trations on averaging time
     Arbitrary number and location of receptors
     Individual point, area source culpability list for each
       receptor (CDMQC)
     Point, area concentration rose for each receptor

p.  Computer Requirements

     Digital computer required
     Core requirements are moderate

q.  Limitations

     Useable for urban areas only
     Area source emission densities must not vary rapidly from
       one area source to the next
                           B-14

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B.4  REAL-TIME AIR QUALITY SIMULATION MODEL (RAM)
Reference:
Abstract:
Equations:
            Turner, D, B., and J, H. Novak.  "User's Guide for RAM,"
            Environmental Protection Agency, Research Triangle Park
            North Carolina  27711 , 1977.

            RAM is a steady state Gaussian plume model for estimating
            concentrations of relatively stable pollutants for averaging
            times from an hour to a day from point and area sources.
            Level or gently rolling terrain is assumed.  Calculations
            are performed for each hour.  Both rural and urban versions
            are available.
            Contribution from single upwind area source
                    Xn = jj-  /  f dx    integral evaluated numerically

                            xl
               , x~ = points of intersection of ray from receptor
                     through area source in question

                 q = emission rate per unit area of the area source(g/s-m2)
                 u = mean  wind speed  (m/s)
        For stable conditions:    f = —
                                              92
                 xpoint   2TTU a  az 9192
        For neutral or unstable conditions, a
                                                ^
                 •p =
                         -
                 xoint  ~  2iru  a   a   9193
                               y

                              B-15

-------
   For  neutral or unstable conditions, a  > 1.6L
            f  =
            (point
                      2TT  uL  a.
       In  which
               91  =  exP   -  2
               g2 = exp
[-1
                             rZ-H,
+ exo I- 1 (z^
    " I   ?  a  '
                      exp |- 1 (£^)' |  +  exp
a.  Source-Receptor Relationship

     Arbitrary location for point sources
     Receptors may be
       (1) arbitrarily located
       (2) internally located near individual  source maxima
       (3) on a program-generated hexagonal  grid to give good
           coverage to a user-specified portion of the region of
           interest
     Receptors all at same height above (or at) ground
     Flat terrain assumed
     Unique stack height for each point source
     User may specify up to three effective release heights for
       area sources, each assumed appropriate for a 5 m/sec wind
       speed.  Value used for any given area source must be one
       of these three
     Unique separation for each source-receptor pair
                          B-16

-------
b.  Emission Rate

     Unique, constant emission rate for each point, area source
     Area source treatment-
       Narrow plume approximation
       Area source used as input; not subdivided into uniform
       elements
       Arbitrary emission heights input by user
       Areas must be squares; side lengths = integer multiples
       of a basic unit
       Effective emission height = that appropriate for 5 m/s wind
       Area source contributions obtained by numerical integration
       along upwind distance of narrow-plume approximation formulae
       for contribution from area source with given effective
       release height

c.  Chemical Composition

     Treats a single inert pollutant

d.  Plume Behavior

     Briggs8'9'10 plume rise formulas
     Does not treat fumigations or downwash
     If plume height exceeds mixing height, ground level concen-
       tration is assumed zero

e.  Horizontal Wind Field

     Uses user-supplied hourly wind speeds
     Uses user-supplied hourly wind directions  (nearest 10°),
       internally modified by addition of a random integer value
       between -4° and +5°
     Wind speeds corrected for release height based on power  law
       variation, exponents  from DeMarrais*; different exponents
       for  different stability classes, reference  height = 10 meters
     Constant, uniform (steady-state) wind assumed within each hour

f.  Vertical Wind Speed

     Assumed equal to zero

g.  Horizontal Dispersion

     Semi-empirical/Gaussian plume
     Hourly stability class  determined internally  by  Turner3
       procedure, six classes used
     Dispersion  coefficients from McElroy and Pooler4 (urban)  or
       Turner7  (rural).   No  further adjustments made  for variations
       in  surface roughness  or  transport  time


                           B-17

-------
h.  Vertical  Dispersion

     Semi-empirical/Gaussian plume
     Hourly stability class determined internally
     Dispersion coefficients from McElroy and Pooler4 (urban) or
       Turner7 (rural).  No further adjustments made for variations
       in   surface roughness

i.  Chemistry/Reaction Mechanism

     Exponential  decay, user-input half-life

j.  Physical  Removal

     Exponential  decay, user-input half-life

k.  Background

     Not treated

1.  Boundary Conditions

     Lower boundary:  perfect reflection
     Upper boundary:  perfect reflection
     Neutral  and unstable conditions
       Multiple reflections numerically accounted for by summation
       of series until a  = 1.6 times mixing height
       Uniform mixing assumed in vertical thereafter
     Stable conditions:  ignore effect of upper boundary
     Mixing height for a given hour is obtained by suitable
       interpolation using data from soundings taken twice a day
     Interpolation technique dependent on mode of operation  (urban
       or rural) and calculated stability class for the hour in
       question as well as the stability class for the hour
       just preceding sunrise

m.  Emission and Meteorological Correlation

     User supplies hourly values of wind speed, wind direction,
       mixing height and other meteorological variables required
       for determination of stability class and plume rise

 n.  Validation/Calibration

     No calibration option provided
     No documented validation or comparison with observational data
                         B-18

-------
o.  Output

     Hourly and average (up to 24 hours)  concentrations  at each
       receptor
     Limited individual source contribution list
     Cumulative frequency distribution based on 24-hour  averages
       and up to 1 year of data at a limited number of receptors

p.  Computer Requirements

     Digital computer required
     Core requirements are moderate

q.  Limitations

     Flat or gently rolling terrain
                           B-19

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B.5  SINGLE  SOURCE  (CRSTER) MODEL
Reference:
Abstract:
Equations:
Environmental  Protection Agency.  "User's  Manual  for
Single Source  (CRSTER) Model."  Publication  No.  EPA-450/
2-77-013 (NTIS PB 271360).  Office of Air  Quality Planning
and Standards, Research Triangle Park, North Carolina
27711, July  1977.

CRSTER is a  steady  state Gaussian plume technique applicable
to both rural  and urban areas in uneven terrain.   The  purpose
of the technique is:   (1) to determine the maximum concen-
trations,for certain averaging times between 1-hour and
24-hours, over a one year period due to a  single point source
of up to 19  stacks,  (2) to determine the meteorological
conditions which cause the maximum concentrations, and
(3) to store concentration information useful in calculating
frequency distributions for various averaging times.   The
concentration  for each hour of the year is calculated  and
midnight - to  - midnight averages are determined for each
24-hour period.
                          gl
                           for a  <_
1.61.
            X =
                /2T uL a.
                            for a  > 1.61.
             x = 0 (stability class 7)

             L = mixing  height  (m)

             H = (stack  height  +  plume rise)-(difference in elevation
                 between receptor and base of stack) (m)
            9, - exp
          - \ 
-------
a.  Source-Receptor Relationship

     Up to 19 point sources, no area sources
     All point sources assumed at the same location
     Unique stack height for each source
     Receptor locations restricted to 36 azimuths (every 10°)
       and 5 user-specified radial distances
     Unique topographic elevation for each receptor; must be
       below top of stack

b.  Emission Rate

     Unique average emission rate for each source
     Monthly variation in emission rate allowed

c.  Chemical Composition

     Treats a single inert pollutant

d.  Plume Behavior

     Briggs8'9'10 final plume rise formulas
     Does not treat fumigation or downwash
     If plume height exceeds mixing height, concentrations further
       downwind assumed equal to zero

e.  Horizontal Hind Field

     Uses user-supplied hourly wind speeds
     Uses user-supplied hourly wind directions (nearest 10°),
       internally modified by addition of a random integer value
       between -4° and +5°
     Wind speeds corrected for release height based on power law
       variation, exponents from DeMarrais6; different exponents
       for different stability classes, reference height = 10
       meters
     Constant, uniform (steady-state) wind assumed within each
       hour

f.  Vertical Wind Speed

     Assumed equal to zero

g.  Horizontal Dispersion

     Semi-empirical/Gaussian  plume
     7  stability classes used; Turner Class 7:   extremely stable,
       elevated plume assumed not to touch the ground
     Dispersion coefficients  from Turner; no further adjustments
       made for variations  in surface roughness, transport or
       averaging time

                           B-21

-------
h.  Vertical Dispersion

     Semi-empirical/Gaussian plume
     7 stability classes
     Dispersion coefficients from Turner; no further adjustments
       made

i.  Chemistry/Reaction Mechanism

     Not treated

j.  Physical Removal

     Not treated

k.  Background

     Not treated

1.  Boundary Conditions

     Lower boundary:  perfect reflection at the same height as
       the receptor
     Upper boundary:  perfect reflection
       Multiple reflections handled by summation of series until
       a  = 1.6 x mixing height
       Uniform vertical distribution thereafter
     Mixing height is constant and follows topographic variations:
       Taken from base of stack for determining whether plume
       punches through
       Taken from receptor elevation for determining vertical
       concentration distribution
     Mixing height for a given hour is obtained by suitable
       interpolation using data from soundings taken twice a day.
       Interpolation technique dependent on mode of operation
       (urban or rural) and calculated stability class for the
       hour in question as well as the stability class for the
       hour just preceding sunrise.

m.  Emission and Meteorological Correlation

     User supplies hourly values of wind speed, direction, mixing
       height and other meteorological variables required for
       determination of stability class and plume rise
     Monthly emission variation allows limited emission -
       meteorology correlation

n.  Validation/Calibration

     No calibration option provided
     Comparison with observations around at least 5 separate
       power plants have been made

                         B-22

-------
o.  Output

     Highest and second highest concentrations for the year at
       each receptor for averaging times of 1, 3, and 24-hours,
       plus a user-selected averaging time which may be 2,  4,  6,
       8, or 12 hours
     Annual arithmetic average at each receptor
     For each day, the highest 1-hour and 24-hour concentrations
       over the receptor field
     Hourly concentrations for each receptor on magnetic tape

p.  Computer Requirements

     Digital computer required
     Core requirements are moderate

q.  Limitations

     Not applicable to area and line sources
     Use care when applying to low-level sources
                           B-23

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B.6  TEXAS CLIMATOLOGICAL MODEL
References:
Abstract:
Equations:
Porter, R. A. and Christiansen, J.  H..   "Two Efficient
Gaussian Plume Models Developed at the Texas Air Control
Board."  Proceedings, of the 7th NATO/CCMS International
Technical Meeting on Air Pollution Modeling, Airlie House
Va., September, 1976.

Christiansen, J. H. and Porter, R.  A..   Users Guide to the
Texas Climatological Model, Texas Air Control Board, Austin
Texas,  May, 1976.

The TCM is a climatological model that predicts long-term
arithmetic mean concentrations of nonreactive pollutants
from point sources and area sources.
             Area sources are handled by an algorithm proposed by Gifford
             and Hanna11.  The concentration due to area sources is
             given by
                    XA = FQ/U


             where XA = concentration  (yg/m3)

                    Q = area source emission rate in the vicinity of
                        the receptor (yg/s-m2)

                    U = mean ground-level wind speed (m/s)

                    F = a dimension!ess constant
              Gifford  and  Hanna  have  suggested a  value  of  F  =  50  for  S02
              and  225  for  total  suspended  particulate (TSP).   The area
              emission rate,  Q,  is determined by  averaging the emissions
              in  the area  source square  containing  the  receptor and in
              the  neighboring squares.   The  extent  of the  region  around
              each receptor to use for emission rate averaging is an  input
              parameter.
                                 B-24

-------
The TCM uses steady-state Gaussian plume point source
logic, with  the crosswind distribution averaged across
22.5° azimuthal sectors.  The only meteorological input
required for area source calculations is the mean wind
speed, but the point source calculations require a mete-
orological joint frequency function with sixteen 22'.5°
wind sectors, six wind speed classes (0-3, 4-6, 7-10,
11-16, 17-21, and > 21 knots), and six stability classes
(Turner classes A, B, C, D (day), D (night), and E plus F)

The basic equation is:

             6
            m=l
                 (K(x,H,mMk,m)/ll). (decay tenn)
where K(x,H,m) = (32xl06/[(21f)3/2x QZ]) exp (-H2/2az2)

(K is precalculated for 20 distances, 9 effective source
heights, and six stability classes)
S = (2/[/2TUm oz]) exp (-H/2a
                                  exp (-.692x/[UmT1/2])
U  is a wind speed characteristic of an entire stability
class, and is computed in the model by the equation:
      16
       I
      k=l
            I  4>(k,£,m)
16
 I
k=l
6
I
                                                   -1
with x = concentration, yg/m3

     k = the wind sector index appropriate to source i
         at the receptor

     £ = wind speed class index

     m = stability class index

     <{> = meteorological joint frequency function
                B-25

-------
            Q.  =  emission  rate  of  source  i  (g/s)

            H.  =  effective height  of  source i  (mi)

            a  =  standard  deviation of vertical  Gaussian  concentra-
             z    tion  distribution (m)

          T,/2  =  half-life for  first-order  pollutand  decay (s)

            LL  =  central wind speed of class i (m/s)

             x  =  downwind  distance (m)
a.  Source-Receptor Relationship

     Arbitrary location for each point source
     Unlimited number of sources
     Arbitrary location and square grid width for each area source
     The model will allocate area sources into a uniform square grid
     Receptor location is arbitrary grid (max. 50 x 50)
     Release heights for point sources
     The area source algorithm (Gifford-Hanna) does not consider
       height of release
     Receptors are at ground level
     No terrain difference between sources and receptors

b.  Emission Rate

     All sources have a single average emission rate for the
       averaging time period (i.e., month, season, year)

c.  Chemical Composition

     One, two, or three inert pollutants are treated simultaneously

d.  Plume Behavior

     Plume rise calculated according to Briggs9 neutral/unstable
       equation
     Effective stack heights less than 10 meters are considered
       10 meters
     Effective stack heights greater than 300 meters are considered
       300 meters
     No plume rise  for area sources
     Downward and  fumigation not  considered
                         B-26

-------
e.  Horizontal Wind Field

     Climatological approach
     16 wind directions
     Mean wind speed calculated for each stability clas- from
       the joint frequency function of stability, wind direction,
       and wind speed
     Wind speed corrected for physical stack height (same as COM)

f.  Vertical Hind Speed

     Assumed equal to zero

g.  Horizontal Dispersion

     Assumed to be uniform within each 22.5 degree sector (same
       as COM)

h.  Vertical Dispersion

     Gaussian plume
     6 stability classes  (Pasquill-Gifford-Turner) A, B, C, D-Day,
       D-Night, E and F
     No provision for variation in surface roughness

i.  Chemistry/Reaction Mechanism

     Exponential decay according to user input half-life (same
       as COM)

j.  Physical Removal

     Same as i above

k.  Background

     Background may be entered by calibration coefficient for
       each pollutant

1.  Boundary Conditions

     Perfect reflection assumed at ground
     Mixing height not considered

m.  Emission and Meteorological Correlation

     Emissions not varied
                            B-27

-------
n .   Validation/Correlation

     Model  is self-calibrating with input of field receptor
       observations
     High correlation achieved of observed to calculated values
       for Houston TSP 1975, Houston S02 1972, Dallas TSP 1972
     Arithmetic mean concentration for the averaging time of the
       climatological input and emission data (one month to one
       year
     Any combination of the following outputs are available:
       (1)  Listing of concentration for an arbitrarily spaced
            square grid of up to 50 by 50 elements
       (2)  A print plot of the grid concentrations
       (3)  Punched card output for isopleth maping (same as COM)
       (4)  A listing of the five high contributors to the concen-
            concentration (by % concentration) at each grid point

p.  Computer Requirements

     Digital computer required
     Core requirements are moderate

q.  Limitations

     Flat terrain, relatively constant emissions
                          B-28

-------
B.7  TEXAS EPISODIC MODEL ("I EM)
References:
Abstract:
Equations:
Porter, R. A. and Christiansen, J. H.  "Two  Efficient
Gaussian Plume Models Developed at the Texas Air Control
Board."  Proceedings of the 7th NATO/CCMS International
Technical Meeting »n Air Pol 1ution Model ing, Airlie House,
Va., September, i()/f).

Christiansen, J. H.  Users Guide to the Texas Episodic Model,
Texas Air Control Board, May,  i'^76.

The Texas Episodic Model TEM is a short-term (10 minute
to 24 hour averaging time) Gaussian Plume Model for pre-
diction of concentrations of nonreactive pollutants due to
up to 300 elevated poinc sources and up to 200 area sources.
Concentrations are calculated for 1 to 24 scenarios of
meteorological conditions, averaging time, and mixing height
             The area source algorithm is due to Gifford and Hannai1
             Each area source square is affected by its own diffuse
             emissions and those in the N area source squares directly
             upwind of it:
             x -
             x
                          +   I  Q, [(2i+l)1-b - (2i-l)1-bj
             with Ax = length of side of area source grid squares  (m)

                  U  - surface wind speed  (m/s)

                  QQ - area emission rate  of square containing the
                       receptor  (ug/s-m2)

                  Q.J = area emission rates of the upwind area sources
                 a,b = stability and downwind distance-dependent
                       parameters  from  the  equation o   = ax

             The TEM employs steady-state bivariate Gaussian  plume
             point  source  logic.   The concentration due to an  elevated
             point  source  is given by
                              B-29

-------
x = ^-^jjexp (-y2/2o2) exp (-H2/2a^) exp (-.692x/UT1/2)
      y  z

where  x = concentration (yg/m3)

       Q = emission rate (g/s)

       U = wind speed at physical source height (m/s)

       H = effective source height (m)

       x = downwind distance  (m)

       y = crosswind distance (m)

                       pollutant decay half-life (s)
a  , a     -  the standard deviations of the plume concen
tration distribution,
a  = ax
with stability and downwind distance-dependent coeffi-
cients a, b, c, and d from Busse and Zimmerman12 and
Turner7.  The wind speed U is the surface wind speed
adjusted to the physical source height.  Let K  and K
be defined by                                 ^
           exp  (-y2/2ay2),
 KZ =       exp  (.
Then,        x =  yz   (decay  term)
                  B-30

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        K  was calculated for each of the 1120 combinations  of
        twenty downwind distances from 2 to 60 km, eight crosswind
        angles (Tan^y/x) from 0° to 7°, with 6  varying from 1° to
        5° depending on stability, and seven stability classes.
        For total vertical mixing below a mixing height of L meters,
        x = -^	 exp (-yV2o ")
            /2T a LU            y


                                                    KVK2Q
        This can be represented in the equation x =   	 (decay term)

        by setting  KZ * 398L
a.  Source-Receptor Relationship

     Up to 300 arbitrarily located point sources
     Up to 200 arbitrarily located area sources
     A uniform square receptor grid of arbitrary spacing with up
       to 50 by 50 rows or columns
     Terrain assumed flat
     Unique release height for each source
     All receptors at ground level

b.  Emission Rate

     Unique emission rate for each source

c.  Chemical Composition

     One, two, or three inert pollutants treat simultaneously

d.  Plume Behavior

     Plume rise according to one of six equations from Briggs
       selected according to stability and distance from source.
       Effective stack heights less than 10 meters are considered
       10 meters.  Effective stack heights greater than 2000
        meters are considered 2000 m.
     Mixing height penetration factor (P) is a user input.  If
       effective source height (h) is greater than P times the
       mixing height the plume escapes.  Otherwise the .47L mixing
       scheme from Turner-7 is used.
     Does not treat downwash or fumigation
                          B-31

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e.  Horizontal  Wind Fijld

     User supplied stability, wind speed, and direction for the
       averaging time period (10 minutes to 3 hours) or for each
       3 hour period to build a 24-hour day.
     Power law variation of wind speed with release height (same
       as COM).
     Steady state wind for each scenario

f.  Vertical  Wind Speed

     Equal to zero

g.  Horizontal  Dispersion

     Semi-empirical Gaussian plume
     User supplied stability class for each scenario (Pasquill-
       Gifford-Turner)
     Turner7 dispersion coefficients
     No adjustment for surface roughness

h.  Vertical  Dispersion

     Semi-empirical Gaussian plume
     User supplied stability classes (Pasquill-Gifford-Turner)
       for each scenario
     Turner7 dispersion coefficients
     No adjustment for surface roughness

i.  Chemistry/Reaction Mechanism

     Exponential decay with user supplied half-life

j.  Physical  Removal

     Same as i above

k.  Background

     May be input with calibration factor

1.  Boundary Conditions

     Lower boundary:  perfect reflection
     Upper boundary:   reflection from  top of mixed  layer  by the
        .47L scheme of Turner7  except as  described in d above
                          B-32

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m.  Emission/Meteorological Correlation

     User supplied values of wind speed, wind direction, stability
       class, mixing height, ambient temperature for each scenario
       up to 24 scenarios

n.  Validation/Calibration

     Limited validation with observed vinyl chloride observations
     Calibration by user supplied coefficients (A, B) so that
     xcal = A + Bxpredicted

o.  Output

     Concentration  for  each receptor grid  point for averaging
       times of:
       10 minutes
       30 minutes
        1 hour
        3 hours
       24 hours (based  on eight 3-hour scenarios)
     Output is available for from 1 to 24  scenarios in the fol-
       lowing formats:
       listing
       print plot
       punched cards for isopleth maps
       culpability  list of the high five contributors to the
       concentration at each receptor grid point

p.  Computer Requirements

     Digital computer required
     Core requirements  are moderate

q.  Limitations

     Relatively uncomplicated terrain
                          B-33

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                            References


 1.   Larsen,  R.  I.   "A  Mathematical Model for  Relating Air Quality
     Measurements  to Air  Quality  Standards."   Office of Air Programs
     Publication No. AP-89  (NTIS  PB 205277), Environmental Protection
     Agency,  Research Triangle  Park,  North  Carolina  27711, November
     1971.

 2.   Holland,  J. Z.   "A Meteorological  Survey  of  the Oak  Ridge Area."
     Atomic  Energy Commission  Report  ORO-99, Oak  Ridge National
     Laboratory, Oak  Ridge,  Tennessee,  1953.

 3.   Turner,  D.  B.   "A  Diffusion  Model  of an Urban Area." Journal  of
     Applied  Meteorology, American Meteorological  Society, Boston,
     Massachusetts,  February 1964.

 4.   McElroy,  J. L.  and F.  Pooler.     St. Louis Pispersion Study,_ Voltime
     II  -  Analyses.   AP-53.   National  Air Pollution  Control Administra-
     tion, Arlington, Virginia   22203,  December 1968.

 5.   Leighton, P.  A.  and  R.  B.  Dittmar.   "Behavior of Aerosol  Clouds
     within  Cities."  Joint  Quarterly Progress Reports Nos. 2, 4,  5, 6
     (2  vols.),  Contracts DA-18-064-CMC-1856 and  DA-18-064-CMC-2282.
     Stanford University  and Ralph M.  Parsons  Co.  DDC Nos. AD 7261,
     AD  31509, AD  31508,  AD  31507, AD 31510, AD 31511, Respectively,
     1952.

 6.   DeMarrais,  G.  A.   "Wind Speed Profiles at Brookhaven National
     Laboratory."   Journal  of Meteorology,  Americal  Meteorological
     Society,  Boston, Massachusetts,  1959.

 7.   Turner,  D.  B.   "Workbook of Atmospheric Dispersion  Estimates."
     PHS Publication  No.  999-AP-26  (NTIS  PB 191482),  Environmental
     Protection  Agency, Research Triangle  Park, North Carolina  27711,
     1969.

 8.   Briggs.  Gary  A.   PJ[ume  Rise.  U.S.  Atomic Energy  Commission
     Critical  Review Series, (NTIS TID-25075)  Oak Ridge  National
     Laboratory, Oak  Ridge,  Tennessee, 1969.

 9.   Briggs,  Gary  A.   "Some  Recent Analyses of Plume Rise Observations."
     Proceedings of the Second International  Clean Air  Congress, edited
     by  H.  M. Englund and W. T. Berry, Academic  Press,  New York, 1971.

10.   Briggs,  Gary  A.   Discussion on  Chimney Plumes in  Neutral  and
     Stable  Surroundings.  Atmospheric Environment,  July 1972.
                                B-34

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11.   Gifford, F.  A.  and S. R. Hanna.   "Modeling Urban Air Pollution."
     Atmospheric  Environment. January 1973.

12.   Busse, A. D. and J. R. Zimmerman.  "User's Guide for the Cli-
     matological  Dispersion Model."  Publication No.  EPA-RA-73-024
     (NTIS PB 227346/AS), Environmental  Protection Agency, Research
     Triangle Park,  North Carolina   27711, December 1973.
                               B-35

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