REPORT TO THE U. S. EPA
         OF THE
SPECIALISTS' CONFERENCE
         ON THE
EPA MODELING GUIDELINE
    February 22-24, 1977
      Chicago, Illinois

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           REPORT TO U.S.  EPA OF THE SPECIALISTS CONFERENCE
                     ON THE EPA MODELING GUIDELINE
                         February 22-24, 1977

                           Chicago, Illinois
       Organized by:  Energy and Environmental  Systems Division

                      Argonne National Laboratory*
       Conference Chairman:

       Conference Organizers:



       Conference Coordi nators:
John J. Roberts

Donald M. Rote
Albert E. Smith
Kenneth L. Brubaker

Miriam L. Holden, Director
Conference Planning & Management

Albert E. Smith
      Sponsored by:  Office of Air Quality Planning and Standards
                     United States Environmental Protection Agency
      EPA Project Officer:
Herschel H. Slater
*Under Interagency Agreement No. EPA-IAG-D7-0013 between ERDA and EPA;

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                              TABLE OF CONTENTS
Contents                                                               Page

List of Participants	    7

Conference Schedule  	   11

Preface	   13

1.  Policy Issues and General Technical Matters Addressed in
    the Plenary Sessions 	    23

    1.1  The Guideline Tends to Standardize Air Quality
         Analysis	    23
    1.2  Specification of Computer Codes Versus Approved
         Algorithms	    25
    1.3  Application of Standard Computer Codes for "Screening"
         in the New Source Review Process	    26
    1.4  Evaluation and Calibration  	    27
    1.5  The Draft Guideline Suggest a Heirarchy which Evolves
         Toward Rollback as the Problems Become More Difficult ....    28
    1.6  Estimating Short-Term Air Quality Impacts:  Statistical
         Versus Enumerative Approaches  . .	    29
    1.7  To What Extent Should the User's Burden in
         Acquisition of Data and Performance of Calculations
         be Considered in Recommendations Set Forth in the
         Guideline?	    31
    1.8  The EPA Office of Air Quality Management and
         Standards Decided Not to Include Oxidant Modeling
         in this Edition of the Guideline	    32
    1.9  Air Quality Data	    32
    1.10 Meteorological Data	    32
    1.11 Proprietary Computer Codes  	    33
    1.12 Supplemental Control Systems   	    34
    1.13 Structure of the Guideline	    34
    1.14 Periodic Review and Upgrading of the Guideline	    35

2.  Working Group Reports  	    37
    2.1  Group 1-1:  Multi-Source, Short-Term and Long-Term for
         Set 1 Pollutants	    39
         2.1.1  Changes in Models Recommended in Draft
                Guideline	    40
         2.1.2  Meteorological Data	    44
         2.1.3  Source Data	    44
         2.1.4  Supplementary Comments  and Information  	    45
         2.1.5  Further Recommendations	   47
    2.2  Group 1-2:  Single-Source, Short-Term and Long-Term,
         Set 1 Pollutants	   49
         2.2.1  Distinction Between Models and Computational
                Procedures	   50
         2.2.2  State-of-the-Art Point  Source Model   	 .   50

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         2.2.3  Availability of Regulatory Algorithms
                (Programs, Codes)  	     52
         2.2.4  Recommendation for "Screening Procedure 	     53
         2.2.5  On the Question of Enumerative vs.  Statistical
                Use of the Estimates of Short-Term Concentrations .  .     54
    2.3  Group 1-3:  Set 2 Pollutants (CO, NO™, Photochemical
         Oxidant)	     59
         2.3.1  Overview and Philosophy	     60
         2.3.2  General Considerations  	     61
         2.3.3  Carbon Monoxide Model Recommendations 	     63
         2.3.4  Short-Term Photochemical Pollutant Model
                Recommendations 	     64
         2.3.5  Long-Term N02 Model Recommendations 	     67
    2.4  Group II-l:  Long-Range Transport and Loss Mechanisms  ...     69
         2.4.1  Summary of Discussion	     70
         2.4.2  Recommendations	     71
    2.5  Group II-2:  Plume Dynamics Under Specail Conditions ....     75
         2.5.1  Introductory Remarks  	     76
         2.5.2  Aerodynaic Dovrawash	     76
         2.5.3  Sea Breeze and Other Anomalous Circulation  .....     77
         2.5.4  Interactions of the Plume with an Elevated
                Inversion Layer	     78
    2.6  Group II-3:  Complex Terrain	     81
         2.6.1  Summary of Discussions  ......... 	     82
         2.6.2  Recommended Procedure for Complex Terrain 	     84
         2.6.3  Assistance in Defining Screening Techniques in
                Complex Terrain	     85
         2.6.4  Supplementary Comments and Information  	     95
    2.7  Group II-4:  Characterization of Turbulence  	     99
         2.7.1  Introductory Remarks	    100
         2.7.2  Vertical Profiles of Wind Speed	    100
         2.7.3  Comparison of STAR, AT and OQ	    101
         2.7.4  Mixing Height Interpolation Scheme  .........    104
         2.7.5  Vertical Dispersion Estimates ..... 	    104
         2.7.6  Plume Dimension Stabilization Height  	    105
         2.7.7  Horizontal Dispersion Estimates 	    105
         2.7.8  Randomization of Wind Vector	    106
         2.7.9  Review and Comment on Pasquill's Recommendations
                for Interim Changes to the Pasquill-Gifford
                Curves	    107
         2.7.10 Use of Models in Urban vs. Rural Areas	    107
    2.8  Group II-5:  Validation and Calibration  	  .    Ill
         2.8.1  Principles	    112
         2.8.2  Considerations	    113
         2.8.3  Other Issues  	 ........    114
         2,8.4  Proposed Mechanism for Meeting Requirements 	    114
         2.8.5  Example Model Evaluation Information  	    115
         2.8.6  Levels of Model Performance and Quality 	    116

3.  Supplementary Materials 	    121

    3.1  Group 1-1	    122
         3.1.1  Description of Texas Climatological Model 	    122

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     3.1.2  Description of Texas Episodic Model 	  126
     3.1.3  Minority Report on Application o£ Multi-Source,
            Urban Model	130
     3.1.4  Description of the ERTAQ Model	131
3.2  Group 1-2	135
     3.2.1  Comments on Sec. 2.2.5	135
     3.2.2  Comments on Short-Term Analysis and on M. Williams'
            "Rationale for Elimination of the Maximum of the
            Second Highest for Modeling Purposes" (3.2.3,
            below)	137
     3.2.3  Rationale for Elimination of the Maximum of the
            Second Highest for Modeling Purposes  	  138
3.3  Group 1-3	139
     3.3.1  Description of the SAI Reactive Plume Model	139
     3.3.2  Description of the DEPICT Model	
     3.3.3  Description of the SAI Urban Air Pollution Simulation
            Model	
     3.3.4  Description of the ARTSIM Model	149
     3.3.5  Description of the TAPS Model	153
3.4  Group II-l	159
     3.4.1  Description of the EGAMA Model	159
3.5  Group II-2	I63
     (No  supplementary materials)  	  163
3.6  Group II-3	165
     3.6.1  Description of  the TAPAS Model	165
     3.6.2  Validation Data  on  the Valley Model	170
     3.6.3  Comments by D. Henderson	176
     3.6.4  Comments by M. Williams	178
3.7  Group II-4	179
     3.7.1  Comments on the  Group  II-4  Discussion of the
            Pasquill Gifford Oz  Curves   	  179
     3.7.2  Supplementary Comments on Discussion Topics  of
            Working Group II-4	184
     3.7.3  Comments on Report  of  Working Group  II-4	198
3.8  Group  II-5	199
      (No  supplementary materials)  	 199
3.9  General  Supplementary Materials   	 201
     3.9.1  Use  and Formulation of the  Hanna-Gifford Model   .... 201
     3.9.2  Recommended Changes in Draft Guidelines 	 206
     3.9.3  Comments on the RAM Urban Model	213
     3.9.4  Comments Regarding EPA Models	• 214
      3.9.5  Descriptions  of C.E.G.B. Air Quality Models 	 216
      3.9.6  Description of the Air Quality Short-Term Model
             (AQSTM)	221
      3.9.7  Application of Air Quality  Models Under the
            Ontario Environmental Protection Act  	 225
      3.9.8  Comments Regarding CRSTER 	 228
      3.9.9   Comments on the Need for Model Accuracy	231

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    3.10  Policy Issue Supplementary Materials  	  233
          3.10.1  On the Use of  Statistical Techniques  for  the
                  Prediction of  Second High 24-Hour  Concentra-
                  tions  	233
          3.10.2  Methods for Estimating Levels Not  to  be
                  Exceeded More  than Once per Year	237
          3.10.3  Position on Recently Proposed Amendments  to
                  the Clean Air  Act	  238
          3.10.4  Consistency and Standardization  	  241
          3.10.5.  Statistical Evaluation of Compliance  	  244
          3.10.6  Statistical Evaluation of Compliance  (Revised)  .  .  .  245
          3.10.7  Comment on Statistical Guide  for  Compliance  ....  246
          3.10.8  Validity of Hour-by-Hour Estimates of Air
                  Quality (The Use of  the Nowcast in Pollution
                  Potential Forecasting)	247
          3.10.9  A Comment Concerning the Use  of Unverified
                  Models as "Relative  Predictions"  	  248
          3.10.10 Comments on Various  Issues  	  249
          3.10.11 Comments on the Use  of Proprietary Models
                  and Some Examples	254
    3.11  Description of Air Quality Models	259

4.  Materials Distributed to Participants Prior to  the  Conference  .  .  315

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                       Conference Invited Participants
Dr.  Sumner Barr
Section Leader, Atmospheric
  Science Section
Los Alamos Scientific Laboratory
P.O. Box 1663, M.S. 490
Los Alamos, NM  37545

Dr.  Norman Bowne
Certified Consulting Meteorologist
TRC Research Corporation of NE
125 Silas Deane Highway
Wethersfield, CT  06109

Mr.  A1,E. Boyer
Senior Meteorologist
Ontario Hydro
700 University Avenue
Toronto, Ontario, Canada

Dr.  Harrison E. Cramer
President, H.E. Cramer Co., Inc.
P.O Box 8049
Salt Lake City, UT  84108

Dr.  Noel de Nevers
Professor, Department of
  Chemical Engineering
University of Utah
Salt Lake City, UT  84112

Dr.  Bruce A. Egan
Technical Director of
  Environmental Services
Environmental Research and
  Technology, Inc.
429 Marrett Road
Lexington, MA  02173

Dr.  Douglas G. Fox
Meteorologist and Project Leader
Rocky Mountain Forest & Range
  Experiment Station
Forest Service
U.S. Department of Agriculture
240 W. Prospect Street
Fort Collins, CO  80521
Dr. Steven R. Hanna
Meteorologist
Atmospheric Turbulence and Diffusion
  Laboratory
National Oceanic and Atmospheric
  Administration
Cheyenne Hall Building
P.O. Box E
Oak Ridge, TN  37830

Mr. Donald Henderson
Meteorologist
Region VIII, Environmental Protection
  Agency
1860 Lincoln Street
Denver, CO  80203

Dr. Eugene Y. Leong
Chief, Air and Water Quality Division
Association of Bay Area Governments
Hotel Clairemont
Berkley, CA  94705

Mr. Gary L. Melvin
Manager of Air Resource Analysis Section
Division of Air Pollution Control
Illinois Environmental Protection Agency
2200 Churchill Road
Springfield, IL  62706

Dr. David J. Moore
Central Electricity Research Laboratory
Leatherhead, Surrey KT22 7SE
England

Mr. Lawrence E. Niemeyer
Director, Met. and Asses. Division
U.S. Environmental Protection Agency
Research Triangle Park, NC  27711

Dr. William A. Perkins
Special Consultant
Member, EPA Science Advisory Board
Metronics
3174 Porter Drive
Palo Alto, CA  94304

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Mr. Richard A. Porter
Institute for Environmental Sciences
University of Texas, Dallas
P.O. Box 688
Richardson, TX  75080

Dr. Andrew J. Ranzieri
Senior Air Quality Engineer
California Air Resource Board,
  Modeling Section
1709 llth Street
Sacramento, CA  95814

Dr. Phillip M. Roth
Director of Research
Systems Applications, Inc.
950 Northgate Drive
San Rafael, CA  94903

Dr. Jerry L. Shapiro
Chief Environmental Engineer
Bechtel Power Corporation
12400 E. Imperial Highway
Norwalk, CA  90650

Dr. Conrad Simon
Director, Environmental Programs
  Division
Region II, Environmental Protection
  Agency
Federal Officer Building
26 Federal Plaza
New York, NY  10007
Mr. Maynard E. Smith
President, Smith-Singer Meterologists,
  Inc.
134 Broadway
Amityville, NY  11701

Dr. D. Bruce Turner
Chief, Environmental Applications
Branch, MD, ESRL
U.S. EPA
Environmental Sciences Research
  Laboratory
Research Triangle Park, NC  27711

Dr. Isaac Van Der Hoven
Chief, Air Resource Environmental
  Laboratory
National Oceanic and Atmospheric
  Administration
Granax Building
8060 13th Street
Silver Spring, MD  20910

Mr. Robert I. Wevodau, Jr.
Engineering Services Division
E.I. DuPont de Nemours & Co., Inc.
13W38 Louviers Building
Wilmington, DE  19898

Dr. Michael D. Williams
Research Coordinator
John Muir Institute for Environmental
  Studies, Inc.
Route #5, Box 299A
Santa Fe, NM  87501

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                  EPA Resource Staff
    Dr.  David K. Berry
    Director, Policy Analysis Staff
    Office of Air Quality Planning and Standards
    U.S. Environmental Protection Agency
    Research Triangle Park, NC  27711

    Dr.  Peter Finkelstein
    Regional Meteorologist
    U.S. Environmental Protection Agency
    Region III
    6th and Walnut Street
    Philadelphia, PA  19106

    Mr.  Michael Lazaro
    Chief, Technical Analyst Section
    Air and Hazardous Materials Division
    U.S. Environmental Protection Agency
    230 South Dearborn
    Chicago, IL  60631

    Mr.  Herschel H. Slater
    Physical Scientist
    Monitoring and Data Analysis Division (MDAD)
    U.S. Environmental Protection Agency
    Research Triangle Park, NC  27711

    Mr.  Joseph A. Tikvart
    Chief, Source Receptor Analysis
    Branch (MD-14), OAQPS
    U.S. Environmental Protection Agency
    Research Triangle Park, NC  27711
Energy and Environmental Systems Division/ANL Staff


    Dr. John J. Roberts
    Deputy Division Director

    Dr. Donald M. Rote
    Air Resources Section Director

    Dr. Albert E. Smith

    Dr. Kenneth L. Brubaker

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10

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                               11
Schedule - Specialists Conference on the EPA Modeling Guidelines
Tuesday


9:00


Registration



12:00
12:00
Lunch

1:00
1:00 _ . ,.
Opening address
1:45

1:45
Orientation
3:45
Wednesday
Breakfast
9:00
9:00 TT ..
Working groups:
1-1
1-2
1-3
(Coffee at 10:00)


12:00
12:00 „ ,.
Working group
lunch
1:00
1:00 _,
Plenary session -
Working group
reports
3:00

3:00 „ -_ , ,
Coffee break
o /c ! 3=30
3:45
Coffee break
' 4:15

4 : 15
Plenary session
on policy issues
6:00
6:00 _
Open
7:00
7:00 _.
Dinner
8:00
8:00 _-
Plenary session
(Cont'd)

10:30

3:30 „ .,
Working groups:
1-1

1-2
1-3
5:00
5:00 _
Open
6:30
6=30 nj
Dinner
7:30
7:30 TT
Writing sessions
for working groups
1-1, 1-2, 1-3
10:30
Thursday
Breakfast
9:00
9:00 TT . .
Working groups:
II-l
II-2
II-3
II-4
II-5
(Coffee at 10:00)
12:00
12:00 IT . .
Working group
lunch
1:00
Plenary session -
Working group
reports
3:00

3:00 _ .... , .
Coffee break
3:30

3:30 TT .
Working groups:
II-l II-4

II-2 II-5
II-3
5:00
5:00 _
Open
6:00
6:00
Dinner
7:00
7:00 T31
Plenary session -
Wrap-up of
issues
10:30

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12

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                                    13

                                  PREFACE
                               J. J.  Roberts

       The problem of air pollution control can be approached via two
alternative though not mutually exclusive policies:  management of emissions
and management of air quality.  In the former (which could include emission
tax policies), emphasis is placed on the technology for control of effluent
(including in-plant process changes); regulatory requirements based pri-
marily on cost/effectiveness considerations are expressed in terms of
emission standards reflecting reasonably available or best available control
technology.
       Air quality management, on the other hand, entails the establishment
of air quality goals based upon stated criteria or public policies.
Emissions are then limited to the extent necessary to attain and maintain
such goals.  For example, the Clean Air Act Amendments of 1970 require the
federal Environmental Protection Agency to establish National Ambient Air
Quality Standards  (NAAQS) in  terms of a national policy requiring the pro-
tection of public health and welfare.  Air quality increments designed to
prevent significant  deterioration  (PSD) reflect an air quality rather than
an emission management policy.
       In  the Clean  Air Act of 1967, the amendments of 1970, and in  the
most recently proposed amendments of 1976,  the U.S. Congress has con-
sistently  endorsed a national policy of air quality management.  Though
not exclusive of  specific  requirements  for  emission control  (e.g., new
source performance standards, motor vehicles), the thrust of these legis-
lative acts is  the achievement of  air quality  goals through a combination
of emission limitations  and land-use-related measures.
       The success of a policy of air quality management depends very
much upon  the availability of calculational procedures to relate emissions
of air contaminants  to resulting levels of pollution in the ambient  air.

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                                     14
Such "air quality models"* must be sensitive to quantities and geographical
location of emitted pollutants, meteorological conditions governing the dis-
persion of airborne contaminants, and, as appropriate, the physical and
chemical processes affecting transformation and removal.
       A number of regulatory programs which implicitly call for the applica-
tion of air quality models are listed in Fig. 1.  Proportional rollback and,
in some cases, multi-source urban dispersion models such as the Air Quality
Display Model (AQDM) were employed in the development of State Implementation
Plans (SIP) pursuant to clean air legislation of 1967 and 1971.  Currently many
such SIPs are being revised to reflect regional growth, recent pressures to
permit substitution of coal for scarce petroleum resources, and other reasons
including failure of original analyses to properly predict future levels of
air quality and failure of emission sources to achieve required levels of
control.  Recent court rulings and subsequent EPA regulations concerning pre-
vention of significant deterioration  (PSD) call for use of air quality models
in two modes: (1) initial classification or reclassification of geographical
areas, where the decision requires estimates of the economic and energy impacts
of alternative PSD increments; and (2) determination of compliance of a pro-
posed new source with established PSD increments.  The latter determination
is part of a federally mandated new source review (NSR) process which also
entails a determination that the source will not cause a violation of the NAAQS.
Where the source is a "major" one as defined by EPA directives and where the
source would cause or exacerbate a violation of the NAAQS, then the source can
be constructed if and only if a balancing or emission offset procedure is
followed.**  In many situations, the emission offset evaluation will require
use of an air quality model in what is, in effect, a SIP revision.
        Figures 2 and 3 present the NAAQS and PSD increments (current EPA
 values and those recently proposed by Congress).  They set forth the quanti-
 tative targets and limits which govern the air quality management process.
 *We shall distinguish such "air quality models" from individual algorithms
  (e.g. estimation of plume rise) which may be combined in the model.
  Further, for our purposes a Gaussian dispersion kernel may be considered
  a sub-model employing algorithms to estimate the rate of dispersion as a
  function of downwind distance or time.  Finally, since complex models
  are frequently implemented on a digital computer, they may be termed
  herein computer models or computer codes.  Often the name of the
  computer code (e.g. AQDM) is used synonymously for the air quality model
  from which it is derived.
**41 Fed. Reg. 55525 (Dec. 21, 1976)

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SIP
c
       APPLICATIONS OF AIR QUALITY MODELS
       IN AIR QUALITY MANAGEMENT
DEVELOPMENT

REVISION
PSD CLASSIFICATION
        t
       PSD
       NAAQS
                       NON-
                     ATTAINMENT
INDIRECT SOURCE REVIEW

ENVIRONMENTAL IMPACT STATEMENTS

LITIGATION (ENFORCEMENT/ VARIANCE )
                                 EMISSION
                                 OFFSET"
                      FIG.

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                              16
        NATIONAL AMBIENT AIR QUALITY STANDARDS  (NAAQS)
POLLUTANTS
PARTICULATES
PRIMARY
SECONDARY
S02
PRIMARY
SECONDARY
CO
HYDROCARBONS
OXIDANTS
N02
CONCENTRATIONS IN /-ig/m3
1-hr


40°

160

3-hr

1300

160+


8-hr


10°



24- hr
260
150
. 365




1-YEAR
75 (G)
60 (G)*
80 IA)



100 (A)
 0  CO CONCENTRATIONS MEASURED IN  mg/m3
(A) ARITHMETIC AVERAGE
(6) GEOMETRIC  AVERAGE
* AS A GUIDE  TO BE USED IN ASSESSING IMPLEMENTATION PLANS TO ACHIEVfc
      THE 24  HOUR STANDARD
+  AS A GUIDE  TO BE USED IN DEVISING IMPLEMENTATION PLANS FOR
     ACHIEVING OXIDANT STANDARDS
                         FIG.   2

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                PREVENTION OF SIGNIFICANT DETERIORATION (PSD)
                            INCREMENTS
POLLUTANT
                    CLASS  I AREAS    CLASS II AREAS   CLASS III AREAS
 PARTICULATE
 MATTER
   ANNUAL
   24-HOUR
                      5/5

                      10/10
10/19

30/37
UP TO NAAQS/37

UPTONAAQS/75
 SULFUR
 DIOXIDE
   ANNUAL
   24 HOUR
   3 HOUR
                      2/2
                      5/5
                    25/25
15/29
100/19
700/512
UPTONAAQS/40
UP TO NAAQS/182
UP TO NAAQS/700
TOP NUMBER -EPA,
                         BOTTOM NUMBER-CONFERENCE REPORT ON  S.32I9 TO
                               AMEND  THE CLEAN  AIR  ACT, CONGRESSIONAL
                               RECORD,  SEPTEMBER  30,  1976
                                  FIG.  3

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                                     18
Simultaneously, they establish implicitly a level of accuracy desirable in
applicable air quality models.
       Scientists have been engaged for many years in the study of atmos-
pheric dispersion of windborne material from industrial and other pollutant
sources, including military activities.  Much has been published on the
subject.  In addition to definitive texts such as Pasquill (1961 and 1974)
and to summary reports such as Turner (1967) are numerous articles and
reports describing research on the formulation and evaluation of algorithms
and models characterizing the various aspects of the problem.  Given these
research activities, coupled with the emergence of air pollution control
agencies at state and local levels and of consulting firms established
to aid both industry and government, one should not be surprised to find
extensive proliferation of algorithms, sub-models, models and related
computer codes, each designed to facilitate one or more aspects of air
quality analysis.  Further, even though many models employ a Gaussian
kernel to characterize the dispersion phenomenon, results can vary widely
depending upon other calculational features such as manipulation of emission
and meteorological data, treatment of plume rise and interactions with
elevated inversions, evaluation of dispersion coefficients, averaging
techniques and statistics routines to estimate compliance with short- and
long-term standards.  Different user-oriented input/output features may
distinguish two otherwise identical calculational procedures.  And,
finally, the same computer code can be employed in different ways (e.g.
one versus five years of meteorological data) or results interpreted in
different ways (e.g., in determining compliance with short-term NAAQS).
       Thus, with numerous models in the literature and numerous users in
the field, the potential exists for chaos, or at least for frequent, honest
disagreement, even among different units within the same organization.
Confusion of this sort has already emerged where an industry seeking a
construction permit in a multistate air quality control region is faced
with differing calculational procedures (and thus, in effect, differing
emission limits) among the various state and local agencies and, quite
possibly, between adjoining Regional Offices of the federal EPA.
       It is evident that there is a need for some measure of standardiza-
tion in air quality analysis.  As a minimum such standardization should be

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                                    19

applied to calculatlonal procedures used throughout the federal EPA and by
state and local agencies in their official dealings with the EPA.  The
issue then becomes one of determining the degree of specificity (e.g.,
specific computer codes), what in fact will be standardized (e.g., models
vs. algorithms), the rigidity of the requirements (or conversely, the
flexibility of the user to select or even develop alternative calculational
methods), the extent of coverage (e.g., specification of a particular model
vs. specification of the model along with procedures for input of data and
interpretation of results), and finally, and perhaps most importantly, the
performance criteria which a calculational procedure must meet to quality
for inclusion in any official statement of such standards.
       The need within the national air pollution control program for a
guidance document on air quality analysis persists.  Despite the difficulties
outlined above, the EPA/Office of Air Quality Planning and Standards  (OAQPS)
has been given the responsibility for developing the requisite Guideline.
A first draft was prepared and circulated for initial in-house review during
December 1976.  A second draft was published in early February.  The target
date for a final version of the Guideline is July 1, 1977.
       In December Argonne was apprised of this schedule and requested to
organize a three-day conference/workshop wherein specialists in air quality
analysis would critique the second draft of the Guideline.  Recognizing that
the federal EPA and the states had to fulfill the important air quality
management responsibilities outlined in Fig. 1 employing the state-of-the-
art in analytical methods, the conferees were challenged to advise EPA on
the best available approaches to modeling air quality impacts and to concur
with or where possible recommend improvements to the many aspects of
the problem addressed in the Guideline.
       The Specialists Conference on the EPA Modeling Guideline was held at
the Nordic Hills Inn, Itasca (Chicago), Illinois on February 22-24, 1977.
Members of the staff of the Energy and Environmental Systems Division of
Argonne National Laboratory served as conference organizers and reporters.
Several representatives of the EPA/OAQPS assisted as resource persons.
The twenty-four conferees were drawn from private industry, universities,
public interest organizations, and federal and state government.  A list
of participants and affiliations appears at the front of this report.

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                                     20
       These participants and the conference, as a body, are considered essential
parts of the decision-making process for development of the Guideline.  The
sum of the scientific group's experience in the private sector included many
years of service as staff members for large corporations and as consultants
to the entire spectrum of industrial activity of this Nation.  The experience
and procedures followed in Canada and in Western Europe were brought to the
group by scientists from Canada and the United Kingdom.  Those involved in
the public sector are active at the local, state, federal and even inter-
national levels.  Several of the conferees are valued consultants to both
private and public activities as well as independent public interest associa-
tions promoting environmental issues.  All are highly knowledgeable of the capa--
bilities and limitations of air quality modeling.
       The conference was organized around a mix of plenary sessions and
concurrent workshops.  Major policy issues and general technical matters
were reviewed by the conferees at-large.  These general discussions provided
guidance to working groups responsible for examining specific aspects of the
Guideline (see Fig. 4).  To assist everyone in preparing for the conference,
a reference notebook was prepared and distributed along with the draft
Guideline several weeks in advance.  This notebook contains a brief charac-
terization of each model referenced in Tables 2 and 3 of the draft Guideline
along with key excerpts of material referenced in the Guideline.  The
portion of the notebook containing the model descriptions is reproduced here
in Section 3.11.
       I wish to thank my colleagues at Argonne for their assistance in
preparing the background material, planning the conference, and in general
seeing to the myriad of details essential to a successful meeting.  We have
acted only as organizers of the conference and as recorders and reporters
of the positions articulated by the conferees.  These conferees endured a
very intensive three-day marathon.  Their exceptional efforts attest to
the seriousness with which they assumed their responsibilities.
       The following quotation from Chief Justice David L. Bazelon of the
U.S. Court of Appeals for the District of Columbia seems appropriate to con-
clude these introductory comments.  Speaking on the difficulties that the
courts face in resolving environmental debates, this jurist observes that
the courts are not the proper forum

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                     WORKING  GROUPS

 I-l   MULTISOURCE,  SHORT  TERM AND LONG TERM,  SET I POLLUTANTS
 1-2   SINGLE SOURCE, SHORT TERM AND LONG TERM, SET I POLLUTANTS
 1-3   SET  2 POLLUTANTS - N02 , OXIDANT.CO
     LONG RANGE TRANSPORT AND LOSS MECHANISMS
H-2  PLUME DYNAMICS: WAKES, DOWNWASH, FUMIGATION,MIXING LAYER EFFECTS
E-3  COMPLEX  TERRAIN
U.-4  CHARACTERIZATION OF  TURBULENCE
E-5  VALIDATION AND CALIBRATION
                           FIG. 4

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                                     22
       ...either to resolve the factual disputes or to make the
       painful value choices.   What the courts and judges can do,
       and do well, when conscious of their role and limitations -
       is scrutinize and monitor the decision-making process to
       make sure that it is thorough, complete, and rational; that
       all relevant information has been considered; and that
       insofar as possible, those who will be affected by a
       decision have had the opportunity to participate in it.

       [Decision makers should openly disclose] where and why the
       experts disagree as well as where they concur, and where the
       information is sketchy as well as complete.  When the issues
       are controversial, any decision which is reached may be
       unsatisfactory to large portions of the community.  But
       those who are dissatisfied with a particular decision will
       be more likely to acquiesce in it if they perceive that their
       views and interests were given fair hearing.

       (Address to the Atomic Industrial Forum, January 10, 1977)
This Specialists' Conference and the report which now follows represents
an important element in such a decision-making process.

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                                    -23-
               1   POLICY ISSUES AND GENERAL TECHNICAL MATTERS
                       ADDRESSED IN THE PLENARY SESSIONS
                       Moderator/Reporter:   J.J.  Roberts
       Policy issues and technical matters addressed in the plenary sessions
dealt primarily with the broader considerations surrounding the application
of models, purpose and structure of the guideline document, and criteria and
procedures for inclusion therein of "approved" models.  These discussions
provided guidance to the working groups so that the individual position
papers are consistent with philosophical and technical perspectives of the
conferees at large.  Fourteen specific policy issues and general technical
matters are summarized below.

1.1  THE GUIDELINE TENDS TO STANDARDIZE AIR QUALITY ANALYSIS
       The principal issue for the conferees is the degree to which it is
appropriate for a Guideline to prescribe calculational methods and/or
specific computer codes to be used in the analysis of air quality problems.
The desirability of some guidance is generally recognized; the degree of
specificity of that guidance is the issue.  Reference was made to the
Congressional Conference Report of September 30, 1976 on Clean Air Act
Amendments of 1976 where an amended Section 318 addressed  "Standardized Air
Quality Modeling" and set forth a general prescription for a conference on
this subject which presumably would lead to a "guidance document," possibly
published in the Federal Register.  Again, here, the  degree of standardiza-
tion is left unclear in that the word "standardized"  never appears within
the text of the article and, further, that the term "appropriate modeling"
appears somewhat in its stead.
       A related aspect to the discussion on the extent of standardization
concerns  the likelihood of approved models attaining  some  special status in
legal contests.  It was generally recognized that such would,  in fact, be
the case  although wording could be incorporated to soften  a rigid interpre-
tation by emphasizing the opportunity and/or desirability  under certain
circumstances  for  the user to, at his discretion, select alternative
analytical procedures.  The near  certainty that the Guideline  would attain
special status in  regulatory matters argues for a cautious approach to
designating any particular model  as "approved" and emphasizes  the importance

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                                    -24-
of proper evaluative procedures.   .These legal  implications also  led  to
recommendations of "conservative" modeling techniques for initial "screening"
in the new source review process.2
       Two widely different perspectives on the issue of standardization
were expressed by the conferees.   On the one hand,  a "scientific" viewpoint
emphasized that air quality problems are generally  unique and should  be
treated on a case-by-case basis with appropriate professional counseling.
Reference in this case was made to a position recently drafted by the
American Meteorological Society (not yet approved by the AMS Council) which
responds principally to the above-referenced proposed amendments  to the
Clean Air Act.  The draft position statement expressed specific concern
with "the concept that there should be currently established one  particular
model which will be capable of analyzing all conceivable situations," and
went on to point out that the wide range of meteorological, geographical,
and other aspects of the air quality modeling problem, especially regarding
short-averaging-time standards, precludes the uniform application of  a
single model and, in some cases, is "not amenable to simple mathematical
treatment."
       The other distinct perspective could be termed a "regulatory
perspective."  It was most clearly articulated by spokesmen from industries
having to deal with the new source review process as applied to state
implementation plans and prevention of significant  deterioration.  Here,
the call was for clearly defined analytical procedures for determining air
quality impacts and, thus, compliance with applicable limitations.  In
this sense, the Guideline was seen as requiring the use of reasonably
accepted, state-of-the-art mathematical models by which the applicant could
independently assure himself of a reasonable likelihood of compliance
before the investment of significant monies in the pre-construction phases
of a new development.  The call was to identify those types of problems
which could be treated by approved models and the gray areas within which
special case-by-case analysis would be necessary.  It was  felt that
industry's need for clarity, for reducing uncertainties in the review
     Section 2.4 and report of the Working Group II-5
2See Section 2.3

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                                    -25-
process, was the principal motivation behind the above-referenced section
in the Congressional Conference Report.  A related argument in favor of some
degree of standardization concerned the review of new pollution sources
impacting on more than one state and possibly more than one federal EPA
region.  This situation is likely to occur under the prevention of signifi-
cant deterioration regulations.  With different states and EPA regional
offices applying different air quality models to determine compliance of the
proposed source, chaotic regulatory situations can occur.
       The conferees have reached a consensus on this issue in that it is
agreed that for certain standard types of situations, it would be desirable
for the Guideline to identify an "appropriate" or, possibly, "approved" set
of algorithms or particular computer code(s) to be applied if the particular
model(s) have been properly verified under  the conditions for which the
standard application would be prescribed.   The decision in this regard would
be made in accord with an approved set of procedures for testing, validation,
and possibly calibration.  Secondly, significant flexibility should be
provided to the user whereby, with appropriate professional counsel, other
analytical methods could be employed where  the standard situation was not
encountered.

1.2   SPECIFICATION OF COMPUTER CODES VERSUS APPROVED ALGORITHMS
        The Guideline as currently proposed  lists approved  or recommended
computer codes  to perform different air  quality analyses.  In fact, however,
with  the exception of rollback,  they all employ  the  same Gaussian dispersion
kernel and  thus could be viewed  as the same models.  It  is also  recognized
that  a computer code consists  of many  algorithms critical  to  the estimation
of air pollution  levels;  among  these are emission  inventory estimates
 (seasonal average and temporally varying),  statistical analysis  of meteoro-
logical data  including  the  initial acquisition and processing of such  data,
prediction  of  plume  rise,  dispersion coefficients, and approximation of  area
sources.
        Thus,  while  there  is  a  strong sentiment among the conferees  to  have
the Guideline focus  as  exclusively as  possible on  the  identification of
approved algorithms  which  could  then be used  in  creating an  (implicitly)
approved computer code,  it  becomes readily  apparent  that the  number

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                                    -26-
of steps in performing the overall air quality analysis are so closely
interrelated that such an approach would be difficult to pursue,  especially
at this time.  Thus, the Guideline should, where appropriate, reference
specific computer codes, although evaluation studies should be conducted on
individual algorithms as well as on such complex air quality models.   The
working groups, however, had many reservations about the specific models
listed in the Guideline, due in part to the lack of sufficient verification
experience, and in many cases could not give blanket approval to  their use.

1.3  APPLICATION OF STANDARD COMPUTER CODES FOR "SCREENING" IN THE NEW
     SOURCE REVIEW PROCESS
       Whereas there is strong reluctance on the part of many conferees to
accept a particular code as a standard for a particular application,  there
is general agreement that certain codes could be applied in a "screening"
process wherein a determination could be made that the source is  in compli-
ance because the associated estimate of air quality impact is suitably far
below any regulatory threshold.  The concept is one of applying the analyti-
cal procedure with  sufficient conservatism to identify those situations
which are clearly within the allowable limitations.  This approach would
distinguish a computer model as being appropriate  for  screening but not
necessarily sufficiently reliable to be endorsed as a  standard for analysis
where the decision  is a close one and where a more refined analysis and/or
interpretation of the results of such an analysis may  be required.  There
still remains the necessity of characterizing those geometrical, meteoro-
logical and other conditions under which the code  is applicable in the
screening process.
       The use of standard models as screening tools to eliminate
"non-problems" from more extensive new source review requirements has
several constructive ramifications:
       1.  If the conservative estimates demonstrate that air quality
           goals could be readily achieved, a quick approval action is
           possible.
       2.  If the conservative estimates indicate a potential problem,
           on the basis of the results, the source can alter its
           design or implement a program involving further measurements
           and/or a refined modeling approach which might yield a more
           realistic assessment of air quality impact.  The fact that

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                                    -27-
           the  standardized model  results  were  conservative  provides a
           procedural recourse for sources to provide more information
           on the expected impact.
       3.   From an advancement-of-technology point-of-view,  pressure
           is maintained on all parties to produce accurate  modeling
           techniques.  If the standardized models are  too conserva-
           tive, too few "non-problems" are identified  and EPA will
           therefore strive for more realistic  prediction methods
           to reduce the conservatism.   Similarly, because refined
           approaches will be required  for a reconsideration of  a
           potential denial,  sources will be interested in engaging
           in efforts which will tend to improve our understanding  of
           the technical issues.
1.4  EVALUATION AND CALIBRATION
       The ability of a computational procedure or any of its algorithms to
properly characterize the physical processes that govern the transport and
aerochemistry of air pollutants and thus to predict pollution concentra-
tions with acceptable accuracy is clearly the most important criterion in
evaluating that model for inclusion in this Guideline.  Currently, there is
no systematic procedure  being employed by the federal EPA for the
evaluation of air quality models.  Such a procedure would require criteria
for acceptability and for inclusion in a guideline, procedures for carrying
out a test program including specifications on data and on the range of
conditions under which the model must be evaluated.  The evaluation process
would include an internal (EPA) review as well as, ultimately, an independent
peer group review.  This evaluation process certainly would be an extensive
one involving significant effort on the part of persons developing and
promoting particular theories  and models.  A more extensive discussion of
the requirements for such a  systematic procedure are described in the report
of Working Group II-5.
       Most of  the models referenced in Table  3 of the proposed Guideline
and recommended by the EPA have undergone some evaluation.  However, in
general, it was agreed that  the existing models would not meet the criteria
likely to be imposed via the above-mentioned  systematic evaluation procedure.
EPA itself is well aware of  these limitations  and will welcome specific
recommendations for a comprehensive evaluation procedure.

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                                     -25-
       Th e multi-source, long-term air quality models in Table 3 of the
draft Guideline generally employ a calibration step to fit, usually by linear
regression techniques, the model predictions to a set of observed long-term
averages.  The resulting regression coefficients are then employed in all
further use of that model for predictions of future levels of air pollution
under varying source and meteorological conditions.  Positions for and
against such calibration were expressed.  That calibration has been commonly
used with apparently acceptable results would argue for its endorsement as
an acceptable procedure; that the use of calibration implies a failure of
the model to properly represent the physical processes would suggest that
more attention be paid to those failings that to a statistical procedure to
conceal their details.  The decision on this matter was left up to the
working group on multi-source models, wherein the use of calibration
techniques was endorsed for evaluation of long-term average concentrations
in urban, multi-source configurations.

1.5  THE DRAFT GUIDELINE SUGGESTS A HIERARCHY WHICH EVOLVES TOWARD ROLLBACK
     AS THE PROBLEMS BECOME MORE DIFFICULT
       The conferees could not understand the rationale for this seemingly
contradictory proposition.  Rollback techniques, as the EPA admitted, are
rarely an acceptable option in the eyes of the EPA regional offices.  While
perhaps appropriate for a first-cut estimate of the degree of required
emission control and possibly where the geography and aerochemistry of the
problem would permit an assumption of homogeneity, it makes no sense to use
a super-simple model in complex situations.  In general, it was recommended
that rollback not be included as an acceptable option for S02, particulates,
and CO.*  Its application to photochemical oxidants and N02 is discussed in
more detail under the report from Working Group 1-3.
       Another aspect of concern for the use of rollback is under condi-
tions where there are an insufficient number of monitoring sites to assure
that the maximum pollution levels are used in determining the constants of
*Working Group 1-1 did, however, accept the proposition that "if the
 meteorological or topographic complexities of the region are such that the
 use of any available air quality model is precluded, then the model used
 for strategy evaluation may be limited to a Rollback Model."

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                                     -29-
proportionality.  Thus, one might significantly underestimate the degree of
required controls in areas not monitored.  Conversely, rollback can over-
estimate the degree of control required by focusing on "hot spots" which are
not characteristic of  the area.

1.6  ESTIMATING SHORT-TERM AIR QUALITY IMPACTS:  STATISTICAL VERSUS
     ENUMEEATIVE APPROACHES
       Two very controversial issues emerge under this general topic.
Firstly, the PSD increments are stated as increments never to be exceeded, a
condition which has somewhat frightening implications when viewed in terms
of a statistical treatment of meteorology and air pollution and the
unpredictability of rare events.  The EPA explains that this dilemma would
be resolved by applying the models to a fixed and limited meteorological
history (up to five years) wherein, if the model did not indicate a violation
of the PSD increments, approval would be given for construction of the source.
Further, it is suggested that the PSD increments would be enforced only
during the review process and thus constitute a procedural type of regulation
rather than one which  would be enforced after the fact if monitored
pollution levels showed the increments to be violated.  This position seems
somewhat contradictory to the basic concept of SIP development and revision.
       The second issue which comes under this general heading concerns  the
procedure for the calculation of short-term impacts, whether for determining
compliance with short-term  (three and twenty-four hour) national ambient air
quality standards or with PSD increments.  The most common way of employing a
model such as CRSTER is to  calculate air pollution levels at each receptor
site for each hour in  a given period of recent history, usually one  to five
years.  The results of this analysis are then applied in an enumerative  sense
whereby as long as no  more  than one violation per year at any site is found
to exist, the NAAQS short-term values are assumed met, and where no value
exceeds the appropriate PSD increment, that limitation is assumed satisfied.
       An alternative, and  to many persons a more satisfying approach, would
be to generate or in some way employ a statistical representation of the air
quality at any given receptor site and apply the results of such an analysis
to the  test of compliance.  This approach would require some, possibly minor,
redefinition of the NAAQS and PSD limitations.  Consistent with this

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                                   -30-
statistical approach, a model such as CRSTER would be run over an
appropriate period of time with the resulting computed values fitted to a
suitable cumulative frequency distribution function.  It was observed that
similar problems occur in interpreting the short-term data from air quality
monitoring sites which may be subject to occasional and undetermined
malfunctions producing erroneously high values.
       It is common engineering practice to use statistically computed
criteria for design to meet extreme meteorological conditions.  Drainage
for flood control and wind loads on structures are two examples.  Appro-
priate meteorological parameters are treated by special statistical
procedures to estimate conditions for specified periods ranging from ten to
one hundred years.  These estimates often exceed the period of record by a
factor of two or more.  Although meteorological conditions associated with
atmospheric transport models are very different from those associated with
the above examples, the concept of using a statistical approach to determine
compliance is analogous to these established practices.
       Although the consensus is that a statistical approach is theoret-
ically more valid, it is recognized that a detailed investigation of
the nature of the statistics should be completed prior to implementation..
In particular, concern is expressed regarding the appropriate form of the
cumulative distribution function to be used in any curve-fitting procedure.
       To the extent that enumerative (i.e., deterministic) methods are
used  to determine compliance with short-term standards, two approaches
suggest themselves:   (1) the above-described enumerative approach using
CRSTER, where the major question is the period of record over which calcu-
lations are  to be made; and  (2) a "worst case" approach where the challenge
is to identify the conditions under which maximum ground level  concentra-
tions might  occur.
       In  the first  situation,  it was argued by one conferee  that the period
of record be equivalent to the  plant life  (i.e., as long as twenty to thirty
years).  It  was  also recognized that even a five-year set of  calculations
could be unreasonably  costly for certain smaller sources.
       The worst  case  approach  via models such as CRSTER makes  sense to  the
conferees as a screening mechanism whereby compliance can be  assumed if  the

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                                    -32-
worst case estimate is conservatively below the allowable increment.
However, the use of an idealized plume rise algorithm (as in CRSTER)  might
overlook special high pollution conditions associated, for example,  with
aerodynamic downwash.  Other specialized situations such as unusual
circulations (e.g., sea breezes) and fumigation should be considered as
potentially creating the worst case situation.
       Finally, in applying a "worst case" analysis, some recognition must
be taken of the potential for occurrence of such a situation in order to
determine compliance with short-term NAAQS.

1.7  TO WHAT EXTENT SHOULD THE USER'S BURDEN IN ACQUISITION OF DATA AND
     PERFORMANCE OF CALCULATIONS BE CONSIDERED IN RECOMMENDATIONS SET FORTH
     IN THE GUIDELINE?
       It is generally recognized that for some types of calculations and
some computer codes  the burden of acquisition of emission and meteorological
data and the computer costs can be excessive for small pollution control
agencies and for many industrial applicants.  Thus, another argument can be
made in favor of an  inexpensive screening process  to  limit the number of
situations in which more extensive air quality analysis would be required.
However, in those  situations where the standards are  in fact threatened and
a more refined  analysis is required,  the best available methods should be
employed.  This burden would most likely fall upon the federal EPA and upon
state agencies.
        To  the  extent the Guideline recommends models  for  screening purposes,
such models may reflect a  desire for  simplicity and ease  of use.  To the
extent  the Guideline recommends models for  general application in air
quality assessments,  they  should be  characterized  in  terms of the potential
burden  upon  the user but  the decision as  to  the appropriateness of that
burden  should  be  left to  the user.
        In  a  somewhat related aspect,  the  Guideline would  permit the
Regional  Administrator  to  select a model  other  than that  recommended "if  the
data bases  required" are  "unavailable or  inadequate." The degree to which
it  is  incumbent upon the  EPA or upon the  applicant in a  new  source review
procedure to  delay the  project until adequate data are assembled should be
clarified.

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                                     -32-
1.8  THE EPA OFFICE OF AIR QUALITY PLANNING AND STANDARDS DECIDED NOT TO
     INCLUDE OXIDANT MODELING IN THIS EDITION OF THE GUIDELINE
       EPA representatives indicate that they have recently organized a task
force to develop methods for relating levels of photochemical oxidants to
the required degree of hydrocarbon controls.  Thus, they chose to leave
oxidant modeling out of this edition of the Guideline.  The appropriateness
of currently available models for the description of chemically reactive
pollutants is considered briefly in the report of Working Group  1-3.

1.9  AIR QUALITY DATA
       The importance of air quality data in evaluating the performance of
an air quality model is evident.  The conferees are concerned that,  in
general, insufficient consideration has been given to criteria for proper
siting of monitors and acquisition and processing of air quality data for
validation and for calibration purposes.  The EPA has a task force currently
studying the monitoring situation in the USA, but the major thrust of this
effort would appear to be a cost/effectiveness approach to minimizing the
number of monitors necessary to assure compliance with ambient air quality
standards.  The establishment of appropriate criteria for monitoring data
to be employed in model validation studies would be a part of the evalua-
tion procedure developed pursuant to policy issue 1.4, above.  Finally, in
addition to the need for better air quality data, there is a need for
modelers to be more aware of the data which is available.

1.10  METEOROLOGICAL DATA
       In a matter analogous to the concern for the quality of monitoring
data, concern is expressed for the inadequacy of meteorological data
currently available via the National Weather Service.  Further, hourly
meteorological observations are not available on magnetic tape from the
National Weather Records Center for many sites in the USA.  Finally, the
NWS program of twice per day urban soundings has been discontinued.
       It is generally agreed that little can be done in the near future to
change the method of observation and to reinstate the sounding program.  The
National Climatic Center is preparing hourly records  for selected locations at
the request of EPA under an interagency reimbursement agreement.  Several other
parties from time to time obtain climatic data of this type under similar arrange-
ments.

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                                    -33-
       Concern is also expressed for a lack of adequate meteorological data
at elevations approximating the effective stack height of major  elevated
sources, as well as for how the available meteorological data are used in
evaluation and in various algorithms.

1.11 PROPRIETARY COMPUTER CODES
       The appropriateness of referencing proprietary computer codes in the
Guideline is a controversial issue.  The large majority of conferees,
including several private consultants, oppose the inclusion of such models
in this government publication.  EPA representatives indicate that they do
not intend to reference such models in the Guideline.
       Arguments in favor of including references to proprietary models are
consistent with a structure for the Guideline which does not recommend
specific computer codes but rather approved, generic methods and algorithms
for air quality analysis.  It is felt by some that this approach would
allow a listing of computer models, public and private, which satisfied the
generic criteria.  This position is also consistent with a concern for
excessive standardization of calculational methods in  contrast to more
emphasis on  case-by-case analysis by  qualified experts.  Regarding the
image of secrecy associated with a proprietary model,  it is  argued that the
regulatory agency could be provided with documentation and possibly
listings of  the proprietary code as  long as  the agency did not release  this
information  to  the public.  Finally,  proprietary models can  be tested
against sample  problems  in order to  demonstrate their  consistency with
other,  approved, public  domain  models.
        This  rationale for  including  proprietary models and the associated
procedures  for  their  evaluation as  well  as  their safeguard are countered by
a number of  arguments.   From  the viewpoint  of  a government agency, it would
appear  to be impractical,  if  not directly  contrary to  law, for proprietary
models  to be used  in  new source review and,  quite probably,  in the design
of regulations  and SIP revisions.   Regarding new source review,  spokesmen
from industry express great  concern that the agency  could be using a  model
to which  they as applicants  had no  access.   The agency would at  the same
time face  requirements for compliance with various public disclosure  laws,
state and  federal, and in all likelihood could not honor an  agreement to

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                                    -34-
safeguard the details of the proprietary model.  For example,  the general
counsel for EPA Region V forbade the use of a proprietary model in
regulation development for these very reasons.
       As to the use of standardized test problems for establishing some
degree of equivalence between models approved via the Guideline and
alternate, proprietary models, it is recognized that such may be desirable
whenever a private party chooses to use a proprietary model in a permit
application or in litigation.  The challenge then becomes one of developing
a set of test problems which explore the full range of application of the
models in order to determine with a fair degree of confidence the question
of equivalence .

1. 12  SUPPLEMENTAL CONTROL SYSTEMS  (SCS)
       The Guideline makes no reference to SCS.  Although many of the
computer codes referenced in the draft Guideline are applicable to SCS
determinations, some codes more suitable to transient analysis were not
considered.  Further, an important part of an SCS strategy is the on-line
analysis of air quality and meteorological data and the related, near-
term forecasting techniques.
       Since the Guideline does not address SCS, the conferees did not
consider this subject in significant detail.  It is recommended that the
Guideline be amended to indicate, probably by footnote, that SCS is not
considered within the Guideline and then to provide one or more references
for the user who is interested in pursuing the federal requirements and
available methods for the implementation of SCS.

1. 12  STRUCTURE OF THE GUIDELINE
       The utility of the draft Guideline would be greatly improved if
certain minor structural modifications were incorporated.  Chapter
subheadings and greater reliance on tabular presentations along with a
table of contents would make the document more readable.  As mentioned
earlier, SCS must be referenced in order to clarify the intended scope of
the Guideline.  Similarly, the section on growth does not provide adequate
guidance for handling this complex subject.  It would be preferable for the

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                                     -35-
Guideline to acknowledge its limitation in this regard and provide
references wherein the user can assess the state-of-the-art of growth
projections for air quality management.  Finally, the section on model
application should be expanded to provide more guidance on use of appropriate
emission data.

1.14  PERIODIC REVIEW AND UPGRADING OF THE GUIDELINE
        By recognizing the generally unsatisfactory status of past efforts
in validation of air quality models and the need for a more extensive,
formalized procedure (see policy issue 1.4), the conferees wish to under-
score the need for periodic review and upgrading of the Guideline.  Further,
the  absence of coverage of topics such as SCS and photochemical oxidants
and the limited treatment of oxides of nitrogen call for a significant
expansion in the scope of future editions.
        The conferees would be pleased to examine subsequent drafts of this
first edition and to participate in the review of future editions.  However,
it is generally agreed that a more extended and less intensive review
schedule, perhaps involving several standing committees, would be preferable
to the highly compressed schedule imposed by a two- or three-day intensive
conference/workshop.

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-36-

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                                     -37-
                          2  WORKING GROUP REPORTS

       The working group reports are based on discussions held within the
working group meetings.  These reports incorporate corrections and editorial
changes recommended by working group members.  Extensive revisions or addi-
tional material submitted subsequent to the group meetings by group members
or other conference participants have been referenced where appropriate and
collected together in Section 3.

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-38-

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                                    -39-
2.1  GROUP 1-1:   MULT I-SOURCE,  SHORT-TERM AND LONG-TERM FOR SET  1 POLLUTANTS
                                PARTICIPANTS

           N. Bowne                               R.  A. Porter
           A. E. Boyer                            C.  Simon
           H. E. Cramer                           J.  A. Tikvart
           S. R. Hanna                            D.  B. Turner
           W. A. Perkins                          M.  Lazaro

                       Moderator/Reporter: A. E. Smith
                                  CONTENTS

 2.1.1   Changes  in Models Eecommended in  Draft  Guideline
 2.2.2   Meteorological  Data
 2.1.3   Source Data
 2.1.4   Supplementary Comments and Information
 2.1.5   Further  Recommendations

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                                     -40-
2.1.1  Changes in Models Recommended in Draft Guidelines
       The consensus of the group was that Gaussian plume models are the best
available at this time for multi-source situations.  The situation most fre-
quently referred to throughout the discussions was the urban area with a large
number of sources.  Concerns were expressed regarding the need to specify the
constraints on the use of a model and on various algorithms implementing a
model.  In this regard, the group recognized the need for well written user's
guides which detail the limits of an algorithm's applicability.  It was sug-
gested that a list of features which would make algorithms more desirable might
be developed.  This was considered a reasonable suggestion but no attempt was
made to produce such a list.
       After accepting the Gaussian plume model as the best currently available,
the specific algorithms in Table 3 of the draft guideline document were con-
sidered.  It was concluded that knowledge of the state of validation of these
models was lacking and that a need existed for validation studies and, more
generally, packaged test  cases to test new algorithms.  The group considered
whether to retain the hierachical structure of the draft Table 3 or to simply
list acceptable algorithms with the choice of a prrticular algorithm being re-
lated to the level of detail required by the task at hand.  The consensus
retained the hierachy.
       The use of the Larsen procedure for estimating short-term averages in
AQDM and CDM was discussed.  The group concluded that hour-by-hour calculations
as done by RAM are a preferable method but did not feel that the statistical
conversion of averaging times should be precluded in urban areas where large
point sources are not a major influence.  The possibility of giving CRSTER the
capability of handling multiple sources was discussed and some validation data
on CRSTER were presented.  It was noted that short-term predictions tend to be
accurate to within about a factor of two and that the suggested long-term algor-
ithms tend to overpredict.  The point was also made that AQDM, CMD, and the
Hanna-Gifford model (properly applied) give equivalent correlations with mea-
sured data.  The importance of representative meteorological data and a good
emissions inventory was stressed by several members.  Possible problems with
the existing parameterizations of a  in the guideline models were noted.
                                   z
       The Texas Climatological Model (TCM) and Texas Episodic Model  (TEM)
were suggested as additional models for Table 3.  These models were repre-
sented as being essentially computerized algorithms applying the Hanna-Gifford

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                                     -41-
model with provisions for including the effects of individual point sources.
Although there was some reluctance to including models not available from
EPA or models in which a member of the group had a particular interest in
Table 3, the Texas models were recommended for inclusion given the fact that
they have been widely used.  So that other models might be included in future
revisions of Table 3, development of a formal procedure for approving equiva-
lent models was suggested.
       Throughout its deliberations, the group was aware that the data to
make the best technical, scientific decision are frequently unavailable but
that a need does exist for guidance in making the administrative decisions
required by law.
       The final concensus of the group for specific changes and additions to
the February 1977 draft guideline follow.  Where doubt might exist as to where
changes were made, additions or changes have been indicated by a bar in the
left hand margin.  Recommendations of the group for areas needing attention
or study follow the suggested changes in the guideline.
On page 15, change Table 3;

            Table 3.  Multi-Source Models Applicable to Specific
                       Pollutants and Averaging Timesa
S02 and TSP
Annual Average
AQDM/CDM26'2?/TCMb
SO and TSP
24-Hour Average
RAM31'32
TEMb
AQDM*/CDM*26'27
so2
3-Hour Average
RAM31'32
TEMb
* *?fi 77
AQDM /COM Zb'
 Statistical conversion of averaging times required.
 Numerical references  in  the  table  cite  references  in  the draft Guideline.
 Appropriate reference to user's manual.  Descriptions  of TCM and TEM are
 contained in  Sees.  3.1.1 and 3.1.2.
 On page 16:
                                                                         9
        Similar models have been summarized and discussed by Lamb et.al.,
 Moses  , Stern   , and others.  They are available from private consultants
 and other governmental agencies.  However, to meet the need for consistency

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                                    -42-
identifled in the Introduction to this guideline, selected models have
been specified.  Based on a determination by the Regional Administrator
that another air quality model already in use by a state agency provides
equivalent or more reliable concentration estimates, that model may be
                 12
used.  Guidelines   which provide a procedure for comparing air quality
models are in preparation.
       There are limitations to the use of all atmospheric transport and
dispersion models.  Users must be aware of these limitations and not apply
the models outside their limitations.  Models listed in Table 3 are readily
available; there are many other published multi-source models.
On page 17:

Multi-Source Models Required for Sulfur Dioxide and Total Suspended
Partioulates  (Annual Average)
                                           j /-
       The Air Quality  Display Model  CA-QDM)   or the Climatological Dispersion
           27
Model  (CDM)   ,  or TCM  may be used  to evaluate multi-source complexes.
On page 18;
       If a more detailed or more suitable model is available, especially in
a Region with major meteorological  or topographic complexities, that model
may  be used.
       Also,  if the meteorological  or topographic complexities of the region
are  such that the use of any available air quality model  is precluded, then
the  model used for strategy evaluation may be limited to  a Rollback Model.

Multi-Source Models Required for Sulfur Dioxide and Total Suspended
Particulates  (Short-Tern Averages)
                                                       31 32
       The Real-Time Air-Quality-Simulation Model (RAM)   '   may be used to
evaluate multi-source complexes.
       If the data bases required to apply RAM are unavailable or inadequate,
the  TEM may be used.
       If the resources required to operate RAM or TEM are not available,
AQDM or CDM may be used to estimate short-term concentrations of SCL  and
particulate matter.  These models must be used with procedures for the

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                                    -43-
                                                                25
statistical conversion of averaging times as discussed by Larsen   to
convert annual average concentration estimates to 3-hour and 24-hour average
concentrations.  This technique is valid only in urban, multi-source situations
and should not be used in situations dominated by single point sources.
Other similar techniques for making this conversion may also be used.
On page 19:
       If a more detailed or more suitable model is available, especially
in a Region which has major meteorological or topographic complexities,
that model may be used.
       If the meteorological or topographic complexities  of the Region are
such that the use of any available air quality model is precluded, then the
model used for control strategy evaluation may be limited to a Rollback
Model.30

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                                     -44-
2.1.2  Meteorological Data
       In discussing the section on meteorological data it was pointed out
that the data listed was that required by the models listed and that more
detailed data was likely to be needed as models improve.  Several comments
indicated that representative meteorological data is not always readily
available despite an implication to the contrary in the draft guideline.   The
group felt that the requirement of a five year data base was reasonable if
five years of data  were available.   This was strengthened by one
member who reported on a situation where consistent results began to be ob-
tained at about 5 years when a total dosage model was used with increasingly
long meteorological data bases in the 1-10 year range.
       The consensus of the group is expressed in the following recommendations
for specific changes in the draft guideline.
On page 30:
       Specific meteorological data required to describe transport and dis-
persion in the atmosphere are wind direction, wind speed, wind shear, atmos-
pheric stability and mixing height appropriate to the site.  These parameters
may be derived from routine measurements by National Weather Service (NWS)
stations and the data may be available both as individual observations and in
summarized form from the National Climatic Center, Asheville, N.C.  If other
sets of data which encompass wind direction, wind speed, atmospheric stability,
mixing height or other indicators of atmospheric turbulence and mixing are
available, they may be used.  Local universities, industrial companies, pol-
lution control agencies and consultants  may be sources of such data.  A five
year data base is desirable.
On page 32 add the paragraph:
       It is to be noted that future availability of meso and micro meteor-
ological data collections will make practical more detailed meteorological
analysis and subsequent improvement of model estimates.

2.1.S  Source Data
       There were no major problems with the draft guideline statements about
source data for multi-source urban situations.  The consensus held, however,
that other than area-wide diurnal variations could be important and that

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                                     -45-
emission models should be used where available.   The group suggested the
following change in wording to express its consensus.
On page 35:
       For multi-source urban situations, detailed source data are generally
impossible to obtain.  In these cases, source data shall be based on annual
average conditions.  Area source information required are types and amounts
of pollutant emissions, the physical size of the area over which emissions
are prorated, representative stack height for the area, the location of the
centroid or the southwest corner of the source in appropriate coordinates.
Where emission models are available, output from such models should be used.
Short-term models may be modified to accept such data.

2.1.4  Supplementary Comments and Information
       Supplementary comments and information on multi-source modeling were
submitted by several conference participants.  Both members of the working
group itself and others contributed.  This material can be found in Sec. 3
and is referenced below.
       R.  Porter submitted additional information on the  Texas models.  Sections
3.1.1 and  3.1.2 describe  the Texas  Climatological Model  (TCM) and  the Texas
Episodic Model (TEM),  respectively,  in  the  format used in the conference note-
book.
       G.  Melvin presented a description of Illinois'  Air Quality  Short Term
Model  (AQSTM)  in the format  of the notebook in  Sec.  3.9.6.   The group did  not
discuss  this model.
        A.  Boyer presented a  minority report (Sec.  3.1.3)  suggesting limitations
on who  should  be required to apply multi-source urban models.  The group  as
a whole did not take a position on this subject.  He has also described the
use of  urban and single source models by the Ontario EPA in Sec.  3.9.7.
        S.  Hanna has provided an extensive description of the development,
application, and  suggested uses of the Hanna-Gifford  model in Sec. 3.9.1.
 Included is a description of the model in the notebook format including a
 formulation treating chemical reactions.

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                                     -46-
       M. Williams has suggested adding monitoring requirements to the condi-
tions under which rollback may be used and has commented on the choice of
receptor sites.  He recommends specific wording changes for pp. 19 and 29
of the draft guideline in Sec.  3.9.2.
       M. Smith submitted criticisms of RAM in Sees.  3.9.3 and 3.9.4.  Section
3.9.4 is a letter from Dr. Howard M. Ellis of Enviroplan, Inc.  The criticisms
relate to the lack of validation, problems with a  values, and the full load
                                                 z
operation assumption.    Section 3.9.4 also suggests that some measure of
actual operating rates be used if five, rather than one, years of meteorology
data are employed.

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                                     -47-
2.1.5  Further Recommendations

1.   Packaged test cases should be developed for comparing  and testing models.
    These cases should include the required emissions  and  meteorological  data
    for input and the appropriate measured air quality data against which
    model predictions can be checked.
2.   User's Guides should delineate clearly the limits  of applicability  of
    model algorithms in order to avoid misuse.  There  is also a frequent  lack
    of clarity in the specification of the inputs needed by particular
    algorithms.
3.   EPA should encourage continuing effort, either in-house or under  contract,
    for validating the models they are recommending.  Such information  is
    necessary for an informed scientific endorsement of particular models and
    such information has not been presented at the conference.
4.   A formal procedure should be developed for approving other models and
    placing them in the guideline.

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-48-

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                                     -49-
2.2  GROUP 1-2:  SINGLE SOURCE,  SHORT-TERM AND LONG-TERM,,  SET 1  POLLUTANTS
                                PARTICIPANTS

            S. Barr                                J. L. Shapiro
            N. H. de Nevers                        M. E.  Smith
            D. Henderson                           I. Van der Hoven
            D. J. Moore                            R- !• Wevodau, Jr.
            H. H. Slater                           M. D. Williams

                       Moderator/Reporter:  D. M. Rote
                                   CONTENTS

 2.2.1   Distinction  Between  Models  and Computational  Procedures
 2.2.2   State-of-the-Art Point Source Model
 2.2.3   Availability of Regulatory  Algorithms (Programs,  Codes)
 2.2.4   Recommendation for "Screening Procedure"
 2.2.5   On the Question of Enumerative vs.  Statistical Use of the
        Estimates of Short-Term Concentrations

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                                     -50-
2.2.1  Distinction Between Models and Computation Procedures
       For the purposes of these deliberations a clear distinction should
be made between models and computational algorithms.   The latter are
sometimes called computer programs or computer codes.  A model is a set
of mathematical relationships, based on scientific principles, often using
adjustable parameters.  Examples of atmospheric concentration models are:
rollback, fixed box models, Gaussian models, and moving box models.
       A computational algorithm is a set of detailed instructions for
implementing a model-  For example, AQDM, CDM, CRSTER, PTMAX, PTDIS, and
PTMTP are all computational algorithms for implementing the same basic
Gaussian model.  For  identical input data they must give the same result,
if they faithfully represent the agreed underlying model.
       This distinction was made because the group agreed that the Gaussian
model is the state-of-the-art model for operational estimates of TSP and
SO .  The choice of particular algorithms to be used depends on the amount
and quality of input  data available, the detail required in the answer,
the available budget, and other factors.

2.2.2  State-of-the-Art Point Source Model
       The working group recognized that no model available today, nor
probably in the foreseeable future, is free of serious limitations, but
it was unanimous in recommending the Gaussian statistical model as the
                                                         1
state-of-the-art model for all point source evaluations.
       It should be mentioned that the use of the term "Gaussian" is
understood to refer to the distribution of the pollutant about a plume
centerline.  Consequently, the use of a particular algorithm to compute
the position of the plume centerline does not alter the fact that the
model is still basically a Gaussian model.
       Each of the computational algorithms listed on page 14 (Table 2)
of the draft Guideline contains the Gaussian assumptions for the repre-
sentation of plume dispersion as a key feature.  Therefore, the group
agreed that each is acceptable in principle.

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                                     -51-
       Several concerns were expressed regarding specific assumptions  or

the management of data in several of these algorithms.   However,  none  were

sufficiently basic to warrant discarding the Gaussian model in favor of
                                                      2  3
another approach.  The major concerns are listed below:

       1.  There  is evidence that the az curve representing
           the A  stability  category  in the Pasquill-Gifford
           formulation may result in serious overestimates of short-term
           maximum values from tall-stack sources.

       2.  The system of using a random adjustment to
           winds  recorded in 10° intervals may not be
           adequate to represent full horizontal plume spread.  This
           system is used in CRSTER  (see results of Group II-4 dis-
           cussions) .

       3.  From analysis of limited  power plant data it  appears that
           the 24-hour predictions of CRSTER may be  too  low for relatively
           flat terrain.  It is worth determining whether this is a typical
           result of the model.

       4.  There is controversy  over the  technique used  for adjusting
           the algorithm to  terrain  variations.   More generally, how
           should  the  Gaussian model be modified  when a  plume approaches
           elevated terrain?

       5.  There is evidence  that stability estimates,  and  the resultant
            az and possibly ay values, should be functions of source
           height,  and not  independent  of it  as  shown in the Pasquill-
           Giff ord  curves.   This may influence the applicability of
            the Gaussian model  to tall stack calculations.

       6.  Effluent released during one segment  of time may have an
            effect  on the concentration  at a receptor at some  later
            time.   This could influence multi-hour concentration  estimates.

        7.   Plume rise is not considered as a  final,  settled issue.  As
            with  the diffusion algorithms these estimates should be reviewed.

        It  should be noted that some of these  concerns are specific to the

 "CRSTER" algorithm, namely points 2 and  3,     while others apply  to  the

 Gaussian model in general.   It should be emphasized  that in all  cases,
 the participants were concerned with the problem of  calculating  short-term

 ground-level concentrations resulting from point  source and especially

 tall-stack emissions.  Each of the items listed were only briefly  discussed
 by this working group since the subsequent working groups on specific

 issues would presumably deal with these concerns.

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                                     -52-
       This working group also expressed concern about applying this set
of algorithms beyond 50 kilometers from the source.   The concerns ranged
from vague uneasiness because of the lack of data on dispersion and model
validation at greater distances to the recognition that physio-chemical
processes, such as deposition at the air-land or air-sea interface would
alter the Gaussian distribution of the pollutant.  Conversion and loss
processes are not treated by the Guideline models.  Neglect of these
latter factors should result in an overestimate of ground level concentra-
tions.  However, extrapolation of the Pasquill-Gifford dispersion curves
to large distances could result in an underestimate of ground level con-
centrations.
       Although not specifically  agreed upon as a recommendation, it
was clear that all members of the group favored expanded efforts to refine
and improve  the algorithms listed in the proposed Guidelines by careful
comparison with data.

2.2.3  Availability of Regulatory Algorithms  (Programs, Codes)
       It was unanimously agreed that regulatory  agencies  (EPA, state
and local agencies) should be required  to state clearly which models and
algorithms (programs,  codes) they will use  to review  construction permit
applications.
       If published, widely available algorithms  (e.g., those on the
UNAMAP tape, available from NTIS) are used, then  a listing of algorithms
together with a brief  description of how optional features are selected
and which algorithms apply to particular cases  should be adequate.  If,
on the other hand, they use models which are not  readily available, they
should be required to  make copies of computer tapes and complete program
documentation, and make these available to  any  interested  party at nominal
cost.
       The purpose of  this requirement  is to  enable those  who apply for
permits  to know  exactly how  their request will  be evaluated.
       Wherever  practical, regulatory agencies  should use  nationally-
available models,  rather than ones  peculiar to  their  agency.  This
recommendation minimizes the number  of  different regulatory  situations

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                                     -53-
a multi-state source must face.  If there is some significant advantage
(i.e., special algorithms for accurate treatment of land/sea breeze,
fumigation, etc.) in using a non-standard model, then this recommendation
should be ignored.  In such cases the requirement for adequate documentation
must be re-emphasized.
       There should be some criteria for deciding whether alternative
algorithms or models are acceptable.  It was unanimously agreed that  such
criteria could include the use of reference or bench-mark type test problems
designed by the EPA.

2.2.4  Recommendation for "Screening Procedure"
       The computational effort and resource expenditure required to carry
out single source calculations with the CRSTER algorithm as outlined in the
Guideline were judged likely to be excessive for small sources.  This
judgment took into account the fact that both the CRSTER and alternative
computation algorithms (some of which are much easier to implement) were
based on the same Gaussian model.  Instead  of the indiscriminate use of
CRSTER for all sources, a screening procedure was recommended.  Only
sources failing  the screening  test or failing to qualify for the screening
test  should be subjected to an analysis with the more elaborate CRSTER
algorithm.
       The question of precisely what criteria  should be used  in qualifying
a source for the  screening procedure and  in deciding compliance with the
controlling standard  was discussed at some  length.  It was  generally agreed
that  sources having stack heights  greater than  100 meters would automatically
be  subjected to  the more complete  analysis  by the  "CRSTER"  model.  Applica-
tion  of the "CRSTER"  model must  of course be limited to cases  where the
physical stack height is substantially  greater  than the surrounding terrain
variations.  Reservations were expressed  because applicants might use this
cirteria as a basis for  stack  design.   Hence, it may be necessary to consider
additional criteria which take into  account effluent or heat emission rate
as  well as stack height.
       To  pass the  screening test  the predicted concentration using the
screening  procedure must be  less than or  equal  to  50% of  the controlling
standard concentration.

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                                    -54-
       The recommended  screening  procedure  is as  follows:
       a)   A hypothetical  joint frequency distribution  of wind  speed
           and  stability  is  developed  containing  all  possible reasonable
           combinations of those  2 meteorological variables.  This dis-
           tribution is to be used  in  the selection of  appropriate short-
           term meteorological conditions.
       b)   Computations are carried  out  using one of  the UNAMAP point
           source models  to determine  the 1-hr, maximum concentrations.
       c)   Longer-term concentrations  estimates are developed using
           simple ratios  to the hourly maximum concentration estimate.
           It is tentatively suggested that the ratio of  1-hr,  maximum
           to 24-hr, maximum be taken  as 3  for stack heights less than 100m.
       d)   The estimated maximum concentration  is compared  with the
           controlling standard concentration.  If the estimated
           value  is <_ 50% of that standard,the test  is passed  and no
           further analysis using the CRSTER model may be required.
       It should be pointed out that the screening procedure may have  to
take into consideration special effects such as  fumigation, downwash,
or terrain complexities using appropriate algorithms.  (See the reports
of the Working Groups on Complex Terrain and Plume Dynamics.)
       This recommended screening procedure is quite similar to that
outlined in "Draft-Guidelines for Air Quality Maintenance Planning and
Analysis, Volume 10: Reviewing New Stationary Sources" SRAB, MDAD,  OAQPS,
EPA, February 1977.  The latter guideline suggests the use of an
approach similar to that used by the "VALLEY" model for complex terrain
cases.
       See footnote 4 for  further comments.
2.2.5  On  the Question of Enumerative vs. Statistical  Use of the Estimates
       of Short-Term Concentrations
       In  regard to this  question there was general, although not unanimous,
agreement  that a statistical  approach should be  used.  However  there were
differences of opinion regarding the precise nature of the statistical
approach,  the  length of meteorological records required, and which con-
centration value  (highest,  second highest) should be applied as the control.
It was generally felt  that  the question of the precise nature  of the
approach  is sufficiently  important  to warrant a  detailed investigation
subsequent to  the  conference.

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                                     -55-
       The following statement reflects the consensus of the majority of
participants of this working group:
       In evaluating the modeling results to predict compliance with short-
term NAAQS (not to be exceeded more than once a year) or PSD (not  to be
exceeded), the test shall consist of computing the predicted concentrations
at each of the several worst receptor points, for a specified  period of time  ,
using the state-of-the-art modeling technique (e.g., CRSTER or its equivalent)
These worst receptor points are defined by the highest concentrations for  the
averaging time of concern (3 hours or 24 hours).  The resulting set of
computed values for each of the several points chosen shall be fitted by a
suitable cumulative frequency distribution function  .
       In order to be consistent with both the inherent uncertainties
in the frequency of occurrence of rare events and the use of the second
highest concentration as required by the NAAQS, the following demonstration
of compliance seems appropriate:  The distributions shall be considered
to demonstrate compliance if the cumulative distributions functions indicate
that the short-term NAAQS will not be exceeded twice in a calendar year,
more than one year in each jc years at any given point, and that the PSD
increments will not be exceeded more than once per £ years.
       We understand that the use of the second highest value was chosen
to correspond to the realities of ambient air quality monitoring.   The
realities of modeling are different.  Thus - for modeled compliance -
a preferable demonstration of compliance would be:  The distributions shall
be considered to demonstrate compliance if the cumulative distribution
functions indicate that the short-term NAAQS will not be exceeded, on the
average, more than once per calendar year, and that  the PSD increments will
not be exceeded more than once per y years.  In the  latter statement the
term "on the average" is understood to be based upon a limited but specified
length of record, say one to five years.
       The x and y in the above paragraphs have been purposely left undefined.
It was believed that a thorough study of the consequences of possible choices
of those values should be made before values can be  assigned  .

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                                     -5(5-
                           FOOTNOTES  FOR SECTION  2.2.

1.  This recommendation was made for  cases where  there is no appreciable
    depletion of plume material and where the topography is relatively  simple.
2.  In a post-conference contribution (see Section 3.9.2) M. Williams
    expressed some additional concerns regarding  CRSTER and RAM and pointed
    out a documented case in which plume rise was suppressed by an elevated
    inversion layer.  Such phenomenon are not properly treated by these
    algorithms.
3.  At the conference, M. Smith passed out copies of comments prepared  by
    H. Ellis regarding the "RAM" and other guideline recommended algorithms.
    See Sections 3.9.3 and 3.9.4.
4.  It is worthwhile calling attention to a post-conference submittal in
    which A. Boyer  (see Section 3.9.7) describes a regulatory approach taken
    in the Ontario  Environmental Protection Act.  This approach, which is
    a variant of the screening procedure  concept, requires  the applicant to
    make estimates  of  impact based on a simple point source model applied
    under certain meteorological conditions.  It is then  the responsibility
    of the control  agency  to determine whether the combined effects of mul-
    tiple sources,  each complying with single source standards, when operating
    under a wide spectrum  of conditions,  is acceptable.
5.  The length of meteorological record to be used for the  calculations was
    not firmly agreed  upon.  Some were happy with a minimum of one year and
    a maximum of five  years if the data were available while others preferred
    a minimum of two years.  One participant suggested that the projected
    life time of the plant might be  a more appropriate record  length to insure
    compliance with the standard.  The majority, however, were reluctant to
    recommend a  specific record length without first examining the whole
    question  in  much greater detail.
6.  At  least  one working group member expressed  formal concern over the
    difficulty  of  fitting  air  quality data with  a suitable  functional  form.
    See  comments by Wevodau, Section 3.2.1.  This concern plus others  lead
    this member  to conclude that  the enumerative approach,  as  recommended
    in  the  draft guideline,  was more acceptable  at  this  time.  Also see a
    comment submitted  by a non-working  group member,  R.  Porter,  Section 3.2.2,

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                                 -57-
in which the suggestion was made to include with the fitting procedure
a formal test of "goodness of fit".
In addition to the statement above, Mike Williams expressed reserva-
tions about the use of modeled second highest concentration estimates.
His statement was submitted after the Group 1-2 meeting was adjourned
and was therefore placed in Section 3.2.3 under the title "Rationale for
Elimination of the Maximum of Second Highest for Modeling Purposes".
An additional comment made outside the group meeting which addressed
William's comment was submitted by R. Porter and was placed in Section
3.2.2.

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-58-

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                                    -59-
2.3  GROUP 1-3:  SET 2 POLLUTANTS (CO,  M? ,  PHOTOCHEMICAL OXIDANT)
                                PARTICIPANTS
            B. A.  Egan
            P. Finkelstein
            D. G.  Fox
            E. Y.  Leong
                     Moderator/Reporter: K. L.  Brubaker
G. L. Melvin
L. E. Niemeyer
A. J. Ranzieri
P. M. Roth
                                  CONTENTS

2.3,1  Overview and Philosophy
2.3.2  General Considerations
2.3.3  Carbon Monoxide Model Recommendations
2.3.4  Short-Term Photochemical Pollutant Model Recommendations
2.3,5  Long-Term NO  Model Recommendations
                   &

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                                    -60-
2.S.I  Overview and Philosophy
       In order to recommend a model for general application, it is necessary
that the model meet appropriately specified performance criteria.  In our
opinion, while a number of models are now available for use, it is not
possible as of this writing to appraise the performance of these models for
one or more of the following reasons:
       - performance criteria do not now exist
       - evidence of verification has not been satisfactorily reviewed
       - adequate evidence of verification is not available
       - acceptable evaluation exercises have not been undertaken
As a consequence, we recommend the  identification of only generic classes
of models  for  use, avoiding the recommendation of any specific model.
       While  this position  is  in our view a wise and appropriate one to
adopt, we  realize that  it falls short of meeting the short-term needs of
the user community.  In order  to partially satisfy this need, we propose
a two  level hierarchy of model usage —
       - screening  (general estimation and potential problem identification)
       - refined exercise (attempt  at "best"  prediction)
                                                             *
In the case of CO prediction models, we tentatively recommend   specific
models for screening use, but  at this time we can only recommend several
generic types  of model  as being potentially suitable for  refined calculations
in practical  applications.  The specific models tentatively  recommended
for screening  purposes  may be deemed suitable for certain types of refined
calculation at some time in the future upon completion and/or presentation
of adequate verification studies.
       In  the  case of the photochemical pollutants, we again recommend
specific methods for screening purposes but only generic  types  of approaches
for refined calculation.  In  this case however, the screening procedures
are not potentially suitable  for refined predictions.
       In  any case, it  is incumbent upon the  user to assure  himself  that a
model  is  truly appropriate  for a particular application,  be  it  for screening
purposes or refined calculation.
  Subject  to evidence of verification.

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                                    -61-
       Since we are unable to recommend specific models for refined use
at this time and recognizing the eventual need for such recommendation, we
would strongly encourage that the following steps be undertaken as quickly
as possible and with sufficient talent and resources:
       - Establish relevant and unambiguous performance criteria.  We
         would encourage the convening of a panel of experts to achieve
         this end.
       - Establish an independent group having a charter to perform  a
         continuing evaluative function — that is, determining if
         candidate models satisfy the specified performance criteria.
       - Incorporate new information and knowledge into these recommenda-
         tions annually and introduce specific model endorsements as rapidly
         as feasible (perhaps bimonthly or quarterly).

2.3.2  General Considerations
       Applications requiring the use of air quality models for set  2
pollutants fall into a wide variety of categories, of which the following
are examples:
       - State Implementation Plan Revisions
       - Air Quality Maintenance Plans
       - Regional Transportation Plans,  including Transporation
         Control Plans, Transportation System Management,  Trans-
         portation Improvement Program Requirements
       - EIS/EIR's
       - NSR
       - Indirect Source Review
       - PSD
       - Others
A need for both regional and  local models  is  apparent  from a  consideration
of these applications  and  both needs  can rarely be  satisfied  by a single
model. By our definition,  local models  deal  with distance scales of the
order  of 1 km or less and regional models with distance  scales greater
than 1 km.
       Several generic classes of models can  be identified,  each having
advantages and disadvantages.   The  principal  classes and  some of their
limitations  are:

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                                    -62-
       - Gaussian

          a) considerable simplification of real phenomena
          b) cannot treat low wind speed cases
          c) cannot properly treat cases involving complex terrain
          d) cannot properly treat cases involving complex source geometries
             (mainly depressed and elevated roadway sections)
          e) cannot treat time varying behavior

       - Numerical

          a) specification of the appropriate values of the diffusion
             parameters can be difficult
          b) generally requires more meteorological data than routinely
             available
          c) the effects of numerical errors (e.g., pseudo-diffusion) must
             be evaluated

       - Statistical/Empirical

          a) low spatial and/or temporal resolution
          b) often site or region-specific

       - Physical

          a) specification of the appropriate values of the diffusion
             parameters difficult
          b) site-specific
          c) difficulty in achieving dynamic and geometric similarity

       A general assignment of model classes to local and regional scale
applications may be made as follows:

       Local/Site-Specific                       Regional

         Gaussian                                Numerical
         Numerical                               Statistical/Empirical
         Physical                                Gaussian

Care needs to be exercised in complex circumstances in which these general
assignments may be inappropriate and in which a sophisticated analysis may

be required.  We emphasize that the burden is on the user to consider his

specific application and needs carefully, taking the limitations of different
models or model classes into account.

       The main criterion that should be used in selecting a model for a
specific application, is the past performance of the model in situations

which approximate the applications at hand as closely as possible.  This

verification evidence should include work by an independent group other than
the model developer if possible, and should cover a wide range of conditions

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                                    -63-
in order to clearly define the limitations of the model.   If past verification
is inadequate, a verification study should be required as part of the
analysis, including the specification of evaluation procedures and  per-
formance guidelines.  Verification is important because it provides the
best mechanism for identifying model limitations  in practice and for
determining the suitability of different models for specific applications
in an appropriate manner.
       Cost/benefit considerations should play a role in determining the
level of sophistication with which the air quality analysis is to be carried
out.  Some of the relevant factors are:
       - the economic consequences or cost of making a specific
         decision compared to the cost of the analysis necessary
         to justify the decision technically,
       - the extent to which an increased level of effort in doing
         an analysis is justified by an increased level of confidence
         in the results,
       - the available resources (money, skilled manpower, computer)
       - the data requirements (meteorological, emissions, air
         quality).
We suggest that the information in the Guideline on the following topics
be updated periodically:
       - the codification or  identification  of  typical applications
       - model verification  information, classified particularly with
         respect  to typical  applications

  2.2.3   Carbon Monoxide Model Recommendations
         Consistent with  the  philosophy outlined in Sec. 2.3.1, we can
tentatively recommend  certain models  for use as "screening"  tools, subject
to  evidence of verification.  The  term "screening" tools  applies to
methodologies which provide  approximate and  conservative  estimates of
the  air  quality  impact of a  specific  source  type  so as to identify those
situations which require no  further  air quality impact evaluation:

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                                     -64-
              Model
              HIWAY
     Hanna - Gifford - HIWAY
     (Point and Area Source
      Models Recommended  in
      Sections 2.1 and 2.2)
    Source Category
Individual Project Reviews
Transportation Network Review
Point Sources
       These models must, of course, be used in a manner consistent with the
scientific principles and assumptions inherent in their development.  Further,
the user should apply these models only to situations similar to those in
which they have been verified.  Also, it is recommended that U.S. EPA
immediately subject these models to further verification procedures.

2.2.4  Short-Term Photochemical Pollutant Model Recommendations
       In this section we divide our discussion into two parts, one dealing
with the use of models for screening purposes and one with the use of models
for more refined predictions.  We also give recognition to the problems
of point source and regional modeling of photochemically reactive pollutants.

       2.3.4.1  Screening Purposes
       The purpose of a screening exercise is to eliminate from further
consideration those sources which are very unlikely to cause or contribute
to ambient concentrations in excess of the air quality standards.  A suggested
technique for screening NO  sources with respect to NO  standards is to use
                          X                           ^
a simple gaussian diffusion model in conjunction with the appropriate
Briggs formula to compute plume rise.  It should be assumed that all NO
is emitted in the form of NO  and that NO  is a conservative pollutant.
Future verification work may allow the assumption of 100% conversion of
NO  to NO  to be relaxed, thereby increasing the utility of this technique
  A      £
as a screening procedure.  The Gaussian model is used to estimate the
highest 1-hr, maximum ground level concentration taking into account the
existing background concentration, potential for downwash of the plume,
 Subject to evidence of verification.  A specific concern is the validity
 of this model for "worst-case" low wind speed conditions.

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                                    -65-
terrain,  other sources and the full range of possible meteorological
conditions.  The approach should be conservative in nature.
       As a first approximation, several techniques may be used to  estimate
the regional impact on oxidant levels of the emission of oxidant precursors.
The technique under development by EPA (EPA, January 1977),  commonly  known
as the Dimitriades - Dodge Isopleth technique, should be viewed at  present
as the preferred screening procedure.  The technique, if adapted for  the
user's specific area and circumstances, provides a quick estimate of  the
degree of control required.  (A code for such use is now being developed
under contract to EPA).  If the necessary data are unavailable for develop-
ing a site-specific system of curves, the more general curves should  be
used.  If it is not possible for either set of curves to be used, the only
available alternative is Rollback; however, we have serious reservations
concerning the use of this procedure.

       2.3.4. 2  Eefined Predictions
       Once it has been determined through a  screening exercise that
exceedances of the air quality  standard may occur, it may be appropriate
to pursue a more refined modeling approach.   Such approaches can be
divided  into point source and regional models.
       In order to calculate the temporal and spatial distributions of
NO  and  0  that occur in the atmosphere,  it is  essential  that  a numerical
  £*      J
modeling approach be used.  Numerical models  have the ability  to treat
through  realistic mathematical  representation of the  transport, diffusion,
and chemical reaction processes that occur  in the atmosphere.  Two classes
(grid and  trajectory) of regional numerical models have been developed.
These models are currently undergoing  testing and evaluation.  Until these
validation studies are completed,  these  models  must  be exercised with caution.

       2.3.4.3  Models for Reactive Plumes from Point Sources
       Several models which are designed for  this specific  type of application
have recently been developed or are  currently under  development.  Simulation of
the transport, dispersion and chemical evolution of  plumes  of  reactive material
from point sources requires treatments of the simultaneous  spread of the plume,
entrailment of ambient air with pollutant concentrations  and chemical composition

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                                    -66-
different from that within the plume, the effects of these phenomena on the
chemical dynamics within the plume,  and advection of the plume,  all on a
time dependent basis.  Clearly,  models designed specifically for this type
of application should be used.
       The data requirements for reactive plume models are more demanding
than for Gaussian models.  In many cases considerable care must be exercised
in selecting the most appropriate and representative inputs, in particular
the meteorological and ambient air quality variables.  This may require the
expertise of experienced modelers and meteorologists, depending on the type
of problem.  Of course, this does not preclude the applicant from supple-
menting existing data bases, designing an aerometic monitoring system, and
collecting field data to more adequately establish inputs.  A model's
performance can be evaluated only through appropriate and well considered
verification studies based  on sound  inputs and comparative data.

       2.3.4.4  Regional Models
       The assessment of the temporal and spatial impacts of a new
emission source of NO and RHC on regional air quality requires the use
of regional airshed models.  Airshed models require  substantial amounts
of data, including a gridded  emission  inventory  for  both mobile and  sta-
tionary sources of NO   (NO + NO) and  RHC.  In addition, depending on  the
       }              x          2
chemical mechanism,  it may  be required to further classify  the RHC according
to subgroups (such as olefins,  aldehydes, paraffins  and aromatics).  This
gridded emission inventory  generally requires a  resolution  of 1 to 5 km
depending on the application.   In addition detailed  meteorological data
are  required to describe the wind flow, diffusion, and  inversion  fields.
Grid models are most appropriate for assessing air quality  impacts on  a
systems basis while  reactive  plume models are most useful  for assessing  the
impact of  isolated sources.   These models are  complex and  require experienced
modelers who can properly  interpret  the results  and  assess  the  impacts.   It
is of  importance  to  note that because  of complexities of  the models  and  their
substantial data requirements,  their use will  most  likely  be restricted
to applications  in those regions where sufficient data  are available.
Even in such  regions,  a careful editing of  the data bases  must  be undertaken
to ensure  proper  interpretation of  model results.

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                                    -67-
Again, as with the reactive plume models, the reliability of model predictions
can only be assessed through carefully planned and designed validation studies,
Where input data are available, it is strongly recommended that validation
studies be considered an integral part of the modeling endeavor.

2.3.5  Long-Term N0y Model Recommendations
                   ILl
       An appropriate screening procedure for determining impacts of new
sources on long-term (annual) NO  levels would be to make use of an appro-
priate model now in use for conservative pollutants  (such as COM in urban
areas, for example) together with the assumptions: 1) that all NO  emissions
                                                                 X
are really NO  emissions and 2) that NO  is a conservative pollutant.  After
sufficient experience with this procedure, it may be possible to assume less
than  100% conversion of NO  to NO  and thereby to improve the usefulness of
                          X      ^L
the technique as a screening tool.
       In order to obtain a refined estimate it will be necessary to make
use of more sophisticated techniques applied on a case-by-case basis.
                           Supplementary Material
       Descriptions of two reactive plume models may be found in Sec. 3.3.1
and 3.3.2.

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-68-

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                                    -69-
2.4  GROUP II-I: LONG-RANGE TRANSPORT AND LOSS MECHANISMS
                                PARTICIPANTS

                  A. E. Boyer                A. J. Ranzieri
                  E. Y. Leong                J. A. Tikvart
                  D. J. Moore                M. D. Williams

                     Moderator/Reporter: K. L. Brubaker
                                  CONTENTS
2.4,1  Swmayy of Disaussi-on
2.4.2  Recommendations

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                                    -70-
2.4.1  Summary of Discussion

       The section in the EPA draft Guideline that is directly related to long-

range transport considerations is the first paragraph on page 21, which

reads in part "It is recommended that estimates should not be made for
distances greater than 100 kilometers and that air quality impact beyond

this distance be assumed to be negligible."  The point is made further on

in the paragraph that impacts at this distance and beyond are "likely to
be small and for all except very large facilities," and the Regional

Administrator is directed to consider specific large individual sources

on a case-by-case basis if a threat to air quality standards or prevention
of significant deterioration (PSD) increments exists at these distances.

       In the general discussion which arose from these remarks, the follow-
ing points were made and agreed upon by the participants:

        • The guideline should refrain from specifying a single distance
         cutoff.  It was considered preferable to give a range of distances
         to indicate some degree of uncertainty regarding the maximum distance
         at which relatively simple models could be used for estimating pollu-
         tant concentrations, and a range of 50-100 km was deemed appropriate
         although a lower value of 25 km was also considered.

        • Observational evidence indicating that Class I PSD increments
         have been or could be exceeded at distances of the order
         of 100 km from a single or a small number of sources was presented
         and discussed.  It was felt that the assumption of negligible
         impact beyond 100 km would be invalid in a sufficiently large
         number of cases that it should be deleted from the guideline.
         (Specifically, data from Ref. 2 were presented which indicated
         that S02 concentrations approaching 50 yg/m^ were observed
         approximately 90 km downwind of a group of four power plants in
         eastern England. Several participants reported knowledge of other
         studies which also provided evidence for potential PSD increment
         exceedances.   These were provided after the conference and are
         given as Refs. 1, 3-5.)

       • Regarding the different averaging times specified in the PSD
         regulations for S02, it was felt that if the short-term (3 and
         24-hour) increments were not exceeded, then neither would the
         annual increment.  Attention should therefore be focused on the
         possibility of a short-term exceedance in evaluating the potential
         impact of a new source.

       • The need for treatment of individual situations on a case-by-case
         basis should be emphasized.

       • Plume depletion and chemical conversion in plumes from elevated
         sources can be significant at distances of the order of 25 km,

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                                    -71-
         and are normally significant at distances of 50-100 km and  beyond
         under conditions of appreciable vertical mixing.   Plume depletion  is
         normally significant at considerably shorter distances for  near-
         ground level sources.

Attention was then focused on questions relating to the modeling of  transport,

dispersion and depletion over distances beyond 50 km and the following
points were agreed upon:

       •  Techniques for long range transport modeling are available.
         (Refs. 6-16, provided by participants after the conference.)

       •  Only limited comparison of predictions of these models with
         observational data is available and the models cannot be
         considered validated.

       •  A treatment of vertical dispersion using an approach based
         on K-theory is generally more appropriate than one using
         a Gaussian approach whenever plume depletion by dry deposition
         is significant,due to the ability of the former to incorporate a
         more realistic boundary condition at the earth's surface.

       •  Trajectory analysis of the type usually done in the available
         models requires wind, pressure and temperature data both at
         ground level and aloft at representative sites within  the
         area of interest.

       Based upon this discussion, several changes in the EPA guideline

were recommended.
2.4.2  Peeomnendati-ons

       It is recommended  that  the paragraph  in the draft EPA Guideline on
Air Quality Models and Associated Data Bases which deals with long range

transport, specifically the  first paragraph  on page 21 of that document,

be altered to read as follows:


       "The administration of  the national prevention of significant

deterioration 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.  There

are several reasons for this.   Our knowledge of  the dispersion coefficients

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                                     -72-
                      *
for air quality models  becomes increasingly tenuous with downwind distance.
Distances beyond 50-100 kilometers require substantial travel time at the
most frequently experienced wind speeds.  As travel time increases, the
daily weather cycle is more likely to alter plume trajectories and dis-
persion characteristics.  The impact at distances greater than 50-100
kilometers  is likely to be small, however the Impacts are not necessarily
negligible  for large sources.     Techniques are available to examine
these impacts,     but only limited experience in their use is currently
available.  If it appears that a large source (for example, a 2000-MW
coal-fired  power plant meeting new source performance standards) may
constitute  a threat to ambient air quality standards or prevention of
significant deterioration increments at large distances, that source
should be considered on a case-by-case basis with available techniques."
                           Supplementary Material
       A description of models used by the Central Electricity Generating
Board, England, for medium and long range predictions may be found in
Sec. 3.9.5.
  Vertical dispersion in these situations  is more appropriately treated
  with models which are based on K-theory.  There also are  inherent
  difficulties with Gaussian models  in  cases where plume depletion is
  significant.  Plume depletion would normally be significant  at dis-
  tances of the order of 50-100 kilometers and beyond under conditions
  of appreciable vertical mixing, and at considerably shorter  distances
  for near-ground level sources.

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                                     -73-
                          REFERENCES FOR SECTION 2.4.

  Observations of Effects from Single Sources
  at Long Range (50-100 km and beyond)


 1.   Drivas, P.  and  F.  H.  Shair,  A Tracer Study  of Pollutant  Transport and
     Dispersion in the  Los Angeles Area,  Atmos.  Env. 8^,  1155  (1974).

 2.   Fisher, B.  E. A. and  P.  R.  Maul,  The Mathematical Modeling  of the
     Transport of Sulphur Dioxide Across Country,  presented at the symposium
     "Systems  and Models  in Air and Water Pollution,"  organized  by the
     Institute of Measurement and Control, London,  England, Sept.  22-24  (1976)

 3.   Heimbach, J.  A., A.  B. Super and J.  T. McPartland,  Dispersion from  an
     Elevated  Source Over Colstrip, Montana, presented as paper  no.  75-26.6
     at  the  68th Annual Air Pollution Control Association Meeting, Boston,
     Massachusetts (1976).

 4.   Lamb, B.  K.,  A.  Lorenzen, F. H. Shair, Tracer Study of Power Plant
     Emission  Transport and Dispersion from the  Oxnard/Venture Plain,
     final report prepared for the California State Air  Resources Board
     under contract  no. ARB-5-306, February (1977).

 5.   Williams, M.  D.  and  R. Cudney, Predictions  and Measurements of Power
     Plant Plume Visibility Reductions and Terrain Interactions, pp.  415-
     420, preprints  of  the Third Symposium on Atmospheric Turbulence,
     Diffusion and Air  Quality, sponsored by the American Meteorological
     Society,  October 19-22 (1976).
 Long Range  Transport Models
 6.   Bolin,  B. and C. Persson, Regional Dispersion and Deposition of Atmos-
     pheric  Pollutants  with Particular Application to Sulfur Pollution over
     Western Europe,  Tellus _24_, 281-310 (1975) .

 7.   Egan, B.  A., K.  S. Rao and A. Bass, A Three-Dimensional  Adveotive-
     Diffusive Model for Long Range Sulfate Transport and Transformation,
     presented at the 7th International Technical Meeting on Air Pollution
     Modeling  and Its Application, Airlie, VA, Sept. 7-10 (1976).

 8.   Eliassen, A. and J.  Saltbones, Decay and Transformation Rates of SO*,
     as  Estimated from Emission Data, Trajectories and Measured Air Con-
     centrations, Atmos.  Env. 9., 425-429  (1975).

 9.   Fisher, B. E. A. and P. R. Maul, The Mathematical Modelling of the
     Transport of Sulphur Dioxide Across  Country, Presented at the symposium
     "Systems  and Models in Air and Water Pollution," organized by the
     Institute of Measurement and Control, London, England, Sept. 22-24  (1976)

10.   Heffter,  J. L.  and G. A. Ferber, A Regional Continental Scale Transport,
     Diffusion and Deposition Model, NOAA Technical Memorandum ERL-ARL-50,
     June (1975).

11.   Knox,  J., Numerical Modeling of the  Transport, Diffusion and Deposition
     of Pollutants for Regions and Extended Scales, J.APCA. 24,  660-664  (1974)

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                                     -74-
12.  Sao, K. S., J. S. Lague and B. A. Egan, An Air Trajectory Model for
     Regional Transport of Atmospheric Sulfates, pp. 325-331, preprints
     of the Third Symposium on Atmospheric Turbulence, Diffusion and Air
     Quality, sponsored by The American Meteorological Society, October
     19-22 (1976).

13.  Scriven, R. A. and B. E. A. Fisher, The Long Range Transport of Airborne
     Material and Its Removal by Deposition and Washout - I.   General Consi-
     derations, Atmos. Env. 9_» 49-58 (1975).

14.  Scriven, R. A. and B. E. A. Fisher, The Long Range Transport of Airborne
     Material and Its Removal by Deposition and Washout - II,  The Effect of
     Turbulent Diffusion, Atmos. Env. 9^ 59-68 (1975).

15.  Sheih, C. M., Application of a Lagrangian Statistical Trajectory Model
     to the Simulation of Sulfur Pollution over Northeastern United States,
     pp. 311-317, preprints of the Third Symposium on Atmospheric Turbulence,
     Diffusion and Air Quality, sponsored by The American Meteorological
     Society, October 19-22, (1976).

16.  Wendell, L. L. D. C. Powell and R. L. Drake, A Regional Scale Model for
     Computing Deposition and Ground Level Air Concentrations of 30% and
     Sulfates from Elevated and Ground Sources, pp. 318-324, preprints of
     the Third Symposium on Atmospheric Turbulence, Diffusion and Air Quality,
     sponsored by The American Meteorological Society, October 19-22 (1976).

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2.5  GROUP II-2:  PLUME DYNAMICS UNDER SPECIAL CONDITIONS
                                PARTICIPANTS

                 L.  E.  Niemeyer                    C. Simon
                 R.  A.  Porter                      R. I.  Wevodau
                 P. Finkelstein
                       Moderator/Reporter:  J.  J.  Roberts
                                  CONTENTS

 2,5.1  Introductory Remarks
 2.5.2  Aerodynamic Dotinuash
 2.5.3  Sea Breeze and Other  Anomalous  Circulation  Patterns
 2.5.4  Interactions of the Plume Dith  an Elevated  Inversion  Layer

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                                     -76-
2.5.1  Introductory Remarks
       Despite the wide range of problems implied by this title, the working
group focused on only a few situations not routinely treated by the standard
models but which may lead to violations of short-term NAAQS or PSD incre-
ments, especially in Class I areas.  Three situations were considered:
       1.  Aerodynamic downwash;
       2.  Sea breeze and other anomalous circulation patterns;
       3.  Interactions of the plume with an elevated inversion layer:
           a.  Fumigation
           b.  Trapping
       The members were in unanimous agreement on the proper way to approach
these issues  in a guideline document.  Firstly,  the guideline should state
clearly  that  it is incumbent upon  the various parties involved  in the
decision making process to determine whether or  not any of these phenomena
are  likely  to occur, and,  if so, whether  in a manner likely  to  cause viola-
tions of short-term  limits.  Secondly, this screening process,  as well as
any  subsequent analyses, must be carried  out under  the guidance of persons
knowledgeable in  the physical processes involved.   Thirdly,  since the
situations  for downwash and unusual circulations require  a case-by-case
analysis, no  standard models or rules-of-thumb should be  formally recommended
in the guideline.  And, finally, to assist the user of the guideline  in
selecting the best available and most appropriate analytical methods  for
estimating  such impacts, one or more pertinent references should be cited.

2.5.2  Aerodynamic Downuash
       Included in this problem area are  all situations wherein the dynamics
of the effluent in the  immediate vicinity of  the source are  influenced in a
significant manner by nearby structures and  terrain.  Two closely related
approaches  have,  in  general, been  taken  to  simulate these conditions  within
the  structure of  the standard Gaussian plume model:
       1.   An equivalent volumetric  source  is  assumed, with  a  cross-
            sectional area  approximating  that of  the obstacles  -
            usually the  building upon which the stack or vent is
            situated;

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                                     -77-
        2.  An enhanced DZ is employed, usually with an assumption of
            zero plume rise.
 These approaches are documented in references 1, 2, and 3 in sufficient
 detail to permit the user to handle the most common problem of a single
 building and its stack.
        In a closely related matter, the members were agreed that the  rule-
 of-thumb, "a proper height for a stack is 2 1/2 times that of any nearby
 structure," should not be arbitrarily applied to avoid downwash
 conditions since (a) in many instances the rule is unreasonably conserva-
 tive, causing unnecessary expense and unsightly high stacks, and (b)  in
 other instances it may be inadequate especially if localized terrain  effects
 influence the air flow in the vicinity of the source.  (See Ref. 4 for a
 further discussion on these topics.)
        The members recommended that the guideline include a listing of
 situations in which downwash effects might occur.  These would serve  as
 warnings to the user that special attention should be given to the potential
 for downwash.  They would not represent explicit criteria.  Included  in  this
 list should be:
        1.  Relative heights of stack and nearby structures or land
            features (both upwind and downwind);
        2.  Dimensions of the building and stack, in particular the
            aspect ratio relative to the wind direction;
        3.  Emission characteristics - thermal flux and effluent
            velocity at part load operation as well as at design
            values;  and
        4.  Multiple stack configurations.

2.5.5   Sea Breeze and Other Anomalous Circulation Patterns
        The phenomenon commonly known as a"sea breeze" can have a substantial
 influence on the transport and diffusion of pollutants.  The EPA modeling
 guideline should recommend  that  is  be  accounted  for  in major modeling studies
 in areas where the sea breeze can be important.  Three areas in which the
 sea breeze can be important are in long—term modeling, short—term modeling
 on a regional scale, and short—term modeling of individual sources.   For
 individual sources the sea breeze fumigation, documented by Lyons  and

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                                     -78-
 Hales ,   can lead to high short-term concentrations of  pollutants.  For
 short-term studies of a large area,  the  sea breeze  can strongly  influence
 local circulation,changing wind  and  stability patterns and,  again,
 possibly causing high short-term levels  in areas  where they  wouldn't be other-
 wise expected.  This has been documented by Gatz  .    For  long-term models
 the  sea breeze effect can have a marked  effect on the stability  climatology
 of the shore area,  affecting wind patterns, stability, cloud cover, and
 precipitation patterns.  Great care  must be taken to ensure  that the data
                                                                  Q
 used in such a model is representative of the area.   (See  Estoque ).
        A meteorological problem  which is similar  physically  - but dissimilar
 topographically - is that of the mountain/valley  winds.  This, too, is a
 local, thermally-induced wind field  which must be taken  into account in any
 type of diffusion modeling in an area prone to these winds,  or serious
 errors can and will occur.  The  need for correct, representative data is
 never more crucially felt than while modeling in  both these  problem areas.
        While the committee feels compelled to point out  this deficiency in the
 present draft guideline, we do not feel  competent to  draft an alternative
 and  wish to suggest that an experienced, knowledgeable scientist in this
 area be retained to draft this portion of the Guideline.

2.5.4   Interactions of the Plume with an Elevated Inversion  Layer
        The group considered two  topics under this heading:
        1.  Short-term downwash of a  plume during  transition  from stable
            to unstable conditions (fumigation)
        2.  Plume interaction with mixing height
        Fumigation downwash may be a  significant factor in  evaluating large
 sources relative to Class I 3-hr. PSD increments.  The guideline user should
 be aware of this and seek expert advice  if fumigation is reasonably
 anticipated to be a significant  factor.   The group  recognizes that there are
 many cases in which fumigation will  not  significantly affect 3-hr,  (or
 longer) concentrations, but we cannot offer specific procedures  to determine
 if fumigation is a significant factor.  Fumigation  is discussed  in Turner's
 Workbook^, "Meteorology and Atomic Energy"10 and  TVA  Studies11.   In
 evaluating the effect of fumigation on 3-hr, concentration,  the  user must

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                                     -79-
take into consideration the short duration of fumigation at a given receptor
site.
       Current models (e.g., CRSTER) assume no concentration at ground level
if effective stack height is greater than the mixing height.*  The group
generally agreed this assumption is not uniformly valid.**  Trapping
probably does occur sometimes even when the calculated effective stack height
is greater than the height of the mixed  layer, and thus the idealized
assumption in models such as CRSTER may lead to underestimation of short-term
maxima.  However, we are unable to recommend a change in current models at
this time, in part because of the uncertainty in the manner currently used to
estimate hourly mixing heights.  The group believes this topic needs addi-
tional field evaluation in order to identify those physical conditions under
which plume trapping can be assumed to occur.
 * See  Sec.  3.9.6 for a summary of a model called "AQSTM" which was
   contributed by G.  Melvin.  This model treats atmospheric trapping  and
   other  phenomenon discussed in this section.
 **See  Sec.  3.9.2 for a. discussion of a case in point by M. Williams  which
   was  submitted after the conference.

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                                      -80-
                           REFERENCES  FOR SECTION 2.5.
 1.  Briggs, G. A., Diffusion Estimation for1 Small Emissions, Environmental
     Research Laboratories, Air Resources Atmosphere  Turbulence and Diffusion
     Laboratory 1973 Annual Report, USAEC Report ATDL-106, National Oceanic
     and Atmospheric Administration, December  1974.

 2.  Huber, A. H. and Snyder, W. H., Stack Placement  in  the Lee of a Mountain
     Ridge - A Wind Tunnel Study, Environmental Protection Agency, EPA-600/4-
     76-047, 1976.

 3.  Riley, J. J., et al, A Numerical and Experimental Study of Stably
     Stratified Flow Around Complex Terrain, Environmental Protection Agency,
     EPA-600/4-76-021 and 022, Vols. 1 and 2,  1976.

 4.  Snyder, W. H. and Lawson, R. E., Jr., Determination of a Necessary
     Height for a Stack Close to a Building - A Wind Study, Atmospheric
     Environment, Vol. 10, pp. 683-691, 1976.

 5.  Lyons, W. A., et al, Detailed Field Measurements and Numerical Models of
     SC>2 from Power Plants in the Lake Michigan Shoreline Environment, Report
     to Wisconsin Electrical Power Company, p. 218, 1974.

     Lyons, W. A. and Rubin, E. M., Aircraft Measurements of the Chicago Urban
     Plume at 100 KM Downwind, Proceedings of  the 3rd Symposium on Atmospheric
     Turbulence, Diffusion and Air Quality, Raleigh, N.C., pp. 358-365, 1976.

 6.  Hales, J. Vern, Draft EIS for Indian River Power Plant, Delmarva Power
     and Light Company, 1974.

 7.  Gatz, D., Chicago Air Pollution System Analysis Program," Final Report,
     ANL/ES-CC-099, J. J. Roberts, Editor, Sec. 4.3 and 4.4, Feb. 1971.

 8.  Estoque, M. G., A Numerical Model of the Atmospheric Boundary Layer,
     Journal of Geophysical Research, 68:4, pp. 1103-1113, 1963.

 9.  Turner, D. B., Workbook of Atmospheric Dispersion Estimates, Office at
     Air Programs, Environmental Protection Agency, Pub. No. AP-26, 1970.

10.  Slade, D. H., Meteorology and Atomic Energy - 1968, U.S. Atomic Energy
     Commission, July 1968.

11.  Full Scale Study of Inversion Breakup at Large Power Plants, Tennessee
     Valley Authority report, p. 177, 1970.

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                                     -81-
2.6  GROUP II-3:   COMPLEX TERRAIN
                                  PARTICIPANTS

              S. Barr                            D. Henderson
              B. A.  Egan                         G. L.  Melvin
              D. G.  Fox

                             Moderator:  H. H. Slater
                             Reporter:    A. E. Smith
                                   CONTENTS

2.6.1  Summary of Discussions
2.6.2  Recommended Procedure for Complex Terrain
2.6.3  Assistance in Defining Screening Techniques in Complex Terrain
2.6.4  Supplementary Comments and Information

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                                     -82-
2.6.1  Summary of Discussions
       The  group  was  in  unanimous agreement  that  it  is not  possible  to  specify
a  general model for providing a definitive statement concerning  the  air qual-
ity  impact  of a source or  group of sources locating  in complex terrain.  While
a  number of generic types  and specific algorithms are available,  it  is  not
possible to appraise  the performance of  these models or algorithms during
this workshop for one or more of the following reasons:
          Lack of performance criteria,
          Lack of evidence for adequate  simulation of physical
          processes for  all possible situations,  and
          Lack of acceptable evaluation  exercises.
       The  discussion was  limited to topographic  complexities (e.g., lake or
sea breezes were  not  discussed) and single sources.   Considerable discussion
was devoted to the use of  the Valley algorithm.   Some validation data for
Valley were presented and  explained(see Sec.  3.6.2).  it was clear that Val-
ley was not uniquely  defined for various members  of  the group.  One  opinion
held that specific meteorological conditions, 2.5 m/sec winds and F  stability
for a total of 6  out  of  24 hours, were intended to be used  in the algorithm.
(This is the version  described in the conference  notebook.)  Other users re-
ported that they  relax this restriction  in practice  and use  site-specific
meteorology.  The point  was also made that conditions conducive to highest
concentrations depend on the specific situation and  may frequently occur with
moderate wind speeds, not  always under stable conditions.  It was also  noted
that even as a screen, Valley is not applicable in all situations because it
might not treat critical situations like circular flows and inversions  in
valleys.  Doubts  were also expressed concerning the  use of Valley as a  conser-
vative screening  technique.  Concern was also expressed that specifying the
meteorology would allow  some group to seize  on these  conditions and say they
should always be  used to estimate maximum concentrations even when such an
assumption  would  be incorrect.  One Valley user expressed confidence in using
the algorithm as  a screening procedure in mountainous terrain with the  specific
meteorological input.  Another noted that in less severe terrain results ob-
tained from Valley with  site-specific meteorology were only negligibly less
well correlated with observed concentrations than results from a Gaussian
model using  a plume half-height correction and that  in general it appears to

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                                    -83-
give satisfactory results, at least from viewpoint of a regulatory agency.   It
was decided that two positions concerning Valley had developed and should be
noted:
          Some members of the group felt comfortable with Valley, at
          least as a reasonable screening procedure in complex ter-
          rain, and
          Most members of the group, however, felt that Valley could
          not be recommended for general use in complex terrain or
          even as a screening tool in all situations although steps
          could be taken to render its use  less objectionable as a
          screen in certain circumstances.
       Additional discussion centered on other available algorithms for and
experience with treating complex terrain.   Results using a half-height correc-
tion  to a Gaussian plume model  in  three river valleys  in Illinois were pre-
sented.  This experience also indicated that the worst agreement occurred
under calm conditions  or with circulating  flows.  It was noted that poor
agreement between predicted and measured values  is usually attributable  to
poor  source or meteorological data.   In general, the  group felt  that  good
meteorological data is almost always  a problem,  particularly in  complex  ter-
rain.
       A method of correcting the  Gaussian formula based  on  theoretical  con-
siderations of  flow around  a hemispherical obstacle was also presented.   The
method appears  to work better  than the  full-lift assumption.
       One proposal considered  the use  of  a combination of meteorological and
air quality modeling  to  develop the concept of air  shed zoning.  3   (See  also
 Sec. 3.6.1.)   This approach was presented  as being  particularly  applicable
 to the designation  of land  areas for the purpose of the prevention of signi-
 ficant deterioration.
       A computerized flow correction model for use in mountainous terrain
was also described  but the group did not take any position on such work.
       The group made several attempts to develop some guidance material.
 One suggestion was  to enumerate the limitations on the use of various types
 of models  (Gaussian,  K-theory,  statistical, etc.)  in complex terrain.  This
 approach was  abandoned in favor of suggesting an administrative procedure
 for use  in complex terrain situations.   The procedure is described in
 Section  2.6.2.  It  includes a screening  technique which is

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                                    -84-
detailed in  Section 2.6.3. Pertinent references are cited including some
analytical routines.  Given the dearth of applicable, validated techniques,
the group's  consensus called for  requiring that a thorough and comprehensive
effort be made to evaluate sources in complex terrain and for assigning con-
siderable priority  to developing and validating models for complex terrain.

2.6.2  Recommended  Procedure for Complex Terrain
       Given the agreement that no single model can adequately treat all com-
plex  terrain situations, the group suggested that  a specific procedure be estab-
lished that will lead to a rational and adequate assessment of a  source locating
in complex terrain and that will result in a two level hierarchy  of a model
usage  consisting of conservative  screening procedures  followed by  detailed
analytical  studies  when the  screens are not  passed  and in the development of
data  which  will lead to further model  refinement  or  input data acquisition.
       The following process is recommended for use in evaluating sources desir-
ing  to locate in complex terrain:
       1)   Review available  data  (air  quality,  meteorological, source
            data, etc.)  and the physical situation (action by source
            alone).
       2)   Develop  a rationale with regard  to a particular  analysis
            technique (by analogy  if necessary)  (action by source
            alone).
       3)   As a screening procedure, the  source reviews  initial re-
            sults with Agency.   (See recommendations  in subsection  3.)
       4)   If no agreement is  reached  based  on  the screen  or if  the
            source does  not pass the screen,  then  the source  has an
            option to develop detailed  site-specific  data and perform
            detailed analyses.
       5)   Agency evaluates  detailed analyses.
       6)   Source constructs (if  approval granted);  however, air
            quality  monitoring  may be required.
       7)   Air quality  data  reviewed as necessary.
       This approach requires  control  agencies  to have access to  the services
of qualified personnel  who are highly  knowledgeable  in dispersion climatology
and  the  characteristics of atmospheric flow in  complex terrain.   In addition,
 the  EPA  must serve  as a clearinghouse  for data  concerning  the application of
various  models in complex terrain situations.

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                                     -55-
       It  is  further  recommended  that  specific  procedures  can be applied for use
as  screening tools  for specific physical situations.  Within the time limits
of the session, such a procedure could not be fully developed.   However,  an
outline of phenomena which should be considered in various complex terrain
situations and suggestions for modifications to flat-terrain models are pre-
sented in the following section of this report along with the references  to
some of the appropriate literature.
       For those sources which do not pass the screening procedures, detailed
studies may be conducted by the sources to develop the meteorological data
necessary to select and verify an analysis procedure which will result in an
air quality impact assessment sufficiently refined to make the necessary sit-
ing decisions.  At this point the assistance of a meteorologist knowledgeable
in complex terrain problems and familiar with the area of interest is required.

2.6.3  Assistance in Defining Screening Techniques in Complex Terrain
       This section provides a general discussion of phenomena which should
be considered in addressing compliance with air quality goals in regions of
rough terrain.  The purpose is to provide some guidance on appropriate model-
ing techniques as a function of various source configurations with respect to
topography and meteorological situations which might be expected.  Modifica-
tions are suggested which could be incorporated in a dispersion model origi-
nally developed for flat terrain analyses and which would be one of a number
of generic types (e.g., Gaussian, "K-theory").  The modifications are proposed
in the context of developing "conservative" models which would tend to over-
predict air quality impact and which would be appropriately used for "screening"
purposes.  Two broad categories of meteorological flow situations have been
defined:  1) plume interactions with  terrain under organized flow conditions,
and 2) effects of local meteorological phenomena on dispersion in complex
terrain.

       2.6.5.1  Plume Interactions with Terrain under Organized Flow Conditions
       This  section is concerned with how a plume affects ground-level concen-
trations in flow situations which might be considered to be organized in
the sense that the wind field would have high spatial correlations from one
location to another and would not be  significantly affected by local circula-

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                                    -86-
tions.  These are flows which lend themselves to some simplified types of
theories (e.g., potential flow, modified potential flow, etc.).  The approaches
discussed below are categorized by different geometry and atmospheric stabil-
ity classification considerations.

(A)  Alterations to Plume Trajectories Due to Terrain Effects
       Plume Height Greater than the Height of Nearby Terrain.  Under these
conditions, models which are appropriate for flat terrain can generally be
utilized with some modest modifications.  If the plume height is much greater
than the terrain, flat terrain assumptions may be valid if some consideration
is given to the possible enhancement of turbulence due to "roughness."1  For
plume heights not much greater than terrain heights, some modifications to
the plume centerline  trajectory seem appropriate.  If the plume is embedded
in a stable layer above the terrain, it is a reasonable approximation to
assume  that the plume trajectory would be horizontal for purposes of computing
ground  surface concentrations.  (Note, however, the comment about the possible
effects of "lee wave" phenomena. )
        For a plume approaching a  terrain object during neutral atmospheric
stability conditions, modifications which are suggested from  potential flow
theory  appear appropriate.  A commonly used modification to Gaussian models
involves adding an increment to the plume height over the terrain equal to
approximately one-half the terrain height (distances estimated from the valley
floor).  A more conservative estimating technique would be to allow a smaller
lift of the plume centerline above the terrain.  This is, however, a poten-
tially  poor assumption when the plume height is nearly equal  to the terrain
height.  Under non-stable conditions, allowing full lift of the plume, that
is, a terrain following trajectory, would appear to be generally nonconserva-
tive, although it appears from a  theoretical point of view to be reasonable
for flow normal to two-dimensional type ridge orientations.2
       Plume Height Lower than the Height of Nearby Terrain.  If the plume
height  is initially lower than the height of nearby terrain,  the approach to
defining the flow pattern and, hence, plume centerline trajectories, depends
critically on the local topography, atmospheric stability, and airflow.  Poten-
tial flow theory provides some insight on the approach for neutral atmospheric
stability conditions.  If the flow is normal to a ridge, a plume embedded in

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                                     -87-
the flow will accelerate and lift over the ridge with an actual center line-
standoff distance equal to approximately one-half the plume height above the
upwind valley flow.  Because of the flow acceleration, however, the stream-
line spacing decreases and, through distortion effects, the vertical dimen-
sions of the plume decrease.  The net effect suggest no alternation of the
                                                                        o
effective standoff distance in the exponential term in a Gaussian model.
A numerical simulation appropriate for this flow situation should account for
these kinematic effects automatically.  If the flow is normal  to a more iso-
lated three-dimensional terrain object (as opposed to a two-dimensional
ridge), the effective standoff distance (considering both trajectory alter-
nations and plume distortion effects) will be less than that for flow over a
ridge shape.  A value of about one-half the effective plume height  in the
absence of terrain is an approximation suggested for  terrain objects with
roughly equal horizontal and vertical dimensions.  Under stable atmospheric
conditions, for  flow normal to a  two-dimensional ridge, the flow may pass
over  the ridge if  the stability  is weak and wind speed  is not  too  low.   If
the  temperature  inversion  is strong  and the wind weak,  the flow may "block"
and  effluents would  tend  to stagnate upwind of  the terrain.  In either  case,
careful consideration would be given to how the principle of conservation  of
mass will act to alter  plume trajectories and  constrain the types  of  flow
situations possible.  On-site meteorological measurements, as  well as any
information on air quality concentration  patterns,  are  extremely  useful in
such situations.   For  stable flow normal  to more  three-dimensionally  shaped
objects,  the  possibility  of  "blocking"  is greatly  reduced because the flow
will tend  to  pass  around  the sides of the terrain.   The possibilities for
relatively high  ground  level concentrations  exists under  these conditions,
however,  if  the  plume  flows  directly toward  the terrain.   The  frequency of
occurrence of possible  meteorological conditions  which would  cause this flow
situation should be  carefully  assessed.
        Under  these postulated  situations, insight regarding the expected plume
behavior  can  be  greatly enhanced with field observations  and/or scaled-down
physical  modeling studies (stratified wind tunnel or water-towing tank experi-
ments).   Eowever,  the  frequency of occurrence and persistance of  meteorological
conditions  cannot be determined by these techniques.

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                                    -88-
(B)  Alterations to Turbulent Diffusion fates in Complex Terrain Modeling
     Studies
       General Considerations.   A number of field measurement studies have
indicated that the association utilized in flat terrain of stability classi-
fication indices with turbulent diffusion rates are not necessarily valid in
complex terrain.  In particular, a number of phenomena tend to decrease the
possibilities for very low diffusion rates associated with stable classifica-
tions. 3»6»7  A shift of stable classifications toward neutral appears
often to be appropriate for purposes of estimating atmospheric turbulence
levels.  Under stable conditions, the presence of terrain may also greatly
enhance crosswind "meandering" of a plume which in effect reduces time-
averaged concentration values.
       Considerations in the Near-Field of Sources.  For purposes of examining
the impact of emission sources on nearby high terrain (within 10 km) careful
consideration should be given to the effects of buoyancy-induced entrainment
on plume dilution during the rising phase of plume growth.1*'5  Inclusion of
buoyancy-induced turbulence can result in markedly different estimations of
plume centerline and ground-level concentration values.
       Far-Field Air Quality Impact.  Additional considerations must be given
to estimating the impact of plumes which travel large distances (30 km or
more) before encountering high terrain:
       1)  If at the location of the encounter, the plume dimensions
           are the same order or larger than the terrain dimensions
           and the plume centerline is below the terrain height, then
           near centerline concentrations can be expected to occur at
           ground level.
       2)  The transport wind field may be highly variable both spa-
           tially and temporally.  Therefore, methods for estimating
           dispersion rates under these conditions need to be given
           special attention.
       3)  Removal of gases and particulates by chemical reactions,
           scavenging, and deposition processes may be important to
           include in the estimation techniques to provide realistic
           estimates.

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                                     -89-
(C)   Other Factors Affecting the Estimation of Air Quality Levels under
     Organized Flow Conditions
       Lee-Side FloDs.   Observational data and physical model experiments
show that under stable atmospheric conditions, the flow may accelerate on the
lee side of mountain ranges and cause streamlines to closely approach the lee
surface.  It is possible that this type of flow could cause a down draft of a
plume toward the surface and would increase ground-level concentrations.
This type of flow is often characterized by the production of lee-waves down-
wind of the mountains.  Flow separation effects on effluent dispersion of
sources both upwind and downwind of an obstruction are discussed  in a subse-
quent section.
       Channeling Effects.  Topographic features will alter larger scale
meteorological flows to tend to  follow terrain contours.  This results in
"channeling" of winds  into preferred directions and  therefore an increased
persistence of winds in directions along valleys.  These  effects are of  spe-
cial importance in  the analysis  and application of wind data at  a site for
purposes  of estimating 24-hour  and annual  average  concentrations where,  for
point sources, persistence of wind direction  is a  major concern.
       Fumigation and  Limited Mixing Depth Effect.   Special  attention  should
be  given  to possible high  ground-level  concentrations  on  high  terrain  during
meteorological  flow conditions  characterized  by  fumigation of  elevated  plumes
to  the  surface by vigorous  mixing.   Other  factors  being equal,  larger  ground-
level  concentrations  can be expected  under these  conditions  in rough terrain
than in flat  terrain.  Another  concern is  limitations  to  dilution rates
associated with  the "lidding"  effects of an elevated inversion above high
terrain.   This  phenomena needs  further understanding;  an approach similar  to
that adopted  for  fumigation simulations may be reasonable.  Numerical simula-
tion models which can account for the effects of spatially and time varying
meteorological parameters would be especially appropriate for applications of
this type.

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                                    -90-
       2.6.S.2  Effects of Local Meteorological Phenomena on Dispersion in
                Complex Terrain
                   (A)  Surface generated flow systems
                        (i)    Upslope-downslope
                        (ii)   Surface inversion dynamics
                        (iii)  Mechanical forcing and turbulence
                   (B)  Interaction of surface generated flows with
                        larger scale meteorology
                        (i)    Separation and decoupling
                        (ii)   Channeling
                        (iii)  Lee waves and blocking
                   (C)  Worst case estimates
       (A)  Surface Generated Flow Systems
       A major complication  in dealing with dispersion over  irregular  topog-
raphy is the fact that a simple atmosphere-surface interface does not  e^ist
as it does over a flat surface.  The amount of solar  energy  received by
irregular terrain surfaces varies widely with elevation  and  orientation of
the surface toward the sun  (aspect).  This differential  heating gives  rise
to locally forced wind systems and atmospheric stability variations which
have to be considered.  The  irregular terrain, simply by its presence, also
acts to alter an otherwise straight wind.
       (i)  Upslope-Downs lope
       The general character of surface winds in mountainous terrain is
readily identified.   Winds blow upslope  (toward higher elevation  terrain) on
clear sunny days and  downslope  (toward lower elevation terrain) at other
times.12'13'11**19  This description is deceptively simple because complex
valley-mountain geometry often disrupts  this flow.11*'19  Modeling  the disper-
sion from ground or low level sources must take into  account these persistent
and regular upslope-downslope winds.  Since such winds are  rather shallow,
often not exceeding 100-200  meters depth,  10,13,19 tall  sources may be sub-
jected to quite different  flows underlining the need  for site  specific data
taken at both the source emission and effective plume height levels.   One
must be certain in areas of  complex terrain to  ensure that  the winds used for
dispersion analysis in fact  represent the  immediate environment of the site.

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                                     -92-
       (ii)   Surface Inversion Dynamics
       Surface based inversions often form in mountain valleys.13'19  Ground
based emissions, low level sources, as well as tall sources, can be trapped
by such conditions.  Maximum surface concentrations may occur as a result of
the limited vertical mixing, especially during times of minimum inversion
height.  Gaussian models are of limited value for calculating such concentra-
tions.  Rather, a model which divides the total emission by the volume flow
rate of air in  the valley below the inversion, a so-called box model, is more
appropriate.15'22  This technique may be suitable as a conservative screening
method.
       (iii)  Mechanical Forcing and Turbulence
       Turbulence  can be forced, enhancing mixing and dispersion, by  the
presence of  topographic relief.16   Such  a condition is difficult  to analyze
although terrain types can  be  identified which  give rise  to mechanical  turbu-
lence.  Abrupt  changes in elevation,  isolated topographic features  and  very
irregular surfaces all can  contribute to mechanical forcing.11'     Physical
modeling techniques  (wind tunnel,  water channels)  can be  used most  success-
fully to qualify and quantify  such mechanical turbulence  generation.     Physi-
cal  modeling techniques  do  not,  however, deal with the  frequency of occurrence
and  persistance of the meteorological  conditions modeled.

        (B)   Interactions  of Surface Generated Flows with Larger Scale
             Meteorology
        A major complication of mountain meteorology is the fact that normal
 passage of  large-scale meteorological systems is greatly altered by the
 topography.  The expected surface induced flows, for example, may be strongly
 altered or not occur at all as a result of larger  scale patterns.  These
 local effects of larger scale systems can have a major effect on dispersion
 modeling.
        (i)   Separation and Decoupling
        Particularly when wind blows perpendicular  to a ridge the flow may
 separate from  the downwind  (leeward) side.   Separation causes a closed circu-
 lation which can  bring a plume down to  the surface as well as a wake effect
 often far downwind of the  obstacle.9   It is  not uncommon  for various types

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                                     -92-
of flow systems to exist within a mountain valley reasonably independent of,
or decoupled from, the aloft meteorology. 9»1'*'19  The modeling of a plume
under such situations can be very complex, especially if the plume travels
between each flow regime.  Phenomena based flow fields can be introduced into
Gaussian models to be used as screening techniques.  However, a preferred
modeling technology is again the use of wind tunnels and/or water channels,
but the problem of persistence would need further evaluation.
        (i,i)  Channel-ing
       Wind components blowing parallel with a valley often descend well into
the valley and channel a strong flow along the valley axis. 9 »12 »Jl* »19  The
conventional Gaussian model is appropriate so long as the wind  field is
properly specified.
        (Hi)  Lee Waves and Blocking
        Lee waves  generally occur with  stable  flows and set up a standing pat-
tern  of waves downwind  of major terrain  features.9'16  Depending upon how
close to  the surface  these waves and  their associated flows  occur,  either
separation  or a  high  velocity  surface  flow may  result.9'19   Blocking, another
result of stable flow,  occurs  upwind  (windward)  of terrain  and  basically
represents  a region of  stagnation.9'18  Upwind  slopes with  a reasonable  flow
component perpendicular to the ridge  line may be subject to  such blocking
under stable atmospheric  conditions.   A  box model such as described above
could be  calculated using  the  ridge top  as mixing height and a  surface  area
appropriately defined.   Such a process could  be used as  a screening technique.
An alternative  is the use  of combined  meteorological and dispersion modeling.

        (C)  Worst Case  Estimates
        Worst  case estimates  of ground level  concentrations  under terrain-
disturbed  flow  conditions  may  be  addressed with simple modeling techniques
 if care is  taken in  selecting  model input variables.  Perhaps the most impor-
 tant effect of  topographic flow features is  the severe constraint on repre-
 sentativeness  of input  data including mean wind, stability,  and turbulence,
 since these flows exhibit strong space-time  variability, particularly in the
vertical.   Some determination must be made that the  plume is involved in the
 same flow regime as  the meteorological input data throughout its path to the
 potential receptor site.
2 3

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                                     -93-
       Many drainage flows are relatively shallow (< 200 m in depth)  and wind
measurements near ground level may not apply to an elevated plume if  it is
above the drainage regime.  Also the lateral radius of representativeness of
wind measurements becomes very small in broken terrain.
       If the net transport is properly estimated and if alterations  to dilu-
tion rate due to local effects are accounted for, Gaussian plume or box model-
ing concepts may be reasonably applied to obtain bounds on ground level
concentrations of pollutants.  In establishing procedures for estimating the
impact of a proposed facility the following steps should be considered :
       1.  Examination of topographic charts on several scales with
           varying degrees of smoothing will aid  in determining  the
           basic setting with regard to basin structure, channeling,
           downwash, drainage,and stagnation potential.
       2.  A quantitative critical  review of existing meteorological
           records from sites within the  same general topographic
           domain will aid in establishing  the existence and  occur-
           rence frequency of topographic effects on  transport.
           These data are likely to be  sparse  and not properly
           located  for a  site analysis,  so  site  meteorology must
           be  considered  next.
        3.  Based on  the  insights gained from the first two steps,
            perform  a series  of  observations to establish the radius
            of  representativeness.   This should include elevated
           winds by  means of pibals and concurrent observations from
            combinations  of  topography such as different slope faces,
            valley-mesa,   hillside-plain,  and source-receptor points.
            In documenting local flows,  consideration should be given
            to their recurrence frequency, depth, lateral extent,
            diurnal  pattern,  net wind speed,and some measure of tur-
            bulence  intensity.

        4.  The preliminary analysis outlined above gives a basis for
            estimating bounds on ground level concentrations for screen-
            ing purposes and aids in the design of subsequent monitoring

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                                     -94-
           networks.  Using the morphology of local flows and the
           design of the proposed facility, an assessment can be made
           of the interaction of the plume with these wind domains,
           including the likely pollutant trajectories.
       Examples of simple model applications given the insights from steps 1-
4 above are:
       a.  Plume Involved in Drainage Wind.  These flows maintain a
           downslope component and are estimable from smoothed topo-
           graphic charts.  The turbulence in these winds is often
           greater than would be implied by a simple AT predictor
           and should be measured in order to not underestimate
           dilution.  Flows may vary from 1 to 10 m/sec but are
           often remarkably steady and amenable to Gaussian plume
           modeling along  the determined  (possibly curved) plume
           axis.  The daytime counterpart  to  the drainage wind is
           generally weak, brief and sporadic and  is dominated by
            the large scale flow regime of  the day.
       b.  Plume Elevated  above Drainage Flow.  If it  is determined
            that the plume  and receptor are in the more uniform wind
            field above  the local drainage wind ,traditional modeling
            techniques  [for organized flows] may be applicable to
           worst case determinations.
       c.  Stagnation.  A  plume imbedded  in a stagnant flow  created
           by  a basin structure or blocking by a ridge may be treated
            using modified  Gaussian plume  techniques.21'22   (The Valley
           model is an  example of one such  technique.)   Plumes above
            a stagnant region  in a well defined wind  field may be  more
            amenable to  the traditional Gaussian plume  model  applica-
            tions .
       d.  Separated Flows.   These are local  in character and depend
            on  the  existence of some  general meteorological  conditions
            of  stability and wind speed.   The  geometry  of the source
            and the  obstacle producing the  separation is  very important.
            With an upstream source estimated  to be involved  in  the

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                                     -PS-
           separated flow region, a well mixed zone on the lee side of
           the obstacle ("box" model) may be adopted.  With the source
           in a lee side downwash, maximum ground level concentrations
           would be due to a nearly direct path of pollutant to the
           ground in a fumigation mode.

2.6.4  Supplementary Comments and Information
       Supplementary comments and information were submitted by both members
of the working group and others.  This material can be found in Sec. 3.6  and
is referenced below.
       D. Fox submitted a description of the TAPAS model in the format of the
conference notebook (Sec. 3.6.1).  This model can be  applied in complex  terrain.
       In Sec. 3.6.2,  H. Slater discusses the Valley model and some compari-
sons of observed and estimated concentrations including a description of the
conditions under which the data were obtained.
       M. Williams  (Sec. 3.9.2) has suggested a specific change in the wording
for p. 30 of the draft Guideline.  The suggestion concerns the choice of wind
speed when an elevated source impacts high terrain.  He presents data on the
unacceptability of  ground level inferences of stability, wind speed, and direc-
tion during stable  conditions.  He also suggests some changes in the Valley
algorithm.  In Sec.  3.6.4,he also comments on the use of plume half-height
corrections, the uniformity of the wind field in the lower atmosphere under
stable conditions,  and the influence of terrain elements on stable plumes.

       The letter from Howard M. Ellis of Enviroplan  submitted by M. Smith
(Sec. 3.9.4) comments on the use of a half-height rather than a full-height
(CRSTER treatment)  reduction in effective stack height in complex terrain and
on turbulence and dispersion enhancement.
       G. Melvin  (Sec. 3.9.6) has presented a description, in the notebook
format, of the Illinois' Air Quality Short Term Model (AQSTM) which can be
used  in complex terrain.
       In Sec. 3.6.3,D. Henderson  has commented  on the  use  of  local  meteoro-
logical  conditions  in Valley  and on the conditions  under which  the model is
most  applicable.  He  has also  noted factors which determine  the degree of con-
servatism of a box  model.

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                                    -96-
                         REFERENCES FOR SECTION 2.6.


1.  Hovind, E.  L.,  T.  C.  Spangler, and A. J. Anderson,  1974:   The influence
    of rough mountainous  terrain upon plume dispersion  from an elevated
    source.  Presented at the  Symposium  on Atmospheric  Diffusion and Air
    Pollution  - American Meteorological  Society.   Santa Barbara, California,
    September  1974.  pp.  214-217.

2.  Hunt,  J. C. R., and P. J.  Mulhearn,  1973:   Turbulent dispersion from
    sources near tuo-dimensional obstacles.  J. Fluid Mech.,  61.  pp.  245-274.

3.  Egan,  B. A., 1975:  Turbulent  diffusion in complex  terrain.  Workshop on
    Air Pollution Meteorology  and  Environmental Assessment, American Meteor-
    ological  Society, September 30-October  3,  1975, Boston, Massachusetts.

4.  Pasquill,  F., 1976:  Atmospheric Parameters In Gaussian Plume Modeling
    Part II,  EPA-600/4-76-030b.

5.   Egan,  B.  A., and A. Bass,  1976:   Air quality  modeling on  effluent  plumes
    in rough  terrain.  Third Symposium on Atmospheric  Turbulence  Diffusion
     and Air Quality.  October 26-29, 1976,  Raleigh, North Carolina.

6.   Start, G.  E., C. R. Dickson,  and L.  L.  Wendell, 1975:  Diffusion in a
     canyon within rough mountainous terrain.  J.  Appl.  Meteor.,  24,  3, pp.
     333-346.

 7.   Hinds, W.  T., 1970:  Diffusion over coastal mountains of southern  Cali-
    fornia.   Atmospheric Environment, 2.  pp.  541-558.

 8.   VanderHoven, I., 1976:  A survey of field measurements of atmospheric
     diffusion  under low wind speed inversion conditions.  Nuclear Safety,
     Vol. 17,  No. 2, March-April 1976.

9.  Alaka, M.  A. ed., 1968:  The airflow over mountains .  World Meterol.
     Organ. Tech. Note 34, WMO No.  98 R.P. 43.

10.   Bergen,  J. D., 1969:  J. Appl. Meteorol. 8.  pp. 884-895.

11.   Davidson,  B. and J. Halitsky,  1961:   Studies of the field of turbulence
     in the lee of mountain ridges  and tree lines.  New York University.

12.   DeFant,  F., 1951:  Local wind.  In "Compendium of Meteorology," ed. T. F.
     Malone.   American Meteorol. Society, Boston, MA.

13.   Geiger,  R., 1959:  The climate near the ground.  Howard Univ. Press,
     Ch. 18-20.

14.   Panofsky,  H. and B. Prasad, 1967:  J. of Applied Meteorol. 6(3):493-499.

15.   Thyer, N.  and K. J. K. Buettner, 1962:  On valley and mountain winds.
     Dept.  of Atmos. Sci., Univ. of Wash. AFCRL-62-1082.

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                                     -97-
                            REFERENCES (Cont'dJ
16.  Sato, Y. Onda and T. Saito, 1974:  Turbulent diffusion in environmental
     pollution,  eds. Frankiel and Munn, Academic Press, New York, p. 241-251.

17.  Snyder, W. H., 1972:  Boundary Layer Meteorology 33 1.

18.  Yih, C. S. 1965:  Dynamics of Nonhomogeneous Fluids.  Macmillan Co.
     New York.

19.  Yoshino, M. M. , 1975:  Climate in a Small Area,  University of Tokyo
     Press.

20.  Sagendorf and Dickson, 1974:  Diffusion under low wind speed inversion
     conditions.  Proc. Symposium on Atmospheric Diffusion and Air Pollution,
     American Meteorological Society, Santa Barbara, Calif.  September, 1974.

21.  Leahey, D. M. and J. Halitsky, 1973:  Low wind turbulence statistics and
     related diffusion estimates from a site located in  the Hudson River
     Valley.  Atmos Env. 7, 49.

22.  Fox, D. G. 1975:  Man, leisure, and wildlands: A complex interaction.
     Proc. First Eisenhower Consortium Res. Symp., Sept. 14-19, 1975, Vail,
     Coro.  Eisenhower Consortium Bull. 1.  pp. 259-273.

23.  Fox, D. G., M. A. Fosberg, W. E. Marlatt, and W. Reeser, 1976:  Analysis
     of Mountain Air Quality.  Third Symposium on Atmospheric Turbulence Dif-
     fusion and Air Quality.  October 26-29, 1976.  Raleigh, N.C.

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-98-

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                                    -99-
2.7  GROUP II-4:   CHARACTERIZATION OF TURBULENCE
                                PARTICIPANTS
            H. E. Cramer
            S. R. Hanna
            M. E. Smith
                       Guest Participant:  D. J. Moore
                       Moderator/Reporter:  D. M. Rote
D. Bruce Turner
I. Van der Hoven
M. Lazaro
                                   CONTENTS
 2.7.1   Introductory Remarks
 2.7.2   Vertical Profiles of Wind Speed
 2.7.3   Comparison of STAR,  A27 and aQ
 2.7.4   Mixing Height Interpolation Scheme
 2.7.5   Vertical Dispersion Estimates
 2.7.6   Plume Dimension Stabilization Height
 2.7.7   Horizontal Dispersion Estimates
 2.7.8   Randomization of Wind Vector
 2.7.9  Review and Comment on Pasquill's Recommendations for
        Interim Chnages to the Pasquill-Gifford Curves
2.7.10  Use of Models in Urban vs. Rural Areas

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                                    -100-
2.7.1  Introductory Remarks
       This working group dealt primarily with detailed aspects of the
Gaussian plume model in general and with some issues specific to some of
the algorithms recommended in the proposed guideline.  The problems asso-
ciated with special modeling situations such as complex terrain were not
discussed.
       The general consensus of the group was that while some serious
concerns were raised and some important questions needed to be answered,
none were so serious as to prevent interim use of the  Guideline recommended
simple terrain algorithms such as "RAM" and "CRSTER".
       The most  important issue raised by the group was the validity of the sub-
jectively extrapolated portion of the a  curve for the Pasquill-Gifford stability
                                       z
class A and its  applicability to the computation of ground level concentra-
tions from tall  stack  emissions.  Similar questions were also raised re-
garding the upward turning stability B curve and the downward turning
stability D, E,  and F  curves for a  .  The group  supported the method of
                                  z
multiple images  for treating dispersion in a limited mixing layer and
stressed that for elevated releases the surface based  stability parameter
must be accompanied by information about the layer containing the plume.
Of special importance  is the position of the plume relative to a ground
based or elevated inversion layer.
    There was significant difference of opinion about the validity of
the prediction of neutral hours by the "STAR" computer program and the
implications for both  ground based and elevated releases.
       The group unanimously agreed that these questions deserved serious
attention and recommended that a thorough and systematic study be con-
ducted of both the theoretical arguments and available data.
       These issues are more fully discussed in the following text.
Discussions on several additional issues, including some specific to the
 CRSTER  algorithm are also presented.

2.7.2  Vertical  Profiles of Wind Speed
       The working group unanimously agreed that the representation of
the increase of  wind speed with height now employed in the EPA modeling

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                                     -101-
systems is satisfactory.  This system takes the form of a power law with the
exponent varying according to stability categories.

2.7.S  Comparison of STAR, A27, and a0
       In order to make any dispersion evaluation, the available meteorological
data must be stratified according to categories representing the diffusive
capacity of the atmosphere.  A variety of simplified approximations have been
used to represent this variable.
       The group notes that the various systems, when applied to the same
data, give widely different distributions of stability categories.  Many
authors have pointed out these differences, but a pair of examples show the
extent of the problem.
       Table 1 compares the frequency of the Pasquill-Gifford stability
categories estimated from  tower temperature difference (AT) measurements with
a set derived from standard deviation of the wind fluctuations (aA) measure-
                                                                 0
ments.  The discrepancies  are enormous, with differences as great as a factor
of 3 or 4 among the major  unstable, neutral and stable groups.  Table 2 com-
pares a "STAR" calculation with the AT and a  estimates.  Again the variations
are huge.
       A question was raised regarding the prediction of neutral stability
categories by the "STAR" program.  While the group did not unanimously agree,
some participants expressed the opinion that the  "STAR" program tended to pre-
dict unrealistically high  numbers of neutral hours especially for low level
        2
sources.
        It is  the consensus that the technique of  estimating stability now used
in the  EPA computer evaluations  (STAR) can be used in  the  interim but that  it
be reviewed to  resolve  this question and to develop  an alternate system which
will eliminate  the unrealistically  large percentages of neutral hours if nec-
cessary.
        We doubt that  AT,  at  least over modest height intervals, will serve  as
an alternative  because  of instrumental accuracy problems,  and because it  is
based  on only one of  the  several key factors responsible  for turbulence.  A
possible alternative  might be based  on wind  fluctuations  for estimates  of hori-
zontal dispersion and either  AT or  net radiaion measurements for vertical dis-
persion.

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                                                 Table 1


                          JOINT FREQUENCY OF OQ (30')  and AT_nnl    .,  DATA*  (%)
                                              W             ZOO   -  J


                                        MARCH 1971  - FEBRUARY 1972
Category
A
B
C
D
E
F
G
Sum

A

0.20
0.57
1.41
0.82
0.01
0.00
3.20
I


B
0.10

0,33
0.84
0.45
0.03
0.00
1.84
13.13
Unstable
C
0.51
0.72

3.61
1.87
0.13
0.00
8.09


D
1.24
0.89
1.88

2.78
0.19
0.00
12.87
12.87
Neutral
E
5.58
4.02
9.97
29.21
0.26
0.00
60.29


F
2.03
1.33
1.97
3.35
1.50
0.00
12.29
76.23
Stable
G Sum
1.75 11.4(Tj
0.66 7.91 )36 28 Unstable
1.00 16.97J
1.59 45.90 45.90 Neutral
0.61 11.28^ ,
I ***
0.04 0.77 )20.05 Stable <»
I

5.65

102.23

From: Environmental Report, Trojan Nuclear Power Plant, Oregon,

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                                               Table 2

**
STAR
AT
ae
STAR
AT
ae
ABC
1.08 7.68 7.93
3.20 1.84 8.09
11.40 7.91 16.97
Unstable
16.69
13.13
36.28
D E
57.17 8.74
12.87 60.29
45.90 11.28
Neutral
57.17
12.87
45.90
F
17.39
12.29
0.77
Stable
26.13
76.23
20.05
G

5.65
0.00



*
**
 Data based  on an Environmental Report on the Troj.an Nuclear Power Plant,  Oregon as
 reported  in a letter dated  November  22,  1976 from James Carson to Maynard Smith.
 (See reference materials at end of report)

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                                    -104-
       For further discussion on problems associated with surface based meteoro-
logical data, see Section 3.9.2 for some remarks by M. Williams regarding the
LAPPES Program data.

2.7.4  Mixing Height Interpolation Scheme Used in CRSTER
       There are reservations about the interpolation scheme used in CBSTER to
obtain hourly mixing heights.  This system should be tested against available
data and the results should be documented.  The scheme should be examined and
could be improved using hourly surface temperatures to interpolate the mixing
heights between the standard hours of radiosonde observations.

2.7.5  Vertical Dispersion Estimates
                        3
       There is evidence  that in their present form, the Pasquill-
Gifford a  curves are unsuitable for calculation of the ground level concen-
         z
tration due  to emissions from tall stacks.  In particular, the stability A
and, to some extent, the stability B curves may result in large overestimates
of  the short-term maximum ground-level concentration  (1-hr, to 3-hr, averages
especially)  and in underestimates of its  distance when compared with observa-
tions.  Extrapolation of the A curves for a   and 
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                                    -105-
elimination of the A curve and the use of the B curve for both A and B
stability categories.  Alternatively, the 0  curves for the A, D, E, and
                                           z
F categories may be reformulated to show an approximate linear dependence
on distance (on a log-log plot), starting at a distance 100 meters from
the source.  If this procedure is adopted, the effects of thermal stratifi-
cation in limiting the vertical growth of plumes can be accounted for by
the use of multiple reflection terms in combination with a specified mixing
depth.  These adjustments (i.e., inclusion of multiple reflections) apply
only to plumes that stabilize within the mixing layer and thus in practice
are most applicable to the A and D categories.  It should be noted that the
algorithms RAM and CRSTER treat limited mixing in this manner for stabilities
A through D.
       Additional comments on  this subject were submitted after the conference
by M. Williams (see Sec.  3.7.1) and D. B. Turner and L. E. Niemeyer (see Sec.  3.7.3)
       Finally, in order to account for variations in  surface roughness
and urban/rural differences, it was suggested  that some  latitude be given
to exercise professional judgement in making minor adjustments  in  stability
class assignments.   See related comments by Moore below.

2.7.6  Initial Plume Dimension
       Plume  size at the plume stabilization distance  (at downwind
distances  of  approximately ten stack  heights)  should be  determined  and
incorporated  into a  and o  determinations  by  adding variances  or  use of
virtual distances to account  for  initial size  of  buoyant plumes.   A
                                                                  2
possible algorithm for doing  this  has been  discussed by  G.  Briggs  .

2.7.7  Horizontal Dispersion  Estimates
       D.  Moore3 of  the  Central Electricity Generating Board  (CEGB)
presented  some  recent  results  of  the  determination of  ground  level a
values due to elevated sources.   The  results abtained  from  surface sampling
 (with a  sampling  time  of roughly  one  hour)  to  distances  of  14 km,  indi-
cated  linear  variation with  distance  for all cases  studied.  The constant
of  proportionality varied  from 0.04  under strong  winds to 0.2 under light
winds.   These results  appeared to be  very similar to  the Pasquill-Gifford

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                                    -106-
curves which apply to the plume distribution relative to its centerline and
which are characteristic of 3-minute sampling time.  Currently, the CEGB
is using a  values which are a continuous rather than discrete function of
          y           s
the stability variable .
       The group concurred that the present PG curves for a  should be used
for rural cases and for 1-hour sampling periods for elevated sources.  This
position takes into consideration the original basis for these curves (ground-
level releases 3 to 10 minute sampling times) and the influence of wind shear
for elevated sources which effectively enhances the horizontal spread near
ground level.
       The position was somewhat less clear for near ground releases.  The
general opinion was that for ground level sources and 1-hour sampling times
the present PG curves for a  should be adjusted for sampling time using the
1/5 power law.
       To account  for increased site roughness or heat flux, a  values
should be shifted  one stability category toward unstable (except a shift
from B to A).
       The incorporation of the Briggs1  urban a   curves in urban models
warrants further consideration.
       Extreme caution is advised in the use of so-called "stability G"
category.  Contrary to the trend for decreased dispersion with increasing
stability, the stable, light wind situation which  characterizes the
"stability G" case has been shown (see for example Van der Hoven ) to
occur with rather  large horizontal plume meander.  Thus, the use of Pasquill G
results in serious overestimates of short-term concentrations  from low-
level sources.

2.7.8  Wind Direction Randomization
       Most wind data available for air pollution  studies are  recorded  to
10° direction intervals, whereas the time hourly-mean values would have

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                                    -107-
shown variation within these intervals.   To assume that the winds  are  actually
fixed on the 10° radials will result in  unrealistic maximization of  concentra-
tions in these directions.  To overcome  this tendency, a randomizer  has been
introduced in the  CRSTER  computer program to force deviation of  the  wind
from one hour to the next.
       Occasionally this randomizer will repeat the same random number for
several hours in a row.  It may therefore create an unrealistically  large
3-hourly calculation.  The repetition effect becomes very unlikely over
longer periods, and presents no problem.
       It is recommended that this problem be reviewed, since three  consecu-
tive identical wind directions is exceedingly unlikely in nature.  A
possible solution would involve an automatic override to prevent  successive
duplication of random numbers for short-term evaluations.
       See further comments contributed after the conference by M. Smith,
Section 3.

2.7.9  Review and Comment on Pasquill's Recommendations for Interim
       Changes to the Pasquill-Giffovd Curves1
1.  No change should be made to the present "PG-curves" for O  .  It was
    felt that Pasquill's recommendations regarding  o"  could not be generally
                                                    D
    implemented at this time.
2.  In regard to adjustments to stability  classes  to account for surface
    roughness and the urban heat  island effect, a practical compromise was
    recommended:  In an urban area  the  stability class  should be changed
    by one unit in the direction  of unstable  to account for the combined
    effects of surface roughness  and  the heat  island.
3.  A correction should be made in  the  Gaussian model which takes account
    of the plume dimension at an  appropriate  distance downwind from the
    release point  (see Section 2.7.6  of this  report) but not necessarily
    following  precisely Pasquill's  suggestion.

2.7.10   Use of Models  in  Urban vs.  Rural Areas
       It was  noted  that   CRSTER   is  intended specifically for rural
calculations  (i.e.,  relatively smooth surface without heat island effects).

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                                     -108-
       For urban cases (rough surface and urban heat island effect) the
appropriate version of  RAM  should be used in lieu of  CRSTER.   However,
the group emphasized that» the EPA should be certain that the  RAM
algorithm has been validated by comparison with field data.
       If  CRSTER  were to be used in urban-type situations the stability
class should be changed by one unit toward the unstable.

-------
                                    -109-
                         FOOTNOTES  FOR SECTION  2.7.
1.  Cramer, in a post-conference contribution,  see Sec.  3.7.2,  emphasized
    that this statement should not be interpreted to mean that  the power-law
    exponents currently used by the EPA are not subject  to change.  He
    suggested some possible modifications.

2.  Ibid.  Cramer feels that the PG stability categories predicted by
    STAR are very satisfactory provided that mixing height, vertical temper-
    ature gradient, and the wind profile exponent are also specified for
    each combination of wind speed and stability.

3.  In his post-conference contribution, H. Cramer (see  Sec. 3.7.2) has
    prepared a well documented argument for modifying the PG 0Z curves,
    this argument deserves careful attention.

4.  The group was cognizant of the fact that the original az curves for
    stability A was based on direct measurements, i.e.,  observations of
    vertical distribution, only out to 100m and on indirect measurement,
    i.e., reduction from observations of ground level distribution, out
    to 800m (see Ref. 1).

5.  In his presentation, Moore advocated that az and av  be expressed as
    analytical/empirical functions of source height, sampling time, and
    surface roughness, as well as stability, wind speed  and downwind dis-
    tance.  See Ref. 4 and Sec. 3.9.5.

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                                     -110-
                         REFERENCES FOR SECTION 2.7.
1.  Pasquill, F.,  Atmospheric Dispersion Parameters in Gaussian Plume
    Modeling.  Part II.  Possible Requirements for Change in the Turner
    Workbook Values, Environmental Sciences Research Laboratory, Office
    of Research and Development, U.S. Environmental Protection Agency,
    EPA-600/4-76-030b, June 1976.

2.  Briggs, G.A.,  Lectures on Air Pollution and Environmental Impact
    Analysis, AMS, 1975, TD883.A23.

3.  Moore, D.J., A Simple Boundary-Layer Model for Predicting Time Mean
    Ground-Level Concentrations of Material Emitted from Tall Chimneys,
    paper presented for Ordinary Meeting of the Institution (Thermodynamics
    and Fluid Mechanics Group) in London, November 1974.

4.  Moore, D.J., Calculation of Ground Level Concentrations for Different
    Sampling Periods and Source Locations, Atmospheric Pollution, pg. 51-60,
    Elsevier Scientific Publishing Company, Amsterdam, Netherlands.

5.  Briggs, G.A.,  Diffusion Estimation for Small Emissions, Environmental
    Research Laboratories, Air Resources Atmosphere Turbulence and Diffusion
    Laboratory 1973 Annual Report, USAEC Report ATDL-106, National Oceanic
    and Atmospheric Administration, December 1974.

6.  Van der Hoven, I., A Survey of Field Measurements of Atmospheric Diffusion
    Under Low-Wind Speed Inversion Conditions, Nuclear Safety Vol. 17, No. 2,
    March-April 1976.

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                                    -111-
2.8  GROUP II-5:  VALIDATION. AND CALIBRATION
                                PARTICIPANTS

                N. E. Bowne                    W. A. Perkins
                N. deNevers                    P. M. Roth
                J. L. Shapiro

                       Moderator/Reporter: P. M. Roth
                                  CONTENTS

 2.8.1  Principles
 2.8.2  Considerations
 2.8.3  Other Issues
 2.8.4  Proposed Mechanism for Meeting Requirements
 2.8.5  Example Model Evaluation Information
 2.8.6  Levels of Model Performance and Quality

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                                    -112-
2.8.1
       While the desirability of providing model recommendations to the
user community is well recognized, it is clearly unwarranted to offer such
recommendations when supporting information is inadequate or unavailable.
Consistent with this philosophy are the following requirements:
       - The specification that models must meet certain standards of
         performance if they are to be endorsed  in the Guideline   for
         general use.
       - The acceptance of model verification as a common practice in
         overall model use where performance is not established.
To provide an orderly basis for determining model performance, we foresee
the following needs:
                                                   *
       - Prescribed standards for model performance
       - Specified model verification procedures
       - Established performance  evaluation procedures
In order to meet these needs, we  strongly recommend that:
       - An effort be mounted to  establish the appropriate
         standards and procedures (under the auspices of EPA).
       - A continuing function be established within EPA expressly for
         the purpose of model evaluation.
       - Established standards and procedures be revisited on a. regular
         basis.
See Section 2.8.4.
       Recognizing that these recommendations do not satisfy the short-term
needs of model users, we recommend that the following practices be adopted
on an interim basis until the needed functions are put  into practice:
 Also  of  concern  is  the extent of generality of model applicability.
 In other words,  there is a need to place constraints on  the range of
 model applicability over which the performance standards are expected
 to be met.

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                                     -113-
       - the extent of evaluation undertaken for models mentioned  in  the
         Guideline be indicated in a manner consistent with Section
         2.8.6.
       - areas of applicability of the models and constraints (areas  of
         non-applicability) be listed.
Examples of the type of information to be supplied are shown in Section
2.8.5.

2.8.2  Considerations
       We believe that attention must be given to the following considerations
in attempting to prepare guidelines for model evaluation practices and
procedures:
       - Verification should be viewed in two contexts - general and site-
         specific usage.  The distinction between the two contexts should
         be maintained wherever appropriate, giving recognition to potentially
         differing verification requirements.
       - Performance  standards should vary  in stringency and content, giving
         recognition  to:
           -  the differences in models
           -  variations in desired  level of  accuracy  (compare screening
              use and  refined use  of models)
           -  loftiness of  performance goals.  (This gives recognition of
              the two-step  process of  (1) selecting a model with a  certain
              potential predictive capability and  (2)  establishing  the extent
              to which its  capability  is realized.  See  Section 2.8.6.

       - Verification activities  should be carried out with  overall  project
         and  information-gathering-goals in mind, i.e., give proper  consi-
         deration  to:
            -  the problem  to  be resolved
            -  the nature of the Standard  to be met (its form, its  averaging
              time,  etc.)
        - Verification goals  should  be in concert  with potential level of
         achievement insofar as it  is limited by  the  accuracy  of  data input
         to the model and data to be compared with predictions.

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                                    -114-
       -  Obligations of  the user  (in  contrast with  the  control agency)  should
         be specified
       -  Recognition should be given  to the trade-offs  between costs  and
         benefits prior  to mounting any substantial verification  program.

2.8.3  Other Issues
       We wish to call attention to certain other issues that bear on model
evaluation:
       - the "veriflability" of a model - Can a particular model, as  a
         practical matter, be evaluated?  In some cases, it cannot.
       - the permissibility of calibration - Under what conditions is the
         practice warranted?
       - the differences  in verification requirements for long-term versus
         short-term  predictions
       - the importance of  evaluating models under conditions which "stress"
         the model,  i.e., which are  designed to most readily uncover suspected
         flaws or shortcomings
       - the differing evaluative  requirements for relative  (e.g., rollback)
         versus  absolute  (e.g., Gaussian plume models)  predictors
       - indication  of the  types or  categories of statistical procedures
         to be used  in model  evaluation, at a minimum
We believe that  each of these issues should receive full  consideration in
preparing  a guideline document.

2.8.4   Proposed Mechanism for Meeting  Requirements
       Clearly a well defined mechanism must be  established  as quickly as
possible to meet the requirements  outlined above and indeed  to insure in  a
larger sense that suitable models  are  properly developed  and rationally
applied.
       The Clean Air Act  of 1970 requires  modeling as  a tool to  achieve  the
intent and goals of the Act.   Accordingly  EPA has  the  total  responsibility
for  all  aspects  of  modeling from concept through application.  Since the

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                                    -115-
requirements for validation and performance as described herein are a key
part of the overall modeling problem, EPA has the responsibility to carry
out these tasks.  It is a major effort on a continuing basis requiring  addi-
tional internal support together with assistance by grant,  contract and
consultants.  In short, EPA must establish within its organization a group
responsible for carrying out the tasks described; to do so   on an ad hoc
basis is totally inadequate.
       There are a number of advantages in having an identifiable group
within EPA with total responsibility for the modeling effort including:
1.  Model evaluation, performance, and applicability can be more readily
    achieved and documented and the results made available to all parties.
2.  Results of model applications can be evaluated and retained in a data
    bank for use in subsequent model improvements.
3.  Uniform methods can be established for processing new models.
4.  A higher degree of standardization can be achieved in the use of models
    both for enforcement and air quality management.
This proposed mechanism would provide for a periodic review of models in use
as well as those proposed for adoption and probably would involve formal
meetings with outside  experts.  However, the effectiveness of such formal
meetings would be greatly enhanced by having the benefit of the results of
a continuous effort within EPA.

2.8.5   Example Model Evaluation Information
       Examples of  evaluation  information  for models listed in  the Guideline
may  include the following  information.
       CDM - An urban  multiple-source model  that has been subjected to
             limited  evaluation for 	cities.  Results  indicate that
             SO  calculations  are made more  accurately  than those  for TSP.
             Correlation coefficients ranged  from	 to 	 for SO
             and correlation coefficients  ranged from	 to 	 for TSP.
     CRSTER - A single point  source model for  the calculation  of the dis-
             tribution of  short-term concentrations  around  the  source.
             Studies  at four power plants  indicate the  maximum  concentrations

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             calculated are within a factor of 2 of the observed concentrations
             at local monitoring stations.
Examples of limitations are:
       CDM is an urban model that should not be applied to rural situations.
        RAM is a multi-source short-term model that has not been evaluated.
        It is  composed from algorithms such as the Gaussina plume model and
        plume  rise  equations similar  (identical?) to GRSTER and in a similar
        application could  be expected to give similar  results.
        None of the models listed have been validated  in  complex terrain.
        Use of differential receptor  heighrs  has not been validated.

  2.8.6 Levels of Model  Performance and Quality
       We  should recognize that models operate at varying levels of performance
 and  quality, and should  be tested (validated, verified) against the standards
 of the various levels.   We suggest that  there are roughly four such levels,
 which in descending order  are:
 Top  Level.  A  model in this level will  predict  concentration of any pollutant
 at any time, any place,  any averaging time, with uncertainty equal to the
 uncertainty in the  input data  (emissions,  meteorology).   An example of this
 type from  physics is F = ma.   For that model  we truly  can predict with
 accuracy equal to input  data accuracy,  for all  size and  time scales, excluding
 relativistic and uncertainty principle  limitations.
        To  validate  a model in  this category would  require comparing its time
 and  space  resolved  predictions with equally  time and  space resolved observa-
 tions, using all  sorts of  meteorological inputs.
 Second Level.  A  second level  model would  not claim to give accurate time
 and  space  resolved  answers but would  attempt  to predict  time-resolved dis-
 tributions of  results which were comparable to observed  ones.   For  example,
 the  observed mean and s.d. of  the concentration at any point  should agree
 with the predicted one, within the uncertainty of  the input data.   An  example
 of this type from biology is  mendelian  genetics, which predicts the distribution
 of properties  (quite accurately) but generally does not predict, for  example,
 which seed will contain which properties.   After the fact we have worked  out

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                                    -117-
a great deal of "first principles" to explain the distribution,  but  that came
after the fact.
Third Category.  A third category would be models for which we have  plausible
scientific bases, but for which we have not yet been able to show that  the
models do truly make accurate predictions.  For such models presumably  a
verification of their predictive powers is possible, but not necessarily
easy.  An example from physics of this type is the existence of "black  holes."
Current theory says they may exist, while direct observation currently  seems
possible but not easy.
Fourth Category.  A fourth category of models are ones for which the scientific
bases is questionable but perhaps plausible and for which experimental  veri-
fication is unlikely or impossible.  A non-related example is the way we  all
raise our children; we have poorly-founded but plausible ideas of how one
should do it, and no effective measures of their performance.
       If we accept the idea that there are such categories of models,  we can
enunciate the following ideas:
1.  Although models in lower categories may have uses for screening purposes,
for disputed regulatory decisions one should use as high a category model as
is available.
2.  There should be little effort devoted to models of a lower category if
a higher category model is available for  the same task.
3.  The category into which a model falls should be determined on the basis
of validation and testing, rather than any other way.
4.  The validation requirements  for the various categories should be different
from category  to category.  For  the first category  the requirements must be
for space and  time resolved correspondence between  calculated and observed
concentrations.  For  the  second  category  it would be  correspondence between
computed and observed space (but not time) resolved means and standard devia-
tions of the concentration distributions.  For  the  lower two categories,
validation within the category would seem unnecessary; the objective of
validation would be to move the  model to  a better category.
5.  EPA  should  be asked to classify existing EPA models into these categories
and  indicate what levels  of performance they believe  can be reached in the next
five years  for  each pollutant and  each averaging time.

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                                     -118-
       This whole set of ideas is summarized in Table 2.8.1.

                            Supplementary Material

       A comment on the use of unverified models for making relative predictions
may be found in Sec. 3.10.10.

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                      Table 2.8.1.   Categorization  of  Models  by  Validation Technique
Category
      Description
Air Pollution Models which
may fall into this Category*
 Proposed Validation
      Technique
                  Time and Space Resolved
                  Source-Receptor Models
                                                                    Comparison Between
                                                                    Time  &  Space  Resolved
                                                                    Observed  & Calculated
II
Statistical Property
Only Models
(Spaced Resolved)
  Single Source Gaussian
  Plume (e.g., CRSTER) ?
  Multi-Source Gaussian?
Comparison of Statis-
tical Properties of
Space Resolved
Observed & Calculated
III
Models with Plausible
Scientific Bases, but
Currently not Validated
to Above Levels
  Most CO Models,
  Any Model Requiring
  Empirical Calibration,
  Photochemical Models
Move to Upper Category
if Validatable
IV
Models with Less Plaus-
ible Scientific Bases,
Probably not Validatable
  Rollback
Not Validatable?
 These choices are to some extent arbitrary and as shown some model fall in more  than  one  category
 depending on use.

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-120-

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                                     -121-
                          3  SUPPLEMENTARY MATERIALS
       This section contains commentary, opinions, and other written material
submitted by the participants both during and after the conference.   Except
for retyping and minor editorial corrections, no changes have been made
in the content or expression of these contributions.
       Sections 3.1 - 3.8 contain material germane to the working group
reports (Sections 2.1 - 2.8, respectively).
       Material which is of more general interest has been placed in Section 3.9,
       Comments which refer to the issues discussed in the plenary sessions of
the conference and additional issues are contained in Section 3.10.

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                               -122-
3.1  GROUP 1-1

3.1.1  Description of Texas Climatologiaal Model (TCM)

Submitted by Richard A. Porter at conference.

                   TEXAS CLIMATOLOGICAL MODEL
Reference:  Porter, R. A. and Christiansen, J. H.; "Two Efficient
     Gaussian Plume Models Developed at the Texas Air Control Board."
     Proceedings of_ the 7th NATQ/CCMS International Technical Meeting
     on Air Pollution Modeling, Airlie House, Va., September, 1976.
     (Copy attached.)

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

Abstract:  The TCM is a climatological model that predicts long-term
     arithmetic mean concentrations of nonreactive pollutants from point
     sources and area sources.  The TCM is conceptually similar to the
     Climatological Dispersion Model (COM) but incorporates design features
     that reduce the model run time by as much as two orders of magnitude.

Equations:  See references cited above.

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                               -123-
A.  Source-Receptor Relationship

     1.  Arbitrary locationMB^ for each point source.  Unlimited number
         of sources.
     2.  Arbitrary location and square grid width for each area source.
         The model will allocate area sources into a uniform square grid.
     3.  Receptor location is arbitrary grid  (max. 50 x 50).
     4.  Release heights for point sources are accepted for any height.
     5.  The area source algorithm (Gifford-Hanna) does not consider
         height of release.
     6.  Receptors are at ground level.
     7.  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 pollutants are treated simultaneously.

D.  Plume Behavior

     1.  Plume rise calculated according  to  Briggs  (1971) neutral/unstable
         equation.
     2.  Effective  stack heights  less  than 10 meters are considered 10 meters.
     3.  Effective  stack heights  greater  than 300 meters are considered  300
         meters.
     4.  No plume rise  for  area sources.
     5.  Down-wash  and  fumigation not  considered.

E,  Horizontal Wind Field

     1.  Climatological approach
     2.  16 wind direction
     3.  Mean wind  speed  calculated  for  each stability class  from  the joint
         frequency  function of  stability, wind direction,  and  wind speed.
     4.  Wind speed corrected for physical stack height (same  as CDM).

F.  Vertical Wind Speed

     Assumed  equal  to zero.

G.  Horizontal  Disperion

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

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                               -124-
H.  Vertical Dispersion

     1.  Gaussian plume.
     2.  6 stability classes (Pasquill-Gifford-Turner) A, B, C, D-Day,
         D-Night, E+F.
     3.  No provision for variation in surface roughness.

*•  Chemistry/Reaction Mechanism

     Exponential decay according to user input halflife  (same as CDM).

J.  Physical Removal

     Same as I above.

K.  Background

     Background may be entered by calibration coefficient for each
     pollutant.

L.  Boundary Conditions

     Perfect reflection assumed at ground.  Mixing height not a factor
     because investigation shows no effect  for typical climatology using
     the CDM .47L total mixing scheme.

M.  Emission and Meteorological Correlation

     Emissions not varied.

N.  Validation/Correlation

     1.  Model is self-calibrating with input of field receptor observations.
     2.  High correlation achieved of observed to calculated values  for
         Houston TSP 1975, Houston S<>2 1972, Dallas TSP  1972.

0.  Output

     1.  Arithmetic mean concentration for  the averaging time of the Clima-
         tological input data and emission  data (one month  to one year).
     2.  Any combination of the following outputs are available:
         a.  Listing of concentration for an arbitrarily spaced square grid
             of up to 50 by 50 elements.
         b.  A print plot of the grid concentrations.
         c.  Punched card output for isopleth maping  (same  as CDM).
         d.  A listing of the five high contributors to  the concentration
             (by Z concentration) at each grid point.

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                              -125-
P.  Activity
     The TCM has been widely applied for evaluation of new source impact
     upon existing air quality and for evaluating the impact of growth in
     urban areas.  The model is in use by more than 50 industrial firms,
     environmental consultants, and government (federal, state and local)
     agencies throughout the U. S. and Canada.  Speed of operation (up to
     2 orders of magnitude faster than the CDM) and convenient output formats
     have made this model popular with a wide variety of users.

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                               -126-


3.1.2  Description of Texas Episodic  Model (TEM)

Submitted by Richard A. Porter at conference.

                   TEXAS EPISODIC MODEL (TEM)


References:  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.  (Copy attached.)

     Christiansen, J. H.; Users  Guide t£ the Texas Episodic Model. Texas Air
     Control Board, May, 1976.

Abstract:  The Texas Episodic Model  (TEM) is a short-term (10 minute  to 24
     hour averaging time)  Gaussian Plume Model for prediction of  concentra-
     tions of nonreactive  pollutants due to up to 300 elevated point  sources
     and up to 200 area sources.  Concentrations are calculated for 1 to 24
     scenarios of meteorological conditions, averaging time, and  mixing height,

Equations:  See references cited above.

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                               -127-
A.  St>urce-Receptor Relationship

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

B.  Emission Rate

     Unique emission  rate for  each source.

C.  Chemical Composition

     One, two, or  three pollutants treated simultaneously.

D.  Plume Behavior

     1.  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.
     2.  Mixing height penetration factor (P)  is a user input.   If effec-
         tive  source height (H)  is greater than P times the mixing height
         the plume escapes.  Otherwise the .47L mixing scheme from Turner's
         Workbook is used.
      3.  Does not treat down-wash or fumigation.

 E.   Horizontal  Wind  Field

      1.  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.
      2.   Power law variation of wind speed with release height  (same as COM).
      3.   Steady state wind for each scenario.

 F.   Vertical Wind Speed

      Equal to zero.

 G.   Horizontal Dispersion

      1.   Semi-Empirical Gaussian Plume.
      2.   User supplied stability class for each scenario  (Pasquill-Gifford-
          Turner) .
      3.   Turner  (1969) dispersion coefficients.
      4.   No adjustment for surface  roughness.

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                              -128-
H.  Vertical Dispersion

     1.  Semi-empirical Gaussian plume.
     2.  User supplied stability classes (Pasquill-Gifford-Turner) for
         each scenario.
     3.  Turner  (1969) dispersion coefficients.
     4.  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.

L.  Boundary Conditions

      1.  Lower  boundary:   perfect reflection.
      2.  Upper  boundary:   reflection from top of mixed layer by the
         scheme of Turner (1969)  except as described in D.2 above.

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

      1.  Limited validation with observed vinyl chloride observations.
      2.  Calibration by user supplied coefficients (A, B) so that
         Xcal  - A + BX predicted

 0.  Output

      1.   Concentration mean for each receptor grid point for averaging
          times of:
          a.   10 minutes
          b.   30 minutes
          c.   1 hour
          d.   3 hours                                   ^
          e.  24 hours (based on eight  3-hour scenarios.)

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                          -129-
1.   Output is available for from 1 to 24 scenarios in the following
    formats:
    a.  listing.
    b.  print plot.
    c.  punched cards for isopleth maps.
    d.  culpability list of the high five contributors to the concen-
        tration at each receptor grid point.

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                                      -130-
3.3.3  Minority Report on Application of Multi-Source,  Urban Model
Submitted by A. Boyer at conference.

                               MINORITY REPORT

                 The Application  of Multi-Source, Urban Models

      The application of complex  multi-source urban models  is to be primarily
 the responsibility of control agencies.  The results of multi-source simulations
 may be used by control agencies  to assess the effects  of changing source
 strengths, control measures, or  changing land-use  patterns.  Multi-source sim-
 ulations may also be used by control agencies for  amending guidelines for the
 application of single source models in areas where many single sources inter-

 act.
      Individual sources sould not be expected to do more than apply single
 source models.  In those cases where industries or members of the public choose

 to apply multi-source urban models control  agencies should encourage such

 activity by providing urban source inventories.

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                                    -131-
  3.1.4
  Description of the  Environmental Research  & Technology_, Inc. Model EETAQ
                    Post  Conference  Submission by  B. Egan

Abstract:   ERTAQ is a steady-state sector-averaged Gaussian plume model
     that calculates concentrations  of up to six pollutants from an
     unlimited number of point, line, and area sources.  The model may
     be operated in either the "sequential" mode to calculate one-,
     three-, or 24-hour concentrations for analysis of historical "worst-
     case" impacts or in the "climatological average" mode to calculate
     long-term averages for periods  represented statistically by stability
     windroses.  Dispersion coefficients may be user-specified.  In the
     sequential mode, a fourth class of source, "tall stacks", is available
     that provides for optional use  of distinct dispersion coefficients
     more representative of this class of source.  The model may be
     applied in both  flat and hilly  terrain.  Up to 128 receptor points
     may be specified, at each of which the user may specify background
     concentrations as well as calibration factors.  The contributions
     of individual sources to selected receptors may be isolated at the
     user's option.   In addition, program options  are available for
     user-specified input format, storage of output files, and manipulation
     of the results of intermediate  computations.

Equations:

          X = u  glg2

     For sources  other than "tall stacks", at user's option, the crosswind
     dispersion  function  g, may be  sector-averaged over 22.5° by

             = 	1_
          gl ~ 2x tan
      For  "tall  stacks",  the crosswind dispersion function gj  is  given by
      the  statistically "expected" value within the 22.5° sector  for
      receptors  within the downwind sector,  i.e.

                    1      . ,x TT/16^
           SI  =  TT x/8  erf (o-y HT };

      for  receptors in the sectors adjacent  to the downwind sector,
                   i        I - er
      This formulation avoids the difficulty of using centerline one-hour
      values when accumulating concentration estimates for multiple-hour
      averages.

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                                   -132-
     The vertical dispersion function g~ is  given by

                          U>u<£f]*?,
                          tu.           *     J" •
     where L   =    mixing depth
           H   =    height of plume centerline  above the ground-level
                   receptor.

     Terrain Correction  (tall stacks only):

          H = H  +4H -  T~ x Min (z  - z   H +AH),
               o          1         i    o   i>

     wherein

          H    =    height of plume above terrain  at receptor
          H    =    height of stack above stack base
         AH   =    plume rise
          Z    =    topographic height of receptor (above  sea-level)
          Zg   =    topographic height of stack base  (above  sea-level)
          To   =    stability dependent, user-specified terrain correction
                   factor.

A.   Source-Receptor  Relationship

     Unlimited number of point, area, line, and tall-stack sources at
     any locations.

     Up to 128 receptor  points at any selected  locations.

     Unique topographic  elevation for each receptor.

     Receptors must be at ground level.

B.   Emission Rates

     Unique emission  rate for each source that  may be varied according
     to diurnal,  weekly,  or monthly scheduling.

C.   Chemical Composition

     N/A

D.   Plume Behavior

     Briggs (1970) final plume-rise formulas
     Stack-tip downwash  (Gifford) for tall stacks
     If plume height  exceeds mixing height, concentrations further
     downwind assumed equal to zero.
     Plume and mixing depth both respond to terrain obstacles  (see Equations)

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                                     -733-
E.    Horizontal Wind Field

     Wind direction constant at all heights over all space.
     Wind speed varies with height according to user-specified power-
     laws dependent on stability class.

F.    Vertical Wind Speed

     Assumed equal to zero except as implied by terrain correction
     factors.

G.    Horizontal Dispersion

     Gaussian plume sector-averaged in various ways depending upon
     application.   5 stability classes used with user-specified dispersion
     coefficients; different classes of sources may have different
     coefficients.  "Urban" and "rural" options.

H.    Vertical Dispersion

     Gaussian plume.
     5 stability classes used with user-specified dispersion coefficients;
     different classes of sources may have different coefficients.
     "Urban" and "rural" options.
     Option for initial vertical source dimension.

I.    Chemistry/Reaction Mechanism

     Not treated directly (see J).

J.    Physical Removal

     Half-life decay factors.

K.    Background

     May be specified for each receptor or for all receptors.  May be
     calculated if appropriate emissions inventory is input.

L.    Boundary Conditions

     Perfect reflection at the ground and at the top of the mixing
     layer.  Mixing height follows terrain with correction factor
     (see Equations).

M.    Emission and Meteorological Correlation

     None specifically, but see  B. Emission Rates  (above).

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                                      -134-
N.   Validation/Calibration

     Calibration option available which involves external determination
     of linear calibration coefficients; slope and intercept may be
     applied in subsequent runs.  Comparison with observations made in a
     number of studies.

0.   Output

     Concentration values at each receptor.  Output routines search
     complete data sets for high values and identify time periods of
     interest.

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                                   -135-
      3.2  GROUP 1-2
     3. 2.1  Comments on Section 2. 2. 5 Submitted by /?. Wevodau After the Conference
EN-I02S
              ESTA9USHEOI802
 E. I. DU PONT DE NEMOURS & COMPANY
             INCORPORATED

     WILMINGTON, DELAWARE 19898
 ENGINEERING DEPARTMENT

 LOUVIERS BUILDING
     March  9,  1977
     Dr. D. M.  Rote
     Argonne National Laboratory
     9700  South Cass Avenue
     Argonne, IL 60439

     Dear  Don:

     I  offer  the following comments on the first round report of
     Working  Group 1-2:

           Item 6 - "On the Question of Enumerative Vs.
           Statistical Use of the Estimates of Short-Term
           Concentrations"	__	

           I  agree with the consensus opinion that a  statistical
           approach is theoretically more valid.  However, I
           believe it is very important that the detailed inves-
           tigation on the precise nature of the statistical
           approach be completed prior to endorsement of this
           approach.  At this time, I favor the enumerative
           approach as recommended in the draft guideline.  My
           main concern with a  statistical approach involves
           the difficulty in fitting air quality data, whether
           measured or predicted, to a suitable cumulative
           frequency  distribution function.

           In model applications to evaluate compliance with air
           quality standards, I favor use of the second highest
           value   I  agree that fumigation, stagnation, thunder-
           storm" downdraft and  terrain-induced mixing, none of
           which are  considered by CRSTER, can be  important
           factors  in potential short-term violations. However,
           substitution of the  highest computed value for second
           highest  value  is not an appropriate alternative in
           these situations.  This approach  skirts the issue.

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                             -136-
2
March 9, 1977
Dr. D. M. Rote
     In these abnormal situations alternative procedures
     which do consider such factors should be employed
     (or developed) instead of using the highest computed
     value from a model which does not consider these
     factors .

I believe the report is accurate and representative.  You have
done an excellent job in coordinating and reporting the opinions
exposed during our workshop sessions.

Very truly yours,

ENGINEERING SERVICE DIVISION
R. I. Wevodau

RIW:braw

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                                     -137-
3.2.2  Comments on Short-Term Analysis and on M. William's "Rationale for
       Elimination of the Maximum of the Second Highest for Modeling Purposes"
       C3.2.3 below).
Submitted by R. Porter after the conference.

Comment 1;  Lake shore fumigation should be included in consideration of any
short-term standard.  Also Briggs (1969, p 51)  recommends that plumes will
not escape in a lid situation unless the calculated plume rise Ah is greater
than 2 times the mixing height.  Normal fumigation and thunderstorm downwash
are too short in duration to influence a 3-hour mean.

Comment 2:  The problem with the  second high  standard  for modeling is that
we are attempting  to  predict concentrations  too far  out on  the tail  of  the
distribution already.   1/365 is  even harder  to predict than 2/365.   Any
probabilistic  scheme  should  include a  test of the goodness  of  fit of the
calculated  data  to the  assumed  frequency  distribution.  (See the submission
to the NATO committees) .   Results in Frankfurt showed that  if  the data
does not  fit  the distribution  absurd numbers are generated  for the  second
high.   See Section 3.10.1 for a more expanded discussion of this topic by
 Porter.

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                                    -138-
3.2.3  Rationale for Elimination of the Maximum of Second Highest for
       Modeling Purposes
Submitted by M. Williams after the Working Group 1-2 meeting.

       Since the modeling used will not consider model uncertainties, the
observed second highest concentration at a point may indeed be somewhat
greater than the standard if the second highest model value is permitted to
approach the standard.  It should be noted that CRSTER does not calculate
concentrations during fumigation or stagnation.  It is also possible that
thunderstorm downdraft circumstances or rapid terrain induced mixing may
result in high concentration in the real world.  The model does not reflect
such circumstances,  thus  it may not represent the actual frequency of high
concentrations.  Thus, a  procedure which uses model calculations for the second
highest does not assure compliance with standard.  In order to provide a
greater margin of  safety, i.e., to provide greater assurance that the
observed concentrations will not  exceed the  standard when the model
estimate does  not, the  quantity to be  compared with the  standard should be
the estimated maximum concentration.

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                                    -139-
3.3.2   Description of the SAI Reactive Plume Model


 NAME  OF f'X)DEL:   Reactive  Plume  Model (RPM)

      Source of  Model:   Systems  Applications,  Inc.
                        950 Northgate Dri ve
                        San Rafael, California  94903

      Sponsor:  California Air Resources Board
                Sacramento, California  95825

      References:  Liu, M. K., M. A. Yocke,  and P.  Mundkur,  "Numerical Simu-
           lation of Reactive Plumes," 68th Meeting of American  Institute
           of Chemical  Engineers, Los Angeles, California, November 1975.

           Liu,  M. K.,  D.  Durran, P. Mundkur, M. Yocke, and  J. Ames,  "The
           Chemistry, Dispersion, and Transport of Air Pollutants  Emitted from
           Fossil Fuel  Power Plants in California," Draft Final  Reoort submitted
           to California Air Resources Board. SAI Report ER  76-18, April, 1976.

      Type of Model: Lagrangian—for either single point or areal source.

      Special Feature of Model:  This model is designed to estimate concentra-
           tions of reactive species  downwind of a single point  or areal source
           of pollutants.  Assuming  the pollutants are well  mixed  in  the vertical,
           this model is more suitable for plume fumigation and  trapping
           conditions.

      Status of Model Development:   Operational


 CHARACTERISTICS OF THE MODEL FORMULATION

      Model Equation:  Mass  conservation

                  dC_.
                  dt     ^  'chem

      Plume  Rise:   Input  to  model.

      Turbulent  Dispersion:   This model  contains two options:  either the
           measured plume width and plume depth (as a function o, downwind
           distance)  of the  .classical  Pasquill-Gifford methods (Turner, 19o9)
           may be used.  Plume dispersion is determined either by the clas-
            sical Pasquill-Gifford method (Turner, 1969) or from  the observed
            plume width and plume depth as a function of downwind distance.
            Provision  has been made for entrapment of background pollutants.

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                                                                  11-22
                                  -140-
     Wind Shear:  Wind velocity  at  plume  height must be used.  Although
          a simple correction  according to a power law can be easily
          incorporated,  no treatment of wind shear is currently in the model.

     Terrain Interaction:  No current treatment  but simple consideration
          (similar to the Valley model) can easily be incorporated.

     Chemistry:  This model  is written in a modular form which can accept
          any chemical kinetics  submodel  with  a maximum of 50 reaction steps.
          The kinetic mechanism  that is currently in this model is a mod-
          ified version of the Hecht-Seinfeld-Dodge mechanism (1974) for
          hydrocarbon-NO-SO?  system.
                        ^\   f—                    ' •
     Spatial Scale:  Medium scale  (~tens of  kilometers)

     Temporal Average:  Short  and Medium  (hourly  averages).


DATA BASE REQUIRED

     Source:  Stack location
              Either stack emission rates or initial  pollutant  concentrations
                within the plume.

     Meteorology:  Wind speeds
                   Stability class  (or plume width  and plums depth)
                   Radiation intensity

     Other:  Kinetic mechanism
             Ambient pollutant concentrations


CHARACTERISTICS OF THE MODEL COMPUTATION

     Computer Language:  FORTRAN IV

     Computing Time:  10-50 PCU  seconds  (CDC 7600)  for a typical  run

     Storage Requirement:  -145,000 actual  large core.

     IBM 370/168 Compatibility:   Yes


KNOWN MODEL APPLICATION:  This model was applied to the following seven
     point sources (Liu et al., 1975, 1976; Tesche et a!., 1976):
                 Moss Landing Power Plant, Monterey, California
                 Los Alamitos Power Plant, Los Angeles, California
                 Haynes. Power Plant, Los Angeles, California
                 Mobile Oil Refinery, Los Angeles, California
                 Four Corners Power Plant, Farmington, New Mexico
                 Hobbs Power Plant, Hobbs, New Mexico
                 Jefferson Power Plant,  Jefferson, Texas

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                                    -141-
S.2.2  Description of the DEPICT Model
Reference;  l) Sklarew, R.C., and J.C. Wilson,
            "Applications of DEPICT to the Garfield, Navajo,  and
             Ormond Beach Air Quality Data Bases"
             Science Applications, Inc. Report prepared for
             Southern California Edison, July 19T6
            2) Sklarew, R.C., Wilson, J.C., and Frabrick, A.,
            "Evaluation of Air Quality Models Point Source Models"
             Science Applications, Inc., July 1976, under contract
             to the California Air Resources Board, Sacramento,  CA.
Abstract;   The DEPICT (Detailed Examination of Plume JBnpact in Complex
            Terrain) is a 3-dimemsional eulerian numerical point source
            model.  The model calculates the temporal and spatial
            concentrations of inert or reactive pollutants in flat or
            complex "terra in.  The model is modular in design and has the
            ability to update algorithms in an efficient manner.  The
            model currently uses either the Eschenroeder l6-step or the
            Hecht-Selnfeld 39-step chemical mechanisms for the reactive
            pollutants.  The model is applicable to assess air quality
            impact for point sources located in rural environments.
Equations;  Conservation of Specie Equation
A.  Source-Receptor Relationship

       Point sources  only;  (10 maximum)  uniform grid squares, user defined grid.
       Sources can be treated as ground  level or elevated releases.
       Receptors are  located at  center of grids.
       Receptor locations are 3-dimensional.
B.  Emission Rate
       User specified emission rate for each pollutant for each point source.
       Emission  rates can vary hourly for each source.

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                                     -142-
C.  Chemical Composition
D.  Plume Behavior
       Plune  rise calculation is based on the work of Briggs with simple
       modifications to obtain estimates of inversion penetration.

       Four cases are considered:
          1}  vholly unstable atmosphere
          2)  deep ground base inversion
          3)  elevated stable layer
          if)  shallow ground base inversion.
 E.  Wind FLov Field

        The model calculates a three dimensional wind field using the f olloving
        equations.
                                 ~ o
                <            di

           2)   U-


           3)  *^
    Where {> is the perturbation velocity potential, and^are tramstaission coefficients
    based on temperature profile or stability classes (1-7)   Wind observations are
    projected upward  frcm the point measured through the portion of the grid
    without any measurements based on the power law  £/-  ^>(.^»|
    The initial horizontal wind components are calculated using a
    interpolation schsoe of the vertical profiles for each layer.

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                                      -143-
F.  Vertical Dispersion

    The vertical diffusivlty parameters are calculated based on  the
    algorithms of Smith and Howard.
       where  U  = wind speed at point of interest

                 = standard deviation of the wind vane fluctuation and is
                   dependent on stability class.

                 = turbulence scale length (in meters) and depends on height
                   above ground and stability.
G.  Horizontal Dispersion

    The horizontal values of diffusivity are calculated using the relationship
                    K^ocK*
    where  o    depends on stability class.
H.  Chemistry and Reaction. Mechanism

    The node! has the option of using either the Eschenroeder 16-step or the
    Hecht-Seinfeld 39-st®P mechanism.
 •  Physical Removal

    The l6-step mechanism includes a reaction between NOg and participates.  The
    Hecht-Seinfeld has no physical removal process.
J.  Background

    Treated as an hourly input for the chemical mechanisms.



K.  Boundary Conditions

    Lower boundary (surface of earth) perfect reflection.
    Upper boundary - see pluae rise,

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                                      -144-
L.  Emission and Meteorological Correlations

    User supplies hourly values of vind speeds measurements (surface and aloft),
    nixing height measurements, stability fields, and emissions.
M.  Validation/Calibration

    Preliminary validation of the DEPICT Model has been completed for the following
    areas:
        1) Garfield-aaelter

        2) Navajo Generating Station (SO  )

        3)  Ormond Beach Generating Station
              (SF6, NO, B02, 03)
N.   Output
     The model predicts the hourly temporal and spatial concentrations for each
     grid for inert or reactive pollutants.

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                                  -245-
3.3.3  Description of the SAI Urban Airshed Model

       Post Conference Submission by Philip M.  Roth
References:   (a)
              (b)
Roth, P. M., S. D. Reynolds, P. J. W. Roberts, and
J. H. Seinfeld (1971), "Development cf a Simulation
Model for Estimating Ground Level Concentrations of
Photochemical Pollutants," Final Report and 6 Appendices,
APTD 0908-0914, Systems Applications, Inc., San Rafael, CA.

Reynolds, S. D., M. K. Liu, T. A. Hecht, P. M. Roth,
and J. H. Seinfeld (1973), "Urban Airshed Photochemical
Simulation Model Study-Volumes I-III, EPA-R4-73-030 a-h.
Systems Applications, Inc., San Rafael, CA.
               (c)   Reynolds, S. D. et al.  (1976, 1977), "Continued Research
                    in Mesoscale Air Pollution Simulation Modeling," Volumes
                    I-IV (EPA 600/4-76-016 a-d) and Volumes V-VII (in review
                    by sponsor), Systems Applications, Inc., San Rafael, CA.
 Abstract:
 The SAI Urban Airshed Model is a fully three dimensional
 grid-based model capable of predicting the spatial and
 temporal distribution of both inert and chemically
 reactive pollutants.  Basic inputs to the model include
 the specific meteorological, emissions, and chemical
 characteristics of the region of interest. Predictions
 for up to 13 pollutants may be obtained, including CO,
 SO?, 03, N02, NO, four hydrocarbon classes, total aerosol,
 PAN, HN02, and H202.  A 42 step kinetic mechanism is em-
 ployed to represent the" pertinent chemical phenomena.
 The atmospheric diffusion equation is solved numerically
 on the three dimensional grid to predict the dynamic
 changes  in pollutant  concentration levels over a period
 of up to a few  days.  The model is applicable to the
 examination of  regional air pollution problems, such as
 the evaluation  of alternative emission control straegies.
  Equations:
 3C.
                                                                           S,

-------
                                 -146-
where:


           c. = concentration of specie i

          ZT,7 = horizontal components of the wind

            w = vertical component of the wind

           KH = horizontal turbulent diffusivity

           KV = vertical turbulent diffusivity
           R. = rate of formation of specie i by chemical reactions

           S. = rate of emission of specie i



A.   Source-Receptor Relationship

     1)   Emissions from line and area sources are apportioned to
          each grid cell and are assumed to be emitted at ground level.

     2)   Each point source is treated separately.  The stack height
          and plume rise determine the cell in which the emissions
          enter the grid.

     3)   Ambient concentrations are calculated for each grid cell.
          Each concentration represents a spatial average over the
          volume of a grid cell.

B.   Emission Rates

          Emissions are calculated external to the main program using
          EPA or other appropriate emission factors.  All emission
          rates can vary in time.

C.   Chemical Composition

          Both inert and reactive species are considered.  The thirteen
          pollutants for which predictions are made include: CO, SOU, NO,
          NOa, 03 four hydrocarbon classes, PAN, H202, HN02, and total
           aerosol  nitrate, sulfate, and organic).

D.   Plume Behavior

          A point  source plume enters  the modeling  gri^l  at a  height
          calculated  from  the actual  stack  height plus the plume  rise
          given  by  the  formula of Briggs.   Consideration  is  given to  the
          determination  of whether the  plume penetrates  an elevated
          inversion  layer,  and,  if so,  whether it breaks  through  the  layer.

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                                 -147-
E.   Horizontal Wind Field
          Horizontal wind components are calculated external to the main
          program using objective analysis techniques in conjunction with
          avaliable data taken at the surface and aloft.  The wind field
          is fully three-dimensional, thus allowing for the treatment
          of wind shear effects.  Temporal variations are also considered.
F.   Vertical Wind
          The vertical wind component  in each grid cell is calculated
          using the continuity equation and the horizontal wind component
          inputs.
G.   Horizontal Dispersion
          The horizontal turbulent  diffusivity  (KH)  is assumed to
          be a constant.

H.   Vertical Dispersion

          The vertical  turbulent  diffusivity  (Kv)  varies in space and
          time and depends  on  the height  above  the ground, wind speed,
          surface roughness, and  the  atmospheric  stability class.
          Algorithms developed by Liu et  al.,  and Lamb et a!., are
          included in  the model.

I.   Chemistry/Reaction Mechanism

          A generalized lumped mechanism  consisting  of 32 reaction steps
          developed  by Whitten et al.,  is employed to describe the
          chemical interaction of organics,  NOX and  03.  Organics are
          segmented  into four  groups  determined by the following carbon
          bond characteristics:  single bonds,  relatively reactive (fast)
          double  bonds, slow double bonds, and carbonyl bonds.  Six
          reaction steps are included to  treat the oxidation of S02- The
          formation  of nitrate,  sulfate,  and  organic aerosol products is
          parameterized by  four reaction  steps.

J.   Physical Removal

          Surface deposition of species is treated in each ground-level
          grid cell.   The  rate of deposition  depends on the type of
          vegetation or ground surface in that cell.

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                                 -148-
K.    Background Pollutants
          The influence of background pollutant concentrations is treated
          in the initial and boundary conditions of the governing equa-
          tions.

L.   Boundary Conditions

          The pollutant flux for each species must be specified at
          all points on the boundary (both horizontal and vertical) where
          the wind  is blowing into the modeling region.  The boundary
          condition at  the ground incorporates the influence of ground-
          level  emissions as well as surface removal  processes.

M.    Emission and Meteorological Correlation

          Emissions and meteorological  inputs  to  the  model  are compiled
          in special  data preparation  programs.   These  programs  are
          tailored  to each  application of the  model  in  order  to  provide
          an effective interface between the existing observational  data
          and the  emissions and meteorological  input file  needs  of the
          SAI model.

 N.    Validation/Cali brati on

           Evaluation  studies using an early version of the SAI  Model
           developed in 1973 were carried out for Los Angeles, Las Vegas,
           and Denver for CO, NO, N02, 03 and hydrocarbons.  The latest
           version has been  applied to Denver and is currently being
           adapted to St.  Louis, Los Angeles, and Sacramento.
 0.   Output
           The model produces gridded maps illustrating the spatial
           distribution of one-hour-average pollutant concentrations
           over the entire region of interest.  Vertical concentration.
           profiles and predictions at air monitoring stations or other
           user-selected sites may also be displayed.  The model output
           may also be interfaced with contour plotting routines to
           generate diagrams of concentration isopleths.

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                                    -149-

3.3.4  Description of the Environmental Research & Technology, Inc. Model ARTSIM 2.0
        Post Conference Submission by B. Egan
 Reference:   "Lagrangian Photochemical/Diffusion Model", i-nvironmental
             Research £ Technology, Inc. (In preparation), May 1977.

 Abstract:   ARTSIM is a trajectory-oriented model intended for regional
      application.  It simulates chemistry and diffusion in a moving
      polluted air mass.  The chemical model contains 54 reactions and
      explicitly treats four hydrocarbon classes, namely, alkenes, alkanes,
      aromatics and aldehydes as well  as photochemical oxidants, SCL, and
      sulfate.  The model computes pollutant concentration as a function
      of height and time.  It contains 3-modules:   (1) trajectory generation;
      (2) source emissions;  (3) chemical/meteorological.

 Equations:


           |f= |f  UCU.t) H ) * »{e)

      wnere            c  = vector of pollutant  concentrations
                K(z,t)   = diffusion coefficient which varies with height
                          and time
                   R(c)  = vector of chemical reaction rates

 A.  Source-Receptor Relationship
      Area and point sources are used.  Elevated point  sources may be used.
      Generalized  input  structure  allows use of source  emissions data at
      various  levels of resolution.   Source-oriented and receptor-oriented
      trajectories may be specified.

 B.  Emission  Rate
      Rates  must  be  specified for each primary pollutant.  Hourly rates
      for  traffic and stationary sources required.

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ARTSIM - page 2                     -150-

C.  Chemical Composition
     54-reaction chemical model including NO, NCL, 0_, S0~, sulfate, alkenes,
     alkanes, aromatic hydrocarbons, and aldehydes.

D.  Plume Behavior
     N/A

E.  Horizontal  Wind Field
     Hourly u-  and  v-components must be input for  trajectory  calculations.

F.  Vertical Wind  Speed
      Not included.

G.  Horizontal  Dispersion
      Assumed negligible

H.  Vertical Dispersion
      Diffusivities are specified at up to 10 vertical levels  with arbitrary
      time resolution.  Any atmospheric stability class can be used for
      various times of day.

 J.   Chemistry - Reaction Mechanism
      See Section C

 J.   Physical Removal
      Physical removal is simulated by a variety of ground boundary conditions
      and/or chemical reactions in kinetic model.

 K.   Background
      Background concentrations can be specified as initial or boundary
      conditions.

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ARTSIM - page 3                     -151-
 L.  Boundary Conditions
      a)   Source  boundary condition at ground:
                  3c.
           K(o,t)  —;JY = - 0. (t)   ,   i denotes ith species,  0. (t)  =  source flux
      b)   Constant-concentration boundary condition:
           c   = constant
            i
      c)   Absorption at the ground:
           K(o,t)   | = vd C? ,
           Vj = deposition velocity
            n = power of c^

      d)  Top boundary condition  (impermeable boundary)


           —^- = 0    , for all i.
 M.  Emission and Meteorological Correlation
      N/A

 N.  Validation - Calibration
      a)  A. Q. Eschenroeder, J. R.  Martinez,  and  R. A. Nordsieck,
          "Evaluation of  a  Diffusion Model  for Photochemical Smog
          Simulation",  General  Research  Corporation, CR-1-273, Oct. 1972
      b)  AeroVironment,  Inc.,  "Las  Vegas Valley Air Quality Study",
          April 1976
      c)  AeroVironment,  Inc.,  "Truckee  Meadows Basin  Air Quality Study",
          April 1976.

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- ARTSIM - page 4
  N.   d)  Stanford Research Institute, "Present and Prospective San Francisco
           Bay Area Air  Quality", December  1974.

  0.   Output
        Species  concentrations  as  functions  of time and height above ground.
        Time resolution is arbitrary down  to 1 minute.   Variable vertical  mesh
        spacing  may be used.

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                                    -153-
3.3.5  Description of the Environmental Research & Technology3  Inc. Model LAPS
       Post conference submission by B. 3gan

Reference:  R. A.  Nordsieck.  "A Local Air Pollution Simulator  (LAPS);
            Volume I,  User's  Guide,"  Environmental Research £ Technology,
            2030  Alameda Padre Serra, Santa Barbara, California  93103,
            1977  (In preparation),

Abstract:  LAPS employs numerical techniques to calculate concentration
      fields downwind of single or multiple concentrated sources.   These
      sources  may  be individual point sources at various heights,  an  area
      source strip on the ground at arbitrary orientation with  respect to
      the  wind, or combination of the two.  The model uses the  Lagrangian
      or trajectory approach with lateral dispersion.  Steady  or  unsteady
      conditions  may be modeled and the averaging times associated with
      the calculated concentrations are related to the averaging  periods
      of the input emissions and meteorology.  Vertical mixing conditions
      are simulated using time- and space-varying eddy diffusivities.  Up
      to seven pollutants may  be modeled  simultaneously with optional
      equilibrium  chemical coupling between the species.

 Equations/.
      The basic transport equation modeled by LAPS  is:
          8c      8c
          3lf  + U^x
      where
                    c = pollutant concentration
                    u = wind speed in x-direction
                D (z) = lateral eddy diffusivity at z
                 y
                D (z) = vertical eddy diffusivity at z
                 z
            Q(x,y,z,t)= source-sink term to model pollutant emission fluxes
                        and simple chemical reactions

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                                   -154-
LAPS - Page 2

     In the Lagrangian formulation, the coordinate system of the air
parcel is oriented with x in the direction of the wind and moves at the
wind speed u.  Thus, the wind speed relative to the moving coordinate
                             3c
system is zero and the term u— is removed from the equation.  LAPS
                             oX
solves the reduced equation by a combination of finite difference tech-
niques in the z and t dimensions and multiple superposition of an analytical
solution for Gaussian spreading from a finite-width source in the y and t
directions.

A.  Source-Receptor Relationship
     Up to 10 point sources at arbitrary  locations.
     Unique  stack height  and efflux  parameters  for  each  stack.
     Separate uniform area  sources allowed over entire region and/or within
          a  finite-width  strip  located  and oriented by user  input.
     Specific receptor  locations  not currently  calculated, but  a ground
          concentration map is  provided and  vertical  profiles  arc
          available at  user specified time intervals.

B.   Emission Rate
      Unique  average emission rates of all pollutants  for each point  and
          area  source.

C.   Chemical Composition
      Up  to  seven chemical species can be specified  by name and  molecular
          weight.

D.   Plume Behavior
      Br'iggs  (1971)  final  plume rise formula for neutral  or unstable
           conditions with u > 3.1 mph.
      Downwash not treated.
      Plume rise limited to inversion height unless stack penetrates base
           of inversion layer.

-------
LAPS - page 3                      ~155-

E.  Horizontal Wind Field
     Wind speed is uniform over y and z, but may be varied with time.
     Vertical mixing induced by  wind shear can be modeled through adjust-
          ment of the vertical eddy diffusivities.

F,  Vertical Wind Speed
     Neglected.

G.  Horizontal Dispersion
     Lateral dispersion is treated using an analytic solution for one-
          dimensional diffusion from a  finite-width source.  Each cell
          in a row at each vertical station is treated as an isolated
          source which diffuses laterally  for one  time step.  The results
          of these calculations are superposed to  give the complete solution
          in each row of cells.
     The  lateral eddy diffusivities can be varied  with height and time.

H.  Vertical Dispersion
     Vertical  dispersion is  modeled using  a Crank-Nicolson finite difference
          formulation for vertical diffusion with  variable eddy diffusivities.
     The  vertical eddy diffusivities  can be varied with  height and time.

 I.  Chemistry  -  Reaction Mechanism
      Equilibrium chemical  coupling of NO,  N02>  and 03 is optional.
      First  order conversion of S02 to sulfate  is optional.
      Governing rate constants and conversion  rates are input parameters.

 J.   Physical Removal^
      Fallout not modeled.
      Rainout not modeled.

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                                   -156-
LAPS - page  4

J.  (cont'd)
      Impaction of particulates or complete chemical uptake at the ground
           is  optional  for any species.

 K.   Background
      Vertical profiles of background  concentrations are  specified by the
           user  for each species.

 L.   Boundary Conditions
      Optional time varying surface fluxes of each species,  independently
           specified for a source step and for the remaining area.
      Constant ground concentration may be specified for  any species to
           enable calculation of surface uptake.
      Reflection coefficient at sidewalls may be set by the user.

 M.   Emission and Meteorological Correlation
      N/A

 N.   Validation - Calibration
      Thus far, validations have consisted of comparisons with various
           analytical  solutions; for example, for steady-state Gaussian
           plumes and  step changes in flux or concentration boundary con-
           ditions.  In each case, it has been possible to achieve accuracy
           in the 10%  and under range, which is certainly within the accuracy
           of real-world input data.  No  comparisons with measured data
           have been attempted as yet, owing partially to the scarcity of
           good measured data, either roadside CO profiles or point source
           plume concentrations collected with simultaneous  car counts or
           pollutant emissions and meteorological data.

  0.   Output
       Vertical  concentration maps  giving concentrations  at mesh points in
           an array of up to 10 vertical  stations and  20 horizontal  stations
           at user-specified time  intervals.   (Optional)

-------
LAPS - page 5                      -357-

0.   (cont'd)
      Ground concentration naps showing ground concentrations at up to
           20 stations normal to the wind direction at user-specified time
           intervals along the air trajectory.  (Optional)
      Ground concentration contours - A printer-plotted symbol nap derived
           from the array of ground concentrations delineates up to  10 user-
           specified contour levels.  The user nay select which species  are
           to be plotted and specify different contour levels for  each
           selected species.
      Concentration vs. distance plot - This option produces a printer-plot
           of ground concentrations vs. distance along the trajectory for
           up to five user-selected species.
      Ground concentration crossplot - This optional output gives  ground
           concentration profiles normal to a roadway  (i.e. a source strip)
           at user-specified intervals along the road.

-------
-158-

-------
                                    -159-


3.4  GROUP II-1
3.4.1  Description of the Environmental Research & Technology3 Inc.  Model EGAMA

       Post conference submission by B. Egan

Reference:  Egan, B.A. and J.R. Mahoney, 1972a:  "Numerical modeling
            of advection  and diffusion of urban area-source pollutants."
            J. Appl. Meteor.,  11.   312-322.

            Egan, B.A. and J.R. Mahoney, 1972:  "Applications of a
            numerical  air pollution transport model  to dispersion
            in the  atmospheric boundary layer." J. App1. Meteor., 11.
            1023-1039.

Abstract:   The  Egan-Mahoney advection-diffusion  model (EGAMA) simulates
     the  dispersion mechanisms of grid-cell  emissions using a numerical
     solution to the  basic tracer equation.  The  model utilizes moments
     of the concentration distribution within  each grid  cell in a computation
     scheme designed  to  virtually eliminate  numerical, pseudo-diffusion
     effects.  The  capability  of  this  model  to  treat spatial and temporal
     variations  in  the wind  and  diffusivity profiles allows for specialized
     adaptations for  a wide  range of modeling,  applications, e.g., near-
     field dispersion of highway sources,  fumigation episodes due to
     sources  located  near land-water interfaces,  and long  range transport
     problems.

Equations:  The  basic mass  conservation equation for a pollutant species
      in  a planer non-divergent flow field  may  be written:

       3c_   „ 3c   vi£ + JL(K-^)+Q-R
       aT  - " u ax ' v  9y  sz  l   9z }    *

     where     C    =    pollutant concentration
                U    =    component wind speed  in mean (x)  direction
                V    =    cross wind speed component
                K    =    vertical eddy diffusivity
                Q    =    emission rate
                R    =    Removal or production rate (specific form
                          depends upon mechanism)

 A.   Source-Receptor Relationship

      Emission rates may be assigned to each grid cell.   Average concentrations
      within each model grid cell are  computed by step-wise integration
      for evenly spaced time intervals.  Two or three dimensional grid
      systems may be specified.

 B.   Emission Rate

      Time and spatially variable emissions within each  grid cell can
      vary with  time and represent  average values over the geographical
      space encompassed by a single grid cell.

-------
                                     -160-
C.   Chemical Composition

     Transformation and decay as well as surface deposition of the
     contaminant species under examination may be incorporated.  These
     processes are of primary concern for long range transport applications.

D.   Plume  Behavior

     No plume rise formula per se is incorporated by the model.  Emissions
     are  assumed well mixed  in the vertical within each grid cell.
     Preliminary plume rise  calculations (e.g., Briggs formulae) can be
     performed to establish  the vertical location of each emission
     source.

E.   Horizontal Wind Field

     Steady-state or spatially/temporally varying ambient winds may be
     specified throughout the grid system.  The speed at any point is
     considered constant during a given time step.  The effects of
     obstacles on altering the flow field may be simulated.

F.   Vertical Wind Speed

     Normally assigned zero  initial value.  Vertical wind speeds resulting
     from flow over obstacles or downwash are internally generated (for
     example return flow circulation in depressed highway sections).

G.   Horizontal Dispersion

     Turbulent diffusivities can be specified to simulate horizontal
     dispersion.  In applications with large grid cells, horizontal
     dispersion is often neglected compared to the transport terms.

H.   Vertical Dispersion

     The vertical diffusion component is simulated by a conventional
     forward-time, center-difference technique modified so that variable
     grid spacing can be specified in the vertical.  In regions where
     parameters or concentrations vary rapidly with height, resolution
     and accuracy can be improved by smaller vertical grid spacing.

I.   Chemistry/Reaction Mechanisms

     Program presently allows incorporation of two specie reactive
     chemistry.   Modifications are underway to expand capability to
     multiple species.

J.   Physical Removal

     Surface deposition of the contaminant species modeled is simulated
     at the lower boundary of the grid system.  Other removal processes,
     including chemical transformation and decay can also be incorporated.

-------
                                     -262-
K.   Background Concentrations

     Can be specified as initial conditions throughout the grid system
     and in the form of boundary values which are advected into the
     computational region.

L.   Boundary Conditions

     Winds, diffusivities, and pollutant fluxes at top and bottom of
     grid system may be specified.

M.   Emission and Meteorological Correlation

     Could be specified.

N.   Va1idation/Ca1ibration

     Extensive validation/calibration study performed for highway
     applications (2D-version) limited validation of 3D version performed
     in long range SOX modeling study.

0.   Output

     Concentration fields at all grid locations at specified time intervals
     or time averaged.

-------
-162-

-------
                                    -163-
3.5  NO SUPPLEMENTARY MATERIALS

-------
-164-

-------
                                     -165-
 3. 6  GROUP II-3
 3.6.1.  .Description of TAPAS Model
 Submitted  by  D.  G. Fox at  the conference.
                                       TAPAS
Reference:  (a)  Fosberg, M.A. and D.G. Fox.  "A Topographic Air Pollution
                 Analysis System" to be submitted to Atmospheric Environment,

            (b)  Fosberg, M.A. and D.G. Fox.  "An Air Quality Index to Aid
                 in Determining Mountain Land Use".  In Proceedings of the
                 Fourth National Conference on Fire and Forest Meteorology,
                 Nov., 1976, USDA For. Serv. Gen. Tech. Rep. RM-32, p. 167-
                 170, 1977.

            (c)  Fosberg, M.A., D.G. Fox, E.A. Howard and J.D. Cohen.
                 "Nonturbulent Dispersion Processed in Complex Terrain",
                 Atmos. Env. 10, p. 1053-1055, 1976.

            (d)  Fosberg, M.A., W.E. Marlatt and L. Krupnak.  "Estimating
                 Airflow Patterns Over Complex Terrain, USDA Forest Service
                 Research Paper No. 162,'16 p., 1976.

            (e)  D.G. Fox, G. Wooldridge, and others.  "An Experimental
                 Study of Mountain Meteorology".  In Proceedings of the
                 Third Symposium on Atmospheric Turbulence Diffusion and
                 Air Quality.  Amer. Met. Soc., Raleigh, NC, 1976
 Abstract:
                 TAPAS combines a simulation of the wind field over mountain-
                 ous terrain with a Gaussian derived diffusion model.  The
                 diffusion model is employed in each grid cell of the calculation
                 in order to provide an estimate of the mixing conditions
                 within these cells.  These conditions are combined with
                 the Pollutant Standards Index such that a maximum allowable
                 emission is calculated.  These in turn represent an
                 atmospheric constraint for planners to work with.
Equations:  U)
                 Wind Model
                 (a)  Cressman  objective analysis
                 (b)  Potential flow over topography
                 (c)  Influences of  surface temperature  and  roughness.
                      See  reference  (d)
             (2)  Dispersion Model
                                   
-------
or
      u  -
          o
                         -166-
           tv\eav\  diWaev^  i*  £&cU  cell
                       0
                         V
                                                =  ^- * |0
                                     Q
                                b      T
1 v\uw    o
                          f


           Ql -  £MAiiiJoiA  v-afc  cf  life-   MSiCLuH  ^o /•  cie-lcr iu i m u

-------
                                    -167-
TAPAS
A.   Source-Receptor Relationship

          (a)   Sources  are  evaluated  on the basis of  their instantaneous
               emission rate  (rag/sec)  for/fvalues and in  terms of their
               total emission (mg) over the standard  time period for the
              "Upvalues.

          (b)   There is no  distinction made between point, line and area
               sources.

          (c)   There are no specific  receptors, Analysis  is for ground
               level concentrations.
B.  Emission Rates

          (a)  Calculated  from EPA emission  factors external to the model.

          (b)  Model provides the total allowable emission within each grid
              cell (ranging from .25 km2  to 9 km2) to achieve a preselected
              level of air quality.


C.  Chemical Composition

              Only non-reactive pollutants  are treated.
D.  Plume Behavior

              No explicit treatment of plume behavior.


E.  Horizontal Wind Field

              The wind component calculates an overall driving wind by
              Cressman objective analysis.  This is then altered by
              topography within the restrictions of potential flow.  The
              potential flow is corrected by surface temperature and
              surface roughness considerations.


F.  Vertical Wind

              The calculated wind is constrained to follow the terrain at
              the surface.  The rate of change of vertical velocity
              is explicitly calculated as the divergence.

-------
                                  -168-
TAPAS
G.  Horizontal Dispersion
              A Gaussian formulation is altered to include the effects of
              mass divergence ( see ref. c) on horizontal dispersion.
              This effect is coupled with the values of 
-------
                                   -169-
TAPAS
0.  Output
              A matrix of the allowable emissions within  each grid cell is
              output for each individual  ambient air  quality standard, i.e.
              1 hr.CO, 8 hr. CO, 24 hr. TSP, or for any other preselected
              level of air quality, i.e.  for 24 hr. TSP ^= 100,
                   75 /e/m3             or <^ = 8,7*  =75 /Jg/m3  (class I)
                   = 17,^=  TS^g/m3  (class II)..

              The model is able  to accomodate selection of different values
              of V at different  grid  points so that class I, II and III
              areas can be analysed together.

-------
                                  -170-
3.6.2  Validation Data on the VALLEU Model
Submitted by Herschel H. Slater, post-conference
       Valley Model:  Comparisons of Observed and Estimated
                      Concentrations and Related Observations

     The Valley Model was initially used to estimate the impact of
emissions from single sources on elevated terrain.  There are few data
available to evaluate this or any other model or analytical routine in
rough terrain situations, because:  (1) it is difficult to locate and
operate monitoring  equipment in complex terrain; (2) the representa-
tiveness of meteorological data is often uncertain; and (3) some short-
term standards are  addressed to rare events.  Sensing  the rare event may
require a very highly reliable, continuously operating monitoring
program.
      The dramatic example of the  latter  is provided by data collected  by
the  Kennecott Copper Corporation  at  two  of their sampling sites on
January 20,  1976.  The  bearings from the main stack to the monitors were
within 5° of each other (about  250° True).   One monitor was about 2.7
miles  (4.5  km) from and 300 feet  (100  m) above the stack  top.   It meas-
ured a 24-hour S02  concentration  of 2.71 ppm.  The second monitor was
about  3.0 miles  (5  km)  from and 1100 feet  (350 m) above the stack top.
 It measured 0.02  ppm for the same 24-hour period.  The maximum  concen-
trations at monitors with elevations near or below the base of  the
stacks were  at most 10-22% of the highest  concentrations  measured on  the
hillside.   (It is probable the  concentrations at  the  monitors near  the
elevations  of the stack bases were  caused  by sources  other  than the
 pollutants  emitted  from the stacks.)   It is  quite apparent  that the
effluents from the  stacks were  contained  in  or  below  an  inversion and
 lay  along the hillside  above the  base  and  below  the  crest.

-------
                           -171-
     Comparisons of estimated and observed 24-hour sulfur dioxide con-
centrations measured at sites located at elevations greater than the
stack top of a nearby source are shown in Table A-l.  In five cases, the
emission rate was well documented.  To make the concentration estimates
it was assumed that stable conditions (F) and light wind speeds (2.5
mps) described the dispersion conditions.
     Quantitative comparisons have been made which digress from the
original purposes and applications of the Valley Model; e.g., compari-
sons made for 1-hour averaging  times.  Also, observations exist which
can be only qualitatively related to the plume  impingement concept of
the Valley Model.  Some examples which may provide some useful insights
follow.
     Lantz, Hoffnagle and Pahwa^5' developed 1-hour  estimates from  the
Valley Model and  compared estimated and observed  concentrations  from the
Navajo Generating Plant Study.   Using 4 sets of meteorological  inputs
they developed  12 data-pairs.   The ratio of estimated  to  observed
maximum  1-hour  concentrations  ranged  from  0.7  to  2.4.
     Slowik and Pica^ used the Valley Model  to  estimate concentrations
at a sampling  site on  Laurel Ridge,  Pa.,  near  the Conemaugh  Generating
Station.   Comparisons  were  made with  one year  of 1-hour sulfur  dioxide
concentrations.   The data were screened on the basis of wind directions
collected  at  a  wind sensing site on  Chestnut  Ridge,  14 km from  the
monitoring site.   The study presumes  that the  wind observation  on
Chestnut Ridge defines  the  plume trajectory in all  cases.  If this

-------
                                    -172-
       TABLE A-l    Comparisons  Between Estimated  Maximum and Second
                    Highest and  Observed  24-Hour S02  Concentrations.
                    Estimated Concentrations  Are Based  On Valley Model
                    Assuming F Stability  and  2.5 mps  Wind Speed.
Location
Crusher* '

Lower /9x
Lake UJ
S1te,,x
106 w
S1tem
107 IJ;
C-H111°'4)
Jones /i x
Ranchu'
Source
Garfleld
Smelter

Garfleld
Smelter
Navajo
P. P.
Navajo
P. P.
Anaconda
Smelter
Morencl
Smelter
Miami
Smelter
Period
4/15/73-
1/31/74
2/01/74-
1/31/75
3/08-
12/16/75
1/1-25/76
10/1/74-
2/17/75
10/1/74-
2/17/75
24-Hour Concentration^9'
Estimated
2480
2480
1.18 ppm
1.18ppm(b)
36
25
Observed
Max
2564
6130
2.66 ppm
2"T1 MMMt\CJ
.71 ppm
32
30
2nd
High
2473
3130
1.20 ppm
2.14 ppm(c)
19
15
Ratio
Est/Obs
Max
1.0
0.4
0.4
0.4
1.1
0.8
2nd
High
1.0
0.8
1.0
0.6
1.9
1.7
Although some data have been acquired at C-Hin near the
Anaconda smelter, unresolved uncertainties with the data
make It Inadvisable to apply them for validation purposes
1975
1974
1975
15490(b)
8610(b)
8610(b)
2547
2042
2642
2416
1760
1548
6.1
4.2
3.3
6.4
4.9
5.6
(a) Mlcrograms per cubic meter except where otherwise Indicated.

(b) Emission rates not well-documented.
(c) Reliability of air quality data not ascertained.
                                                                    4-77

-------
                        -173-
very tenuous assumption is accepted, then the ratio of estimated to
observed 1-hour concentrations was 40 for the maximum hourly, 65 for the
second highest and 30 for the 90th percent!le concentration.
     Keen observers of plume configuration have long noted that plumes
from the stacks of sources located in hilly terrain usually have greatest
impact on higher terrain.  Studies of the plume travel of the Clifty
Creek power plant and observations near the Widow's Creek facility
confirm this observation in a qualitative sense.  (The Navajo study
provides quantitative confirmation.)  Observers do not agree that a
particular plume configuration such as high-wind fumigation, inversion
break-up fumigation or impingement is associated with greatest ground
level concentrations.  The critical configuration is influenced by the
characteristics of the source, its site, the frequency of weather events,
the location and orientation of nearby terrain features, and the operating
schedule of the facility.
     Scorer^ ' has described several circumstances which support quali-
tatively the assumptions on which the Valley Model is based; namely, in
hilly terrain the highest 24-hour concentrations caused by pollutants
emitted from a stack whose top is lower than the elevation of nearby
terrain features occur under light wind stable conditions.  Scorer
describes plume characteristics of a number of sources.  He states that
in Bohemia (p. 36):  "The gases from large power stations drift with
very little dilution above the height at which they were emitted, but
below the mountain tops, until they impinge on the hillsides and damage
the pine trees	"  He describes a situation in New Zealand where,

-------
                           -174-
due to steeply sloping nearby mountains, air pollutants are trapped in
calm weather and often impinge on the hillsides.  Further, on page 46,
he cites the coastal area of Lebanon where "...the plumes from the
factory chimneys at the coast impinge on the rich fruit-growing fields
which slope up steeply inland..."
                                                     fo\
     The classic Trail, B.C. study of Hewson and Gillv ' suggests that,
on occasion, higher concentrations occurred on the slopes of the Columbia
River Valley, above the valley floor but below the crests of the sur-
rounding hills, than elsewhere.  •
                                                             fq)
     An oft cited  tracer study by Start, Dickson, and Wendellv   was
conducted  in Huntington Canyon, Arizona.  Based upon the single line-of-
best-fit through observed vs. estimated concentrations from the four  1-
hour valid tracer  tests conducted during stable conditions, within a  48-
hour period, the authors conclude that  the dilution in the Canyon was  15
times that to be expected over flat terrain.  Estimates were made by  the
bivariate  Gaussian formulation, using the flat-terrain Pasquill-Gifford
diffusion  parameters corresponding to the stability observed in the
Canyon.  However,  when the  same data are expressed as  the  ratio of the
estimated  concentration to  the study-period maximum observed concen-
tration at each of the four sampling arcs (at distances about  2.2, 2.8,
4.2, and 6.2 km),  we obtain values of 0.95, 1.1,  2.6,  and  1.0, respectively.
Relative to many modeling results, this is an excellent  relationship.
It is  interesting  to  note that each maximum occurred  at  an end sampler
of the  respective  arc.  How adequately  this set of data  represents the
dispersion conditions when  the highest  concentrations  of the year  occur
has  not been resolved.

-------
                                    -175-
                        REFERENCES FOR SECTION 3.6.2.
1.  Office of Air Quality Planning and Standards (AEROS data system),
    U.S. Environmental Protection Agency, Research Triangle Park, N.C.

2a. Eeaney, R.J.:  Statement on Behalf of Kennecott Copper Corporation,
    December 16, 1975.

-b.  Kennecott Copper Corporation:  Air Pollution Emergency Episode,
    January 21 through 24, 1976.  KCC Technical Report (undated).

3.  Rockwell International;Meteorology Research, Inc.; and Systems
    Applications, Inc.:  Navajo Generating Station Sulfur Dioxide
    Field Monitoring Program.  Vol. IV:  Ground-Level S02 Measurements.
    September 1975.

4.  Anaconda Copper Company Observational data.

5.  Lantz, R.B., G.F. Hoffnagle, and S.B. Pahwa:  Diffusion Model
    Comparisons  to Measured Data in Complex Terrain.  Third Symposium
    On Atmospheric Turbulence, Diffusion, and Air Quality.  AMS, Raleigh,
    N.C., p. 476, October  1976.

6.  Slowik, A.A. and  G. N. Pica:  A field comparison  of the EPA "Valley"
    Model, "Half-Height" Model and a suggested new model  in complex
    terrain under stable atmospheric conditions.  Interim Report
    No.  1, Pennsylvania Electric Co., August 31, 1976.

7.  Scorer, R.S.:  Pollution  in the Air  - Problems. Policies and
    Priorities.  Rutledge  & Kegan Paul,  London and Boston,  1973.

8.  Hewson, E.W. and  G.C.  Gill:  Part III - Meteorological  Investigations
    in  the Columbia River  Valley, near Trail, B.C.  Report  Submitted to
    the Trail Smelter Arbitral Tribunal   (by R.S. Dean and  R. E. Swain)
    U.S.  Bureau of Mines,  Bulletin 4537  1944.

9.  Start, G.E., C.R.Dickson  and L.L. Wendell:   Diffusion in a Canyon
    within Rough Mountainous  Terrain.  NOAA TM ERL-38,  August 1973.

-------
                                        -176-

           3.6.3  Comments by D.  Henderson

           Submitted after the conference
      \
       o     UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

,,4   ^ '                               REGION VIII
                                 I860 LINCOLN STREET
                                - "'ER. COLORADO  8O2O3
    March 23, 1977

    REF:  8AH-A

    Mr. Albert E. Smith
    Energy and Environmental Systems Division
    Argonne National Laboratory
    9700 South Cass Avenue
    Argonne, IL  60439

    Dear Al:

    As a follow-up to our recent telephone conversation concerning comments
    on the EPA Modeling Guideline Working Group II-3, I am submitting the
    following suggestions.  My first suggestions are related to use of
    the EPA Valley Model in complex terrain.  These are followed by sug-
    gestions on the written statements prepared by Bruce Eagan, Douglas
    Fox, and Sumner Barr.

    As I indicated in the workshop I have used the Valley Model differently
    than presented by Herschel Slater at the workgroup.  I have attempted
    to use local meteorological conditions which would take into consideration
    the frequency of occurrence of the particular stability, wind direction
    and wind speed frequency distribution.  This allows more versatility
    in applying the model, but requires consideration of persistence of
    the prescribed meteorological condition.

    Several suggested changes to the Valley Model User's Guide were given
    by me to Herschel Slater and Ed Burt on the telephone.  I suggested
    that in the introduction a statement similar to that given below be
    included.

    "The EPA Valley Model is a modified Gaussian technique designed for
    making ground concentration estimates for plumes emitted from area
    sources and elevated point sources.  Experience indicates that maximum
    24-hour ground level concentrations frequently occur in elevated ter-
    rain when plumes are contained in a stable layer below the height
    of the terrain with the flow blocked under light wind conditions.
    This condition was given primary consideration in the Valley Model
    development for short term estimates."

-------
                                   -177-
Th e following comments pertain to the submission referred to at the
beginning of this letter.
On page 11 at the end of section (ii), the statement is made that
the box model approach is a conservative screening method.  Without
specifying averaging times, and how the top of the box is defined
one cannot generally conclude that a box model is conservative.
The results of the box model are very sensitive to the designation
of the top boundary.

At the bottom of page 11 and also in one of Dr. Eagan's sections,
physical modeling is proposed as a useable technique for source im-
pact analysis in complex terrain.  The guidelines should point out
that the frequency of ocurrance and persistence of meteorological
conditions are not determined 1n physical modeling techniques.

Several other suggestions were given to you on the telephone, but I
believe the above covers those items I agreed to write you about.
I hope these suggestions will be helpful to you.

Sincerely yours,
Donald Henderson
Regional Meteorologist

-------
                                   -178-
3.6.4  Comments by M.  Will-Loans




       Submitted after the conference.





    O n t he occasions of which I am aware,  the use of a plume half height




correction w ould have produced drastically lower values than those observed





or those predicted  by Valley.   These cases  involve stable flow toward terrain





as high as the approximate plume  height.




    With respect to the use of the principle of conservation of mass to con-




sider what flow situations may be  possible,  it is very important that ^overly





simplistic models  not be used.  There  is a tendency on the part  of some to





use very simple models which assume that  the wind is approximately uniform





in direction   the over than lower  2000 feet  of the atmosphere.   Under stable




conditions this assumption is frequently false   Furthermore, the use of this





assumption may suggest restrictions on plume  behavior which do not,  in fact,





exist.   We have found cases where a stable  plume reached  a  distant ridge top




(55 km) with little or no standoff distance.   The ridge was approximately a two





dimensional feature oriented  at 45° to the direction of travel. Winds were ap-





proximately 3. 5 m/sec.




    With respect to alteration of diffusion rates we have found that terrain





elements do not appear to influence the diffusion of stable plumes unless they





are at full plume height.  As  long  as there is no intervening terrain between




an obiect and the source, it seems that no alteration in diffusion rates is justi-





fied for elevated plumes during stable  conditions.

-------
                                 -179-
3. 7  GROUP II-4
'6.7.1  Comments on  the Group II-4 Discussion of the
       a  Curves
        %
        Submitted by M. Williams after the conference.
      Unfortunately I was not a party to the discussion which produced these
conclusions  so I do not  know what basis was used to produce the stated con-
clusions.  However,  I have reviewed data which does  relate to the question
at hand.  First,  the Central Electricity Generating Board has reported on
measurements near to power plants with stacks of 137 meters and 183 meters
         I,
in height. The calculational technique was that of the Pasquill H <•»;*»  method
which is  similar to the  Turner method except that the  J-*.-5 are a little
smaller in the Pasquill technique and the plume rise is calculated differently.
The plume heights calculated through the Kr^i* method are significantly
smaller than those calculated through Brigg's plume rise used in CRSTER.
Measurements near (1.4 km) the plant with the taller  stack generally gave
values comparable to those predicted, the exception being that during A
stability with winds of 2.9 m/sec a 3-minute  concentration of 58 pphm was
predicted while the measured value was only  32 pphm and the highest measur-
ed value was only  36  pphm.  Using a Brigg's  plume rise  with Turner disper-
sion parameters the  calculated value is 40 pphm which appears to be in good
agreement.  Thus this  evidence indicates that farily good agreement is ob-
tain.d if the' Turner values are taken as 3-10 minute values.  The same docu-
ment suggests that the  maximum one-hour values are from half to three-
quarters of  the 3 minute values.  Thus the Pasquill curves may overpredict

-------
                                  -180-
by  33 to ] 00% if used as on--hour values at distances of about 1.4 km.   This




would suggest that the corrections  should be applied to rs^, not  0\  .  In addi-



tion to  the ground-level measurements there are measurements of actual



plume dimensions.   These indicate that vertical spreads will be well in ex-



cess (more than twice) of those expected for  Class B occurred even though



the wind speeds were too high (5.8 m/sec) for Category A.   This data does



not seem to support either the  replacement of Category A by Category  B or  the



linear extrapolation technique.  Both of these procedures would lead to dra-



matic underpredictiontfor low wind speed, near plant cases.

                                •

      The  measurements do  suggest some underprediction at 5 km.  Further-



more- some of GX   for categories B-D were greater than given by Turner
      in addition to the- English work there is work in eastern Montana which




      sts that the Turnrr C, curves are  .   appropriate.   These data  are based


                      • 6 C <^ «.

on work v.ith a silver      tracer.  I have  enclosed Figures 5 and 6 from this
v. or .
      Finally,  the  Lappes study also suggests  the importance of looping type



?:tua:ions".  Tbi- \\ork shew? that the highest ground level concentrations en-



countered occurred close in.  There were two occasions where it looked as



thoue'n the- plume was underooinc classic  looping type  behavior near the  Key-


          £.
stone plant.' In both cases peak values were comparable to those predicted



with a Erigg's plume rise-Turner dispersion parameters combination.  Fur -




mere, repeated instances of high concentrations were found at 11:00 a.m.





                                     -2-

-------
                                 -292-
These data suggest that while the computational procedures may require



some revision it would be imprudent to change the 0^ curves without much



more careful analyses.  With the existing data as strong an argument can be



made for changing the  ff^ curves  as  can be made for changing the  *%. curves.



The suggested changes for A stability might veyy well lead to drainatic under-



predictions.


       The recommended changes for the other categories also appear to be
                                              .; "•    -            " ' •'•     *

unsupportable.   Certainly the measurements of dispersion  during stable con-



ditions in the Southwest show a much  different behavior. TV A  experience al$«



indicates behavior similar tq that found in the ffouthwest.  Experiments re-



ported by Slade  also are inconsistent  with the proposed  changes.








1!     A.  !-5artin and F.  R.  Barker, "Further Measurgttents Around-


      Stations I-III",   Atnospheri c Envir&nment, ypl.  7,  1973,
                                                                   :-.Power


                                                             pp |T-37.
?.   Jar.es  A.  I'.einbscb, arlin B. Super, John T.  M^rtland, "Dispersion Fron


      An Elevated Source Over Clostrip, Montana"*.; raper #7p-?6.6 presented


      ^ the  66th Annual Meetinr of the Air ?cliuticr  Centre! Association


      in Boston, June  1975.     •   -



3.   F. Peeler, Jr and  L.E. TTi^iejrer, "Dispersion from  Tall Stacks:; An; Evaluation


      ir. Proceedings of the Second'International Clean Air Congresp edited by


      Enruund 8: Beery, pi01i9-i056,
                                     -3-

-------
                              -182-
Francis A. Schiermeier and Lawrence E.  Kiemeyer,  "Larre  Power Plant Effiueri




  ~tudy \IAPPES) Volume I-Instrumentation,  Procedures, and  Data Tabulations




  (1968)", U. S. Dept. of H.  E.  W., June 1970 pp

-------
                           -183-
75-26.6
Figure 5.   Comparison of the  Colstrip vertical diffusion
coefficients to those of Turner  and Bovme's urban curves.
               il    11   11 MI   i  i
                                                            eon.
Figure 6.    Comparison of the Colstrip horizontal diffusion
coefficients to those  of  Turner and  Bowne's  suburban curves.

                              14

-------
                                    -184-
3,7.2  Supplementary Comments on Discussion  Topics of
       Working  Group II-4
Submitted by H.  Cramer after the conference  adjourned.
 1.       VERTICAL PROFILES OF WIND SPEED

         I am in agreement with the working group' s statement that the representa-
 tion of the increase of wind speed with height employed in the EPA models by means
 of a power law with the exponent varying according to stability categories is  satis-
 factory.  However, this statement of the working group should not be interpreted to
 mean that the values of the power-law exponents now used by EPA are fixed and not
 subject to change.  For example, wind-profile exponents are known to vary not
 only with stability  category but also with wind speed and surface roughness.  Spec-
 ifically, it may prove desirable to vary the present values of the power-law expon-
 ents for the C and  D stability categories with moderate and high wind speeds and
 with large changes in surface roughness parameters.

 2.       CONFLICTS AMONG SYSTEMS FOR ESTIMATING STABILITY CATEGORIES

         In my experience the STAR program incorporating Turner1 s system using
 hourly surface observations to assign Pasquill stability categories is very satisfac-
 tory provided it is  recognized that other meteorological parameters  such as  the
 mixing height, vertical gradient of potential temperature and wind-profile exponent
 must additionally be specified for each of the various combinations of wind-speed
 and stability categories.  Of these parameters,  the mixing height is  by far the most
 variable and should not be considered fixed for any stability  category,  especially
 neutral.  (One of the basic deficiencies of the Pasquill-Gifford a  curves is that
                                                             z
 they contain implicit mixing heights — see comments below under Topic 3.)  In my
 view, the opinion expressed by the working group that the STAR progrem tended to
 predict unrealistic ally high frequencies of occurrence  of neutral stability reflects

-------
                                    -185-
a lack of understanding of the importance of taking the mixing height into considera-
tion when considering the behavior of tall-stack plumes during neutral stability.
The STAR program output yields a stability classification that strictly applies in
the first 10 meters or so above the surface.  If the plume stabilization height is in
a stable layer above the surface layer, which is frequently the case for tall stack
plumes, the plume behavior does not and should not correlate with the behavior ex-
pected on the basis of the stability category assigned  to the surface layer.   The
STAR program stability classification must be supplemented by detailed specifica-
tion of the wind and temperature profiles along the vertical from the ground surface
beyond  the height attained by the upper edge of the plume as it travels downwind.
As shown in Figure 1,  the meteorological inputs required for application of the
Gaussian plume model to tall stacks are thus directly related to vertical profiles of
wind  velocity  and temperature (and in some cases to  the vertical profiles of humidity
and turbulent intensity as well) representative of a large reference air volume. This
reference volume, which includes the source and the points on the ground at which
concentrations are to be calculated, extends vertically to the top of the mixing layer
and has horizontal dimensions large enough to contain significant ground-level
concentrations.

         I would like to register my complete and irrevocable opposition to the AT
method for determining stability categories.  Even if one ignores the very  consider-
 able  and insurmountable practical difficulties in measuring and interpreting small
 vertical temperature differences of the order of 0.1   C, the  AT observations are
 strictly applicable only over the measurement height interval which is generally
 100m or less.  In working with tower AT measurements and following the AEC
 (ERDA) guidelines relating  AT to stability categories, I have never been able to
 obtain  results that appear to be reasonable on the basis of any conventional criteria.
 On the other hand, use of the Turner and STAR program procedures yields results
 that  appear to be consistent with the wind measurements made on the towers and
 other conventional criteria.

-------
                        z   =  H   =  Depth of surface  mLxins layer ~    1 kilometer
                         h     m

                        v   =  x   =  Maximum downwind distances ~  100 kilometers
                        Jh     h
    zh = Hm
-
RH{x,y,z,t}


I  {x.y.z.t}

       Figure 1.  Schematic representation of the reference air volume.

-------
                                    -187-
3.       VERTICAL DISPERSION ESTIMATES

        As we have discussed over the telephone,  the first sentence on page 7 of
the 2 March First Round Report of Working Group II-4 was typed incorrectly and
should be changed to read:
              "In their present form, the Pasquill-Gifford crz curves are
        unsuitable for calculating  the ground-level concentrations produced
        by tall stack emissions.  Specifically,  the a  curve for A  stab-
        ility results in large overestimates of the short-term maximum
        ground-level  concentrations (1-hour to 24-hour averages) and in
        large underestimates of the distances to the maximum concentra-
        tion compared with observations."

        There are two basic deficiencies in the Pasquill-Gifford a curves that
                                                                z
make them inherently unsuitable for describing the vertical dispersion of tall stack
plumes:
         •    They refer specifically to the vertical dispersion of plumes
              from sources located at or near  ground level and thus con-
              tain the effects of the large vertical gradients of atmospheric
              density (temperature gradients) and turbulence near the air-
              ground interface

         •    They are principally based on measurements of vertical
              plume dispersion made at distances less than 1 km  from the
               source; the portions of the curves extending beyond 1  km are
               extrapolations and were originally intended only to serve as
               rough approximations to vertical dispersion from ground
               sources at these longer distances

         The light lines in Figure 2 show the Pasquill-Gifford curves for the A, B,
 C, D, E,  and F  stability categories.  The strong curvature of the A, D, E and F

-------
                       -188-
                     DOWNWIND  DISTANCE (km)

FIGURE 2.  Pasquill-Gifford az curves (light lines) and suggested modi-
           fications (heavy lines).

-------
                                   -189-
curves reflects the influence of the vertical gradients of temperature and turbulence
in the air layers close to the ground on the upward vertical dispersion of plumes
under very unstable conditions (A stability) and under neutral or slightly stable to
stable conditions (D,  E and F stability). Both measurements and theoretical rea-
soning indicate that the downward dispersion of plumes from elevated sources
toward the ground surface  is characterized by cr  curves that plot as straight
                                              z
rather than curved lines on double logarithmic paper (i.e.,  cr K x) as shown by
                                                           Z
the heavy lines in Figure 2 labeled A' through Fr.  The upward dispersion of plumes
from elevated sources depends on the vertical temperature gradients and turbulence
in the air layers above the plume stabilization height.

         In the absence of elevated temperature inversions that restrict upward
plume growth, there  appears to be an approximate linear relationship between ver-
tical plume dispersion and  cr .   For example, Pasquill (1974, p.  202) cites experi-
                            Z
ments by Hogstrom (1964) in which smoke puffs were released at a height of 50 m
with the result that a   encreased linearly with travel  distance out to about 300 m
                     z
in all stabilities; at longer distances,  a  tended to increase less  rapidly with dis-
                                       Z
tance possibly because of a restriction on further upward expansion by an elevated
stable layer at the top of the mixing layer. Pasquill (1974, p. 200) aslo cites
experiments by Hay and Pasquill (1957) involving continuous tracer releases at a
height of 150 m which showed a linear relationship between vertical spread  (o^)
and the travel time to downwind distances of  500 meters, the maximum distance
 at which measurements were made.   Briggs  (1975, p. 36) notes that recent studies
of tall stack plumes show  az approximately  linear with distance in the most un-
stable categories.
         The strongest evidence of the inapplicability of the  Pasquill-Gifford  
-------
                                  -190-
                         max
  2Q



7T6U
                                                 max
where  H  is the plume stabilization height and both cr  and  a  are evaluated at


the distance x     of the maximum ground-level concentration.  Assuming all


parameters to be fixed except a  and  
-------
                                    -191-
stability places the maximum ground-level concentration about twice as close to
the stack as the modified cr   curve and yields a maximum concentration twice
                          £*
as large as the modified  &z  curve.  Similarly, the P. G.  o"z  curve for D stability
places the  maximum ground-level concentration about five times farther from the
stack than  the modified  a  curve and yields a maximum concentration four times
smaller than the modified az  curve.

     Table  2 shows the  GZ/VV  ratios for all the Pasquill-Gifford curves at distances
from 0.1 to 10 kilometers.   Note that the Cg/a   ratios for C stability are approxi-
mately constant with increasing distance (which reflects the condition that az « x)
while the ratios for other stability categories either increase with distance  (A and
B stability) or decrease with distance (D, E and F stability).  Assuming that the
vertical dispersion of tall stack plumes toward the ground when the plumes  are con-
tained in the surface mixing layer  requires that  0.6) or underestimation (vz/vy < 0. 6) of
                                    J
the maximum hourly ground-level  concentration.  It follows that the P. G.  crz
curves lead to large overestimates of the maximum ground-level concentrations for
A and B stability  and to large underestimates for D, E and F stability.  Similarly,
the P.  G.  crz  curves lead to large underestimates of the distance to the maximum
ground-level concentration in A and B stability and to very large overestimates of
the distance to the maximum ground-level concentration for D, E and F stability.
The degree of overestimation or underestimation in a particular stability category
 is directly related to the plume  stabilization height.  The minimum plume stabilization
 heights by stability category at which these effects become significant are approxi-
Veil and Hoult (1973) in a study of SOg observations from the Keystone plant found
 that a value of crz/a  = 0. 6 correlated best with hourly maximum ground-level con-
 centrations during unstable conditions.

-------
                    -192-
                    TABLE2
RATIOS OF az/o- FROM THE PASQUILL-GIFFORD CURVES
Distance
(km)
0.1
0.2
0.5
0.7
1.0
1.5
2.0
3.0
4.0
5.0
6.0
8.0
10.0
Stability A
Category
0.53
0.57
0.96
1.32
1.97
3.39
5.05
8.49





B
0.54
0.57
0.63
0.66
0.69
0.74
0.79
0.85
0.91
0.96
1.00
1.07
1.14
C
0.59
0.60
0.58
0.58
0.58
0.58
0.58
0.59
0.59
0.59
0.59
0.60
0.60
D
0.60
0.56
0.50
0.47
0.44
0.41
0.38
0.35
0.33
0.30
0.29
0.27
0.26
E
0.60
0.56
0.49
0.46
0.42
0.38
0.35
0.31
0.28
0.26
0.24
0.22
0.20
F
0.57
0.54
0.47
0.44
0.40
0.36
0.33
0.28
0.26
0.23
0.21
0.19
0.17

-------
                                   -193-

mately 100m (A stability),  300m (B stability), 45m (D stability), 22m (E stability)
and llm (F stability).

         Confirmation of the effects described above is readily found in the results
of model validation studies sponsored by EPA.  For example, Lee, et_aL_  (1975)
describe a validation study of the EPA CRSTER Model (which uses the Pasquill-
Gifford curves) that involved the application of the model to four power plants.  In
each case, there was no significant positive correlation between concurrent calcu-
lated and observed hourly and 24-hour average SC^ concentrations.  For a year of
data, the CRSTER Model tended to overpredict the maximum observed hourly SO2
concentrations and to underpredict to maximum observed 24-hour average concen-
trations.  The poorest model performance was at the  Canal Plant, which is located
near Cape  Cod Bay and consequently has a much greater frequency of occurrence
of D, E and F stability than the other plants studied.  Table 3 shows a comparison
of hourly SO2 concentrations observed at monitor stations in Tacoma, Washington
downwind from the 172-meter stack of the  ASARCO copper smelter with two sets of
concurrent calculated concentrations.  One set of calculated  concentrations was
made by means of the short-term Gaussian plume model described by Cramer, et al.
(1975)  which, except for the use of crz  curves similar to those shown by the heavy
lines in Figure 2,  is practically identical to the EPA  CRSTER Model.  The second
set of  calculated concentrations (Pasquill-Gifford) was made by means of the same
short-term model except that the Pasquill-Gifford curves  were used.  Differences
in the  two  sets of calculated values in Table 3 are essentially due to the  differences in
the  a   curves because the same source and meteorological  inputs were used for both
     z
sets.  For all the  cases shown in Table 3, the average ratio  of calculated and ob-
served concentrations is 0.11 for the model calculations using the Pasquill-Gifford
curves and approximately unity for the model calculations  using the  modified curves.
The principal explanation is that the Pasquill-Gifford curves for D and E stability do
not allow the plume to come to the ground  at the distances  of the monitors. This is
the same result found by Lee,  et al.  (1975) at the Canal Plant.   The modeling
                                        10

-------
                                             TABLE 3

                            COMPARISON OF CALCULATED AND OBSERVED
                                   HOURLY SO2 CONCENTRATIONS
Case
No.
2
3
4
6
7
8
Monitor
N26th and Pearl
Reservoir
Highlands
N26th and Pearl
N26th and Pearl
Reservoir
N26th and Pearl
N26th and Pearl
N2Gth and Pearl
N26th and Pearl
Reservoir
Observed
Concentration
(ppm)*
(1.23)
0.27
0.73
0.40 (0.46)
0.56 (0.68)
0.62
0.30 (0.22)
0.26 (0.26)
0. GO (0.46)
0.32 (0.21)
0.37
Calculated Concentration
(ppm)*
Pasquill-
Gifford
0.03
0.00
0.06
0.12
0.00
0.00
0.04
0.01
0.00
0.00
0.00
Cramer, et al.
(1975)
0.88
0.04
0.44
0.25
0.61
0.64
0.30
0.23
0.26
0.44
0.39
Ratios of Calculated and
Observed Concentrations**
Pasquill-
Gifford
(0. 02)
0.00
0.08
0.30 (0.26)
0.00 (0.00)
0.00
0.13 (0.18)
0.04 (0.04)
0.00 (0.00)
0.00 (0.00)
0.00
Cramer, et al.
(1975)
(0.72)
0.15
0.60
0.63 (0.54)
1.09 (0.90)
1.03
1.00 (1.36)
0. 88 (0. 88)
0.43 (0.57)
1.38 (2.10)
1.05
Pasquill
Stability
Category
D
D
D
C
E
E
D
D
D
E
E
^Numbers enclosed by parentheses are concentrations measured by the ASARCO SO2 monitor at
                                                                                          and Pearl.
**Numbers enclosed by parentheses are ratios of calculated and observed concentrations for the ASARCO SO2 mon-
  itor at N26th and Pearl.

-------
                                          TABLE  3 (Continued)


Case
No.


9

12



13



14

19



Monitor


N2Gth and Pearl
N26th and Pearl
N26th and Pearl
McMicken Heights
Meeker
Meeker-Brown
Meeker
Meeker-Brown
Meeker
Meeker-Brown
McMicken Heights
Tukwila
N26th and Pearl
Reservoir
N26th and Pearl

Observed
Concentration
(ppm)*

0.37 (0.67)
0.31 (0.21)
0.25 (0.25)
0.50
0.28
0.31
0.38
0.42
0.59
0.49
0.30
0.41
0.42 (0.35)
0.50
0.27 (0.17)
Calculated Concentration
(ppm)*


Pasquill-
Gifford
0.00
0.06
0.00
0.25
0.04
0.06
0.06
0.07
0.06
0.05
0.06
0.06
0.00
0.00
0.07

Cramer, et al.
(1975)
0.64
0.11
0.31
0.52
0.28
0.35
0.42
0.50
0.56
0.53
. 0.09
0.10
0.87
1.00
0.10
Mean Ratios
Ratios of Calculated and
Observed Concentrations**


Pasquill-
Gifford
0.00 (0.00)
0.19 (0.29)
0.00 (0.00)
0.50
0.14
0.19
0.16
0.17
0.10
0.10
0.20
0.15
0.00 (0.00)
0.00
0.26 (0.44)
0.11 (0.12)

Cramer, et al.
(1975)
1.73 (0.96)
0.35 (0.52)
1.36 (1.36)
1.04
1.00
1.13
1.11
1.19
0.95
1.08
0.33
0.24
2.07 (2.49)
2.00
0.37 (0.59)
0.97 (1.00)

Pasquill
Stability
Category

D
A
E
D
D
D
D
D
D
D
C
C
E
E
B

                                                                                                             I
                                                                                                             V-4
                                                                                                             Oi
                                                                                                             I
 *Numbers enclosed by parentheses are concentrations measured by the ASARCO SOa' monitor at N26th and Pearl
**Numbers enclosed by parentheses are ratios of calculated and observed concentrations for the ASARCO SO2 mon-

  itor at N26th and Pearl.

-------
                                   -196-
techniques, emissions data, meteorological data and the air quality observations
referenced in Table 3 are described in detail in the report prepared for EPA by
Cramer, et^al.  (1976).
                                       13

-------
                                  -197-
                               REFERENCES


Briggs, G. A., 1975:  Plume rise predictions.  Paper presented at the AMS
     Workshop on Meteorology and Environmental Assessment,  Boston,  Mass.,
     September 29-October 3, 1975.

Cramer, H. E., H. V. Geary and J.  F.  Bowers, 1975:  Diffusion-model calcula-
     tions of long-term and short-term ground-level SC>2 concentrations in
     Allegheny County, Pennsylvania.  H. E. Cramer Company Technical
     Report TR-75-102-01 prepared for the U. S. Environmental Protection
     Agency,  Region in, Philadelphia, Pennsylvania.  EPA Report 903/9-75-018.
     NTIS Accession No. PB-245262/AS.

Cramer, H. E., J. F. Bowers and H. V. Geary, 1976:  Assessment of the air
     quality impact of SO^  emissions from the ASARCO-Tacoma smelter.  EPA
     Report No. EPA 910/9-76-028.  U.  S. Environmental Protection Agency,
     Region X,  Seattle, Washington.

Hay, J. S. and F.  Pasquill, 1957:  Diffusion from a fixed source at a height of a
     few hundred feet in the atmosphere.  J. Fluid Mech., 3, 299.

Hogstrom, U.  1964:  An experimental study on atmospheric diffusion. Tellus, 16,
     205.

Lee, R.  F., M. T. Mills and R. W.  Stern, 1975: Validation of a single source
     dispersion model. Paper presented at the 6th NATQ/CCMS International
     Technical Meeting on Air Pollution Modeling, Frankfurt/Main, Germany,
     24-26 September 1975.

Pasquill,  F.,  1974:  Atmospheric Diffusion (Second Edition).  Ellis Horwood
      Limited,  Sussex, England, 429.

Weil, J.  C. and D. P. Hoult, 1973:  A correlation of ground-level concentrations of
      sulfur dioxide downwind of the Keystone stacks.  Atmospheric Environment,
      T  707-721.
                                      14

-------
                                    -198-
3. 7. 3  Comments on Report of Working Group II-4
       Submitted by D. Bruce Turner and L. E.  Niemeyer after the conference.

       Group II-4 expressed concern with the adequacy of  the Pasquill-
Gifford vertical dispersion coefficients for unstable conditons when
applied to sources with tall stacks.  Nevertheless,  this  distinguished
group did not see fit to make a recommendation for changes at this time.
The Pasquill-Gifford curves have stood the test of over a decades' use.
The prudent course of action is to continue to use the time-tested factors
until such time as the data which are now becoming available as sampling  data
from the vicinity of facilities with tall stacks are organized, until they
are subjected to full scientific review and scrutiny and  until  the
scientific community agrees that current values for  the vertical dispersion
parameters result in wholly unsatisfactory estimates.  It is our current
task to encourage, enhance and aid a prompt scientific analysis and review
of  this matter.  To our knowledge such activities are underway by several
atmospheric scientists and by professional scientific societies.

-------
                                    -199-
3.8  GROUP II-S
     No Supplementary Materials

-------
-200-

-------
                                  -201-

3.9  GENERAL SUPPLEMENTARY MATERIALS

3.9.1  Use and Formulation of the Hanna-Gifford Model
 February 25, 1977
                                U.S. DEPARTMENT OF COMMERCE
                                National Oceanic and Atmospheric Administration
                                ENVIRONMENTAL RESEARCH LABORATORIES
                                 Post Office Box E
                                 Oak Ridge, Tennessee   37830
 Mr. Kenneth Brubaker
 Energy and Environmental  Systems Division
 Argonne National Laboratory
 9700 S. Cass Avenue
 Argonne, Illinois  60439

 Dear Mr. Brubaker:

 I've had a chance  to review  the section  on my model  on pages
 2.18 through 2.21  of your report "Descriptions of  Air  Quality
 Models and Abstracts of Reference Materials."  There are a
 few corrections that should  be made.   I  suppose these  problems
 are basically my fault, since we have published our  model in
 a  series of articles,  rather than in  a comprehensive user's
 guide.

 You and the EPA break  up  our model  into  a  short-term and a
 long-term model.   In reality, we never intended such a division
 and believe the model  is  equally applicable  to averages from
 20 or 30 minutes on up.   Historically, we began in late 1969
 with the equation
                         /2 1 (Az/2V^
                        V (7 o(l - W
t = i
                                                            (1)
 where  az  = axb.   After applying this equation to several gridded
 urban  areas,  we  discovered that the formula

                   X = C(Qo/U)                              (2)

 works  just fine  for most areas, where
                         f1 —
                                                            (3)

-------
                                    -202-
The "constant" C theoretically equals about 600, 200, and 50 for
stable, neutral, and unstable conditions, respectively.  So, you
see we do have theoretical expression for C, in contrast to the
statement made in your review.  Further, we always caution that
(1) should be used in place of (2) whenever the local emission
Q0 is much less than the Q's of neighboring grid blocks.  Also,
it has always been recommended as part of our model that strong
point sources (there are usually 10 or 20 in an urban area) be
treated separately using the standard plume model.

Of course, whenever good observations of concentrations are
available, the constant C in equation (3) or the expression
/tTiKAx^^-lVUCl-b)) in equation (1) should be replaced by
a calibrated value. The diurnal variation of C has always been
questionable, but through the analysis of much CO data from
several states I have recently developed a diurnal curve for
C.  This will be reported in the open literature during the next
few months.

We have also found from studying observed pollutant concentrations
that the calibrated C varies with the pollutant, being highest
for CO, lowest for S02, and intermediate for suspended particles.
There are several hand waving arguments for these differences.
Other modelers at the Nordic Hills workshop reported exactly the
same behavior with their models.

In 1973 I extended this model to Include the photochemical
pollutants NO, N02, and oxidants.  The seven-step reaction
mechanism proposed by Friedlander and Seinfeld was used, although
any kinetic mechanism could be plugged into the model.  Predictions
of this model were compared with predictions of other models in
the Los Angeles basin, showing that our model was just as good
as the others.  In this case as well as in the other reprints
that I have enclosed, we test or validate our model extensively.
Because of the ease with which our model is applied, I can
confidently state that it has been validated in the open
literature much more often than any other urban dispersion
model.  Whenever a new set of observations comes out, we test
it.

The  following outline is my suggestion for a revision to pages
2.18-2.21 of your report:

  References:l)Hanna, S. R.  A  Simple Method of Calculating
               Dispersion  from  Urban Area  Sources.  J. Air
               Poll.  Cont. Assoc., 12_f 774-777  (Dec. 1971).
             2)Gifford,  F. A. and  S. R. Hanna.  Modeling Urban
               Air Pollution.   Atmcs. Environ., _7»  131-136  (1973).
             3)Hanna, S. R.  A  Simple Dispersion  Model  for  the
               Analysis  of Chemically Reactive  Pollutants.
               Atmos. Environ.,  7_, 803-817 (1973).
                                 -2-

-------
                                  -203-
Abstract:  This is basically an area source model that can
be applied to any size grid square.  It can be used in
conjunction with the Gaussian plume model, which is used to
treat the largest point sources in the region.  Chemical
reactions and physical removal mechanisms can also be
incorporated into the area source model.

Equations:  For grid squares in which the local area sources
emissions are much less than those in neighboring  grid squares:

for area sources across which emissions are nearly uniform

                X = C(Q/U)

where

                      ^	  _. 1 _  y-1
If good data on x» Q» and U are available, C can be estimated
or "calibrated" with  these data.

A.  Source-receptor relationship
      Uniform grid squares defined by user.
      Receptors and area sources at ground level.
      Receptor at center of grid square.
      Point sources at any location.

B.  Emission rate
      User-specified for each grid square or point source,
      emissions not time-varying over the period of interest.

C.  Chemical composition
      Define the normalized concentration

             X±* = XiU/CQi                              (4)

      When chemical reactions are not important, then according
      to eq. (3), X* = 1.0.
      Assume that C = Ax/Z,   where Ax is the width of the region
      and  Z  is the height of vertical dispersion.  For illustration,
      use the Friedlander-Seinfeld (1969) seven step photochemical
      kinetic mechanism.  Then
                              —3—

-------
                                   -204-
[0 .]  = 6 -              6  a constant
  ->      INOJ
              la [NO]* =     - I - [NO,]* [RH]«  a

                             ~ ! + P^01* IRH]*
                                                               (5)
                                                              (8)
                        - ONOJ*/[NO]')
where t* = tU/Ax and a,  X,  9, and y are  rate constants.  These
equations can be rewritten, using the  reference,  for any kinetic
mechanism.

D.  Plume behavior
      Gaussian plume model with Briggs1  plume rise for point
      sources.

E.  Horizontal wind field
      User-supplied wind speed and direction over a 16 point wind rose.
      No variation of wind with height.
      Constant winds for each calculation.

F.  Vertical wind speed
      Assumed equal to zero.

6.  Horizontal dispersion
      Narrow plume approximation for area sources.
      Power law ay = axb for point sources.

H.  Vertical dispersion
      oz = axb assumption used, with a and b from Smith (1968)
      or Briggs (1973).
                              -4-

-------
                                -205-
  I.   Chemistry and Reaction Mechanism
        Friedlander and Seinfeld (1969).
        Any mechanism could be input,  however.

  J.   Physical removal
        1/(1 + C(vd/u)) factor applied  to  concentration
        prediction, where v
-------
                                  -206-
3 9.2  Recommended Changes in Draft Guidelines

Submitted by Michael D. Williams
On page 6

      I believe that the second highest of all estimated concentrations should
be used.  Thus on page 6 the relevant sentence would read:

      "Thus,  emission limits which are to be based on an averaging time of
24 hours or less shall be based on the second highest of all estimated concen-
trations (plus a background concentration which can reasonably be assumed to
occur with that concentration; see section on background concentrations). "

      This is consistent with the protocol between EPA  and Salt River Project
with respect to the measurement program for  the Navajo Generating Station
Sulfur Dioxide Field Monitoring Program.

      I note that if the highest, second highest concept is to be used, then
meteorological data equivalent to the life of the facility should be used rather
than for a five-year period.   It seems likely that the second highest at an in-
dividual point in a year will increase as the number of years tested.

Page 19

      Under item (4),  with respect to an area with meteorological or topo-
graphic complexities, I believe this item should read:

      "(4) If the meteorological or topographic complexities of the region
are such that the use of any available air quality model is precluded,  then the
model used for control strategy evaluation may be limited to a rollback model30,
if a dense monitoring  network is in place and has been operating for  at least
two years.  In this context a dense monitoring network would be one  in which
all points expected to  receive high concentrations on the basis of models" appli-
cable to the terrain are well  covered. "

      I believe this change is justified because a rollback model based on a
limited sampling network which does not address the  points of controversy
is likely to be much worse than best estimates based on existing models.

Page 29

      The last sentence in the first paragraph should read:

-------
                                 -207-
      "The receptor grid must allow sufficient spatial detail and resolution
so that the location of the maximum or highest,  second highest concentration
is identified for all areas. "

      The phrase "which are generally accessible to the public" could, under
some interpretations, be limited to roadside areas.  The mountain climber
struggling with a difficult pitch probably needs clear air too.  Furthermore,
the intent of the standards is to protect more than just human life.  Finally,
the health of vegetation on a steep  slope not generally accessible to the public
may be very important for  esthetic or soil holding purposes.

Page 30

      After the second sentence  in the second paragraph, insert the following
sentences:

      "In cases where the impact of elevated sources on high terrain during
stable conditions is to be considered the relevant winds are those measured
at expected plume height rather  than ground level or near ground level. In
cases where the frequency of A  stability conditions is important, methods
should be used to either make direct measurement of the frequency through
the use of a bivane or adjust the frequency given by the Turner categoriza-
tion scheme to provide a better  estimate of the actual frequency. "

      During stable conditions the  ground level inferences of stability, wind
speed and wind direction are likely to be so poor as to make their use unac-
ceptable. Table I reports all days of stable conditions (at plume height) re-
ported in Schiermeier's "Large  Power Plant Effluent Study (LAPPES) Volume
HI.  Instrumentation, Procedures  and Data Tabulations (1970)"  January,  1972.
                                    -2-

-------
 -208-
TABLE I
Date
April 20


April 22


April 2 7


April 28


April 30


May 5


May 8


May 9


May I'l


May 15


Oct. 14


Nov. 9


Time
0708


0700


0635


0800


0830


0700


•800


0700


0630


0830


0800


0900


Height
Sfc
50 m
400 m
Sfc
50 m
400 m
Sfc
50 m
400 m
Sfc
50 m
400 m
Sfc
50 m
400 m
Sfc
50 m
400
Sfc
50 m
400
Sfc
50 m
400 m
Sfc
50 m
400 m
Sfc
50 m
400 m
Sfc
50 m
400 m
Sfc
50 m
400 m
Speed
(mps)
2.2
4.3
12.0
0.0
2,6
12.1

-------
                                  -209-
      Exhibit #1 also shows that surface winds tend to be poor predictors.

      With respect .to the second line work by Lun«uand Church "A Comparison
of Turbulance Intensity and Stability Rates Measurements to Pasquall Stability
Classes, " Journal of Applied Meteorology, Volume 11, June, 1972; pp. 663-
669, illustrates the large discrepancy between stability as indicated by fluctua-
tions  in the vertical component of the wind and by Turner stability categories.
      Finally I have difficulties with both CRSTER and \jAlley.   The principal
defects in CRSTER are that fumigation is not considered and that reduced
plume rise with a capping inversion is not considered.  I note that a group
(Dames and Moore) modeling for utilities in the Southwest has suggested that
the plume rise may be restricted to 2/3 of its normal value.  There is a case
in the  Lap^se study in which the plume rise was about one-half of its expected
values (May 4, 1970,  near Ho«%er City).  I believe some changes could be in-
corporated into CRSTER which would permit better predictions in the case of
limited mixing situations.

      With respect toMdlley,  I believe a number of modifications are reason-
able which would improve its prediction capabilities.  These can be drawn
from experience at the Navajo Plant plus aircraft measurements of stable
flow dispersion in the Navajo region, Four Corners region and the TV A.
For more distant travel I recommend that time of travel be considered.
The principal elements of the model changes are described below.
                                    -4-

-------
                                  -210-
Modifications to CRSTER

      Schiermeir's LAPPES study carries a case where the mixing layer
clearly inhibited plume rise.  On May 4,  1970, ground level concentrations
were consistently about 0. 5 ppm at 3  km downwind between 10:30 and 10:45
(p. 80).  Helicopter temperature profiles gave a  strong inversion at 765
meters (p.  218); pilot balloons indicated an average wind speed between stack
top and 750 meters of 2. 2 to 2. 5 m/sec (p.  244).   Plant operational parameters
(pp. 287-288) gave one unit  at 2126 gm/sec,  VS = 21.3, T = 149° fi,  DT = 137° (L,
while the other unit was at 2556 gm/sec, VS = 21. 3, T = 158, DT = 106.  With
a stack height of 244 meters and stack radius of 3.65 meters, the expected
stack height will be 1380-1535 meters.  Under these circumstances CRSTER
would give a zero concentration because the expected height would be greater
than the  height of the mixing layer.  A B stability calculation using the actual
height 765 meters would give x = 1376 ug/m3 as opposed to a measured maxi-
mum of  1493 ug/m3.  The predicted maximum would occur at 4. 5 km. On the
other hand, an A stability calculation  would give 3742 at 1. 2 km.  Thus,  for A
stability one would have to use an assumption that one -half of the plume pene-
trated  the stable layer  and was trapped.  On this  basis I suggest that

     He = L if L i Hs + . 5 _fc he

however, for A stability

(1)   Qe = Q+Q  (L -  Hs- >5 Ah )   .
                                     .               ^  £
                         Ahc         •>         s  '
Replacement for Valley
      Ihe results of the Navajo study indicated that the maximum 3 -hour con-
centraliu~ ~ould be  represented by:
(2)       y.  -~
                     J. Tr u J"-
with the values considered as 10-minute averages and extrapolated to 3 hours
via a technique:
                                 -.T-
      Note that the equation for'X does not use plume reflection.   The parameters
cry and oz sure. Turner values for  E stability.  However, aircraft data in TVA,
Four Corners and the  Navajo area suggest  that the distance dependence is in-
correct.  Instead a form:
                             0

-------
                                 -211-
is suggested.   Thus I recommend

      rfy = 140. 6 x • 5*  )      oy, o*z in meters
                       )      x in kilometers
      Oz = 68. Ox -21  )

      Finally,  I believe •$••'*.« of flight should be considered.  Thus the model
would calculate the concentration expected at the receptor at x and then examine
the wind directions for the V**« x/u. If the wind direction were reasonably  con-
sistent  during that time the calculated concentrations would be presumed to
have occurred at the receptor.  Otherwise, they would not.  Work by Start,
et al. in Utah suggests similar relations apply to unstable and neutral condi-
tions.

-------
O  O   0    O   O

^	T	1	?	*-
                              O   O   O
                                         o
                                         O
                                         . n
                                             O   O
         4OOO
                    ••Illi   lili
                                              •I
         35OO'
             .   .  .11..li..  Him
         3OOO
         250O
                  j   '
             i .   .1..  I -Nil
         2OOO'
              .--•••   •••iiil.il
         15UO'
             .  •  i..  .. •-•- M--
         1000'
          5OO
             .lii-iii--   •  •-!-  11
                        -# Cases
      SURFACE
                         .lllllll..  .
        O   O
                                  o   o
                                            o   o
                                            r»   \o
                      Direction
Figure 6. The arrays of direction by height for 70 pilot balloon runs
from surface to 4000 feet above San Juan Plant Site, March 15-23, 1972.
                         -23-

-------
                                  -213-

 3.9.S  Conments on the RAM Urban Model

   itft-*Sinffer <^Auieoro/ogists, >jnc.           DATE:   February  4,  1977	


TO:  (MESy Model Study File    (Maynard E. Smith)	

FROM: JRM	,	,	

RE:   The RAM Urban Model	


     Howard Ellis  made  many points in his study of the RAM Model,  a
 few of which deserve comment in regard to the  Argonne Conference.

     RAM is an urban model designed to calculate maximum 24-hour con-
 centrations on a grid using a year's worth of  data.  It  could be used
 for 1-hour  and 3-hour predictions.  Criticisms of it follow:

     1.  The model has  not been validated.  An extensive validation
         study must be  done before the predicted results can be con-
         sidered realistic.

     2.  o'z values are  excessively high for Pasquill A and B (they
         are equal for  these two classes).  The az curve for Pasquill
         A  and B is not as extreme as the CRSTER Model.   However, the
         •effect would be a doubling or tripling of the predicted max-
         imum  concentration compared to our modeling of tall stacks
         for 1-hour periods under very unstable and unstable conditions.

     3.  These o"2  values were developed from the St. Louis tests in  .
         the mid-60*s,  using low-level tracer releases and do not ap-
         pear  to be applicable to even low-level sources, let alone
         elevated  sources.  In brief, <$z  is considered inversely pro-
         portional to  the crosswind integrated concentrations observed
         in these  tests.  Ellis documents several studies showing that
         the urban plume centerline is lifted by a mean upward wind
         component, leading to very low surface CIC and the resulting
         inflated  
-------
                              -214-
3.9.4  Corrments Regarding EPA Models
                   ENVIROPLAN,  INC.
                   An Environmental Planning Company
                 47 ORIENT WAY. RUTHERPORD. N J. O7O7O
                           2O1 935-5O98
                                 February 16, 1977
Mr. Maynard E. Smith
"Smith-Singer Meterolegists, Inc.
134 Broadway
Amityville, New York   11701

Dear Maynard:

          Concerning your 1/29/77 letter, I would like to stress
the following points concerning the E.P.A. Models no\7 in use.

          1.  RAM Model (Urban Version).  This model was used
for the first time in E.P.A. regulation setting in Ohio and
has seven serious problems.  The enclosed evaluation adequately
describes our concerns as well as recommendations for a Revised
RAM Model for urban areas.  I and many of the electric utilities
in Ohio with plants in urban areas will be very grateful if you
would read the enclosed critical evaluation, perhaps discuss it
with me prior to the meeting, and then forcefully present the
criticisms you concur with at the 2/22 meeting.  Perhaps the
most, serious problem with Urban RAM is the use of vertical
dispersion rates that are greatly in excess of Pasquill-Gifford
vertical dispersion rates and that lead to predicted maximum
short-term concentrations from power plants that are tv;o to
four times previous predictions using Pasquill-Gifford dispersion
rates.  I consider the use of Urban RAM as presently constituted
to be the single most serious problem with present E.P.A. models
for urban areas.

          20  Use of Pasquill Stability Class A as the Most
Extreme Vertical Dispersion Rate.  My concerns with this issue
and the necessity of using a less extreme vertical dispersion
rate under the most unstable meteorological conditions are
presented in the appendix to the Muskingum River Plant report
prepared by Enviroplan in late  1976.  Your office has a copy
of this report.

          3.  Prediction Modeling in Complex Terrain.  Two  issues
are of special importance here:T5the assumed point of closest
approach of the plume centerline to ground-level as the terrain
rises, and 2) the enhancement of the turbulence and dispersion
as the plume approaches elevated terrain.  The Egan approach to
issue 1) of reducing the effective stack height by half the

-------
                                  -215-
     Mr.  Maynard E. Smith
     February 16, 1977
     Page 2
     increase in ground elevation seems more reasonable than using
     the E.P.A. CRSTER Model's full ground displacement procedure
     of subtracting the entire difference in ground elevation
     between receptor and stack base from the effective stack
     height.  Progress on this issue and issue 2) would be helpful
     in eliminating some of the very major conservatisms in E.P.A.
     prediction modeling with CRSTER.

               4.  Proposed Use of Five Years of Meteorology in the
     Prediction Modeling.  If E.P.A. adopts this procedure of using
     five individual years of meteorology in modeling, it is important
     some measure of actual operating rates rather than constant
     maximum possible operating rates be used.  One excuse for using
     constant maximum operating rates is that only a  single year of
     meteorology is being used for analysis.  If the  number of years
     of meteorology increases, some adjustment in the assumption on
     operating rates is needed to produce reasonable  predicted con-
     centrations.

               Finally, I would certainly welcome any future
     opportunities there may be to participate directly in future
     meetings*of this type.


                                      Sincerely,

                                      ENVIROPIAN, INC.
                                       Dr.  Howard M.  Ellis
                                       President
     HME/pl
     Enc.
ENVIROPLAN. INC.      • An Environmental Planning Company •

-------
                                  -216-
3.9.5  Descriptions of C.E.G.B.  Air Quality Models
                                  Central Electricity Research Laboratories   —
               Description of C.E.G.B. Air Quality Models
 References   Papers by Moore D.J. listed in EPA-600/4-76 030a, also:
 Scriven, R.A. and Fisher B.E.A. and Fisher B.E.A. 1975 Atms Env. 9^
   49 and 59 and 1063.
 Fisher B.E.A. and Maul P.R. and Moore D.J. 1976 Proceedings of Symposium
   'Systems and Models in Air and water Pollution*  Institute of Measurement
   and Control, London.
 Moore, D.J. 1975 Proc. Inst. Mech Eng. 189, 33. and 1976 Atmospheric
   Pollution 51-30 Ed. M. Benarie, Elsvier.
 Abstract
           The C.E.G.B. models include
      (i)  Gaussian models for predicting maximum g.l.c.s. from single
           sources, the vertical spread being related to an average
           vertical diffusivity, conservation of emitted material assumed.
     (ii)  Diffusivity profile models for calculating medium and long
           range effects including wet and dry deposition and chemical
          '. reactions.

-------
                                   -217-

Gaussian Model

A.   Source-Receptor Relationship

          Point source only, twin stacks assumed separate for plume height

calculation single source for g.l.c. calculation.  Multi-flue stacks as single

sources.  Receptor measuring points usually at 2 m above surface.

B.   Emission Rate

          Take from station load and fuel data.

C.   Plume Behaviour

          Flume rise calculated form C.E.G.B. plume rise equations.  Growth

due to relative motion taken into account in calculating location of maximum

g.l.c. but not its magnitude.

D.   Horizontal Wind Field

          Wind speed and direction measurements made to about 2  stack heights

on adjacent tall  towers.  Value of B based on measurements of crosswind spread

and fluctuations  in measured wind direction.

                            2
E.   Vertical Dispersion  o    =  LX.
      - - J  - r T~~ "-••*•  —  '      Z

     L is a function of:

Height of source  and/or height of mixing layer  (H,h)  (m)

Free stream wind  speed                          (U)  (ms  )

Lapse rate  above  stack top                      (36/3z)  (Km   )

Sensible heat flux due to   (i) solar heating of         „
                               ground.          (E.)  (Wm )
                                                 o
                       or  (ii) advection effects
                               or cooling of            _
                               cloud-tops       (Ej)  (Wm~ )

Surface roughness length                        (Z )  (m)

Coriolis parameter                              (f)  (s   )

Assumed independent of sampling the T  for T > 3 min.
                           2            22
F.   Lateral Dispersion  a    =  LX +  B X  .

     B is a function of free stream wind speed and  sampling  time T(s)  (for

given source location and wind direction).

-------
                                   -218-
G.   Emission and Meteorological Correlations




          The calculations give ensemble mean values of maximum g.l.c.  under




a restricted range of meteorological conditions and emission rates when plume




material remains within the mixing layer.  Vertical wind effects and interaction




with the top of the mixing layer cause variations about the ensemble mean and




lead to a scatter with values falling within a range 0 to 2 times the ensemble




mean value.



          Terrain effects are not included as the sites studied are mostly




flat.



          Calculations refer to SO- but could be applied to any other




conservative emission with negligible fall velocity.




          Background is estimated from observations.  The model may be used




to  make estimates of concentration out to about 3 times the distance of




maximum g.l.c.



          Sampling periods from 3 minutes to 1 year.




H.   Validation/Calibration




          Comparison with 3000 hours of observations at 2 power  stations.




r.m.s. residuals 
-------
                                  -219-

B.   Horizontal Wind Field


          Average wind direction calculated using available meteorological


data (wind or pressure field).


C.   Vertical Wind Speed - None.


D.   Vertical Dispersion


          K profile, K proportional to height in layer of depth Z.., constant

                »<
from Z, to h, zero above h.  Constant value of h consistent with values of  L


in gaussian model (K..  =  -*-  where L and K are both independent of z).


E.   Crosswind Dispersion


          Based on gaussian model for hourly average and wind rose or


distribution of wind trajectories for annual average.


F.   Surface Deposition


          Use is made of effective deposition velocity  (V ) at the top of
                                                         O

the surface layer, which is accurate for long range transport.



          V   .
           g





For near and middle distance transport exact K model uses V  at ground.
                                                           O

G.   Removal by Precipitation


          Can be included by suitable decay constant for hourly or daily


average.  Precipitation included statistically with constant expected duration


of dry and wet periods for annual average.


H.   Chemical reactions


          Conversion of S02 to  SO, included.


Background; Man-made contribution may be calculated if  source inventory available.


I.   Validation/Calibration


          Flux of S02 leaving U.K. compared with aircraft measurements of


vertical profiles for middle distance transport.


          Dry and wet deposition of S over W. Europe compared with OECD


network.  Observations of concentrations of S02 and SO^ at distances up to


80 km  from groups of power stations compared with  aircraft  cross-sections.

-------
                                  -220-
          G.l.c. of S0_ and SO, at distances up to 150 km from urban




complex.




J.   Output




          Variable to suit requirement - e.g.  surface deposition pattern,




g.l.c. pattern, SO. flux.

-------
                                 -221-

3.9.6  Description of the Air Quality Short Term Model
Reference:
Abstract:
 Equations:
"Air Quality Short Term Model," Illinois Environmental
Protection Agency, Springfield, Illinois, January 1976.

The Air Quality Short Term Model (AQSTM) is a steady
state Gaussian plume model for estimating concentrations
of relatively stable pollutants for averaging times from
an hour to a day from multiple point sources in .level or
complex terrain.  Concentrations can be computed to simu-
late inversion break-up fumigation, lake shore fumigation,
and atmospheric trapping.
             1)   Contribution from each upwind point  source
                            Q
                             cr  u
             2)   Trapping
Q
'27T OyLu
i
~X2
ry \
^ay
2~
             3)   Fumigation
	 	 — __ «xp
^Truery,hr
•\
\
y
i",.J
.2
                                        wh«r«i   h,:  H -f 2CTZ
              4) Continuous  Lake  Shore Fumigation

-------
                                       STABLE  AIR
                                                       >v_>ta**^^<>«fcA^«^A--*»-*^-.*--A-A
                                                                       >
                                                                       Co
                                                                       •>
                                                                       •'
                                                                       I
Figure 2. Lake Shore Fumigation.

-------
                                 -223-
A.  Source - Receptor Relationship

    Arbitrary location for a maximum of 200 point sources
    Up to 900 receptors located on uniform rectangular grid
    Unique release height for each point source
    Unique separation for each source-receptor pair
    Unique topographic elevation for each receptor and source
    Receptors must be at ground level


B.  Emission Rate

        Unique constant emission rate for each source


C.  Chemical Composition

        Treats one or two pollutants simultaneously


D.  Plume Behavior

        Briggs (1971, 1972) final plume rise formulae
        Does not treat downwash.  If plume height exceeds mixing height,  maximum
        plume height is limited to the mixing height.  Treats fumigation  by:  (a)
        user specifying specific height of limiting lid and averaging time,  or (b)
        user specifying rate of rise of inversion breakup (mixing height) *»nd  starting
E.  Horizontal Wind Field                                            height.

        User specifies hourly wind speed and direction
        Wind speed corrected for release height based on power law variation.
        Constant, uniform (steady-state) wind assumed.  In complex terrain,  plume
        allowed to rise % the distance between the base of the stack and  the height
        of a ground-based receptor.   Plume assumed to remain at constant  height  above
F.  Vertical Wind Speed                               ground following initial rise.

        Assumed equal to zero


G.  Horizontal Dispersion

        Dispersion coefficients from Turner  (1969); no adjustments made  for
          variations in surface roughness  or travel time.
        User-supplied hourly stability class
        Averaging time adjustment  according to Turner (1969)(to one  hour).

H.  Vertical Dispersion

        Dispersion coefficients from Turner  (1969); no adjustments made  for
          variations in surface roughness, averaging  time or  travel  time
        User-supplied hourly stability class


I.  Chemistry/Reaction Mechanism

        Not treated

-------
                                 -224-
J.  Physical Removal

        Not treated


K.  Background

        Not treated


L.  Boundary Conditions

        Lower boundary: perfect reflection
        Upper boundary: user-input mixing height used; perfect reflection
                        assumed
        Permits user  to input continuous non-horizontal boundary layer
        or rising boundary layer.

M.  Emission and Meteorological Correlations

        N/A


N.  Validation/Calibration

        No calibration option provided
        Direct application of Turner (1969) procedures


0.  Output

        Average concentration, source contributions at each receptor  for
          total period of interest
        Individual  point  culpability list for 5 maximum receptors
P.   Computer  Time  Requirements

         14  sources with  800 receptors requires 8 seconds CPU  time
           (IBM 370/168)

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                                -225-
3.9.7  Application of Air Quality Models Under the Ontario Environmental
      Protection Act

Submitted by A. E. Boyer: post-conference.
          The design of pollution control equipment and the
administration of environmental protection legislation are both
complicated by the wide variability of air quality resulting from
the interaction of pollution source characteristics and limiting
meteorological variables.

          From the point of view of source design, it is often
desirable to design facilities to operate under whatever range of
meteorological and operating conditions are likely to occur during
the life of the source.  An evaluation of limiting meteorological
conditions, however, often shows that these limiting conditions
occur a small fraction of this time; to design facilities to
operate under all conditions, including these infrequent happenings,
may result in greatly increased design costs.

          The development of environmental legislation requires
resolution of the need for laws which are easily  understood and
enforced and at the same time, cover a wide range of complex
possibilities.  These two ends are not easily resolved.

          The development of several air quality  simulation models
provides tools for the analysis of the interaction of important
source and meteorological conditions.  The problem then  is how to
apply these models within the legal and engineering  limitations
noted above.

          The Ontario Environmental Protection Act provides the
framework for agencies and pollution  sources to  function within
a  simply enforced law based on a point source model.  This Act
also allows  for redefinition of these  simple controls  in the
event subsequent ambient air quality  does not meet  desired goals.

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                                -226-
          An example of the application of both simple point
source and complex urban air quality simulation models within
Ontario legislation is illustrated by Boyer and Shenfeld in,
"Atmospheric Impact of Coal Firing", Power Magazine,  March 1975.
The question of converting an urban power generating  station to
an all coal-fired operation is examined.   In this example, the
Ontario Ministry of the Environment applied a multi-source urban
model to the question of air quality impact under the assumption
of the interruption of natural gas consumption by industrial and
commercial users, limiting meteorological conditions  and con-
version of a large power generating station to an all coal-fired
operation.

          The conclusions of the above study are less important
here than the methodology and the division of responsibility
between the individual sources and the control agency, in this
case, the Ontario Ministry of the Environment.

          Each of the sources contributing to the S02 levels under
study had previously been required to meet short-term (30-minute)
air quality criteria for various pollutants.   Compliance with
this regulation can be satisfied by estimates of impingement
concentrations based on a simple point source model.   Certain
frequently occurring meteorological conditions are specified for
the basis of this estimate.

          The responsibility of the control agency is then to
estimate the combined effects of multiple sources under a wide
spectrum of weather and operating conditions.  This is accomplished
in a variety of ways, including the application of urban air
quality simulation models. ^»2

          If after application of the urban model, the control
agency decides that the combined effect of multiple sources all
meeting the regulation for a single source is unacceptable, then
the single source limits may be changed by the issuance of new
guidelines for the application of single source models.  Other
options may be included in the amended guidelines as, for example,
the use of supplementary control systems.

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                                -22?-
          Working within this framework,  each source is assured
that so long as ambient air quality objectives are met, the
single source requirements with which they are forced to comply,
will not be made more stringent.

          The advantage of the guideline described above is that
it allows for enforcement based on a simple straightforward point
source model, while at the same time, allowing for a more detailed
assessment of complex multi-source model applications by the con-
trol agency.  In addition, while the various evaluations are in
progress, it is in the interest of both the agency and the various
sources that ambient air quality goals are met.

          The procedure suggested above is similar to the use of
emissions offset regulations used by the Environmental Protection
Agency.  They are different in that the Ontario guidelines make
allowances for not only emission offsets to achieve desired air
quality, but also variations in stack design, emission temperature
and velocity, and in some cases, may include options for supplemental
controls under limiting meteorological conditions.
 1.  Boyer, A.E. and Shenfeld, L.  "Atmospheric Impact of Coal
    Firing", Power, March  1975.

 2.  P.S. Wong, K. Heidorn  and D. Yap, "Modeling Sulphur Dioxide
    Levels in the Sarnia Area", Water, Air and Soil Pollution
    (1976) 407-414.

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                                 -225-
5,3.8  Comments Regarding CRSTER
                                t  wwc.
                       134 BROADWAY. AMITYVILLE, N. Y. 117O1 TEL: 516-691-3395
                                        March 21, 1977
 Dr. John J. Roberts
 Deputy Division Director
 Energy and Environmental Systems
 Argonne National Laboratory
 9700 South Cass Avenue
 Argonne, Illinois   60439

 Dear Dr. Roberts:

      Your letter of March 4 about the Specialist Conference on the
 EPA Modeling Guideline suggested that individual letters be sub-
 mitted for the Appendix where participants have comments which are
 pertinent to the sessions, but were not actually a part of pro-
 ceedings.

      In Session II-4 the randomizer system used in the CRSTER model
 was discussed, and the working group decided that the method should
 be studied to see whether wind direction variability is adequately
 represented.  This letter is a partial response to that suggestion.

      The CRSTER model provides for a random adjustment to the wind
 directions which are typically reported only in 10° intervals by
 the National Weather Service.  This procedure is supposed to simu-
 late the real wind fluctuation from hour to hour.
                                                               \
      The effect of the system on calculated ground-level concentra-
 tions has been evaluated.  Table 1 shows the concentrations directly
 downwind of the three-hourly mean wind under three conditions;
 (a) with no direction fluctuation, (b) with a JIl° shift, and
 (c) with a +4°, -5° shift.  The latter is the maximum variability
 possible in the existing CRSTER system.  Only with the maximum fluc-
 tuation is the mean ground-level concentrations reduced appreciably.

      Most modeling systems based on real data use a method sumulating
 a more rapid lateral plume diffusion than allowed by CRSTER.  TVA
 uses Oy values which are larger than Pasquill-Gifford; both Smith-
 Singer and the state of Maryland use wind shear terms; Cramer assumes
 more rapid diffusion while the plume is rising after release.

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                             -225-
Dr. John J. Roberts                                March  21,  1977
     Two studies are suggested to EPA  to define a better randomizer
system:

     1.   Study real hourly mean wind  direction fluctuations
          to determine the typical fluctuations over  3-hourly
          and 24-hourly periods.

     2.   Examine the oy values implied by the TVA, Maryland
          and Smith-Singer systems.  These data should show
          how the randomizer might be  improved.

                                       Sincerely yours,
                                       «ayna¥d E. Smith
                                       Mark L. Kramer
                                       John R. Martin
MES:la
Enclosure

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             EFFECT OF WIND DIRECTION RANDOMIZATION ON GROUND-LEVEL  CONCENTRATIONS
  Wind
 Speed

(m/sec)
Wind Categories:

Fixed Direction

Stability

Unstable



Neutral

(Hypothetical 300-meter stack)
o
Three-Hour Concentrations Along 180° Radial (ug/m )
i 	 — • - 	 - 	 ^ ;
Fixed Minimum Maximum
Distance Direction Variation Variation
(km)
2 97 96 84
5 769 761 649
10 453 448 371
15 261 258 209
5 3 3 .1.5
/ 10 264 212 •' 116
' 15 535 497 219
ies: 1st Hour 2nd Hour 3rd Hour
ion 360° 360° 360°
ation 360° 359° 001°
ation 360° 355° 004°
                                                                                                   tNO
                                                                                                   CJ
                aiW*, vjrn

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                                -231-
3.9.9  Cormsnts On the Need for Model Aaaupaey
Post Conference Contribution by J.  L. Shapiro
      In several different parts of the  report,  references are made
to the required accuracy for uniformity of validity of models.  For
example, the Preface refers to an implicit "level of accuracy desirable. "
In Section 2. 5. 4 "the group generally agreed this assumption is not
uniformly valid. " • There are other references to this subject,  both
explicit and implicit.
      While some attention was directed to this by Group II-5,  with an
indication that performance standards of models should be related to the
"loftiness of performance goals, "  the overall tenor of the report does not
adequately reflect this view.  It appears that the validity of models as
evaluated by the workshop participants is readily destroyed by citing
instances where the models' predictions were exceeded.  In general,
the group is relatively satisfied with models that over-predict as
compared to models that under-predict.
       This traditional approach obviously is necessary in many
applications but it is also obviously highly over-restrictive  in some
cases, particularly where PSD limits are on the border line of
 significance.  In such cases,  models  should be adopted which are the
best available and which most closely represent an "expected value"
with the recognition that some cases will over-predict while others
 will under-predict.  As  experience is gained and we learn more about
the details, we would expect that  such models will be  refined to reduce
the magnitude of the errors.

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-232-

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                                   -233-

3.10  POLICY ISSUE SUPPLEMENTARY MATERIALS
3.10.1  On the Use of Statistical Techniques for the Prediction of Second
        High 24 Hour Concentrations
        Post Conference Contribution
        Richard A. Porter3 P.E.
       Air pollution standards styled as levels "not to be exceeded more than
one time per year" require a probalistic approach in modeling.  However, models
that are currently available (listed in the draft guideline) are not suitable
for determining the second high value.  Only the RAM model is available with
a statistical post processor, but the RAM model contains no method for testing
the model-generated data for goodness of fit to the proposed log-normal dis-
tribution.  Predictions of values at the extreme end of the probability dis-
tribution function are very sensititive to the fit of the data to the proposed
distribution.  Order of magnitude errors may be encountered when the data is
forced to fit the wrong distribution.
       Many investigators have examined the frequency distribution of air
pollution concentrations at air pollution monitoring facilities.  A wide range
of  skewed frequency distributions have been fitted to the empirical data.  The
distributions proposed include Weibull  (Barlow, 1971; Milokai, 1972) and neg-
ative-binomial distributions (Prinz and Stratman, 1966); but by far the most
extensively fitted distributions are log-normal (Bencala, and Seinfeld, 1976;
Knox and Pollack, 1974; Shoji and Tsukatani, 1973) in the multiple source ur-
ban environment and exponential  (Gifford, 1959; Scriven, 1965; Gartrell, 1966)
in  an environment dominated by a single source.  In the case of the urban
environment the work of R. I. Larsen (Larsen, 1970; 1971) based on analysis of
seven years of continuous monitoring data from USEPA monitors in urban areas
is  the most widely cited investigation of the log-normality of air pollution
concentration.  Studies by P. J. Barry  (Barry, 1971; 1975) support the theory
that receptors,dominated by a single source,record concentrations of pollu-
tant that are exponentially distributed.  The work by Barry is based on sev-
eral years of data using ARGON-41 injected in the plume of a power plant.
(Porter and' Christiansen, 1976).
       In the paper "Predictions of Annual Sulfur Dioxide Concentrations for
Frankfurt An Main, Federal Republic of Germany, Aug., 1971 to July 1972
(Porter and Christiansen, 1976) ,  a frequency distribution of concentrations

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                                     -234-
 was generated using the Texas Episodic Model  for  242 receptor points in the
 Frankfurt area.   Two methods were used to  estimate  the parameters of the
 log-normal distribution: 1)  the graphical  method  of Larsen  (Larsen, 1971),
 essentially the concept used in RAM;  2)  the Delta- Log-normal distribution
 (Atehison and Brown, 1957),  a three parameter log-normal distribution that
 accepts zero variates.  Tests for log-normality using the Kolmogorov-Smirnov
 Statistic (Lillienfors,  H. W., 1967)  failed at 73 of the 242 receptors for
 one or both of the methods used to estimate the log-normal parameters.
        All but two of the receptors that failed the test for log-normality
 were located on the edge of  the urban area.   Both theory and observed data
 suggest that the frequency distribution of concentrations at a receptor due
 to a single point source is  exponential  (Gifford, 1959; Barry, 1975).  In
 theory, a receptor located in the  center of a uniform area source will have
 a log-normal distribution of concentrations (Bencala,  and Seinfeld, 1976).
 One can expect that receptors that  are located in urban areas (away from the
 influence of a strong single source) will have log-normal distributions, and
 receptors that are influenced  by a  single source will have exponential dis-
 tributions.   However,  the frequency distributions of concentrations at recep-
 tors that do not  fit  in  one  of  the above categories are probably a mixture
 of the two distributions.  The  experience with the Frankfurt study cited
 above indicated that  unreasonably large values for the second high concentra-
 tion were predicted  for  non-urban receptors that had low predicted mean con-
 centration.   These receptors  failed the test for log-normality.  It is sug-
 gested that  any model that uses statistically processed data to estimate con-
 centrations  should include a  test of the data for goodness of fit to the pro-
 posed distribution.

        Alternative Methods
        The alternative to the  statistical approach is  to use the "worst case"
 estimate  for the  concentrations.  The RAM model  can be used to survey a spec-
 ified  period  of meteorological record to determine the worst case condition
 in  terms  of  predicted concentration.  Or a short term  model (such as the Texas
 Episodic  Model) can be exercised on selected scenarios of meteorology that
 can be expected to result in the highest concentrations for the sources in-
volved .

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                                     -235-
                        REFEPENCES FOR SECTION 3.20.1
 1.     Barlow, R.E., Averaging Time and Maxima for Air Pollution Concentra-
       tions, NTIS AD  727/413, ORC 71-17  (1971).

 2.     Milokaj , P.G.,  Environmental Applications of the Weibull Distribu-
       tion Function:  Oil Pollution, Science, 176:1019-1021  (1972).

 3.     Prinz, B. and Stratman, H., The Statistics of Propagation Conditions,
       Staub, Vol. 26, Number 12, pp. 4-12  (1966).

 4.     Gifford, F.A.,  Statistical Properties of a Fluctuating Plume Model,
       Proceedings of  Symposium  on Atmospheric Diffusion and Air Pollution,
       Advances in Geophysics, 6:117-137  (1959)

 5.     Scriven, R.A.,  Some  Properties of  Ground Level Pollution Patterns
       Based on a Fluctuating Plume Model,  CEGB Lab Note No. RD/L/N 60/65
       (1965).

 6.     Gartrell, F.E., Control of Air Pollution From Large Thermal Power
       Stations, Revue Mensuelle 1966 de  la Sec. Beige des Ingenieurs et
       des Industrials Bruxelles, pp. 1-12  (1966).

 7.     Larsen, R., Relating Air  Pollutant Effects to Concentration and
       Control, Journal  APCA,  20:214-225  (1970).

 8.     Larsen, R., A Mathematical Model for Relating Air Quality Measure-
       ments to Air Quality Standards3 USEPA Pub. No. AP-89  (1971).

 9.     Barry, P.J., Uses of Argon 41 to Study the Dispersion of Effluent
       from Stacks,  Nuclear Techniques in Environmental Pollution, Inter-
       national Atomic Energy Association,  Vienna, pp. 241-255 (1971).

10.     Barry, P.J., Stochastic Properties of Atmospheric Diffusivity,
       Atomic Energy of  Canada Ltd. AECL  5012  (1975).

11.     Shoji, H. and Tsukatani,  T., Statistical Model of Air Pollutant
       Concentration and Its Application  to the Air Quality Standards,
       Atmospheric Environment,  Vol. 7, pp. 485-501 (1973).

12.     Atchison and Brown,  The Lognormal  Distribution, Cambridge Univer-
       sity Press ,  (1957) .

13.     Lillienfors, H.W., On the Kolmogorov-Smirnov Test for Normality
       With Mean and Variance Unknown, J. Am. Stat. Assoc. 62:399-402
        (1967).

14.      Porter,  R.A.  and  J.H. Christiansen,  Predictions of Annual Sulfur Dioxide
       Concentrations  for Frankfurt An Main, FRG, Aug; 71  to July; 72,
       Practical Demonstration of Urban Air Quality Simulation Models,
       NATO/CCMS Air Pollution Pilot Study  in print (1976).

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                                    -236-
15.     Bencala, K.E. and J.H.  Seinfeld,  On Frequenot Distributions  of Air
        Pollutant Concentrations,  Atmospheric Environment,  Vol.  10,  pp. 941-
        950, (1976).

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                                    -237-
3.10.2  Methods for Estimating Levels Not to Be Exceeded or Not to  Be
        Exceeded Move Than Onae Per Year
        Distributed at Conference
        Richard A.  Porter
        The sound scientific approach to estimating compliance with standards
 styled "not to be exceeded more than xxx times per xxx" is a probabilistic ap-
 proach.  However, none of the models that are now widely available contain
 the statistical post processor necessary to make such a probability state-
 ment. :  Therefore, some method must be stated for estimating compliance with
 such short-term standards without having the necessary statistical post
 processor.  There are two possible methods: 1) survey of historic  data;
 2) estimation of worst case from meteorological and source considerations.

        Survey of Historic Data
        This is the method used by RAM.  The question becomes : What period of
 record should be used ?  Using more than one year of data causes problems with
 computational time.  Using only one year of data limits the number of meteor-
 ological conditions examined.  Practical considerations can decree the per-
 iod of record examined.  Less than one year of data would be unacceptable.

        Estimation of Worst Case
        An experienced modeler can estimate the worst case conditions when
 only a few sources are being considered.  Such an option is very economical
 and should be available as a tool for estimating worst case.
  JThe paper Predictions of Annual Sulfur Dioxide Concentrations for Frankfort
 Air Main Germany, Aug. 71 - July 73.  7th Technical Meeting of the NATO/CCMS
 Committee.  (Porter and Christiansen, 1976), discusses the problems associated
 with frequency distribution determination.

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                                    -238-
3.10.3  Position on Recently Proposed Amendments to the Clean Air- Act
        (Draft; prepared by the Committee on the Meteorological Aspects
        of Air Pollution of the American Meteorological Society and
        not yet approved as of the date of the conference by the Amer-
        ican Meteorological Society Council.)
        Distributed at Conference
        Bruce A. Egan
        The 94th Congress considered, at length, proposals to the Clean Air Act
of  1964, as revised in 1970.  Many issues were discussed and presented to
Congress and its committees in consideration of these amendments; a joint
House-Senate conference bill was also prepared.  Congress adjourned without
passing any amendments.  The Committee on the Meteorological Aspects of Air
Pollution of the American Meteorological Society feels it necessary to estab-
lish  certain issues of policy regarding the use of meteorological knowledge
in  air  pollution studies.  Inasmuch as these policy issues were addressed in
the Clean Air Act Amendments, we feel it is important to comment on areas
where meteorological expertise is required.
        Existing legislation and Environmental Protection Agency regulations
have  established National Ambient Air Quality Standards (NAAQS) designed to
protect the health and welfare of the general public by not allowing air
pollution levels to exceed certain values for different averaging times.
Under these regulations, the establishment or continuation of any source of
air pollution is allowed if it can be shown that pollution from that facil-
ity will not add to the pollution burden by an amount which would cause an
excess  of the NAAQS.  A second type of regulation adopted by the Environmen-
tal Protection Agency addresses the emission of pollution from new industrial
and other sources.  New Source Performance Standards (NSPS) require that a
facility must restrict emissions of certain criterion pollutants per unit of
production using Best Available Control Technology (BACT).  Thus there are
in  existence two types of standards,  one which addresses ambient air quality,
and a second which requires that each new source be controlled so as to lim-
it, within prescribed amounts, its pollution emissions to the atmosphere.
       The Supreme Court of the United States has determined (Sierra Club
vs. Ruckelshaus) that the Clean Air Act Amendments of 1970 empower the Envi-
ronmental Protection Agency to issue  a third type of regulation to prevent

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                                    -239-
the significant deterioration of air quality and in particular to preserve
existing clean air regions.  Regulations were promulgated by the Environ-
mental Protection Agency in 1974 which provide for the classification of
areas within the U.S. according to allowable increments of deterioration.
In Class I (pristine areas) no significant deterioration of air quality
would be allowed.  Class II areas would allow very little deterioration
and Class III regions would allow air quality to deteriorate to the NAAQS.
       A number of methods or  'models' have been used historically by federal,
state, and local agencies, by  industrial sources and others for calculating
the ambient air quality resulting from various configurations of air pollu-
tion  sources.  Mathematical models, when used responsibly with good meteoro-
logical data, have proven  invaluable  in analyzing the effects of new construc-
tion  on ambient air quality.
       This committee is particularly concerned that language in certain of
the versions of the Clean  Air  Amendments proposed to the 94th Congress and
in the Conference Report  (House of  Representatives Report No. 94-1742, Sep-
tember 30, 1976) included  wording to  the effect that a model or a group of
models would be designated by  the Administrator of the Environmental Pro-
tection Agency to evaluate the impact of new and existing sources of emis-
sions on ambient air quality.
       The term  'model', with  respect to air quality considerations, has
been  taken to mean a relationship,  analytical or empirical, which relates
the ambient air quality as measured at  a receptor to the emission of mate-
rial  from sources influencing  that  receptor.  Such relationships can be
established and have, in  fact, been validated on many occasions, if meteo-
rological conditions are  adequately known.  While there  is  much  active re-
 search  in progress to improve  the current  state-of-the-art  of  such models,
 the present models do not  permit accurate  predictions of ambient air qual-
 ity when meteorological conditions  are  not  known.  Further, many meteoro-
 logical phenomena which are  responsible for threats  to  short  averaging  time
 standards are  not amenable to  simple  mathematical  treatment.
Our  specific concern is with the concept that there  should  be  currently
 established one particular model which  will be  capable  of analyzing all  con-
 ceivable  situations.  From a professional meteorological point of view,  we

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                                     -240-
 feel  this  is not  a  tenable position to support.  The scales of atmospheric
 motion  which are  responsible for dispersing pollutants vary from site to
 site  and exhibit  quite variable behavior.  While meaningful averages can
 generally  be calculated  and established from data, there is no model cur-
 rently  available  which has a proven reliability for a range of commonly en-
 countered  topographical  and meteorological situations of importance.  The
 diversity  of meteorological, geographic, and site-specific conditions which
 exist is such  that  very  careful choice of a model is important and atten-
 tion  must  be given  to  whether a given modeling approach adequately handles
 the situation  of  concern.  Models, after all, are simply an extension of
 professional opinion and abilities on the part of the model maker.  To in-
 cautiously or  casually apply a model to a specific situation without care-
 ful meteorological  and other professional consideration is not appropriate
 for problems having significant economic and social implications.  While
 it  may  be  desirable from a legal point of view to adopt one uniform method
 for calculating air quality, it is a scientifically indefensible and un-
 reasonable procedure,  given the non-uniformity and complexity of the atmos-
 phere,  and the wide variability in meteorologically relevant geometric fac-
 tors.
        The  above discussion has been implicitly considering the pollutants
 for which  non-deterioration standards are presently being proposed, namely
 sulfur  dioxide and  particulates.   When one considers the problems of mod-
 eling the  transport and  transformation of other pollutants of concern such
 as  sulfates or oxidants which may involve atmospheric chemical reactions
 and other  physical  phenomena still not well understood (for example, dis-
 persion of  pollutants  over long distances) then the arguments are even more
 compelling  to not advocate the adoption of uniform modeling techniques.
 The state-of-the-art simply does not justify it.
        The  atmosphere  is a large but finite resource.   Used responsibly, it
 can provide a disposal mechanism for many of the pollutants of our modern
 society.  Responsible use, however, includes continual consultation with
 professional meteorologists whose knowledge and experience will provide
guidance on the best current methods for evaluating the effects of emissions
on air quality, and who can provide even more reliable methods as our under-
standing of atmospheric phenomena increases.

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                                    -241-
3.10.4   Consistency and Standardization
        Distributed at Conference
        Bruce A.  Egan
       Considerable discussion at the conference centered on the issues of
needs for consistency and standardization and the compromises inherent in
adopting standardized models.
       Standardization of approach was stated as a concept which would great-
ly relieve the work load presently within the various EPA regional offices
and support facilities with respect to reviews of SIP revisions, New Source
Reviews, PSD, etc.   Standardization was also stated as an objective expressed
to EPA by various industry groups who were concerned that different EPA re-
gional offices had different approaches and this may result in variations in
required emission limitations for what otherwise could be considered similar
source situations.   Therefore, differences resulted simply as a matter of
location.  A case where two regional offices disagreed on the methodologies
for the same source affecting both regions was cited as indicative of the need
for a consistent approach.  It was stated that new sources needed to be
assured that, after construction was started, the ground rules would not
change so as to require still more stringent emission limitations.  Section
318 of the September 30, 1976 House of Representatives Conference Report was
cited as requiring that a modeling conference be held and that..."special
attention shall be given to appropriate modeling necessary for carrying out
subtitle C of Title I  (relating  to prevention of significant deterioration
of air quality)."
       Standardization of modeling approaches has the potential for creating
other problems.  The present state-of-the-art does not permit the identifica-
tion of a single specific model which would be most appropriate for most
sources.  Site by site analyses which include room for professional judgment
regarding choice of modeling techniques and meteorological parameters is
generally required.  They are especially appropriate for the analysis of
siting and control alternatives  of sources where large capital  investments
are involved.  The problem with  the use of a specific standard model is that
if this model is non-conservative  (that is, it is considered to be appropriate
for a "most probable"  concentration estimate) it can be expected to err
sometimes by overpredicting  and  sometimes by underpredicting.  Over predictions

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                                      -242-
  would suggest emission limitations which are beyond that  called for in order
  to maintain ambient  air quality  standards and therefore not cost-effective;
  underpredictions  would suggest emission limitations which would result in
  concentrations in excess of  the  standards - therefore not responsive to EPA
  regulations.   Thus,  a  standardized approach if used to define emission
  limitations which would result in concentrations close to an air quality goal
  (NAAQS  or  PSD increments) has hidden costs associated with potential "over-
  kill" and  "underkill"  on a site by site basis.
         A second problem associated with standardization as discussed above
  is  that it  tends  to discourage the advancement  of our scientific understand-
  ing of  air  pollution phenomenon.   A standardized approach would have resolved
  the debate  of  the  two regional offices cited  above but the resolution might
  not have satisfied either's concerns  about  the  specific technical issues at
  hand.   Science advances more responsively when  technical differences are
 aired and a judgment passed on the basis of the  technical arguments.
        A solution  proposed in these guidelines which meets the  concerns  out-
 lined above is to  move  toward the adoption of standardized models for screen-
 ing purposes.   These  models would be  purposefully made  to  produce  "conserva-
 tive" estimates of ambient air quality  levels.   They would be used  to  iden-
 tify and efficiently  deal with "non-problems."   If, for  example, the conser-
 vative model indicated  that a new source would not  threaten air quality  goals,
 a quick  approval could  be granted.  If  the model  indicated a potential pro-
 blem,  then  a refined modeling and analysis approach should be defined which
 would  attempt  to produce more realistic estimates of the actual impact.  The
 required approach  could  involve the collection of more complete data, more
 applicable  (site-specific) meteorological parameterization and model valida-
 tion efforts.   The  guideline  document can then provide guidance on the choice
 of  generic models  (basic  model types)  which are thought to be most appropriate
 for  the  various source  types and for various combinations of source config-
 uration, local  meteorology, and local topography.
       Discussion concluded in the topic of providing in the guideline docu-
ment a strong statement regarding  the rights of  sources to choose alternative
modeling approaches for the refined types  of estimates.   The predictions which
result from the use of alternative models must be defensible in  a scientific
sense implying adherence to known  physical  principles  and the presentation of

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                                    -243-
model validation data.   A corollary to this discussion was the recognition
that if a new model were to prove superior to the screening model from an
accuracy point of view (adjusting for the built-in conservatism of the
screening model) then the new model might be a candidate for upgrading the
standardized model at a future date.  A conclusion was the recognition of
the need to establish a very good data base in the public domain for purposes
of evaluating alternative modeling approaches.

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                                    -244-

3.10. 5  Statistical Evaluation of Compliance
        Distributed at Conference
        Noel de Nevers
       In evaluating the modeling results to determine compliance with short-
term AAQS (not to be exceeded more than once per year) or PSD (not to be
exceeded), the test shall'consist of computing the predicted concentration
at the worst receptor point, for each applicable period (1 hr.,  3 hr., or
24 hr. where applicable) for a period of not less than	 years and longer
if data are available and using the most probable modeling techniques.  The
resulting computed values shall be fitted by a suitable cumulative frequency
distribution function.  The distribution shall be considered to demonstrate
compliance if the cumulative distribution function indicates that the second
high short term standard will not be exceeded twice in a calendar year, more
than one year in each 	 years, and that the PSD increments will not be
exceeded more than once per 	 years.
 This version should be regarded  as  a  draft  version.   The final form is
 incorporated in Section 2.2.5.

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                                    -245-
3.10.6  Statistical Evaluation of Compliance  (Revised) *
Distributed at conference by Noel de Nevers
       In evaluating the modeling results to determine compliance with
short-term AAQS (not to be exceeded more than once per year) or PSD (not
to be exceeded), the test shall consist of computing the predicted concen-
tration at the worst receptor point, for each applicable period (1 hr.,
3 hr. or 24 hr. where applicable) for a period of not less than _1 year
and longer if data are available (but generally not more than 5_ years) and
using the most probable modeling techniques.  The resulting computed values
shall be fitted by a suitable cumulative frequency distribution function.
The distribution shall be considered to demonstrate compliance if the
cumulative distribution function indicates that the (second high) short
term ambient air quality standard will not be exceeded twice in a calendar
year, more than one year in each xx years, and that the PSD increments
will not be exceeded more than once per y_ years.
       (The above assumes we MUST use the same format (second high) as
is used in the definition of the AAQS.  We understand that the format was
chosen to correspond to the realities of ambient air quality sampling.
The realities of modeling are different.  Thus for modeled compliance -
we would prefer the appropriate section of the final sentence to read
"... indicates that the short term ambient air quality standard will
not be exceeded, on the average, more than once per calendar year, and
that the PSD . . . "}
       We have purposely left the x and y in the above paragraphs undefined.
We believe that a thorough study of the consequences of possible choices
of those values should be made before values are assigned.
*
 This  version should  be regarded as a draft version.   The final form
 is incorporated  in Section 2.2.5.

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                                    -246-


3.10.7  Comment on Statistical Guide for Compliance
        Distributed at Conference
        W.  A.  Perkins
       It is common engineering practice to use statistically computed cri-
teria for structural design to meet extreme meteorological conditions.  Drain-
age as flood control improves and wind loads on structures are two examples.
Appropriate meteorological parameters are treated by special statistical
procedures to estimate conditions for specified return periods ranging from
10 to 100 years (10% to 1% of the distribution function).   These estimates
often exceed the period of record by a factor of two or more.
       Meteorological conditions required for atmospheric  transport models
are very different from those used in the above examples.   However, the con-
cept of using a statistical approach to determine compliance is analogous to
established practice.

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                                    -247-
3.10.8  Validity of Hour-by-Hour Estimates of Air Quality (The Use of the
        Nowoast in Pollution Potential Forecasting)
        Distributed at Conference
        A.  E.  Boyer
       A practice developed in pollution potential forecasting has been to
analyze current conditions so as to prepare a consistent picture of air
quality and meteorological data in map format.  This process is very similar
to the meteorological exercise called "map analysis."  In the case of the
weather map, selected reports of pressure, temperature, wind, precipitation,
and other variables are used to estimate the continuous initial distribution
of air mass and frontal characteristics.
       In the case of pollution potential forecasting, an exercise referred
to as the "nowcast" consists of analyzing initial values of wind velocity and
shear, continuous temperature gradients, stabilities, along with measured and
simulated air quality values to estimate the continuous initial state of
velocity and thermal structure of the atmosphere.
       The  nowcast  is an attempt to use a limited amount of air quality and
meteorological data in the vicinity of a pollution source to present a snap-
shot or analysis of the continuous distribution of air quality and meteor-
ology in three dimensions.  It is based on the concept that if one is given
the height  of an elephant at eye level, it is likely that one could estimate
other dimensions of the elephant.  In a similar fashion, given a limited
number of meteorological, source, and air quality measurements, a skilled
meteorologist using mathematical simulation models can fill in the gaps
between the reporting points.  This initial state analysis is then the basis
of decisions for the need for environmental control in the coming hours.  It
is a corollary of meteorological predictions that no prediction can be more
detailed or more accurate than the initial state analysis upon which it is
based.  It is for this reason that the concept of the nowcast or of the
initial state analysis of air quality and meteorology is so important.

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                                     -248-
 3.10.9  A Comment Concerning the Use of Unverified Models as "Relative
         Predictions "
Post-conference contribution submitted by Philip M. Roth
       Oftentimes there is doubt as to the accuracy of a particular model
as an "absolute predictor" due to shortcomings in its formulation.  On
occasion such models are used instead to estimate differences in concentra-
tion between a base case and a case of interest.  Insofar as we can see,
there is no evidence to support the hypothesis that a model whose absolute
predictive capability is in doubt will, in general, perform more reliably
when used as a relative predictor.  Hence, those notes of caution raised
concerning the use of models as absolute predictors, i.e., the need for
model evaluation and verification for a variety of relevant conditions,
should apply when these models are used as relative predictors as well.

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                                -249-

3.10.10  Comments on Various Issues
                        134 BROADWAY. AMITYVILLE, N. Y. 117O1 TEL: 516-691-3395


                                          March 25,  1977
  Dr. John J. Roberts
  Deputy Division Director
  Energy and Environmental Systems
  Argonne National Laboratory
  9700 South Cass Avenue
  Argonne, Illinois    60439

  Dear Dr. Roberts:

      This letter summarizes several ideas which we consider
  pertinent to the issues raised at the recent Specialist's Con-
  ference on the EPA Modeling  Guidelines.   We would like to have
  it included in the Appendix.

      1.  Screening Procedures

          Over the past  decade the  effort  required for the approval
          of any facility emitting  pollutants has grown enormously.
          Much of this effort  is good because it represents genuine
          concern about  environmental problems, but some is totally
          unnecessary paperwork and computation.  We strongly favor
          the use of screening techniques  to simplify approval proce-
          dures .

          As recommended by  Working Group  1-2, it should often be
          possible to reach  a  "yes-no" decision about a new facil-
          ity from a limited set of calculated hourly mean concen-
          trations.  These  should cover a  wide range of hypotheti-
          cal wind and stability conditions, but need not involve
          any site data.   If none of these test calculations exceed
          a  specified concentration level, the facility could be
          accepted without  further study.

          To apply such  a  system one should know how hourly values
          are related to 3-hourly and 24-hourly concentrations.
          The recent paper  by  Martin and Reeves* should be helpful
          to EPA in  developing such a screening procedure.  They
          show  the following ratio.s apply for a typical group of
          power  plants:
   * Martin,  J.  R.  and Reeves, R. W. , Relationships Among Observed
   Short-Term Maximum Sulfur Dioxide Concentrations Near Coal-Fired
   Power Plants. 1977 Annual APCA Meeting (in press).

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                          -250-
Dr. John J. Roberts
                                   March  25,  1977
                                                Q 77
                                              = n 28
            Maximum 3-Hour  Concentration

            Maximum 1-Hour  Concentration

            Maximum 24-Hour Concentration

            Maximum 1-Hour  Concentration

            Maximum 24-Hour Concentration _  Q  37

            Maximum 3 -Hour  Concentration

2.   Air Quality Standards

    Although we understand  why EPA is  reluctant  to revise
    the established air quality standards, a number of the
    Conference participants favored a  change in  expressing
    the standards so that more reasonable and  reliable
    statistics could be employed.   Revising  the  air quality
    standards so that maximum values are  not involved would
    be a great improvement  for the following reasons :

    a.  The maximum or second maximum, whether computed or
        observed, is an inherently controversial quantity.
        One can always argue that  the  examination of a
        longer period of record or a more adverse set of
        assumptions would reveal a higher concentration than
        had been discovered to date.

    b.  A standard based on extreme values tends to involve
        spurious data.  Improper recorder operation, computer
        errors or even one  untenable assumption  may suggest
        an apparent violation where none  exists.

    c.  Protecting against  an absolute maximum is not a real-
        istic objective. All human activity involves risk
        and it is reasonable to balance the  severity of the
        event against the probability  of  occurrence.  Total
        elimination of risk is an  unreasonable objective.

    There is no technical reason why the  short-term standards
    cannot be redefined so  that the limiting values fall with-
    in the normal statistical population. Careful choice of
    new limiting values would still prevent  the  existing max-
    imums from being exceeded very often, but  it would elimi-
    nate the controversial  "never" concept.

    For example, one might  specify in  a new  3-hour ly SC^ stand-
    ard that a concentration of 200 /ig/m^ should not be exceeded
    in more than 1% of the  cases.   Based  on  typical probability
    distributions this would insure that  a l,300Aig/m3 level
    would seldom be exceeded.

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                              -251-
Dr.  John J.  Roberts                     March 25,  1977


    3 .   Alternative Models or Algorithms

        The participants emphasized the basic conflict  between
        the need for more consistent regulatory treatment and
        the need to retain flexibility.  In striving for con-
        sistency,  EPA Guidelines should not stifle alternative
        approaches to solving air quality and emission  problems.
        If they do, certain situations would be treated unrea-
        sonably, and both research and imaginative review would
        be discouraged.

        On the other hand, capricious acceptance or rejection
        of alternatives at the state and regional levels could
        destroy regulatory consistency, a primary objective of
        the Guideline.

        We recommend a review procedure for alternative algorithms
        in which the systems would be evaluated by the  EPA techni-
        cal staff in Raleigh.  Once an alternative system has been
        approved,  it would no longer be necessary to justify its
        use for each subsequent application in any region, " other
        than to assure that the system is appropriate for a par-
        ticular problem.

        The term "equivalent" should not have the same stringent
        limitations as would be true of air quality measurements.
        Diffusion modeling is much less precise, and there is no
        certainty that a given EPA algorithm is significantly bet-
        ter than some other alternatives.  The limits of accept-
        ability should therefore be correspondingly broad.

    4.   Rollback

        The initial reaction of most of the Conference participants
        was to reject rollback entirely as a modeling technique.
        It seems to us, however, that rollback still has a place in
        control strategy.  It is appropriate when applied to a large
        group of very similar sources which cannot be modeled or con-
        trolled in any other way.  Residential heating and automo-
        biles are good examples of this type of source.

        Rollback should not be used as a substitute for a more
        erudite analysis merely because the problem is complex, the
        modeling is difficult, or because it is time-consuming.
        Application in these circumstances is precisely what has
        given rollback a bad name.

    5.   Background Concentrations

        The definition of background concentrations is still an
        unsettled problem.  However, there are serious flaws  in

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                              -252-
Dr. John J. Roberts                     March 25,  1977


        two methods which are frequently proposed.

        a.  For an isolated source, or closely-packed group of
            sources, the proposed background is the maximum
            concentration at a nearby air quality station
            determined when the source cannot affect it.   This
            definition suffers from the same drawbacks  as the
            maximum in an air quality standard.  Instrument
            problems and very unusual meteorological circum-
            stances may be responsible for this single  value.

            It is erroneous and overly conservative to  assume
            that this maximum background exists throughout the
            region and on all time scales.   A significantly
            lower value should be selected.

        b.  The draft Guideline suggests an optional method to
            determine background when no monitors are near the
            source.  This method consists of using as background
            an average monitored concentration from sites in
            similar topographic and climatological settings .
            The flaw in this technique is that the average moni-
            tored concentration at one location is influenced by
            its own local sources.  It does not represent back-
            ground even at the point of measurement. Transferring
            this concentration from one location and calling it
            the background at another site compounds errors .

    6.  Use of Measured Air Quality Data

        Accurate air quality data, regardless of the agency re-
        sponsible for measurement, must play a more important role
        in assessing compliance with standards and in model vali-
        dation.

        As mentioned earlier, the frequency distributions of air
        quality data are more valuable than isolated maximums .
        They provide a more complete picture of the impact of a
        pollutant on the area.  Distribution curves will reflect
        the effect of emission changes more faithfully  than maxi-
        mum values, since they are much less sensitive  to minor
        variations in meteorology or measurements.

        Another important use for the data distributions is in
        the validation of diffusion models.   Comparing  the pre-
        dicted and observed concentration distributions is more

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                             -253-
Dr.  John J. Roberts
                                         March 25, 1977
       effective than relying upon matching the observed and
       predicted maximums ,  especially for short-time scales.
                                         Sincerely yours,
                                         Mark L. Kramer
                                         "John R. Martin
MES/MLK/JRM:rg

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ERT
                         -254-

3.10.11  Corrments on the  Use of Proprietary Models and Some Examples

         Post conference  submission by B. Egan
ENVIRONMENTAL RESEARCH &TECHNOLOGY, INC.
CONCORD,MASS. • CHICAGO • LOS ANGELES • WASHINGTON. DC
         REF: AQSD-1428


         25 April 1977
         Dr. John J. Roberts
         Argonne National Laboratory
         9700 South Cass Avenue
         Argonne, Illinois  60439

         Dear Dr. Roberts:

              I would like to offer some supplementary material  relating to the
         issue of proprietary models and the inclusion of descriptions  of models
         in  the Report of the Specialists Conference  on  the EPA  Modeling Guideline.

              ERT feels that it will be very important for EPA to  clearly articulate
         its position regarding its intended exclusion of proprietary models  in
         the Modeling Guideline recommendations.   In  particular, a clarification
         should be made regarding whether the intent  is  to discourage the use of
         proprietary models by EPA regional offices or whether the intent is  more
         generally to not accept the results of analyses performed by anyone  on
         the basis of proprietary model computations.

              As a consulting firm with business in the  areas of model  development
         and application, ERT is concerned about the  implications  of the  latter
         possible intention.  We feel strongly that,  just as  patents and  copyrights
         protect investments in other commercial areas,  establishing a  proprietary
         status to models or algorithms  which have commercial value, provides a
         means of encouraging and protecting private  investments in the development
         of advanced modeling techniques.  Our company,  for  example, over the
         years has  invested well over one-half million  dollars in internal  development
         projects relating to model development.  We  believe that private investments
         have historically contributed substantially  to advancing the state-of-
         the-art of modeling and we, therefore, wish  to discourage EPA from
         "legislating away" incentives for further advancing our capabilities in
         the area of modeling air pollution impacts.   In our opinion, proprietary
         status does not  at all preclude the possibility that a model (including
         computer code) can be reviewed and tested in detail by concerned parties.

              Having stated our feelings about the principles involved, as a
         matter of practice we recognize the potential  benefits of having a
         number of our models more widely appreciated.   We are,  therefore, offering
         for inclusion in the Report of the Specialists Conference, the enclosed
         descriptions of  models which ERT currently utilizes in air quality
         studies.  We have summarized the main features of four of the models in
         accordance with  the format Argonne used in the referenced document:
         Descriptions of  Air Quality Models and Abstracts of Reference Materials.
      696 \flRGINA ROAD CONCORD. MASSACHUSETTS 01742 (617) 369-8910  TELEX 923 335 ENVIRORES CNCM  CABLE; ERTCON

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Dr. John J.  Roberts                                         27 April 1977
REF:  AQSD-1428                      -255-
Versions of each of the models have been considered non-proprietary by
ERT in the past.  If it becomes a prerequisite for inclusion in the
modeling guideline, ERT would offer the current versions as non-proprietary
also.

     I would suggest that the ERTAQ description be included in the
section on multiple-source, set 1 pollutants: the ARTSIM and LAPS models
be included in the section on set 2 pollutants and EGAMA be included in
the Long Range Transport and Loss Mechanism
     For more general interest we have  enclosed  a  tabular  description of
computer models utilized by  ERT  for  a variety of applications.  An
important point is the recognition that a number of important  environmental
assessment problems require  special  models which include detailed treatment
of effluents as a function of unique source configurations or  thermodynamics
(e.g. cooling towers, gas turbines). Other applications,  such as SCS
(Supplementary Control Systems)  feasibility studies,  require specialized
programs which utilize system operational input  data and provide output
information which includes considerations of a variety of  engineering
and operational constraints.

     I hope these materials  will be  of  use to you  in the  final report.
                                             jrely yours,

                                                          CAJL*S^
                                               A. Egah"    /
                                         Manager
                                         Air Quality Studies Division

 /mk

 Enclosures
  Section  3.1.4

 +Sections 3.3.4  and  3.3.5

  Section  3.4.1

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                         -255-
      TAHULAR SUMMARY OF SONlli KliY  ERT,  INC. AIR QUALITY M011HLS
Model
    Key Elements/Features
     Typical Applications
ERTAQ
• Gaussian Plume  in Vertical
• Sector Crosswind Averaging
• Climatological  Wind Rose  Input
• Briggs Final Plume Rise

• Turner (Pasquill/Gifford) o
• Point, Line and Area Sources

• Climatological  Mixing Depth
• Regional Studies

• "Background" AQ

• Seasonal/Annual Averages

• Arbitrary Source-Receptor
  Geometry

• Level Terrain Only

• Low Level Sources

• Transportation Links
PSDM
• Double Gaussian Plume  (ASME
  coefficients)
• One or Two  Point Source Sites
  Only (£ 10  stacks per  source)
• Sector Centerline Concentra-
  tions Only
• "Weather Condition"  Inputs
  Designed  for  stack height
  determination

  Isolated  Source  DEC
  Feasibility Studies

  Identifications  of "Worst
  Case" Meteorology

  Identification of Maximum
  Ground  Level  Concentrations
EGAMA
• High Resolution Numerical
  Simulation Model based on
  Conservation of Mass Equation
• Eddy Diffusivity  (K Theory)
  vs.  Gaussian Dispersion
• Time and Spatially Variable
  Meteorological Inputs Allowed
• Requires independent determina-
  tion of flow fields (Done
  internally for Highway Appli-
  cations)

• Forward time-step "Marc' ing"
  Computation Routine
• Highway  Impact  Studies
  (Especially well  suited for
  near roadway AQ estimates)

• Special  Studies:
  Downwash
  Street Canyon
  Terrain  Effects
  Fumigation

• Research Tool
  Long range transport
  Chemical Transformation
  Sea/Lake Breeze Flows
ATRAJBOX
  Air Trajectory Transport
  Model with well-mixed  BOX
  approach
  Linear Chemistry  and Depo-
  sition processes
  Tracks transport  of air from
  its origin to receptor for  a
  specified time
• Regional  Transport of S02
  and  SO.
• Ideal  for predicting darly
  mean concentrations at a
  receptor  for an episode

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                                  -257-
    TABULAR SUMMARY OF SOME KEY I-.RT, INC. AIR QUALITY MODELS continued
Model
    Key Elements/Features
     Typical  Applications
COOLTOKR
• Based on Convective Rise of
  Cumulus Cloud Formations
• Moisture Parametrization, Phase
  Change and Plume Merger
  Considerations
• Meteorological Data Inputs
  Cooling Tower Studies:  Visible
  Plume Length, Icing and Fogging
  Potentials
DEPOT
• Drift Deposition Model which
  Includes Consideration
  of Plume Dynamics, Droplet
  size and Mass Distributions,
  evaporation effects on drop
  fall velocity
• Cooling Tower Salt Deposition
  Predictions
PSDM-2
• PSDM modified to incorporate
  fall velocity and ground
  deposition rate
  Particulate AQ studies
 FUMIG
• Based on EGAMA  and Bounda -y
  Laver Parameterization
• Time dependent inversion
  breakup fumigation
• Sea Breeze Fumigation
 SULFA3D
• Three-Dimensional Adveci-ive-
  Diffusive  Grid  Model
• Linear  Deposition and Transfor-
  mation  processes with first
  order Chemistry
• Long range regional transport

• Regional Sulfate Predictions
 MONITOR
 •  Dispersion Model  inputs  used
   to  rank  useful 1'ness  of
   alternative monitoring
   locations
  Optimal Monitoring  Site
  Location
 PROBL
 • Dispersion Model inputs
   used to generate statistics
   for probability analysis of
                                              • SCS Feasibility Studies

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                       -255-
    TABULAR SUMMMIY OP SOM1I KI-Y URT, INC. AIH QUALITY MODELS continued
Model
    Key Elements/Features
       Typical  Applications
RATE BOX
• One-Dimensional Sector/Square
  Model with well-mixed BOX
  Approach for Point/Urban Area
  Sources

• Finite-Difference Numerical
  Solution

• Includes Washout and Rainout
  Point  or  Urban  Scale Sulfate
  Predictions

  Ideal  for local SO-, SO. and
  H2S04
DIFDEP
(Diffu-
sion/Dep-
osition)
CRSVAL
DIFKIN
• Same as Plume with  TWO Tracer
  Equations for SO. and SO.

• Linear Chemistry and Removal
  Processes
                                               Point Source Sulfate Problems
  Acid Rain Predictions
• Modification of EPA CRSTER
  Models for Complex  Terrain

• Incorporates soi:e features /
  of EPA-Valley Model
• Gaussian Plume r.oncentration
  profile with Multiple  Images
  Estimates of hourly ground
  level concentrations due  to
  large point source in  areas
  of complex terrain


  National and State Ambient
  Air Quality Standards  Studies
• Finite Difference solutions
  based on mass conservation Eqn.
• Chemical kinetics and the
  upward spread through a
  series of vertical cells
• Vertical diffusion coefficients
  Regional maximum oxident
  concentrations

  "Background" 0, concentration

  Transportation strategy
  evaluation of 0- concentration
ASTEC
• Gridded Area Sources
• Gifford/Hanna Concept
             «
• Computationally Inexpensive
• Regional Model  (Flat Terrain)

• Multiple Area Sources (Homo-
  geneous Emission  Geometry)
PLUME
• Rising Plume Deflected by
  Crosswind

• Morton, Turner and Taylor
  Local Similarity Hypothesis
• Conservation Equations and
  Entrainment Coefficients
• Meteorological Sounding Data
  Inputs
• Gas Turbine  Plume  Studies


• Unconventional  Stack Geometry
  or Effluent  Characteristics

• Final  Plume  Rise Predictions

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                                    -259-
3.21  Descriptions of Air Quality Models
       This section is an exact reproduction of Sections 1 and  2  of  the
notebook entitled "Descriptions of Air Quality Models and Abstracts  of
Reference Materials" prepared by ANL staff and distributed to the parti-
cipants prior to the conference.  A supplement to that notebook was  prepared
and distributed at the conference.  It has also been reproduced here.
       Section 3 of the notebook, which has not been reproduced here, contained
abstracts of the references cited in the draft Guideline.

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-260-

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   -261-
SECTION 1
 GENERAL

-------
                                     -262-
                                 Introduction
       This section contains two outlines,  the first  listing the aspects of
atmospheric dispersion which simulation models must treat and the second
providing a list of features defining applications to which models are applied.

       The first outline of model characteristics provides the framework for
the model descriptions in Section 2.   It also  provides a  common basis for
comparing and evaluating models.

       The second outline of features of model applications is intended to aid
in the identification of specific features  characterizing situations related
to various issues to be addressed.

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                                    -263-
                            Model Characteristics
 I.   General  considerations
     A.   Method  as  implemented
     B.   Basis for  parameterization

II.   Specific elements
     A.   Emission characteristics
         1.   Source-receptor relationship
             a.  Downwind  distance
             b.  Orientation
             c.  Release height-elevation
             d.  Horizontal  location
             e.  Ground level vs. elevated
         2.   Emission rate
             a.  Spatial variation
             b.  Temporal  variation
         3.   Chemical composition of  emissions
     B.   Transport  characteristics
         1.   Plume  behavior
             a.  Plume  rise
             b.  Fumigation  - inversion  breakup
             c.  Downwash
             d.  Looping
             e.  Trapping
         2.   Horizontal wind field
             a.  Shear  (variation  in  speed  and direction)
             b.  Periodic  variations  (seasonal,  diurnal)
             c.  Terrain effects (e.g.,  channeling)
             d.  Persistence
         3.   Vertical wind speed
             a.  Terrain effects
             b.  Lake breeze
         4.   Horizontal dispersion
             a.  Stability
             b.  Surface roughness
             c.  Averaging time as  compared to transport  time
         5.   Vertical dispersion
             a.  Stability
             b.  Surface roughness
             c.  Mixing height
             d.  Elevated  vs. ground  level  sources
     C.   Removal and transformation
         1.   Chemistry  and reaction mechanism
             a.  Secondary production
             b.  Chemical  removal
         2.   Physical removal
             a.  Dry deposition
             b.  Precipitation  scavenging
             c.  Resuspension

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                                      -264-
      D.  Background
          1.  Long term
          2.  Short term
          3.  Directional dependence
      E.  Initial conditions
      F.  Boundary conditions
          1.  Ground
          2.  Mixing height
          3.  Sides in complex terrain
      G.  Correlations
          1.  Emissions
          2.  Meteorological parameters

III.  Validation
      A.  Sensitivity analysis
      B.  Field studies

 IV.  Calibration - confidence limits

  V.  Requirements for implementation
      A.  Resource requirements
          1.  Personnel
          2.  Monetary
          3.  Computer
      B.  Data requirements

 VI.  Output
      A.  Averaging time
      B.  Spatial resolution
      C.  Source culpability list
      D.  Frequency distribution
      E.  Special output (e.g., amount  deposited

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                                     -265-

                       Features of Model Applications
 I.  Pollutant characteristics
     A.  Production
         1.  Primary
         2.  Secondary
         3.  Resuspension
     B.  Removal
         1.  None
         2.  Chemical
         3.  Physical
     C.  Chemical  identity
         1.  Single substance
         2.  Mixture

 II.  Averaging time
     A.  Long-term: (month,  season,  year)
     B.  Short-term:  1-24 hrs.  (1,  3,  8,  24)

III.  Source  characteristics
     A.  Number
         1.  Single
         2.  Multiple
     B.  Geometry
          1.  Point
         2.  Area
          3.  Line
         4.  Combination
     C.   Release height
          1.  Ground level
          2.  Elevated
     D.   Temporal
          1.  Constant
          2.  Time-varying

 IV.  Transport characteristics
     A.   Geography
          1.   Simple  terrain
          2.   Complex  terrain (rough terrain, lake breeze)
          3.   Urban
          4.   Rural
      B.   Range
          1.   Short
          2.   Meso
          3.   Long
      C.   Transport time

  V.   Output
      A.  Mean
          1.   Specific location
          2.   Arbitrary locations or isopleths
      B.   Frequency distribution
          1.   Specific location
          2.   Arbitrary locations or isopleths
      C.   Maximum at  specific receptor or overall maximum


 VI.  Activity
     A.   Overall planning
     B.   Source-specific review

-------
-266-

-------
         -267-
       SECTION 2
DESCRIPTIONS OF MODELS

-------
                                    -268-
                                Introduction

       This section provides brief descriptions  of  the  models  suggested for use
in the guidelines.   Rollback has not been included.   A  brief discussion of the
limitations of rollback is provided in Reference 30 in  Section 3.   The format
of each description follows the outline of model  characteristics given in
Section 1.  The descriptions are in alphabetical  order.

-------
                                     -269-
                                                                      APRAC-1A
                                  APRAC-1A
Reference:  Nos. 34, and 35 in the guideline.

            Ludwig, F.L. and R.L. Mancuso.  "User's Manual for the APRAC-1A
            Urban Diffusion Model Computer Program."  Prepared for Division
            of Meteorology, Environmental Protection Agency, under Contract
            CAPA-3-68C 1-69) (NTIS PB 213091), Research Triangle Park, North
            Carolina 27711, September 1972.

            Ludwig, F.L. and W.F. Dabberdt.  "Evaluation of the APRAC-1A
            Urban Diffusion Model for Carbon Monoxide."  Prepared for Di-
            vision of Meteorology, Environmental Protection Agency, under
            Contract CAPA-3-68 (1-69) (NTIS PB 210819), Research Triangle
            Park, North Carolina 27711,  February 1972.
Abstract:
Equations
            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; introurban,
            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.
                                UL
                                                 %tn 22. £

                                                    32 kn\ -h> 1000 km
                                   i-b--   /.(>.. i
                       r *       / __!U — l*i — I
                       J   uau  \    f-bf    /
                                  o

-------
                               -270-                          APRAC-lfl
~$-l\ u)livcU/        X =  WI'I.CA





                  ^ =  ketaU- 
-------
                                    .271-                            APRAC-1A
APRAC-1A

A.  Source-Receptor Relationship

      User specifies set of traffic links (line sources) by providing  link
        endpoints, road type, daily traffic volume
      The traffic links may have arbitrary length and orientation
      Off-link traffic allocated to 2 mi x 2 ml 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.   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  2 mi 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.   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

 D.   Horizontal Wind Field

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

 E.   Vertical  Wind Speed

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

-------
                                     .272-                            APRAC-1A
F.  Horizontal Dispersion

      Sector averaging uniform distribution within sectors
        22.5  sectors beyond 1 km
        45.0  sectors within 1 km

G.  Vertical Dispersion

      Semi-empirical/Gaussian plume
      6 stability classes; stability class determined internally from user-
        supplied meteorological data [modified from Turner  (1964)]
      Dispersion coefficients from McElroy and Pooles (1968), modified
        using information in Leighton and Ditmar (1953)
      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

H.  Chemistry/Reaction Mechanism

      Not treated

I.  Physical Removal

      Not treated


J.  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

K.  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

L.  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

-------
                                     -273-                           APRAC-1A
M.  Validation/Calibratlon

      No calibration option provided
      Some documented validation experience available

N.  Output

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

-------
                                     -274-
                                                                         AQDM
                                     AQDM
Reference:  No. 26 in the guideline.

            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.
Abstract:
Equations:
            The Air Quality Display Model (AQDM)  is a climatological
            steady state Gaussian plume model  that estimates annual
            arithmetic average sulfur dioxide  and particulate concentra-
            tions at ground level.  A statistical model  based on Larsen
            (1969) 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.
                                Sources '•
                k-l f-l
                                                               W
                                                                   X ^
                           u,L
              C=
                            «//S-f«nc€  be-kueen reiepb

                                            low ha*
                                                                 /c center-tine

-------
                                    -275-
                                                               AQDM
AQDM

A.  Source-Receptor Relationship

      Arbitrary location 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 pollutants simultaneously

 D.  Plume Behavior

       Holland (1953) formula, with adjustment for stability
       No olume 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

 E.  Horizontal VHnd Field

       Climatological approach
       16 wind directions
       6 wind soeed classes
       No variation in windspeed with height
       Constant, uniform (steady-state) wind assumed

 F.  Vertical Wind Speed

       Assumed equal to zero

 G.  Horizontal Dispersion
        Climatolonical  approach
          Linear  interpolation  between  22.5° sector  renter!ines;
         center value calculated by sector averaging procedure
          (narrow plume  approximation)
       Averaging time = 1 month -  1 year

-------
                                     -276-                       AQDM
H.  Vertical Dispersion

      Semi-empirical/Gaussian plume
      5 stability classes  (Turner, 1964)
      Neutral stability split internally into 60% day, 40% night
      Dispersion coefficients from Pasquill (1961) and Gifford  (1961)
      Neutral dispersion coefficients used for all neutral and  stable  classes
      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

L.  Boundary Conditions

      Lower boundary (ground):  perfect reflection
      Upper boundary (mixing ht):  no effect until a ^.47H
                                   (occurs at x=x.) z
                                for x > 2x, uniform mixing
                                in between-linear interpolation
                                  transition region used

M.  Emission and Meteorological  Correlation

      Hind 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 x afternoon climatological  value
        Class D (night, internally divided)
          average of 100 meters  and afternoon  cl imatological  value
        Class E - assumes 100 meters

N.  Vali dation/Calibration

      Calibration option available
      Substantial experience but limited documentation

-------
                                    -177-
0.  Output
      1 month - 1 year averaging time simulated (arithmetic mean only)
      Arbitrary averaging time by Larsen (1969) procedure
        (typically 1-24 hr)
        Assumes
           1)   lognormal concentration  distribution,
           2)   power  law dependence  of  median  and maximum
                 concentrations  on averaging  time
       Up to 225  gridded receptor locations, 12 arbitrary locations
       Individual point, area source  culpability list for each receptor

-------
                                     -278-
CDM
                                     COM
Reference:  No. 27 in the guideline.

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

Abstract:   The Climatological Dispersion Model (COM) is a climatological
            steady-state Gaussian plume model for determining long-term
            (seasonal or annual) arithmetic average pollutant concentra-
            tions at any ground level receptor in an urban area.

            A  statistical model based on Larsen (1968) is used to trans-
            form the average concentration data from a limited number
            of receptors into expected geometric mean and maximum con-
            centration  values for several different averaging times.

 Equations :                   lA   <,  (,
                                   StcWk
                                                                     0.8 L
                  * cix

-------
                                  -279-
                                                               CDM

COM

A.  Source-Receptor Relationship

      Arbitrary location for each point source
      Area sources equal uniform grid squares
      Receptor location arbitrary
      Arbitrary release hts for pt. 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 pollutants simultaneously

D.  Plume Behavior

      Only Briggs (1971) neutral/unstable formula used.
      If stack height + plume rise is greater than mixing height,
        ground level  concentrations assumed equal to zero.
      Alternative to Briqgs - 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  (1959)
      Constant, uniform (steady-state) wind assumed

F.  Vertical Mind Speed

      Assumed equal to zero

-------
                                 -280-                              CDM
G.  Horizontal Dispersion

      Climatological
      Uniform distribution within each of 16 sectors
         (narrow-plume approximation)
      Averaging time =  1 month to 1 year

H.  Vertical  Dispersion

      Semi-empirical/Gaussian plume
      5  stability  classes as defined by Turner  (1964)
      Neutral stability split into day/night cases on input
      Dispersion coefficients taken from Turner (1970)
      Area  sources - stability class is decreased by 1 category  from
         input values (to account for urban effects)
      Neutral dispersion coefficients are used  for all neutral
         ajid stable classes.
      No provision for  variations in surface roughness

 I.   Chemistry/Reaction  Mechanism

      Exponential  decay, user-input halflife

 J.   Physical Removal

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

 K.   Background

       Input single constant background value for each pollutant.

 L.   Boundary Conditions

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

 M.   Emission and Meteorological Correlation

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

-------
                                                               CDH
N.  Validation/Calibration

      Limited validation experience
      Calibration option available

0.  Outnut

      One month to one-year averaging time simulated (arithmetic
        mean only)
      Arbitrary averaging time by Larsen (1969) procedure
        (typically 1-24 hr.)
        Assumes
          1)  lognormal concentration distribution,
          2)  power  law dependence of median  and maximum
                concentrations on averaging time
      Arbitrary number  and  location  of  receptors
      Individual  point, area  source  culpability list for each receptor
      Point,  area concentration  rose for each receptor

-------
                                     -282-
                                                                      CRSTER
                                    CRSTER
Reference:  No. 13 in guideline.
Abstract:
Environmental Protection Agency.   "A User's Guide to the
Single Source (CRSTER) Model."  Office of Air Quality Plan-
ning and Standards, Research Triangle Park, North Carolina
27711, 1977.  (In preparation)

CRSTER is a steady state Gaussian plume technique applicable
in uneven terrain.  The purpose of the technique is to:
1) determine the maximum 24-hour concentration from a single
point source of up to 19 stacks for one year, 2) to deter-
mine 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.
Equations:
                                             T  4=  1,6
            X=
                                     >  (.6 L
   « O    (
                                  cUss 7
 L =

 V4 - (stock
                                           f
                                              ba.se  of

-------
                                     -283-                          CRSTER
CRSTER

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 less
        than stack height
      Receptors must be at ground level

B.  Emission Rate

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

C.  Chemical Composition

      N/A

D.  Plume Behavior

      Briggs (1971, 1972) 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 Wind Field

      Same as RAM

F.  Vertical Wind Speed

      Assumed equal to zero

G.  Horizontal Dispersion

      Semi-empirical/Gaussian  plume
      7  stability classes used; Turner  (1964),  Pasquil   (1961)
         Class 7:  extremely  stable,  elevated plume assumed not
          to touch the ground
      Dispersion coefficients  from Turner  (1969); no  further adjustments
        made for variations  in surface  roughness, transport or
         averaging time

H.  Vertical Dispersion

      Semi-empirical/Gaussian  plume
      7  stability classes
      Dispersion coefficients  from Turner  (1969); no  further adjustments
        made

-------
                                     -294-                           CRSTER
I.  Chemistry/Reaction Mechanism

      Not treated

J.  Physical Removal

      Not treated

K.  Background

      Not treated

L.  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                                            z
        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
      Hourly mixing height obtained from radiosonde data using same
        interpolation algorithm as RAM

M.  Emission and Meteorological Correlation

      Same as RAM
      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
      Additional work in progress

0.  Output

      Highest and second highest 1-hour and 24-hour concentrations at each
        receptor for the year plus the annual  arithmetic average at each
        receptor
      For each day in the year, the highest 1-hour and highest 24-hour
        concentration values found in the field of receptors
      Hourly concentrations for each receptor are output onto magnetic tape
        for further processing, for example to obtain the frequency
        distribution

-------
                                    -285-
                                                            HANNA-GIFFORD
                                 HANNA-GIFFORD
Reference:  Numbers 28, 29 in guidelines.

            Hanna, S.R.  "A Simple Method of Calculating Dispersion from
            Urban Area Sources."  J. Air Pollution Control Assn., Vol.  21,
            No. 12, pp. 774-777, December 1971.

            Gifford, G.A., and S.R. Hanna.  "Modeling Urban Air Pollution."
            Atmospheric Environment, Vol. 7, pp. 131-136, 1973.
            (also S.R. Hanna, private communication*)

Abstract:   Two slightly different versions of  the same model are de-
            scribed.  The first  is a purely deterministic version which
            does not require any local  air  quality data for its imple-
            mentation.  The form of the second  version may be derived
            from the first on the assumption  that area source emissions
            are uniform.  For actual use however, it  is recommended that
            local air quality data be used  to,  in essence, calibrate the
            model .
Equations
          a,V>
  See also  Section 3.9.1.

-------
                                      -286-                      HANNA-GIFFORD
HANNA-GIFFORD  (Short-Term)

A.  Source-Receptor Relationship

      Area sources only; uniform grid squares; user-defined grid
      Sources  assumed at ground level
      Receptors assumed at ground level
      Each receptor is assumed located at center of a grid square
      Unique separation for each source-receptor pair

B.  Emission Rate

      Arbitrary user-specified emission rate for each grid square
      Emission rates assumed constant

C.  Chemical Composition

      N/A

D.  Plume Behavior

      Plume rise not treated
      Does not treat fumigation, downwash

E.  Horizontal Wind Field
      User-supplied hourly wind speed and direction
      No variation of wind speed or direction with height
      Constant, uniform (steady-state) wind assumed within each hour

F.  Vertical Hind Speed

      Assumed equal to zero

6.  Horizontal Dispersion

      Narrow plume approximation; horizontal dispersion not treated explicitly

H.  Vertical Dispersion

      Semi-empirical/Gaussian plume                     .
      Dispersion coefficient assumed of the form a  = ax
      Analytic integration of upwind area source  z
        contributions assuming each can be represented
        as an infinite crosswind strip of width equal to
        area grid spacing.
      Dispersion coefficient parameters a, b modified from Smith (1968)
      Stability classes modified from Smith (1968)
      No adjustments made for variations in surface roughness

I.  Chemistry and Reaction Mechanism

      Not treated

-------
                                      -287-                       HANNA-GIFFORD
J.  Physical Removal
      Not treated
K.  Background
      Not treated
L.  Boundary Conditions
      Lower boundary:  perfect  reflection
      Upper boundary:  mixing height  assumed  high enough to have no effect;
        treats only effects  of  lower  boundary
M.  Emission and Meteorological  Correlations
      Emission rates, meteorological  parameters  all  input  by user on hourly
        basis
N.  Validation/Calibration
      Calibration  not used in previous  applications.   Some validation experience
        has been published
      In  nearly all  applications,  a single set of dispersion coefficient
        parameter  values (a  = 0.15, b = 0.75) has been used, corresponding
        to  neutral stability
0.  Output
      Hour-by-hour average concentration values at  each receptor

-------
                                                                 HANNA-GIFFORD
                                     -288-
HANNA-GIFFORD (Long Term)
       This model is intended for use in predicting an area-wide average
pollutant concentration, and is expected to work best for long averaging
times.  The working equation is
       X = CQ/u
in which
       X = average concentration within a suitably defined region,
       Q = average emission rate per unit area within the same region,
       u = average wind speed in the polluted layer over the desired
           averaging time, and
       C = proportionality constant to be determined empirically for
           each different region
Specific values of C for both S(L and TSP concentrations in a large number of
U.S.  cities have been presented by Gifford and Hanna (1973).  It is recom-
mended that local emission, meteorological and air quality data be used to
determine the value of C appropriate for the region of interest.  If such
data  is not available, an approximate average value of C = 225 has been
recommended for use in evaluating the true area source effect in the absence
of removal or decay processes.

-------
                                      -289-
                                                                        HIWAY
                                    HIWAY
Reference:  No. 36 in guideline.
Abstract:
Equations
Zimmerman, J.R.  and  R.S.  Thompson.   "User's  Guide  for HIWAY:
A Highway Air Pollution Model."   Publication No. EPA-650/4-74-
008  {NTIS PB 239944/AS),  Environmental  Protection  Agency, Re-
search Triangle  Park,  North  Carolina 27711,  February 1975.

HIWAY is a Gaussian  plume model  that computes the  hourly con-
centrations of non-reactive  pollutants  downwind  of roadways.
It is applicable for uniform wind conditions and level  terrain.
Although best suited for  at-grade highways,  it can also be
applied to depressed highways (cut sections).
           p
       o   r

X =  ~uT }  ^
Q
     * CO -emission
                                     /uv*4
                                  or
                                            lorvcj Uruyfk of  line,

                                             usiWi 4meoi'^Al ru/e
                                                         L

                   I "~  1-rr.
                                                           1.6>L
            for
                                        ^i
                                          _}
                       =  2-

-------
                                                                         HI WAY

                                     -290-
HIWAY

A.  Source-Receptor Relationship

      Horizontal finite line, multiple line sources (up to 24 lines)
      Straight lines, arbitrary orientation and length
      One road or highway segment per run
      Arbitrarily located receptors, downwind of the sources
      Unique source-receptor distance defined
      Arbitrary receptor heights
      Arbitrary release heights
      Cut section mode
        Receptors cannot be located in the cut
        Emissions treated as coming from 10 equal uniform line sources
          at the top of the cut
      Flat terrain assumed
      Line sources treated as sequence of point sources; the number used is
        such that convergence to within 2% is achieved

 B.  Emission Rate

      Constant uniform emission rate for each lane

 C.  Chemical Composition

      N/A

 D.  Plume Behavior

      Not treated

 E.  Horizontal Wind Field

      User specifies arbitrary wind speed and direction
      No variation of wind speed and direction with height
      Uniform, constant  (steady-state) wind assumed

 F.  Vertical Wind Speed

      Assumed equal to zero

 G.  Horizontal Dispersion

      Semi-empirical/Gaussian plume
      User specifies which of 6 stability classes to be used; Turner  (1964)
      Dispersion coefficients from Turner (1969); for
        distances less than 100 m, dispersion coefficients  from
        Zimmerman and Thompson  (1975)
      Level grade mode - initial value of dispersion coefficient  equals
        3 meters
      Cut section mode - initial value of dispersion coefficient  approximated
        as a function of wind speed
      No further adjustments to dispersion coefficients are made

-------
                                                                        HIWAY
                                     -291-
H.  Vertical Dispersion
      Semi-empirical/Gaussian plume
      User specifies stability class
      Dispersion coefficients from Turner (1969); for distances  less
        than 100 m, dispersion coefficients from Zimmerman and Thompson  (1975)
      Level grade mode - initial cr  =1.5 meters
      Cut section mode - initial cr = function of wind speed
I.  Chemi stry/Reaction Mechani sm
      Not treated
J.  Physical Removal
      Not treated
K.  Background
      Not treated
L.  Boundary Conditions
      Lower boundary:  perfect  reflection
      Upper boundary:  perfect  reflection
          £)  Stable conditions  or  mixing  height greater  than 5000 m:
                 assumes  no  effect,  treats  only reflection from ground
         U)  Other stabilities  with  a > 1.6  times mixing heights
                 assumes  uniform mixing
         Hi]  Other neutral  er  unstable conditions:   perfect reflection,
                 multiple reflections  treated  by summation of  series
M.   Emission and Meteorological  Correlation
      N/A;  user inputs all  specific parameter values for  hour in question
N.   Va1i dati on/Cali brati on
      Validation studies have been published
0.   Output
       1-hour average  concentration at each receptor

-------
                                     -292-
                                                                         PTDIS
                                     PTDIS
Reference:  Same as PTMTP.
Abstract:
PTDIS is a steady-state Gaussian plume model that estimates
short-term center-line concentrations directly downwind
of a point source at distances specified by the user.  The
effect of limiting vertical dispersion by a mixing height
can be included and gradual plume rise to the point of
final rise is also considered.  An option allows the cal-
culation of isopleth half-widths for specific concentra-
tions at each downwind distance.
Equations:

                     if

-------
                                   ~293~                       PTDIS
PTDIS
A.  Source-Receptor Relationship
      Single stack of arbitrary height
      Up to 50 receptors specified by user; all  receptors  at
        qround level, below plume centerline, at user-specified
        downwind distances
      Flat terrain assumed

B.  Emission Rate
      Single constant value
C.  Chemical Composition
      N/A
D.  Plume Behavior
      Briggs  (1971,  1972) plume  rise  formulae
      Alternatively, one user-supplied plume rise value can be used
      Does not treat fumigation  or  downwash
      If plume height exceeds  mixing  height, ground level concen-
        tration  assumed equal  to zero
E.   Horizontal Wind  Field
      Wind directions implicit along  source-receptor direction
      Uses user-defined wind speed
      No variation  in wind  speed with height
      Constant,  uniform  (steady-state) wind  assumed
 F.   Vertical  Wind Speed
      Assumed equal  to  zero
 G.   Horizontal  Dispersion
      Semi-empirical/Gaussian plume
      Calculations for  a single user-specified stability  class,
      Dispersion coefficients from Turner (1969);  no  adjustments
         made for variations in surface  roughness,  averaging  time
         or travel time

-------
                                                                        PTDIS
H.  Vertical Dispersion
      Semi-empirical/Gaussian plume
      Calculations done for user-specified stability class
      Dispersion coefficients from Turner (1969); no adjustments
        made for variations in surface roughness
I.  Chemistry/Reaction ftechanism
      Not treated
J.  Physical Removal
      Not treated
K,  Background
      Not treated
L.  Boundary Conditions
      Lower boundary:  perfect reflection
      Upper boundary:  user-input mixing height used; perfect
        reflection  assumed
      Multiple  reflections numerically accounted for by summation
        of  series
M.  Emission and Meteorological Correlations
      N/A
N,  Validation/Calibration
      No calibration option provided
      Direct application of Turner (1969) procedures
0.  Output
      Centerline, ground-level values of concentration and normalized
        concentration  (concentration x wind speed/emission rate) a  , a
        and plume height at user-supplied downwind distances      y
      Isopleth  halfwidths of up to eight user-specified ground
        level concentrations, at same downwind distances as above

-------
                                      -295-
                                                                         PTMAX
                                    PTMAX
Reference:

Abstract:
Equations:
Same as PTMTP.

PTMAX is a steady-state Gaussian plume model that performs
an analysis of the maximum short-term concentrations from
a single point source as a function of stability and wind
speed.  The final plume height  is  used for  each computation,
A separate analysis must be made for each individual stack;
but the model cannot give the maximum concentrations of a
combination of stacks.
                                 g
                                w.

-------
                                                                PTMAX


PTMAX

A.  Source-Receptor Relationship

      Single stack used
      Determines downwind distance to ground-level maximum concentration
      Flat terrain
      Unique release heiqht

B.  Emission Rate

      Single value

C.  Chemical Composition

      N/A

D.  Plume Behavior

      Briggs (1971, 1972) final olume rise formulae
      Does not  treat downwash or fumigation

E.  Horizontal  Hind Field

      Wind direction implicit along source-receptor direction
      Calculations done for fixed, internally defined set of wind
         speed values
      No variation in wind speed with height
      Constant, uniform (steady-state) wind assumed

F.  Vertical Hind Speed

      Assumed egual to zero

G.  Horizontal  Dispersion

      Senri-empirical/Gaussian olume
      6  stability classes as defined by Turner (1964)
      Calculations done for dispersion coefficients from Turner  (1969);
         no adjustments made for variations in surface roughness,
         travel  or averaging times

H.  Vertical Dispersion

      Semi-empirical/Gaussian plume
      Calculations done for 6 stability classes
      Dispersion coefficients from Turner  (1969); no adjustments made
         for variations in surface roughness

I.  Chemistry/Reaction Mechanism

      Not treated

-------
                               -297-                              PTMAX
J.  Physical Removal
      Not treated
K.  Background
      Not treated
L.  Boundary Conditions
      Lower boundary:  perfect reflection
      Upper boundary:  mixing height  assumed high enough to have no effect
M.  Emission and Meteorological Correlation
      N/A
N.  Val i dation/Cali brati on
      No calibration option  provided
      Direct application  of  Turner  (1969) procedures
0.  Output
      Maximum  around  level concentrations,  distances to maximum
        values  and  final  olume heights  for  all  stability classes
        and wind soeeds
      Averaninq time  less than 1  hour (Turner,  1969)

-------
                                      -298-
                                                         PTMTP
                                   PTMTP
Reference:  No. 17 in the guideline.

            Environmental Protection Agency.  "User's Network for
            Applied Modeling of Air Pollution (UNAMAP).  (Computer
            program on tape for point source models, HIWAY, Clima-
            tological Dispersion Model, and APRAC-1A), NTIS PB 229771,
            National Technical Information Service, Springfield, Vir-
            ginia, 1974.
Abstract:
PTMTP is a steady-state, Gaussian plume model  that estimates
for a number of arbitrarily located receptor points at or
above ground-level, the concentration from a number of point
sources.  Plume rise is determined for each source.  Down-
wind and crosswind distances are determined for each source-
receptor pair.  Concentrations at a receptor from various
sources are assumed additive.   Hour by hour calculations
are made based on hourly meteorological data;  both hourly
concentrations and averages over any averaging time from
one to 24 hours can be obtained.
Equations:

                   if

-------
                              -299-
                                                                PTMTP
PTMTP
A.  Source-Receptor Relationship

      Up to 25 arbitrarily located point sources
      Up to 30 arbitrarily located receptors
      Unique separation for each source-receptor pair
      Unique release height for each source
      Terrain assumed flat
      Arbitrary height above ground for each receptor

B.  Emission Rate

      Unique constant emission rate for each source

C.  Chetni ca 1 Compos i ti on

      N/A

D.  Plume Behavior

      Briggs (1971, 1972) plume rise formulae
      Does not treat fumigation or downwash
      If plume height exceeds mixing height, concentration at
        any receptor further downwind is assumed equal to zero

E.  Horizontal Wind Field

      Uses user-supplied hourly wind speed and  direction
      No variation in speed and direction with  height
      Constant, uniform  (steady-state) wind assumed within each hour

F.  Vertical Wind Speed

      Assumed equal to zero

G.  Horizontal Dispersion

      Semi-empirical/Gaussian  plume
      User-supplied hourly stability class, Turner (1969)
      Dispersion coefficients  from Turner  (1969);  no adjustments
        made for variations in  surface  roughness,  averaging time
        or  travel time

H.  Vertical Dispersion

      Semi-empirical/Gaussian  plume
      User-supplied hourly stability class	
      Dispersion coefficients  from Turner  (1969);  no adjustments
        made for variations  in  surface  roughness

-------
                                 -300-                          pTMTp
I.  Chemistry/Reaction Mechanism

      Not treated

J.  Physical Removal

      Not treated

K.  Background

      Not treated

L.  Boundary Conditions

      Lower boundary:  perfect reflection
      Upper boundary:  user-input mixing height used; perfect
        reflection assumed
      Multiple reflections numerically accounted for by summation
        of series

M.  Emission and Meteorological Correlation

      User-supplied hourly values of wind speed, direction, stability
        class, mixing height, ambient temperature (used in plume
        rise calculations) are correlated

N.  Validation/Calibration

      No calibration option provided
      Direct application of Turner (1969) procedures

0.  Output

      Hourly concentration, individual  source contribution list at
        each receptor
      Average concentration, source contributions at each receptor
        for total  period of interest

-------
                                      -301-
                                                                           RAM
                                     RAM
Reference:   No. 32 in guideline.
Abstract:
Hrenko, J. and D.B. Turner.  "An Efficient Gaussian-Plume
Multiple Source Air Quality Algorithm."  Paper presented
to the Annual Meeting of the Air Pollution Control Association,
Boston, Mass., 1975. (Available upon request from EPA, OAQPS,
Monitoring and Data Analysis Division, Research Triangle Park,
North Carolina 27711)

RAM is a steady state Gaussian plume model for estimating
concentrations of  relatively stable pollutants for averag-
ing times from an  hour  to  a day  in urban areas from point
and area sources.  Level or gently rolling terrain is
assumed.  Calculations  are performed for each hour.
Equations:
                  from  Siv\^U
                                           ^

                                T

                                         *  of  ro-^j

                                      Source i»\ Qt


                                       I
                                                       tntt»*(
                                                                  7
                                     r-:r*
                                                   >  l.fcL
                                     u

-------
-302-
                               RAM
  rU
-------
                                                                        RAM
                                     -303-


RAM

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.  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 imput  by user
        Areas must be squares;  side lengths = integer multiples of a
          basic unit
        Effective emission height = that  appropriate for  5 m/sec wind
        Area source contributions obtained  by numerical integration
          along upwind distance of narrow-plume approximation formulae
          for contribution from area source with given effective re-
          lease height.

  C.   Chemical  Composition

        N/A

  D.   Plume  Behavior

        Briggs  (1971,  1972}  plume rise  formulas
        Does not treat fumigations or downwash
        If plume height exceed mixing height,  ground level concentration assumed
          zero

  E.   Horizontal  Wind Field

        lkp<; user-suDDlipH hniirlv wind soeeds
        Uses user-supplied hourly wind directions (nearest IQO), internally
          modified by addition of a random  integer value  between -4° and +50
        WinS speeds corrected for release height based on power law variation,
          exponents from DeMarris (1959); 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-empiricalI/Gaussian plume
      Hourly stability class determined internally by Turner (1964) procedure
        six classes used
      Dispersion coefficients from McElroy and Pooler (1968) (urban) or Turner
        (1969) (rural).  No further adjustments made for variations in surface
        roughness or transport time

 H.   Vertical  Dispersion

       Semi-empirical/Gaussian plume
       Hourly  stability class determined internally
       Dispersion  coefficients from McElroy and Pooler (1968)  (urban) or Turner
         (1969)  (rural).   No further adjustments made for variations  in surface
         roughness

 I.   Chemistry/Reaction Mechanism

       Exponential  decay,  user-input halflife

 J.   Physical  Removal

       Exponential  decay,  user-input halflife

 K.   Background

       Not treated

 L.   Boundary  Conditions

       Lower boundary:  perfect  reflection
       Upper boundary:  perfect  reflection
       Neutral and unstable conditions
         Multiple  reflections numerically accounted  for  by  summation  of series
           until o*^  =  1.6  x 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 depen-
         dent  on mode  of operation (urban or rural)  and  calculated stability
         class  for the  hour in question as well as the stability classes for
         the hours  just preceding sunrise and  sunset.  See  attached description
         and figure

 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

 N.   Validation/Calibration

      No  calibration option provided
      No  documented validation  or comparison  with observational  data

-------
                                                                        RAM
                                     -305-
0.  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 can be obtained from
        special versions of RAM  (RAMF, RAMFR)

-------
                               -306-
     Two different mixing hciyhts are calculated by the preprocessor.
One is for basically rural surroundings; the other is for urban
locations.  The user is given the option to specify which he wants
to use.  Hourly mixing heights are determined from maximum  heights
(MXDP) for yesterday (i-1), today (i) and tomorrow (i+1) jnd from
minimum mixing  heights  (MflDP)  for toddy (i)  c/nd  tomorrow (i H )
 (See  Figure*  1.)

      For urban mixing height,  between midnight und  sunrise; if the
stability is  neutral interpolate belv.vm MXDP.. ,  and  MX!'P.  'J, if
stability is  stable use MflDP^  •'?•.  Tor  hours  between  sunrise  and
1100,  if the  hour  before  sunrise w;>;, neutral,  interpolii^f  he-two LMI
HXL'P^j  and MXDPi  Q.  For sunn*so to 1400,  if the  hour before sunrise
war; stable, interpolate between MKHP. and MXDP.  (-'r.   For 14UO i:o
sunset,  use MXDP.  ("5,,.  For hours between sunset  and inidirinht;  i i;
stability  is neutral interpolute between MXDP. and MXDP.+,  ;6, if
stability  is stable interpolate between MXDP. and MNDP.    '7.

     For rural mixing height between midnight and sunri;..,  inU'.-polate
between MXDP1_1 and MXDP.  ($  For hours between  sunrise and 1400, if
the hour before sunrise was neutral interpolate  between I1XDP.    and
MXDP.'3X   For sunrise to 1400, if the hour before sunns': was  stable,
interpolate between 0 and MXDP.  (To..  For 1400 to sunse1'., use  MXDP.
iV.  For sunset, to midiniiht,  interpoljte Ivtwc, n MX1.1P.  mil  MXUP.+  ;j--
A listing  and detailed description of input  formats for the preprocessor
are given  in Appendix A.

-------
                                                 TC-r.'.Y
JA1-' !
MXDP. , '.v.
"i ~ ' • .'•/ ^ V v f' p
(,._^1....L.J / -.
1 / *<&
\ / ^ \
"l - s \
y / _>
----- ^\
j.-.-.j. .
t
1 II
SR 14 SS
or,i
i
'"'"'i-i 1 ® ® ,«pp
...^,, 	 — •— - — ^-— ^_ ' MALI .

(NEUTRAL) / fT Oj)
- • 	 MXDPi + l

i
cb
7°
0
1—1
LU
=
                                                      /
/
/
                                           SR
    14
                                                    TIME
SS
                                 FIGURE 1.  DETERMINATION OF MIXING HEIGHTS

-------
                                      -308-
                                    VALLEY
                                                                        VALLEY
Reference:  No. 14 in guideline.
Abstract:
Environmental Protection Agency.   "User's Guide to the
Valley Model."  Office of Air Quality Planning and Stand-
ards, Research Triangle Park, North Carolina 27711, 1977.
(In preparation)

VALLEY is intended for use in calculating annual  and maximum
24-hour average SOa and TSP concentrations from single point
sources in complex terrain.  A climatological  approach is
used in calculating the annual  average.   The maximum 24-hour
averages are calculated by assuming F stability and a wind
speed of 2.5 m/sec and are intended to apply to the situation
in which the plume impinges on a  hill.
Equations:
           l*  6  t

     t '  Z Z  Z
                                                    Xfcfm  Q£
                  or
                 1C  * o  ff  K > U
                                                 nse
                     t> *  C
                       10

                       lo   m


-------
                         -809-                         VALLEY
U- 2,-Sr vr\/s«c awd k= G^|>rt)pn.ftie v^cAtjr  '(p/ reapW of i




If  H>U    S^ 14 = L.

-------
                                 -320-
                                                                VALLEY
VALLEY

A.  Source-Receptor Relationship

      Arbitrary location for each point source
      Arbitrary location and size for each area source
      112 receptors on radial grid, 16 directions; relative
        radial distances internally fixed, overall scale may
        be modified by user; location of grid center defined by user
      Unique release height for each point, area source
      Receptors at ground level; ground level elevations above
        mean sea level defined by user
      Total number of sources less than or equal to 50

B.  Emission Rate

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

C.  Chemical Composition

      N/A

D.  Plume Behavior

      Briggs (1971, 1972) plume rise formula for both point/area sources
      Alternatively, a single constant plume rise value may be input
        for any or all sources
      Does not treat fumigation or downwash
      If  plume height exceeds mixing height:
        A.  for long-term calculations, ground level concentrations
              assumed equal to zero
        B.  for short-term calculations, maximum plume height is
              limited to the mixing height

E.  Horizontal Wind Field

      A.  For long-term calculations
            Climatological approach
            16 wind directions
            6 wind speed classes
            No variation in windspeed with height
            Constant, uniform (steady-state) wind assumed
            User must specify wind speeds  representative of
              each class; these are not internally defined
      B.  For short-term calculations, specifically to predict
            the second highest 24-hour concentration expected in  1 year:
            Class F stability and 2.5 m/sec wind  speed assumed with
              user-defined direction.  These conditions are assumed
              to exist for 25? of the 24-hour period; an internal ad-
              justment is made for this

-------
                                                           VALLEY

E.  Horizontal  Wind Field  (Cqnt'd)

        C.  In stable conditions,  in  complex terrain, concentrations
              for receptors located above the point of impingement are
              obtained by  linear interpolation between the value  ob-
              tained at the point of  impingement and a value of zero
              at a height  of 400 meters  above that point.  The value
              at the point of impingement is taken to be equal to the
              value 10 meters below plume center! ine.  For receptors
              located below the point of impingement, the effective
              plume height is equal  to the  height of the plume above
              receptor elevation or 10 meters, whichever is larger.
              The plume is assumed to remain at a constant elevation
              following the initial  rise                      .
             In  neutral or  unstable conditions,  in complex terrain,  the
               plume  is assumed  to remain at a  constant height above
               topography,  following the initial  use
          No variation of wind  speed with height
          Constant,  uniform (steady-state) wind assumed


 F.  Vertical Hind Speed

       In  stable conditions, assumed equal  to zero.
       In  neutral and  unstable conditions,  assumed such that
        the plume  remains  at a  fixed height above terrain.

 G.   Horizontal  Dispersion
         cav™             pl« approbation) for calculating
         center values  of each of 16 sectors; linear mterpolation
         hptween centerlines  as  in AQDM
       Averaging time 1  month to 1 year  for long-term calculations.

 H.  Vertical Dispersion

       Semi -empirical /Gaussian plume
       Urban mode:                    1Qe/n


                                              for al  neutral and stable  classes
                                                             topographic ejects

        Rural mode:

                                                         SKi'B
                     s    rs -

-------
                                 -312-                          VALLEY
 I.   Chemistry/Reaction Mechanism

      Exponential decay,  user-input halflife

 J.   Physical Removal

      Exponential decay,  user-input halflife

 K.   Background

      Not treated in any mode

 L.   Boundary Conditions

      Lower boundary:  perfect reflection
      Upper boundary:  perfect reflection
        Neutral, unstable conditions  - multiple reflections accounted
          for by summation of series
        Stable conditions - ignores effect of upper boundary,
          treats only reflection from lower boundary.

 M.   Emission and Meteorological  Correlation

      Wind speed, direction, stability correlated via wind rose approach
      Emission rate not correlated with any other parameter
      Non-sequential; limited correlation
      Mixing height adjusted according to stability class
        Urban, long term:
          Class  A - 1.5 x afternoon climatological  value
          Class  D (night)  - 0.5  x afternoon climatological value
          Class  E - assumes morning climatological  value
        Rural, long term:
          Class  D (night)  - 0.5  x afternoon climatoloqical value
          Stable classes - ignores existence of any mixing height
            (assumes no limit)
        Short term calculations  - input value is ignored, only F
          stability is considered

N.  Validation/Calibration

      No calibration option available.
      Some validation experience, but limited documentation

0.  Output

      Long-term mode:
        Long-term arithmetic means, source contribution list for
          each receptor
      Short-term mode:
        Second highest 24-hour concentration, source contribution
          list for each receptor

-------
                                    -313-
                                References


Briggs, G.A. 1971.  Some Recent Analyses of Plume Rise Observation In:  Proc.
    Second International Clean Air Congress, Englund, H.M. and W.T. Biery (ed.).
    New York, Academic Press.

Briggs, G.A. 1972.  Discussion on Chimney Plumes in Neutral and Stable  Surround-
    ings.  Atmos. Env. 6_, 507-510.

DeMarrais, G.A.  1959.  Wind Speed Profiles of Brookhaven National.  J. App.
    Met. 16., 181-189.

Gifford, F.A. and Hanna, S.R.  1973.  Modeling Urban Air Pollution* Atmos.
    Env. 7., 131-136.

Larsen, R.I.  1971.  A Mathematical Model for Relating Air Quality Measure-
    ments to Air Quality Standards.  OAQPS  Publication No. AP-89  (NTIS PB
    205 277) Office of Technical  Information and  Publications, EPA, Research
    Triangle Park, N.C.

Leighton, P.A. and R.B. Dittmar  1952.   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.

McElroy, O.L. and Pooler,  F.   1968.  St. Louis Dispersion Study.   U.S. Public
    Health  Service,  National  Air Pollution Control Administration Report  No.
    AP-53.

Smith   M.   1968.  Recommended Guide for the Prediction of the Dispersion  of
    Air-bom Effluents.   1st ed.   The American  Society of Mechanical Engineers,
    New York.

Turner, D.B.   1964.   A  Diffusion Model for an Urban  Area, 0.  App. Met. 3,  83-
    91!

Turnpr  DB   1969   Workbook of Atmospheric Dispersion Estimates.   PHS
    Publication  No.  999-AP-26 (NTIS PB 191482),  Office of Technical  Information
    and Publications, EPA, Research Triangle Park, N.C.

 Zimmerman  J.R.  and Thompson, R.S.  1975.  User's Guide for HIWAJ  a Highway
      ™r Pollution Model.    EPA Publication No. EPA-650/4-74-008.  National
     Environmental Research Center, EPA, Research Triangle Park, N.C.

-------
-314-

-------
                                      -315-
      4  Materials Distributed to Participants Prior to the Conference
       In order to prepare the invited participants for productive
deliberations, a number of letters and documents were distributed at least
two weeks prior to the conference.  This section contains a listing of  the
documents and reproductions of the correspondence.
       The distributed documents were:
       1. A draft of the "Guideline on Air Quality Models and Associated
          Data Bases:, Source Receptor Analysis Branch, MDAD, OAQPS,
          U.S. EPA, January 1977.
       2. "Descriptions of Air Quality Models  and Abstracts of
          Reference Materials",  a notebook prepared by K. Brubaker
          and A. Smith from EES/ANL with assistance from  the staff
          of OAQPS/EPA, describing the  technical details  of the
          models suggested  in  the draft guideline, and abstracting
          the references cited  therein.
       3. "Turbulent Diffusion  - Typing Schemes: A Review", F. A.
          Gifford, Nuclear  Safety, Vol.  17,  No.  1, Jan.-Feb. 1976.
       4. "Atmospheric Dispersion Parameters In  Gaussian  Plume
          Modeling".
             Part  I - "Review of  Current Systems  and Possible
                       Future Developments",  A. H. Weber,
                       EPA-600/4-76-030a,  July 1976.
             Part  II  -"Possible Requirements  for  Change in the
                       Turner Workbook Values", F.  Pasquill,
                       EPA-600/4-76-030b,  June 1976.

 Reproductions of  other correspondence follows:

-------
A
                                            -316-
ARGONNE NATIONAL  LABORATORY
             We are pleased  that you have agreed to participate  in a workshop for
        the  peer-review of  modeling guidelines proposed by  the U.S. Environmental
        Protection Agency.  We realize that your schedule is full and appreciate
        your willingness to assist in this important task.

             The EPA Office  of Air Quality Planning and Standards has  prepared these
        modeling guidelines for application in the review of new  sources  under federal
        regulations  such as those governing prevention of significant deterioration
        and  as  an aid in the  revision of  State Implementation Plans.  EPA as well as
        the  States must fulfill these important review responsibilities now; there-
        fore, our challenge is to advise  on the best approach to  modeling air quality
        impacts within the  current state  of the art and with consideration for required
        data and resources.  As participants  in the conference, we should view the
        forthcoming  draft guidelines as a well-considered attempt by EPA  to meet this
        challenge.  Hopefully, our efforts will build upon  their  draft  to produce the
        final version.
                                               •
             The conference  will be held at  Carson Inn/Nordic Hills near Chicago,
        Illinois, from February 22-25, 1977.  A tentative conference  schedule and bro-
        chures  describing Nordic Hills are included.  As you can  see, our schedule is
        full but the accommodations are excellent.

             Also enclosed is a registration and housing form and a  return envelope.
        Please  indicate the nights for which  you will require lodging.   Since the
        conference does not begin until noon  on Tuesday, February 22, you may find
        it convenient to arrive in Chicago on Tuesday morning.

             By February 8 you should have received a copy of the proposed guide-
        lines and background  information. Please contact Donald  Rote [(312)739-7711
        ext.  5266 or FTS 388-5266] or Albert  Smith  [(312)739-7711 ext.  3259, 3240 or
        FTS  388-3259, 3240] if you have not received the material by  this date.
9700 South Cass Avenue. Argonne. Illinois 60439 • Telephone 312-739-7711 • TWX 910-258-3285 • WUX LB, Argonne, Illinois

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                                     -317-
      Tlie costs of rooms and  scheduled meals will be paid directly by the
sponsors.  To cover  the cost  of meals outside  the conference, per diem of
up to $15/day will be paid  to eligible participants.  As government agencies,
we cannot make cash  advances. Forms for  reimbursement of allowable expenses
(air fares, ground transportation,  and other conference related expenses)
will be available at the conference.  Please note that receipts are required
and that government  employees can be reimbursed  only  if their expenses are not
covered by their agencies.   If you  wish  us to  purchase your  airline tickets
in advance, please fill out and  return  the attached "Request For Airline
Ticket11.  We must receive  these  ticket  requests  before February 4.

      In addition to the expenses noted  above, you  will be paid an honorarium
of $150.00 per conference  day.

      A list of attendees  has been  included for  your  information.

      Again, let me  express my sincere  appreciation for  your attendance  at
 the conference.  With  your support  I  feel sure that the  conference will  yield
a workable set of guidelines.

                                     Very truly yours,
                                     John J. Roberts
                                     Energy and Environmental Systems Division
 JJR/blt
 attachments

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                                -318-
Dear

     I wish to thank you for accepting an invitation to participate
in the Modeling Workshop and helping us to develop more uniform and
consistent modeling guidance.

     The choice of analytical procedures to assess environmental
impacts has become a significant, and often the  paramount*  issue in
the administration of the new source, nonattainment, and prevention
of significant deterioration policies.   I believe that we have gathered
a distinguished and balanced group of scientists to consider this
important problem.  Certainly the group represents a broad  spectrum of
interests and concerns.  But more important, you and other  participants
are recognized not only for your scientific competence, but for your
appreciation and depth of understanding of the nature of the problems
that arise in administering a national  environmental program.

     I wish you success and have asked  my staff  and their associates
at Argonne National Laboratory to do whatever  is necessary  to help
you make the workshop a success.

                                Sincerely yours,
                                Walter C.  Barber
                                    D1 rector
                         Office of Air Quality Planning
                                  and  Standards

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                                            -319-
  UriC 1UI USEMH
ARGONNE  NATIONAL  LABORATORY
                                                          February  3, 1977
       TO:        Conference Participants

       FROM:      John J. Roberts
       SUBJECT:   Background Material  for Conference on  EPA Modeling Guidelines


                 Enclosed please find some information  which you will find  of use
       in preparing for the upcoming  Conference on EPA  Modeling Guideline:  a
       draft of  the guideline  itself; descriptions of the models suggested  for  use
       in the guideline; and excerpts from the references cited in the guideline.
       Several additional references  will  be sent under separate cover.

                 If you intend to  propose  alternate models or submodels for
       consideration by the conference,  it would be most helpful if you could
       document your position  in a format  similar to that used in Sec. 2 of
       the enclosed Descriptions of Air  Quality Models  and Abstracts of Reference
       Materials.  Thirty-five (35) copies should suffice for the conference
       participants; we will also  have copying facilities available on the
       premises at the conference. In addition we will be able to make 8* x 11
       viewgraphs.  An overhead projector  for the viewgraphs and a 2 x 2 slide
       projector will also be  available.

                 We look forward  to meeting with you and to discussing your ideas
       for improving the guidelines.


       JJR:tb

       Enclosures
                           m n«;« fina-^q • Teleohone 312-739-7711 • TWX 910-258-3285 • WUX LB, Argonne, Illinois
 9700 South Cass Avenue, Argonne, Illinois 60439  leiepnone oi
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                                           -320-
  MC-MM

ARGON NE  NATIONAL LABORATORY
                                                          February 4, 1977
       TO:       Participants in the Specialist Conference on EPA Modeling Guideline

       FROM:     John J. Roberts

       SUBJECT:  Potential Conference Issues


                 We have some additional* information which may be of use to you
       in connection with the up-coming Specialist Conference on EPA Modeling  Guideline.
       A number of issues and questions are likely to arise as you review the
       draft and discuss it with your colleagues.   Without focusing attention  on
       specifics and without the intention of biasing your thinking, we have
       prepared a list of potential issues.  This list is certainly not complete
       and we welcome additions or other modifications.  Furthermore, we do not
       expect to discuss each of these issues in detail unless the participants
       feel such discussion is warranted.   The issues identified so far fall
       into three categories:

              1)  Policy Issues

              2)  Guideline Contents

              3)  Guideline Format or Structure

                 The major objective of the conference is, of course, not to
       simply identify issues but to reach consensus regarding the guideline
       itself.  This consensus could presumably take one or more of the following
       forms as appropriate:

              1)  Statements regarding the adequacy of the guideline in
                  terms of the issues (majority and minority opinions may evolve).

              2)  Recommendations of specific changes and additions or deletions
                  to the guideline including both the content and format thereof.

              3)  Identification of issues which are regarded as important but
                  which can not be adequately addressed at this time because of
                  incomplete information or other constraints.

                 Again we look forward to meeting with you and to a fruitful
       conference.
       JJR:tb

       *A copy of the draft guideline along with other materials  is  being sent
        under separate cover.
9700 South Cass Avenue, Argonne, Illinois 60439 • Telephone 312-739-7711 • TWX 910-258-3285 • WUX L8, Argonne, Illinois

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                                     -321-



                         Potential Conference Issues


 1.   Policy  Issues

     1.1  What  should be the limits of  the discretionary  powers of users?
     1.2  The guideline will be subject to a periodic  review and updating
         process.   What should be the  form and period of review?

     1.3  Which concentration estimates should be used for specific policy
         questions?  That is, which short term estimate  (highest, 2nd highest,
         etc.)  should be used for SIP  and NSR?

     1.4  Should  pragmatic considerations  (computer  and/or manpower cost and
         time)  enter into model choice guidance?


2.  Applications  (or problems associated  with specific applications)

    2.1  Features which are associated with specific  applications (for
         general  list  of  such features see the table  in  Sec. 1 of the
         notebook prepared by ANL)

    2.2  Importance of  each of these features for specific applications
         and the requirements for accuracy of treatment  by models.
    2.3  Criteria for  deciding whether features such  as  downwash, complex
         terrain, anomalous meteorological conditions, etc., deserve
         special treatment.


3.  Suitability of models  specified in the  guideline  and  criteria for
    selecting alternatives  or updating

    3.1  Matching models  to  application requirements  (qualitative
         considerations)

    3.2  Accuracy determination and specification

         -  What measures of  accuracy should be used.  How should they
           be determined?

         -  What constitutes  acceptable  validation? (purpose,  procedures,
           documentation)

         -  Calibration - What role should it play?
                         What method(s) should be used?
                         Criteria for use?

    3.3  Criteria for acceptability of  alternatives to the proposed models
         or  portions thereof?

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                                      -322-
4.  Specialized technical issues
    4.1  Characterization of turbulent diffusion
         4.1.1  Typology of regimes
         4.1.2  Algorithms within each regime
    4.2  Boundary conditions
    4.3  Formation and loss mechanisms - guidance on when to worry  and
         how to deal with
    4.4  Estimation of short-term concentrations
         4.4.1  Calculations of multi-hour concentrations
         4.4.2  Statistics of 2nd highest value
         4.4.3  Suitability of peak-to-mean  ratio   technique
    4.5  Receptor points: density, location, grid  or  cell  size

5.  Data requirements - Representativeness (location,  duration); Accuracy
    (sampling method, quality control)
    5.1  Air quality data
         - Background estimation
         - Model validation
         - Model calibration
    5.2  Meteorological data
    5.3  Emission data
         - Temporal and spatial resolution,  accuracy

6.  Structure and content of guideline
    6.1  Appendices - Summary and/or  documentation of models?
    b.2  Guidelines for modeling photochemical smog?
                                                 US GOVERNMENT PRINTING OFFICE 1977 - 751 254

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