Federal Register, Volume 61 Issue 156 (Monday, August 12, 1996)

[Federal Register Volume 61, Number 156 (Monday, August 12, 1996)]
[Rules and Regulations]
[Pages 41838-41894]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 96-17031]

[[Page 41837]]

_______________________________________________________________________

Part II

Environmental Protection Agency

_______________________________________________________________________

40 CFR Parts 51 and 52

Requirements for Preparation, Adoption, and Submittal of Implementation
Plans; Final Rule

Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules
and Regulations

[[Page 41838]]

ENVIRONMENTAL PROTECTION AGENCY

40 CFR Parts 51 and 52

[AH-FRL-5531-6]
RIN 2060-AS01

Requirements for Preparation, Adoption, and Submittal of
Implementation Plans

AGENCY: Environmental Protection Agency (EPA).

ACTION: Direct final rule.

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SUMMARY: Though codified as appendix W in July 1993, the Guideline on
Air Quality Models (``Guideline'') had never been properly organized to
conform with the CFR format (which features sequentially numbered
paragraphs) imposed by the Office of the Federal Register. Thus, this
direct final rule republishes the Guideline to reflect the format
appropriate for appendix W. In addition, reference lists are
alphabetized and updated, technical contacts and availability for
models are updated, and typographical errors are corrected. Two new
models presented at the 6th Conference on Air Quality Modeling (August
1995) are added to Guideline appendix B for case-by-case use; several
outdated models are removed from appendix B. Appendix A models
considered to be ``obsolete'' (i.e., CRSTER & MPTER, replaced by ISC3)
are removed, as is Table 4-1. In addition, minor amendments to 40 CFR
51.112, 51.160. 51.166, and 52.21 are necessary to bring respective
references to appendix W up to date.

DATES: This rule is effective October 11, 1996 unless notice is
received by September 11, 1996 that adverse or critical comments will
be submitted or that an opportunity to submit such comments at a public
hearing is requested. If such comments or a request for a public
hearing are received by the Agency, EPA will then publish a subsequent
Federal Register document withdrawing from this action only those
amendments which are specifically listed in those comments or in the
request for a public hearing.

ADDRESSES: Substantial adverse or critical comments may be sent to
Docket No. A-96-39 at the following address: Air Docket (6102), Room M-
1500, Waterside Mall, U.S. Environmental Protection Agency, 401 M
Street, S.W., Washington, D.C. 20460. This docket is available for
public inspection and copying between 8:00 a.m. and 5:30 p.m., Monday
through Friday, at the address above. Please furnish duplicate comments
to Tom Coulter, Air Quality Modeling Group, U.S. Environmental
Protection Agency (MD-14), Research Triangle Park, NC 27711.

FOR FURTHER INFORMATION CONTACT: Joseph A. Tikvart, Leader, Air Quality
Modeling Group (MD-14), Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711;
telephone (919) 541-5561 or C. Thomas Coulter, telephone (919) 541-
0832.
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\1\ In reviewing this preamble, note that appendix W (Guideline)
itself contains several appendices which are mentioned. Appendix A
is the repository for preferred models, while appendix B is the
repository for alternate models justified for use on a case-by-case
basis.
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SUPPLEMENTARY INFORMATION:

Background ^{1

The purpose of the Guideline is to promote consistency in the use
of modeling within the air management process. The Guideline provides
model users with a common basis for estimating pollution
concentrations, assessing control strategies and specifying emission
limits; these activities are regulated at 40 CFR 51.112, 51.117,
51.150, 51.160, 51.166, and 51.21. The Guideline was originally
published in April 1978. It was incorporated by reference in the
regulations for the Prevention of Significant Deterioration of Air
Quality in June 1978. The Guideline was subsequently revised in 1986,
and later updated with supplement A in 1987 and supplement B in July
1993. The revisions in supplement B included techniques and guidance
for situations where specific procedures had not previously been
available, and also improved several previously adopted techniques. As
mentioned before, the Guideline was published as appendix W to 40 CFR
part 51 when supplement B was promulgated.
During the public comment period for supplement B, EPA received
requests to consider several additional new modeling techniques and
suggestions for enhanced technical guidance. However, because there was
not sufficient time for the public to review the new techniques and
technical guidance before promulgation of supplement B, the new models
and enhanced technical guidance could not be included in the supplement
B rulemaking. Thus, in a subsequent regulatory proposal, EPA proposed
to further revise the Guideline with supplement C and sought public
comment on four specific items. After reviewing and addressing public
comments, EPA promulgated the last revision in August 1995.

Final Action

Today's action republishes appendix W to 40 CFR part 51 and, in
large part, is pursuant to an agreement between EPA and the Office of
the Federal Register (OFR) to reorganize appendix W to conform with
normal CFR format imposed by OFR. This reorganization mainly involves
the systematic identification of paragraphs, in this case using
sequential letters of the alphabet. As a practical matter, such a
format should facilitate the process by which future revisions of
appendix W are made, in which reference to specific paragraphs can be
more easily made. Because the appendices (A, B, and C) do not
inherently lend themselves to the sequencing structure imposed on the
rest of appendix W, these appendices are organized much as they have
been in the past. EPA has made an agreement with OFR that, when future
revisions become necessary to appendix A or B, the entire model
description will be set out in the amendatory instruction. Likewise,
appendix C would be set out in its entirety.
Another element of this action involves models that are listed in
appendix B (summaries of Alternative Air quality Models) of appendix W,
which are available for use on a case-by-case basis. Of the 31 models
currently listed in appendix B, 14 have been identified for removal
because they have seen little or no use in recent years and have been
superseded by other modeling techniques. Prior to this deletion effort,
respective model developers were contacted and they concurred. The
deleted models are: Air Quality Display Model (AQDM), Air Resources
Regional Pollution Assessment (ARRPA) Model, APRAC-3/MOBILE 1 Emissions
and Diffusion Modeling Package (APRAC-3), COMPTER, HIWAY-2, Integrated
Model for Plumes and Atmospheric Chemistry in Complex Terrain (IMPACT),
Models 3141 and 4141, MULTIMAX, Pacific Gas and Electric PLUME5 Model,
PLMSTAR Air Quality Simulation Model, Random-walk Advection and
Dispersion Model (RADM), Regional Transport Model (RTM-II), Texas
Climatological Model (TCM-2) and Texas Episodic Model (TEM-8).
Two models were presented by their respective developers at the 6th
Conference on Air Quality Modeling, August 9-10, 1995 in Washington,
D.C., as candidates for appendix B. One of these models is HOTMAC/
RAPTAD, a mesoscale meteorological/transport and diffusion model
system. HOTMAC, Higher Order Turbulence Model for Atmospheric
Circulation, is a mesoscale

[[Page 41839]]

weather prediction model that forecasts wind, temperature, humidity,
and atmospheric turbulence distributions over complex surface
conditions. RAPTAD, Random Puff Transport and Diffusion, is a
Lagrangian random puff model that is used to forecast transport and
diffusion of airborne materials over complex terrain. The other model,
PANACHE, is an Eulerian (and Lagrangian for particulate matter), 3-
dimensional finite volume fluid mechanics model designed to simulate
continuous and short-term pollution dispersion in the atmosphere, in
simple or complex terrain. In the docket established for the 6th
Conference, no adverse public comments were received during the comment
period that followed. EPA is therefore adding HOTMAC/RAPTAD and PANACHE
to appendix B.
Two models in appendix A (Summaries of Preferred Air quality
Models) of appendix W, Multiple Point Gaussian Dispersion Algorithm
with Terrain Adjustment (MPTER) and Single Source (CRSTER) Model, have
long been known to be virtually superseded by the Industrial Source
Complex (ISC) Model. Accordingly, EPA believes it is appropriate to
remove these models from appendix A. Conforming edits have been made to
appendix W sections 2.2, 3.2.2, 4.1, 7.2.2, and 9.3.4.2 where
references to either MPTER, CRSTER, or both occurred. With this
removal, it appears to EPA that appendix W may be simplified by
removing Table 4-1 as well, and this was done. Conforming edits have
been made to appendix W section 4.2.2 which referenced Table 4-1, and
to section 7.2.2 to note that CDM 2.0 may be used for long-term
applications, while RAM may be used for short-term applications.
In addition, there were several typographical errors which appeared
when the appendix was first published in the Federal Register in 1993;
these errors have been corrected. Appendices A and B of appendix W
referenced page numbers which were incorrect (conforming with the
earlier edition of the Guideline, when it was incorporated by reference
and maintained as a separate EPA document); these errors have been
corrected. Reference lists, i.e., A.REF and B.REF, have been
alphabetized and updated as a result of the model deletions discussed
above. The Availability and (where appropriate) Technical Contact
sections have been updated, as well. Elements of the technical
description of some appendix B models have been updated to reflect
current status.
Minor amendments to 40 CFR 51.112, 51.160, 51.166, and 52.21 are
necessary to update respective references to appendix W. The paragraphs
generally make reference to ``supplements'' which are no longer used as
vehicle for revision. Also, NTIS is no longer an agent of distribution
for the Guideline.

Administrative Requirements

A. Executive Order 12866

Under Executive Order (E.O.) 12866 [58 FR 51735 (October 4, 1993)],
the Agency must determine whether the regulatory action is
``significant'' and therefore subject to review by the Office of
Management and Budget (OMB) and the requirements of the Executive
Order. The Order defines ``significant regulatory action'' as one that
is likely to result in a rule that may:
(1) have an annual effect on the economy of $100 million or more or
adversely affect in a material way the economy, a sector of the
economy, productivity, competition, jobs, the environment, public
health or safety, or State, local, or tribal governments or
communities;
(2) create a serious inconsistency or otherwise interfere with an
action taken or planned by another agency;
(3) materially alter the budgetary impact of entitlements, grants,
user fees, or loan programs of the rights and obligations of recipients
thereof; or
(4) raise novel legal or policy issues arising out of legal
mandates, the President's priorities, or the principles set forth in
the Order.
It has been determined that this rule is not a ``significant
regulatory action'' under the terms of E.O. 12866 and is therefore not
subject to OMB review.

B. Paperwork Reduction Act

This final rule does not contain any information collection
requirements subject to review by OMB under the Paperwork Reduction Act
on 1980, 44 U.S.C. 3501 et seq.

C. Regulatory Flexibility Act

The Regulatory Flexibility Act (5 U.S.C. 601 et seq.) requires EPA
to consider potential impacts of regulations on small ``entities''. The
direct final action taken today is a supplement to the final rule that
was published on July 20, 1993 (58 FR 38816). As described earlier in
this preamble, the revisions here promulgated merely update and
reformat appendix W to 40 CFR Part 51, update references to that
appendix in several places in Part 51 and 52, and impose no new
regulatory burdens. As such, there will be no additional impact on
small entities regarding reporting, recordkeeping, compliance
requirements, as stated in the final rule (aforementioned).
Furthermore, this final rule does not duplicate, overlap, or conflict
with other federal rules. Thus, pursuant to the provisions of 5 U.S.C.
605(b), EPA hereby certifies that the attached final rule will not have
a significant impact on a substantial number of such entities.

D. Submission to Congress and the General Accounting Office

Under section 801(a)(1)(A) of the Administrative Procedures Act
(APA) as amended by the Small Business Regulatory Enforcement Fairness
Act of 1996, EPA submitted a report containing this rule and other
required information to the U.S. Senate, the U.S. House of
Representatives and the Comptroller General of the General Accounting
Office prior to publication of the rule in today's Federal Register.
This rule is not a ``major rule'' as defined by section 804(2) of the
APA as amended.

E. Unfunded Mandates

Under Section 202 of the Unfunded Mandates Reform Act of 1995
(``Unfunded Mandates Act'', Pub. L. 104-4), signed into law on March
22, 1995, EPA must prepare a budgetary impact statement to accompany
any proposed or final rule that includes a Federal mandate that may
result in estimated costs to State, local, or tribal governments in the
aggregate; or to the private sector, of $100 million or more. Under
Section 205, EPA must select the most cost-effective and least
burdensome alternative that achieves the objectives of the rule and is
consistent with statutory requirements. Section 203 requires EPA to
establish a plan for informing and advising any small governments that
may be significantly or uniquely impacted by the rule.
EPA has determined that the action promulgated today does not
include a Federal mandate that may result in estimated costs of $100
million or more to either State, local, or tribal governments in the
aggregate, or to the private sector. Therefore, the requirements of the
Unfunded Mandates Act do not apply to this action.

List of Subjects

40 CFR Part 51

Environmental Protection, Administrative practice and procedure,
Air pollution control, Intergovernmental relations, Reporting and
recordkeeping requirements, Ozone, Sulfur oxides, Nitrogen dioxide,
Lead, Particulate matter, Hydrocarbons, Carbon monoxide.

[[Page 41840]]

40 CFR Part 52

Air pollution control, Ozone, Sulfur oxides, Nitrogen dioxide,
Lead.

Dated: June 26, 1996.
Carol M. Browner,
Administrator.

Parts 51 and 52, chapter I, title 40 of the Code of Federal
Regulations are amended as follows:

PART 51--REQUIREMENTS FOR PREPARATION, ADOPTION, AND SUBMITTAL OF
IMPLEMENTATION PLANS

1. The authority citation for part 51 continues to read as follows:

Authority: 42 U.S.C. 7401-7671q.

2. Sec. 51.112 is amended by revising paragraph (a)(1) and the
first sentence of paragraph (a)(2) to read as follows:

Sec. 51.112 Demonstration of adequacy.

(a) * * *
(1) The adequacy of a control strategy shall be demonstrated by
means of applicable air quality models, data bases, and other
requirements specified in appendix W of this part (Guideline on Air
Quality Models).
(2) Where an air quality model specified in appendix W of this part
(Guideline on Air Quality Models) is inappropriate, the model may be
modified or another model substituted. * * *
* * * * *
3. Sec. 51.160 is amended by revising paragraph (f)(1) and the
first sentence of paragraph (f)(2) to read as follows:

Sec. 51.160 Legally enforceable procedures.

* * * * *
(f) * * *
(1) All applications of air quality modeling involved in this
subpart shall be based on the applicable models, data bases, and other
requirements specified in appendix W of this part (Guideline on Air
Quality Models).
(2) Where an air quality model specified in appendix W of this part
(Guideline on Air Quality Models) is inappropriate, the model may be
modified or another model substituted. * * *
* * * * *
4. Sec. 51.166 is amended by revising paragraph (l)(1) and the
first sentence of paragraph (l)(2) to read as follows:

Sec. 51.166 Prevention of significant deterioration of air quality.

* * * * *
* * *
(1) All applications of air quality modeling involved in this
subpart shall be based on the applicable models, data bases, and other
requirements specified in appendix W of this part (Guideline on Air
Quality Models).
(2) Where an air quality model specified in appendix W of this part
(Guideline on Air Quality Models) is inappropriate, the model may be
modified or another model substituted. * * *
* * * * *
5. Appendix W to Part 51 revised to read as follows:

Appendix W to Part 51--Guideline on Air Quality Models

Preface

a. Industry and control agencies have long expressed a need for
consistency in the application of air quality models for regulatory
purposes. In the 1977 Clean Air Act, Congress mandated such
consistency and encouraged the standardization of model
applications. The Guideline on Air Quality Models (hereafter,
Guideline) was first published in April 1978 to satisfy these
requirements by specifying models and providing guidance for their
use. The Guideline provides a common basis for estimating the air
quality concentrations used in assessing control strategies and
developing emission limits.
b. The continuing development of new air quality models in
response to regulatory requirements and the expanded requirements
for models to cover even more complex problems have emphasized the
need for periodic review and update of guidance on these techniques.
Four primary on-going activities provide direct input to revisions
of the Guideline. The first is a series of annual EPA workshops
conducted for the purpose of ensuring consistency and providing
clarification in the application of models. The second activity,
directed toward the improvement of modeling procedures, is the
cooperative agreement that EPA has with the scientific community
represented by the American Meteorological Society. This agreement
provides scientific assessment of procedures and proposed techniques
and sponsors workshops on key technical issues. The third activity
is the solicitation and review of new models from the technical and
user community. In the March 27, 1980 Federal Register, a procedure
was outlined for the submittal to EPA of privately developed models.
After extensive evaluation and scientific review, these models, as
well as those made available by EPA, are considered for recognition
in the Guideline. The fourth activity is the extensive on-going
research efforts by EPA and others in air quality and meteorological
modeling.
c. Based primarily on these four activities, this document
embodies all revisions to the Guideline Although the text has been
revised from the original 1978 guide, the present content and topics
are similar. As necessary, new sections and topics are included. EPA
does not make changes to the guidance on a predetermined schedule,
but rather on an as needed basis. EPA believes that revisions of the
Guideline should be timely and responsive to user needs and should
involve public participation to the greatest possible extent. All
future changes to the guidance will be proposed and finalized in the
Federal Register. Information on the current status of modeling
guidance can always be obtained from EPA's Regional Offices.

Table of Contents

List of Tables

1.0 Introduction
2.0 Overview of Model Use
2.1 Suitability of Models
2.2 Classes of Models
2.3 Levels of Sophistication of Models
3.0 Recommended Air Quality Models
3.1 Preferred Modeling Techniques
3.1.1 Discussion
3.1.2 Recommendations
3.2 Use of Alternative Models
3.2.1 Discussion
3.2.2 Recommendations
3.3 Availability of Supplementary Modeling Guidance
3.3.1 The Model Clearinghouse
3.3.2 Regional Meteorologists Workshops
4.0 Simple-Terrain Stationary Source Models
4.1 Discussion
4.2 Recommendations
4.2.1 Screening Techniques
4.2.2 Refined Analytical Techniques
5.0 Model Use in Complex Terrain
5.1 Discussion
5.2 Recommendations
5.2.1 Screening Techniques
5.2.2 Refined Analytical Techniques
6.0 Models for Ozone, Carbon Monoxide and Nitrogen Dioxide
6.1 Discussion
6.2 Recommendations
6.2.1 Models for Ozone
6.2.2 Models for Carbon Monoxide
6.2.3 Models for Nitrogen Dioxide (Annual Average)
7.0 Other Model Requirements
7.1 Discussion
7.2 Recommendations
7.2.1 Fugitive Dust/Fugitive Emissions
7.2.2 Particulate Matter
7.2.3 Lead
7.2.4 Visibility
7.2.5 Good Engineering Practice Stack Height
7.2.6 Long Range Transport (LRT) (i.e., beyond 50km)
7.2.7 Modeling Guidance for Other Governmental Programs
7.2.8 Air Pathway Analyses (Air Toxics and Hazardous Waste)
8.0 General Modeling Considerations
8.1 Discussion
8.2 Recommendations
8.2.1 Design Concentrations
8.2.2 Critical Receptor Sites
8.2.3 Dispersion Coefficients
8.2.4 Stability Categories
8.2.5 Plume Rise
8.2.6 Chemical Transformation
8.2.7 Gravitational Settling and Deposition
8.2.8 Urban/Rural Classification
8.2.9 Fumigation
8.2.10 Stagnation
8.2.11 Calibration of Models
9.0 Model Input Data
9.1 Source Data

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9.1.1 Discussion
9.1.2 Recommendations
9.2 Background Concentrations
9.2.1 Discussion
9.2.2 Recommendations (Isolated Single Source)
9.2.3 Recommendations (Multi-Source Areas)
9.3 Meteorological Input Data
9.3.1 Length of Record of Meteorological Data
9.3.2 National Weather Service Data
9.3.3 Site-Specific Data
9.3.4 Treatment of Calms
10.0 Accuracy and Uncertainty of Models
10.1 Discussion
10.1.1 Overview of Model Uncertainty
10.1.2 Studies of Model Accuracy
10.1.3 Use of Uncertainty in Decision-Making
10.1.4 Evaluation of Models
10.2 Recommendations
11.0 Regulatory Application of Models
11.1 Discussion
11.2 Recommendations
11.2.1 Analysis Requirements
11.2.2 Use of Measured Data in Lieu of Model Estimates
11.2.3 Emission Limits
12.0 References
13.0 Bibliography
14.0 Glossary of Terms
Appendix A to Appendix W of Part 51--Summaries of Preferred Air
Quality Models
Appendix B to Appendix W of Part 51--Summaries of Alternative Air
Quality Models
Appendix C to Appendix W of Part 51--Example Air Quality Analysis
Checklist

List of Tables
------------------------------------------------------------------------
Table No. Title
------------------------------------------------------------------------
5-1a...................................... Neutral/Stable
Meteorological Matrix for
CTSCREEN.
5-1b...................................... Unstable/Convective
Meteorological Matrix for
CTSCREEN.
5-2....................................... Preferred Options for the
SHORTZ/LONGZ Computer Codes
When Used in a Screening
Mode.
5-3....................................... Preferred Options for the
RTDM Computer Code When
Used in a Screening Mode.
9-1....................................... Model Emission Input Data
for Point Sources.
9-2....................................... Point Source Model Input
Data (Emissions) for PSD
NAAQS Compliance
Demonstrations.
9-3....................................... Averaging Times for Site-
Specific Wind and
Turbulence Measurements.
------------------------------------------------------------------------

1.0 Introduction

a. The Guideline recommends air quality modeling techniques that
should be applied to State Implementation Plan (SIP) \1\ revisions
for existing sources and to new source reviews,\2\ including
prevention of significant deterioration (PSD).\3\ It is intended for
use by EPA Regional Offices in judging the adequacy of modeling
analyses performed by EPA, State and local agencies and by industry.
The guidance is appropriate for use by other Federal agencies and by
State agencies with air quality and land management
responsibilities. The Guideline serves to identify, for all
interested parties, those techniques and data bases EPA considers
acceptable. The guide is not intended to be a compendium of modeling
techniques. Rather, it should serve as a basis by which air quality
managers, supported by sound scientific judgment, have a common
measure of acceptable technical analysis.
b. Due to limitations in the spatial and temporal coverage of
air quality measurements, monitoring data normally are not
sufficient as the sole basis for demonstrating the adequacy of
emission limits for existing sources. Also, the impacts of new
sources that do not yet exist can only be determined through
modeling. Thus, models, while uniquely filling one program need,
have become a primary analytical tool in most air quality
assessments. Air quality measurements though can be used in a
complementary manner to dispersion models, with due regard for the
strengths and weaknesses of both analysis techniques. Measurements
are particularly useful in assessing the accuracy of model
estimates. The use of air quality measurements alone however could
be preferable, as detailed in a later section of this document, when
models are found to be unacceptable and monitoring data with
sufficient spatial and temporal coverage are available.
c. It would be advantageous to categorize the various regulatory
programs and to apply a designated model to each proposed source
needing analysis under a given program. However, the diversity of
the nation's topography and climate, and variations in source
configurations and operating characteristics dictate against a
strict modeling ``cookbook.'' There is no one model capable of
properly addressing all conceivable situations even within a broad
category such as point sources. Meteorological phenomena associated
with threats to air quality standards are rarely amenable to a
single mathematical treatment; thus, case-by-case analysis and
judgment are frequently required. As modeling efforts become more
complex, it is increasingly important that they be directed by
highly competent individuals with a broad range of experience and
knowledge in air quality meteorology. Further, they should be
coordinated closely with specialists in emissions characteristics,
air monitoring and data processing. The judgment of experienced
meteorologists and analysts is essential.
d. The model that most accurately estimates concentrations in
the area of interest is always sought. However, it is clear from the
needs expressed by the States and EPA Regional Offices, by many
industries and trade associations, and also by the deliberations of
Congress, that consistency in the selection and application of
models and data bases should also be sought, even in case-by-case
analyses. Consistency ensures that air quality control agencies and
the general public have a common basis for estimating pollutant
concentrations, assessing control strategies and specifying emission
limits. Such consistency is not, however, promoted at the expense of
model and data base accuracy. This guide provides a consistent basis
for selection of the most accurate models and data bases for use in
air quality assessments.
e. Recommendations are made in this guide concerning air quality
models, data bases, requirements for concentration estimates, the
use of measured data in lieu of model estimates, and model
evaluation procedures. Models are identified for some specific
applications. The guidance provided here should be followed in all
air quality analyses relative to State Implementation Plans and in
analyses required by EPA, State and local agency air programs. The
EPA may approve the use of another technique that can be
demonstrated to be more appropriate than those recommended in this
guide. This is discussed at greater length in Section 3.0. In all
cases, the model applied to a given situation should be the one that
provides the most accurate representation of atmospheric transport,
dispersion, and chemical transformations in the area of interest.
However, to ensure consistency, deviations from this guide should be
carefully documented and fully supported.
f. From time to time situations arise requiring clarification of
the intent of the guidance on a specific topic. Periodic workshops
are held with the EPA Regional Meteorologists to ensure consistency
in modeling guidance and to promote the use of more accurate air
quality models and data bases. The workshops serve to provide
further explanations of Guideline requirements to the Regional
Offices and workshop reports are issued with this clarifying
information. In addition, findings from on-going research programs,
new model submittals, or results from model evaluations and
applications are continuously evaluated. Based on this information
changes in the guidance may be indicated.
g. All changes to the Guideline must follow rulemaking
requirements since the Guideline is codified in this Appendix W of
Part 51. EPA will promulgate proposed and final rules in the Federal
Register to amend this Appendix W. Ample opportunity for public
comment will be provided for each proposed change and public
hearings scheduled if requested.
h. A wide range of topics on modeling and data bases are
discussed in the Guideline. Chapter 2 gives an overview of models
and their appropriate use. Chapter 3 provides specific guidance on
the use of ``preferred'' air quality models and on the selection of
alternative techniques. Chapters 4 through 7 provide recommendations
on modeling techniques for application to simple-terrain stationary
source problems, complex terrain problems, and mobile source
problems. Specific modeling requirements for selected regulatory
issues are also addressed. Chapter 8 discusses issues common to many
modeling analyses, including acceptable model components. Chapter 9
makes recommendations for data inputs to models including source,
meteorological and background air quality data. Chapter 10 covers
the uncertainty in model estimates and how that information can be
useful to the

[[Page 41842]]

regulatory decision-maker. The last chapter summarizes how estimates
and measurements of air quality are used in assessing source impact
and in evaluating control strategies.
i. This Appendix W itself contains three appendices: A, B, and
C. Thus, when reference is made to ``Appendix A'', it refers to
Appendix A to this Appendix W. Appendices B and C are referenced in
the same way.
j. Appendix A contains summaries of refined air quality models
that are ``preferred'' for specific applications; both EPA models
and models developed by others are included. Appendix B contains
summaries of other refined models that may be considered with a
case-specific justification. Appendix C contains a checklist of
requirements for an air quality analysis.

2.0 Overview of Model Use

a. Before attempting to implement the guidance contained in this
appendix, the reader should be aware of certain general information
concerning air quality models and their use. Such information is
provided in this section.

2.1 Suitability of Models

a. The extent to which a specific air quality model is suitable
for the evaluation of source impact depends upon several factors.
These include: (1) The meteorological and topographic complexities
of the area; (2) the level of detail and accuracy needed for the
analysis; (3) the technical competence of those undertaking such
simulation modeling; (4) the resources available; and (5) the detail
and accuracy of the data base, i.e., emissions inventory,
meteorological data, and air quality data. Appropriate data should
be available before any attempt is made to apply a model. A model
that requires detailed, precise, input data should not be used when
such data are unavailable. However, assuming the data are adequate,
the greater the detail with which a model considers the spatial and
temporal variations in emissions and meteorological conditions, the
greater the ability to evaluate the source impact and to distinguish
the effects of various control strategies.
b. Air quality models have been applied with the most accuracy
or the least degree of uncertainty to simulations of long term
averages in areas with relatively simple topography. Areas subject
to major topographic influences experience meteorological
complexities that are extremely difficult to simulate. Although
models are available for such circumstances, they are frequently
site specific and resource intensive. In the absence of a model
capable of simulating such complexities, only a preliminary
approximation may be feasible until such time as better models and
data bases become available.
c. Models are highly specialized tools. Competent and
experienced personnel are an essential prerequisite to the
successful application of simulation models. The need for
specialists is critical when the more sophisticated models are used
or the area being investigated has complicated meteorological or
topographic features. A model applied improperly, or with
inappropriately chosen data, can lead to serious misjudgments
regarding the source impact or the effectiveness of a control
strategy.
d. The resource demands generated by use of air quality models
vary widely depending on the specific application. The resources
required depend on the nature of the model and its complexity, the
detail of the data base, the difficulty of the application, and the
amount and level of expertise required. The costs of manpower and
computational facilities may also be important factors in the
selection and use of a model for a specific analysis. However, it
should be recognized that under some sets of physical circumstances
and accuracy requirements, no present model may be appropriate.
Thus, consideration of these factors should not lead to selection of
an inappropriate model.

2.2 Classes of Models

a. The air quality modeling procedures discussed in this guide
can be categorized into four generic classes: Gaussian, numerical,
statistical or empirical, and physical. Within these classes,
especially Gaussian and numerical models, a large number of
individual ``computational algorithms'' may exist, each with its own
specific applications. While each of the algorithms may have the
same generic basis, e.g., Gaussian, it is accepted practice to refer
to them individually as models. For example, the Industrial Source
Complex (ISC) model and the RAM model are commonly referred to as
individual models. In fact, they are both variations of a basic
Gaussian model. In many cases the only real difference between
models within the different classes is the degree of detail
considered in the input or output data.
b. Gaussian models are the most widely used techniques for
estimating the impact of nonreactive pollutants. Numerical models
may be more appropriate than Gaussian models for area source urban
applications that involve reactive pollutants, but they require much
more extensive input data bases and resources and therefore are not
as widely applied. Statistical or empirical techniques are
frequently employed in situations where incomplete scientific
understanding of the physical and chemical processes or lack of the
required data bases make the use of a Gaussian or numerical model
impractical. Various specific models in these three generic types
are discussed in the Guideline.
c. Physical modeling, the fourth generic type, involves the use
of wind tunnel or other fluid modeling facilities. This class of
modeling is a complex process requiring a high level of technical
expertise, as well as access to the necessary facilities.
Nevertheless, physical modeling may be useful for complex flow
situations, such as building, terrain or stack downwash conditions,
plume impact on elevated terrain, diffusion in an urban environment,
or diffusion in complex terrain. It is particularly applicable to
such situations for a source or group of sources in a geographic
area limited to a few square kilometers. If physical modeling is
available and its applicability demonstrated, it may be the best
technique. A discussion of physical modeling is beyond the scope of
this guide. The EPA publication ``Guideline for Fluid Modeling of
Atmospheric Diffusion,''\4\ provides information on fluid modeling
applications and the limitations of that method.

2.3 Levels of Sophistication of Models

a. In addition to the various classes of models, there are two
levels of sophistication. The first level consists of general,
relatively simple estimation techniques that provide conservative
estimates of the air quality impact of a specific source, or source
category. These are screening techniques or screening models. The
purpose of such techniques is to eliminate the need of further more
detailed modeling for those sources that clearly will not cause or
contribute to ambient concentrations in excess of either the
National Ambient Air Quality Standards (NAAQS) \5\ or the allowable
prevention of significant deterioration (PSD) concentration
increments.\3\ If a screening technique indicates that the
concentration contributed by the source exceeds the PSD increment or
the increment remaining to just meet the NAAQS, then the second
level of more sophisticated models should be applied.
b. The second level consists of those analytical techniques that
provide more detailed treatment of physical and chemical atmospheric
processes, require more detailed and precise input data, and provide
more specialized concentration estimates. As a result they provide a
more refined and, at least theoretically, a more accurate estimate
of source impact and the effectiveness of control strategies. These
are referred to as refined models.
c. The use of screening techniques followed by a more refined
analysis is always desirable, however there are situations where the
screening techniques are practically and technically the only viable
option for estimating source impact. In such cases, an attempt
should be made to acquire or improve the necessary data bases and to
develop appropriate analytical techniques.

3.0 Recommended Air Quality Models

a. This section recommends refined modeling techniques that are
preferred for use in regulatory air quality programs. The status of
models developed by EPA, as well as those submitted to EPA for
review and possible inclusion in this guidance, is discussed. The
section also addresses the selection of models for individual cases
and provides recommendations for situations where the preferred
models are not applicable. Two additional sources of modeling
guidance, the Model Clearinghouse \6\ and periodic Regional
Meteorologists' workshops, are also briefly discussed here.
b. In all regulatory analyses, especially if other than
preferred models are selected for use, early discussions among
Regional Office staff, State and local control agencies, industry
representatives, and where appropriate, the Federal Land Manager,
are invaluable and are encouraged. Agreement on the data base to be
used, modeling techniques to be applied and the overall

[[Page 41843]]

technical approach, prior to the actual analyses, helps avoid
misunderstandings concerning the final results and may reduce the
later need for additional analyses. The use of an air quality
checklist, such as presented in Appendix C, and the preparation of a
written protocol help to keep misunderstandings at a minimum.
c. It should not be construed that the preferred models
identified here are to be permanently used to the exclusion of all
others or that they are the only models available for relating
emissions to air quality. The model that most accurately estimates
concentrations in the area of interest is always sought. However,
designation of specific models is needed to promote consistency in
model selection and application.
d. The 1980 solicitation of new or different models from the
technical community \7\ and the program whereby these models are
evaluated, established a means by which new models are identified,
reviewed and made available in the Guideline. There is a pressing
need for the development of models for a wide range of regulatory
applications. Refined models that more realistically simulate the
physical and chemical process in the atmosphere and that more
reliably estimate pollutant concentrations are required. Thus, the
solicitation of models is considered to be continuous.

3.1 Preferred Modeling Techniques

3.1.1 Discussion

a. EPA has developed approximately 10 models suitable for
regulatory application. More than 20 additional models were
submitted by private developers for possible inclusion in the
Guideline. These refined models have all been organized into eight
categories of use: rural, urban industrial complex, reactive
pollutants, mobile sources, complex terrain, visibility, and long
range transport. They are undergoing an intensive evaluation by
category. The evaluation exercises ^{8 9 10 include statistical
measures of model performance in comparison with measured air
quality data as suggested by the American Meteorological Society
\11\ and, where possible, peer scientific reviews.^{12 13 l4
b. When a single model is found to perform better than others in
a given category, it is recommended for application in that category
as a preferred model and listed in Appendix A. If no one model is
found to clearly perform better through the evaluation exercise,
then the preferred model listed in Appendix A is selected on the
basis of other factors such as past use, public familiarity, cost or
resource requirements, and availability. No further evaluation of a
preferred model is required if the source follows EPA
recommendations specified for the model in the Guideline. The models
not specifically recommended for use in a particular category are
summarized in Appendix B. These models should be compared with
measured air quality data when they are used for regulatory
applications consistent with recommendations in Section 3.2.
c. The solicitation of new refined models which are based on
sounder scientific principles and which more reliably estimate
pollutant concentrations is considered by EPA to be continuous.
Models that are submitted in accordance with the provisions outlined
in the Federal Register notice of March 1980 (45 FR 20157) \7\ will
be evaluated as submitted. These requirements are:
i. The model must be computerized and functioning in a common
Fortran language suitable for use on a variety of computer systems.
ii. The model must be documented in a user's guide which
identifies the mathematics of the model, data requirements and
program operating characteristics at a level of detail comparable to
that available for currently recommended models, e.g., the
Industrial Source Complex (ISC) model.
iii. The model must be accompanied by a complete test data set
including input parameters and output results. The test data must be
included in the user's guide as well as provided in computer-
readable form.
iv. The model must be useful to typical users, e.g., State air
pollution control agencies, for specific air quality control
problems. Such users should be able to operate the computer
program(s) from available documentation.
v. The model documentation must include a comparison with air
quality data or with other well-established analytical techniques.
vi. The developer must be willing to make the model available to
users at reasonable cost or make it available for public access
through the National Technical Information Service; the model cannot
be proprietary.
d. The evaluation process will include a determination of
technical merit, in accordance with the above six items including
the practicality of the model for use in ongoing regulatory
programs. Each model will also be subjected to a performance
evaluation for an appropriate data base and to a peer scientific
review. Models for wide use (not just an isolated case!) found to
perform better, based on an evaluation for the same data bases used
to evaluate models in Appendix A, will be proposed for inclusion as
preferred models in future Guideline revisions.

3.1.2 Recommendations

a. Appendix A identifies refined models that are preferred for
use in regulatory applications. If a model is required for a
particular application, the user should select a model from Appendix
A. These models may be used without a formal demonstration of
applicability as long as they are used as indicated in each model
summary of Appendix A. Further recommendations for the application
of these models to specific source problems are found in subsequent
sections of the Guideline.
b. If changes are made to a preferred model without affecting
the concentration estimates, the preferred status of the model is
unchanged. Examples of modifications that do not affect
concentrations are those made to enable use of a different computer
or those that affect only the format or averaging time of the model
results. However, when any changes are made, the Regional
Administrator should require a test case example to demonstrate that
the concentration estimates are not affected.
c. A preferred model should be operated with the options listed
in Appendix A as ``Recommendations for Regulatory Use.'' If other
options are exercised, the model is no longer ``preferred.'' Any
other modification to a preferred model that would result in a
change in the concentration estimates likewise alters its status as
a preferred model. Use of the model must then be justified on a
case-by-case basis.

3.2 Use of Alternative Models

3.2.1 Discussion

a. Selection of the best techniques for each individual air
quality analysis is always encouraged, but the selection should be
done in a consistent manner. A simple listing of models in this
guide cannot alone achieve that consistency nor can it necessarily
provide the best model for all possible situations. An EPA document,
``Interim Procedures for Evaluating Air Quality Models'',^{15 16
has been prepared to assist in developing a consistent approach when
justifying the use of other than the preferred modeling techniques
recommended in this guide. An alternative to be considered to the
performance measures contained in Chapter 3 of this document is set
forth in another EPA document ``Protocol for Determining the Best
Performing Model''.\17\ The procedures in both documents provide a
general framework for objective decision-making on the acceptability
of an alternative model for a given regulatory application. The
documents contain procedures for conducting both the technical
evaluation of the model and the field test or performance
evaluation.
b. This section discusses the use of alternate modeling
techniques and defines three situations when alternative models may
be used.

3.2.2 Recommendations

a. Determination of acceptability of a model is a Regional
Office responsibility. Where the Regional Administrator finds that
an alternative model is more appropriate than a preferred model,
that model may be used subject to the recommendations below. This
finding will normally result from a determination that (1) A
preferred air quality model is not appropriate for the particular
application; or (2) a more appropriate model or analytical procedure
is available and is applicable.
b. An alternative model should be evaluated from both a
theoretical and a performance perspective before it is selected for
use. There are three separate conditions under which such a model
will normally be approved for use: (1) If a demonstration can be
made that the model produces concentration estimates equivalent to
the estimates obtained using a preferred model; (2) if a statistical
performance evaluation has been conducted using measured air quality
data and the results of that evaluation indicate the alternative
model performs better for the application than a comparable model in
Appendix A; and (3) if there is no preferred model for the specific
application but a refined model is needed to satisfy regulatory
requirements. Any one of these three separate conditions may warrant
use of an alternative model. Some known

[[Page 41844]]

alternative models that are applicable for selected situations are
contained in Appendix B. However, inclusion there does not infer any
unique status relative to other alternative models that are being or
will be developed in the future.
c. Equivalency is established by demonstrating that the maximum
or highest, second highest concentrations are within 2 percent of
the estimates obtained from the preferred model. The option to show
equivalency is intended as a simple demonstration of acceptability
for an alternative model that is so nearly identical (or contains
options that can make it identical) to a preferred model that it can
be treated for practical purposes as the preferred model. Two
percent was selected as the basis for equivalency since it is a
rough approximation of the fraction that PSD Class I increments are
of the NAAQS for SO2, i.e., the difference in concentrations
that is judged to be significant. However, notwithstanding this
demonstration, use of models that are not equivalent may be used
when the conditions of paragraph e of this section are satisfied.
d. The procedures and techniques for determining the
acceptability of a model for an individual case based on superior
performance is contained in the document entitled ``Interim
Procedures for Evaluating Air Quality Models'', \15\ and should be
followed, as appropriate.^{a Preparation and implementation of an
evaluation protocol which is acceptable to both control agencies and
regulated industry is an important element in such an evaluation.
---------------------------------------------------------------------------

\a\ Another EPA document, ``Protocol for Determining the Best
Performing Model'', \17\ contains advanced statistical techniques
for determining which model performs better than other competing
models. In many cases, this protocol should be considered by users
of the ``Interim Procedures for Evaluating Air Quality Models'' in
preference to the material currently in Chapter 3 of that document.
---------------------------------------------------------------------------

e. When no Appendix A model is applicable to the modeling
problem, an alternative refined model may be used provided that:
i. The model can be demonstrated to be applicable to the problem
on a theoretical basis; and
ii. The data bases which are necessary to perform the analysis
are available and adequate; and
iii. Performance evaluations of the model in similar
circumstances have shown that the model is not biased toward
underestimates; or
iv. After consultation with the EPA Regional Office, a second
model is selected as a baseline or reference point for performance
and the interim procedures \15\ protocol \17\ are then used to
demonstrate that the proposed model performs better than the
reference model.

3.3 Availability of Supplementary Modeling Guidance

a. The Regional Administrator has the authority to select models
that are appropriate for use in a given situation. However, there is
a need for assistance and guidance in the selection process so that
fairness and consistency in modeling decisions is fostered among the
various Regional Offices and the States. To satisfy that need, EPA
established the Model Clearinghouse and also holds periodic
workshops with headquarters, Regional Office and State modeling
representatives.

3.3.1 The Model Clearinghouse

3.3.1.1 Discussion

a. The Model Clearinghouse is the single EPA focal point for
review of air quality simulation models proposed for use in specific
regulatory applications. Details concerning the Clearinghouse and
its operation are found in the document, ``Model Clearinghouse:
Operational Plan.'' \6\ Three primary functions of the Clearinghouse
are:
i. Review of decisions proposed by EPA Regional Offices on the
use of modeling techniques and data bases.
ii. Periodic visits to Regional Offices to gather information
pertinent to regulatory model usage.
iii. Preparation of an annual report summarizing activities of
the Clearinghouse including specific determinations made during the
course of the year.

3.3.1.2 Recommendations

a. The Regional Administrator may request assistance from the
Model Clearinghouse after an initial evaluation and decision has
been reached concerning the application of a model, analytical
technique or data base in a particular regulatory action. The
Clearinghouse may also consider and evaluate the use of modeling
techniques submitted in support of any regulatory action. Additional
responsibilities are: (1) Review proposed action for consistency
with agency policy; (2) determine technical adequacy; and (3) make
recommendations concerning the technique or data base.

3.3.2 Regional Meteorologists Workshops

13.3.2.1 Discussion

a. EPA conducts an annual in-house workshop for the purpose of
mutual discussion and problem resolution among Regional Office
modeling specialists, EPA research modeling experts, EPA
Headquarters modeling and regulatory staff and representatives from
State modeling programs. A summary of the issues resolved at
previous workshops was issued in 1981 as ``Regional Workshops on Air
Quality Modeling: A Summary Report.'' \17\ That report clarified
procedures not specifically defined in the 1978 version of the
Guideline and was issued to ensure the consistent interpretation of
model requirements from Region to Region. Similar workshops for the
purpose of clarifying Guideline procedures or providing detailed
instructions for the use of those procedures are anticipated in the
future.

3.3.2.2 Recommendations

a. The Regional Office should always be consulted for
information and guidance concerning modeling methods and
interpretations of modeling guidance, and to ensure that the air
quality model user has available the latest most up-to-date policy
and procedures.

4.0 Simple-Terrain Stationary Source Models

4.1 Discussion

a. Simple terrain, as used in this section, is considered to be
an area where terrain features are all lower in elevation than the
top of the stack of the source(s) in question. The models
recommended in this section are generally used in the air quality
impact analysis of stationary sources for most criteria pollutants.
The averaging time of the concentration estimates produced by these
models ranges from 1 hour to an annual average.
b. Model evaluation exercises have been conducted to determine
the ``best, most appropriate point source model'' for use in simple
terrain.^{8 12 However, no one model has been found to be clearly
superior. Based on past use, public familiarity, and availability,
ISC is the recommended model for a wide range of regulatory
applications. Similar determinations were made for the other refined
models that are identified in section 4.2.

4.2 Recommendations

4.2.1 Screening Techniques

a. Point source screening techniques are an acceptable approach
to air quality analyses. One such approach is contained in the EPA
document ``Screening Procedures for Estimating the Air Quality
Impact of Stationary Sources''.\18\ A computerized version of the
screening technique, SCREEN, is available.^{19 20 For the current
version of SCREEN, see 12.0 References.\20\
b. All screening procedures should be adjusted to the site and
problem at hand. Close attention should be paid to whether the area
should be classified urban or rural in accordance with Section
8.2.8. The climatology of the area should be studied to help define
the worst-case meteorological conditions. Agreement should be
reached between the model user and the reviewing authority on the
choice of the screening model for each analysis, and on the input
data as well as the ultimate use of the results.

4.2.2 Refined Analytical Techniques

a. A brief description of preferred models for refined
applications is found in Appendix A. Also listed in Appendix A are
the model input requirements, the standard options that should be
selected when running the program, and output options.
b. When modeling for compliance with short term NAAQS and PSD
increments is of primary concern, a short term model may also be
used to provide long term concentration estimates. However, when
modeling sources for which long term standards alone are applicable
(e.g., lead), then the long term models should be used. The
conversion from long term to short term concentration averages by
any transformation technique is not acceptable in regulatory
applications.

5.0 Model Use in Complex Terrain

5.1 Discussion

a. For the purpose of the Guideline, complex terrain is defined
as terrain exceeding the height of the stack being

[[Page 41845]]

modeled. Complex terrain dispersion models are normally applied to
stationary sources of pollutants such as SO2 and particulates.
b. A major outcome from the EPA Complex Terrain Model
Development project has been the publication of a refined dispersion
model (CTDM) suitable for regulatory application to plume impaction
assessments in complex terrain.\21\ Although CTDM as originally
produced was only applicable to those hours characterized as neutral
or stable, a computer code for all stability conditions,
CTDMPLUS,\19\ together with a user's guide,\22\ and on-site
meteorological and terrain data processors,^{23 24 is now
available. Moreover, CTSCREEN,^{19 25 a version of CTDMPLUS that
does not require on-site meteorological data inputs, is also
available as a screening technique.
c. The methods discussed in this section should be considered in
two categories: (1) Screening techniques, and (2) the refined
dispersion model, CTDMPLUS, discussed below and listed in Appendix
A.
d. Continued improvements in ability to accurately model plume
dispersion in complex terrain situations can be expected, e.g., from
research on lee side effects due to terrain obstacles. New
approaches to improve the ability of models to realistically
simulate atmospheric physics, e.g., hybrid models which incorporate
an accurate wind field analysis, will ultimately provide more
appropriate tools for analyses. Such hybrid modeling techniques are
also acceptable for regulatory applications after the appropriate
demonstration and evaluation.\15\

5.2 Recommendations

a. Recommendations in this section apply primarily to those
situations where the impaction of plumes on terrain at elevations
equal to or greater than the plume centerline during stable
atmospheric conditions are determined to be the problem. If a
violation of any NAAQS or the controlling increment is indicated by
using any of the preferred screening techniques, then a refined
complex terrain model may be used. Phenomena such as fumigation,
wind direction shear, lee-side effects, building wake- or terrain-
induced downwash, deposition, chemical transformation, variable
plume trajectories, and long range transport are not addressed by
the recommendations in this section.
b. Where site-specific data are used for either screening or
refined complex terrain models, a data base of at least 1 full-year
of meteorological data is preferred. If more data are available,
they should be used. Meteorological data used in the analysis should
be reviewed for both spatial and temporal representativeness.
c. Placement of receptors requires very careful attention when
modeling in complex terrain. Often the highest concentrations are
predicted to occur under very stable conditions, when the plume is
near, or impinges on, the terrain. The plume under such conditions
may be quite narrow in the vertical, so that even relatively small
changes in a receptor's location may substantially affect the
predicted concentration. Receptors within about a kilometer of the
source may be even more sensitive to location. Thus, a dense array
of receptors may be required in some cases. In order to avoid
excessively large computer runs due to such a large array of
receptors, it is often desirable to model the area twice. The first
model run would use a moderate number of receptors carefully located
over the area of interest. The second model run would use a more
dense array of receptors in areas showing potential for high
concentrations, as indicated by the results of the first model run.
d. When CTSCREEN or CTDMPLUS is used, digitized contour data
must be first processed by the CTDM Terrain Processor \23\ to
provide hill shape parameters in a format suitable for direct input
to CTDMPLUS. Then the user supplies receptors either through an
interactive program that is part of the model or directly, by using
a text editor; using both methods to select receptors will generally
be necessary to assure that the maximum concentrations are estimated
by either model. In cases where a terrain feature may ``appear to
the plume'' as smaller, multiple hills, it may be necessary to model
the terrain both as a single feature and as multiple hills to
determine design concentrations.
e. The user is encouraged to confer with the Regional Office if
any unresolvable problems are encountered with any screening or
refined analytical procedures, e.g., meteorological data, receptor
siting, or terrain contour processing issues.

5.2.1 Screening Techniques

a. Five preferred screening techniques are currently available
to aid in the evaluation of concentrations due to plume impaction
during stable conditions: (1) for 24-hour impacts, the Valley
Screening Technique \19\ as outlined in the Valley Model User's
Guide; \26\ (2) CTSCREEN,\19\ as outlined in the CTSCREEN User's
Guide; \25\ (3) COMPLEX I; \19\ (4) SHORTZ/LONGZ; ^{19 27 and (5)
Rough Terrain Dispersion Model (RTDM) ^{19 90 in its prescribed
mode described below. As appropriate, any of these screening
techniques may be used consistent with the needs, resources, and
available data of the user.
b. The Valley Model, COMPLEX I, SHORTZ/LONGZ, and RTDM should be
used only to estimate concentrations at receptors whose elevations
are greater than or equal to plume height. For receptors at or below
stack height, a simple terrain model should be used (see Chapter 4).
Receptors between stack height and plume height present a unique
problem since none of the above models were designed to handle
receptors in this narrow regime, the definition of which will vary
hourly as meteorological conditions vary. CTSCREEN may be used to
estimate concentrations under all stability conditions at all
receptors located ``on terrain'' above stack top, but has limited
applicability in multi-source situations. As a result, the
estimation of concentrations at receptors between stack height and
plume height should be considered on a case-by-case basis after
consultation with the EPA Regional Office; the most appropriate
technique may be a function of the actual source(s) and terrain
configuration unique to that application. One technique that will
generally be acceptable, but is not necessarily preferred for any
specific application, involves applying both a complex terrain model
(except for the Valley Model) and a simple terrain model. The Valley
Model should not be used for any intermediate terrain receptor. For
each receptor between stack height and plume height, an hour-by-hour
comparison of the concentration estimates from both models is made.
The higher of the two modeled concentrations should be chosen to
represent the impact at that receptor for that hour, and then used
to compute the concentration for the appropriate averaging time(s).
For the simple terrain models, terrain may have to be ``chopped
off'' at stack height, since these models are frequently limited to
receptors no greater than stack height.

5.2.1.1 Valley Screening Technique

a. The Valley Screening Technique may be used to determine 24-
hour averages. This technique uses the Valley Model with the
following worst-case assumptions for rural areas: (1) P-G stability
``F''; (2) wind speed of 2.5 m/s; and (3) 6 hours of occurrence. For
urban areas the stability should be changed to ``P-G stability E.''
b. When using the Valley Screening Technique to obtain 24-hour
average concentrations the following apply: (1) multiple sources
should be treated individually and the concentrations for each wind
direction summed; (2) only one wind direction should be used (see
User's Guide,\26\ page 2-15) even if individual runs are made for
each source; (3) for buoyant sources, the BID option may be used,
and the option to use the 2.6 stable plume rise factor should be
selected; (4) if plume impaction is likely on any elevated terrain
closer to the source than the distance from the source to the final
plume rise, then the transitional (or gradual) plume rise option for
stable conditions should be selected.
c. The standard polar receptor grid found in the Valley Model
User's Guide may not be sufficiently dense for all analyses if only
one geographical scale factor is used. The user should choose an
additional set of receptors at appropriate downwind distances whose
elevations are equal to plume height minus 10 meters. Alternatively,
the user may exercise the ``Valley equivalent'' option in COMPLEX I
or SCREEN and note the comments above on the placement of receptors
in complex terrain models.
d. When using the ``Valley equivalent'' option in COMPLEX I, set
the wind profile exponents (PL) to 0.0, respectively, for all six
stability classes.

5.2.1.2 CTSCREEN

a. CTSCREEN may be used to obtain conservative, yet realistic,
worst-case estimates for receptors located on terrain above stack
height. CTSCREEN accounts for the three-dimensional nature of plume
and terrain interaction and requires detailed terrain data
representative of the modeling domain. The model description and
user's instructions are contained in the user's guide.\25\ The
terrain data must be digitized in the same manner as for CTDMPLUS
and a terrain processor is available.\23\ A discussion of the
model's performance characteristics is provided in a technical
paper.\91\ CTSCREEN is designed to execute a fixed matrix of
meteorological values for wind speed (u),

[[Page 41846]]

standard deviation of horizontal and vertical wind speeds
(v, G5w), vertical potential temperature
gradient (d/dz), friction velocity (ux), Monin-
Obukhov length (L), mixing height (zi) as a function of terrain
height, and wind directions for both neutral/stable conditions and
unstable convective conditions. Table 5-1 contains the matrix of
meteorological variables that is used for each CTSCREEN analysis.
There are 96 combinations, including exceptions, for each wind
direction for the neutral/stable case, and 108 combinations for the
unstable case. The specification of wind direction, however, is
handled internally, based on the source and terrain geometry. The
matrix was developed from examination of the range of meteorological
variables associated with maximum monitored concentrations from the
data bases used to evaluate the performance of CTDMPLUS. Although
CTSCREEN is designed to address a single source scenario, there are
a number of options that can be selected on a case-by-case basis to
address multi-source situations. However, the Regional Office should
be consulted, and concurrence obtained, on the protocol for modeling
multiple sources with CTSCREEN to ensure that the worst case is
identified and assessed. The maximum concentration output from
CTSCREEN represents a worst-case 1-hour concentration. Time-scaling
factors of 0.7 for 3-hour, 0.15 for 24-hour and 0.03 for annual
concentration averages are applied internally by CTSCREEN to the
highest 1-hour concentration calculated by the model.

5.2.1.3 COMPLEX I

a. If the area is rural, COMPLEX I may be used to estimate
concentrations for all averaging times. COMPLEX I is a modification
of the MPTER model that incorporates the plume impaction algorithm
of the Valley Model.\19\ It is a multiple-source screening technique
that accepts hourly meteorological data as input. The output is the
same as the normal MPTER output. When using COMPLEX I the following
options should be selected: (1) Set terrain adjustment IOPT (1)=1;
(2) set buoyancy induced dispersion IOPT (4)=1; (3) set IOPT (25)=1;
(4) set the terrain adjustment values to 0.5, 0.5, 0.5 0.5, 0.0,
0.0, (respectively for six stability classes); and (5) set Z MIN=10.
b. When using the ``Valley equivalent'' option (only) in COMPLEX
I, set the wind profile exponents (PL) to 0.0, respectively, for all
six stability classes. For all other regulatory uses of COMPLEX I,
set the wind profile exponents to the values used in the simple
terrain models, i.e., 0.07, 0.07, 0.10, 0.15, 0.35, and 0.55,
respectively, for rural modeling.
c. Gradual plume rise should be used to estimate concentrations
at nearby elevated receptors, if plume impaction is likely on any
elevated terrain closer to the source than the distance from the
source to the final plume rise (see Section 8.2.5).

5.2.1.4 SHORTZ/LONGZ

a. If the source is located in an urbanized (Section 8.2.8)
complex terrain valley, then the suggested screening technique is
SHORTZ for short-term averages or LONGZ for long-term averages.
SHORTZ and LONGZ may be used as screening techniques in these
complex terrain applications without demonstration and evaluation.
Application of these models in other than urbanized valley
situations will require the same evaluation and demonstration
procedures as are required for all Appendix B models.
b. Both SHORTZ and LONGZ have a number of options. When using
these models as screening techniques for urbanized valley
applications, the options listed in Table 5-2 should be selected.

5.2.1.5 RTDM (Screening Mode)

a. RTDM with the options specified in Table 5-3 may be used as a
screening technique in rural complex terrain situations without
demonstration and evaluation.
b. The RTDM screening technique can provide a more refined
concentration estimate if on-site wind speed and direction
characteristic of plume dilution and transport are used as input to
the model. In complex terrain, these winds can seldom be estimated
accurately from the standard surface (10m level) measurements.
Therefore, in order to increase confidence in model estimates, EPA
recommends that wind data input to RTDM should be based on fixed
measurements at stack top height. For stacks greater than 100m, the
measurement height may be limited to 100m in height relative to
stack base. However, for very tall stacks, see guidance in Section
9.3.3.2. This recommendation is broadened to include wind data
representative of plume transport height where such data are derived
from measurements taken with remote sensing devices such as SODAR.
The data from both fixed and remote measurements should meet quality
assurance and recovery rate requirements. The user should also be
aware that RTDM in the screening mode accepts the input of measured
wind speeds at only one height. The default values for the wind
speed profile exponents shown in Table 5-3 are used in the model to
determine the wind speed at other heights. RTDM uses wind speed at
stack top to calculate the plume rise and the critical dividing
streamline height, and the wind speed at plume transport level to
calculate dilution. RTDM treats wind direction as constant with
height.
c. RTDM makes use of the ``critical dividing streamline''
concept and thus treats plume interactions with terrain quite
differently from other models such as SHORTZ and COMPLEX I. The
plume height relative to the critical dividing streamline determines
whether the plume impacts the terrain, or is lifted up and over the
terrain. The receptor spacing to identify maximum impact
concentrations is quite critical depending on the location of the
plume in the vertical. Analysis of the expected plume height
relative to the height of the critical dividing streamline should be
performed for differing meteorological conditions in order to help
develop an appropriate array of receptors. Then it is advisable to
model the area twice according to the suggestions in Section 5.2.

5.2.1.6 Restrictions

a. For screening analyses using the Valley Screening Technique,
COMPLEX I or RTDM, a sector greater than 22\1/2\ deg. should not be
allowed. Full ground reflection should always be used in the Valley
Screening Technique and COMPLEX I.

5.2.2 Refined Analytical Techniques

a. When the results of the screening analysis demonstrate a
possible violation of NAAQS or the controlling PSD increments, a
more refined analysis may need to be conducted.
b. The Complex Terrain Dispersion Model Plus Algorithms for
Unstable Situations (CTDMPLUS) is a refined air quality model that
is preferred for use in all stability conditions for complex terrain
applications. CTDMPLUS is a sequential model that requires five
input files: (1) General program specifications; (2) a terrain data
file; (3) a receptor file; (4) a surface meteorological data file;
and (5) a user created meteorological profile data file. Two
optional input files consist of hourly emissions parameters and a
file containing upper air data from rawinsonde data files, e.g., a
National Climatic Data Center TD-6201 file, unless there are no
hours categorized as unstable in the record. The model description
and user instructions are contained in Volume 1 of the User's
Guide.\22\ Separate publications \23\ \24\ describe the terrain
preprocessor system and the meteorological preprocessor program. In
Part I of a technical article \92\ is a discussion of the model and
its preprocessors; the model's performance characteristics are
discussed in Part II of the same article.\93\ The size of the
CTDMPLUS executable file on a personal computer is approximately
360K bytes. The model produces hourly average concentrations of
stable pollutants, i.e., chemical transformation or decay of species
and settling/deposition are not simulated. To obtain concentration
averages corresponding to the NAAQS, e.g., 3- or 24-hour, or annual
averages, the user must execute a postprocessor program such as
CHAVG.\19\ CTDMPLUS is applicable to all receptors on terrain
elevations above stack top. However, the model contains no
algorithms for simulating building downwash or the mixing or
recirculation found in cavity zones in the lee of a hill. The path
taken by a plume through an array of hills cannot be simulated.
CTDMPLUS does not explicitly simulate calm meteorological periods,
and for those situations the user should follow the guidance in
Section 9.3.4. The user should follow the recommendations in the
User's Guide under General Program Specifications for: (1) Selecting
mixed layer heights, (2) setting minimum scalar wind speed to 1 m/s,
and (3) scaling wind direction with height. Close coordination with
the Regional Office is essential to insure a consistent, technically
sound application of this model.
c. The performance of CTDMPLUS is greatly improved by the use of
meteorological data from several levels up to plume height. However,
due to the vast range of source-plume-hill geometries possible in
complex terrain, detailed requirements for meteorological monitoring
in support of refined analyses using CTDMPLUS should be determined
on a case-by-case basis. The following general guidance should be
considered in the development of a meteorological monitoring
protocol for regulatory applications of CTDMPLUS and

[[Page 41847]]

reviewed in detail by the Regional Office before initiating any
monitoring. As appropriate, the On-Site Meteorological Program
Guidance document \66\ should be consulted for specific guidance on
siting requirements for meteorological towers, selection and
exposure of sensors, etc. As more experience is gained with the
model in a variety of circumstances, more specific guidance may be
developed.
d. Site specific meteorological data are critical to dispersion
modeling in complex terrain and, consequently, the meteorological
requirements are more demanding than for simple terrain. Generally,
three different meteorological files (referred to as surface,
profile, and rawin files) are needed to run CTDMPLUS in a regulatory
mode.
e. The surface file is created by the meteorological
preprocessor (METPRO) \24\ based on on-site measurements or
estimates of solar and/or net radiation, cloud cover and ceiling,
and the mixed layer height. These data are used in METPRO to
calculate the various surface layer scaling parameters (roughness
length, friction velocity, and Monin-Obukhov length) which are
needed to run the model. All of the user inputs required for the
surface file are based either on surface observations or on
measurements at or below 10m.
f. The profile data file is prepared by the user with on-site
measurements (from at least three levels) of wind speed, wind
direction, turbulence, and potential temperature. These measurements
should be obtained up to the representative plume height(s) of
interest (i.e., the plume height(s) under those conditions important
to the determination of the design concentration). The
representative plume height(s) of interest should be determined
using an appropriate complex terrain screening procedure (e.g.,
CTSCREEN) and should be documented in the monitoring/modeling
protocol. The necessary meteorological measurements should be
obtained from an appropriately sited meteorological tower augmented
by SODAR if the representative plume height(s) of interest exceed
100m. The meteorological tower need not exceed the lesser of the
representative plume height of interest (the highest plume height if
there is more than one plume height of interest) or 100m.
g. Locating towers on nearby terrain to obtain stack height or
plume height measurements for use in profiles by CTDMPLUS should be
avoided unless it can clearly be demonstrated that such measurements
would be representative of conditions affecting the plume.
h. The rawin file is created by a second meteorological
preprocessor (READ62) \24\ based on NWS (National Weather Service)
upper air data. The rawin file is used in CTDMPLUS to calculate
vertical potential temperature gradients for use in estimating plume
penetration in unstable conditions. The representativeness of the
off-site NWS upper air data should be evaluated on a case-by-case
basis.
i. In the absence of an appropriate refined model, screening
results may need to be used to determine air quality impact and/or
emission limits.

Table 5-1a.--Neutral/Stable Meteorological Matrix for CTSCREEN
----------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
Variable Specific values

----------------------------------------------------------------------------------------------------------------
U (m/s).................................... 1.0 2.0 3.0 4.0 5.0
v (m/s)........................... 0.3 0.75
w (m/s)........................... 0.08 0.15 0.30 0.75
DQ/Dz (K/m)................................ 0.01 0.02 0.035
WD
(4) (Wind direction optimized internally
for each meteorological combination)
----------------------------------------------------------------------------------------------------------------
Exceptions:
(1) If U 2 m/s and v 0.3 m/s, then include w = 0.04 m/s.
(2) If w = 0.75 m/s and U 3.0 m/s, then ^{DU/^{Dz is limited to 0.01 K/m.
(3) If U 4 m/s, then w 0.15 m/s.
(4) w v

Table 5-1b.--Unstable/Convective Meteorological Matrix for CTSCREEN
----------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
Variable Specific values

(2) (where h = terrain height)
----------------------------------------------------------------------------------------------------------------

Table 5-2.--Preferred Options for the SHORTZ/LONGZ Computer Codes When Used in a Screening Mode
----------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
Option Selection

----------------------------------------------------------------------------------------------------------------
I Switch 9................................ .......................................... If using NWS data, set =
0, If using site-
specific data, check
with the Regional
Office.
I Switch 17............................... .......................................... Set = 1 (urban option).
GAMMA 1................................... .......................................... Use default values (0.6
entrainment
coefficient).
GAMMA 2................................... .......................................... Always default to
``stable''.
XRY....................................... .......................................... Set = 0 (50m rectilinear
expansion distance).
NS, VS, FRQ (SHORTZ)
(particle size, etc.) Do not use (applicable
only in flat terrain).
NUS, VS, FRQ (LONGZ)
ALPHA..................................... .......................................... Select 0.9.
SIGEPU
(dispersion parameters)................... Use Cramer curves
(default); if site-
specific turbulence
data are available, see
Regional Office for
advice.
SIGAPU
P (wind profile).......................... .......................................... Select default values
given in Table 2-2 of
User's Instructions; if
site-specific data are
available, see Regional
Office for advice.
----------------------------------------------------------------------------------------------------------------

[[Page 41848]]

Table 5-3.--Preferred Options for the RTDM Computer Code When Used in a Screening Mode
----------------------------------------------------------------------------------------------------------------
Parameter Variable Value Remarks
----------------------------------------------------------------------------------------------------------------
PR001-003.......................... SCALE................. ...................... Scale factors assuming
horizontal distance is in
kilometers, vertical
distance is in feet, and
wind speed is in meters
per second.
PR004.............................. ZWIND1................ Wind measurement See Section 5.2.1.4.
height.
ZWIND2................ Not used.............. Height of second
anemometer.
IDILUT................ 1..................... Dilution wind speed scaled
to plume height.
ZA.................... 0 (default)........... Anemometer-terrain height
above stack base.
PR005.............................. EXPON................. 0.09, 0.11, 0.12, Wind profile exponents.
0.14, 0.2, 0.3
(default).
PR006.............................. ICOEF................. 3 (default)........... Briggs Rural/ASME \139\
dispersion parameters.
PR009.............................. IPPP.................. 0 (default)........... Partial plume penetration;
not used.
PR010.............................. IBUOY................. 1 (default)........... Buoyancy-enhanced
dispersion is used.
ALPHA................. 3.162 (default)....... Buoyancy-enhanced
dispersion coefficient.
PR011.............................. IDMX.................. 1 (default)........... Unlimited mixing height for
stable conditions.
PR012.............................. ITRANS................ 1 (default)........... Transitional plume rise is
used.
PR013.............................. TERCOR................ 6*0.5 (default)....... Plume patch correction
factors.
PR014.............................. RVPTG................. 0.02, 0.035 (default). Vertical potential
temperature gradient
values for stabilities E
and F.
PR015.............................. ITIPD................. 1..................... Stack-tip downwash is used.
PR020.............................. ISHEAR................ 0 (default)........... Wind shear; not used.
PR022.............................. IREFL................. 1 (default)........... Partial surface reflection
is used.
PR023.............................. IHORIZ................ 2 (default)........... Sector averaging.
SECTOR................ 6*22.5 (default)...... Using 22.5 deg. sectors.
PR016 to 019; 021; and 024......... IY, IZ, IRVPTG, 0..................... Hourly values of
IHVPTG; IEPS; IEMIS. turbulence, vertical
potential temperature
gradient, wind speed
profile exponents, and
stack emissions are not
used.
----------------------------------------------------------------------------------------------------------------

6.0 Models for Ozone, Carbon Monoxide and Nitrogen Dioxide

6.1 Discussion

a. Models discussed in this section are applicable to pollutants
often associated with mobile sources, e.g., ozone (O3), carbon
monoxide (CO) and nitrogen dioxide (NO2). Where stationary
sources of CO and NO2 are of concern, the reader is referred to
Sections 4 and 5
b. A control agency with jurisdiction over areas with
significant ozone problems and which has sufficient resources and
data to use a photochemical dispersion model is encouraged to do so.
Experience with and evaluations of the Urban Airshed Model show it
to be an acceptable, refined approach, and better data bases are
becoming available that support the more sophisticated analytical
procedures. However, empirical models (e.g., EKMA) fill the gap
between more sophisticated photochemical dispersion models and
proportional (rollback) modeling techniques and may be the only
applicable procedure if the available data bases are insufficient
for refined dispersion modeling.
c. Models for assessing the impact of carbon monoxide emissions
are needed for a number of different purposes, e.g., to evaluate the
effects of point sources, congested intersections and highways, as
well as the cumulative effect on ambient CO concentrations of all
sources of CO in an urban area.^{94 95
d. Nitrogen oxides are reactive and also an important
contribution to the photochemical ozone problem. They are usually of
most concern in areas of high ozone concentrations. Unless suitable
photochemical dispersion models are used, assumptions regarding the
conversion of NO to NO2 are required when modeling. Site-
specific conversion factors may be developed. If site-specific
conversion factors are not available or photochemical models are not
used, NO2 modeling should be considered only a screening
procedure.

6.2 Recommendations

6.2.1 Models for Ozone

a. The Urban Airshed Model (UAM)^{19 28 is recommended for
photochemical or reactive pollutant modeling applications involving
entire urban areas. To ensure proper execution of this numerical
model, users must satisfy the extensive input data requirements for
the model as listed in Appendix A and the users guide. Users are
also referred to the ``Guideline for Regulatory Application of the
Urban Airshed Model'' \29\ for additional data requirements and
procedures for operating this model.
b. The empirical model, City-specific EKMA,^{19 30-33 has
limited applicability for urban ozone analyses. Model users should
consult the appropriate Regional Office on a case-by-case basis
concerning acceptability of this modeling technique.
c. Appendix B contains some additional models that may be
applied on a case-by-case basis for photochemical or reactive
pollutant modeling. Other photochemical models, including multi-
layered trajectory models, that are available may be used if shown
to be appropriate. Most photochemical dispersion models require
emission data on individual hydrocarbon species and may require
three dimensional meteorological information on an hourly basis.
Reasonably sophisticated computer facilities are also often
required. Because the input data are not universally available and
studies to collect such data are very resource intensive, there are
only limited evaluations of those models.
d. For those cases which involve estimating the impact on ozone
concentrations due to stationary sources of VOC and NOX,
whether for permitting or other regulatory cases, the model user
should consult the appropriate Regional Office on the acceptability
of the modeling technique.
e. Proportional (rollback/forward) modeling is not an acceptable
procedure for evaluating ozone control strategies.

6.2.2 Models for Carbon Monoxide

a. For analyzing CO impacts at roadway intersections, users
should follow the procedures in the ``Guideline for Modeling Carbon
Monoxide from Roadway Intersections''.\34\ The recommended model for
such analyses is CAL3QHC.\35\ This model combines CALINE3 (already
in Appendix A) with a traffic model to calculate delays and queues
that occur at signalized intersections. In areas where the use of
either TEXIN2 or CALINE4 has previously been established, its use
may continue. The capability exists for these intersection models to
be used in either a screening or refined mode. The screening
approach is described in reference 34; a refined approach may be
considered on a case-by-case basis. The latest version of the MOBILE
(mobile source emission factor) model should be used for emissions
input to intersection models.
b. For analyses of highways characterized by uninterrupted
traffic flows, CALINE3 is recommended, with emissions input from the
latest version of the MOBILE model.
c. The recommended model for urban areawide CO analyses is RAM
or Urban Airshed Model (UAM); see Appendix A. Information on SIP
development and requirements for using these models can be found in
references 34, 96, 97 and 98.
d. Where point sources of CO are of concern, they should be
treated using the screening and refined techniques described in
Section 4 or 5 of the Guideline.

[[Page 41849]]

6.2.3 Models for Nitrogen Dioxide (Annual Average)

a. A tiered screening approach is recommended to obtain annual
average estimates of NO2 from point sources for New Source
Review analysis, including PSD, and for SIP planning purposes. This
multi-tiered approach is conceptually shown in Figure 6-1 and
described in paragraphs b and c of this section. Figure 6-1 is as
follows:

Figure 6-1.--Multi-tiered Screening Approach for Estimating Annual NO2
Concentrations From Point Sources
------------------------------------------------------------------------

-------------------------------------------------------------------------
Tier 1: Assume Total Conversion of NO to NO2
Tier 2: Multiply Annual NOX Estimate by Empirically Derived NO2/NOX
Ratio.
------------------------------------------------------------------------

b. For Tier 1 (the initial screen), use an appropriate Gaussian
model from Appendix A to estimate the maximum annual average
concentration and assume a total conversion of NO to NO2. If
the concentration exceeds the NAAQS and/or PSD increments for
NO2, proceed to the 2nd level screen.
c. For Tier 2 (2nd level) screening analysis, multiply the Tier
1 estimate(s) by an empirically derived NO2/NOX value of
0.75 (annual national default).^{36 An annual NO2/NOX
ratio differing from 0.75 may be used if it can be shown that such a
ratio is based on data likely to be representative of the
location(s) where maximum annual impact from the individual source
under review occurs. In the case where several sources contribute to
consumption of a PSD increment, a locally derived annual NO2/
NOX ratio should also be shown to be representative of the
location where the maximum collective impact from the new plus
existing sources occurs.
d. In urban areas, a proportional model may be used as a
preliminary assessment to evaluate control strategies to meet the
NAAQS for multiple minor sources, i.e. minor point, area and mobile
sources of NOX; concentrations resulting from major point
sources should be estimated separately as discussed above, then
added to the impact of the minor sources. An acceptable screening
technique for urban complexes is to assume that all NOX is
emitted in the form of NO2 and to use a model from Appendix A
for nonreactive pollutants to estimate NO2 concentrations. A
more accurate estimate can be obtained by: (1) Calculating the
annual average concentrations of NOX with an urban model, and
(2) converting these estimates to NO2 concentrations using an
empirically derived annual NO2/NOX ratio. A value of 0.75
is recommended for this ratio. However, a spatially averaged annual
NO2/NOX ratio may be determined from an existing air
quality monitoring network and used in lieu of the 0.75 value if it
is determined to be representative of prevailing ratios in the urban
area by the reviewing agency. To ensure use of appropriate locally
derived annual NO2/NOX ratios, monitoring data under
consideration should be limited to those collected at monitors
meeting siting criteria defined in 40 CFR Part 58, Appendix D as
representative of ``neighborhood'', ``urban'', or ``regional''
scales. Furthermore, the highest annual spatially averaged NO2/
NOX ratio from the most recent 3 years of complete data should
be used to foster conservatism in estimated impacts.
e. To demonstrate compliance with NO2 PSD increments in
urban areas, emissions from major and minor sources should be
included in the modeling analysis. Point and area source emissions
should be modeled as discussed above. If mobile source emissions do
not contribute to localized areas of high ambient NO2
concentrations, they should be modeled as area sources. When modeled
as area sources, mobile source emissions should be assumed uniform
over the entire highway link and allocated to each area source grid
square based on the portion of highway link within each grid square.
If localized areas of high concentrations are likely, then mobile
sources should be modeled as line sources with the preferred model
ISCLT.
f. More refined techniques to handle special circumstances may
be considered on a case-by-case basis and agreement with the
reviewing authority should be obtained. Such techniques should
consider individual quantities of NO and NO2 emissions,
atmospheric transport and dispersion, and atmospheric transformation
of NO to NO2. Where they are available, site-specific data on
the conversion of NO to NO2 may be used. Photochemical
dispersion models, if used for other pollutants in the area, may
also be applied to the NOX problem.

7.0 Other Model Requirements

7.1 Discussion

a. This section covers those cases where specific techniques
have been developed for special regulatory programs. Most of the
programs have, or will have when fully developed, separate guidance
documents that cover the program and a discussion of the tools that
are needed. The following paragraphs reference those guidance
documents, when they are available. No attempt has been made to
provide a comprehensive discussion of each topic since the reference
documents were designed to do that. This section will undergo
periodic revision as new programs are added and new techniques are
developed.
b. Other Federal agencies have also developed specific modeling
approaches for their own regulatory or other requirements. An
example of this is the three-volume manual issued by the U. S.
Department of Housing and Urban Development, ``Air Quality
Considerations in Residential Planning.'' \37\ Although such
regulatory requirements and manuals may have come about because of
EPA rules or standards, the implementation of such regulations and
the use of the modeling techniques is under the jurisdiction of the
agency issuing the manual or directive.
c. The need to estimate impacts at distances greater than 50km
(the nominal distance to which EPA considers most Gaussian models
applicable) is an important one especially when considering the
effects from secondary pollutants. Unfortunately, models submitted
to EPA have not as yet undergone sufficient field evaluation to be
recommended for general use. Existing data bases from field studies
at mesoscale and long range transport distances are limited in
detail. This limitation is a result of the expense to perform the
field studies required to verify and improve mesoscale and long
range transport models. Particularly important and sparse are
meteorological data adequate for generating three dimensional wind
fields. Application of models to complicated terrain compounds the
difficulty. EPA has completed limited evaluation of several long
range transport (LRT) models against two sets of field data. The
evaluation results are discussed in the document, ``Evaluation of
Short-Term Long-Range Transport Models.'' ^{99 100 For the time
being, long range and mesoscale transport models must be evaluated
for regulatory use on a case-by-case basis.
d. There are several regulatory programs for which air pathway
analysis procedures and modeling techniques have been developed. For
continuous emission releases, ISC forms the basis of many analytical
techniques. EPA is continuing to evaluate the performance of a
number of proprietary and public domain models for intermittent and
non-stack emission releases. Until EPA completes its evaluation, it
is premature to recommend specific models for air pathway analyses
of intermittent and non-stack releases in the Guideline.
e. Regional scale models are used by EPA to develop and evaluate
national policy and assist State and local control agencies. Two
such models are the Regional Oxidant Model (ROM) ^{101 102 103
and the Regional Acid Deposition Model (RADM).\104\ Due to the level
of resources required to apply these models, it is not envisioned
that regional scale models will be used directly in most model
applications.

7.2 Recommendations

7.2.1 Fugitive Dust/Fugitive Emissions

a. Fugitive dust usually refers to the dust put into the
atmosphere by the wind blowing over plowed fields, dirt roads or
desert or sandy areas with little or no vegetation. Reentrained dust
is that which is put into the air by reason of vehicles driving over
dirt roads (or dirty roads) and dusty areas. Such sources can be
characterized as line, area or volume sources. Emission rates may be
based on site-specific data or values from the general literature.
b. Fugitive emissions are usually defined as emissions that come
from an industrial source complex. They include the emissions
resulting from the industrial process that are not captured and
vented through a stack but may be released from various locations
within the complex. Where such fugitive emissions can be properly
specified, the ISC model, with consideration of gravitational
settling and dry deposition, is the recommended model. In some
unique cases a model developed specifically for the situation may be
needed.
c. Due to the difficult nature of characterizing and modeling
fugitive dust and fugitive emissions, it is recommended that the
proposed procedure be cleared by the appropriate Regional Office for
each specific situation before the modeling exercise is begun.

[[Page 41850]]

7.2.2 Particulate Matter

a. The particulate matter NAAQS, promulgated on July 1, 1987 (52
FR 24634), includes only particles with an aerodynamic diameter less
than or equal to a nominal 10 micrometers (PM-10). EPA promulgated
regulations for PSD increments measured as PM-10 on June 3, 1993 (58
FR 31621), which are codified at Secs. 51.166(c) and 52.21(c).
b. Screening techniques like those identified in Section 4 are
also applicable to PM-10 and to large particles. It is recommended
that subjectively determined values for ``half-life'' or pollutant
decay not be used as a surrogate for particle removal. Conservative
assumptions which do not allow removal or transformation are
suggested for screening. Proportional models (rollback/forward) may
not be applied for screening analysis, unless such techniques are
used in conjunction with receptor modeling.
c. Refined models such as those in Section 4.0 are recommended
for PM-10 and large particles. However, where possible, particle
size, gas-to-particle formation, and their effect on ambient
concentrations may be considered. For urban-wide refined analyses
CDM 2.0 (long term) or RAM (short term) should be used. ISC is
recommended for point sources of small particles and for source-
specific analyses of complicated sources. No model recommended for
general use at this time accounts for secondary particulate
formation or other transformations in a manner suitable for SIP
control strategy demonstrations. Where possible, the use of receptor
models ^{38 39 105 106 107 in conjunction with dispersion models
is encouraged to more precisely characterize the emissions inventory
and to validate source specific impacts calculated by the dispersion
model. A SIP development guideline,^{108 model reconciliation
guidance,^{106 and an example model application ^{109 are
available to assist in PM-10 analyses and control strategy
development.
d. Under certain conditions, recommended dispersion models are
not available or applicable. In such circumstances, the modeling
approach should be approved by the appropriate Regional Office on a
case-by-case basis. For example, where there is no recommended air
quality model and area sources are a predominant component of PM-10,
an attainment demonstration may be based on rollback of the
apportionment derived from two reconciled receptor models, if the
strategy provides a conservative demonstration of attainment. At
this time, analyses involving model calculations for distances
beyond 50km and under stagnation conditions should also be justified
on a case-by-case basis (see Sections 7.2.6 and 8.2.10).
e. As an aid to assessing the impact on ambient air quality of
particulate matter generated from prescribed burning activities,
reference 110 is available.

7.2.3 Lead

a. The air quality analyses required for lead implementation
plans are given in Secs. 51.83, 51.84 and 51.85. Sections 51.83 and
51.85 require the use of a modified rollback model as a minimum to
demonstrate attainment of the lead air quality standard but the use
of a dispersion model is the preferred approach. Section 51.83
requires the analysis of an entire urban area if the measured lead
concentration in the urbanized area exceeds a quarterly (three
month) average of 4.0 g/m\3\. Section 51.84 requires the
use of a dispersion model to demonstrate attainment of the lead air
quality standard around specified lead point sources. For other
areas reporting a violation of the lead standard, Section 51.85
requires an analysis of the area in the vicinity of the monitor
reporting the violation. The NAAQS for lead is a quarterly (three
month) average, thus requiring the use of modeling techniques that
can provide long-term concentration estimates.
b. The SIP should contain an air quality analysis to determine
the maximum quarterly lead concentration resulting from major lead
point sources, such as smelters, gasoline additive plants, etc. For
these applications the ISC model is preferred, since the model can
account for deposition of particles and the impact of fugitive
emissions. If the source is located in complicated terrain or is
subject to unusual climatic conditions, a case-specific review by
the appropriate Regional Office may be required.
c. In modeling the effect of traditional line sources (such as a
specific roadway or highway) on lead air quality, dispersion models
applied for other pollutants can be used. Dispersion models such as
CALINE3 have been widely used for modeling carbon monoxide emissions
from highways. However, where deposition is of concern, the line
source treatment in ISC may be used. Also, where there is a point
source in the middle of a substantial road network, the lead
concentrations that result from the road network should be treated
as background (see Section 9.2); the point source and any nearby
major roadways should be modeled separately using the ISC model.
d. To model an entire major urban area or to model areas without
significant sources of lead emissions, as a minimum a proportional
(rollback) model may be used for air quality analysis. The rollback
philosophy assumes that measured pollutant concentrations are
proportional to emissions. However, urban or other dispersion models
are encouraged in these circumstances where the use of such models
is feasible.
e. For further information concerning the use of models in the
development of lead implementation plans, the documents
``Supplementary Guidelines for Lead Implementation Plans,'' \40\ and
``Updated Information on Approval and Promulgation of Lead
Implementation Plans,'' \41\ should be consulted.
7.2.4. Visibility
a. The visibility regulations as promulgated in December 1980
^{b require consideration of the effect of new sources on the
visibility values of Federal Class I areas. The state of scientific
knowledge concerning identifying, monitoring, modeling, and
controlling visibility impairment is contained in an EPA report
``Protecting Visibility: An EPA Report to Congress''.\42\ In 1985,
EPA promulgated Federal Implementation Plans (FIPs) for states
without approved visibility provisions in their SIPs. A monitoring
plan was established as part of the FIPs.^{c
---------------------------------------------------------------------------

\b\ Sec. 51.300-307.
\c\ Sec. 51.300-307.
---------------------------------------------------------------------------

b. Guidance and a screening model, VISCREEN, is contained in the
EPA document ``Workbook for Plume Visual Impact Screening and
Analysis (Revised).'' \43\ VISCREEN can be used to calculate the
potential impact of a plume of specified emissions for specific
transport and dispersion conditions. If a more comprehensive
analysis is required, any refined model should be selected in
consultation with the EPA Regional Office and the appropriate
Federal Land Manager who is responsible for determining whether
there is an adverse effect by a plume on a Class I area.
c. PLUVUE II, listed in Appendix B, may be applied on a case-by-
case basis when refined plume visibility evaluations are needed.
Plume visibility models have been evaluated against several data
sets.^{44, 45
7.2.5 Good Engineering Practice Stack Height
a. The use of stack height credit in excess of Good Engineering
Practice (GEP) stack height or credit resulting from any other
dispersion technique is prohibited in the development of emission
limitations by Secs. 51.118 and 51.164. The definitions of GEP stack
height and dispersion technique are contained in Sec. 51.100.
Methods and procedures for making the appropriate stack height
calculations, determining stack height credits and an example of
applying those techniques are found in references 46, 47, 48, and
49.
b. If stacks for new or existing major sources are found to be
less than the height defined by EPA's refined formula for
determining GEP height, ^{d then air quality impacts associated
with cavity or wake effects due to the nearby building structures
should be determined. Detailed downwash screening procedures \18\
for both the cavity and wake regions should be followed. If more
refined concentration estimates are required, the Industrial Source
Complex (ISC) model contains algorithms for building wake
calculations and should be used. Fluid modeling can provide a great
deal of additional information for evaluating and describing the
cavity and wake effects.
---------------------------------------------------------------------------

\d\ The EPA refined formula height is defined as H + 1.5L (see
Reference 46).
---------------------------------------------------------------------------
7.2.6 Long Range Transport (LRT) (i.e., beyond 50km)
a. Section 165(e) of the Clean Air Act requires that suspected
significant impacts on PSD Class I areas be determined. However,
50km is the useful distance to which most Gaussian models are
considered accurate for setting emission limits. Since in many cases
PSD analyses may show that Class I areas may be threatened at
distances greater than 50km from new sources, some procedure is
needed to (1) determine if a significant impact will occur, and (2)
identify the model to be used in setting an emission limit if the
Class I increments are threatened (models for this purpose should be
approved for use on a case-by-case basis as required in Section
3.2). This procedure and the models

[[Page 41851]]

selected for use should be determined in consultation with the EPA
Regional Office and the appropriate Federal Land Manager (FLM).
While the ultimate decision on whether a Class I area is adversely
affected is the responsibility of the permitting authority, the FLM
has an affirmative responsibility to protect air quality related
values that may be affected.
b. If LRT is determined to be important, then estimates
utilizing an appropriate refined model for receptors at distances
greater than 50 km should be obtained. MESOPUFF II, listed in
Appendix B, may be applied on a case-by-case basis when LRT
estimates are needed. Additional information on applying this model
is contained in the EPA document ``A Modeling Protocol For Applying
MESOPUFF II to Long Range Transport Problems''.\111\

7.2.7 Modeling Guidance for Other Governmental Programs

a. When using the models recommended or discussed in the
Guideline in support of programmatic requirements not specifically
covered by EPA regulations, the model user should consult the
appropriate Federal or State agency to ensure the proper application
and use of that model. For modeling associated with PSD permit
applications that involve a Class I area, the appropriate Federal
Land Manager should be consulted on all modeling questions.
b. The Offshore and Coastal Dispersion (OCD) model \112\ was
developed by the Minerals Management Service and is recommended for
estimating air quality impact from offshore sources on onshore, flat
terrain areas. The OCD model is not recommended for use in air
quality impact assessments for onshore sources. Sources located on
or just inland of a shoreline where fumigation is expected should be
treated in accordance with Section 8.2.9.
c. The Emissions and Dispersion Modeling System (EDMS) \113\ was
developed by the Federal Aviation Administration and the United
States Air Force and is recommended for air quality assessment of
primary pollutant impacts at airports or air bases. Regulatory
application of EDMS is intended for estimating the cumulative effect
of changes in aircraft operations, point source, and mobile source
emissions on pollutant concentrations. It is not intended for PSD,
SIP, or other regulatory air quality analyses of point or mobile
sources at or peripheral to airport property that are independent of
changes in aircraft operations. If changes in other than aircraft
operations are associated with analyses, a model recommended in
Chapter 4, 5, or 6 should be used.

7.2.8 Air Pathway Analyses (Air Toxics and Hazardous Waste)

a. Modeling is becoming an increasingly important tool for
regulatory control agencies to assess the air quality impact of
releases of toxics and hazardous waste materials. Appropriate
screening techniques \114\ \115\ for calculating ambient
concentrations due to various well-defined neutrally buoyant toxic/
hazardous pollutant releases are available.
b. Several regulatory programs within EPA have developed
modeling techniques and guidance for conducting air pathway analyses
as noted in references 116-129. ISC forms the basis of the modeling
procedures for air pathway analyses of many of these regulatory
programs and, where identified, is appropriate for obtaining refined
ambient concentration estimates of neutrally buoyant continuous air
toxic releases from traditional sources. Appendix A to the Guideline
contains additional models appropriate for obtaining refined
estimates of continuous air toxic releases from traditional sources.
Appendix B contains models that may be used on a case-by-case basis
for obtaining refined estimates of denser-than-air intermittent
gaseous releases, e.g., DEGADIS; \130\ guidance for the use of such
models is also available.\131\
c. Many air toxics models require input of chemical properties
and/or chemical engineering variables in order to appropriately
characterize the source emissions prior to dispersion in the
atmosphere; reference 132 is one source of helpful data. In
addition, EPA has numerous programs to determine emission factors
and other estimates of air toxic emissions. The Regional Office
should be consulted for guidance on appropriate emission estimating
procedures and any uncertainties that may be associated with them.

8.0 General Modeling Considerations

8.1 Discussion

a. This section contains recommendations concerning a number of
different issues not explicitly covered in other sections of this
guide. The topics covered here are not specific to any one program
or modeling area but are common to nearly all modeling analyses.

8.2 Recommendations

8.2.1 Design Concentrations

8.2.1.1 Design Concentrations for Criteria Pollutants With
Deterministic Standards

a. An air quality analysis for SO2, CO, Pb, and NO2 is
required to determine if the source will (1) Cause a violation of
the NAAQS, or (2) cause or contribute to air quality deterioration
greater than the specified allowable PSD increment. For the former,
background concentration (see Section 9.2) should be added to the
estimated impact of the source to determine the design
concentration. For the latter, the design concentration includes
impact from all increment consuming sources.
b. If the air quality analyses are conducted using the period of
meteorological input data recommended in Section 9.3.1.2 (e.g., 5
years of NWS data or 1 year of site-specific data), then the design
concentration based on the highest, second-highest short term
concentration or long term average, whichever is controlling, should
be used to determine emission limitations to assess compliance with
the NAAQS and to determine PSD increments.
c. When sufficient and representative data exist for less than a
5-year period from a nearby NWS site, or when on-site data have been
collected for less than a full continuous year, or when it has been
determined that the on site data may not be temporally
representative, then the highest concentration estimate should be
considered the design value. This is because the length of the data
record may be too short to assure that the conditions producing
worst-case estimates have been adequately sampled. The highest value
is then a surrogate for the concentration that is not to be exceeded
more than once per year (the wording of the deterministic
standards). Also, the highest concentration should be used whenever
selected worst-case conditions are input to a screening technique.
This specifically applies to the use of techniques such as outlined
in ``Screening Procedures for Estimating the Air Quality Impact of
Stationary Sources, Revised''.\18\ Specific guidance for CO may be
found in the ``Guideline for Modeling Carbon Monoxide from Roadway
Intersections''.\34\
d. If the controlling concentration is an annual average value
and multiple years of data (on-site or NWS) are used, then the
design value is the highest of the annual averages calculated for
the individual years. If the controlling concentration is a
quarterly average and multiple years are used, then the highest
individual quarterly average should be considered the design value.
e. As long a period of record as possible should be used in
making estimates to determine design values and PSD increments. If
more than 1 year of site-specific data is available, it should be
used.

8.2.1.2 Design Concentrations for Criteria Pollutants With Expected
Exceedance Standards

a. Specific instructions for the determination of design
concentrations for criteria pollutants with expected exceedance
standards, ozone and PM-10, are contained in special guidance
documents for the preparation of SIPs for those pollutants.\86\
\108\ For all SIP revisions the user should check with the Regional
Office to obtain the most recent guidance documents and policy
memoranda concerning the pollutant in question.

8.2.2 Critical Receptor Sites

a. Receptor sites for refined modeling should be utilized in
sufficient detail to estimate the highest concentrations and
possible violations of a NAAQS or a PSD increment. In designing a
receptor network, the emphasis should be placed on receptor
resolution and location, not total number of receptors. The
selection of receptor sites should be a case-by-case determination
taking into consideration the topography, the climatology, monitor
sites, and the results of the initial screening procedure. For large
sources (those equivalent to a 500MW power plant) and where
violations of the NAAQS or PSD increment are likely, 360 receptors
for a polar coordinate grid system and 400 receptors for a
rectangular grid system, where the distance from the source to the
farthest receptor is 10km, are usually adequate to identify areas of
high concentration. Additional receptors may be needed in the high
concentration location if greater resolution is indicated by terrain
or source factors.

8.2.3 Dispersion Coefficients

a. Gaussian models used in most applications should employ
dispersion

[[Page 41852]]

coefficients consistent with those contained in the preferred models
in Appendix A. Factors such as averaging time, urban/rural
surroundings, and type of source (point vs. line) may dictate the
selection of specific coefficients. Generally, coefficients used in
Appendix A models are identical to, or at least based on, Pasquill-
Gifford coefficients \50\ in rural areas and McElroy-Pooler \51\
coefficients in urban areas.
b. Research is continuing toward the development of methods to
determine dispersion coefficients directly from measured or observed
variables.\52\ \53\ No method to date has proved to be widely
applicable. Thus, direct measurement, as well as other dispersion
coefficients related to distance and stability, may be used in
Gaussian modeling only if a demonstration can be made that such
parameters are more applicable and accurate for the given situation
than are algorithms contained in the preferred models.
c. Buoyancy-induced dispersion (BID), as identified by
Pasquill,\54\ is included in the preferred models and should be used
where buoyant sources, e.g., those involving fuel combustion, are
involved.

8.2.4 Stability Categories

a. The Pasquill approach to classifying stability is generally
required in all preferred models (Appendix A). The Pasquill method,
as modified by Turner,\55\ was developed for use with commonly
observed meteorological data from the National Weather Service and
is based on cloud cover, insolation and wind speed.
b. Procedures to determine Pasquill stability categories from
other than NWS data are found in subsection 9.3. Any other method to
determine Pasquill stability categories must be justified on a case-
by-case basis.
c. For a given model application where stability categories are
the basis for selecting dispersion coefficients, both
y and z should be determined from the
same stability category. ``Split sigmas'' in that instance are not
recommended.
d. Sector averaging, which eliminates the y term,
is generally acceptable only to determine long term averages, such
as seasonal or annual, and when the meteorological input data are
statistically summarized as in the STAR summaries. Sector averaging
is, however, commonly acceptable in complex terrain screening
methods.

8.2.5 Plume Rise

a. The plume rise methods of Briggs \56\ \57\ are incorporated
in the preferred models and are recommended for use in all modeling
applications. No provisions in these models are made for fumigation
or multistack plume rise enhancement or the handling of such special
plumes as flares; these problems should be considered on a case-by-
case basis.
b. Since there is insufficient information to identify and
quantify dispersion during the transitional plume rise period,
gradual plume rise is not generally recommended for use. There are
two exceptions where the use of gradual plume rise is appropriate:
(1) In complex terrain screening procedures to determine close-in
impacts; (2) when calculating the effects of building wakes. The
building wake algorithm in the ISC model incorporates and
automatically (i.e., internally) exercises the gradual plume rise
calculations. If the building wake is calculated to affect the plume
for any hour, gradual plume rise is also used in downwind dispersion
calculations to the distance of final plume rise, after which final
plume rise is used.
c. Stack tip downwash generally occurs with poorly constructed
stacks and when the ratio of the stack exit velocity to wind speed
is small. An algorithm developed by Briggs (Hanna et al.) \57\ is
the recommended technique for this situation and is found in the
point source preferred models.
d. Where aerodynamic downwash occurs due to the adverse
influence of nearby structures, the algorithms included in the ISC
model ^{58 should be used.

8.2.6 Chemical Transformation

a. The chemical transformation of SO2 emitted from point
sources or single industrial plants in rural areas is generally
assumed to be relatively unimportant to the estimation of maximum
concentrations when travel time is limited to a few hours. However,
in urban areas, where synergistic effects among pollutants are of
considerable consequence, chemical transformation rates may be of
concern. In urban area applications, a half-life of 4 hours \55\ may
be applied to the analysis of SO2 emissions. Calculations of
transformation coefficients from site-specific studies can be used
to define a ``half-life'' to be used in a Gaussian model with any
travel time, or in any application, if appropriate documentation is
provided. Such conversion factors for pollutant half-life should not
be used with screening analyses.
b. Complete conversion of NO to NO2 should be assumed for
all travel time when simple screening techniques are used to model
point source emissions of nitrogen oxides. If a Gaussian model is
used, and data are available on seasonal variations in maximum ozone
concentrations, the Ozone Limiting Method \36\ is recommended. In
refined analyses, case-by case conversion rates based on technical
studies appropriate to the site in question may be used. The use of
more sophisticated modeling techniques should be justified for
individual cases.
c. Use of models incorporating complex chemical mechanisms
should be considered only on a case-by-case basis with proper
demonstration of applicability. These are generally regional models
not designed for the evaluation of individual sources but used
primarily for region-wide evaluations. Visibility models also
incorporate chemical transformation mechanisms which are an integral
part of the visibility model itself and should be used in visibility
assessments.

8.2.7 Gravitational Settling and Deposition

a. An ``infinite half-life'' should be used for estimates of
particle concentrations when Gaussian models containing only
exponential decay terms for treating settling and deposition are
used.
b. Gravitational settling and deposition may be directly
included in a model if either is a significant factor. One preferred
model (ISC) contains a settling and deposition algorithm and is
recommended for use when particulate matter sources can be
quantified and settling and deposition are problems.

8.2.8 Urban/Rural Classification

a. The selection of either rural or urban dispersion
coefficients in a specific application should follow one of the
procedures suggested by Irwin \59\ and briefly described below.
These include a land use classification procedure or a population
based procedure to determine whether the character of an area is
primarily urban or rural.
b. Land Use Procedure: (1) Classify the land use within the
total area, Ao, circumscribed by a 3km radius circle about the
source using the meteorological land use typing scheme proposed by
Auer \60\; (2) if land use types I1, I2, C1, R2, and R3 account for
50 percent or more of Ao, use urban dispersion coefficients;
otherwise, use appropriate rural dispersion coefficients.
c. Population Density Procedure: (1) Compute the average
population density, p per square kilometer with Ao as defined
above; (2) If p is greater than 750 people/km\2\, use urban
dispersion coefficients; otherwise use appropriate rural dispersion
coefficients.
d. Of the two methods, the land use procedure is considered more
definitive. Population density should be used with caution and
should not be applied to highly industrialized areas where the
population density may be low and thus a rural classification would
be indicated, but the area is sufficiently built-up so that the
urban land use criteria would be satisfied. In this case, the
classification should already be ``urban'' and urban dispersion
parameters should be used.
e. Sources located in an area defined as urban should be modeled
using urban dispersion parameters. Sources located in areas defined
as rural should be modeled using the rural dispersion parameters.
For analyses of whole urban complexes, the entire area should be
modeled as an urban region if most of the sources are located in
areas classified as urban.

8.2.9 Fumigation

a. Fumigation occurs when a plume (or multiple plumes) is
emitted into a stable layer of air and that layer is subsequently
mixed to the ground either through convective transfer of heat from
the surface or because of advection to less stable surroundings.
Fumigation may cause excessively high concentrations but is usually
rather short-lived at a given receptor. There are no recommended
refined techniques to model this phenomenon. There are, however,
screening procedures (see ``Screening Procedures for Estimating the
Air Quality Impact of Stationary Sources'' \18\) that may be used to
approximate the concentrations. Considerable care should be
exercised in using the results obtained from the screening
techniques.
b. Fumigation is also an important phenomenon on and near the
shoreline of bodies of water. This can affect both individual plumes
and area-wide emissions. When fumigation conditions are expected to
occur from a source or sources with tall

[[Page 41853]]

stacks located on or just inland of a shoreline, this should be
addressed in the air quality modeling analysis. The Shoreline
Dispersion Model (SDM) listed in Appendix B may be applied on a
case-by-case basis when air quality estimates under shoreline
fumigation conditions are needed.\133\ Information on the results of
EPA's evaluation of this model together with other coastal
fumigation models may be found in reference 134. Selection of the
appropriate model for applications where shoreline fumigation is of
concern should be determined in consultation with the Regional
Office.

8.2.10 Stagnation

a. Stagnation conditions are characterized by calm or very low
wind speeds, and variable wind directions. These stagnant
meteorological conditions may persist for several hours to several
days. During stagnation conditions, the dispersion of air
pollutants, especially those from low-level emissions sources, tends
to be minimized, potentially leading to relatively high ground-level
concentrations.
b. When stagnation periods such as these are found to occur,
they should be addressed in the air quality modeling analysis.
WYNDvalley, listed in Appendix B, may be applied on a case-by-case
basis for stagnation periods of 24 hours or longer in valley-type
situations. Caution should be exercised when applying the model to
elevated point sources. Users should consult with the appropriate
Regional Office prior to regulatory application of WYNDvalley.

8.2.11 Calibration of Models

a. Calibration of long term multi-source models has been a
widely used procedure even though the limitations imposed by
statistical theory on the reliability of the calibration process for
long term estimates are well known.\61\ In some cases, where a more
accurate model is not available, calibration may be the best
alternative for improving the accuracy of the estimated
concentrations needed for control strategy evaluations.
b. Calibration of short term models is not common practice and
is subject to much greater error and misunderstanding. There have
been attempts by some to compare short term estimates and
measurements on an event-by-event basis and then to calibrate a
model with results of that comparison. This approach is severely
limited by uncertainties in both source and meteorological data and
therefore it is difficult to precisely estimate the concentration at
an exact location for a specific increment of time. Such
uncertainties make calibration of short term models of questionable
benefit. Therefore, short term model calibration is unacceptable.

9.0 Model Input Data

a. Data bases and related procedures for estimating input
parameters are an integral part of the modeling procedure. The most
appropriate data available should always be selected for use in
modeling analyses. Concentrations can vary widely depending on the
source data or meteorological data used. Input data are a major
source of inconsistencies in any modeling analysis. This section
attempts to minimize the uncertainty associated with data base
selection and use by identifying requirements for data used in
modeling. A checklist of input data requirements for modeling
analyses is included as Appendix C. More specific data requirements
and the format required for the individual models are described in
detail in the users' guide for each model.

9.1 Source Data

9.1.1 Discussion

a. Sources of pollutants can be classified as point, line and
area/volume sources. Point sources are defined in terms of size and
may vary between regulatory programs. The line sources most
frequently considered are roadways and streets along which there are
well-defined movements of motor vehicles, but they may be lines of
roof vents or stacks such as in aluminum refineries. Area and volume
sources are often collections of a multitude of minor sources with
individually small emissions that are impractical to consider as
separate point or line sources. Large area sources are typically
treated as a grid network of square areas, with pollutant emissions
distributed uniformly within each grid square.
b. Emission factors are compiled in an EPA publication commonly
known as AP-42 \62\; an indication of the quality and amount of data
on which many of the factors are based is also provided. Other
information concerning emissions is available in EPA publications
relating to specific source categories. The Regional Office should
be consulted to determine appropriate source definitions and for
guidance concerning the determination of emissions from and
techniques for modeling the various source types.

9.1.2 Recommendations

a. For point source applications the load or operating condition
that causes maximum ground-level concentrations should be
established. As a minimum, the source should be modeled using the
design capacity (100 percent load). If a source operates at greater
than design capacity for periods that could result in violations of
the standards or PSD increments, this load ^{e should be modeled.
Where the source operates at substantially less than design
capacity, and the changes in the stack parameters associated with
the operating conditions could lead to higher ground level
concentrations, loads such as 50 percent and 75 percent of capacity
should also be modeled. A range of operating conditions should be
considered in screening analyses; the load causing the highest
concentration, in addition to the design load, should be included in
refined modeling. For a power plant, the following paragraphs b
through h of this section describe the typical kind of data on
source characteristics and operating conditions that may be needed.
Generally, input data requirements for air quality models
necessitate the use of metric units; where English units are common
for engineering usage, a conversion to metric is required.
---------------------------------------------------------------------------

\e\ Malfunctions which may result in excess emissions are not
considered to be a normal operating condition. They generally should
not be considered in determining allowable emissions. However, if
the excess emissions are the result of poor maintenance, careless
operation, or other preventable conditions, it may be necessary to
consider them in determining source impact.
---------------------------------------------------------------------------

b. Plant layout. The connection scheme between boilers and
stacks, and the distance and direction between stacks, building
parameters (length, width, height, location and orientation relative
to stacks) for plant structures which house boilers, control
equipment, and surrounding buildings within a distance of
approximately five stack heights.
c. Stack parameters. For all stacks, the stack height and inside
diameter (meters), and the temperature (K) and volume flow rate
(actual cubic meters per second) or exit gas velocity (meters per
second) for operation at 100 percent, 75 percent and 50 percent
load.
d. Boiler size. For all boilers, the associated megawatts, 10\6\
BTU/hr, and pounds of steam per hour, and the design and/or actual
fuel consumption rate for 100 percent load for coal (tons/hour), oil
(barrels/hour), and natural gas (thousand cubic feet/hour).
e. Boiler parameters. For all boilers, the percent excess air
used, the boiler type (e.g., wet bottom, cyclone, etc.), and the
type of firing (e.g., pulverized coal, front firing, etc.).
f. Operating conditions. For all boilers, the type, amount and
pollutant contents of fuel, the total hours of boiler operation and
the boiler capacity factor during the year, and the percent load for
peak conditions.
g. Pollution control equipment parameters. For each boiler
served and each pollutant affected, the type of emission control
equipment, the year of its installation, its design efficiency and
mass emission rate, the data of the last test and the tested
efficiency, the number of hours of operation during the latest year,
and the best engineering estimate of its projected efficiency if
used in conjunction with coal combustion; data for any anticipated
modifications or additions.
h. Data for new boilers or stacks. For all new boilers and
stacks under construction and for all planned modifications to
existing boilers or stacks, the scheduled date of completion, and
the data or best estimates available for paragraphs b through g of
this section above following completion of construction or
modification.
i. In stationary point source applications for compliance with
short term ambient standards, SIP control strategies should be
tested using the emission input shown on Table 9-1. When using a
refined model, sources should be modeled sequentially with these
loads for every hour of the year. To evaluate SIPs for compliance
with quarterly and annual standards, emission input data shown in
Table 9-1 should again be used. Emissions from area sources should
generally be based on annual average conditions. The source input
information in each model user's guide should be carefully consulted
and the checklist in Appendix C should also be consulted for other
possible emission data that could be helpful. PSD NAAQS compliance
demonstrations should follow the emission input data shown in Table
9-2. For purposes of emissions trading, new

[[Page 41854]]

source review and demonstrations, refer to current EPA policy and
guidance to establish input data.
j. Line source modeling of streets and highways requires data on
the width of the roadway and the median strip, the types and amounts
of pollutant emissions, the number of lanes, the emissions from each
lane and the height of emissions. The location of the ends of the
straight roadway segments should be specified by appropriate grid
coordinates. Detailed information and data requirements for modeling
mobile sources of pollution are provided in the user's manuals for
each of the models applicable to mobile sources.
k. The impact of growth on emissions should be considered in all
modeling analyses covering existing sources. Increases in emissions
due to planned expansion or planned fuel switches should be
identified. Increases in emissions at individual sources that may be
associated with a general industrial/commercial/residential
expansion in multi-source urban areas should also be treated. For
new sources the impact of growth on emissions should generally be
considered for the period prior to the start-up date for the source.
Such changes in emissions should treat increased area source
emissions, changes in existing point source emissions which were not
subject to preconstruction review, and emissions due to sources with
permits to construct that have not yet started operation.

Table 9-1.-- Model Emission Input Data for Point Sources \1\
----------------------------------------------------------------------------------------------------------------
Operating factor
Averaging time Emission limit (#/ x Operating level x (e.g., hr/yr, hr/
MMBtu) \2\ (MMBtu/hr) \2\ day)
----------------------------------------------------------------------------------------------------------------
Stationary Point Source(s) Subject to SIP Emission Limit(s) Evaluation for Compliance with Ambient Standards
(Including Areawide Demonstrations)
----------------------------------------------------------------------------------------------------------------

Annual & quarterly............. Maximum allowable Actual or design Actual operating
emission limit or capacity factor averaged
federally (whichever is over most recent
enforceable greater), or 2 years.\3\
permit limit. federally
enforceable
permit condition.
Short term..................... Maximum allowable Actual or design Continuous
emission limit or capacity operation, i.e.,
federally (whichever is all hours of
enforceable greater), or each time period
permit limit. federally under
enforceable consideration
permit condition (for all hours
\4\. of the
meteorological
data base).\5\
----------------------------------------------------------------------------------------------------------------
Nearby Background Source(s)

Same input requirements as for stationary point source(s) above.
----------------------------------------------------------------------------------------------------------------

Other Background Source(s)

If modeled (see Section 9.2.3), input data requirements are defined below.
----------------------------------------------------------------------------------------------------------------

Annual & quarterly............. Maximum allowable Annual level when Actual operating
emission limit or actually factor averaged
federal operating, over the most
enforceable averaged over the recent 2
permit limit. most recent 2 years.\3\
years \3\.
Short term..................... Maximum allowable Annual level when Continuous
emission limit or actually operation, i.e.,
federally operating, all hours of
enforceable averaged over the each time period
permit limit. most recent 2 under
years \3\. consideration
(for all hours
of the
meteorological
data base).\5\
----------------------------------------------------------------------------------------------------------------
\1\ The model input data requirements shown on this table apply to stationary source control strategies for
STATE IMPLEMENTATION PLANS. For purposes of emissions trading, new source review, or prevention of significant
deterioration, other model input criteria may apply. Refer to the policy and guidance for these programs to
establish the input data.
\2\ Terminology applicable to fuel burning sources; analogous terminology (e.g., #/throughput) may be used for
other types of sources.
\3\ Unless it is determined that this period is not representative.
\4\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
causing the highest concentration.
\5\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24 hours) and the
source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
modeled emission rate may be made (e.g., if operation is only 8:00 a.m. to 4:00 p.m. each day, only these
hours will be modeled with emissions from the source. Modeled emissions should not be averaged across non-
operating time periods.)

Table 9-2.--Point Source Model Input Data (Emissions) for PSD NAAQS Compliance Demonstrations
----------------------------------------------------------------------------------------------------------------
Operating factor
Averaging time Emission limit (#/ x Operating level x (e.g., hr/yr, hr/
MMBtu) \1\ (MMBtu/hr) \1\ day)
----------------------------------------------------------------------------------------------------------------
Proposed Major New or Modified Source
----------------------------------------------------------------------------------------------------------------

Annual & quarterly............. Maximum allowable Design capacity or Continuous
emission limit or federally operation (i.e.,
federally enforceable 8760 hours).\2\
enforceable permit condition.
permit limit.
Short term ( 24 hours)......... Maximum allowable Design capacity or Continuous
emission limit or federally operation (i.e.,
federally enforceable all hours of
enforceable permit each time period
permit limit. condition.\3\ under
consideration)
(for all hours
of the
meteorological
data base).\2\
----------------------------------------------------------------------------------------------------------------

[[Page 41855]]

Nearby Background Source(s) \4\
----------------------------------------------------------------------------------------------------------------

Annual & quarterly............. Maximum allowable Actual or design Actual operating
emission limit or capacity factor averaged
federally (whichever is over the most
enforceable greater), or recent 2 years.^{5
permit limit. federally ^{7
enforceable
permit condition.
Short term ( 24 hours)......... Maximum allowable Actual or design Continuous
emission limit or capacity operation (i.e.,
federally (whichever is all hours of
enforceable greater), or each time period
permit limit. federally under
enforceable consideration)
permit (for all hours
condition.\3\ of the
meteorological
data base).\2\

----------------------------------------------------------------------------------------------------------------

Other Background Source(s) \6\
----------------------------------------------------------------------------------------------------------------

Annual & quarterly............. Maximum allowable Annual level when Actual operating
emission limit or actually factor averaged
federally operating, over the most
enforceable averaged over the recent 2 years.^{5
permit limit. most recent 2 ^{7
years.\5\
Short term ( 24 hours)......... Maximum allowable Annual level when Continuous
emission limit or actually operation (i.e.,
federally operating, all hours of
enforceable averaged over the each time period
permit limit. most recent 2 under
years.\5\ consideration)
(for all hours
of the
meteorological
data base).\2\
----------------------------------------------------------------------------------------------------------------
\1\ Terminology applicable to fuel burning sources; analogous terminology (e.g., #/throughput) may be used for
other types of sources.
\2\ If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24 hours) and the
source operation is constrained by a federally enforceable permit condition, an appropriate adjustment to the
modeled emission rate may be made (e.g., if operation is only 8:00 a.m. to 4:00 p.m. each day, only these
hours will be modeled with emissions from the source. Modeled emissions should not be averaged across non-
operating time periods.
\3\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
causing the highest concentration.
\4\ Includes existing facility to which modification is proposed if the emissions from the existing facility
will not be affected by the modification. Otherwise use the same parameters as for major modification.
\5\ Unless it is determined that this period is not representative.
\6\ Generally, the ambient impacts from non-nearby background sources can be represented by air quality data
unless adequate data do not exist.
\7\ For those permitted sources not yet in operation or that have not established an appropriate factor,
continuous operation (i.e., 8760 hours) should be used.

9.2 Background Concentrations

9.2.1 Discussion

a. Background concentrations are an essential part of the total
air quality concentration to be considered in determining source
impacts. Background air quality includes pollutant concentrations
due to: (1) natural sources; (2) nearby sources other than the
one(s) currently under consideration; and (3) unidentified sources.
b. Typically, air quality data should be used to establish
background concentrations in the vicinity of the source(s) under
consideration. The monitoring network used for background
determinations should conform to the same quality assurance and
other requirements as those networks established for PSD
purposes.\63\ An appropriate data validation procedure should be
applied to the data prior to use.
c. If the source is not isolated, it may be necessary to use a
multi-source model to establish the impact of nearby sources.
Background concentrations should be determined for each critical
(concentration) averaging time.

9.2.2 Recommendations (Isolated Single Source)

a. Two options (paragraph b or c of this section) are available
to determine the background concentration near isolated sources.
b. Use air quality data collected in the vicinity of the source
to determine the background concentration for the averaging times of
concern.^{f Determine the mean background concentration at each
monitor by excluding values when the source in question is impacting
the monitor. The mean annual background is the average of the annual
concentrations so determined at each monitor. For shorter averaging
periods, the meteorological conditions accompanying the
concentrations of concern should be identified. Concentrations for
meteorological conditions of concern, at monitors not impacted by
the source in question, should be averaged for each separate
averaging time to determine the average background value. Monitoring
sites inside a 90 deg. sector downwind of the source may be used to
determine the area of impact. One hour concentrations may be added
and averaged to determine longer averaging periods.
---------------------------------------------------------------------------

\f\ For purposes of PSD, the location of monitors as well as
data quality assurance procedures must satisfy requirements listed
in the PSD Monitoring Guidelines. \63\
---------------------------------------------------------------------------

c. If there are no monitors located in the vicinity of the
source, a ``regional site'' may be used to determine background. A
``regional site'' is one that is located away from the area of
interest but is impacted by similar natural and distant man-made
sources.

9.2.3 Recommendations (Multi-Source Areas)

a. In multi-source areas, two components of background should be
determined.
b. Nearby Sources: All sources expected to cause a significant
concentration gradient in the vicinity of the source or sources
under consideration for emission limit(s) should be explicitly
modeled. For evaluation for compliance with the short term and
annual ambient standards, the nearby sources should be modeled using
the emission input data shown in Table 9-1 or 9-2. The number of
such sources is expected to be small except in unusual situations.
The nearby source inventory should be determined in consultation
with the reviewing authority. It is envisioned that the nearby
sources and the sources under consideration will be evaluated
together using an appropriate Appendix A model.
c. The impact of the nearby sources should be examined at
locations where interactions between the plume of the point source
under consideration and those of nearby sources (plus natural
background) can occur. Significant locations include: (1) the area
of

[[Page 41856]]

maximum impact of the point source; (2) the area of maximum impact
of nearby sources; and (3) the area where all sources combine to
cause maximum impact. These locations may be identified through
trial and error analyses.
d. Other Sources: That portion of the background attributable to
all other sources (e.g., natural sources, minor sources and distant
major sources) should be determined by the procedures found in
Section 9.2.2 or by application of a model using Table 9-1 or 9-2.

9.3 Meteorological Input Data

a. The meteorological data used as input to a dispersion model
should be selected on the basis of spatial and climatological
(temporal) representativeness as well as the ability of the
individual parameters selected to characterize the transport and
dispersion conditions in the area of concern. The representativeness
of the data is dependent on: (1) the proximity of the meteorological
monitoring site to the area under consideration; (2) the complexity
of the terrain; (3) the exposure of the meteorological monitoring
site; and (4) the period of time during which data are collected.
The spatial representativeness of the data can be adversely affected
by large distances between the source and receptors of interest and
the complex topographic characteristics of the area. Temporal
representativeness is a function of the year-to-year variations in
weather conditions.
b. Model input data are normally obtained either from the
National Weather Service or as part of an on-site measurement
program. Local universities, Federal Aviation Administration (FAA),
military stations, industry and pollution control agencies may also
be sources of such data. Some recommendations for the use of each
type of data are included in this section 9.3.

9.3.1 Length of Record of Meteorological Data

9.3.1.1 Discussion

a. The model user should acquire enough meteorological data to
ensure that worst-case meteorological conditions are adequately
represented in the model results. The trend toward statistically
based standards suggests a need for all meteorological conditions to
be adequately represented in the data set selected for model input.
The number of years of record needed to obtain a stable distribution
of conditions depends on the variable being measured and has been
estimated by Landsberg and Jacobs \64\ for various parameters.
Although that study indicates in excess of 10 years may be required
to achieve stability in the frequency distributions of some
meteorological variables, such long periods are not reasonable for
model input data. This is due in part to the fact that hourly data
in model input format are frequently not available for such periods
and that hourly calculations of concentration for long periods are
prohibitively expensive. A recent study \65\ compared various
periods from a 17-year data set to determine the minimum number of
years of data needed to approximate the concentrations modeled with
a 17-year period of meteorological data from one station. This study
indicated that the variability of model estimates due to the
meteorological data input was adequately reduced if a 5-year period
of record of meteorological input was used.

9.3.1.2 Recommendations

a. Five years of representative meteorological data should be
used when estimating concentrations with an air quality model.
Consecutive years from the most recent, readily available 5-year
period are preferred. The meteorological data may be data collected
either onsite or at the nearest National Weather Service (NWS)
station. If the source is large, e.g., a 500MW power plant, the use
of 5 years of NWS meteorological data or at least 1 year of site-
specific data is required.
b. If one year or more, up to five years, of site-specific data
is available, these data are preferred for use in air quality
analyses. Such data should have been subjected to quality assurance
procedures as described in Section 9.3.3.2.
c. For permitted sources whose emission limitations are based on
a specific year of meteorological data that year should be added to
any longer period being used (e.g., 5 years of NWS data) when
modeling the facility at a later time.

9.3.2 National Weather Service Data

9.3.2.1 Discussion

a. The National Weather Service (NWS) meteorological data are
routinely available and familiar to most model users. Although the
NWS does not provide direct measurements of all the needed
dispersion model input variables, methods have been developed and
successfully used to translate the basic NWS data to the needed
model input. Direct measurements of model input parameters have been
made for limited model studies and those methods and techniques are
becoming more widely applied; however, most model applications still
rely heavily on the NWS data.
b. There are two standard formats of the NWS data for use in air
quality models. The short term models use the standard hourly
weather observations available from the National Climatic Data
Center (NCDC). These observations are then ``preprocessed'' before
they can be used in the models. ``STAR'' summaries are available
from NCDC for long term model use. These are joint frequency
distributions of wind speed, direction and P-G stability category.
They are used as direct input to models such as the long term
version of ISC.\58\

9.3.2.2 Recommendations

a. The preferred short term models listed in Appendix A all
accept as input the NWS meteorological data preprocessed into model
compatible form. Long-term (monthly seasonal or annual) preferred
models use NWS ``STAR'' summaries. Summarized concentration
estimates from the short term models may also be used to develop
long-term averages; however, concentration estimates based on the
two separate input data sets may not necessarily agree.
b. Although most NWS measurements are made at a standard height
of 10 meters, the actual anemometer height should be used as input
to the preferred model.
c. National Weather Service wind directions are reported to the
nearest 10 degrees. A specific set of randomly generated numbers has
been developed for use with the preferred EPA models and should be
used to ensure a lack of bias in wind direction assignments within
the models.
d. Data from universities, FAA, military stations, industry and
pollution control agencies may be used if such data are equivalent
in accuracy and detail to the NWS data.

9.3.3 Site-Specific Data

9.3.3.1 Discussion

a. Spatial or geographical representativeness is best achieved
by collection of all of the needed model input data at the actual
site of the source(s). Site-specific measured data are therefore
preferred as model input, provided appropriate instrumentation and
quality assurance procedures are followed and that the data
collected are representative (free from undue local or ``micro''
influences) and compatible with the input requirements of the model
to be used. However, direct measurements of all the needed model
input parameters may not be possible. This section discusses
suggestions for the collection and use of on-site data. Since the
methods outlined in this section are still being tested, comparison
of the model parameters derived using these site-specific data
should be compared at least on a spot-check basis, with parameters
derived from more conventional observations.

9.3.3.2 Recommendations: Site-specific Data Collection

a. The document ``On-Site Meteorological Program Guidance for
Regulatory Modeling Applications''\66\ provides recommendations on
the collection and use of on-site meteorological data.
Recommendations on characteristics, siting, and exposure of
meteorological instruments and on data recording, processing,
completeness requirements, reporting, and archiving are also
included. This publication should be used as a supplement to the
limited guidance on these subjects now found in the ``Ambient
Monitoring Guidelines for Prevention of Significant
Deterioration''.\63\ Detailed information on quality assurance is
provided in the ``Quality Assurance Handbook for Air Pollution
Measurement Systems: Volume IV''.\67\ As a minimum, site-specific
measurements of ambient air temperature, transport wind speed and
direction, and the parameters to determine Pasquill-Gifford (P-G)
stability categories should be available in meteorological data sets
to be used in modeling. Care should be taken to ensure that
meteorological instruments are located to provide representative
characterization of pollutant transport between sources and
receptors of interest. The Regional Office will determine the
appropriateness of the measurement locations.
b. All site-specific data should be reduced to hourly averages.
Table 9-3 lists the wind related parameters and the averaging time
requirements.

[[Page 41857]]

c. Solar Radiation Measurements. Total solar radiation should be
measured with a reliable pyranometer, sited and operated in
accordance with established on-site meteorological guidance. \66\
d. Temperature Measurements. Temperature measurements should be
made at standard shelter height (2m) in accordance with established
on-site meteorological guidance. \66\
e. Temperature Difference Measurements. Temperature difference
(^{all) measurements for use in estimating P-G
stability categories using the solar radiation/delta-T (SRDT)
methodology (see Stability Categories) should be obtained using two
matched thermometers or a reliable thermocouple system to achieve
adequate accuracy.
f. Siting, probe placement, and operation of T systems
should be based on guidance found in Chapter 3 of reference 66, and
such guidance should be followed when obtaining vertical temperature
gradient data for use in plume rise estimates or in determining the
critical dividing streamline height.
g. Wind Measurements. For refined modeling applications in
simple terrain situations, if a source has a stack below 100m,
select the stack top height as the wind measurement height for
characterization of plume dilution and transport. For sources with
stacks extending above 100m, a 100m tower is suggested unless the
stack top is significantly above 100m (i.e., 200m). In
cases with stack tops 200m, remote sensing may be a
feasible alternative. In some cases, collection of stack top wind
speed may be impractical or incompatible with the input requirements
of the model to be used. In such cases, the Regional Office should
be consulted to determine the appropriate measurement height.
h. For refined modeling applications in complex terrain,
multiple level (typically three or more) measurements of wind speed
and direction, temperature and turbulence (wind fluctuation
statistics) are required. Such measurements should be obtained up to
the representative plume height(s) of interest (i.e., the plume
height(s) under those conditions important to the determination of
the design concentration). The representative plume height(s) of
interest should be determined using an appropriate complex terrain
screening procedure (e.g., CTSCREEN) and should be documented in the
monitoring/modeling protocol. The necessary meteorological
measurements should be obtained from an appropriately sited
meteorological tower augmented by SODAR if the representative plume
height(s) of interest exceed 100m. The meteorological tower need not
exceed the lesser of the representative plume height of interest
(the highest plume height if there is more than one plume height of
interest) or 100m.
i. In general, the wind speed used in determining plume rise is
defined as the wind speed at stack top.
j. Specifications for wind measuring instruments and systems are
contained in the ``On-Site Meteorological Program Guidance for
Regulatory Modeling Applications''.\66\
k. Stability Categories. The P-G stability categories, as
originally defined, couple near-surface measurements of wind speed
with subjectively determined insolation assessments based on hourly
cloud cover and ceiling height observations. The wind speed
measurements are made at or near 10m. The insolation rate is
typically assessed using observations of cloud cover and ceiling
height based on criteria outlined by Turner.\50\ It is recommended
that the P-G stability category be estimated using the Turner method
with site-specific wind speed measured at or near 10m and
representative cloud cover and ceiling height. Implementation of the
Turner method, as well as considerations in determining
representativeness of cloud cover and ceiling height in cases for
which site-specific cloud observations are unavailable, may be found
in Section 6 of reference 66. In the absence of requisite data to
implement the Turner method, the SRDT method or wind fluctuation
statistics (i.e., the E and A methods)
may be used.
l. The SRDT method, described in Section 6.4.4.2 of reference
66, is modified slightly from that published by Bowen et al. (1983)
\136\ and has been evaluated with three on-site data bases.\137\ The
two methods of stability classification which use wind fluctuation
statistics, the E and A methods, are
also described in detail in Section 6.4.4 of reference 66 (note
applicable tables in Section 6). For additional information on the
wind fluctuation methods, see references 68-72.
m. Hours in the record having missing data should be treated
according to an established data substitution protocol and after
valid data retrieval requirements have been met. Such protocols are
usually part of the approved monitoring program plan. Data
substitution guidance is provided in Section 5.3 of reference 66.
n. Meteorological Data Processors. The following meteorological
preprocessors are recommended by EPA: RAMMET, PCRAMMET, STAR,
PCSTAR, MPRM,\135\ and METPRO.\24\ RAMMET is the recommended
meteorological preprocessor for use in applications employing hourly
NWS data. The RAMMET format is the standard data input format used
in sequential Gaussian models recommended by EPA. PCRAMMET \138\ is
the PC equivalent of the mainframe version (RAMMET). STAR is the
recommended preprocessor for use in applications employing joint
frequency distributions (wind direction and wind speed by stability
class) based on NWS data. PCSTAR is the PC equivalent of the
mainframe version (STAR). MPRM is the recommended preprocessor for
use in applications employing on-site meteorological data. The
latest version (MPRM 1.3) has been configured to implement the SRDT
method for estimating P-G stability categories. MPRM is a general
purpose meteorological data preprocessor which supports regulatory
models requiring RAMMET formatted data and STAR formatted data. In
addition to on-site data, MPRM provides equivalent processing of NWS
data. METPRO is the required meteorological data preprocessor for
use with CTDMPLUS. All of the above mentioned data preprocessors are
available for downloading from the SCRAM BBS.\19\

Table 9-3.--Averaging Times for Site-Specific Wind and Turbulence
Measurements
------------------------------------------------------------------------
Parameter Averaging time
------------------------------------------------------------------------
Surface wind speed (for use in stability 1-hr.
determinations).
Transport direction....................... 1-hr.
Dilution wind speed....................... 1-hr.
Turbulence measurements (E and 1-hr.\1\
A) for use in stability
determinations.
------------------------------------------------------------------------
\1\ To minimize meander effects in A when wind conditions are
light and/or variable, determine the hourly average value
from four sequential 15-minute 's according to the following
formula:

[GRAPHIC] [TIFF OMITTED] TR12AU96.000

9.3.4 Treatment of Calms

9.3.4.1 Discussion

a. Treatment of calm or light and variable wind poses a special
problem in model applications since Gaussian models assume that
concentration is inversely proportional to wind speed. Furthermore,
concentrations become unrealistically large when wind speeds less
than 1 m/s are input to the model. A procedure has been developed
for use with NWS data to prevent the occurrence of overly
conservative concentration estimates during periods of calms. This
procedure acknowledges that a Gaussian plume model does not apply
during calm conditions and that our knowledge of plume behavior and
wind patterns during these conditions does not, at present, permit
the development of a better technique. Therefore, the procedure
disregards hours which are identified as calm. The hour is treated
as missing and a convention for handling missing hours is
recommended.
b. Preprocessed meteorological data input to most Appendix A EPA
models substitute a 1.00 m/s wind speed and the previous direction
for the calm hour. The new treatment of calms in those models
attempts to identify the original calm cases by checking for a 1.00
m/s wind speed coincident with a wind direction equal to the
previous hour's wind direction. Such cases are then treated in a
prescribed manner when estimating short term concentrations.

9.3.4.2 Recommendations

a. Hourly concentrations calculated with Gaussian models using
calms should not be considered valid; the wind and concentration
estimates for these hours should be disregarded and considered to be
missing. Critical concentrations for 3-, 8-, and 24-hour averages
should be calculated by dividing the sum of the hourly concentration
for the period by the number of valid or non-missing hours. If the
total number of valid hours is less than 18 for 24-hour averages,
less than 6 for 8-hour averages or less than 3 for 3-hour averages,
the total concentration should be divided by 18 for the 24-hour
average, 6 for the 8-hour average and 3 for the 3-hour

[[Page 41858]]

average. For annual averages, the sum of all valid hourly
concentrations is divided by the number of non-calm hours during the
year. A post-processor computer program, CALMPRO \73\ has been
prepared following these instructions and has been coded in RAM and
ISC.
b. The recommendations in paragraph a of this section apply to
the use of calms for short term averages and do not apply to the
determination of long term averages using ``STAR'' data summaries.
Calms should continue to be included in the preparation of ``STAR''
summaries. A treatment for calms and very light winds is built into
the software that produces the ``STAR'' summaries.
c. Stagnant conditions, including extended periods of calms,
often produce high concentrations over wide areas for relatively
long averaging periods. The standard short term Gaussian models are
often not applicable to such situations. When stagnation conditions
are of concern, other modeling techniques should be considered on a
case-by-case basis (see also Section 8.2.10).
d. When used in Gaussian models, measured on-site wind speeds of
less than 1 m/s but higher than the response threshold of the
instrument should be input as 1 m/s; the corresponding wind
direction should also be input. Observations below the response
threshold of the instrument are also set to 1 m/s but the wind
direction from the previous hour is used. If the wind speed or
direction can not be determined, that hour should be treated as
missing and short term averages should then be calculated as
described in paragraph a of this section.

10.0 Accuracy and Uncertainty of Models

10.1 Discussion

a. Increasing reliance has been placed on concentration
estimates from models as the primary basis for regulatory decisions
concerning source permits and emission control requirements. In many
situations, such as review of a proposed source, no practical
alternative exists. Therefore, there is an obvious need to know how
accurate models really are and how any uncertainty in the estimates
affects regulatory decisions. EPA recognizes the need for
incorporating such information and has sponsored workshops ^{11
74 on model accuracy, the possible ways to quantify accuracy, and on
considerations in the incorporation of model accuracy and
uncertainty in the regulatory process. The Second (EPA) Conference
on Air Quality Modeling, August 1982,^{75 was devoted to that
subject.

10.1.1 Overview of Model Uncertainty

a. Dispersion models generally attempt to estimate
concentrations at specific sites that really represent an ensemble
average of numerous repetitions of the same event. The event is
characterized by measured or ``known'' conditions that are input to
the models, e.g., wind speed, mixed layer height, surface heat flux,
emission characteristics, etc. However, in addition to the known
conditions, there are unmeasured or unknown variations in the
conditions of this event, e.g., unresolved details of the
atmospheric flow such as the turbulent velocity field. These unknown
conditions may vary among repetitions of the event. As a result,
deviations in observed concentrations from their ensemble average,
and from the concentrations estimated by the model, are likely to
occur even though the known conditions are fixed. Even with a
perfect model that predicts the correct ensemble average, there are
likely to be deviations from the observed concentrations in
individual repetitions of the event, due to variations in the
unknown conditions. The statistics of these concentration residuals
are termed ``inherent'' uncertainty. Available evidence suggests
that this source of uncertainty alone may be responsible for a
typical range of variation in concentrations of as much as
50 percent.\76\
b. Moreover, there is ``reducible'' uncertainty \77\ associated
with the model and its input conditions; neither models nor data
bases are perfect. Reducible uncertainties are caused by: (1)
Uncertainties in the input values of the known conditions--emission
characteristics and meteorological data; (2) errors in the measured
concentrations which are used to compute the concentration
residuals; and (3) inadequate model physics and formulation. The
``reducible'' uncertainties can be minimized through better (more
accurate and more representative) measurements and better model
physics.
c. To use the terminology correctly, reference to model accuracy
should be limited to that portion of reducible uncertainty which
deals with the physics and the formulation of the model. The
accuracy of the model is normally determined by an evaluation
procedure which involves the comparison of model concentration
estimates with measured air quality data.\78\ The statement of
accuracy is based on statistical tests or performance measures such
as bias, noise, correlation, etc.\11\ However, information that
allows a distinction between contributions of the various elements
of inherent and reducible uncertainty is only now beginning to
emerge. As a result most discussions of the accuracy of models make
no quantitative distinction between (1) Limitations of the model
versus (2) limitations of the data base and of knowledge concerning
atmospheric variability. The reader should be aware that statements
on model accuracy and uncertainty may imply the need for
improvements in model performance that even the ``perfect'' model
could not satisfy.

10.1.2 Studies of Model Accuracy

a. A number of studies ^{79 80 have been conducted to examine
model accuracy, particularly with respect to the reliability of
short-term concentrations required for ambient standard and
increment evaluations. The results of these studies are not
surprising. Basically, they confirm what leading atmospheric
scientists have said for some time: (1) Models are more reliable for
estimating longer time-averaged concentrations than for estimating
short-term concentrations at specific locations; and (2) the models
are reasonably reliable in estimating the magnitude of highest
concentrations occurring sometime, somewhere within an area. For
example, errors in highest estimated concentrations of
10 to 40 percent are found to be typical,\81\ i.e.,
certainly well within the often quoted factor-of-two accuracy that
has long been recognized for these models. However, estimates of
concentrations that occur at a specific time and site, are poorly
correlated with actually observed concentrations and are much less
reliable.
b. As noted in paragraph a of this section, poor correlations
between paired concentrations at fixed stations may be due to
``reducible'' uncertainties in knowledge of the precise plume
location and to unquantified inherent uncertainties. For example,
Pasquill \82\ estimates that, apart from data input errors, maximum
ground-level concentrations at a given hour for a point source in
flat terrain could be in error by 50 percent due to these
uncertainties. Uncertainty of five to 10 degrees in the measured
wind direction, which transports the plume, can result in
concentration errors of 20 to 70 percent for a particular time and
location, depending on stability and station location. Such
uncertainties do not indicate that an estimated concentration does
not occur, only that the precise time and locations are in doubt.

10.1.3 Use of Uncertainty in Decision-Making

a. The accuracy of model estimates varies with the model used,
the type of application, and site-specific characteristics. Thus, it
is desirable to quantify the accuracy or uncertainty associated with
concentration estimates used in decision-making. Communications
between modelers and decision-makers must be fostered and further
developed. Communications concerning concentration estimates
currently exist in most cases, but the communications dealing with
the accuracy of models and its meaning to the decision-maker are
limited by the lack of a technical basis for quantifying and
directly including uncertainty in decisions. Procedures for
quantifying and interpreting uncertainty in the practical
application of such concepts are only beginning to evolve; much
study is still required.^{74 75 77
b. In all applications of models an effort is encouraged to
identify the reliability of the model estimates for that particular
area and to determine the magnitude and sources of error associated
with the use of the model. The analyst is responsible for
recognizing and quantifying limitations in the accuracy, precision
and sensitivity of the procedure. Information that might be useful
to the decision-maker in recognizing the seriousness of potential
air quality violations includes such model accuracy estimates as
accuracy of peak predictions, bias, noise, correlation, frequency
distribution, spatial extent of high concentration, etc. Both space/
time pairing of estimates and measurements and unpaired comparisons
are recommended. Emphasis should be on the highest concentrations
and the averaging times of the standards or increments of concern.
Where possible, confidence intervals about the statistical values
should be provided. However, while such information can be provided
by the modeler to the decision-maker, it is unclear how this
information should be used to make an air

[[Page 41859]]

pollution control decision. Given a range of possible outcomes, it
is easiest and tends to ensure consistency if the decision-maker
confines his judgment to use of the ``best estimate'' provided by
the modeler (i.e., the design concentration estimated by a model
recommended in the Guideline or an alternate model of known
accuracy). This is an indication of the practical limitations
imposed by current abilities of the technical community.
c. To improve the basis for decision-making, EPA has developed
and is continuing to study procedures for determining the accuracy
of models, quantifying the uncertainty, and expressing confidence
levels in decisions that are made concerning emissions
controls.^{83 84 However, work in this area involves ``breaking
new ground'' with slow and sporadic progress likely. As a result, it
may be necessary to continue using the ``best estimate'' until
sufficient technical progress has been made to meaningfully
implement such concepts dealing with uncertainty.

10.1.4 Evaluation of Models

a. A number of actions are being taken to ensure that the best
model is used correctly for each regulatory application and that a
model is not arbitrarily imposed. First, the Guideline clearly
recommends the most appropriate model be used in each case.
Preferred models, based on a number of factors, are identified for
many uses. General guidance on using alternatives to the preferred
models is also provided. Second, all the models in eight categories
(i.e., rural, urban, industrial complex, reactive pollutants, mobile
source, complex terrain, visibility and long range transport) that
are candidates for inclusion in the Guideline are being subjected to
a systematic performance evaluation and a peer scientific
review.\85\ The same data bases are being used to evaluate all
models within each of eight categories. Statistical performance
measures, including measures of difference (or residuals) such as
bias, variance of difference and gross variability of the
difference, and measures of correlation such as time, space, and
time and space combined as recommended by the AMS Woods Hole
Workshop,\11\ are being followed. The results of the scientific
review are being incorporated in the Guideline and will be the basis
for future revision.^{12 13 Third, more specific information has
been provided for justifying the site specific use of alternative
models in the documents ``Interim Procedures for Evaluating Air
Quality Models'',\15\ and the ``Protocol for Determining the Best
Performing Model''.\17\ Together these documents provide methods
that allow a judgment to be made as to what models are most
appropriate for a specific application. For the present, performance
and the theoretical evaluation of models are being used as an
indirect means to quantify one element of uncertainty in air
pollution regulatory decisions.
b. In addition to performance evaluation of models, sensitivity
analyses are encouraged since they can provide additional
information on the effect of inaccuracies in the data bases and on
the uncertainty in model estimates. Sensitivity analyses can aid in
determining the effect of inaccuracies of variations or
uncertainties in the data bases on the range of likely
concentrations. Such information may be used to determine source
impact and to evaluate control strategies. Where possible,
information from such sensitivity analyses should be made available
to the decision-maker with an appropriate interpretation of the
effect on the critical concentrations.

10.2 Recommendations

a. No specific guidance on the consideration of model
uncertainty in decision-making is being given at this time. There is
incomplete technical information on measures of model uncertainty
that are most relevant to the decision-maker. It is not clear how a
decisionmaker could use such information, particularly given
limitations of the Clean Air Act. As procedures for considering
uncertainty develop and become implementable, this guidance will be
changed and expanded. For the present, continued use of the ``best
estimate'' is acceptable and is consistent with Clean Air Act
requirements.

11.0 Regulatory Application of Models

11.1 Discussion

a. Procedures with respect to the review and analysis of air
quality modeling and data analyses in support of SIP revisions, PSD
permitting or other regulatory requirements need a certain amount of
standardization to ensure consistency in the depth and
comprehensiveness of both the review and the analysis itself. This
section recommends procedures that permit some degree of
standardization while at the same time allowing the flexibility
needed to assure the technically best analysis for each regulatory
application.
b. Dispersion model estimates, especially with the support of
measured air quality data, are the preferred basis for air quality
demonstrations. Nevertheless, there are instances where the
performance of recommended dispersion modeling techniques, by
comparison with observed air quality data, may be shown to be less
than acceptable. Also, there may be no recommended modeling
procedure suitable for the situation. In these instances, emission
limitations may be established solely on the basis of observed air
quality data as would be applied to a modeling analysis. The same
care should be given to the analyses of the air quality data as
would be applied to a modeling analysis.
c. The current NAAQS for SO2 and CO are both stated in
terms of a concentration not to be exceeded more than once a year.
There is only an annual standard for NO2 and a quarterly
standard for Pb. The PM-10 and ozone standards permit the exceedance
of a concentration on an average of not more than once a year; the
convention is to average over a 3-year period.^{5 86 103 This
represents a change from a deterministic to a more statistical form
of the standard and permits some consideration to be given to
unusual circumstances. The NAAQS are subjected to extensive review
and possible revision every 5 years.
d. This section discusses general requirements for concentration
estimates and identifies the relationship to emission limits. The
recommendations in section 11.2 apply to: (1) revisions of State
Implementation Plans; (2) the review of new sources and the
prevention of significant deterioration (PSD); and (3) analyses of
the emissions trades (``bubbles'').

11.2 Recommendations

11.2.1 Analysis Requirements

a. Every effort should be made by the Regional Office to meet
with all parties involved in either a SIP revision or a PSD permit
application prior to the start of any work on such a project. During
this meeting, a protocol should be established between the preparing
and reviewing parties to define the procedures to be followed, the
data to be collected, the model to be used, and the analysis of the
source and concentration data. An example of requirements for such
an effort is contained in the Air Quality Analysis Checklist
included here as Appendix C. This checklist suggests the level of
detail required to assess the air quality resulting from the
proposed action. Special cases may require additional data
collection or analysis and this should be determined and agreed upon
at this preapplication meeting. The protocol should be written and
agreed upon by the parties concerned, although a formal legal
document is not intended. Changes in such a protocol are often
required as the data collection and analysis progresses. However,
the protocol establishes a common understanding of the requirements.
b. An air quality analysis should begin with a screening model
to determine the potential of the proposed source or control
strategy to violate the PSD increment or NAAQS. It is recommended
that the screening techniques found in ``Screening Procedures for
Estimating the Air Quality Impact of Stationary Sources'' \18\ be
used for point source analyses. Screening procedures for area source
analysis are discussed in ``Applying Atmospheric Simulation Models
to Air Quality Maintenance Areas''.\87\ For mobile source impact
assessments the ``Guideline for Modeling Carbon Monoxide from
Roadway Intersections'' \34\ is available.
c. If the concentration estimates from screening techniques
indicate that the PSD increment or NAAQS may be approached or
exceeded, then a more refined modeling analysis is appropriate and
the model user should select a model according to recommendations in
Sections 4.0-8.0. In some instances, no refined technique may be
specified in this guide for the situation. The model user is then
encouraged to submit a model developed specifically for the case at
hand. If that is not possible, a screening technique may supply the
needed results.
d. Regional Offices should require permit applicants to
incorporate the pollutant contributions of all sources into their
analysis. Where necessary this may include emissions associated with
growth in the area of impact of the new or modified source's impact.
PSD air quality assessments should consider the amount of the
allowable air quality increment that has already been granted to any
other sources. Therefore, the most recent source applicant should
model

[[Page 41860]]

the existing or permitted sources in addition to the one currently
under consideration. This would permit the use of newly acquired
data or improved modeling techniques if such have become available
since the last source was permitted. When remodeling, the worst case
used in the previous modeling analysis should be one set of
conditions modeled in the new analysis. All sources should be
modeled for each set of meteorological conditions selected and for
all receptor sites used in the previous applications as well as new
sites specific to the new source.

11.2.2 Use of Measured Data in Lieu of Model Estimates

a. Modeling is the preferred method for determining emission
limitations for both new and existing sources. When a preferred
model is available, model results alone (including background) are
sufficient. Monitoring will normally not be accepted as the sole
basis for emission limitation determination in flat terrain areas.
In some instances when the modeling technique available is only a
screening technique, the addition of air quality data to the
analysis may lend credence to model results.
b. There are circumstances where there is no applicable model,
and measured data may need to be used. Examples of such situations
are: (1) complex terrain locations; (2) land/water interface areas;
and (3) urban locations with a large fraction of particulate
emissions from nontraditional sources. However, only in the case of
an existing source should monitoring data alone be a basis for
emission limits. In addition, the following items should be
considered prior to the acceptance of the measured data:
i. Does a monitoring network exist for the pollutants and
averaging times of concern?
ii. Has the monitoring network been designed to locate points of
maximum concentration?
iii. Do the monitoring network and the data reduction and
storage procedures meet EPA monitoring and quality assurance
requirements?
iv. Do the data set and the analysis allow impact of the most
important individual sources to be identified if more than one
source or emission point is involved?
v. Is at least one full year of valid ambient data available?
vi. Can it be demonstrated through the comparison of monitored
data with model results that available models are not applicable?
c. The number of monitors required is a function of the problem
being considered. The source configuration, terrain configuration,
and meteorological variations all have an impact on number and
placement of monitors. Decisions can only be made on a case-by-case
basis. The Interim Procedures for Evaluating Air Quality Models \15\
should be used in establishing criteria for demonstrating that a
model is not applicable.
d. Sources should obtain approval from the Regional Office or
reviewing authority for the monitoring network prior to the start of
monitoring. A monitoring protocol agreed to by all concerned parties
is highly desirable. The design of the network, the number, type and
location of the monitors, the sampling period, averaging time as
well as the need for meteorological monitoring or the use of mobile
sampling or plume tracking techniques, should all be specified in
the protocol and agreed upon prior to start-up of the network.

11.2.3 Emission Limits

11.2.3.1 Design Concentrations

a. Emission limits should be based on concentration estimates
for the averaging time that results in the most stringent control
requirements. The concentration used in specifying emission limits
is called the design value or design concentration and is a sum of
the concentration contributed by the source and the background
concentration.
b. To determine the averaging time for the design value, the
most restrictive National Ambient Air Quality Standard (NAAQS)
should be identified by calculating, for each averaging time, the
ratio of the applicable NAAQS (S)- background (B) to the predicted
concentration (P) (i.e., (S-B)/P). The averaging time with the
lowest ratio identifies the most restrictive standard. If the annual
average is the most restrictive, the highest estimated annual
average concentration from one or a number of years of data is the
design value. When short term standards are most restrictive, it may
be necessary to consider a broader range of concentrations than the
highest value. For example, for pollutants such as SO2, the
highest, second-highest concentration is the design value. For
pollutants with statistically based NAAQS, the design value is found
by determining the more restrictive of: (1) the short-term
concentration that is not expected to be exceeded more than once per
year over the period specified in the standard, or (2) the long-term
concentration that is not expected to exceed the long-term NAAQS.
Determination of design values for PM-10 is presented in more detail
in the ``PM-10 SIP Development Guideline''.\108\
c. When the highest, second-highest concentration is used in
assessing potential violations of a short term NAAQS, criteria that
are identified in ``Guideline for Interpretation of Air Quality
Standards''^{88 should be followed. This guidance specifies that
a violation of a short term standard occurs at a site when the
standard is exceeded a second time. Thus, emission limits that
protect standards for averaging times of 24 hours or less are
appropriately based on the highest, second-highest estimated
concentration plus a background concentration which can reasonably
be assumed to occur with the concentration.

11.2.3.2 NAAQS Analyses for New or Modified Sources

a. For new or modified sources predicted to have a significant
ambient impact \63\ and to be located in areas designated attainment
or unclassifiable for the SO2, Pb, NO2, or CO NAAQS, the
demonstration as to whether the source will cause or contribute to
an air quality violation should be based on: (1) the highest
estimated annual average concentration determined from annual
averages of individual years; or (2) the highest, second-highest
estimated concentration for averaging times of 24-hours or less; and
(3) the significance of the spatial and temporal contribution to any
modeled violation. For Pb, the highest estimated concentration based
on an individual calendar quarter averaging period should be used.
Background concentrations should be added to the estimated impact of
the source. The most restrictive standard should be used in all
cases to assess the threat of an air quality violation. For new or
modified sources predicted to have a significant ambient impact \63\
in areas designated attainment or unclassifiable for the PM-10
NAAQS, the demonstration of whether or not the source will cause or
contribute to an air quality violation should be based on sufficient
data to show whether: (1) the projected 24-hour average
concentrations will exceed the 24-hour NAAQS more than once per
year, on average; (2) the expected (i.e., average) annual mean
concentration will exceed the annual NAAQS; and (3) the source
contributes significantly, in a temporal and spatial sense, to any
modeled violation.

11.2.3.3 PSD Air Quality Increments and Impacts

a. The allowable PSD increments for criteria pollutants are
established by regulation and cited in Sec. 51.166. These maximum
allowable increases in pollutant concentrations may be exceeded once
per year at each site, except for the annual increment that may not
be exceeded. The highest, second-highest increase in estimated
concentrations for the short term averages as determined by a model
should be less than or equal to the permitted increment. The modeled
annual averages should not exceed the increment.
b. Screening techniques defined in Sections 4.0 and 5.0 can
sometimes be used to estimate short term incremental concentrations
for the first new source that triggers the baseline in a given area.
However, when multiple increment-consuming sources are involved in
the calculation, the use of a refined model with at least 1 year of
on-site or 5 years of off-site NWS data is normally required. In
such cases, sequential modeling must demonstrate that the allowable
increments are not exceeded temporally and spatially, i.e., for all
receptors for each time period throughout the year(s) (time period
means the appropriate PSD averaging time, e.g., 3-hour, 24-hour,
etc.).
c. The PSD regulations require an estimation of the SO2,
particulate matter, and NO2 impact on any Class I area.
Normally, Gaussian models should not be applied at distances greater
than can be accommodated by the steady state assumptions inherent in
such models. The maximum distance for refined Gaussian model
application for regulatory purposes is generally considered to be
50km. Beyond the 50km range, screening techniques may be used to
determine if more refined modeling is needed. If refined models are
needed, long range transport models should be considered in
accordance with Section 7.2.6. As previously noted in Sections 3.0
and 7.0, the need to involve the Federal Land Manager in decisions
on potential air quality impacts,

[[Page 41861]]

particularly in relation to PSD Class I areas, cannot be
overemphasized.

11.2.3.4 Emissions Trading Policy (Bubbles)

a. EPA's final Emissions Trading Policy, commonly referred to as
the ``bubble policy,'' was published in the Federal Register in
1986.^{89 Principles contained in the policy should be used to
evaluate ambient impacts of emission trading activities.
b. Emission increases and decreases within the bubble should
result in ambient air quality equivalence. Two levels of analysis
are defined for establishing this equivalence. In a Level I analysis
the source configuration and setting must meet certain limitations
(defined in the policy) that ensure ambient equivalence; no modeling
is required. In a Level II analysis a modeling demonstration of
ambient equivalence is required but only the sources involved in the
emissions trade are modeled. The resulting ambient estimates of net
increases/decreases are compared to a set of significance levels to
determine if the bubble can be approved. A Level II analysis
requires the use of a refined model and the most recent readily
available full year of representative meteorological data.
Sequential modeling must demonstrate that the significance levels
are met temporally and spatially, i.e., for all receptors for each
time period throughout the year (time period means the appropriate
NAAQS averaging time, e.g., 3-hour, 24-hour, etc.).
c. For those bubbles that cannot meet the Level I or Level II
requirements, the Emissions Trading Policy allows for a Level III
analysis. A Level III analysis, from a modeling standpoint, is
generally equivalent to the requirements for a standard SIP revision
where all sources (and background) are considered and the estimates
are compared to the NAAQS as in Section 11.2.3.2.
d. The Emissions Trading Policy allows States to adopt generic
regulations for processing bubbles. The modeling procedures
recommended in the Guideline apply to such generic regulations.
However, an added requirement is that the modeling procedures
contained in any generic regulation must be replicable such that
there is no doubt as to how each individual bubble will be modeled.
In general this means that the models, the data bases and the
procedures for applying the model must be defined in the regulation.
The consequences of the replicability requirement are that bubbles
for sources located in complex terrain and certain industrial
sources where judgments must be made on source characterization
cannot be handled generically.

12.0 References ^{g h
---------------------------------------------------------------------------

\g\ Documents not available in the open literature or from the
National Technical Information Service (NTIS) have been placed in
Docket No. A-80-46 or A-88-04. Item Numbers for documents placed in
the Docket are shown at the end of the reference.
\h\ Some EPA references, e.g., model user's guides, etc., are
periodically revised. Users are referred to the SCRAM BBS^{19 to
download updates or addenda; see Section A.0 of this appendix.
---------------------------------------------------------------------------

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Protection

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99. Environmental Protection Agency, 1986. Evaluation of Short-
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102. Young, J.O., M. Aissa, T.L. Boehm et al., 1989. Development
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114. Environmental Protection Agency, 1992. Workbook of
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117. Environmental Protection Agency, 1989. Procedures for
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119. Environmental Protection Agency, 1989. U.S. EPA Office of
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127. Environmental Protection Agency, 1992. A Tiered Modeling
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129. Environmental Protection Agency, 1992. Toxic Modeling
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130. Environmental Protection Agency, 1989. User's Guide for the
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American Society of Mechanical Engineers, New York, NY.

13.0 Bibliography ^{i
---------------------------------------------------------------------------

\i\ The documents listed here are major sources of supplemental
information on the theory and application of mathematical air
quality models.
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American Meteorological Society, 1971-1985. Symposia on
Turbulence, Diffusion, and Air Pollution (1st-7th), Boston, MA.
American Meteorological Society, 1977-1984. Joint Conferences on
Applications of Air Pollution Meteorology (1st-4th). Sponsored by
the American Meteorological Society and the Air Pollution Control
Association, Boston, MA.
American Meteorological Society, 1978. Accuracy of Dispersion
Models. Bulletin of the American Meteorological Society, 59(8):
1025-1026.
American Meteorological Society, 1981. Air Quality Modeling and
the Clean Air Act: Recommendations to EPA on Dispersion Modeling for
Regulatory Applications, Boston, MA.
Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission
Critical Review Series, Oak Ridge National Laboratory, Oak Ridge,
TN.
Dickerson, W.H. and P.H. Gudiksen, 1980. ASCOT FY 79 Program
Report. Report UCRL--52899, ASCOT 80-1. Lawrence Livermore National
Laboratory, Livermore, CA.
Drake, R.L. and S.M. Barrager, 1979. Mathematical Models for
Atmospheric Pollutants. EPRI EA-1131. Electric Power Research
Institute, Palo Alto, CA.
Environmental Protection Agency, 1978. Workbook for Comparison
of Air Quality Models. EPA Publication No. EPA-450/2-78-028a and b.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
Fox, D.G., and J.E. Fairobent, 1981. NCAQ Panel Examines Uses
and Limitations of Air Quality Models. Bulletin of the American
Meteorological Society, 62(2): 218-221.
Gifford, F.A., 1976. Turbulent Diffusion Typing Schemes: A
Review. Nuclear Safety, 17(1): 68-86.
Gudiksen, P.H., and M.H. Dickerson, Eds., Executive Summary:
Atmospheric Studies in Complex Terrain Technical Progress Report FY-
1979 Through FY-1983. Lawrence Livermore National Laboratory,
Livermore, CA. (Docket Reference No. II-I-103).
Hales, J.M., 1976. Tall Stacks and the Atmospheric Environment.
EPA Publication No. EPA-450/3-76-007. U.S. Environmental Protection
Agency, Research Triangle Park, NC.
Hanna, S.R., G.A. Briggs, J. Deardorff, B.A. Egan, G.A. Gifford
and F. Pasquill, 1977. AMS Workshop on Stability Classification
Schemes And Sigma Curves--Summary of Recommendations. Bulletin of
the American Meteorological Society, 58(12): 1305-1309.
Hanna, S.R., G.A. Briggs and R.P. Hosker, Jr., 1982. Handbook on
Atmospheric Diffusion. Technical Information Center, U.S. Department
of Energy, Washington, D.C.
Haugen, D.A., Workshop Coordinator, 1975. Lectures on Air
Pollution and Environmental Impact Analyses. Sponsored by the
American Meteorological Society, Boston, MA.
Hoffnagle, G.F., M.E. Smith, T.V. Crawford and T.J. Lockhart,
1981. On-site Meteorological Instrumentation Requirements to
Characterize Diffusion from Point Sources--A Workshop, 15-17 January
1980, Raleigh, NC. Bulletin of the American Meteorological Society,
62(2): 255-261.
McMahon, R.A. and P.J. Denison, 1979. Empirical Atmospheric
Deposition Parameters--A Survey. Atmospheric Environment, 13: 571-
585.
McRae, G.J., J.A. Leone and J.H. Seinfeld, 1983. Evaluation of
Chemical Reaction Mechanisms for Photochemical Smog. Part I:
Mechanism Descriptions and Documentation. EPA Publication No. EPA-
600/3/83-086. U.S. Environmental Protection Agency, Research
Triangle Park, NC.
Pasquill, F. and F.B. Smith, 1983. Atmospheric Diffusion, 3rd
Edition. Ellis Horwood Limited, Chichester, West Sussex, England,
438 pp.
Randerson, D., Ed., 1984. Atmospheric Science and Power
Production. DOE/TIC 2760l. Office of Scientific and Technical
Information, U.S. Department of Energy, Oak Ridge, TN.
Roberts, J.J., Ed., 1977. Report to U.S. EPA of the Specialists'
Conference on the EPA Modeling Guideline. U.S. Environmental
Protection Agency, Research Triangle Park, NC.
Smith, M.E., Ed., 1973. Recommended Guide for the Prediction of
the Dispersion of Airborne Effluents. The American Society of
Mechanical Engineers, New York, NY.
Stern, A.C., Ed., 1976. Air Pollution, Third Edition, Volume I:
Air Pollutants, Their Transformation and Transport. Academic Press,
New York, NY.
Turner, D.B., 1979. Atmospheric Dispersion Modeling: A Critical
Review. Journal of the Air Pollution Control Association, 29(5):
502-519.
Whiteman, C.D. and K.J. Allwine, 1982. Green River Ambient Model
Assessment Program FY-1982 Progress Report. PNL-4520. Pacific
Northwest Laboratory, Richland, WA.

14.0 Glossary of Terms

Air quality. Ambient pollutant concentrations and their temporal
and spatial distribution.
Algorithm. A specific mathematical calculation procedure. A
model may contain several algorithms.
Background. Ambient pollutant concentrations due to:
(1) Natural sources;
(2) Nearby sources other than the one(s) currently under
consideration; and
(3) Unidentified sources.
Calibrate. An objective adjustment using measured air quality
data (e.g., an adjustment based on least-squares linear regression).
Calm. For purposes of air quality modeling, calm is used to
define the situation when the wind is indeterminate with regard to
speed or direction.
Complex terrain. Terrain exceeding the height of the stack being
modeled.
Computer code. A set of statements that comprise a computer
program.

[[Page 41866]]

Evaluate. To appraise the performance and accuracy of a model
based on a comparison of concentration estimates with observed air
quality data.
Fluid modeling. Modeling conducted in a wind tunnel or water
channel to quantitatively evaluate the influence of buildings and/or
terrain on pollutant concentrations.
Fugitive dust. Dust discharged to the atmosphere in an
unconfined flow stream such as that from unpaved roads, storage
piles and heavy construction operations.
Model. A quantitative or mathematical representation or
simulation which attempts to describe the characteristics or
relationships of physical events.
Preferred model. A refined model that is recommended for a
specific type of regulatory application.
Receptor. A location at which ambient air quality is measured or
estimated.
Receptor models. Procedures that examine an ambient monitor
sample of particulate matter and the conditions of its collection to
infer the types or relative mix of sources impacting on it during
collection.
Refined model. An analytical technique that provides a detailed
treatment of physical and chemical atmospheric processes and
requires detailed and precise input data. Specialized estimates are
calculated that are useful for evaluating source impact relative to
air quality standards and allowable increments. The estimates are
more accurate than those obtained from conservative screening
techniques.
Rollback. A simple model that assumes that if emissions from
each source affecting a given receptor are decreased by the same
percentage, ambient air quality concentrations decrease
proportionately.
Screening technique. A relatively simple analysis technique to
determine if a given source is likely to pose a threat to air
quality. Concentration estimates from screening techniques are
conservative.
Simple terrain. An area where terrain features are all lower in
elevation than the top of the stack of the source.

Appendix A to Appendix W of part 51--Summaries of Preferred Air Quality
Models

Table of Contents

A.0 Introduction and Availability
A.1 Buoyant Line and Point Source Dispersion Model (BLP)
A.2 Caline3
A.3 Climatological Dispersion Model (CDM 2.0)
A.4 Gaussian-Plume Multiple Source Air Quality Algorithm (RAM)
A.5 Industrial Source Complex Model (ISC3)
A.6 Urban Airshed Model (UAM)
A.7 Offshore and Coastal Dispersion Model (OCD)
A.8 Emissions and Dispersion Modeling System (EDMS)
A.9 Complex Terrain Dispersion Model Plus Algorithms For Unstable
Situations (CTDMPLUS)
A.REF References

A.0 Introduction and Availability

This appendix summarizes key features of refined air quality
models preferred for specific regulatory applications. For each
model, information is provided on availability, approximate cost,
regulatory use, data input, output format and options, simulation of
atmospheric physics, and accuracy. These models may be used without
a formal demonstration of applicability provided they satisfy the
recommendations for regulatory use; not all options in the models
are necessarily recommended for regulatory use.
Many of these models have been subjected to a performance
evaluation using comparisons with observed air quality data. A
summary of such comparisons for models contained in this appendix is
included in Moore et al. (1982). Where possible, several of the
models contained herein have been subjected to evaluation exercises,
including (1) statistical performance tests recommended by the
American Meteorological Society and (2) peer scientific reviews. The
models in this appendix have been selected on the basis of the
results of the model evaluations, experience with previous use,
familiarity of the model to various air quality programs, and the
costs and resource requirements for use.
All models and user's documentation in this appendix are
available from: Computer Products, National Technical Information
Service (NTIS), U.S. Department of Commerce, Springfield, VA 22161,
Phone: (703) 487-4650. In addition, model codes and selected,
abridged user's guides are available from the Support Center for
Regulatory Air Models Bulletin Board System \19\ (SCRAM BBS),
telephone (919) 541-5742. The SCRAM BBS is an electronic bulletin
board system designed to be user friendly and accessible from
anywhere in the country. Model users with personal computers are
encouraged to use the SCRAM BBS to download current model codes and
text files.

A.1 Buoyant Line and Point Source Dispersion Model (BLP)

Reference

Schulman, Lloyd L. and Joseph S. Scire, 1980. Buoyant Line and
Point Source (BLP) Dispersion Model User's Guide. Document P-7304B.
Environmental Research and Technology, Inc., Concord, MA. (NTIS No.
PB 81-164642)

Availability

The computer code is available on the Support Center for
Regulatory Models Bulletin Board System and also on diskette (as PB
90-500281) from the National Technical Information Service (see
Section A.0).

Abstract

BLP is a Gaussian plume dispersion model designed to handle
unique modeling problems associated with aluminum reduction plants,
and other industrial sources where plume rise and downwash effects
from stationary line sources are important.

a. Recommendations for Regulatory Use

The BLP model is appropriate for the following applications:
Aluminum reduction plants which contain buoyant, elevated line
sources;
Rural areas;
Transport distances less than 50 kilometers;
Simple terrain; and
One hour to one year averaging times.
The following options should be selected for regulatory
applications:
Rural (IRU=1) mixing height option;
Default (no selection) for plume rise wind shear (LSHEAR),
transitional point source plume rise (LTRANS), vertical potential
temperature gradient (DTHTA), vertical wind speed power law profile
exponents (PEXP), maximum variation in number of stability classes
per hour (IDELS), pollutant decay (DECFAC), the constant in Briggs'
stable plume rise equation (CONST2), constant in Briggs' neutral
plume rise equation (CONST3), convergence criterion for the line
source calculations (CRIT), and maximum iterations allowed for line
source calculations (MAXIT); and
Terrain option (TERAN) set equal to 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
For other applications, BLP can be used if it can be
demonstrated to give the same estimates as a recommended model for
the same application, and will subsequently be executed in that
mode.
BLP can be used on a case-by-case basis with specific options
not available in a recommended model if it can be demonstrated,
using the criteria in Section 3.2, that the model is more
appropriate for a specific application.

b. Input Requirements

Source data: point sources require stack location, elevation of
stack base, physical stack height, stack inside diameter, stack gas
exit velocity, stack gas exit temperature, and pollutant emission
rate. Line sources require coordinates of the end points of the
line, release height, emission rate, average line source width,
average building width, average spacing between buildings, and
average line source buoyancy parameter.
Meteorological data: hourly surface weather data from punched
cards or from the preprocessor program RAMMET which provides hourly
stability class, wind direction, wind speed, temperature, and mixing
height.
Receptor data: locations and elevations of receptors, or
location and size of receptor grid or request automatically
generated receptor grid.

c. Output

Printed output (from a separate post-processor program)
includes:
Total concentration or, optionally, source contribution
analysis; monthly and annual frequency distributions for 1-, 3-, and
24-hour average concentrations; tables of 1-, 3-, and 24-hour
average concentrations at each receptor; table of the annual (or
length of run) average concentrations at each receptor;
Five highest 1-, 3-, and 24-hour average concentrations at each
receptor; and
Fifty highest 1-, 3-, and 24-hour concentrations over the
receptor field.

d. Type of Model

BLP is a gaussian plume model.

[[Page 41867]]

e. Pollutant Types

BLP may be used to model primary pollutants. This model does not
treat settling and deposition.

f. Source-Receptor Relationship

BLP treats up to 50 point sources, 10 parallel line sources, and
100 receptors arbitrarily located.
User-input topographic elevation is applied for each stack and
each receptor.

g. Plume Behavior

BLP uses plume rise formulas of Schulman and Scire (1980).
Vertical potential temperature gradients of 0.02 Kelvin per
meter for E stability and 0.035 Kelvin per meter are used for stable
plume rise calculations. An option for user input values is
included.
Transitional rise is used for line sources.
Option to suppress the use of transitional plume rise for point
sources is included.
The building downwash algorithm of Schulman and Scire (1980) is
used.

h. Horizontal Winds

Constant, uniform (steady-state) wind is assumed for an hour.
Straight line plume transport is assumed to all downwind
distances.
Wind speeds profile exponents of 0.10, 0.15, 0.20, 0.25, 0.30,
and 0.30 are used for stability classes A through F, respectively.
An option for user-defined values and an option to suppress the use
of the wind speed profile feature are included.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Rural dispersion coefficients are from Turner (1969), with no
adjustment made for variations in surface roughness or averaging
time.
Six stability classes are used.

k. Vertical Dispersion

Rural dispersion coefficients are from Turner (1969), with no
adjustment made for variations in surface roughness.
Six stability classes are used.
Mixing height is accounted for with multiple reflections until
the vertical plume standard deviation equals 1.6 times the mixing
height; uniform mixing is assumed beyond that point.
Perfect reflection at the ground is assumed.

l. Chemical Transformation

Chemical transformations are treated using linear decay. Decay
rate is input by the user.

m. Physical Removal

Physical removal is not explicitly treated.

n. Evaluation Studies

Schulman, L.L. and J.S. Scire, 1980. Buoyant Line and Point
Source (BLP) Dispersion Model User's Guide, P-7304B. Environmental
Research and Technology, Inc., Concord, MA.
Scire, J.S. and L.L. Schulman, 1981. Evaluation of the BLP and
ISC Models with SF6 Tracer Data and SO2 Measurements at
Aluminum Reduction Plants. APCA Specialty Conference on Dispersion
Modeling for Complex Sources, St. Louis, MO.

A.2 CALINE3

Reference

Benson, Paul E., 1979. CALINE3--A Versatile Dispersion Model for
Predicting Air Pollutant Levels Near Highways and Arterial Streets.
Interim Report, Report Number FHWA/CA/TL-79/23. Federal Highway
Administration, Washington, D.C. (NTIS No. PB 80-220841)

Availability

The CALINE3 model is available on diskette (as PB 95-502712)
from NTIS. The source code and user's guide are also available on
the Support Center for Regulatory Models Bulletin Board System (see
Section A.0).

Abstract

CALINE3 can be used to estimate the concentrations of
nonreactive pollutants from highway traffic. This steady-state
Gaussian model can be applied to determine air pollution
concentrations at receptor locations downwind of ``at-grade,''
``fill,'' ``bridge,'' and ``cut section'' highways located in
relatively uncomplicated terrain. The model is applicable for any
wind direction, highway orientation, and receptor location. The
model has adjustments for averaging time and surface roughness, and
can handle up to 20 links and 20 receptors. It also contains an
algorithm for deposition and settling velocity so that particulate
concentrations can be predicted.

a. Recommendations for Regulatory Use

CALINE-3 is appropriate for the following applications:
Highway (line) sources;
Urban or rural areas;
Simple terrain;
Transport distances less than 50 kilometers; and
One-hour to 24-hour averaging times.

b. Input Requirements

Source data: up to 20 highway links classed as ``at-grade,''
``fill'' ``bridge,'' or ``depressed''; coordinates of link end
points; traffic volume; emission factor; source height; and mixing
zone width.
Meteorological data: wind speed, wind angle (measured in degrees
clockwise from the Y axis), stability class, mixing height, ambient
(background to the highway) concentration of pollutant.
Receptor data: coordinates and height above ground for each
receptor. c.

c. Output

Printed output includes concentration at each receptor for the
specified meteorological condition.

d. Type of Model

CALINE-3 is a Gaussian plume model.

e. Pollutant Types

CALINE-3 may be used to model primary pollutants.

f. Source-Receptor Relationship

Up to 20 highway links are treated.
CALINE-3 applies user input location and emission rate for each
link. User-input receptor locations are applied.

g. Plume Behavior

Plume rise is not treated.

h. Horizontal Winds

User-input hourly wind speed and direction are applied.
Constant, uniform (steady-state) wind is assumed for an hour.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Six stability classes are used.
Rural dispersion coefficients from Turner (1969) are used, with
adjustment for roughness length and averaging time.
Initial traffic-induced dispersion is handled implicitly by
plume size parameters.

k. Vertical Dispersion

Six stability classes are used.
Empirical dispersion coefficients from Benson (1979) are used
including an adjustment for roughness length.
Initial traffic-induced dispersion is handled implicitly by
plume size parameters.
Adjustment for averaging time is included.

l. Chemical Transformation

Not treated.

m. Physical Removal

Optional deposition calculations are included.

n. Evaluation Studies

Bemis, G.R. et al., 1977. Air Pollution and Roadway Location,
Design, and Operation--Project Overview. FHWA-CA-TL-7080-77-25,
Federal Highway Administration, Washington, D.C.
Cadle, S.H. et al., 1976. Results of the General Motors Sulfate
Dispersion Experiment, GMR-2107. General Motors Research
Laboratories, Warren, MI.
Dabberdt, W.F., 1975. Studies of Air Quality on and Near
Highways, Project 2761. Stanford Research Institute, Menlo Park, CA.

A.3 Climatological Dispersion Model (CDM 2.0)

Reference

Irwin, J.S., T. Chico and J. Catalano, 1985. CDM 2.0--
Climatological Dispersion Model--User's Guide. U.S. Environmental
Protection Agency, Research Triangle Park, NC. (NTIS No. PB 86-
136546)

Availability

The source code and user's guide is available on the Support
Center for Regulatory Models Bulletin Board System. The computer
code is also available on diskette (as PB 90-500406) from the
National Technical Information Service (see Section A.0).

[[Page 41868]]

Abstract

CDM is a climatological steady-state Gaussian plume model for
determining long-term (seasonal or annual) arithmetic average
pollutant concentrations at any ground-level receptor in an urban
area.

a. Recommendations for Regulatory Use

CDM is appropriate for the following applications:
Point and area sources;
Urban areas;
Flat terrain;
Transport distances less than 50 kilometers;
Long term averages over one month to one year or longer.
The following option should be selected for regulatory
applications:
Set the regulatory ``default option'' (NDEF=1) which
automatically selects stack tip downwash, final plume rise,
buoyancy-induced dispersion (BID), and the appropriate wind profile
exponents.
Enter ``0'' for pollutant half-life for all pollutants except
for SO2 in an urban setting. This entry results in no decay
(infinite half-life) being calculated. For SO2 in an urban
setting, the pollutant half-life (in hours) should be set to 4.0.

b. Input Requirements

Source data: location, average emissions rates and heights of
emissions for point and area sources. Point source data requirements
also include stack gas temperature, stack gas exit velocity, and
stack inside diameter for plume rise calculations for point sources.
Meteorological data: stability wind rose (STAR deck day/night
version), average mixing height and wind speed in each stability
category, and average air temperature.
Receptor data: cartesian coordinates of each receptor.

c. Output

Printed output includes:
Average concentrations for the period of the stability wind rose
data (arithmetic mean only) at each receptor, and
Optional point and area concentration rose for each receptor.

d. Type of Model

CDM is a climatological Gaussian plume model.

e. Pollutant Types

CDM may be used to model primary pollutants. Settling and
deposition are not treated.

f. Source-Receptor Relationship

CDM applies user-specified locations for all point sources and
receptors.
Area sources are input as multiples of a user-defined unit area
source grid size.
User specified release heights are applied for individual point
sources and the area source grid.
Actual separation between each source-receptor pair is used.
The user may select a single height at or above ground level
that applies to all receptors.
No terrain differences between source and receptor are treated.

g. Plume Behavior

CDM uses Briggs (1969, 1971, 1975) plume rise equations.
Optionally a plume rise-wind speed product may be input for each
point source.
Stack tip downwash equation from Briggs (1974) is preferred for
regulatory use. The Bjorklund and Bowers (1982) equation is also
included.
No plume rise is calculated for area sources.
Does not treat fumigation or building downwash.

h. Horizontal Winds

Wind data are input as a stability wind rose (joint frequency
distribution of 16 wind directions, 6 wind classes, and 5 stability
classes).
Wind speed profile exponents for the urban case (Irwin, 1979;
EPA, 1980) are used, assuming the anemometer height is at 10.0
meters.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Pollutants are assumed evenly distributed across a 22.5 or 10.0
degree sector.

k. Vertical Dispersion

There are seven vertical dispersion parameter schemes, but the
following is recommended for regulatory applications:
Briggs-urban (Gifford, 1976).
Mixing height has no effect until dispersion coefficient equals
0.8 times the mixing height; uniform vertical mixing is assumed
beyond that point.
Buoyancy-induced dispersion (Pasquill, 1976) is included as an
option. Perfect reflection is assumed at the ground.

l. Chemical Transformation

Chemical transformations are treated using exponential decay.
Half-life is input by the user.

m. Physical Removal

Physical removal is not explicitly treated.

n. Evaluation Studies

Busse, A.D. and J.R. Zimmerman, 1973. User's Guide for the
Climatological Dispersion Model--Appendix E. EPA Publication No.
EPA/R4-73-024. Office of Research and Development, Research Triangle
Park, NC.
Irwin, J.S. and T.M. Brown, 1985. A Sensitivity Analysis of the
Treatment of Area Sources by the Climatological Dispersion Model.
Journal of Air Pollution Control Association, 35: 359-364.
Londergan, R., D. Minott, D. Wachter and R. Fizz, 1983.
Evaluation of Urban Air Quality Simulation Models, EPA Publication
No. EPA-450/4-83-020. U.S. Environmental Protection Agency, Research
Triangle Park, NC.
Zimmerman, J.R., 1971. Some Preliminary Results of Modeling from
the Air Pollution Study of Ankara, Turkey, Proceedings of the Second
Meeting of the Expert Panel on Air Pollution Modeling, NATO
Committee on the Challenges of Modern Society, Paris, France.
Zimmerman, J.R., 1972. The NATO/CCMS Air Pollution Study of St.
Louis, Missouri. Presented at the Third Meeting of the Expert Panel
on Air Pollution Modeling, NATO Committee on the Challenges of
Modern Society, Paris, France.

A.4 Gaussian-Plume Multiple Source Air Quality Algorithm (RAM)

Reference

Turner, D.B. and J.H. Novak, 1978. User's Guide for RAM.
Publication No. EPA-600/8-78-016, Vol. a and b. U.S. Environmental
Protection Agency, Research Triangle Park, NC. (NTIS Nos. PB 294791
and PB 294792)
Catalano, J.A., D.B. Turner and H. Novak, 1987. User's Guide for
RAM--Second Edition. U.S. Environmental Protection Agency, Research
Triangle Park, NC.

Availability

The source code and user's guide is available on the Support
Center for Regulatory Models Bulletin Board System. The computer
code is also available on diskette (as PB 90-500315) from the
National Technical Information Service (see Section A.0).

Abstract

RAM is a steady-state Gaussian plume model for estimating
concentrations of relatively stable pollutants, for averaging times
from an hour to a day, from point and area sources in a rural or
urban setting. Level terrain is assumed. Calculations are performed
for each hour.

a. Recommendations for Regulatory Use

RAM is appropriate for the following applications:
Point and area sources;
Urban areas;
Flat terrain;
Transport distances less than 50 kilometers; and
One hour to one year averaging times.
The following options should be selected for regulatory
applications:
Set the regulatory ``default option'' to automatically select
stack tip downwash, final plume rise, buoyancy-induced dispersion
(BID), the new treatment for calms, the appropriate wind profile
exponents, and the appropriate value for pollutant half-life.

b. Input Requirements

Source data: point sources require location, emission rate,
physical stack height, stack gas exit velocity, stack inside
diameter and stack gas temperature. Area sources require location,
size, emission rate, and height of emissions.
Meteorological data: hourly surface weather data from the
preprocessor program RAMMET which provides hourly stability class,
wind direction, wind speed, temperature, and mixing height. Actual
anemometer height (a single value) is also required.
Receptor data: coordinates of each receptor. Options for
automatic placement of

[[Page 41869]]

receptors near expected concentration maxima, and a gridded receptor
array are included.

c. Output

Printed output optionally includes:
One to 24-hour and annual average concentrations at each
receptor,
Limited individual source contribution list, and
Highest through fifth highest concentrations at each receptor
for period, with the highest and high, second-high values flagged.

d. Type of Model

RAM is a Gaussian plume model.

e. Pollutant Types

RAM may be used to model primary pollutants. Settling and
deposition are not treated.

f. Source-Receptor Relationship

RAM applies user-specified locations for all point sources and
receptors. Area sources are input as multiples of a user-defined
unit area source grid size.
User specified stack heights are applied for individual point
sources.
Up to 3 effective release heights may be specified for the area
sources. Area source release heights are assumed to be appropriate
for a 5 meter per second wind and to be inversely proportional to
wind speed.
Actual separation between each source-receptor pair is used.
All receptors are assumed to be at the same height at or above
ground level.
No terrain differences between source and receptor are accounted
for.

g. Plume Behavior

RAM uses Briggs (1969, 1971, 1975) plume rise equations for
final rise.
Stack tip downwash equation from Briggs (1974) is used.
A user supplied fraction of the area source height is treated as
the physical height. The remainder is assumed to be plume rise for a
5 meter per second wind speed, and to be inversely proportional to
wind speed.
Fumigation and building downwash are not treated.

h. Horizontal Winds

Constant, uniform (steady state) wind is assumed for an hour.
Straight line plume transport is assumed to all downwind
distances.
Separate wind speed profile exponents (Irwin, 1979; EPA, 1980)
for urban cases are used.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Urban dispersion coefficients from Briggs (Gifford, 1976) are
used.
Buoyancy-induced dispersion (Pasquill, 1976) is included.
Six stability classes are used.

k. Vertical Dispersion

Urban dispersion coefficients from Briggs (Gifford, 1976) are
used.
Buoyancy-induced dispersion (Pasquill, 1976) is included.
Six stability classes are used.
Mixing height is accounted for with multiple reflections until
the vertical plume standard deviation equals 1.6 times the mixing
height; uniform vertical mixing is assumed beyond that point.
Perfect reflection is assumed at the ground.

l. Chemical Transformation

Chemical transformations are treated using exponential decay.
Half-life is input by the user.

m. Physical Removal

Physical removal is not explicitly treated.

n. Evaluation Studies

Ellis, H., P. Lou, and G. Dalzell, 1980. Comparison Study of
Measured and Predicted Concentrations with the RAM Model at Two
Power Plants Along Lake Erie. Second Joint Conference on
Applications of Air Pollution Meteorology, New Orleans, LA.
Environmental Research and Technology, 1980. SO2 Monitoring
and RAM (Urban) Model Comparison Study in Summit County, Ohio.
Document P-3618-152, Environmental Research & Technology, Inc.,
Concord, MA.
Guldberg, P.H. and C.W. Kern, 1978. A Comparison Validation of
the RAM and PTMTP Models for Short-Term Concentrations in Two Urban
Areas. Journal of Air Pollution Control Association, 28: 907-910.
Hodanbosi, R.R. and L.K. Peters, 1981. Evaluation of RAM Model
for Cleveland, Ohio. Journal of Air Pollution Control Association,
31: 253-255.
Kennedy, K.H., R.D. Siegel and M.P. Steinberg, 1981. Case-
Specific Evaluation of the RAM Atmospheric Dispersion Model in an
Urban Area. 74th Annual Meeting of the American Institute of
Chemical Engineers, New Orleans, LA.
Kummier, R.H., B. Cho, G. Roginski, R. Sinha and A. Greenburg,
1979. A Comparative Validation of the RAM and Modified SAI Models
for Short Term SO2 Concentrations in Detroit. Journal of Air
Pollution Control Association, 29: 720-723.
Londergan, R.J., N.E. Bowne, D.R. Murray, H. Borenstein and J.
Mangano, 1980. An Evaluation of Short-Term Air Quality Models Using
Tracer Study Data. Report No. 4333, American Petroleum Institute,
Washington, D.C.
Londergan, R., D. Minott, D. Wackter and R. Fizz, 1983.
Evaluation of Urban Air Quality Simulation Models. EPA Publication
No. EPA-450/4-83-020. U.S. Environmental Protection Agency, Research
Triangle Park, NC.
Morgenstern, P., M.J. Geraghty, and A. McKnight, 1979. A
Comparative Study of the RAM (Urban) and RAMR (Rural) Models for
Short-term SO2 Concentrations in Metropolitan Indianapolis.
72nd Annual Meeting of the Air Pollution Control Association,
Cincinnati, OH.
Ruff, R.E., 1980. Evaluation of the RAM Using the RAPS Data
Base. Contract 68-02-2770, SRI International, Menlo Park, CA.

A.5 Industrial Source Complex Model (ISC3)

Reference

Environmental Protection Agency, 1995. User's Guide for the
Industrial Source Complex (ISC3) Dispersion Models, Volumes 1 and 2.
EPA Publication Nos. EPA-454/B-95-003a & b. Environmental Protection
Agency, Research Triangle Park, NC. (NTIS Nos. PB 95-222741 and PB
95-222758, respectively)

Availability

The model code is available on the Support Center for Regulatory
Air Models Bulletin Board System. ISCST3 (as PB 96-502000) and
ISCLT3 (PB 96-502018) are also available on diskette from the
National Technical Information Service (see Section A.0).

Abstract

The ISC3 model is a steady-state Gaussian plume model which can
be used to assess pollutant concentrations from a wide variety of
sources associated with an industrial source complex. This model can
account for the following: settling and dry deposition of particles;
downwash; area, line and volume sources; plume rise as a function of
downwind distance; separation of point sources; and limited terrain
adjustment. ISC3 operates in both long-term and short-term modes.

a. Recommendations for Regulatory Use

ISC3 is appropriate for the following applications:
Industrial source complexes;
Rural or urban areas;
Flat or rolling terrain;
Transport distances less than 50 kilometers;
1-hour to annual averaging times; and
Continuous toxic air emissions.
The following options should be selected for regulatory
applications: For short term or long term modeling, set the
regulatory ``default option''; i.e., use the keyword DFAULT, which
automatically selects stack tip downwash, final plume rise, buoyancy
induced dispersion (BID), the vertical potential temperature
gradient, a treatment for calms, the appropriate wind profile
exponents, the appropriate value for pollutant half-life, and a
revised building wake effects algorithm; set the ``rural option''
(use the keyword RURAL) or ``urban option'' (use the keyword URBAN);
and set the ``concentration option'' (use the keyword CONC).

b. Input Requirements

Source data: location, emission rate, physical stack height,
stack gas exit velocity, stack inside diameter, and stack gas
temperature. Optional inputs include source elevation, building
dimensions, particle size distribution with corresponding settling
velocities, and surface reflection coefficients.
Meteorological data: ISCST3 requires hourly surface weather data
from the preprocessor program RAMMET, which provides hourly
stability class, wind direction, wind speed, temperature, and mixing
height. For ISCLT3, input includes

[[Page 41870]]

stability wind rose (STAR deck), average afternoon mixing height,
average morning mixing height, and average air temperature.
Receptor data: coordinates and optional ground elevation for
each receptor.

c. Output

Printed output options include:
Program control parameters, source data, and receptor
data;
Tables of hourly meteorological data for each specified
day;
``N''-day average concentration or total deposition
calculated at each receptor for any desired source combinations;
Concentration or deposition values calculated for any
desired source combinations at all receptors for any specified day
or time period within the day;
Tables of highest and second highest concentration or
deposition values calculated at each receptor for each specified
time period during a(n) ``N''-day period for any desired source
combinations, and tables of the maximum 50 concentration or
deposition values calculated for any desired source combinations for
each specified time period.

d. Type of Model

ISC3 is a Gaussian plume model. It has been revised to perform a
double integration of the Gaussian plume kernel for area sources.

e. Pollutant Types

ISC3 may be used to model primary pollutants and continuous
releases of toxic and hazardous waste pollutants. Settling and
deposition are treated.

f. Source-Receptor Relationships

ISC3 applies user-specified locations for point, line, area and
volume sources, and user-specified receptor locations or receptor
rings.
User input topographic evaluation for each receptor is used.
Elevations above stack top are reduced to the stack top elevation,
i.e., ``terrain chopping''.
User input height above ground level may be used when necessary
to simulate impact at elevated or ``flag pole'' receptors, e.g., on
buildings.
Actual separation between each source-receptor pair is used.

g. Plume Behavior

ISC3 uses Briggs (1969, 1971, 1975) plume rise equations for
final rise.
Stack tip downwash equation from Briggs (1974) is used.
Revised building wake effects algorithm is used. For stacks
higher than building height plus one-half the lesser of the building
height or building width, the building wake algorithm of Huber and
Snyder (1976) is used. For lower stacks, the building wake algorithm
of Schulman and Scire (Schulman and Hanna, 1986) is used, but stack
tip downwash and BID are not used.
For rolling terrain (terrain not above stack height), plume
centerline is horizontal at height of final rise above source.
Fumigation is not treated.

h. Horizontal Winds

Constant, uniform (steady-state) wind is assumed for each hour.
Straight line plume transport is assumed to all downwind
distances.
Separate wind speed profile exponents (Irwin, 1979; EPA, 1980)
for both rural and urban cases are used.
An optional treatment for calm winds is included for short term
modeling.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Rural dispersion coefficients from Turner (1969) are used, with
no adjustments for surface roughness or averaging time.
Urban dispersion coefficients from Briggs (Gifford, 1976) are
used.
Buoyancy induced dispersion (Pasquill, 1976) is included.
Six stability classes are used.

k. Vertical Dispersion

Rural dispersion coefficients from Turner (1969) are used, with
no adjustments for surface roughness.
Urban dispersion coefficients from Briggs (Gifford, 1976) are
used.
Buoyancy induced dispersion (Pasquill, 1976) is included.
Six stability classes are used.
Mixing height is accounted for with multiple reflections until
the vertical plume standard deviation equals 1.6 times the mixing
height; uniform vertical mixing is assumed beyond that point.
Perfect reflection is assumed at the ground.

l. Chemical Transformation

Chemical transformations are treated using exponential decay.
Time constant is input by the user.

m. Physical Removal

Dry deposition effects for particles are treated using a
resistance formulation in which the deposition velocity is the sum
of the resistances to pollutant transfer within the surface layer of
the atmosphere, plus a gravitational settling term (EPA, 1994),
based on the modified surface depletion scheme of Horst (1983).

n. Evaluation Studies

Bowers, J.F. and A.J. Anderson, 1981. An Evaluation Study for
the Industrial Source Complex (ISC) Dispersion Model, EPA
Publication No. EPA-450/4-81-002. U.S. Environmental Protection
Agency, Research Triangle Park, NC.
Bowers, J.F., A.J. Anderson and W.R. Hargraves, 1982. Tests of
the Industrial Source Complex (ISC) Dispersion Model at the Armco
Middletown, Ohio Steel Mill. EPA Publication No. EPA-450/4-82-006.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
Environmental Protection Agency, 1992. Comparison of a Revised
Area Source Algorithm for the Industrial Source Complex Short Term
Model and Wind Tunnel Data. EPA Publication No. EPA-454/R-92-014.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
(NTIS No. PB 93-226751)
Environmental Protection Agency, 1992. Sensitivity Analysis of a
Revised Area Source Algorithm for the Industrial Source Complex
Short Term Model. EPA Publication No. EPA-454/R-92-015. U.S.
Environmental Protection Agency, Research Triangle Park, NC. (NTIS
No. PB 93-226769)
Environmental Protection Agency, 1992. Development and
Evaluation of a Revised Area Source Algorithm for the Industrial
source complex Long Term Model. EPA Publication No. EPA-454/R-92-
016. U.S. Environmental Protection Agency, Research Triangle Park,
NC. (NTIS No. PB 93-226777)
Environmental Protection Agency, 1994. Development and Testing
of a Dry Deposition Algorithm (Revised). EPA Publication No. EPA-
454/R-94-015. U.S. Environmental Protection Agency, Research
Triangle Park, NC. (NTIS No. PB 94-183100)
Scire, J.S. and L.L. Schulman, 1981. Evaluation of the BLP and
ISC Models with SF6 Tracer Data and SO2 Measurements at
Aluminum Reduction Plants. Air Pollution Control Association
Specialty Conference on Dispersion Modeling for Complex Sources, St.
Louis, MO.
Schulman, L.L. and S.R. Hanna, 1986. Evaluation of Downwash
Modification to the Industrial Source Complex Model. Journal of the
Air Pollution Control Association, 36: 258-264.

A.6 Urban Airshed Model (UAM)

Reference

Environmental Protection Agency, 1990. User's Guide for the
Urban Airshed Model, Volume I-VIII. EPA Publication Nos. EPA-450/4-
90-007a-c, d(R), e-g, and EPA-454/B-93-004, respectively. U.S.
Environmental Protection Agency, Research Triangle Park, NC (NTIS
Nos. PB 91-131227, PB 91-131235, PB 91-131243, PB 93-122380, PB 91-
131268, PB 92-145382, and PB 92-224849, respectively, for Vols. I-
VII).

Availability

The model code is available on the Support Center for Regulatory
Air Models Bulletin Board System (see Section A.0).

Abstract

UAM is an urban scale, three dimensional, grid type numerical
simulation model. The model incorporates a condensed photochemical
kinetics mechanism for urban atmospheres. The UAM is designed for
computing ozone (O3) concentrations under short-term, episodic
conditions lasting one or two days resulting from emissions of
oxides of nitrogen (NOx), volatile organic compounds (VOC), and
carbon monoxide (CO). The model treats urban VOC emissions as their
carbon-bond surrogates.

a. Recommendations for Regulatory Use

UAM is appropriate for the following applications: urban areas
having significant ozone attainment problems and one hour averaging
times.
UAM has many options but no specific recommendations can be made
at this time on all options. The reviewing agency should be
consulted on selection of options to be used in regulatory
applications.

b. Input Requirements

Source data: gridded, hourly emissions of PAR, OLE, ETH, XYL,
TOL, ALD2, FORM,

[[Page 41871]]

ISOR, ETOTH, MEOH, CO, NO, and NO2 for low-level sources. For
major elevated point sources, hourly emissions, stack height, stack
diameter, exit velocity, and exit temperature.
Meteorological data: hourly, gridded, divergence free, u and v
wind components for each vertical level; hourly gridded mixing
heights and surface temperatures; hourly exposure class; hourly
vertical potential temperature gradient above and below the mixing
height; hourly surface atmospheric pressure; hourly water mixing
ratio; and gridded surface roughness lengths.
Air quality data: concentration of all carbon bond 4 species at
the beginning of the simulation for each grid cell; and hourly
concentrations of each pollutant at each level along the inflow
boundaries and top boundary of the modeling region.
Other data requirements are: hourly mixed layer average,
NO2 photolysis rates; and ozone surface uptake resistance along
with associated gridded vegetation (scaling) factors.
c. Output
Printed output includes:
Gridded instantaneous concentration fields at user-
specified time intervals for user-specified pollutants and grid
levels;
Gridded time-average concentration fields for user-
specified time intervals, pollutants, and grid levels.
d. Type of Model
UAM is a three dimensional, numerical, photochemical grid model.
e. Pollutant Types
UAM may be used to model ozone (O3) formation from oxides
of nitrogen (NOx) and volatile organic compound (VOC)
emissions.
f. Source-Receptor Relationship
Low-level area and point source emissions are specified within
each surface grid cell. Emissions from major point sources are
placed within cells aloft in accordance with calculated effective
plume heights.
Hourly average concentrations of each pollutant are calculated
for all grid cells at each vertical level.
g. Plume Behavior
Plume rise is calculated for major point sources using
relationships recommended by Briggs (1971).
h. Horizontal Winds
See Input Requirements.
i. Vertical Wind Speed
Calculated at each vertical grid cell interface from the mass
continuity relationship using the input gridded horizontal wind
field.
j. Horizontal Dispersion
Horizontal eddy diffusivity is set to a user specified constant
value (nominally 50 m^{2/s).
k. Vertical Dispersion
Vertical eddy diffusivities for unstable and neutral conditions
calculated using relationships of Lamb et al. (1977); for stable
conditions, the relationship of Businger and Arya (1974) is
employed. Stability class, friction velocity, and Monin-Obukhov
length determined using procedure of Liu et al. (1976).

l. Chemical Transformation

UAM employs a simplified version of the Carbon-Bond IV Mechanism
(CBM-IV) developed by Gery et al. (1988) employing various steady
state approximations. The CBM-IV mechanism incorporated in UAM
utilizes an updated simulation of PAN chemistry that includes a
peroxy-peroxy radical termination reaction, significant when the
atmosphere is NOx-limited (Gery et al., 1989). The current CBM-
IV mechanism accommodates 34 species and 82 reactions.

m. Physical Removal

Dry deposition of ozone and other pollutant species are
calculated. Vegetation (scaling) factors are applied to the
reference surface uptake resistance of each species depending on
land use type.

n. Evaluation Studies

Builtjes, P.J.H., K.D. van der Hurt and S.D. Reynolds, 1982.
Evaluation of the Performance of a Photochemical Dispersion Model in
Practical Applications. 13th International Technical Meeting on Air
Pollution Modeling and Its Application, Ile des Embiez, France.
Cole, H.S., D.E. Layland, G.K. Moss and C.F. Newberry, 1983. The
St. Louis Ozone Modeling Project. EPA Publication No. EPA-450/4-83-
019. U.S. Environmental Protection Agency, Research Triangle Park,
NC.
Dennis, R.L., M.W. Downton and R.S. Keil, 1983. Evaluation of
Performance Measures for an Urban Photochemical Model. EPA
Publication No. EPA-450/4-83-021. U.S. Environmental Protection
Agency, Research Triangle Park, NC.
Haney, J.L. and T.N. Braverman, 1985. Evaluation and Application
of the Urban Airshed Model in the Philadelphia Air Quality Control
Region. EPA Publication No. EPA-450/4-85-003. U.S. Environmental
Protection Agency, Research Triangle Park, NC.
Layland, D.E. and H.S. Cole, 1983. A Review of Recent
Applications of the SAI Urban Airshed Model. EPA Publication No.
EPA-450/4-84-004. U.S. Environmental Protection Agency, Research
Triangle Park, NC.
Layland, D.E., S.D. Reynolds, H. Hogo and W.R. Oliver, 1983.
Demonstration of Photochemical Grid Model Usage for Ozone Control
Assessment. 76th Annual Meeting of the Air Pollution Control
Association, Atlanta, GA.
Morris, R.E. et al., 1990. Urban Airshed Model Study of Five
Cities. EPA Publication No. EPA-450/4-90-006a-g. U.S. Environmental
Protection Agency, Research Triangle Park, NC.
Reynolds, S.D., H. Hogo, W.R. Oliver and L.E. Reid, 1982.
Application of the SAI Airshed Model to the Tulsa Metropolitan Area,
SAI No. 82004. Systems Applications, Inc., San Rafael, CA.
Schere, K.L. and J.H. Shreffler, 1982. Final Evaluation of
Urban-Scale Photochemical Air Quality Simulation Models. EPA
Publication No. EPA-600/3-82-094. U.S. Environmental Protection
Agency, Research Triangle Park, NC.
Seigneur C., T.W. Tesche, C.E. Reid, P.M. Roth, W.R. Oliver and
J.C. Cassmassi, 1981. The Sensitivity of Complex Photochemical Model
Estimates to Detail In Input Information, Appendix A--A Compilation
of Simulation Results. EPA Publication No. EPA-450/4-81-031b. U.S.
Environmental Protection Agency, Research Triangle Park, NC.
South Coast Air Quality Management District, 1989. Air Quality
Management Plan--Appendix V-R (Urban Airshed Model Performance
Evaluation). El Monte, CA.
Stern, R. and B. Scherer, 1982. Simulation of a Photochemical
Smog Episode in the Rhine-Ruhr Area with a Three Dimensional Grid
Model. 13th International Technical Meeting on Air Pollution
Modeling and Its Application, Ile des Embiez, France.
Tesche, T.W., C. Seigneur, L.E. Reid, P.M. Roth, W.R. Oliver and
J.C. Cassmassi, 1981. The Sensitivity of Complex Photochemical Model
Estimates to Detail in Input Information. EPA Publication No. EPA-
450/4-81-031a. U.S. Environmental Protection Agency, Research
Triangle Park, NC.
Tesche, T.W., W.R. Oliver, H. Hogo, P. Saxeena and J.L. Haney,
1983. Volume IV--Assessment of NOx Emission Control
Requirements in the South Coast Air Basin--Appendix A. Performance
Evaluation of the Systems Applications Airshed Model for the 26-27
June 1974 O3 Episode in the South Coast Air Basin, SYSAPP 83/
037. Systems Applications, Inc., San Rafael, CA.
Tesche, T.W., W.R. Oliver, H. Hogo, P. Saxeena and J.L. Haney,
1983. Volume IV--Assessment of NOx Emission Control
Requirements in the South Coast Air Basin--Appendix B. Performance
Evaluation of the Systems Applications Airshed Model for the 7-8
November 1978 NO2 Episode in the South Coast Air Basin, SYSAPP
83/038. Systems Applications, Inc., San Rafael, CA.
Tesche, T.W., 1988. Accuracy of Ozone Air Quality Models.
Journal of Environmental Engineering, 114(4): 739-752.
A.7 Offshore and Coastal Dispersion Model (OCD)
Reference
DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and
Coastal Dispersion Model, Version 4. Volume I: User's Guide, and
Volume II: Appendices. Sigma Research Corporation, Westford, MA.
(NTIS Nos. PB 93-144384 and PB 93-144392)
Availability
This model code is available on the Support Center for
Regulatory Air Models Bulletin Board System and also on diskette (as
PB 91-505230) from the National Technical Information Service (see
Section A.0).
Technical Contact
Minerals Management Service, Attn: Mr. Dirk Herkhof, Parkway
Atrium Building, 381 Elden Street, Herndon, VA 22070-4817, Phone:
(703) 787-1735.
Abstract
OCD is a straight-line Gaussian model developed to determine the
impact of offshore emissions from point, area or line sources on the
air quality of coastal regions. OCD incorporates overwater plume
transport

[[Page 41872]]

and dispersion as well as changes that occur as the plume crosses
the shoreline. Hourly meteorological data are needed from both
offshore and onshore locations. These include water surface
temperature, overwater air temperature, mixing height, and relative
humidity.
Some of the key features include platform building downwash,
partial plume penetration into elevated inversions, direct use of
turbulence intensities for plume dispersion, interaction with the
overland internal boundary layer, and continuous shoreline
fumigation.

a. Recommendations for Regulatory Use

OCD has been recommended for use by the Minerals Management
Service for emissions located on the Outer Continental Shelf (50 FR
12248; 28 March 1985). OCD is applicable for overwater sources where
onshore receptors are below the lowest source height. Where onshore
receptors are above the lowest source height, offshore plume
transport and dispersion may be modeled on a case-by-case basis in
consultation with the EPA Regional Office.

b. Input Requirements

Source data: point, area or line source location, pollutant
emission rate, building height, stack height, stack gas temperature,
stack inside diameter, stack gas exit velocity, stack angle from
vertical, elevation of stack base above water surface and gridded
specification of the land/water surfaces. As an option, emission
rate, stack gas exit velocity and temperature can be varied hourly.
Meteorological data (over water): wind direction, wind speed,
mixing height, relative humidity, air temperature, water surface
temperature, vertical wind direction shear (optional), vertical
temperature gradient (optional), turbulence intensities (optional).
Meteorological data (over land): wind direction, wind speed,
temperature, stability class, mixing height.
Receptor data: location, height above local ground-level,
ground-level elevation above the water surface.

c. Output

All input options, specification of sources, receptors and land/
Water map including locations of sources and receptors.
Summary tables of five highest concentrations at each receptor
for each averaging period, and average concentration for entire run
period at each receptor.
Optional case study printout with hourly plume and receptor
characteristics. Optional table of annual impact assessment from
non-permanent activities.
Concentration files written to disk or tape can be used by
ANALYSIS postprocessor to produce the highest concentrations for
each receptor, the cumulative frequency distributions for each
receptor, the tabulation of all concentrations exceeding a given
threshold, and the manipulation of hourly concentration files.

d. Type of Model

OCD is a Gaussian plume model constructed on the framework of
the MPTER model.

e. Pollutant Types

OCD may be used to model primary pollutants. Settling and
deposition are not treated.

f. Source-Receptor Relationship

Up to 250 point sources, 5 area sources, or 1 line source and
180 receptors may be used.
Receptors and sources are allowed at any location.
The coastal configuration is determined by a grid of up to 3600
rectangles. Each element of the grid is designated as either land or
water to identify the coastline.

g. Plume Behavior

As in MPTER, the basic plume rise algorithms are based on
Briggs' recommendations.
Momentum rise includes consideration of the stack angle from the
vertical.
The effect of drilling platforms, ships, or any overwater
obstructions near the source are used to decrease plume rise using a
revised platform downwash algorithm based on laboratory experiments.
Partial plume penetration of elevated inversions is included
using the suggestions of Briggs (1975) and Weil and Brower (1984).
Continuous shoreline fumigation is parametrized using the Turner
method where complete vertical mixing through the thermal internal
boundary layer (TIBL) occurs as soon as the plume intercepts the
TIBL.

h. Horizontal Winds

Constant, uniform wind is assumed for each hour.
Overwater wind speed can be estimated from overland wind speed
using relationship of Hsu (1981).
Wind speed profiles are estimated using similarity theory
(Businger, 1973). Surface layer fluxes for these formulas are
calculated from bulk aerodynamic methods.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Lateral turbulence intensity is recommended as a direct estimate
of horizontal dispersion. If lateral turbulence intensity is not
available, it is estimated from boundary layer theory. For wind
speeds less than 8 m/s, lateral turbulence intensity is assumed
inversely proportional to wind speed.
Horizontal dispersion may be enhanced because of obstructions
near the source. A virtual source technique is used to simulate the
initial plume dilution due to downwash.
Formulas recommended by Pasquill (1976) are used to calculate
buoyant plume enhancement and wind direction shear enhancement.
At the water/land interface, the change to overland dispersion
rates is modeled using a virtual source. The overland dispersion
rates can be calculated from either lateral turbulence intensity or
Pasquill-Gifford curves. The change is implemented where the plume
intercepts the rising internal boundary layer.

k. Vertical Dispersion

Observed vertical turbulence intensity is not recommended as a
direct estimate of vertical dispersion. Turbulence intensity should
be estimated from boundary layer theory as default in the model. For
very stable conditions, vertical dispersion is also a function of
lapse rate.
Vertical dispersion may be enhanced because of obstructions near
the source. A virtual source technique is used to simulate the
initial plume dilution due to downwash.
Formulas recommended by Pasquill (1976) are used to calculate
buoyant plume enhancement.
At the water/land interface, the change to overland dispersion
rates is modeled using a virtual source. The overland dispersion
rates can be calculated from either vertical turbulence intensity or
the Pasquill-Gifford coefficients. The change is implemented where
the plume intercepts the rising internal boundary layer.

l. Chemical Transformation

Chemical transformations are treated using exponential decay.
Different rates can be specified by month and by day or night.

m. Physical Removal

Physical removal is also treated using exponential decay.

n. Evaluation Studies

DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and
Coastal Dispersion Model. Volume I: User's Guide. Sigma Research
Corporation, Westford, MA.
Hanna, S.R., L.L. Schulman, R.J. Paine and J.E. Pleim, 1984. The
Offshore and Coastal Dispersion (OCD) Model User's Guide, Revised.
OCS Study, MMS 84-0069. Environmental Research & Technology, Inc.,
Concord, MA. (NTIS No. PB 86-159803)
Hanna, S.R., L.L. Schulman, R.J. Paine, J.E. Pleim and M. Baer,
1985. Development and Evaluation of the Offshore and Coastal
Dispersion (OCD) Model. Journal of the Air Pollution Control
Association, 35: 1039-1047.
Hanna, S.R. and D.C. DiCristofaro, 1988. Development and
Evaluation of the OCD/API Model. Final Report, API Pub. 4461,
American Petroleum Institute, Washington, D.C.

A.8 Emissions and Dispersion Modeling System (EDMS)

Reference

Segal, H.M., 1991. ``EDMS--Microcomputer Pollution Model for
Civilian Airports and Air Force Bases: User's Guide.'' FAA Report
No. FAA-EE-91-3; USAF Report No. ESL-TR-91-31, Federal Aviation
Administration, 800 Independence Avenue, S.W., Washington, D.C.
20591. (NTIS No. ADA 240528)
Segal, H.M. and Hamilton, P.L., 1988. ``A Microcomputer
Pollution Model for Civilian Airports and Air Force Bases--Model
Description.'' FAA Report No. FAA-EE-88-4; USAF Report No. ESL-TR-
88-53, Federal Aviation Administration, 800 Independence Avenue,
S.W., Washington, D.C. 20591. (NTIS No. ADA 199003)
Segal, H.M., 1988. ``A Microcomputer Pollution Model for
Civilian Airports and Air

[[Page 41873]]

Force Bases--Model Application and Background.'' FAA Report No. FAA-
EE-88-5; USAF Report No. ESL-TR-88-55, Federal Aviation
Administration, 800 Independence Avenue, S.W., Washington, D.C.
20591. (NTIS No. ADA 199794)

Availability

EDMS is available for $40 from: Federal Aviation Administration,
Attn: Ms. Diana Liang, AEE-120, 800 Independence Avenue, S.W.,
Washington, D.C. 20591, Phone: (202) 267-3494.

Abstract

EDMS is a combined emissions/dispersion model for assessing
pollution at civilian airports and military air bases. This model,
which was jointly developed by the Federal Aviation Administration
(FAA) and the United States Air Force (USAF), produces an emission
inventory of all airport sources and calculates concentrations
produced by these sources at specified receptors. The system stores
emission factors for fixed sources such as fuel storage tanks and
incinerators and also for mobile sources such as automobiles or
aircraft. EDMS incorporates an emissions model to calculate an
emission inventory for each airport source and a dispersion model,
the Graphical Input Microcomputer Model (GIMM) (Segal, 1983) to
calculate pollutant concentrations produced by these sources at
specified receptors. The GIMM, which processes point, area, and line
sources, also incorporates a special meteorological preprocessor for
processing up to one year of National Climatic Data Center (NCDC)
hourly data. The model operates in both a screening and refined
mode, accepting up to 170 sources and 10 receptors.

a. Recommendations for Regulatory Use

EDMS is appropriate for the following applications:
Cumulative effect of changes in aircraft operations,
point source and mobile source emissions at airports or air bases;
Simple terrain;
Transport distances less than 50 kilometers; and
1-hour to annual averaging times.

b. Input Requirements

All data are entered through a ``runtime'' version of the Condor
data base which is an integral part of EDMS. Typical entry items are
source and receptor coordinates, percent cold starts, vehicles per
hour, etc. Some point sources, such as heating plants, require stack
height, stack diameter, and effluent temperature inputs.
Wind speed, wind direction, hourly temperature, and Pasquill-
Gifford stability category (P-G) are the meteorological inputs. They
can be entered manually through the EDMS data entry screens or
automatically through the processing of previously loaded NCDC
hourly data.

c. Output

Printed outputs consist of:
A monthly and yearly emission inventory report for each
source entered; and
A concentration summing report for up to 8760 hours
(one year) of data.

d. Type of Model

For its emissions inventory calculations, EDMS uses algorithms
consistent with the EPA Compilation of Air Pollutant Emission
Factors, AP-42. For its dispersion calculations, EDMS uses the GIMM
model which is described in reports FAA-EE-88-4 and FAA-EE-88-5,
referenced above. GIMM uses a Gaussian plume algorithm.

e. Pollutant Types

EDMS inventories and calculates the dispersion of carbon
monoxide, nitrogen oxides, sulphur oxides, hydrocarbons, and
suspended particles.

f. Source-Receptor Relationship

Up to 170 sources and 10 receptors can be treated
simultaneously. Area sources are treated as a series of lines that
are positioned perpendicular to the wind.
Line sources (roadways, runways) are modeled as a series of
points. Terrain elevation differences between sources and receptors
are neglected.
Receptors are assumed to be at ground level.

g. Plume Behavior

Plume rise is calculated for all point sources (heating plants,
incinerators, etc.) using Briggs plume rise equations (Catalano,
1986; Briggs, 1969; Briggs, 1971; Briggs, 1972).
Building and stack tip downwash effects are not treated.
Roadway dispersion employs a modification to the Gaussian plume
algorithms as suggested by Rao and Keenan (1980) to account for
close-in vehicle-induced turbulence.

h. Horizontal Winds

Steady state winds are assumed for each hour. Winds are assumed
to be constant with altitude.
Winds are entered manually by the user or automatically by
reading previously loaded NCC annual data files.

i. Vertical Wind Speed

Vertical wind speed is assumed to be zero.

j. Horizontal Dispersion

Four stability classes are used (P-G classes B through E).
Horizontal dispersion coefficients are computed using a table
look-up and linear interpolation scheme. Coefficients are based on
Pasquill (1976) as adapted by Petersen (1980).
A modified coefficient table is used to account for traffic-
enhanced turbulence near roadways. Coefficients are based upon data
included in Rao and Keenan (1980).

k. Vertical Dispersion

Four stability classes are used (P-G classes B through E).
Vertical dispersion coefficients are computed using a table
look-up and linear interpolation scheme. Coefficients are based on
Pasquill (1976) as adapted by Petersen (1980).
A modified coefficient table is used to account for traffic-
enhanced turbulence near roadways. Coefficients are based upon data
from Roa and Keenan (1980).

l. Chemical Transformation

Chemical transformations are not accounted for.

m. Physical Removal

Deposition is not treated.

n. Evaluation Studies

Segal, H.M. and P.L. Hamilton, 1988. A Microcomputer Pollution
Model for Civilian Airports and Air Force Bases--Model Description.
FAA Report No. FAA-EE-88-4; USAF Report No. ESL-TR-88-53, Federal
Aviation Administration, 800 Independence Avenue, S.W., Washington,
D.C. 20591.
Segal, H.M., 1988. A Microcomputer Pollution Model for Civilian
Airports and Air Force Bases--Model Application and Background. FAA
Report No. FAA-EE-88-5; USAF Report No. ESL-TR-88-55, Federal
Aviation Administration, 800 Independence Avenue, S.W., Washington,
D.C. 20591.

A.9 Complex Terrain Dispersion Model Plus Algorithms for Unstable
Situations (CTDMPLUS)

Reference

Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. Dennis,
M.T. Mills, D.G. Strimaitis, R.J. Yamartino and E.M. Insley, 1989.
User's Guide to the Complex Terrain Dispersion Model Plus Algorithms
for Unstable Situations (CTDMPLUS). Volume 1: Model Descriptions and
User Instructions. EPA Publication No. EPA-600/8-89-041.
Environmental Protection Agency, Research Triangle Park, NC. (NTIS
No. PB 89-181-424)
Paine, R.J., D.G. Strimaitis, M.G. Dennis, R.J. Yamartino, M.T.
Mills and E.M. Insley, 1987. User's Guide to the Complex Terrain
Dispersion Model, Volume 1. EPA Publication No. EPA-600/8-87-058a.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
(NTIS No. PB 88-162169)

Availability

This model code is available on the Support Center for
Regulatory Air Models Bulletin Board System and also on diskette (as
PB 90-504119) from the National Technical Information Service (see
Section A.0).

Abstract

CTDMPLUS is a refined point source Gaussian air quality model
for use in all stability conditions for complex terrain
applications. The model contains, in its entirety, the technology of
CTDM for stable and neutral conditions. However, CTDMPLUS can also
simulate daytime, unstable conditions, and has a number of
additional capabilities for improved user friendliness. Its use of
meteorological data and terrain information is different from other
EPA models; considerable detail for both types of input data is
required and is supplied by preprocessors specifically designed for
CTDMPLUS. CTDMPLUS requires the parameterization of individual hill
shapes using the terrain preprocessor and the association of each
model receptor with a particular hill.

[[Page 41874]]

a. Recommendation for Regulatory Use

CTDMPLUS is appropriate for the following applications:
Elevated point sources;
Terrain elevations above stack top;
Rural or urban areas;
Transport distances less than 50 kilometers; and
One hour to annual averaging times when used with a
post-processor program such as CHAVG.

b. Input Requirements

Source data: For each source, user supplies source location,
height, stack diameter, stack exit velocity, stack exit temperature,
and emission rate; if variable emissions are appropriate, the user
supplies hourly values for emission rate, stack exit velocity, and
stack exit temperature.
Meteorological data: the user must supply hourly averaged values
of wind, temperature and turbulence data for creation of the basic
meteorological data file (``PROFILE''). Meteorological preprocessors
then create a SURFACE data file (hourly values of mixed layer
heights, surface friction velocity, Monin-Obukhov length and surface
roughness length) and a RAWINsonde data file (upper air measurements
of pressure, temperature, wind direction, and wind speed).
Receptor data: receptor names (up to 400) and coordinates, and
hill number (each receptor must have a hill number assigned).
Terrain data: user inputs digitized contour information to the
terrain preprocessor which creates the TERRAIN data file (for up to
25 hills).

c. Output

When CTDMPLUS is run, it produces a concentration file, in
either binary or text format (user's choice), and a list file
containing a verification of model inputs, i.e.,
Input meteorological data from ``SURFACE'' and
``PROFILE''
Stack data for each source
Terrain information
Receptor information
Source-receptor location (line printer map).
In addition, if the case-study option is selected, the listing
includes:
Meteorological variables at plume height
Geometrical relationships between the source and the
hill
Plume characteristics at each receptor, i.e.,
-> distance in along-flow and cross flow direction
-> effective plume-receptor height difference
-> effective y & z values, both flat
terrain and hill induced (the difference shows the effect of the
hill)
-> concentration components due to WRAP, LIFT and FLAT.
If the user selects the TOPN option, a summary table of the top
4 concentrations at each receptor is given. If the ISOR option is
selected, a source contribution table for every hour will be
printed.
A separate disk file of predicted (1-hour only) concentrations
(``CONC'') is written if the user chooses this option. Three forms
of output are possible:
(1) A binary file of concentrations, one value for each receptor
in the hourly sequence as run;
(2) A text file of concentrations, one value for each receptor
in the hourly sequence as run; or
(3) A text file as described above, but with a listing of
receptor information (names, positions, hill number) at the
beginning of the file.
Hourly information provided to these files besides the
concentrations themselves includes the year, month, day, and hour
information as well as the receptor number with the highest
concentration.

d. Type of Model

CTDMPLUS is a refined steady-state, point source plume model for
use in all stability conditions for complex terrain applications.

e. Pollutant Types

CTDMPLUS may be used to model non-reactive, primary pollutants.

f. Source-Receptor Relationship

Up to 40 point sources, 400 receptors and 25 hills may be used.
Receptors and sources are allowed at any location. Hill slopes are
assumed not to exceed 15 deg., so that the linearized equation of
motion for Boussinesq flow are applicable. Receptors upwind of the
impingement point, or those associated with any of the hills in the
modeling domain, require separate treatment.

g. Plume Behavior

As in CTDM, the basic plume rise algorithms are based on Briggs'
(1975) recommendations.
A central feature of CTDMPLUS for neutral/stable conditions is
its use of a critical dividing-streamline height (Hc) to
separate the flow in the vicinity of a hill into two separate
layers. The plume component in the upper layer has sufficient
kinetic energy to pass over the top of the hill while streamlines in
the lower portion are constrained to flow in a horizontal plane
around the hill. Two separate components of CTDMPLUS compute ground-
level concentrations resulting from plume material in each of these
flows.
The model calculates on an hourly (or appropriate steady
averaging period) basis how the plume trajectory (and, in stable/
neutral conditions, the shape) is deformed by each hill. Hourly
profiles of wind and temperature measurements are used by CTDMPLUS
to compute plume rise, plume penetration (a formulation is included
to handle penetration into elevated stable layers, based on Briggs
(1984)), convective scaling parameters, the value of Hc, and
the Froude number above Hc.

h. Horizontal Winds

CTDMPLUS does not simulate calm meteorological conditions. Both
scalar and vector wind speed observations can be read by the model.
If vector wind speed is unavailable, it is calculated from the
scalar wind speed. The assignment of wind speed (either vector or
scalar) at plume height is done by either:
Interpolating between observations above and below the
plume height, or
Extrapolating (within the surface layer) from the
nearest measurement height to the plume height.

i. Vertical Wind Speed

Vertical flow is treated for the plume component above the
critical dividing streamline height (Hc); see ``Plume
Behavior''.

j. Horizontal Dispersion

Horizontal dispersion for stable/neutral conditions is related
to the turbulence velocity scale for lateral fluctuations,
v, for which a minimum value of 0.2 m/s is used.
Convective scaling formulations are used to estimate horizontal
dispersion for unstable conditions.

k. Vertical Dispersion

Direct estimates of vertical dispersion for stable/neutral
conditions are based on observed vertical turbulence intensity,
e.g., w (standard deviation of the vertical velocity
fluctuation). In simulating unstable (convective) conditions,
CTDMPLUS relies on a skewed, bi-Gaussian probability density
function (PDF) description of the vertical velocities to estimate
the vertical distribution of pollutant concentration.

l. Chemical Transformation

Chemical transformation is not treated by CTDMPLUS.

m. Physical Removal

Physical removal is not treated by CTDMPLUS (complete reflection
at the ground/hill surface is assumed).

n. Evaluation Studies

Burns, D.J., L.H. Adams and S.G. Perry, 1990. Testing and
Evaluation of the CTDMPLUS Dispersion Model: Daytime Convective
Conditions. Environmental Protection Agency, Research Triangle Park,
NC.
Paumier, J.O., S.G. Perry and D.J. Burns, 1990. An Analysis of
CTDMPLUS Model Predictions with the Lovett Power Plant Data Base.
Environmental Protection Agency, Research Triangle Park, NC.
Paumier, J.O., S.G. Perry and D.J. Burns, 1992. CTDMPLUS: A
Dispersion Model for Sources near Complex Topography. Part II:
Performance Characteristics. Journal of Applied Meteorology, 31(7):
646-660.

A. REF References

Benson, P.E., 1979. CALINE3--A Versatile Dispersion Model for
Predicting Air Pollution Levels Near Highways and Arterial Streets.
Interim Report, Report Number FHWA/CA/TL-79/23. Federal Highway
Administration, Washington, D.C.
Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission
Critical Review Series, Oak Ridge National Laboratory, Oak Ridge,
TN. (NTIS No. TID-25075)
Briggs, G.A., 1971. Some Recent Analyses of Plume Rise
Observations. Proceedings of the Second International Clean Air
Congress, edited by H.M. Englund and W.T. Berry. Academic Press, New
York, NY.
Briggs, G.A., 1974. Diffusion Estimation for Small Emissions.
USAEC Report ATDL-106. U.S. Atomic Energy Commission, Oak Ridge, TN.

[[Page 41875]]

Briggs, G.A., 1975. Plume Rise Predictions. Lectures on Air
Pollution and Environmental Impact Analyses. American Meteorological
Society, Boston, MA, pp. 59-111.
Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for
the SHORTZ and LONGZ Computer Programs. EPA Publication No. EPA-903/
9-82-004a and b. U.S. Environmental Protection Agency, Region III,
Philadelphia, PA.
Businger, J.A., 1973. Turbulence Transfer in the Atmospheric
Surface Layer. Workshop in Micrometeorology. American Meteorological
Society, Boston, MA, pp. 67-100.
Businger, J.A. and S.P. Arya, 1974. Height of the Mixed Layer in
the Stably Stratified Planetary Boundary Layer. Advances in
Geophysics, Vol. 18A, F.N. Frankiel and R.E. Munn (Eds.), Academic
Press, New York, NY.
Catalano, J.A., 1986. Addendum to the User's Manual for the
Single Source (CRSTER) Model. EPA Publication No. EPA-600/8-86-041.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
(NTIS No. PB 87-145843)
Environmental Protection Agency, 1980. Recommendations on
Modeling (October 1980 Meetings). Appendix G to: Summary of Comments
and Responses on the October 1980 Proposed Revisions to the
Guideline on Air Quality Models. Meteorology and Assessment
Division, Office of Research and Development, Research Triangle
Park, NC.
Gery, M.W., G.Z. Whitten and J.P. Killus, 1988. Development and
Testing of CBM-IV for Urban and Regional Modeling. EPA Publication
No. EPA-600/3-88-012. U.S. Environmental Protection Agency, Research
Triangle Park, NC. (NTIS No. PB 88-180039)
Gery, M.W., G.Z. Whitten, J.P. Killus and M.C. Dodge, 1989. A
Photochemical Kinetics Mechanism for Urban and Regional Scale
Computer Modeling. Journal of Geophysical Research, 94: 12,925-
12,956.
Gifford, F.A., Jr. 1976. Turbulent Diffusion Typing Schemes--A
Review. Nuclear Safety, 17: 68-86.
Horst, T.W., 1983. A Correction to the Gaussian Source-depletion
Model. In Precipitation Scavenging, Dry Deposition and Resuspension.
H. R. Pruppacher, R.G. Semonin and W.G.N. Slinn, eds., Elsevier, NY.
Hsu, S.A., 1981. Models for Estimating Offshore Winds from
Onshore Meteorological Measurements. Boundary Layer Meteorology, 20:
341-352.
Huber, A.H. and W.H. Snyder, 1976. Building Wake Effects on
Short Stack Effluents. Third Symposium on Atmospheric Turbulence,
Diffusion and Air Quality, American Meteorological Society, Boston,
MA.
Irwin, J.S., 1979. A Theoretical Variation of the Wind Profile
Power-Law Exponent as a Function of Surface Roughness and Stability.
Atmospheric Environment, 13: 191-194.
Lamb, R.G. et al., 1977. Continued Research in Mesoscale Air
Pollution Simulation Modeling--Vol. VI: Further Studies in the
Modeling of Microscale Phenomena, Report Number EF77-143. Systems
Applications, Inc., San Rafael, CA.
Liu, M.K. et al., 1976. The Chemistry, Dispersion, and Transport
of Air Pollutants Emitted from Fossil Fuel Power Plants in
California: Data Analysis and Emission Impact Model. Systems
Applications, Inc., San Rafael, CA.
Moore, G.E., T.E. Stoeckenius and D.A. Stewart, 1982. A Survey
of Statistical Measures of Model Performance and Accuracy for
Several Air Quality Model. EPA Publication No. EPA-450/4-83-001.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
Pasquill, F., 1976. Atmospheric Dispersion Parameters in
Gaussian Plume Modeling Part II. Possible Requirements for Change in
the Turner Workbook Values. EPA Publication No. EPA-600/4-76-030b.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
Petersen, W.B., 1980. User's Guide for HIWAY-2 A Highway Air
Pollution Model. EPA Publication No. EPA-600/8-80-018. U.S.
Environmental Protection Agency, Research Triangle Park, NC. (NTIS
PB 80-227556)
Rao, T.R. and M.T. Keenan, 1980. Suggestions for Improvement of
the EPA-HIWAY Model. Journal of the Air Pollution Control
Association, 30: 247-256 (and reprinted as Appendix C in Petersen,
1980).
Segal, H.M., 1983. Microcomputer Graphics in Atmospheric
Dispersion Modeling. Journal of the Air Pollution Control
Association, 23: 598-600.
Turner, D.B., 1969. Workbook of Atmospheric Dispersion
Estimates. PHS Publication No. 999-26. U.S. Environmental Protection
Agency, Research Triangle, Park, NC.
Weil, J.C. and R.P. Brower, 1984. An Updated Gaussian Plume
Model for Tall Stacks. Journal of the Air Pollution Control
Association, 34: 818-827.
Appendix B to Appendix W of Part 61--Summaries of Alternative Air
Quality Models

Table of Contents

B.0 Introduction and Availability
B.1 AVACTA II Model
B.2 Dense Gas Dispersion Model (DEGADIS)
B.3 ERT Visibility Model
B.4 HGSYSTEM
B.5 HOTMAC/RAPTAD
B.6 LONGZ
B.7 Maryland Power Plant Siting Program (PPSP) Model
B.8 Mesoscale Puff Model (MESOPUFF II)
B.9 Mesoscale Transport Diffusion and Deposition Model For
Industrial Sources (MTDDIS)
B.10 Multi-Source (SCSTER) Model
B.11 PANACHE
B.12 PLUME Visibility Model (PLUVUE II)
B.13 Point, Area, Line Source Algorithm (PAL-DS)
B.14 Reactive Plume Model (RPM-IV)
B.15 Shoreline Dispersion Model (SDM)
B.16 SHORTZ
B.17 Simple Line-Source Model
B.18 SLAB
B.19 WYNDvalley Model
B.REF References

B.0 Introduction and Availability

This appendix summarizes key features of refined air quality
models that may be considered on a case-by-case basis for individual
regulatory applications. For each model, information is provided on
availability, approximate cost, regulatory use, data input, output
format and options, simulation of atmospheric physics and accuracy.
The models are listed by name in alphabetical order.
There are three separate conditions under which these models
will normally be approved for use:
1. A demonstration can be made that the model produces
concentration estimates equivalent to the estimates obtained using a
preferred model (e.g., the maximum or high, second-high
concentration is within 2% of the estimate using the comparable
preferred model);
2. A statistical performance evaluation has been conducted using
measured air quality data and the results of that evaluation
indicate the model in Appendix B performs better for the application
than a comparable model in Appendix A; and
3. There is no preferred model for the specific application but
a refined model is needed to satisfy regulatory requirements.
Any one of these three separate conditions may warrant use of
these models. See Section 3.2, Use of Alternative Models, for
additional details.
Many of these models have been subject to a performance
evaluation by comparison with observed air quality data. A summary
of such comparisons for models contained in this appendix is
included in Moore et al. (1982). Where possible, several of the
models contained herein have been subjected to rigorous evaluation
exercises, including (1) statistical performance measures
recommended by the American Meteorological Society and (2) peer
scientific reviews.
A source for some of these models and user's documentation is:
Computer Products, National Technical Information Service (NTIS),
U.S. Department of Commerce, Springfield, VA 22161, Phone: (703)
487-4650. A number of the model codes and selected, abridged user's
guides are also available from the Support Center for Regulatory Air
Models Bulletin Board System^{19 (SCRAM BBS), Telephone (919)
541-5742. The SCRAM BBS is an electronic bulletin board system
designed to be user friendly and accessible from anywhere in the
country. Model users with personal computers are encouraged to use
the SCRAM BBS to download current model codes and text files.

B.1 AVACTA II Model

Reference

Zannetti, P., G. Carboni and R. Lewis, 1985. AVACTA II User's
Guide (Release 3). AeroVironment, Inc., Technical Report AV-OM-85/
520.

Availability

A 3\1/2\'' diskette of the FORTRAN coding and the user's guide
are available at a cost of $3,500 (non-profit organization) or
$5,000 (other organizations) from: AeroVironment, Inc., 222
Huntington Drive, Monrovia, CA 91016, Phone: (818) 357-9983.

[[Page 41876]]

Abstract

The AVACTA II model is a Gaussian model in which atmospheric
dispersion phenomena are described by the evolution of plume
elements, either segments or puffs. The model can be applied for
short time (e.g., one day) simulations in both transport and calm
conditions.
The user is given flexibility in defining the computational
domain, the three-dimensional meteorological and emission input, the
receptor locations, the plume rise formulas, the sigma formulas,
etc. Without explicit user's specifications, standard default values
are assumed.
AVACTA II provides both concentration fields on the user
specified receptor points, and dry/wet deposition patterns
throughout the domain. The model is particularly oriented to the
simulation of the dynamics and transformation of sulfur species
(SO2 and SO4^{=), but can handle virtually any pair of
primary-secondary pollutants.

a. Recommendations for Regulatory Use

AVACTA II can be used if it can be demonstrated to estimate
concentrations equivalent to those provided by the preferred model
for a given application. AVACTA II must be executed in the
equivalent mode.
AVACTA II can be used on a case-by-case basis in lieu of a
preferred model if it can be demonstrated, using the criteria in
Section 3.2, that AVACTA II is more appropriate for the specific
application. In this case the model options/modes which are most
appropriate for the application should be used.

b. Input Requirements

A time-varying input is required at each computational step.
Only those data which have changed need to be input by the user.
Source data requirements are: Coordinates, emission rates of
primary and secondary pollutants, initial plume sigmas (for non-
point sources), exit temperature, exit velocity, stack inside
diameter.
Meteorological data requirements are: surface wind measurements,
wind profiles (if available), atmospheric stability profiles, mixing
heights.
Receptor data requirements are: receptor coordinates.
Other data requirements: coordinates of the computational
domain, grid cell specification, terrain elevations, user's
computational and printing options.

c. Output

The model's output is provided according to user's printing
flags. Hourly, 3-hour and 24-hour concentration averages are
computed, together with highest and highest-second-highest
concentration values. Both partial and total concentrations are
provided.

d. Type of Model

AVACTA II is Gaussian segment/puff model.

e. Pollutant Types

AVACTA II can handle any couple of primary-secondary pollutants
(e.g., SO2 and SO4^{=).

f. Source Receptor Relationship

The AVACTA II approach maintains the basic Gaussian formulation,
but allows a numerical simulation of both nonstationary and
nonhomogeneous meteorological conditions. The emitted pollutant
material is divided into a sequence of ``elements,'' either segments
or puffs, which are connected together but whose dynamics are a
function of the local meteorological conditions. Since the
meteorological parameters vary with time and space, each element
evolves according to the different meteorological conditions
encountered along its trajectory.
AVACTA II calculates the partial contribution of each source in
each receptor during each interval. The partial concentration is the
sum of the contribution of all existing puffs, plus that of the
closest segment.

g. Plume Behavior

The user can select the following plume rise formulas:
Briggs (1969, 1971, 1972)
CONCAWE (Briggs, 1975)
Lucas-Moore (Briggs, 1975)
User's function, i.e., a subroutine supplied by the user
With cold plumes, the program uses a special routine for the
computation of the jet plume rise. The user can also select several
computational options that control plume behavior in complex terrain
and its total/partial reflections.

h. Horizontal Winds

A 3D mass-consistent wind field is optionally generated.

i. Vertical Wind Speed

A 3D mass-consistent wind field is optionally generated.

j. Horizontal Dispersion

During each step, the sigmas of each element are increased. The
user can select the following sigma functions:
Pasquill-Gifford-Turner (in the functional form specified by
Green et al., 1980)
Brookhaven (Gifford, 1975)
Briggs, open country (Gifford, 1975)
Briggs, urban, i.e., McElroy-Pooler (Gifford, 1975)
Irwin (1979a)
LO-LOCAT (MacCready et al., 1974)
User-specified function, by points
User-specified function, with a user's subroutine
The virtual distance/age concept is used for incrementing the
sigmas at each time step.

k. Vertical Dispersion

During each step, the sigmas of each element are increased. The
user can select the following sigma functions:
Pasquill-Gifford-Turner (in the functional form specified by
Green et al., 1980)
Brookhaven (Gifford, 1975)
Briggs, open country (Gifford, 1975)
Briggs, urban, i.e., McElroy-Pooler (Gifford, 1975)
LO-LOCAT (MacCready et al., 1974)
User-specified function, with a user's subroutine
The virtual distance/age concept is used for incrementing the
sigmas at each time step.

l. Chemical Transformation

First order chemical reactions (primary-to-secondary pollutant)

m. Physical Removal

First order dry and wet deposition schemes

n. Evaluation Studies

Zannetti P., G. Carboni and A. Ceriani, 1985. AVACTA II Model
Simulations of Worst-Case Air Pollution Scenarios in Northern Italy.
15th International Technical Meeting on Air Pollution Modeling and
Its Application, St. Louis, Missouri, April 15-19.

B.2 Dense Gas Dispersion Model (DEGADIS)

Reference

Environmental Protection Agency, 1989. User's Guide for the
DEGADIS 2.1--Dense Gas Dispersion Model. EPA Publication No. EPA-
450/4-89-019. U.S. Environmental Protection Agency, Research
Triangle Park, NC 27711. (NTIS No. PB 90-213893)

Availability

The model code is only available on the Support Center for
Regulatory Air Models Bulletin Board System (see Section B.0).

Abstract

DEGADIS 2.1 is a mathematical dispersion model that can be used
to model the transport of toxic chemical releases into the
atmosphere. Its range of applicability includes continuous,
instantaneous, finite duration, and time-variant releases;
negatively-buoyant and neutrally-buoyant releases; ground-level,
low-momentum area releases; ground-level or elevated upwardly-
directed stack releases of gases or aerosols. The model simulates
only one set of meteorological conditions, and therefore should not
be considered applicable over time periods much longer than 1 or 2
hours. The simulations are carried out over flat, level,
unobstructed terrain for which the characteristic surface roughness
is not a significant fraction of the depth of the dispersion layer.
The model does not characterize the density of aerosol-type
releases; rather, the user must assess that independently prior to
the simulation.

a. Recommendations for Regulatory Use

DEGADIS can be used as a refined modeling approach to estimate
short-term ambient concentrations (1-hour or less averaging times)
and the expected area of exposure to concentrations above specified
threshold values for toxic chemical releases. The model is
especially useful in situations where density effects are suspected
to be important and where screening estimates of ambient
concentrations are above levels of concern.

b. Input Requirements

Data may be input directly from an external input file or via
keyboard using an interactive program module. The model is not set
up to accept real-time meteorological

[[Page 41877]]

data or convert units of input values. Chemical property data must
be input by the user. Such data for a few selected species are
available within the model. Additional data may be added to this
data base by the user.
Source data requirements are: emission rate and release
duration; emission chemical and physical properties (molecular
weight, density vs. concentration profile in the case of aerosol
releases, and contaminant heat capacity in the case of a
nonisothermal gas release; stack parameters (i.e., diameter,
elevation above ground level, temperature at release point).
Meteorological data requirements are: wind speed at designated
height above ground, ambient temperature and pressure, surface
roughness, relative humidity, and ground surface temperature (which
in most cases can be adequately approximated by the ambient
temperature).
Receptor data requirements are: averaging time of interest,
above-ground height of receptors, and maximum distance between
receptors (since the model computes downwind receptor distances to
optimize model performance, this parameter is used only for nominal
control of the output listing, and is of secondary importance). No
indoor concentrations are calculated by the model.

c. Output

Printed output includes in tabular form:
Listing of model input data;
Plume centerline elevation, mole fraction,
concentration, density, and temperature at each downwind distance;
y and z values at each
downwind distance;
Off-centerline distances to 2 specified concentration
values at a specified receptor height at each downwind distance
(these values can be used to draw concentration isopleths after
model execution);
Concentration vs. time histories for finite-duration
releases (if specified by user).
The output print file is automatically saved and must be sent to
the appropriate printer by the user after program execution.
No graphical output is generated by the current version of this
program.

d. Type of Model

DEGADIS estimates plume rise and dispersion for vertically-
upward jet releases using mass and momentum balances with air
entrainment based on laboratory and field-scale data. These balances
assume Gaussian similarity profiles for velocity, density, and
concentration within the jet. Ground-level denser-than-air phenomena
is treated using a power law concentration distribution profile in
the vertical and a hybrid top hat-Gaussian concentration
distribution profile in the horizontal. A power law specification is
used for the vertical wind profile. Ground-level cloud slumping
phenomena and air entrainment are based on laboratory measurements
and field-scale observations.

e. Pollutant Types

Neutrally- or negatively-buoyant gases and aerosols. Pollutants
are assumed to be non-reactive and non-depositing.

f. Source-Receptor Relationships

Only one source can be modeled at a time.
There is no limitation to the number of receptors; the downwind
receptor distances are internally-calculated by the model. The
DEGADIS calculation is carried out until the plume centerline
concentration is 50% below the lowest concentration level specified
by the user.
The model contains no modules for source calculations or release
characterization.

g. Plume Behavior

Jet/plume trajectory is estimated from mass and momentum balance
equations. Surrounding terrain is assumed to be flat, and stack tip
downwash, building wake effects, and fumigation are not treated.

h. Horizontal Winds

Constant logarithmic velocity profile which accounts for
stability and surface roughness is used.
The wind speed profile exponent is determined from a least
squares fit of the logarithmic profile from ground level to the wind
speed reference height. Calm winds can be simulated for ground-level
low-momentum releases.
Along-wind dispersion of transient releases is treated using the
methods of Colenbrander (1980) and Beals (1971).

i. Vertical Wind Speed

Not treated.

j. Horizontal Dispersion

When the plume centerline is above ground level, horizontal
dispersion coefficients are based upon Turner (1969) and Slade
(1968) with adjustments made for averaging time and plume density.
When the plume centerline is at ground level, horizontal
dispersion also accounts for entrainment due to gravity currents as
parameterized from laboratory experiments.

k. Vertical Dispersion

When the plume centerline is above ground level, vertical
dispersion coefficients are based upon Turner (1969) and Slade
(1968). Perfect ground reflection is applied.
In the ground-level dense-gas regime, vertical dispersion is
also based upon results from laboratory experiments in density-
stratified fluids.

l. Chemical Transformation

Not specifically treated.

m. Physical Removal

Not treated.

n. Evaluation Studies

Spicer, T.O. and J.A. Havens, 1986. Development of Vapor
Dispersion Models for Nonneutrally Buoyant Gas Mixtures--Analysis of
USAF/N2O4 Test Data. USAF Engineering and Services
Laboratory, Final Report ESL-TR-86-24.
Spicer, T.O. and J.A. Havens, 1988. Development of Vapor
Dispersion Models for Nonneutrally Buoyant Gas Mixtures--Analysis of
TFI/NH3 Test Data. USAF Engineering and Services Laboratory,
Final Report.

o. Operating Information

The model requires either a VAX computer or an IBM--
compatible PC for its execution. The model currently does not
require supporting software. A FORTRAN compiler is required to
generate program executables in the VAX computing environment. PC
executables are provided within the source code; however, a PC
FORTRAN compiler may be used to tailor a PC executable to the user's
PC environment.

B.3 ERT Visibility Model

Reference

ENSR Consulting and Engineering, 1990. ERT Visibility Model:
Version 4; Technical Description and User's Guide. Document M2020-
003. ENSR Consulting and Engineering, 35 Nagog Park, Acton, MA
01720.

Availability

The user's guide and model code on diskette are available as a
package (as PB 96-501978) from the National Technical Information
Service (see Section B.0).

Abstract

The ERT Visibility Model is a Gaussian dispersion model designed
to estimate visibility impairment for arbitrary lines of sight due
to isolated point source emissions by simulating gas-to-particle
conversion, dry deposition, NO to NO2 conversion and linear
radiative transfer.

a. Recommendations for Regulatory Use

There is no specific recommendation at the present time. The ERT
Visibility Model may be used on a case-by-case basis.

b. Input Requirements

Source data requirements are: stack height, stack temperature,
emissions of SO2, NOx, TSP, fraction of NOx as
NO2, fraction of TSP which is carbonaceous, exit velocity, and
exit radius.
Meteorological data requirements are: hourly ambient
temperature, mixing depth, wind speed at stack height, stability
class, potential temperature gradient, and wind direction.
Receptor data requirements are: observer coordinates with
respect to source, latitude, longitude, time zone, date, time of
day, elevation, relative humidity, background visual range, line-of-
sight azimuth and elevation angle, inclination angle of the observed
object, distance from observer to object, object and surface
reflectivity, number and spacing of integral receptor points along
line of sight.
Other data requirements are: ambient concentrations of O3
and NOx, deposition velocity of TSP, sulfate, nitrate, SO2
and NOx, first-order transformation rate for sulfate and
nitrate.

c. Output

Printed output includes both summary and detailed results as
follows: Summary output: Page 1--site, observer and object
parameters; Page 2--optical pollutants and associated extinction
coefficients; Page 3--plume model input parameters; Page 4--total
calculated visual range reduction, and each pollutant's
contribution; Page 5--calculated plume contrast, object contrast and
object contrast degradation at the 550nm wavelength; Page 6--
calculated blue/red ratio and E

[[Page 41878]]

(U*V*W*) values for both sky and object discoloration.
Detailed output: phase functions for each pollutant in four
wavelengths (400, 450, 550, 650nm), concentrations for each
pollutant along sight path, solar geometry contrast parameters at
all wavelengths, intensities, tristimulus values and chromaticity
coordinates for views of the object, sun, background sky and plume.

d. Type of Model

ERT Visibility model is a Gaussian plume model for estimating
visibility impairment.

e. Pollutant Types

Optical activity of sulfate, nitrate (derived from SO2 and
NOx emissions), primary TSP and NO2 is simulated.

f. Source Receptor Relationship

Single source and hour is simulated. Unlimited number of lines-
of-sight (receptors) is permitted per model run.

g. Plume Behavior

Briggs (1971) plume rise equations for final rise are used.

h. Horizontal Wind Field

A single wind speed and direction is specified for each case
study. The wind is assumed to be spatially uniform.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Rural dispersion coefficients from Turner (1969) are used.

k. Vertical Dispersion

Rural dispersion coefficients from Turner (1969) are used.
Mixing height is accounted for with multiple reflection handled by
summation of series near the source, and Fourier representation
farther downwind.

l. Chemical Transformation

First order transformations of sulfates and nitrates are used.

m. Physical Removal

Dry deposition is treated by the source depletion method.

n. Evaluation Studies

Seigneur, C., R.W. Bergstrom and A.B. Hudischewskyj, 1982.
Evaluation of the EPA PLUVUE Model and the ERT Visibility Model
Based on the 1979 VISTTA Data Base. EPA Publication No. EPA-450/4-
82-008. U.S. Environmental Protection Agency, Research Triangle
Park, NC.
White, W.H., C. Seigneur, D.W. Heinold, M.W. Eltgroth, L.W.
Richards, P.T. Roberts, P.S. Bhardwaja, W.D. Conner and W.E. Wilson,
Jr., 1985. Predicting the Visibility of Chimney Plumes: An Inter-
comparison of Four Models with Observations at a Well-Controlled
Power Plant. Atmospheric Environment, 19: 515-528.

B.4 HGSYSTEM

(Dispersion Models for Ideal Gases and Hydrogen Fluoride)

Reference

Post, L. (ed.), 1994. HGSYSTEM 3.0 Technical Reference Manual.
Shell Research Limited, Thornton Research Centre, Chester, United
Kingdom. (TNER 94.059)
Post, L., 1994. HGSYSTEM 3.0 User's Manual. Shell Research
Limited, Thornton Research Centre, Chester, United Kingdom. (TNER
94.059)

Availability

The PC-DOS version of the HGSYSTEM software (HGSYSTEM: Version
3.0, Programs for modeling the dispersion of ideal gas and hydrogen
fluoride releases, executable programs and source code can be
installed from diskettes. These diskettes and all documentation are
available as a package from API [(202) 682-8340] or from NTIS as PB
96-501960 (see Section B.0).

Technical Contacts

Doug N. Blewitt, AMOCO Corporation, 1670 Broadway/MC 2018,
Denver, CO, 80201, (303) 830-5312.
Howard J. Feldman, American Petroleum Institute, 1220 L Street
Northwest, Washington, DC 20005, (202) 682-8340.

Abstract

HGSYSTEM is a PC-based software package consisting of
mathematical models for estimating of one or more consecutive phases
between spillage and near-field and far-field dispersion of a
pollutant. The pollutant can be either a two-phase, multi-compound
mixture of non-reactive compounds or hydrogen fluoride (HF) with
chemical reactions. The individual models are:
Database program:
DATAPROP Generates physical properties used in other HGSYSTEM
models
Source term models:
SPILL Transient liquid release from a pressurized vessel
HFSPILL SPILL version specifically for HF
LPOOL Evaporating multi-compound liquid pool model
Near-field dispersion models:
AEROPLUME High-momentum jet dispersion model
HFPLUME AEROPLUME version specifically for HF
HEGABOX Dispersion of instantaneous heavy gas releases
Far-field dispersion models:
HEGADAS(S,T) Heavy gas dispersion (steady-state and transient
version)
PGPLUME Passive Gaussian dispersion
Utility programs:
HFFLASH Flashing of HF from pressurized vessel
POSTHS/POSTHT Post-processing of HEGADAS(S,T) results
PROFILE Post-processor for concentration contours of airborne
plumes
GET2COL Utility for data retrieval
The models assume flat, unobstructed terrain. HGSYSTEM can be
used to model steady-state, finite-duration, instantaneous and time
dependent releases, depending on the individual model used. The
models can be run consecutively, with relevant data being passed on
from one model to the next using link files. The models can be run
in batch mode or using an iterative utility program.

a. Recommendations for Regulatory Use

HGSYSTEM can be used as a refined model to estimate short-term
ambient concentrations. For toxic chemical releases (non-reactive
chemicals or hydrogen fluoride; 1-hour or less averaging times) the
expected area of exposure to concentrations above specified
threshold values can be determined. For flammable non-reactive gases
it can be used to determine the area in which the cloud may ignite.

b. Input Requirements

HFSPILL input data: reservoir data (temperature, pressure,
volume, HF mass, mass-fraction water), pipe-exit diameter and
ambient pressure.
EVAP input data: spill rate, liquid properties, and evaporation
rate (boiling pool) or ambient data (non-boiling pool).
HFPLUME and PLUME input data: reservoir characteristics,
pollutant parameters, pipe/release data, ambient conditions, surface
roughness and stability class.
HEGADAS input data: ambient conditions, pollutant parameters,
pool data or data at transition point, surface roughness, stability
class and averaging time.
PGPLUME input data: link data provided by HFPLUME and the
averaging time.

c. Output

The HGSYSTEM models contain three post-processor programs which
can be used to extract modeling results for graphical display by
external software packages. GET2COL can be used to extract data from
the model output files. HSPOST can be used to develop isopleths,
extract any 2 parameters for plotting and correct for finite release
duration. HTPOST can be used to produce time history plots.
HFSPILL output data: reservoir mass, spill rate, and other
reservoir variables as a function of time. For HF liquid, HFSPILL
generates link data to HFPLUME for the initial phase of choked
liquid flow (flashing jet), and link data to EVAP for the subsequent
phase of unchoked liquid flow (evaporating liquid pool).
EVAP output data: pool dimensions, pool evaporation rate, pool
mass and other pool variables for steady state conditions or as a
function of time. EVAP generates link data to the dispersion model
HEGADAS (pool dimensions and pool evaporation rate).
HFPLUME and PLUME output data: plume variables (concentration,
width, centroid height, temperature, velocity, etc.) as a function
of downwind distance.
HEGADAS output data: concentration variables and temperature as
a function of downwind distance and (for transient case) time.
PGPLUME output data: concentration as a function of downwind
distance, cross-wind distance and height.

d. Type of Model

HGSYSTEM is made up of four types of dispersion models. HFPLUME
and PLUME simulate the near-field dispersion and PGPLUME simulates
the passive-gas

[[Page 41879]]

dispersion downwind of a transition point. HEGADAS simulates the
ground-level heavy-gas dispersion.

e. Pollutant Types

HGSYSTEM may be used to model non-reactive chemicals or hydrogen
fluoride.

f. Source-Receptor Relationships

HGSYSTEM estimates the expected area of exposure to
concentrations above user-specified threshold values. By imposing
conservation of mass, momentum and energy the concentration,
density, speed and temperature are evaluated as a function of
downwind distance.

g. Plume Behavior

HFPLUME and PLUME: (1) are steady-state models assuming a top-
hat profile with cross-section averaged plume variables; and (2) the
momentum equation is taken into account for horizontal ambient
shear, gravity, ground collision, gravity-slumping pressure forces
and ground-surface drag.
HEGADAS: assumes the heavy cloud to move with the ambient wind
speed, and adopts a power-law fit of the ambient wind speed for the
velocity profile.
PGPLUME: simulates the passive-gas dispersion downwind of a
transition point from HFPLUME or PLUME for steady-state and finite
duration releases.

h. Horizontal Winds

A power law fit of the ambient wind speed is used.

i. Vertical Wind Speed

Not treated.

j. Horizontal Dispersion

HFPLUME and PLUME: Plume dilution is caused by air entrainment
resulting from high plume speeds, trailing vortices in wake of
falling plume (before touchdown), ambient turbulence and density
stratification. Plume dispersion is assumed to be steady and
momentum-dominated, and effects of downwind diffusion and wind
meander (averaging time) are not taken into account.
HEGADAS: This model adopts a concentration similarity profile
expressed in terms of an unknown center-line ground-level
concentration and unknown vertical/cross-wind dispersion parameters.
These quantities are determined from a number of basic equations
describing gas-mass conservation, air entrainment (empirical law
describing vertical top-entrainment in terms of global Richardson
number), cross-wind gravity spreading (initial gravity spreading
followed by gravity-current collapse) and cross-wind diffusion
(Briggs formula).
PGPLUME: This model assumes a Gaussian concentration profile in
which the cross-wind and vertical dispersion coefficients are
determined by empirical expressions. All unknown parameters in this
profile are determined by imposing appropriate matching criteria at
the transition point.

k. Vertical Dispersion

See description above.

l. Chemical Transformation

Not treated.

m. Physical Removal

Not treated.

n. Evaluation Studies

PLUME has been validated against field data for releases of
liquified propane, and wind tunnel data for buoyant and vertically-
released dense plumes. HFPLUME and PLUME have been validated against
field data for releases of HF (Goldfish experiments) and propane
releases. In addition, the plume rise algorithms have been tested
against Hoot, Meroney, and Peterka, Ooms and Petersen databases.
HEGADAS has been validated against steady and transient releases of
liquid propane and LNG over water (Maplin Sands field data), steady
and finite-duration pressurized releases of HF (Goldfish
experiments; linked with HFPLUME), instantaneous release of Freon
(Thorney Island field data; linked with the box model HEGABOX) and
wind tunnel data for steady, isothermal dispersion.
Validation studies are contained in the following references.
McFarlane, K., Prothero, A., Puttock, J.S., Roberts, P.T. and
H.W.M. Witlox, 1990. Development and validation of atmospheric
dispersion models for ideal gases and hydrogen fluoride, Part I:
Technical Reference Manual. Report TNER.90.015. Thornton Research
Centre, Shell Research, Chester, England. [EGG 1067-1151] (NTIS No.
DE 93-000953)
Witlox, H.W.M., McFarlane, K., Rees, F.J. and J.S. Puttock,
1990. Development and validation of atmospheric dispersion models
for ideal gases and hydrogen fluoride, Part II: HGSYSTEM Program
User's Manual. Report TNER.90.016. Thornton Research Centre, Shell
Research, Chester, England. [EGG 1067-1152] (NTIS No. DE 93-000954)

B.5 HOTMAC/RAPTAD

Reference

Mellor, G.L. and T. Yamada, 1974. A Hierarchy of Turbulence
Closure Models for Planetary Boundary Layers. Journal of Atmospheric
Sciences, 31: 1791-1806.
Mellor, G.L. and T. Yamada, 1982. Development of a Turbulence
Closure Model for Geophysical Fluid Problems. Rev. Geophys. Space
Phys., 20: 851-875.
Yamada, T. and S. Bunker, 1988. Development of a Nested Grid,
Second Moment Turbulence Closure Model and Application to the 1982
ASCOT Brush Creek Data Simulation. Journal of Applied Meteorology,
27: 562-578.

Availability

For a cost to be negotiated with the model developer, a \1/4\-
inch data cartridge or a 4mm DAT tape containing the HOTMAC/RAPTAD
computer codes including pre- and post-processors and hard copies of
user manuals (User's Manual, Maintenance Manual, Operations Manual,
Maintenance Interface Manual, Topo Manual, and 3-Dimensional Plume
Manual) are available from YSA Corporation, Rt. 4 Box 81-A, Santa
Fe, NM 87501; Phone: (505) 989-7351; Fax: (505) 989-7965; e-mail:
[email protected]

Abstract

YSA Corporation offers a comprehensive modeling system for
environmental studies. The system includes a mesoscale
meteorological code, a transport and diffusion code, and extensive
Graphical User Interfaces (GUIs). This system is unique because the
diffusion code uses time dependent, three-dimensional winds and
turbulence distributions that are forecasted by a mesoscale weather
prediction model. Consequently the predicted concentration
distributions are more accurate than those predicted by traditional
models when surface conditions are heterogeneous. In general, the
modeled concentration distributions are not Gaussian because winds
and turbulence distributions change considerably in time and space
over complex terrain.
The models were originally developed by using super computers.
However, recent advancement of computer hardware has made it
possible to run complex three-dimensional meteorological models on
desktop workstations. The present versions of the programs are
running on super computers and workstations. GUIs are available on
Sun Microsystems and Silicon Graphics workstations. The modeling
system can also run on a laptop workstation which makes it possible
to run the programs in the field or away from the office. As
technology continues to advance, a version of HOTMAC/RAPTAD suitable
for PC-based platforms will be considered for release by YSA.
HOTMAC, Higher Order Turbulence Model for Atmospheric
Circulation, is a mesoscale weather prediction model that forecasts
wind, temperature, humidity, and atmospheric turbulence
distributions over complex surface conditions. HOTMAC has options to
include non-hydrostatic pressure computation, nested grids, land-use
distributions, cloud, fog, and precipitation physics. HOTMAC can
interface with tower, rawinsonde, and large-scale weather data using
a four-dimensional data assimilation method. RAPTAD, Random Puff
Transport and Diffusion, is a Lagrangian random puff model that is
used to forecast transport and diffusion of airborne materials over
complex terrain. Concentrations are computed by summing the
concentration of each puff at the receptor location. The random puff
method is equivalent to the random particle method with a Gaussian
kernel for particle distribution. The advantage of the puff method
is the accuracy and speed of computation. The particle method
requires the release of a large number of particles which could be
computationally expensive. The puff method requires the release of a
much less number of puffs, typically \1/10\ to \1/100\ of the number
of particles required by the particle method.
The averaging time for concentration estimates is variable from
5 minutes to 15 minutes for each receptor. In addition to the
concentration computation at the receptor sites, RAPTAD computes and
graphically displays hourly concentration contours at the ground
level. RAPTAD is applicable to point and area sources.
The meteorological data produced from HOTMAC are used as input
to RAPTAD. RAPTAD can forecast concentration distributions for
neutrally buoyant gas, buoyant gas and denser-than-air gas. The
models are significantly advanced in both

[[Page 41880]]

their model physics and in their operational procedures. GUIs are
provided to help the user prepare input files, run programs, and
display the modeled results graphically in three dimensions.

a. Recommendation for Regulatory Use

There are no specific recommendations at the present time. The
HOTMAC/RAPTAD modeling system may be used on a case-by-case basis.

b. Input Requirements

Meteorological Data: The modeling system is significantly
different from the majority of regulatory models in terms of how
meteorological data are provided and used in concentration
simulations. Regulatory models use the wind data which are obtained
directly from measurements or analyzed by using a simple constraint
such as a mass conservation equation. Thus, the accuracy of the
computation will depend significantly on the quantity and quality of
the wind data. This approach is acceptable as long as the study area
is flat and the simulation period is short. As the regulations
become more stringent and more realistic surface conditions are
required, a significantly large volume of meteorological data is
required which could become very expensive.
An alternative approach is to augment the measurements with
predicted values from a mesoscale meteorological model. This is the
approach we have taken here. This approach has several advantages
over the conventional method. First, concentration computations use
the model forecast wind while the conventional method extrapolates
the observed winds. Extrapolation of wind data over complex terrain
and for an extended period of time quickly loses its accuracy.
Secondly, the number of stations for upper air soundings is
typically limited from none to at most a few stations in the study
area. The corresponding number in a mesoscale model is the number of
grid points in the horizontal plane which is typically 50 X 50.
Consequently, concentration distributions using model forecasted
winds would be much more accurate than those obtained by using winds
which were extrapolated from the limited number of measurements.
HOTMAC requires meteorological data for initialization and to
provide boundary conditions if the boundary conditions change
significantly with time. The minimum amount of data required to run
HOTMAC is wind and potential temperature profiles at a single
station. HOTMAC forecasts wind and turbulence distributions in the
boundary layer through a set of model equations for solar radiation,
heat energy balance at the ground, conservation of momentum,
conservation of internal energy, and conservation of mass.
Terrain Data: HOTMAC and RAPTAD use the digitized terrain data
from the U.S. Geological Survey and the Defense Mapping Agency.
Extraction of terrain data is greatly simplified by using YSA's GUI
software called Topo. The user specifies the latitudes and
longitudes of the southwest and northeast corner points of the study
area. Then, Topo extracts the digitized elevation data within the
area specified and converts from the latitudes and longitudes to the
UTM (Universal Transverse Mercator) coordinates for up to three
nested grids.
Emission Data: Emission data requirements are emission rate,
stack height, stack diameter, stack location, stack gas exit
velocity, and stack buoyancy.
Receptor Data: Receptor data requirements are names, location
coordinates, and desired averaging time for concentration estimates,
which is variable from 5 to 15 minutes.

c. Output

HOTMAC outputs include hourly winds, temperatures, and
turbulence variables at every grid point. Ancillary codes
graphically display vertical profiles of wind, temperature, and
turbulence variables at selected locations and wind vector
distributions at specified heights above the ground. These codes
also produce graphic files of wind direction projected on vertical
cross sections.
RAPTAD outputs include hourly values of surface concentration,
time variations of mean and standard deviation of concentrations at
selected locations, and coordinates of puff center locations.
Ancillary codes produce color contour plots of surface
concentration, time variations of mean concentrations and ratios of
standard deviation to mean value at selected locations, and
concentration distributions in the vertical cross sections. The
averaging time of concentration at a receptor location is variable
from 5 to 15 minutes. Color contour plots of surface concentration
can be animated on the monitor to review time variations of high
concentration areas.

d. Type of Model

HOTMAC is a 3-dimensional Eulerian model for weather
forecasting, and RAPTAD is a 3-dimensional Lagrangian random puff
model for pollutant transport and diffusion.

e. Pollutant types

RAPTAD may be used to model any inert pollutants, including
dense and buoyant gases.

f. Source-Receptor Relationship

Up to six point or area sources are specified and up to 50
sampling locations are selected. Source and receptor heights are
specified by the user.

g. Plume Behavior

Neutrally buoyant plumes are transported by mean and turbulence
winds that are modeled by HOTMAC. Non-neutrally buoyant plume
equations are based on Van Dop (1992). In general, plumes are non-
Gaussian.

h. Horizontal Winds

RAPTAD uses wind speed, wind direction, and turbulence on a
gridded array that is supplied hourly by HOTMAC. Stability effect
and mixed layer height are incorporated through the intensity of
turbulence which is a function of stability. HOTMAC predicts
turbulence intensity by solving a turbulence kinetic energy equation
and a length scale equation. RAPTAD interpolates winds and
turbulence at puff center locations every 10 seconds from the values
on a gridded array. RAPTAD can also use the winds observed at towers
and by rawinsondes.

i. Vertical Wind Speed

RAPTAD uses vertical winds on a gridded array that are supplied
hourly by HOTMAC. HOTMAC computes vertical wind either by solving an
equation of motion for the vertical wind or a mass conservation
equation. RAPTAD interpolates vertical winds at puff center
locations every 10 seconds from the values on a gridded array.

j. Horizontal Dispersion

Horizontal dispersion is based on the standard deviations of
horizontal winds that are computed by HOTMAC.

k. Vertical Dispersion

Vertical dispersion is based on the standard deviations of
vertical wind that are computed by HOTMAC.

l. Chemical Transformation

HOTMAC can provide meteorological inputs to other models that
handle chemical reactions, e.g., UAM.

m. Physical Removal

Not treated.

n. Evaluation Studies

Yamada, T., S. Bunker and M. Moss, 1992. A Numerical Simulation
of Atmospheric Transport and Diffusion over Coastal Complex Terrain.
Journal of Applied Meteorology, 31: 565-578.
Yamada, T. and T. Henmi, 1994. HOTMAC: Model Performance
Evaluation by Using Project WIND Phase I and II Data. Mesoscale
Modeling of the Atmosphere, American Meteorological Society,
Monograph 47, pp. 123-135.

B.6 LONGZ

Reference

Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for
the SHORTZ and LONGZ Computer Programs, Volumes I and II, EPA
Publication No. EPA-903/9-82-004. U.S. Environmental Protection
Agency, Region III, Philadelphia, PA.

Availability

The computer code is available on the Support Center for
Regulatory Air Models Bulletin Board System and on diskette (as PB
96-501994) from the National Technical Information Service (see
Section B.0).

Abstract

LONGZ utilizes the steady-state univariate Gaussian plume
formulation for both urban and rural areas in flat or complex
terrain to calculate long-term (seasonal and/or annual) ground-level
ambient air concentrations attributable to emissions from up to
14,000 arbitrarily placed sources (stacks, buildings and area
sources). The output consists of the total concentration at each
receptor due to emissions from each user-specified source or group
of sources, including all sources. An option which considers losses
due to deposition (see the description of SHORTZ) is deemed
inappropriate by the authors for complex terrain, and is not
discussed here.

[[Page 41881]]

a. Recommendations for Regulatory Use

LONGZ can be used if it can be demonstrated to estimate
concentrations equivalent to those provided by the preferred model
for a given application. LONGZ must be executed in the equivalent
mode.
LONGZ can be used on a case-by-case basis in lieu of a preferred
model if it can be demonstrated, using the criteria in Section 3.2
of Appendix W, that LONGZ is more appropriate for the specific
application. In this case the model options/modes which are most
appropriate for the application should be used.

b. Input Requirements

Source data requirements are: for point, building or area
sources, location, elevation, total emission rate (optionally
classified by gravitational settling velocity) and decay
coefficient; for stack sources, stack height, effluent temperature,
effluent exit velocity, stack radius (inner), emission rate, and
ground elevation (optional); for building sources, height, length
and width, and orientation; for area sources, characteristic
vertical dimension, and length, width and orientation.
Meteorological data requirements are: wind speed and measurement
height, wind profile exponents, wind direction standard deviations
(turbulent intensities), mixing height, air temperature, vertical
potential temperature gradient.
Receptor data requirements are: coordinates, ground elevation.

c. Output

Printed output includes total concentration due to emissions
from user-specified source groups, including the combined emissions
from all sources (with optional allowance for depletion by
deposition).

d. Type of Model

LONGZ is a climatological Gaussian plume model.

e. Pollutant Types

LONGZ may be used to model primary pollutants. Settling and
deposition are treated.

f. Source-Receptor Relationships

LONGZ applies user specified locations for sources and
receptors. Receptors are assumed to be at ground level.

g. Plume Behavior

Plume rise equations of Bjorklund and Bowers (1982) are used.
Stack tip downwash (Bjorklund and Bowers, 1982) is included.
All plumes move horizontally and will fully intercept elevated
terrain.
Plumes above mixing height are ignored.
Perfect reflection at mixing height is assumed for plumes below
the mixing height.
Plume rise is limited when the mean wind at stack height
approaches or exceeds stack exit velocity.
Perfect reflection at ground is assumed for pollutants with no
settling velocity.
Zero reflection at ground is assumed for pollutants with finite
settling velocity.
LONGZ does not simulate fumigation.
Tilted plume is used for pollutants with settling velocity
specified.
Buoyancy-induced dispersion is treated (Briggs, 1972).

h. Horizontal Winds

Wind field is homogeneous and steady-state.
Wind speed profile exponents are functions of both stability
class and wind speed. Default values are specified in Bjorklund and
Bowers (1982).

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Pollutants are initially uniformly distributed within each wind
direction sector. A smoothing function is then used to remove
discontinuities at sector boundaries.

k. Vertical Dispersion

Vertical dispersion is derived from input vertical turbulent
intensities using adjustments to plume height and rate of plume
growth with downwind distance specified in Bjorklund and Bowers
(1982).

l. Chemical Transformation

Chemical transformations are treated using exponential decay.
Time constant is input by the user.

m. Physical Removal

Gravitational settling and dry deposition of particulates are
treated.

n. Evaluation Studies

Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for
the SHORTZ and LONGZ Computer Programs, Volume I and II. EPA
Publication No. EPA-903/9-82-004. U.S. Environmental Protection
Agency, Region III, Philadelphia, PA.

B.7 Maryland Power Plant Siting Program (PPSP) Model

Reference

Brower, R., 1982. The Maryland Power Plant Siting Program (PPSP)
Air Quality Model User's Guide. Ref. No. PPSP-MP-38. Prepared for
Maryland Department of Natural Resources by Environmental Center,
Martin Marietta Corporation, Baltimore, MD. (NTIS No. PB 82-238387)
Weil, J.C. and R.P. Brower, 1982. The Maryland PPSP Dispersion
Model for Tall Stacks. Ref. No. PPSP-MP-36. Prepared for Maryland
Department of Natural Resources by Environmental Center, Martin
Marietta Corporation, Baltimore, MD. (NTIS No. PB 82-219155)

Availability

The model code and test data are available on diskette for a
nominal cost to defray shipping and handling charges from: Mr. Roger
Brower, Versar, Inc., 9200 Rumsey Road, Columbia, MD 21045; Phone:
(410) 964-9299.

Abstract

PPSP is a Gaussian dispersion model applicable to tall stacks in
either rural or urban areas, but in terrain that is essentially flat
(on a scale large compared to the ground roughness elements). The
PPSP model follows the same general formulation and computer coding
as CRSTER, also a Gaussian model, but it differs in four major ways.
The differences are in the scientific formulation of specific
ingredients or ``sub-models'' to the Gaussian model, and are based
on recent theoretical improvements as well as supporting
experimental data. The differences are: (1) stability during daytime
is based on convective scaling instead of the Turner criteria; (2)
Briggs' dispersion curves for elevated sources are used; (3) Briggs
plume rise formulas for convective conditions are included; and (4)
plume penetration of elevated stable layers is given by Briggs'
(1984) model.

a. Recommendations for Regulatory Use

PPSP can be used if it can be demonstrated to estimate
concentrations equivalent to those provided by the preferred model
for a given application. PPSP must be executed in the equivalent
mode.
PPSP can be used on a case-by-case basis in lieu of a preferred
model if it can be demonstrated, using the criteria in Section 3.2
of Appendix W, that PPSP is more appropriate for the specific
application. In this case the model options/modes which are most
appropriate for the application should be used.

b. Input Requirements

Source data requirements are: emission rate (monthly rates
optional), physical stack height, stack gas exit velocity, stack
inside diameter, stack gas temperature.
Meteorological data requirements are: hourly surface weather
data from the EPA meteorological preprocessor program. Preprocessor
output includes hourly stability class, wind direction, wind speed,
temperature, and mixing height. Actual anemometer height (a single
value) is also required. Wind speed profile exponents (one for each
stability class) are required if on-site data are input.
Receptor data requirements are: distance of each of the five
receptor rings.

c. Output

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

d. Type of Model

PPSP is a Gaussian plume model.

e. Pollutant Types

PPSP may be used to model primary pollutants. Settling and
deposition are not treated.

f. Source-Receptor Relationship

Up to 19 point sources are treated.
All point sources are assumed at the same location.
Unique stack height and stack exit conditions are applied for
each source.

[[Page 41882]]

Receptor locations are restricted to 36 azimuths (every 10
degrees) and five user-specified radial distances.

g. Plume Behavior

Briggs (1975) final rise formulas for buoyant plumes are used.
Momentum rise is not considered.
Transitional or distance-dependent plume rise is not modeled.
Penetration (complete, partial, or zero) of elevated inversions
is treated with Briggs (1984) model; ground-level concentrations are
dependent on degree of plume penetration.

h. Horizontal Winds

Wind speeds are corrected for release height based on power law
variation, with different exponents for different stability classes
and variable reference height (7 meters is default). Wind speed
power law exponents are 0.10, 0.15, 0.20, 0.25, 0.30, and 0.30 for
stability classes A through F, respectively.
Constant, uniform (steady-state) wind assumed within each hour.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Rural dispersion parameters are Briggs (Gifford, 1975), with
stability class defined by u/w* during daytime, and by the method of
Turner (1964) at night.
Urban dispersion is treated by changing all stable cases to
stability class D.
Buoyancy-induced dispersion (Pasquill, 1976) is included (using
/3.5).

k. Vertical Dispersion

Rural dispersion parameters are Briggs (Gifford, 1975), with
stability class defined by u/w* during daytime, and by the method of
Turner (1964).
Urban dispersion is treated by changing all stable cases to
stability class D.
Buoyancy-induced dispersion (Pasquill, 1976) is included (using
/3.5).

l. Chemical Transformation

Not treated.

m. Physical Removal

Not treated.

n. Evaluation Studies

Londergan, R., D. Minott, D. Wackter, T. Kincaid and D.
Bonitata, 1983. Evaluation of Rural Air Quality Simulation Models,
Appendix G: Statistical Tables for PPSP. EPA Publication No. EPA-
450/4-83-003. Environmental Protection Agency, Research Triangle
Park, NC.
Weil, J.C. and R.P. Brower, 1982. The Maryland PPSP dispersion
model for tall stacks. Ref. No. PPSP MP-36. Prepared for Maryland
Department of Natural Resources. Prepared by Environmental Center,
Martin Marietta Corporation, Baltimore, Maryland. (NTIS No. PB 82-
219155)

B.8 Mesoscale Puff Model (MESOPUFF II)

Reference

Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984. User's
Guide to the Mesopuff II Model and Related Processor Programs. EPA
Publication No. EPA-600/8-84-013. U.S. Environmental Protection
Agency, Research Triangle Park, NC. (NTIS No. PB 84-181775)
A Modeling Protocol for Applying MESOPUFF II to Long Range
Transport Problems, 1992. EPA Publication No. EPA-454/R-92-021. U.S.
Environmental Protection Agency, Research Triangle Park, NC.

Availability

This model code is available on the Support Center for
Regulatory Air Models Bulletin Board System and also on diskette (as
PB 93-500247) from the National Technical Information Service (see
Section B.0).

Abstract

MESOPUFF II is a short term, regional scale puff model designed
to calculate concentrations of up to 5 pollutant species (SO2,
SO4, NOX, HNO3, NO3). Transport, puff growth,
chemical transformation, and wet and dry deposition are accounted
for in the model.

a. Recommendations for Regulatory Use

There is no specific recommendation at the present time. The
model may be used on a case-by-case basis.

b. Input Requirements

Required input data include four types: (1) input control
parameters and selected technical options, (2) hourly surface
meteorological data and twice daily upper air measurements, hourly
precipitation data are optional, (3) surface land use classification
information, (4) source and emissions data.
Data from up to 25 surface National Weather Service stations and
up to 10 upper air stations may be considered. Spatially variable
fields at hour intervals of winds, mixing height, stability class,
and relevant turbulence parameters are derived by MESOPAC II, the
meteorological preprocessor program described in the User Guide.
Source and emission data for up to 25 point sources and/or up to
5 area sources can be included. Required information are: location
in grid coordinates, stack height, exit velocity and temperature,
and emission rates for the pollutant to be modeled.
Receptor data requirements: up to a 40 x 40 grid may be used and
non-gridded receptor locations may be considered.

c. Output

Line printer output includes: all input parameters, optionally
selected arrays of ground-level concentrations of pollutant species
at specified time intervals.
Line printer contour plots output from MESOFILE II post-
processor program. Computer readable output of concentration array
to disk/tape for each hour.

d. Type of Model

MESOPUFF II is a Gaussian puff superposition model.

e. Pollutant Types

Up to five pollutant species may be modeled simultaneously and
include: SO2, SO4, NOx, HNO3, NO3.

f. Source-Receptor Relationship

Up to 25 point sources and/or up to 5 area sources are
permitted.

g. Plume Behavior

Briggs (1975) plume rise equations are used, including plume
penetration with buoyancy flux computed in the model.
Fumigation of puffs is considered and may produce immediate
mixing or multiple reflection calculations at user option.

h. Horizontal Winds

Gridded wind fields are computed for 2 layers; boundary layer
and above the mixed layer. Upper air rawinsonde data and hourly
surface winds are used to obtain spatially variable u,v component
fields at hourly intervals. The gridded fields are computed by
interpolation between stations in the MESOPAC II preprocessor.

i. Vertical Wind Speed

Vertical winds are assumed to be zero.

j. Horizontal Dispersion

Incremental puff growth is computed over discrete time steps
with horizontal growth parameters determined from power law
equations fit to sigma y curves of Turner out to 100km. At distances
greater than 100km, puff growth is determined by the rate given by
Heffter (1965).
Puff growth is a function of stability class and changes in
stability are treated. Optionally, user input plume growth
coefficients may be considered.

k. Vertical Dispersion

For puffs emitted at an effective stack height which is less
than the mixing height, uniform mixing of the pollutant within the
mixed layer is performed. For puffs centered above the mixing
height, no effect at the ground occurs.

l. Chemical Transformation

Hourly chemical rate constants are computed from empirical
expressions derived from photochemical model simulations.

m. Physical Removal

Dry deposition is treated with a resistance method.
Wet removal may be considered if hourly precipitation data are
input.

n. Evaluation Studies

Results of tests for some model parameters are discussed in:
Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984.
Development of the MESOPUFF II Dispersion Model. EPA Publication No.
EPA-600/3-84-057. U.S. Environmental Protection Agency, Research
Triangle Park, NC.

[[Page 41883]]

B.9 Mesoscale Transport Diffusion and Deposition Model for Industrial
Sources (MTDDIS)

Reference

Wang, I.T. and T.L. Waldron, 1980. User's Guide for MTDDIS
Mesoscale Transport, Diffusion, and Deposition Model for Industrial
Sources. EMSC6062.1UR(R2). Combustion Engineering, Newbury Park, CA.

Availability

A diskette copy of the FORTRAN coding and the user's guide are
available for a cost of $100 from: Dr. I. T. Wang, Environmental
Modeling & Analysis, 2219 E. Thousand Oaks Blvd., Suite 435,
Thousand Oaks, CA 91362.

Abstract

MTDDIS is a variable-trajectory Gaussian puff model applicable
to long-range transport of point source emissions over level or
rolling terrain. The model can be used to determine 3-hour maximum
and 24-hour average concentrations of relatively nonreactive
pollutants from up to 10 separate stacks.

a. Recommendations for Regulatory Use

There is no specific recommendation at the present time. The
MTDDIS Model may be used on a case-by-case basis.

b. Input Requirements

Source data requirements are: emission rate, physical stack
height, stack gas exit velocity, stack inside diameter, stack gas
temperature, and location.
Meteorological data requirements are: hourly surface weather
data, from up to 10 stations, including cloud ceiling, wind
direction, wind speed, temperature, opaque cloud cover and
precipitation. For long-range applications, user-analyzed daily
mixing heights are recommended. If these are not available, the NWS
daily mixing heights will be used by the program. A single upper air
sounding station for the region is assumed. For each model run, air
trajectories are generated for a 48-hour period, and therefore, the
afternoon mixing height of the day before and the mixing heights of
the day after are also required by the model as input, in order to
generate hourly mixing heights for the modeled period.
Receptor data requirements are: up to three user-specified
rectangular grids.

c. Output

Printed output includes:
Tabulations of hourly meteorological parameters include both
input surface observations and calculated hourly stability classes
and mixing heights for each station;
Printed air trajectories for the two consecutive 24-hour periods
for air parcels generated 4 hours apart starting at 0000 LST; and
3-hour maximum and 24-hour average grid concentrations over
user-specified rectangular grids are output for the second 24-hour
period.

d. Type of Model

MTDDIS is a Gaussian puff model.

e. Pollutant Types

MTDDIS can be used to model primary pollutants. Dry deposition
is treated. Exponential decay can account for some reactions.

f. Source-Receptor Relationship

MTDDIS treats up to 10 point sources.
Up to three rectangular receptor grids may be specified by the
user.

g. Plume Behavior

Briggs (1971, 1972) plume rise formulas are used.
If plume height exceeds mixing height, ground level
concentration is assumed zero.
Fumigation and downwash are not treated.

h. Horizontal Winds

Wind speeds and wind directions at each station are first
corrected for release height. Speed conversions are based on power
law variation and direction conversions are based on linear height
dependence as recommended by Irwin (1979b).
Converted wind speeds and wind directions are then weighted
according to the algorithms of Heffter (1980) to calculate the
effective transport wind speed and direction.

i. Vertical Wind Field

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Transport-time-dependent dispersion coefficients from Heffter
(1980) are used.

k. Vertical Dispersion

Transport-time-dependent dispersion coefficients from Heffter
(1980) are used.

l. Chemical Transformation

Chemical transformations are treated using exponential decay.
Half-life is input by the user.

m. Physical Removal

Dry deposition is treated. User input deposition velocity is
required.
Wet deposition is treated. User input hourly precipitation rate
and precipitation layer depth or cloud ceiling height are required.

n. Evaluation Studies

Carhart, R.A., A.J. Policastro, M. Wastag and L. Coke, 1989.
Evaluation of Eight Short-Term Long-Range Transport Models Using
Field Data. Atmospheric Environment, 23: 85-105.

B.10 Multi-Source (SCSTER) Model

Reference

Malik, M.H. and B. Baldwin, 1980. Program Documentation for
Multi-Source (SCSTER) Model. Program Documentation EN7408SS.
Southern Company Services, Inc., Technical Engineering Systems, 64
Perimeter Center East, Atlanta, GA.

Availability

The SCSTER model and user's manual are available at no charge on
a limited basis through Southern Company Services. The computer code
may be provided on a diskette. Requests should be directed to: Mr.
Stanley S. Vasa, Senior Environmental Specialist, Southern Company
Services, P.O. Box 2625, Birmingham, AL 35202.

Abstract

SCSTER is a modified version of the EPA CRSTER model. The
primary distinctions of SCSTER are its capability to consider
multiple sources that are not necessarily collocated, its enhanced
receptor specifications, its variable plume height terrain
adjustment procedures and plume distortion from directional wind
shear.

a. Recommendations for Regulatory Use

SCSTER can be used if it can be demonstrated to estimate
concentrations equivalent to those provided by the preferred model
for a given application. SCSTER must be executed in the equivalent
mode.
SCSTER can be used on a case-by-case basis in lieu of a
preferred model if it can be demonstrated, using the criteria in
Section 3.2 of Appendix W, that SCSTER is more appropriate for the
specific application. In this case the model options/modes which are
most appropriate for the application should be used.

b. Input Requirements

Source data requirements are: emission rate, stack gas exit
velocity, stack gas temperature, stack exit diameter, physical stack
height, elevation of stack base, and coordinates of stack location.
The variable emission data can be monthly or annual averages.
Meteorological data requirements are: hourly surface weather
data from the EPA meteorological preprocessor program. Preprocessor
output includes hourly stability class wind direction, wind speed,
temperature, and mixing height. Actual anemometer height (a single
value) is optional. Wind speed profile exponents (one for each
stability class) are optional.
Receptor data requirements are: cartesian coordinates and
elevations of individual receptors; distances of receptor rings,
with elevation of each receptor; receptor grid networks, with
elevation of each receptor.
Any combination of the three receptor input types may be used to
consider up to 600 receptor locations.

c. Output

Printed output includes:
Highest and second highest concentrations for the year at each
receptor for averaging times of 1-, 3-, and 24-hours, a user-
selected averaging time which may be 2-12 hours, and a 50 high table
for 1-, 3-, and 24-hours;
Annual arithmetic average at each receptor; and the highest 1-
hour and 24-hour concentrations over the receptor field for each day
considered.
Optional tables of source contributions of individual point
sources at up to 20 receptor locations for each averaging period;
Optional magnetic tape output in either binary or fixed block
format includes:
All 1-hour concentrations.
Optional card/disk output includes for each receptor:
Receptor coordinates; receptor elevation; highest and highest,
second-highest, 1-, 3-,

[[Page 41884]]

and 24-hour concentrations; and annual average concentration.

d. Type of Model

SCSTER is a Gaussian plume model.

e. Pollutant Types

SCSTER may be used to model primary pollutants. Settling and
deposition are not treated.

f. Source-Receptor Relationship

SCSTER can handle up to 60 separate stacks at varying locations
and up to 600 receptors, including up to 15 receptor rings.
User input topographic elevation for each receptor is used.

g. Plume Behavior

SCSTER uses Briggs (1969, 1971, 1972) final plume rise formulas.
Transitional plume rise is optional.
SCSTER contains options to incorporate wind directional shear
with a plume distortion method described in Appendix A of the User's
Guide.
SCSTER provides four terrain adjustments including the CRSTER
full terrain height adjustment and a user-input, stability-dependent
plume path coefficient adjustment for receptors above stack height.

h. Horizontal Winds

Wind speeds are corrected for release height based on power law
exponents from DeMarrais (1959), different exponents for different
stability classes; default reference height of 7m. Default exponents
are 0.10, 0.15, 0.20, 0.25, 0.30, and 0.30 for stability classes A
through F, respectively.
Steady-state wind is assumed within a given hour.
Optional consideration of plume distortion due to user-input,
stability-dependent wind-direction shear gradients.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Rural dispersion coefficients from Turner (1969) are used.
Six stability classes are used.

k. Vertical Dispersion

Rural dispersion coefficients from Turner (1969) are used.
Six stability classes are used.
An optional test for plume height above mixing height before
terrain adjustment is included.

l. Chemical Transformation

Chemical transformations are treated using exponential decay.
Half-life is input by the user.

m. Physical Removal

Physical removal is treated using exponential decay. Half-life
is input by the user.

n. Evaluation Studies

Londergan, R., D. Minott, D. Wackter, T. Kincaid and D.
Bonitata, 1983. Evaluation of Rural Air Quality Simulation Models.
EPA Publication No. EPA-450/4-83-003. U.S. Environmental Protection
Agency, Research Triangle Park, NC.

B.11 PANACHE

Reference

Transoft Group, 1994. User's Guide of Fluidyn-PANACHE, a Three-
Dimensional Deterministic Simulation of Pollutants Dispersion Model
for Complex Terrain; Cary, North Carolina.

Availability

For a cost to be negotiated with the model developer, the
computer code is available from: Transoft US, Inc., 818 Reedy Creek
Road, Cary, NC 27513-3307; Phone: (919) 380-7500, Fax: (919) 380-
7592.

Abstract

PANACHE is an Eulerian (and Lagrangian for particulate matter),
3-dimensional finite volume fluid mechanics code designed to
simulate continuous and short-term pollution dispersion in the
atmosphere, in simple or complex terrain. For single or multiple
sources, pollutant emissions from stack, point, area, volume,
general sources and distant sources are treated. The model
automatically treats obstacles, effects of vegetation and water
bodies, the effects of vertical temperature stratification on the
wind and diffusion fields, and turbulent shear flows caused by
atmospheric boundary layer or terrain effects. The code solves
Navier Stokes equations in a curvilinear mesh espousing the terrain
and obstacles. A 2nd order resolution helps keep the number of cells
limited in case of shearing flow. An initial wind field is computed
by using a Lagrangian multiplier to interpolate wind data collected
on site. The mesh generator, the solver and the numerical schemes
have been adopted for atmospheric flows with or without chemical
reactions. The model code operates on any workstation or IBM--
compatible PC (486 or higher). Gaussian and puff modes are available
in PANACHE for fast, preliminary simulation.

a. Recommendations for Regulatory Use

On a case-by-case basis, PANACHE may be appropriate for the
following types of situations: industrial or urban zone on a flat or
complex terrain, transport distance from a few meters to 50km,
continuous releases with hourly, monthly or annual averaging times,
chemically reactive or non-reactive gases or particulate emissions
for stationary or roadway sources.

b. Input Requirements

Data may be input directly from an external source (e.g., GIS
file) or interactively. The model provides the option to use default
values when input parameters are unavailable.
PANACHE user environment integrates the pre- and post-processor
with the solver. The calculations can be done interactively or in
batch mode. An inverse scheme is provided to estimate missing data
from a few measured values of the wind.
Terrain data requirements:
Location, surface roughness estimates, and altitude
contours.
Location and dimensions of obstacles, forests, fields,
and water bodies.
Source data requirements:
For all types of sources, the exit temperature and plume mass
flow rates and concentration of each of the pollutants are required.
External sources require mass flow rate. For roadways, estimated
traffic volume and vehicular emissions are required.
Meteorological data requirements:
Hourly stability class, wind direction, wind speed, temperature,
cloud cover, humidity, and mixing height data with lapse rate below
and above it.
Primary meteorological variables available from the National
Weather Service can be processed using PCRAMMET (see Section 9.3.3.2
of Appendix W) to an input file.
Data required at the domain boundary:
Wind profile (uniform, log or power law), depending on the
terrain conditions (e.g., residential area, forest, sea, etc.).
Chemical source data requirements:
A database of selected species with specific heats and molecular
weights can be extended by the user. For heavy gases the database
includes a compressibility coefficients table.
Solar reflection:
For natural convection simulation with low wind on a sunny day,
approximate values of temperature for fields, forests, water bodies,
shadows and their variations with the time of the day are determined
automatically.

c. Output

Printed output option: pollutant concentration at receptor
points, and listing of input data (terrain, chemical, weather, and
source data) with turbulence and precision control data.
Graphical output includes: In 3-dimensional perspective or in
any crosswind, downwind or horizontal plane: wind velocity,
pollutant concentration, 3-dimensional isosurface. The profile of
concentration can be obtained along any line on the terrain. The
concentration contours can be either instantaneous or time
integrated for the emission from a source or a source combination. A
special utility is included to help prepare a report or a video
animation. The user can select images, put in annotations, or do
animation.

d. Type of Model

The model uses an Eulerian (and Lagrangian for particulate
matter) 3-dimensional finite volume model solving full Navier-Stokes
equations. The numerical diffusion is low with appropriate
turbulence models for building wakes. A second order resolution may
be sought to limit the diffusion. Gaussian and puff modes are
available. The numerical scheme is self adaptive for the following
situations:
A curvilinear mesh or a chopped Cartesian mesh is
generated automatically or manually;
Thermal and gravity effects are simulated by full
gravity (heavy gases), no gravity (well mixed light gases at ambient
temperature), and Boussinesq approximation methods;
K-diff, K-e or a boundary layer turbulence models are
used for turbulence calculations. The flow behind obstacles such

[[Page 41885]]

as buildings, is calculated by using a modified K-e.
For heavy gases, a 3-dimensional heat conduction from
the ground and a stratification model for heat exchange from the
atmosphere are used (with anisotropic turbulence).
If local wind data are available, an initial wind field
with terrain effects can be computed using a Lagrangian multiplier,
which substantially reduces computation time.

e. Pollutant Types

Scavenging, Acid Rain: A module for water droplets
traveling through a plume considers the absorption and de-absorption
effects of the pollutants by the droplet. Evaporation and chemical
reactions with gases are also taken into account.
Visibility: Predicts plume visibility and surface
deposition of aerosol.
Particulate matter: Calculates settling and dry
deposition of particles based on a Probability Density Function
(PDF) of their diameters. The exchange of mass, momentum and heat
between particles and gas is treated with implicit coupling
procedures.
Ozone formation and dispersion: The photochemical model
computes ozone formation and dispersion at street level in the
presence of sunlight.
Roadway Pollutants: Accounts for heat and turbulence
due to vehicular movement. Emissions are based on traffic volume and
emission factors.
Odor Dispersion: Identifies odor sources for waste
water plants.
Radon Dispersion: Simulates natural radon accumulation
in valleys and mine environments.
PANACHE may also be used in emergency planning and management
for episodic emissions, and fire and soot spread in forested and
urban areas or from combustible pools.

f. Source-Receptor Relationship

Simultaneous use of multiple kinds of sources at user defined
locations. Any number of user defined receptors can identify
pollutants from each source individually.

g. Plume Behavior

The options influencing the behavior are full gravity,
Boussinesq approximation or no gravity.

h. Horizontal Winds

Horizontal wind speed approximations are made only at the
boundaries based on National Weather Service data. Inside the domain
of interest, full Navier-Stokes resolution with natural viscosity is
used for 3-dimensional terrain and temperature dependent wind field
calculation.

i. Vertical Wind Speed

Vertical wind speed approximations are made only at the
boundaries based on National Weather Service data. The domain of
interest is treated as for horizontal winds.

j. Horizontal Dispersion

Diffusion is calculated using appropriate turbulence models. A
2nd order solution for shearing flow can be sought when the number
of meshes is limited between obstacles.

k. Vertical Dispersion

Dispersion by full gravity unless Boussinesq approximation or no
gravity requested. Vertical dispersion is treated as above for
horizontal dispersion.

l. Chemical Transformation

PANCHEM, an atmospheric chemistry module for chemical reactions,
is available. Photochemical reactions are used for tropospheric
ozone calculations.

m. Physical Removal

Physical removal is treated using dry deposition coefficients

n. Evaluation Studies

Goldwire, H.C. Jr, T.G. McRae, G.W. Johnson, D.L. Hipple, R.P.
Koopman, J.W. McClure, L.K. Morris and R.T. Cederhall, 1985. Desert
Tortoise Series Data Report: 1983 Pressurized Ammonia Spills. UCID
20562, Lawrence Livermore National Laboratory; Livermore,
California.
Green, S.R., 1992. Modeling Turbulent Air Flow in a Stand of
Widely Spaced Trees, The PHOENICS Journal of Computational Fluid
Dynamics and Its Applications, 5: 294-312.
Gryning, S.E. and E. Lyck, 1984. Atmospheric Dispersion from
Elevated Sources in an Urban Area: Comparison Between Tracer
Experiments and Model Calculations. Journal of Climate and Applied
Meteorology, 23: 651-660.
Havens, J., T. Spicer, H. Walker and T. Williams, 1995.
Validation of Mathematical Models Using Wind-Tunnel Data Sets for
Dense Gas Dispersion in the Presence of Obstacles. University of
Arkansas, 8th International Symposium-Loss Prevention and Safety
Promotion in the Process Industries; Antwerp, Belgium.
McQuaid, J. (ed), 1985. Heavy Gas Dispersion Trials at Thorney
Island. Proc. of a Symposium held at the University of Sheffield,
Great Britain.
Pavitskiy, N.Y., A.A. Yakuskin and S.V. Zhubrin, 1993. Vehicular
Exhaust Dispersion Around Group of Buildings. The PHOENICS Journal
of Computational Fluid Dynamics and Its Applications, 6: 270-285.
Tripathi, S., 1994. Evaluation of Fluidyn-PANACHE on Heavy Gas
Dispersion Test Case. Seminar on Evaluation of Models of Heavy Gas
Dispersion Organized by European Commission; Mol, Belgium.

B.12 Plume Visibility Model (PLUVUE II)

Reference

Environmental Protection Agency, 1992. User's Manual for the
Plume Visibility Model, PLUVUE II (Revised). EPA Publication No.
EPA-454/B-92-008, (NTIS PB93-188233). U.S. Environmental Protection
Agency, Research Triangle Park, NC.

Availability

This model code is available on the Support Center for
Regulatory Air Models Bulletin Board System and also on diskette (as
PB 90-500778) from the National Technical Information Service (see
Section B.0).

Abstract

The Plume Visibility Model (PLUVUE II) is used for estimating
visual range reduction and atmospheric discoloration caused by
plumes consisting of primary particles, nitrogen oxides and sulfur
oxides emitted from a single emission source. PLUVUE II uses
Gaussian formulations to predict transport and dispersion. The model
includes chemical reactions, optical effects and surface deposition.
Four types of optics calculations are made: horizontal and non-
horizontal views through the plume with a sky viewing background;
horizontal views through the plume with white, gray and black
viewing backgrounds; and horizontal views along the axis of the
plume with a sky viewing background.

a. Recommendations for Regulatory Use

The Plume Visibility Model (PLUVUE II) may be used on a case-by-
case basis as a third level screening model. When applying PLUVUE
II, the following precautions should be taken:
1. Treat the optical effects of NO2 and particles
separately as well as together to avoid cancellation of NO2
absorption with particle scattering.
2. Examine the visual impact of the plume in 0.1 (or 0), 0.5,
and 1.0 times the expected level of particulate matter in the
background air.
3. Examine the visual impact of the plume over the full range of
observer-plume sun angles.
4. The user should consult the appropriate Federal Land Manager
when using PLUVUE II to assess visibility impacts in a Class I area.

b. Input Requirements

Source data requirements are: location and elevation; emission
rates of SO2, NOX, and particulates; flue gas flow rate,
exit velocity, and exit temperature; flue gas oxygen content;
properties (including density, mass median and standard geometric
deviation of radius) of the emitted aerosols in the accumulation
(0.1-1.0m) and coarse (1.0-10.m) size modes; and
deposition velocities for SO2, NOX, coarse mode aerosol,
and accumulations mode aerosol.
Meteorological data requirements are: stability class, wind
direction (for an observer-based run), wind speed, lapse rate, air
temperature, relative humidity, and mixing height.
Other data requirements are: ambient background concentrations
of NOX, NO2, O3, and SO2, and background visual
range of sulfate and nitrate concentrations.
Receptor (observer) data requirements are: location, terrain
elevation at points along plume trajectory, white, gray, and black
viewing backgrounds, the distance from the observer to the terrain
observed behind the plume.

c. Output

Printed output includes plume concentrations and visual effects
at specified downwind distances for calculated or specified lines of
sight.

d. Type of Model

PLUVUE II is a Gaussian plume model. Visibility impairment is
quantified once the

[[Page 41886]]

spectral light intensity has been calculated for the specific lines
of sight. Visibility impairment includes visual range reduction,
plume contrast, relative coloration of a plume to its viewing
background, and plume perceptibility due to its contrast and color
with respect to a viewing background.

e. Pollutant Types

PLUVUE II treats NO, NO2, SO2, H2SO4,
HNO3, O3, primary and secondary particles to calculate
effects on visibility.

f. Source Receptor Relationship

For performing the optics calculations at selected points along
the plume trajectory, PLUVUE II has two modes: plume based and
observer based calculations. The major difference is the orientation
of the viewer to the source and the plume.

g. Plume Behavior

Briggs (1969, 1971, 1972) final plume rise equations are used.

h. Horizontal Winds

User-specified wind speed (and direction for an observer-based
run) are assumed constant for the calculation.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

Constant, uniform (steady-state) wind is assumed for each hour.
Straight line plume transport is assumed to all downwind distances.

k. Vertical Dispersion

Rural dispersion coefficients from Turner (1969) are used, with
no adjustment for surface roughness. Six stability classes are used.

l. Chemical Transformation

The chemistry of NO, NO2, O3, OH, O(^{1D),
SO2, HNO3, and H2SO4 is treated by means of nine
reactions. Steady state approximations are used for radicals and for
the NO/NO2/O3 reactions.

m. Physical Removal

Dry deposition of gaseous and particulate pollutants is treated
using deposition velocities.

n. Evaluation Studies

Bergstrom, R.W., C. Seigneur, B.L. Babson, H.Y. Holman and M.A.
Wojcik, 1981. Comparison of the Observed and Predicted Visual
Effects Caused by Power Plant Plumes. Atmospheric Environment, 15:
2135-2150.
Bergstrom, R.W., Seigneur, C.D. Johnson and L.W. Richards, 1984.
Measurements and Simulations of the Visual Effects of Particulate
Plumes. Atmospheric Environment, 18(10): 2231-2244.
Seigneur, C., R.W. Bergstrom and A.B. Hudischewskyj, 1982.
Evaluation of the EPA PLUVUE Model and the ERT Visibility Model
Based on the 1979 VISTTA Data Base. EPA Publication No. EPA-450/4-
82-008. U.S. Environmental Protection Agency, Research Triangle
Park, NC.
White, W.H., C. Seigneur, D.W. Heinold, M.W. Eltgroth, L.W.
Richards, P.T. Roberts, P.S. Bhardwaja, W.D. Conner and W.E. Wilson,
Jr, 1985. Predicting the Visibility of Chimney Plumes: An Inter-
comparison of Four Models with Observations at a Well-Controlled
Power Plant. Atmospheric Environment, 19: 515-528.

B.13 Point, Area, Line Source Algorithm (PAL-DS)

Reference

Petersen, W.B, 1978. User's Guide for PAL--A Gaussian-Plume
Algorithm for Point, Area, and Line Sources. EPA Publication No.
EPA-600/4-78-013. Office of Research and Development, Research
Triangle Park, NC. (NTIS No. PB 281306)
Rao, K.S. and H.F. Snodgrass, 1982. PAL-DS Model: The PAL Model
Including Deposition and Sedimentation. EPA Publication No. EPA-600/
8-82-023. Office of Research and Development, Research Triangle
Park, NC. (NTIS No. PB 83-117739)

Availability

The computer code is available on diskette (as PB 90-500802)
from the National Technical Information Service (see Section B.0).

Abstract

PAL-DS is an acronym for this point, area, and line source
algorithm and is a method of estimating short-term dispersion using
Gaussian-plume steady-state assumptions. The algorithm can be used
for estimating concentrations of non-reactive pollutants at 99
receptors for averaging times of 1 to 24 hours, and for a limited
number of point, area, and line sources (99 of each type). This
algorithm is not intended for application to entire urban areas but
is intended, rather, to assess the impact on air quality, on scales
of tens to hundreds of meters, of portions of urban areas such as
shopping centers, large parking areas, and airports. Level terrain
is assumed. The Gaussian point source equation estimates
concentrations from point sources after determining the effective
height of emission and the upwind and crosswind distance of the
source from the receptor. Numerical integration of the Gaussian
point source equation is used to determine concentrations from the
four types of line sources. Subroutines are included that estimate
concentrations for multiple lane line and curved path sources,
special line sources (line sources with endpoints at different
heights above ground), and special curved path sources. Integration
over the area source, which includes edge effects from the source
region, is done by considering finite line sources perpendicular to
the wind at intervals upwind from the receptor. The crosswind
integration is done analytically; integration upwind is done
numerically by successive approximations.
The PAL-DS model utilizes Gaussian plume-type diffusion-
deposition algorithms based on analytical solutions of a gradient-
transfer model. The PAL-DS model can treat deposition of both
gaseous and suspended particulate pollutants in the plume since
gravitational settling and dry deposition of the particles are
explicitly accounted for. The analytical diffusion-deposition
expressions listed in this report in the limit when pollutant
settling and deposition velocities are zero, they reduce to the
usual Gaussian plume diffusion algorithms in the PAL model.

a. Recommendations for Regulatory Use

PAL-DS can be used if it can be demonstrated to estimate
concentrations equivalent to those provided by the preferred model
for a given application. PAL-DS must be executed in the equivalent
mode.
PAL-DS can be used on a case-by-case basis in lieu of a
preferred model if it can be demonstrated, using the criteria in
Section 3.2, that PAL-DS is more appropriate for the specific
application. In this case the model options/modes which are most
appropriate for the application should be used.

b. Input Requirements

Source data: point-sources--emission rate, physical stack
height, stack gas temperature, stack gas velocity, stack diameter,
stack gas volume flow, coordinates of stack, initial y
and z; area sources--source strength, size of area
source, coordinates of S.W. corner, and height of area source; and
line sources--source strength, number of lanes, height of source,
coordinates of end points, initial y and
z, width of line source, and width of median. Diurnal
variations in emissions are permitted. When applicable, the settling
velocity and deposition velocity are also permitted.
Meteorological data: wind profile exponents, anemometer height,
wind direction and speed, stability class, mixing height, air
temperature, and hourly variations in emission rate.
Receptor data: receptor coordinates.

c. Output

Printed output includes:
Hourly concentration and deposition flux for each source type at
each receptor; and
Average concentration for up to 24 hours for each source type at
each receptor.

d. Type of Model

PAL-DS is a Gaussian plume model.

e. Pollutant Types

PAL-DS may be used to model non-reactive pollutants.

f. Source-Receptor Relationships

Up to 99 sources of each of 6 source types: point, area, and 4
types of line sources.
Source and receptor coordinates are uniquely defined.
Unique stack height for each source.
Coordinates of receptor locations are user defined.

g. Plume Behavior

Briggs final plume rise equations are used.
Fumigation and downwash are not treated.
If plume height exceeds mixing height, concentrations are
assumed equal to zero.
Surface concentrations are set to zero when the plume centerline
exceeds mixing height.

h. Horizontal Winds

User-supplied hourly wind data are used.

[[Page 41887]]

Constant, uniform (steady-state) wind is assumed within each
hour. Wind is assumed to increase with height.

i. Vertical Wind Speeds

Assumed equal to zero.

j. Horizontal Dispersion

Rural dispersion coefficients from Turner (1969) are used with
no adjustments made for surface roughness.
Six stability classes are used.
Dispersion coefficients (Pasquill-Gifford) are assumed based on
a 3cm roughness height.

k. Vertical Dispersion

Six stability classes are used.
Rural dispersion coefficients from Turner (1969) are used; no
further adjustments are made for variation in surface roughness,
transport or averaging time.
Multiple reflection is handled by summation of series until the
vertical standard deviation equals 1.6 times mixing height. Uniform
vertical mixing is assumed thereafter.

l. Chemical Transformation

Not treated.

m. Physical Removal

PAL-DS can treat deposition of both gaseous and suspended
particulates in the plume since gravitational settling and dry
deposition of the particles are explicitly accounted for.

n. Evaluation Studies

None Cited.

B.14 Reactive Plume Model (RPM-IV)

Reference

Environmental Protection Agency, 1993. Reactive Plume Model IV
(RPM-IV) User's Guide. EPA Publication No. EPA-454/B-93-012. U.S.
Environmental Protection Agency (ESRL), Research Triangle Park, NC.
(NTIS No. PB 93-217412)

Availability

The above report and model computer code are available on the
Support Center for Regulatory Air Models Bulletin Board System. The
model code is also available on diskette (as PB 96-502026) from the
National Technical Information Service (see Section B.0).

Abstract

The Reactive Plume Model, RPM-IV, is a computerized model used
for estimating short-term concentrations of primary and secondary
reactive pollutants resulting from single or, in some special cases,
multiple sources if they are aligned with the mean wind direction.
The model is capable of simulating the complex interaction of plume
dispersion and non-linear photochemistry. If Carbon Mechanism IV
(CBM-IV) is used, emissions must be disaggregated into carbon bond
classes prior to model application. The model can be run on a
mainframe computer, workstation, or IBM-compatible PC with at least
2 megabytes of memory. A major feature of RPM-IV is its ability to
interface with input and output files from EPA's Regional Oxidant
Model (ROM) and Urban Airshed Model (UAM) to provide an internally
consistent set of modeled ambient concentrations for various
pollutant species.

a. Recommendations for Regulatory Use

There is no specific recommendation at the present time. RPM-IV
may be used on a case-by-case basis.

b. Input Requirements

Source data requirements are: emission rates, name, and
molecular weight of each species of pollutant emitted; ambient
pressure, ambient temperature, stack height, stack diameter, stack
exit velocity, stack gas temperature, and location.
Meteorological data requirements are: wind speeds, plume widths
or stability classes, photolytic rate constants, and plume depths or
stability classes.
Receptor data requirements are: downwind distances or travel
times at which calculations are to be made.
Initial concentration of all species is required, and the
specification of downwind ambient concentrations to be entrained by
the plume is optional.

c. Output

Short-term concentrations of primary and secondary pollutants at
either user specified time increments, or user specified downwind
distances.

d. Type of Model

Reactive Gaussian plume model.

e. Pollutant Types

Currently, using the Carbon Bond Mechanism (CBM-IV), 34 species
are simulated (82 reactions), including NO, NO2, O3,
SO2, SO4, five categories of reactive hydrocarbons,
secondary nitrogen compounds, organic aerosols, and radical species.

f. Source-Receptor Relationships

Single point source.
Single area or volume source.
Multiple sources can be simulated if they are lined up along the
wind trajectory.
Predicted concentrations are obtained at a user specified time
increment, or at user specified downwind distances.

g. Plume Behavior

Briggs (1971) plume rise equations are used.

h. Horizontal Winds

User specifies wind speeds as a function of time.

i. Vertical Wind Speed

Not treated.

j. Horizontal Dispersion

User specified plume widths, or user may specify stability and
widths will be computed using Turner (1969).

k. Vertical Dispersion

User specified plume depths, or user may specify stability in
which case depths will be calculated using Turner (1969). Note that
vertical uniformity in plume concentration is assumed.

l. Chemical Transformation

RPM-IV has the flexibility of using any user input chemical
kinetic mechanism. Currently it is run using the chemistry of the
Carbon Bond Mechanism, CBM-IV (Gery et al., 1988). The CBM-IV
mechanism, as incorporated in RPM-IV, utilizes an updated simulation
of PAN chemistry that includes a peroxy-peroxy radical termination
reaction, significant when the atmosphere is NOX-limited (Gery
et al., 1989). As stated above, the current CBM-IV mechanism
accommodates 34 species and 82 reactions focusing primarily on
hydrocarbon/nitrogen oxides and ozone photochemistry.

m. Physical Removal

Not treated.

n. Evaluation Studies

Stewart, D.A. and M-K Liu, 1981. Development and Application of
a Reactive Plume Model. Atmospheric Environment, 15: 2377-2393.

B.15 Shoreline Dispersion Model (SDM)

Reference

PEI Associates, 1988. User's Guide to SDM-A Shoreline Dispersion
Model. EPA Publication No. EPA-450/4-88-017. U.S. Environmental
Protection Agency, Research Triangle Park, NC. (NTIS No. PB 89-
164305)

Availability

The model code is available on the Support Center for Regulatory
Air Models Bulletin Board System (see Section B.0).

Abstract

SDM is a hybrid multi-point Gaussian dispersion model that
calculates source impact for those hours during the year when
fumigation events are expected using a special fumigation algorithm
and the MPTER regulatory model for the remaining hours (see Appendix
A).

a. Recommendations for Regulatory Use

SDM may be used on a case-by-case basis for the following
applications:
Tall stationary point sources located at a shoreline of
any large body of water;
Rural or urban areas;
Flat terrain;
Transport distances less than 50 km;
1-hour to 1-year averaging times.

b. Input Requirements

Source data: location, emission rate, physical stack height,
stack gas exit velocity, stack inside diameter, stack gas
temperature and shoreline coordinates.
Meteorological data: hourly values of mean wind speed within the
Thermal Internal Boundary Layer (TIBL) and at stack height; mean
potential temperature over land and over water; over water lapse
rate; and surface sensible heat flux. In addition to these
meteorological data, SDM access standard NWS surface and upper air
meteorological data through the RAMMET preprocessor.
Receptor data: coordinates for each receptor.

c. Output

Printed output includes the MPTER model output as well as:
special shoreline

[[Page 41888]]

fumigation applicability report for each day and source; high-five
tables on the standard output with ``F'' designation next to the
concentration if that averaging period includes a fumigation event.

d. Type of Model

SDM is hybrid Gaussian model.

e. Pollutant Types

SDM may be used to model primary pollutants. Settling and
deposition are not treated.

f. Source-Receptor Relationships

SDM applies user-specified locations of stationary point sources
and receptors. User input stack height, shoreline orientation and
source characteristics for each source. No topographic elevation is
input; flat terrain is assumed.

g. Plume Behavior

SDM uses Briggs (1975) plume rise for final rise. SDM does not
treat stack tip or building downwash.

h. Horizontal Winds

Constant, uniform (steady-state) wind is assumed for an hour.
Straight line plume transport is assumed to all downwind distances.
Separate wind speed profile exponents (EPA, 1980) for both rural and
urban cases are assumed.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

For the fumigation algorithm coefficients based on Misra (1980)
and Misra and McMillan (1980) are used for plume transport in stable
air above TIBL and based on Lamb (1978) for transport in the
unstable air below the TIBL. An effective horizontal dispersion
coefficient based on Misra and Onlock (1982) is used. For
nonfumigation periods, algorithms contained in the MPTER model are
used (see Appendix A).

k. Vertical Dispersion

For the fumigation algorithm, coefficients based on Misra (1980)
and Misra and McMillan (1980) are used.

l. Chemical Transformation

Chemical transformation is not included in the fumigation
algorithm.

m. Physical Removal

Physical removal is not explicitly treated.

n. Evaluation Studies

Environmental Protection Agency, 1987. Analysis and Evaluation
of Statistical Coastal Fumigation Models. EPA Publication No. EPA-
450/4-87-002. U.S. Environmental Protection Agency, Research
Triangle Park, NC. (NTIS PB 87-175519)

B.16 SHORTZ

Reference

Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for
the SHORTZ and LONGZ Computer Programs, Volumes I and II. EPA
Publication No. EPA-903/9-82-004a and b. U.S. Environmental
Protection Agency, Region III, Philadelphia, PA.

Availability

The computer code is available on the Support Center for
Regulatory Air Models Bulletin Board System and on diskette (as PB
96-501986) from the National Technical Information Service (see
Section B.0).

Abstract

SHORTZ utilizes the steady state bivariate Gaussian plume
formulation for both urban and rural areas in flat or complex
terrain to calculate ground-level ambient air concentrations. The
model can calculate 1-hour, 2-hour, 3-hour etc. average
concentrations due to emissions from stacks, buildings and area
sources for up to 300 arbitrarily placed sources. The output
consists of total concentration at each receptor due to emissions
from each user-specified source or group of sources, including all
sources. If the option for gravitational settling is invoked,
analysis cannot be accomplished in complex terrain without violating
mass continuity.

a. Recommendations for Regulatory Use

SHORTZ can be used if it can be demonstrated to estimate
concentrations equivalent to those provided by the preferred model
for a given application. SHORTZ must be executed in the equivalent
mode.
SHORTZ can be used on a case-by-case basis in lieu of a
preferred model if it can be demonstrated, using the criteria in
Section 3.2, that SHORTZ is more appropriate for the specific
application. In this case the model options/modes which are most
appropriate for the application should be used.

b. Input Requirements

Source data requirements are: for point, building or area
sources, location, elevation, total emission rate (optionally
classified by gravitational settling velocity) and decay
coefficient; for stack sources, stack height, effluent temperature,
effluent exit velocity, stack radius (inner), actual volumetric flow
rate, and ground elevation (optional); for building sources, height,
length and width, and orientation; for area sources, characteristic
vertical dimension, and length, width and orientation.
Meteorological data requirements are: wind speed and measurement
height, wind profile exponents, wind direction, standard deviations
of vertical and horizontal wind directions, (i.e., vertical and
lateral turbulent intensities), mixing height, air temperature, and
vertical potential temperature gradient.
Receptor data requirements are: coordinates, ground elevation.

c. Output

d. Type of Model

SHORTZ is a Gaussian plume model.

e. Pollutant Types

SHORTZ may be used to model primary pollutants. Settling and
deposition of particulates are treated.

f. Source-Receptor Relationships

User specified locations for sources and receptors are used.
Receptors are assumed to be at ground level.

g. Plume Behavior

Plume rise equations of Bjorklund and Bowers (1982) are used.
Stack tip downwash (Bjorklund and Bowers, 1982) is included.
All plumes move horizontally and will fully intercept elevated
terrain.
Plumes above mixing height are ignored.
Perfect reflection at mixing height is assumed for plumes below
the mixing height.
Plume rise is limited when the mean wind at stack height
approaches or exceeds stack exit velocity.
Perfect reflection at ground is assumed for pollutants with no
settling velocity.
Zero reflection at ground is assumed for pollutants with finite
settling velocity.
Tilted plume is used for pollutants with settling velocity
specified. Buoyancy-induced dispersion (Briggs, 1972) is included.

h. Horizontal Winds

Winds are assumed homogeneous and steady-state.
Wind speed profile exponents are functions of both stability
class and wind speed. Default values are specified in Bjorklund and
Bowers (1982).

i. Vertical Wind Speed

Vertical winds are assumed equal to zero.

j. Horizontal Dispersion

Horizontal plume size is derived from input lateral turbulent
intensities using adjustments to plume height, and rate of plume
growth with downwind distance specified in Bjorklund and Bowers
(1982).

k. Vertical Dispersion

Vertical plume size is derived from input vertical turbulent
intensities using adjustments to plume height and rate of plume
growth with downwind distance specified in Bjorklund and Bowers
(1982).

l. Chemical Transformation

Chemical transformations are treated using exponential decay.
Time constant is input by the user.

m. Physical Removal

Settling and deposition of particulates are treated.

n. Evaluation Studies

Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for
the SHORTZ and LONGZ Computer Programs. EPA Publication No. EPA-903/
9-82-004. EPA Environmental Protection Agency, Region III,
Philadelphia, PA.
Wackter, D. and R. Londergan, 1984. Evaluation of Complex
Terrain Air Quality Simulation Models. EPA Publication No. EPA-450/
4-84-017. U.S. Environmental

[[Page 41889]]

Protection Agency, Research Triangle Park, NC.

B.17 Simple Line-Source Model

Reference

Chock, D.P., 1980. User's Guide for the Simple Line-Source Model
for Vehicle Exhaust Dispersion Near a Road. Ford Research
Laboratory, Dearborn, MI.

Availability

Copies of the above reference are available without charge from:
Dr. D.P. Chock, Ford Research Laboratory, P.O. Box 2053; MD-3083,
Dearborn, MI 48121-2053. The short model algorithm is contained in
the User's Guide.

Abstract

The Simple Line-Source Model is a simple steady-state Gaussian
plume model which can be used to determine hourly (or half-hourly)
averages of exhaust concentrations within 100m from a roadway on a
relatively flat terrain. The model allows for plume rise due to the
heated exhaust, which can be important when the crossroad wind is
very low. The model also utilizes a new set of vertical dispersion
parameters which reflects the influence of traffic-induced
turbulence.

a. Recommendations for Regulatory Use

The Simple Line-Source Model can be used if it can be
demonstrated to estimate concentrations equivalent to those provided
by the preferred model for a given application. The model must be
executed in the equivalent mode.
The Simple Line-Source Model can be used on a case-by-case basis
in lieu of a preferred model if it can be demonstrated, using
criteria in Section 3.2, that it is more appropriate for the
specific application. In this case the model options/modes which are
most appropriate for the application should be used.

b. Input Requirements

Source data requirements are: emission rate per unit length per
lane, the number of lanes on each road, distances from lane centers
to the receptor, source and receptor heights.
Meteorological data requirements are: buoyancy flux, ambient
stability condition, ambient wind and its direction relative to the
road.
Receptor data requirements are: distance and height above
ground.

c. Output

Printed output includes hourly or (half-hourly) concentrations
at the receptor due to exhaust emission from a road (or a system of
roads by summing the results from repeated model applications).

d. Type of Model

The Simple Line-Source Model is a Gaussian plume model.

e. Pollutant Types

The Simple Line-Source Model can be used to model primary
pollutants. Settling and deposition are not treated.

f. Source-Receptor Relationship

The Simple Line-Source Model treats arbitrary location of line
sources and receptors.

g. Plume Behavior

Plume-rise formula adequate for a heated line source is used.

h. Horizontal Winds

The Simple Line-Source Model uses user-supplied hourly (or half-
hourly) ambient wind speed and direction. The wind measurements are
from a height of 5 to 10m.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to zero.

j. Dispersion Parameters

Horizontal dispersion parameter is not used.

k. Vertical Dispersion

A vertical dispersion parameter is used which is a function of
stability and wind-road angle. Three stability classes are used:
unstable, neutral and stable. The parameters take into account the
effect of traffic-generated turbulence (Chock, 1980).

l. Chemical Transformation

Not treated.

m. Physical Removal

Not treated.

n. Evaluation Studies

Chock, D.P., 1978. A Simple Line-Source Model for Dispersion
Near Roadways. Atmospheric Environment, 12: 823-829.
Sistla, G., P. Samson, M. Keenan and S.T. Rao, 1979. A Study of
Pollutant Dispersion Near Highways. Atmospheric Environment, 13:
669-685.

B.18 SLAB

Reference:

Ermak, D.L., 1990. User's Manual for SLAB: An Atmospheric
Dispersion Model for Denser-than-Air Releases (UCRL-MA-105607),
Lawrence Livermore National Laboratory.

Availability

The computer code can be obtained from: Energy Science and
Technology Center, P.O. Box 1020, Oak Ridge, TN 37830, Phone (615)
576-2606.
The User's Manual (as DE 91-008443) can be obtained from the
National Technical Information Service. The computer code is also
available on the Support Center for Regulatory Air Models Bulletin
Board System (Public Upload/ Download Area; see Section B.0.)

Abstract

The SLAB model is a computer model, PC-based, that simulates the
atmospheric dispersion of denser-than-air releases. The types of
releases treated by the model include a ground-level evaporating
pool, an elevated horizontal jet, a stack or elevated vertical jet
and an instantaneous volume source. All sources except the
evaporating pool may be characterized as aerosols. Only one type of
release can be processed in any individual simulation. Also, the
model simulates only one set of meteorological conditions; therefore
direct application of the model over time periods longer than one or
two hours is not recommended.

a. Recommendations for use

The SLAB model should be used as a refined model to estimate
spatial and temporal distribution of short-term ambient
concentration (e.g., 1-hour or less averaging times) and the
expected area of exposure to concentrations above specified
threshold values for toxic chemical releases where the release is
suspected to be denser than the ambient air.

b. Input Requirements

The SLAB model is executed in the batch mode. Data are input
directly from an external input file. There are 29 input parameters
required to run each simulation. These parameters are divided into 5
categories by the user's guide: source type, source properties,
spill properties, field properties, and meteorological parameters.
The model is not designed to accept real-time meteorological data or
convert units of input values. Chemical property data are not
available within the model and must be input by the user. Some
chemical and physical property data are available in the user's
guide.
Source type is chosen as one of the following: evaporating pool
release, horizontal jet release, vertical jet or stack release, or
instantaneous or short duration evaporating pool release.
Source property data requirements are physical and chemical
properties (molecular weight, vapor heat capacity at constant
pressure; boiling point; latent heat of vaporization; liquid heat
capacity; liquid density; saturation pressure constants), and
initial liquid mass fraction in the release.
Spill properties include: source temperature, emission rate,
source dimensions, instantaneous source mass, release duration, and
elevation above ground level.
Required field properties are: desired concentration averaging
time, maximum downwind distance (to stop the calculation), and four
separate heights at which the concentration calculations are to be
made.
Meteorological parameter requirements are: ambient measurement
height, ambient wind speed at designated ambient measurement height,
ambient temperature, surface roughness, relative humidity,
atmospheric stability class, and inverse Monin-Obukhov length
(optional, only used as an input parameter when stability class is
unknown).

c. Output

No graphical output is generated by the current version of this
program. The output print file is automatically saved and must be
sent to the appropriate printer by the user after program execution.
Printed output includes in tabular form:
Listing of model input data;
Instantaneous spatially-averaged cloud parameters--time,
downwind distance, magnitude of peak concentration, cloud dimensions
(including length for puff-type

[[Page 41890]]

simulations), volume (or mole) and mass fractions, downwind
velocity, vapor mass fraction, density, temperature, cloud velocity,
vapor fraction, water content, gravity flow velocities, and
entrainment velocities;
Time-averaged cloud parameters--parameters which may be used
externally to calculate time-averaged concentrations at any location
within the simulation domain (tabulated as functions of downwind
distance);
Time-averaged concentration values at plume centerline and at
five off-centerline distances (off-centerline distances are
multiples of the effective cloud half-width, which varies as a
function of downwind distance) at four user-specified heights and at
the height of the plume centerline.

d. Type of Model

As described by Ermak (1989), transport and dispersion are
calculated by solving the conservation equations for mass, species,
energy, and momentum, with the cloud being modeled as either a
steady-state plume, a transient puff, or a combination of both,
depending on the duration of the release. In the steady-state plume
mode, the crosswind-averaged conservation equations are solved and
all variables depend only on the downwind distance. In the transient
puff mode, the volume-averaged conservation equations are solved,
and all variables depend only on the downwind travel time of the
puff center of mass. Time is related to downwind distance by the
height-averaged ambient wind speed. The basic conservation equations
are solved via a numerical integration scheme in space and time.

e. Pollutant Types

Pollutants are assumed to be non-reactive and non-depositing
dense gases or liquid-vapor mixtures (aerosols). Surface heat
transfer and water vapor flux are also included in the model.

f. Source-Receptor Relationships

Only one source can be modeled at a time.
There is no limitation to the number of receptors; the downwind
receptor distances are internally-calculated by the model. The SLAB
calculation is carried out up to the user-specified maximum downwind
distance.
The model contains submodels for the source characterization of
evaporating pools, elevated vertical or horizontal jets, and
instantaneous volume sources.

g. Plume Behavior

Plume trajectory and dispersion is based on crosswind-averaged
mass, species, energy, and momentum balance equations. Surrounding
terrain is assumed to be flat and of uniform surface roughness. No
obstacle or building effects are taken into account.

h. Horizontal Winds

A power law approximation of the logarithmic velocity profile
which accounts for stability and surface roughness is used.

i. Vertical Wind Speed

Not treated.

j. Vertical Dispersion

The crosswind dispersion parameters are calculated from formulas
reported by Morgan et al. (1983), which are based on experimental
data from several sources. The formulas account for entrainment due
to atmospheric turbulence, surface friction, thermal convection due
to ground heating, differential motion between the air and the
cloud, and damping due to stable density stratification within the
cloud.

k. Horizontal Dispersion

The horizontal dispersion parameters are calculated from
formulas similar to those described for vertical dispersion, also
from the work of Morgan et al. (1983).

l. Chemical Transformation

The thermodynamics of the mixing of the dense gas or aerosol
with ambient air (including water vapor) are treated. The
relationship between the vapor and liquid fractions within the cloud
is treated using the local thermodynamic equilibrium approximation.
Reactions of released chemicals with water or ambient air are not
treated.

m. Physical Removal

Not treated.

n. Evaluation Studies

Blewitt, D.N., J.F. Yohn and D.L. Ermak, 1987. An Evaluation of
SLAB and DEGADIS Heavy Gas Dispersion Models Using the HF Spill Test
Data. Proceedings, AIChE International Conference on Vapor Cloud
Modeling, Boston, MA, November, pp. 56-80.
Ermak, D.L., S.T. Chan, D.L. Morgan and L.K. Morris, 1982. A
Comparison of Dense Gas Dispersion Model Simulations with Burro
Series LNG Spill Test Results. J. Haz. Matls., 6: 129-160.
Zapert, J.G., R.J. Londergan and H. Thistle, 1991. Evaluation of
Dense Gas Simulation Models. EPA Publication No. EPA-450/4-90-018.
U.S. Environmental Protection Agency, Research Triangle Park, NC.

B.19 WYNDvalley Model

Reference

Harrison, Halstead, 1992. ``A User's Guide to WYNDvalley 3.11,
an Eulerian-Grid Air-Quality Dispersion Model with Versatile
Boundaries, Sources, and Winds,'' WYNDsoft Inc., Mercer Island, WA.

Availability

Copies of the user's guide and the executable model computer
codes are available at a cost of $295.00 from: WYNDsoft,
Incorporated, 6333 77th Avenue, Mercer Island, WA 98040, Phone:
(206) 232-1819.

Abstract

WYNDvalley 3.11 is a multi-layer (up to five vertical layers)
Eulerian grid dispersion model that permits users flexibility in
defining borders around the areas to be modeled, the boundary
conditions at these borders, the intensities and locations of
emissions sources, and the winds and diffusivities that affect the
dispersion of atmospheric pollutants. The model's output includes
gridded contour plots of pollutant concentrations for the highest
brief episodes (during any single time step), the highest and
second-highest 24-hour averages, averaged dry and wet deposition
fluxes, and a colored ``movie'' showing evolving dispersal of
pollutant concentrations, together with temporal plots of the
concentrations at specified receptor sites and statistical inference
of the probabilities that standards will be exceeded at those sites.
WYNDvalley is implemented on IBM compatible microcomputers, with
interactive data input and color graphics display.

a. Recommendations for Regulatory Use

WYNDvalley may be used on a case-by-case basis to estimate
concentrations during valley stagnation periods of 24 hours or
longer. Recommended inputs are listed below.

------------------------------------------------------------------------
Variable Recommended value
------------------------------------------------------------------------
Horizontal cell dimension................. 250 to 500 meters.
Vertical layers........................... 3 to 5.
Layer depth............................... 50 to 100 meters.
Background (internal to model)............ Zero (background should be
added externally to model
estimates).
Lateral meander velocity.................. Default.
Diffusivities............................. Default.
Ventilation parameter (upper boundary Default.
condition).
Dry deposition velocity................... Zero (site-specific).
Washout ratio............................. Zero (site-specific).
------------------------------------------------------------------------

b. Input Requirements

Input data, including model options, modeling domain boundaries,
boundary conditions, receptor locations, source locations, and
emission rates, may be entered interactively, or through existing
template files from a previous run. Meteorological data, including
wind speeds, wind directions, rain rates (optionally, for wet
deposition calculations), and time of day and year, may be of
arbitrary time increment (usually an hour) and are entered into the
model through an external meteorological data file. Optionally,
users may specify diffusivities and upper boundary conditions for
each time increment. Source emission rates may be constant or
modulated on a daily, weekly, and/or seasonal basis.

c. Output

Output from WYNDvalley includes gridded contour maps of the
highest pollutant concentrations at each time step and the highest
and second-highest 24-hour average concentrations. Output also
includes the deposition patterns for wet, dry, and total fluxes of
the pollutants to the surface, integrated over the simulation
period. A running ``movie'' of the concentration patterns is
displayed on the screen (with optional printout) as they evolve
during the simulation. Output files include tables of daily-averaged
pollutant concentrations at

[[Page 41891]]

every modeled grid cell, and of hourly concentrations at up to eight
specified receptors. Statistical analyses are performed on the
hourly and daily data to estimate the probabilities that specified
levels will be exceeded more than once during an arbitrary number of
days with similar weather.

d. Type of Model

WYNDvalley is a three dimensional Eulerian grid model.

e. Pollutant Types

WYNDvalley may be used to model any inert pollutant.

f. Source-Receptor Relationships

Source and receptors may be located anywhere within the user-
defined modeling domain. All point and area sources, or portions of
an area source, within a given grid cell are summed to define a
representative emission rate for that cell. Concentrations are
calculated for each and every grid cell in the modeling domain. Up
to eight grid cells may be selected as receptors, for which time
histories of concentration and deposition fluxes are determined, and
probabilities of exceedance are calculated.

g. Plume Behavior

Emissions for buoyant point sources are placed by the user in a
grid cell which best reflects the expected effective plume height
during stagnation conditions. Five vertical layers are available to
the user.

h. Horizontal Winds

During each time step in the model, the winds are assumed to be
uniform throughout the modeling domain. Numerical diffusion is
minimized in the advection algorithm. To account for terrain effects
on winds and dispersion, an ad hoc algorithm is employed in the
model to distribute concentrations near boundaries.

i. Vertical Wind Speed

Winds are assumed to be constant with height.

j. Horizontal Dispersion

Horizontal eddy diffusion coefficients may be entered explicitly
by the user at every time step. Alternatively, a default algorithm
may be invoked to estimate these coefficients from the wind
velocities and their variances.

k. Vertical Dispersion

Vertical eddy diffusion coefficients and a top-of-model boundary
condition may be entered explicitly by the user at every time step.
Alternatively, a default algorithm may be invoked to estimate these
coefficients from the horizontal wind velocities and their
variances, and from an empirical time-of-day correction derived from
temperature gradient measurements and Monin-Obukhov similarities.

l. Chemical Transformation

Chemical transformation is not explicitly treated by WYNDvalley.

m. Physical Removal

WYNDvalley optionally simulates both wet and dry deposition. Dry
deposition is proportional to concentration in the lowest layer,
while wet deposition is proportional to rain rate and concentration
in each layer. Appropriate coefficients (deposition velocities and
washout ratios) are input by the user.

n. Evaluation Studies

Harrison, H., G. Pade, C. Bowman and R. Wilson, 1990. Air
Quality During Stagnations: A Comparison of RAM and WYNDvalley with
PM-10 Measurements at Five Sites. Journal of the Air & Waste
Management Association, 40: 47-52.
Maykut, N. et al., 1990. Evaluation of the Atmospheric
Deposition of Toxic Contaminants to Puget Sound. State of
Washington, Puget Sound Water Quality Authority, Seattle, WA.
Yoshida, C., 1990. A Comparison of WYNDvalley Versions 2.12 and
3.0 with PM-10 Measurements in Six Cities in the Pacific Northwest.
Lane Regional Air Pollution Authority, Springfield, OR.

B. REF References

Beals, G.A., 1971. A Guide to Local Dispersion of Air
Pollutants. Air Weather Service Technical Report #214 (April 1971).
Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for
the SHORTZ and LONGZ Computer Programs. EPA Publication No. EPA-903/
9-82-004a and b. U.S. Environmental Protection Agency, Region III,
Philadelphia, PA.
Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission
Critical Review Series, Oak Ridge National Laboratory, Oak Ridge,
TN. (NTIS No. TID-25075)
Briggs, G.A., 1971. Some Recent Analyses of Plume Rise
Observations. Proceedings of the Second International Clean Air
Congress, edited by H.M. Englund and W.T. Berry. Academic Press, New
York, NY.
Briggs, G.A., 1972. Discussion on Chimney Plumes in Neutral and
Stable Surroundings. Atmospheric Environment, 6: 507-510.
Briggs, G.A., 1974. Diffusion Estimation for Small Emissions.
USAEC Report ATDL-106. U.S. Atomic Energy Commission, Oak Ridge, TN.
Briggs, G.A., 1975. Plume Rise Predictions. Lectures on Air
Pollution and Environmental Impact Analyses. American Meteorological
Society, Boston, MA, pp. 59-111.
Briggs, G.A., 1984. Plume Rise and Buoyancy Effects. Atmospheric
Science and Power Production, Darryl Randerson (Ed.). DOE Report
DOE/TIC-27601, Technical Information Center, Oak Ridge, TN. (NTIS
No. DE84005177)
Carpenter, S.B., T.L. Montgomery, J.M. Leavitt, W.C. Colbaugh
and F.W. Thomas, 1971. Principal Plume Dispersion Models: TVA Power
Plants. Journal of Air Pollution Control Association, 21: 491-495.
Chock, D.P., 1980. User's Guide for the Simple Line-Source Model
for Vehicle Exhaust Dispersion Near a Road. Environmental Science
Department, General Motors Research Laboratories, Warren, MI.
Colenbrander, G.W., 1980. A Mathematical Model for the Transient
Behavior of Dense Vapor Clouds, 3rd International Symposium on Loss
Prevention and Safety Promotion in the Process Industries, Basel,
Switzerland.
DeMarrais, G.A., 1959. Wind Speed Profiles at Brookhaven
National Laboratory. Journal of Applied Meteorology, 16: 181-189.
Ermak, D.L., 1989. A Description of the SLAB Model, presented at
JANNAF Safety and Environmental Protection Subcommittee Meeting, San
Antonio, TX, April, 1989.
Gery, M.W., G.Z. Whitten and J.P. Killus, 1988. Development and
Testing of CBM-IV for Urban and Regional Modeling. EPA Publication
No. EPA-600/3-88-012. U.S. Environmental Protection Agency, Research
Triangle Park, NC. (NTIS No. PB 88-180039)
Gery, M.W., G.Z. Whitten, J.P. Killus and M.C. Dodge, 1989. A
Photochemical Kinetics Mechanism for Urban and Regional Scale
Computer Modeling. Journal of Geophysical Research, 94: 12,925-
12,956.
Gifford, F.A. and S.R. Hanna, 1970. Urban Air Pollution
Modeling. Proceedings of the Second International Clean Air
Congress, Academic Press, Washington, D.C.; pp. 140-1151.
Gifford, F.A., 1975. Atmospheric Dispersion Models for
Environmental Pollution Applications. Lectures on Air Pollution and
Environmental Impact Analyses. American Meteorological Society,
Boston, MA.
Green, A.E., Singhal R.P. and R. Venkateswar, 1980. Analytical
Extensions of the Gaussian Plume Model. Journal of the Air Pollution
Control Association, 30: 773-776.
Heffter, J.L., 1965. The Variations of Horizontal Diffusion
Parameters with Time for Travel Periods of One Hour or Longer.
Journal of Applied Meteorology, 4: 153-156.
Heffter, J.L., 1980. Air Resources Laboratories Atmospheric
Transport and Dispersion Model (ARL-ATAD). NOAA Technical Memorandum
ERL ARL-81. Air Resources Laboratories, Silver Spring, MD.
Irwin, J.S., 1979a. Estimating Plume Dispersion--A Recommended
Generalized Scheme. Fourth Symposium on Turbulence, Diffusion and
Air Pollution, Reno, Nevada.
Irwin, J.S., 1979b. A Theoretical Variation of the Wind Profile
Power-Law Exponent as a Function of Surface Roughness and Stability.
Atmospheric Environment, 13: 191-194.
MacCready, P.B., Baboolal, L.B. and P.B. Lissaman, 1974.
Diffusion and Turbulence Aloft Over Complex Terrain. Preprint
Volume, AMS Symposium on Atmospheric Diffusion and Air Pollution,
Santa Barbara, CA. American Meteorological Society, Boston, MA.
Moore, G.E., T.E. Stoeckenius and D.A. Stewart, 1982. A Survey
of Statistical Measures of Model Performance and Accuracy for
Several Air Quality Models. EPA Publication No. EPA-450/4-83-001.
U.S. Environmental Protection Agency, Research Triangle Park, NC.
Morgan, D.L., Jr., L.K. Morris and D.L. Ermak, 1983. SLAB: A
Time-Dependent Computer Model for the Dispersion of Heavy Gas
Released in the Atmosphere, UCRL-53383, Lawrence Livermore National
Laboratory, Livermore, CA.
Pasquill, F., 1976. Atmospheric Dispersion Parameters in
Gaussian Plume Modeling, Part II. EPA Publication No. EPA-600/4-76-
030b. U.S. Environmental Protection Agency, Research Triangle Park,
NC.

[[Page 41892]]

Slade, D.H., 1968. Meteorology and Atomic Energy, U.S. Atomic
Energy Commission, 445 pp. (NTIS No. TID-24190)
Turner, D.B., 1964. A Diffusion Model of An Urban Area. Journal
of Applied Meteorology, 3: 83-91.
Turner, D.B., 1969. Workbook of Atmospheric Dispersion
Estimates. PHS Publication No. 999-AP-26. U.S. Environmental
Protection Agency, Research Triangle Park, NC.
Van Dop, H., 1992. Buoyant Plume Rise in a Lagrangian Frame
Work. Atmospheric Environment, 26A: 1335-1346.

Appendix C to Appendix W of Part 51--Example Air Quality Analysis
Checklist

C.0 Introduction

This checklist recommends a standardized set of data and a
standard basic level of analysis needed for PSD applications and SIP
revisions. The checklist implies a level of detail required to
assess both PSD increments and the NAAQS. Individual cases may
require more or less information and the Regional Meteorologist
should be consulted at an early stage in the development of a data
base for a modeling analysis.
At pre-application meetings between source owner and reviewing
authority, this checklist should prove useful in developing a
consensus on the data base, modeling techniques and overall
technical approach prior to the actual analyses. Such agreement will
help avoid misunderstandings concerning the final results and may
reduce the later need for additional analyses.

EXAMPLE AIR QUALITY ANALYSIS CHECKLIST ^{1

1. Source location map(s) showing location with respect to:
---------------------------------------------------------------------------

\1\ The ``Screening Procedures for Estimating the Air Quality
Impact of Stationary Sources, Revised'', October 1992 (EPA-450/R-92-
019), should be used as a screening tool to determine whether
modeling analyses are required. Screening procedures should be
refined by the user to be site/problem specific.
---------------------------------------------------------------------------

Urban areas ^{2
---------------------------------------------------------------------------

\2\ Within 50km or distance to which source has a significant
impact, whichever is less.
---------------------------------------------------------------------------

PSD Class I areas
Nonattainment areas \2\
Topographic features (terrain, lakes, river valleys,
etc.) \2\
Other major existing sources \2\
Other major sources subject to PSD requirements
NWS meteorological observations (surface and upper air)
On-site/local meteorological observations (surface and
upper air)
State/local/on-site air quality monitoring locations
\2\
Plant layout on a topographic map covering a 1km radius
of the source with information sufficient to determine GEP stack
heights
2. Information on urban/rural characteristics:
Land use within 3km of source classified according to
Auer (1978): Correlation of land use and cover with meteorological
anomalies. Journal of Applied Meteorology, 17: 636-643.
Population
-> total
-> density
Based on current guidance determination of whether the
area should be addressed using urban or rural modeling methodology
3. Emission inventory and operating/design parameters for major
sources within region of significant impact of proposed site (same
as required for applicant)
Actual and allowable annual emission rates (g/s) and
operating rates ^{3
---------------------------------------------------------------------------

\3\ Particulate emissions should be specified as a function of
particulate diameter and density ranges.
---------------------------------------------------------------------------

Maximum design load short-term emission rate (g/s) \3\
Associated emissions/stack characteristics as a
function of load for maximum, average, and nominal operating
conditions if stack height is less than GEP or located in complex
terrain. Screening analyses as footnoted above or detailed analyses,
if necessary, must be employed to determine the constraining load
condition (e.g., 50%, 75%, or 100% load) to be relied upon in the
short-term modeling analysis.
--location (UTM's)
--height of stack (m) and grade level above MSL
--stack exit diameter (m)
--exit velocity (m/s)
--exit temperature ( deg.K)
Area source emissions (rates, size of area, height of
area source)\3\
Location and dimensions of buildings (plant layout
drawing)
--to determine GEP stack height
--to determine potential building downwash considerations for
stack heights less than GEP
Associated parameters
--boiler size (megawatts, pounds/hr. steam, fuel consumption,
etc.)
--boiler parameters (% excess air, boiler type, type of firing,
etc.)
--operating conditions (pollutant content in fuel, hours of
operation, capacity factor, % load for winter, summer, etc.)
--pollutant control equipment parameters (design efficiency,
operation record, e.g., can it be bypassed?, etc.)
Anticipated growth changes
4. Air quality monitoring data:
Summary of existing observations for latest five years
(including any additional quality assured measured data which can be
obtained from any state or local agency or company) ^{4
---------------------------------------------------------------------------

\4\ See footnote 2 of this Appendix C.
---------------------------------------------------------------------------

Comparison with standards
Discussion of background due to uninventoried sources
and contributions from outside the inventoried area and description
of the method used for determination of background (should be
consistent with the Guideline)
5. Meteorological data:
Five consecutive years of the most recent
representative sequential hourly National Weather Service (NWS)
data, or one or more years of hourly sequential on-site data
Discussion of meteorological conditions observed (as
applied or modified for the site-specific area, i.e., identify
possible variations due to difference between the monitoring site
and the specific site of the source)
Discussion of topographic/land use influences
6. Air quality modeling analyses:
Model each individual year for which data are available
with a recommended model or model demonstrated to be acceptable on a
case-by-case basis
--urban dispersion coefficients for urban areas
--rural dispersion coefficients for rural areas
Evaluate downwash if stack height is less than GEP
Define worst case meteorology
Determine background and document method
--long-term
--short-term
Provide topographic map(s) of receptor network with
respect to location of all sources
Follow current guidance on selection of receptor sites
for refined analyses
Include receptor terrain heights (if applicable) used
in analyses
Compare model estimates with measurements considering
the upper ends of the frequency distribution
Determine extent of significant impact; provide maps
Define areas of maximum and highest, second-highest
impacts due to applicant source (refer to format suggested in Air
Quality Summary Tables)
-> long-term
-> short-term
7. Comparison with acceptable air quality levels:
NAAQS
PSD increments
Emission offset impacts if nonattainment
8. Documentation and guidelines for modeling methodology:
Follow guidance documents
-> Appendix W to 40 CFR Part 51
-> ``Screening Procedures for Estimating the Air Quality Impact
of Stationary Sources, Revised'' (EPA-450/R-92-019), 1992
-> ``Guideline for Determination of Good Engineering Practice
Stack Height (Technical Support Document for the Stack Height
Regulations)'' (EPA-450/4-80-023R), 1985
-> ``Ambient Monitoring Guidelines for PSD'' (EPA-450/4-87-007),
1987
-> Applicable sections of 40 CFR Parts 51 and 52.

[[Page 41893]]

Air Quality Summary--For New Source Alone
--------------------------------------------------------------------------------------------------------------------------------------------------------
Highest Highest 2d high Highest Highest 2d high Annual
--------------------------------------------------------------------------------------------------------------------------------------------------------

Pollutant: ________________ ^{1 ________________ ^{2 ________________ ^{2

Concentration due to modeled ....................^{.. ....................^{.. ....................^{.. ....................^{. ....................^{.
source (g/m^{3).
Background concentration (................. .....^{................. .....^{................. .....^{................ .....^{................
m>g/m^{3).
Total concentration (g/ .^{..................... .^{..................... .^{..................... .^{.................... .^{....................
m^{3).
Receptor distance (km) (or UTM ...................... ...................... ...................... ..................... .....................
easting).
Receptor direction ( deg.) (or UTM ...................... ...................... ...................... ..................... .....................
northing).
Receptor elevation (m)............ ...................... ...................... ...................... ..................... .....................
Wind speed (m/s).................. ...................... ...................... ...................... ..................... .....................
Wind direction ( deg.)............ ...................... ...................... ...................... ..................... .....................
Mixing depth (m).................. ...................... ...................... ...................... ..................... .....................
Temperature ( deg.K).............. ...................... ...................... ...................... ..................... .....................
Stability......................... ...................... ...................... ...................... ..................... .....................
Day/month/year of occurrence...... ...................... ...................... ...................... ..................... .....................
Surface air data from............. ...................... ...................... ...................... ..................... .....................
Surface station elevation (m)..... ...................... ...................... ...................... ..................... .....................
Anemometer height above local ...................... ...................... ...................... ..................... .....................
ground level (m).
Upper air data from............... ...................... ...................... ...................... ..................... .....................
Period of record analyzed......... ...................... ...................... ...................... ..................... .....................
Model used........................ ...................... ...................... ...................... ..................... .....................
Recommended model................. ...................... ...................... ...................... ..................... .....................
--------------------------------------------------------------------------------------------------------------------------------------------------------
^{1 Use separate sheet for each pollutant (SO2, PM-10, CO, NOX, HC, Pb, Hg, Asbestos, etc.).
^{2 List all appropriate averaging periods (1-hr, 3-hr, 8-hr, 24-hr, 30-day, 90-day, etc.) for which an air quality standard exists.

Air Quality Summary--For All New Sources
--------------------------------------------------------------------------------------------------------------------------------------------------------
Highest Highest 2nd high Highest Highest 2nd high Annual
--------------------------------------------------------------------------------------------------------------------------------------------------------

Pollutant: ________________ ^{1 ________________ ^{2 ________________ ^{2

Concentration due to modeled ....................^{.. ....................^{.. ....................^{.. ....................^{. ....................^{.
source (g/m^{3).
Background concentration (................. .....^{................. .....^{................. .....^{................ .....^{................
m>g/m^{3).
Total concentration (g/ .^{..................... .^{..................... .^{..................... .^{.................... .^{....................
m^{3).
Receptor distance (km) (or UTM ...................... ...................... ...................... ..................... .....................
easting).
Receptor direction ( deg.) (or UTM ...................... ...................... ...................... ..................... .....................
northing).
Receptor elevation (m)............ ...................... ...................... ...................... ..................... .....................
Wind speed (m/s).................. ...................... ...................... ...................... ..................... .....................
Wind direction ( deg.)............ ...................... ...................... ...................... ..................... .....................
Mixing depth (m).................. ...................... ...................... ...................... ..................... .....................
Temperature ( deg.K).............. ...................... ...................... ...................... ..................... .....................
Stability......................... ...................... ...................... ...................... ..................... .....................
Day/month/year of occurrence...... ...................... ...................... ...................... ..................... .....................
Surface air data from............. ...................... ...................... ...................... ..................... .....................
Surface station elevation (m)..... ...................... ...................... ...................... ..................... .....................
Anemometer height above local ...................... ...................... ...................... ..................... .....................
ground level (m).
Upper air data from............... ...................... ...................... ...................... ..................... .....................
Period of record analyzed......... ...................... ...................... ...................... ..................... .....................
Model used........................ ...................... ...................... ...................... ..................... .....................
Recommended model................. ...................... ...................... ...................... ..................... .....................
--------------------------------------------------------------------------------------------------------------------------------------------------------
^{1 Use separate sheet for each pollutant (SO2, PM-10, CO, NOx, HC, Pb, Hg, Asbestos, etc.).
^{2 List all appropriate averaging periods (l-hr, 3-hr, 8-hr, 24-hr, 30-day, 90-day, etc.) for which an air quality standard exists.

Air Quality Summary--For All Sources
--------------------------------------------------------------------------------------------------------------------------------------------------------
Highest Highest 2nd high Highest Highest 2nd high Annual
--------------------------------------------------------------------------------------------------------------------------------------------------------

Pollutant:________________\1\ ________________\2\ ________________\2\

Concentration due to modeled source .............^{......... .............^{......... .............^{........ .............^{........ .............^{........
(g/m^{3).
Background concentration (................. .....^{................. .....^{................ .....^{................ .....^{................
m>g/m^{3).
Total concentration (g/m^{3) ...................... ...................... ..................... ..................... .....................

[[Page 41894]]

Receptor distance (km) (or UTM ...................... ...................... ..................... ..................... .....................
easting).
Receptor direction ( deg.) (or UTM ...................... ...................... ..................... ..................... .....................
northing).
Receptor elevation (m)............. ...................... ...................... ..................... ..................... .....................
Wind speed (m/s)................... ...................... ...................... ..................... ..................... .....................
Wind direction ( deg.)............. ...................... ...................... ..................... ..................... .....................
Mixing depth (m)................... ...................... ...................... ..................... ..................... .....................
Temperature ( deg.K)............... ...................... ...................... ..................... ..................... .....................
Stability.......................... ...................... ...................... ..................... ..................... .....................
Day/month/year of occurrence....... ...................... ...................... ..................... ..................... .....................
Surface air data from.............. ...................... ...................... ..................... ..................... .....................
Surface station elevation (m)...... ...................... ...................... ..................... ..................... .....................
Anemometer height above local ...................... ...................... ..................... ..................... .....................
ground level (m).
Upper air data from................ ...................... ...................... ..................... ..................... .....................
Period of record analyzed.......... ...................... ...................... ..................... ..................... .....................
Model used......................... ...................... ...................... ..................... ..................... .....................
Recommended model.................. ...................... ...................... ..................... ..................... .....................
--------------------------------------------------------------------------------------------------------------------------------------------------------
\1\ Use separate sheet for each pollutant (SO2, PM-10, CO, NOX, HC, Pb, Hg, Asbestos, etc.)
\2\ List all appropriate averaging periods (1-hr, 3-hr, 8-hr, 24-hr, 30-day, 90-day, etc.) for which an air quality standard exists.

Stack Parameters for Annual Modeling
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Emission rate Building dimensions (m)
for each Stack exit Stack exit Stack exit GEP stack ht. Stack base --------------------------------------------------
Stack No. Serving pollutant (g/ diameter (m) velocity (m/s) temperature ( Physical height Stack (m) (m) elevation (m)
s) deg.K) Height Width Length
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Stack Parameters for Short-Term Modeling ^{1
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Emission rate Building dimensions (m)
for each Stack exit Stack exit Stack exit GEP stack ht. Stack base --------------------------------------------------
Stack No. Serving pollutant (g/ diameter (m) velocity (m/s) temperature ( Physical height Stack (m) (m) elevation (m)
s) deg.K) Height Width Length
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
\1\ Separate tables for 50%, 75%, 100% of full operating condition (and any other operating conditions as determined by screening or detailed modeling analyses to represent constraining operating conditions) should be provided.

PART 52--APPROVAL AND PROMULGATION OF IMPLEMENTATION PLANS

1. The authority citation for Part 52 continues to read as follows:

Authority: 42 U.S.C. 7401-7671q.

2. Sec. 52.21 is amended by revising paragraph (l)(1) and the first
sentence of paragraph (l)(2) to read as follows:

Sec. 52.21 Prevention of significant deterioration of air quality.

* * * * *
(l) * * *
(1) All estimates of ambient concentrations required under this
paragraph shall be based on applicable air quality models, data bases,
and other requirements specified in appendix W of part 51 of this
chapter (Guideline on Air Quality Models).
(2) Where an air quality model specified in appendix W of part 51
of this chapter (Guideline on Air Quality Models) is inappropriate, the
model may be modified or another model substituted. * * *
* * * * *

[FR Doc. 96-17031 Filed 8-9-96; 8:45 am]
BILLING CODE 6560-50-P}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}