[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.

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

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outdated models are removed from appendix B. Appendix A models 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.
---------------------------------------------------------------------------

    \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.
---------------------------------------------------------------------------

SUPPLEMENTARY INFORMATION:

Background <SUP>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

[[Page 41841]]

    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 <SUP>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.<SUP>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'',<SUP>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 SO<INF>2, 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.<SUP>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.<SUP>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.<SUP>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 SO<INF>2 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,<SUP>23 24 is now 
available. Moreover, CTSCREEN,<SUP>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; <SUP>19 27 and (5) 
Rough Terrain Dispersion Model (RTDM) <SUP>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 
(<greek-s><INF>v, <greek-s>G5w), vertical potential temperature 
gradient (d<greek-th>/dz), friction velocity (u<INF>x), Monin-
Obukhov length (L), mixing height (z<INF>i) 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
<greek-s><INF>v (m/s)...........................          0.3           0.75                                         
<greek-s><INF>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 <ls-thn-eq> 2 m/s and <greek-s><INF>v <gr-thn-eq> 0.3 m/s, then include <greek-s><INF>w = 0.04 m/s.              
(2) If <greek-s><INF>w = 0.75 m/s and U <gr-thn-eq> 3.0 m/s, then <SUP>DU/<SUP>Dz is limited to <ls-thn-eq> 0.01 K/m.          
(3) If U <gr-thn-eq> 4 m/s, then <greek-s><INF>w <gr-thn-eq> 0.15 m/s.                                               
(4) <greek-s><INF>w <ls-thn-eq> <greek-s><INF>v                                                                           


                       Table 5-1b.--Unstable/Convective Meteorological Matrix for CTSCREEN                      
----------------------------------------------------------------------------------------------------------------
                                                                                                                
----------------------------------------------------------------------------------------------------------------
    Variable                                                          Specific values                           
                                                                                                                
----------------------------------------------------------------------------------------------------------------
U (m/s)...................................         1.0             2.0            3.0           4.0          5.0
u<INF>x (m/s)..................................         0.1             0.3            0.5                           
L (m).....................................       -10             -50            -90                             
<SUP>DU/<INF>Dz (K/m)                                        0.030                                                        
(3) (potential temperature gradient above                                                                       
 z<INF>i)                                                                                                            
z<INF>i (m)....................................         0.5h            1.0h           1.5h                          
                                                                                                                
(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 (O<INF>3), carbon 
monoxide (CO) and nitrogen dioxide (NO<INF>2). Where stationary 
sources of CO and NO<INF>2 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.<SUP>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 NO<INF>2 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, NO<INF>2 modeling should be considered only a screening 
procedure.

6.2  Recommendations

6.2.1  Models for Ozone

    a. The Urban Airshed Model (UAM)<SUP>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,<SUP>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 NO<INF>X, 
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 NO<INF>2 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 NO<INF>2 
                    Concentrations From Point Sources                   
------------------------------------------------------------------------
                                                                        
-------------------------------------------------------------------------
Tier 1: Assume Total Conversion of NO to NO<INF>2                            
Tier 2: Multiply Annual NO<INF>X Estimate by Empirically Derived NO<INF>2/NO<INF>X     
 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 NO<INF>2. If 
the concentration exceeds the NAAQS and/or PSD increments for 
NO<INF>2, proceed to the 2nd level screen.
    c. For Tier 2 (2nd level) screening analysis, multiply the Tier 
1 estimate(s) by an empirically derived NO<INF>2/NO<INF>X value of 
0.75 (annual national default).<SUP>36 An annual NO<INF>2/NO<INF>X 
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 NO<INF>2/
NO<INF>X 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 NO<INF>X; 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 NO<INF>X is 
emitted in the form of NO<INF>2 and to use a model from Appendix A 
for nonreactive pollutants to estimate NO<INF>2 concentrations. A 
more accurate estimate can be obtained by: (1) Calculating the 
annual average concentrations of NO<INF>X with an urban model, and 
(2) converting these estimates to NO<INF>2 concentrations using an 
empirically derived annual NO<INF>2/NO<INF>X ratio. A value of 0.75 
is recommended for this ratio. However, a spatially averaged annual 
NO<INF>2/NO<INF>X 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 NO<INF>2/NO<INF>X 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 NO<INF>2/
NO<INF>X ratio from the most recent 3 years of complete data should 
be used to foster conservatism in estimated impacts.
    e. To demonstrate compliance with NO<INF>2 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 NO<INF>2 
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 NO<INF>2 emissions, 
atmospheric transport and dispersion, and atmospheric transformation 
of NO to NO<INF>2. Where they are available, site-specific data on 
the conversion of NO to NO<INF>2 may be used. Photochemical 
dispersion models, if used for other pollutants in the area, may 
also be applied to the NO<INF>X 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.'' <SUP>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) <SUP>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 <SUP>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,<SUP>108 model reconciliation 
guidance,<SUP>106 and an example model application <SUP>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 <greek-m>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 
<SUP>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.<SUP>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.<SUP>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, <SUP>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 SO<INF>2, CO, Pb, and NO<INF>2 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 
<greek-s><INF>y and <greek-s><INF>z should be determined from the 
same stability category. ``Split sigmas'' in that instance are not 
recommended.
    d. Sector averaging, which eliminates the <greek-s><INF>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 <SUP>58 should be used.

8.2.6  Chemical Transformation

    a. The chemical transformation of SO<INF>2 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 SO<INF>2 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 NO<INF>2 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, A<INF>o, 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 A<INF>o, 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 A<INF>o 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 <SUP>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.<SUP>5
                                  permit limit.                  federally                      <SUP>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.<SUP>5
                                  permit limit.                  most recent 2                  <SUP>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.<SUP>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 
(<bd/h-l-arr><SUP>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 <triangle>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., <gr-thn-eq>200m). In 
cases with stack tops <gr-thn-eq>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 <greek-s><INF>E and <greek-s><INF>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 <greek-s><INF>E and <greek-s><INF>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 (<greek-s><INF>E and     1-hr.\1\                    
 <greek-s><INF>A) for use in stability                                       
 determinations.                                                        
------------------------------------------------------------------------
\1\ To minimize meander effects in <greek-s><INF>A when wind conditions are  
  light and/or variable, determine the hourly average <greek-s> value   
  from four sequential 15-minute <greek-s>'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 <SUP>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,<SUP>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 
<plus-minus>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 <SUP>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 
<plus-minus>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.<SUP>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.<SUP>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.<SUP>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 SO<INF>2 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 NO<INF>2 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.<SUP>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 SO<INF>2, 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''<SUP>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 SO<INF>2, Pb, NO<INF>2, 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 SO<INF>2, 
particulate matter, and NO<INF>2 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.<SUP>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 <SUP>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<SUP>19 to 
download updates or addenda; see Section A.0 of this appendix.
---------------------------------------------------------------------------

    1. Code of Federal Regulations (Title 40, Part 51): Protection 
of the Environment; Requirements for Preparation, Adoption, and 
Submittal of Implementation Plans.
    2. Environmental Protection Agency, 1977. Guidelines for the 
Regional Evaluation of State and Local New Source Review Program. 
EPA Publication No. EPA-450/2-77-027. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB-275053)
    3. Environmental Protection Agency, 1980. Prevention of 
Significant Deterioration Workshop Manual. EPA Publication No. EPA-
450/2-80-081. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 81-136459)
    4. Environmental Protection Agency, 1981. Guideline for Fluid 
Modeling of Atmospheric Diffusion. EPA Publication No. EPA-600/8-81-
009. U.S. Environmental Protection Agency, Research Triangle Park, 
NC. (NTIS No. PB 81-201410)
    5. Code of Federal Regulations (Title 40, Part 50): Protection 
of the Environment; National Primary and Secondary Ambient Air 
Quality Standards.
    6. Environmental Protection Agency, 1988. Model Clearinghouse: 
Operational Plan (Revised). Staff Report. U.S. Environmental 
Protection Agency, Research Triangle Park, NC. (Docket No. A-88-04, 
II-J-1)
    7. Environmental Protection Agency, 1980. Guidelines on Air 
Quality Models. Federal Register, 45: 20157-20158.
    8. Londergan, R.J., D.H. Minott, D.J. Wackter, T. Kincaid and D. 
Bonitata, 1982. Evaluation of Rural Air Quality Simulation Models. 
EPA Publication No. EPA-450/4-82-020. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 83-182758)
    9. Londergan, R.J., D.H. Minott, D.J. Wackter and R.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. (NTIS No. PB 84-241173).
    10. Londergan, R.J. and D.J. Wackter, 1984. Evaluation of 
Complex Terrain Air Quality Simulation Models. EPA Publication No. 
EPA-450/4-84-017. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 85-119485).
    11. Fox, D.G., 1981. Judging Air Quality Model Performance. 
Bulletin of the American Meteorological Society, 62(5): 599-609.
    12. American Meteorological Society, 1983. Synthesis of the 
Rural Model Reviews. EPA Publication No. EPA-600/3-83-108. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
No. PB 84-121037)
    13. American Meteorological Society, 1984. Review of the 
Attributes and Performance of Six Urban Diffusion Models. EPA 
Publication No. EPA-600/S3-84-089. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No.PB 84-236850)
    14. White, F.D.(Ed.), J.K.S. Ching, R.L. Dennis and W.H. Snyder, 
1985. Summary of Complex Terrain Model Evaluation. EPA Publication 
No. EPA-600/3-85-060. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 85-236891)
    15. Environmental Protection Agency, 1984. Interim Procedures 
for Evaluating Air Quality Models (Revised). EPA Publication No. 
EPA-450/4-84-023. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 85-106060)
    16. Environmental Protection Agency, 1985. Interim Procedures 
for Evaluating Air Quality Models: Experience with Implementation. 
EPA Publication No. EPA-450/4-85-006. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 85-242477)
    17. Environmental Protection Agency, 1992. Protocol for 
Determining the Best Performing Model. EPA Publication No. EPA-454/
R-92-025. U.S. Environmental Protection Agency, Research Triangle 
Park, NC.
    18. Environmental Protection Agency, 1992. Screening Procedures 
for Estimating the Air Quality Impact of Stationary Sources, 
Revised. EPA Publication No. EPA-454/R-92-019. U.S. Environmental 
Protection Agency, Research Triangle Park, NC.
    19. Environmental Protection Agency, 1989. Support Center for 
Regulatory Air Models Bulletin Board System (SCRAM BBS). Source 
Receptor Analysis Branch, Research Triangle Park, NC. (Docket Nos. 
A-88-04, II-J-4a and b)
    20. Environmental Protection Agency, 1995. SCREEN3 User's Guide. 
EPA Publication No. EPA-454/B-95-004. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 95-222766)
    21. Environmental Protection Agency, 1987. EPA Complex Terrain 
Model Development: Final Report. EPA Publication No. EPA-600/3-88-
006. U.S. Environmental Protection Agency, Research Triangle Park, 
NC. (NTIS No. PB 88-162110)
    22. Perry, S.G., D.J. Burns, L.H. Adams, R.J. Paine, M.G. 
Dennis, M.T. Mills, D.J. 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 
Description and User Instructions. EPA Publication No. EPA-600/8-89-
041. U.S. Environmental Protection Agency, Research Triangle Park, 
NC. (NTIS No. PB 89-181424)
    23. Mills, M.T., R.J. Paine, E.A. Insley and B.A. Egan, 1987. 
The Complex Terrain Dispersion Model Terrain Preprocessor System--
User's Guide and Program Description. EPA Publication No. EPA-600/8-
88-003. U.S. Environmental Protection Agency, Research Triangle 
Park, NC. (NTIS No. PB 88-162094)
    24. Paine, R.J., 1987. User's Guide to the CTDM Meteorological 
Preprocessor Program. EPA Publication No. EPA-600/8-88-004. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
No. PB 88-162102)
    25. Perry, S.G., D.J. Burns and A.J. Cimorelli, 1990. User's 
Guide to CTDMPLUS: Volume 2. The Screening Mode (CTSCREEN). EPA 
Publication No. EPA-600/8-90-087. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 91-136564)
    26. Burt, E.W., 1977. Valley Model User's Guide. EPA Publication 
No. EPA-450/2-77-018. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB-274054)
    27. Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions 
for the SHORTZ and

[[Page 41862]]

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. (NTIS Nos. PB 83-146092 and PB 83-146100)
    28. 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; 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)
    29. Environmental Protection Agency, 1991. Guideline for 
Regulatory Application of the Urban Airshed Model. EPA Publication 
No. EPA-450/4-91-013 U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 92-108760/AS)
    30. Environmental Protection Agency, 1989. Procedures for 
Applying City-Specific EKMA (Empirical Kinetic Modeling Approach). 
EPA Publication No. EPA-450/4-89-012. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 90-256777)
    31. Environmental Protection Agency, 1989. User's Manual for 
OZIPM-4 (Ozone Isopleth Plotting with Optional Mechanisms), Volumes 
1 and 2. EPA Publication Nos. EPA-450/4-89-009a and b. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
Nos. PB 90-261488 and PB 90-255399)
    32. Environmental Protection Agency, 1988. A PC Based System for 
Generating EKMA Input Files. EPA Publication No. EPA-450/4-88-016. 
U.S. Environmental Protection Agency, Research Triangle Park, NC. 
(NTIS No. PB 90-255407)
    33. Meyer, Jr., E.L. and K.A. Baugues, 1987. Consideration of 
Transported Ozone and Precursors and Their Use in EKMA. EPA 
Publication No. EPA-450/4-89-010. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 90-255415)
    34. Environmental Protection Agency, 1992. Guideline for 
Modeling Carbon Monoxide from Roadway Intersections. EPA Publication 
No. EPA-454/R-92-005. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 93-210391)
    35. Environmental Protection Agency, 1992. User's Guide for 
CAL3QHC Version 2: A Modeling Methodology for Predicting Pollutant 
Concentrations near Roadway Intersections. EPA Publication No. EPA-
454/R-92-006. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 93-210250)
    36. Chu, S.H. and E.L. Meyer, 1991. Use of Ambient Ratios to 
Estimate Impact of NOx Sources on Annual NO<INF>2 Concentrations. 
Proceedings, 84th Annual Meeting & Exhibition of the Air & Waste 
Management Association, Vancouver, B.C.; 16-21 June 1991. (16 pp.) 
(Docket No. A-92-65, II-A-9)
    37. U.S. Department of Housing and Urban Development, 1980. Air 
Quality Considerations in Residential Planning. U.S. Superintendent 
of Documents, Washington, DC. (GPO Order Nos. 023-000-00577-8, 023-
000-00576-0, 023-000-00575-1)
    38. Environmental Protection Agency, 1981. Receptor Model 
Technical Series. Volume I: Overview of Receptor Model Application 
to Particulate Source Apportionment. EPA Publication No. EPA-450/4-
81-016a (NTIS No. PB 82-139429); Volume II: Chemical Mass Balance. 
EPA Publication No. EPA-450/4-81-016b (NTIS No. PB 82-187345); 
Volume III (Revised): CMB User's Manual (Version 7.0). EPA 
Publication No. EPA-450/4-90-004 (NTIS No. PB 90-185067); Volume IV: 
Technical Considerations In Source Apportionment By Particle 
Identification. EPA Publication No. EPA-450/4-83-018 (NTIS No. PB 
84-103340); Volume V: Source Apportionment Techniques and 
Considerations in Combining their Use. EPA Publication No. EPA-450/
4-84-020 (NTIS No. PB 85-111524); Volume VI: A Guide To The Use of 
Factor Analysis and Multiple Regression (FA/MR) Techniques in Source 
Apportionment. EPA Publication No. EPA-450/4-85-007 (NTIS No. PB 86-
107638). U.S. Environmental Protection Agency, Research Triangle 
Park, NC.
    39. Pace, T.G., 1982. The Role of Receptor Models for Revised 
Particulate Matter Standards. A Specialty Conference on: Receptor 
Models Applied to Contemporary Pollution Problems. Air Pollution 
Control Association, Pittsburgh, PA; pp. 18-28. (Docket No. A-80-46, 
II-P-10)
    40. Environmental Protection Agency, 1978. Supplementary 
Guidelines for Lead Implementation Plans. EPA Publication No. EPA-
450/2-78-038. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 82-232737)
    41. Environmental Protection Agency, 1983. Updated Information 
on Approval and Promulgation of Lead Implementation Plans (DRAFT). 
U.S. Environmental Protection Agency, Research Triangle Park, NC. 
(Docket No. A-80-46, II-B-38)
    42. Environmental Protection Agency, 1979. Protecting 
Visibility: An EPA Report to Congress. EPA Publication No. EPA-450/
5-79-008. U.S. Environmental Protection Agency, Research Triangle 
Park, NC. (NTIS No. PB 80-220320)
    43. Environmental Protection Agency, 1992. Workbook for Plume 
Visual Impact Screening and Analysis (Revised). EPA Publication No. 
EPA-454/R-92-023. U.S. Environmental Protection Agency, Research 
Triangle Park, NC.
    44. Seigneur C., A.B. Hudischewskyj and R.W. Bergstrom, 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. (NTIS No. PB 83-164723)
    45. 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 
Intercomparison of Four Models with Observations at a Well-
Controlled Power Plant. Atmospheric Environment, 19(3): 515-528.
    46. Environmental Protection Agency, 1985. Guideline for 
Determination of Good Engineering Practice Stack Height (Technical 
Support Document for the Stack Height Regulations), Revised. EPA 
Publication No. EPA-450/4-80-023R. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 85-225241)
    47. Environmental Protection Agency, 1981. Guideline for Use of 
Fluid Modeling to Determine Good Engineering Practice (GEP) Stack 
Height. EPA Publication No. EPA-450/4-81-003. U.S. Environmental 
Protection Agency, Research Triangle Park, NC. (NTIS No. PB 82-
145327)
    48. Lawson, Jr., R.E. and W.H. Snyder, 1983. Determination of 
Good Engineering Practice Stack Height: A Demonstration Study for a 
Power Plant. EPA Publication No. EPA-600/3-83-024. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
No. PB 83-207407)
    49. Snyder, W.H. and R.E. Lawson, Jr., 1985. Fluid Modeling 
Demonstration of Good Engineering-Practice Stack Height in Complex 
Terrain. EPA Publication No. EPA-600/3-85-022. U.S. Environmental 
Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-
203107)
    50. Turner, D.B., 1969. Workbook of Atmospheric Dispersion 
Estimates. PHS Publication No. 999-AP-26. U.S. Department of Health, 
Education and Welfare, Public Health Service, Cincinnati, OH. (NTIS 
No. PB-191482)
    51. McElroy, J.L. and F. Pooler, Jr., 1968. St. Louis Dispersion 
Study, Volume II--Analysis. National Air Pollution Control 
Administration Publication No. AP-53, U.S. Department of Health, 
Education and Welfare, Public Health Service, Arlington, VA. (NTIS 
No. PB-190255)
    52. Irwin, J.S., 1983. Estimating Plume Dispersion--A Comparison 
of Several Sigma Schemes. Journal of Climate and Applied 
Meteorology, 22: 92-114.
    53. Briggs, G.A. and F.S. Binkowski, 1985. Research on Diffusion 
in Atmospheric Boundary Layers: A Position Paper on Status and 
Needs. EPA Publication No. EPA-600/3-25-072. U.S. Environmental 
Protection Agency, Research Triangle Park, NC. (NTIS No. PB 86-
122587)
    54. 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. (NTIS No. PB-258036/3BA)
    55. Turner, D.B., 1964. A Diffusion Model for an Urban Area. 
Journal of Applied Meteorology, 3(1): 83-91.
    56. Briggs, G.A., 1975. Plume Rise Predictions. Chapter 3 in 
Lectures on Air Pollution and Environmental Impact Analyses. 
American Meteorological Society, Boston, MA; pp. 59-111.
    57. Hanna, S.R., G.A. Briggs and R.P. Hosker, Jr., 1982. Plume 
Rise. Chapter 2 in Handbook on Atmospheric Diffusion. Technical 
Information Center, U.S. Department of Energy, Washington, DC; pp. 
11-24. DOE/TIC-11223 (DE 82002045)
    58. 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. U.S. Environmental 
Protection

[[Page 41863]]

Agency, Research Triangle Park, NC. (NTIS Nos. PB 95-222741 and PB 
95-222758, respectively)
    59. Irwin, J.S., 1978. Proposed Criteria for Selection of Urban 
Versus Rural Dispersion Coefficients. (Draft Staff Report). 
Meteorology and Assessment Division, U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (Docket No. A-80-46, II-B-8)
    60. Auer, Jr., A.H., 1978. Correlation of Land Use and Cover 
with Meteorological Anomalies. Journal of Applied Meteorology, 
17(5): 636-643.
    61. Brier, G.W., 1973. Validity of the Air Quality Display Model 
Calibration Procedure. EPA Publication No. EPA-R4-73-017. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
No. PB-218716)
    62. Environmental Protection Agency, 1985 and ff. Compilation of 
Air Pollutant Emission Factors, Volume I: Stationary Point and Area 
Sources (Fourth Edition; GPO Stock No. 055-000-00251-7), and 
Supplements; Volume II: Mobile Sources (NTIS PB 87-205266) and 
Supplement(s). EPA Publication No. AP-42. U.S. Environmental 
Protection Agency, Research Triangle Park, NC.
    63. Environmental Protection Agency, 1987. Ambient Air 
Monitoring Guidelines for Prevention of Significant Deterioration 
(PSD). EPA Publication No. EPA-450/4-87-007. U.S. Environmental 
Protection Agency, Research Triangle Park, NC. (NTIS No. PB 90-
168030)
    64. Landsberg, H.E. and W.C. Jacobs, 1951. Compendium of 
Meteorology. American Meteorological Society, Boston, MA; pp. 976-
992.
    65. Burton, C.S., T.E. Stoeckenius and J.P. Nordin, 1983. The 
Temporal Representativeness of Short-Term Meteorological Data Sets: 
Implications for Air Quality Impact Assessments. Systems 
Applications, Inc., San Rafael, CA. (Docket No. A-80-46, II-G-11)
    66. Environmental Protection Agency, 1987. On-Site 
Meteorological Program Guidance for Regulatory Modeling 
Applications. EPA Publication No. EPA-450/4-87-013. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
No. PB 87-227542)
    67. Environmental Protection Agency, 1989. Quality Assurance for 
Air Pollution Measurement Systems, Volume IV--Meteorological 
Measurements. EPA Publication No. EPA-600/4-82-060R. U.S. 
Environmental Protection Agency, Research Triangle Park, NC.
    68. Irwin, J.S., 1980. Dispersion Estimate Suggestion #8: 
Estimation of Pasquill Stability Categories. U.S. Environmental 
Protection Agency, Research Triangle Park, NC (Docket No. A-80-46, 
II-B-10)
    69. Mitchell, Jr., A.E. and K.O. Timbre, 1979. Atmospheric 
Stability Class from Horizontal Wind Fluctuation. Presented at 72nd 
Annual Meeting of Air Pollution Control Association, Cincinnati, OH; 
June 24-29, 1979. (Docket No. A-80-46, II-P-9)
    70. Nuclear Regulatory Commission, 1972. Meteorological Programs 
in Support of Nuclear Power Plants. Regulatory Guide 1.23 (DRAFT). 
U.S. Nuclear Regulatory Commission, Office of Standards Development, 
Washington, DC. (Docket No. A-80-46, II-P-11)
    71. Smedman-Hogstrom, A. and V. Hogstrom, 1978. A Practical 
Method for Determining Wind Frequency Distributions for the Lowest 
200m from Routine Meteorological Data. Journal of Applied 
Meteorology, 17(7): 942-954.
    72. Smith, T.B. and S.M. Howard, 1972. Methodology for Treating 
Diffusivity. MRI 72 FR-1030. Meteorology Research, Inc., Altadena, 
CA. (Docket No. A-80-46, II-P-8)
    73. Environmental Protection Agency, 1984. Calms Processor 
(CALMPRO) User's Guide. EPA Publication No. EPA-901/9-84-001. U.S. 
Environmental Protection Agency, Region I, Boston, MA. (NTIS No. PB 
84-229467)
    74. Burton, C.S., 1981. The Role of Atmospheric Models in 
Regulatory Decision-Making: Summary Report. Systems Applications, 
Inc., San Rafael, CA. Prepared under contract No. 68-01-5845 for 
U.S. Environmental Protection Agency, Research Triangle Park, NC. 
(Docket No. A-80-46, II-M-6)
    75. Environmental Protection Agency, 1981. Proceedings of the 
Second Conference on Air Quality Modeling, Washington, DC. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (Docket 
No. A-80-46, II-M-16)
    76. Hanna, S.R., 1982. Natural Variability of Observed Hourly 
SO<INF>2 and CO Concentrations in St. Louis. Atmospheric 
Environment, 16(6): 1435-1440.
    77. Fox, D.G., 1983. Uncertainty in Air Quality Modeling. 
Bulletin of the American Meteorological Society, 65(1): 27-36.
    78. Bowne, N.E., 1981. Validation and Performance Criteria for 
Air Quality Models. Appendix F in Air Quality Modeling and the Clean 
Air Act: Recommendations to EPA on Dispersion Modeling for 
Regulatory Applications. American Meteorological Society, Boston, 
MA; pp. 159-171. (Docket No. A-80-46, II-A-106)
    79. Bowne, N.E. and R.J. Londergan, 1983. Overview, Results, and 
Conclusions for the EPRI Plume Model Validation and Development 
Project: Plains Site. EPRI EA-3074. Electric Power Research 
Institute, Palo Alto, CA.
    80. 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. 
(NTIS No. PB 83-260810)
    81. Rhoads, R.G., 1981. Accuracy of Air Quality Models. Staff 
Report. U.S. Environmental Protection Agency, Research Triangle 
Park, NC. (Docket No. A-80-46, II-G-6)
    82. Pasquill, F., 1974. Atmospheric Diffusion, 2nd Edition. John 
Wiley and Sons, New York, NY; 479 pp.
    83. Austin, B.S., T.E. Stoeckenius, M.C. Dudik and T.S. 
Stocking, 1988. User's Guide to the Expected Exceedances System. 
Systems Applications, Inc., San Rafael, CA. Prepared under Contract 
No. 68-02-4352 Option I for the U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (Docket No. A-88-04, II-I-3)
    84. Thrall, A.D., T.E. Stoeckenius and C.S. Burton, 1985. A 
Method for Calculating Dispersion Modeling Uncertainty Applied to 
the Regulation of an Emission Source. Systems Applications, Inc., 
San Rafael, CA. Prepared for the U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (Docket No. A-80-46, IV-G-1)
    85. Environmental Protection Agency, 1981. Plan for Evaluating 
Model Performance. Staff Report. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (Docket No. A-80-46, II-G-6)
    86. Environmental Protection Agency, 1979. Guideline for the 
Interpretation of Ozone Air Quality Standards. EPA Publication No. 
EPA 450/4-79-003. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB-292271)
    87. Environmental Protection Agency, 1974. Guidelines for Air 
Quality Maintenance Planning and Analysis, Volume 12: Applying 
Atmospheric Simulation Models to Air Quality Maintenance Areas. EPA 
Publication No. EPA-450/4-74-013. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB-237750)
    88. Environmental Protection Agency, 1977. Guidelines for 
Interpretation of Air Quality Standards (Revised). OAQPS No. 1.2-
008. U.S. Environmental Protection Agency, Research Triangle Park, 
NC. (NTIS No. PB 81-196420)
    89. Environmental Protection Agency, 1986. Emissions Trading 
Policy Statement; General Principles for Creation, Banking, and Use 
of Emission Reduction Credits. Federal Register, 51: 43814-43860.
    90. Environmental Research and Technology, 1987. User's Guide to 
the Rough Terrain Diffusion Model (RTDM), Rev. 3.20. ERT Document 
No. P-D535-585. Environmental Research and Technology, Inc., 
Concord, MA. (NTIS No. PB 88-171467)
    91. Burns, D.J., S.G. Perry and A.J. Cimorelli, 1991. An 
Advanced Screening Model for Complex Terrain Applications. Paper 
presented at the 7th Joint Conference on Applications of Air 
Pollution Meteorology (cosponsored by the American Meteorological 
Society and the Air & Waste Management Association), January 13-18, 
1991, New Orleans, LA.
    92. Perry, S.G., 1992. CTDMPLUS: A Dispersion Model for Sources 
near Complex Topography. Part I: Technical Formulations. Journal of 
Applied Meteorology, 31(7): 633-645.
    93. 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.
    94. Environmental Protection Agency, 1986. Evaluation of Mobile 
Source Air Quality Simulation Models. EPA Publication No. EPA-450/4-
86-002. U.S. Environmental Protection Agency, Research Triangle 
Park, NC. (NTIS No. PB 86-167293)
    95. Shannon, J.D., 1987. Mobile Source Modeling Review. A report 
prepared under a

[[Page 41864]]

cooperative agreement with the Environmental Protection Agency. 
(Docket No. A-88-04, II-J-2)
    96. Environmental Protection Agency, 1991. Emission Inventory 
Requirements for Carbon Monoxide State Implementation Plans. EPA 
Publication No. EPA-450/4-91-011. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 92-112150)
    97. Environmental Protection Agency, 1992. Guideline for 
Regulatory Application of the Urban Airshed Model for Areawide 
Carbon Monoxide. EPA Publication No. EPA-450/4-92-011a and b. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
Nos. PB 92-213222 and PB 92-213230)
    98. Environmental Protection Agency, 1992. Technical Support 
Document to Aid States with the Development of Carbon Monoxide State 
Implementation Plans. EPA Publication No. EPA-452/R-92-003. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
No. PB 92-233055)
    99. Environmental Protection Agency, 1986. Evaluation of Short-
Term Long-Range Transport Models, Volumes I and II. EPA Publication 
Nos. EPA-450/4-86-016a and b. U.S. Environmental Protection Agency, 
Research Triangle Park, NC. (NTIS Nos. PB 87-142337 and PB 87-
142345)
    100. McNider, R.T., 1987. Review of Short-Term Long-Range 
Models. A report prepared under a cooperative agreement with the 
Environmental Protection Agency. (Docket No. A-88-04, II-I-10)
    101. Lamb, R.G., 1983. A Regional Scale (1,000km) Model of 
Photochemical Air Pollution, Part I--Theoretical Formulation, Part 
II--Input Processor Network Design, and Part III--Tests of Numerical 
Algorithms. EPA Publication Nos. EPA-600/3-83-035, EPA-600/3-84-085, 
and EPA-600/3-85-037. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS Nos. PB 83-207688, PB 84-232651, and PB 85-
203818, respectively)
    102. Young, J.O., M. Aissa, T.L. Boehm et al., 1989. Development 
of the Regional Oxidant Model, Version 2.1. EPA Publication No. EPA-
600/3-89-044. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 89-194252)
    103. Environmental Protection Agency, 1991. The Regional Oxidant 
Model (ROM) User's Guide. Part 1: The ROM Preprocessors. EPA 
Publication No. EPA-600/8-90-083a (NTIS No. PB 91-171926); Part 2: 
The ROM Processor Network. EPA Publication No. EPA-600/8-90-083b 
(NTIS No. PB 91-171934); Part 3: The Core Model. EPA Publication No. 
EPA-600/8-90-083c (NTIS No. PB 91-171942). U.S. Environmental 
Protection Agency, Research Triangle Park, NC.
    104. Chang, J.S., R.A. Brost, I.S.A. Isaksen, S. Madronich, P. 
Middleton, W.R. Stockwell and C.J. Waleck, 1987. A Three-Dimensional 
Eulerian Acid Deposition Model: Physical Concepts and Formulation. 
Journal of Geophysical Research, 92(D12): 14681-14700.
    105. Environmental Protection Agency, 1987. Protocol for 
Applying and Validating the CMB. U.S. Environmental Protection 
Agency. EPA Publication No. EPA-450/4-87-010. U.S. Environmental 
Protection Agency, Research Triangle Park, NC. (NTIS No. PB 87-
206496)
    106. Environmental Protection Agency, 1987. Protocol for 
Reconciling Differences Among Receptor and Dispersion Models. EPA 
Publication No. EPA-450/4-87-008. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 87-206504)
    107. Environmental Protection Agency, 1988. Chemical Mass 
Balance Model Diagnostics. EPA Publication No. EPA-450/4-88-005. 
U.S. Environmental Protection Agency, Research Triangle Park, NC. 
(NTIS No. PB 88-208319)
    108. Environmental Protection Agency, 1987. PM-10 SIP 
Development Guideline. EPA Publication No. EPA-450/2-86-001. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
No. PB 87-206488)
    109. Environmental Protection Agency, 1987. Example Modeling to 
Illustrate SIP Development for The PM-10 NAAQS. EPA Publication No. 
EPA-450/4-87-012. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 87-205191)
    110. Sestak, M.L. and A.R. Riebau, 1988. SASEM Simple Approach 
Smoke Estimation Model. U.S. Bureau of Land Management, Technical 
Note 382. BLM/YA/PT-88/003 + 7000. Available from Printed Materials 
Distribution Section, BLM Service Center (SC-658B), Denver, CO 
80225-0047. (NTIS No. PB 90-185653)
    111. Environmental Protection Agency, 1992. A Modeling Protocol 
for Applying Mesopuff II to Long Range Transport Problems. EPA 
Publication No. EPA-454/R-92-021. U.S. Environmental Protection 
Agency, Research Triangle Park, NC.
    112. DiCristofaro, D.C. and S.R. Hanna, 1989. The Offshore and 
Coastal Dispersion (OCD) Model, Volume I: User's Guide, Volume II: 
Appendices. Version 4 Prepared for Minerals Management Services by 
Sigma Research Corporation, Westford, MA. (Docket No. A-88-04, II-D-
A-06)
    113. Federal Aviation Administration, 1988. A Microcomputer 
Pollution Model for Civilian Airports and Air Force Bases, Model 
Description, Model Application and Background, and EDMS User's Guide 
(June 1991). Federal Aviation Administration Publication Nos. FAA-
EE-88-4 and 5; FAA-EE-91-3, respectively. United States Air Force 
Publication Nos. ESL-TR-88-53 and 55; ESL-TR-91-31, respectively. 
Federal Aviation Administration, Office of Environment and Energy, 
Washington, D.C. (NTIS Nos. ADA 199003, ADA 199794, and ADA 240528, 
respectively)
    114. Environmental Protection Agency, 1992. Workbook of 
Screening Techniques for Assessing Impacts of Toxic Air Pollutants 
(Revised). EPA Publication No. EPA-454/R-92-024. U.S. Environmental 
Protection Agency, Research Triangle Park, NC.
    115. Environmental Protection Agency, 1990. User's Guide to 
TSCREEN: A Model for Screening Toxic Air Pollutant Concentrations. 
EPA Publication No. EPA-450/4-90-013. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 91-141820)
    116. Environmental Protection Agency, 1989. Hazardous Waste TSDF 
Fugitive Particulate Matter Air Emissions Guidance Document. EPA 
Publication No. EPA-450/3-89-019. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (NTIS No. PB 90-103250)
    117. Environmental Protection Agency, 1989. Procedures for 
Conducting Air Pathway Analyses for Superfund Applications, Volume I 
Applications of Air Pathway Analyses for Superfund Activities and 
Volume IV Procedures for Dispersion Modeling and Air Monitoring for 
Superfund Air Pathway Analysis, EPA-450/1-89-001 and 004. U.S. 
Environmental Protection Agency, Research Triangle Park, NC. (NTIS 
Nos. PB 89-113374 and PB 89-113382)
    118. Environmental Protection Agency, 1988. Air Dispersion 
Modeling as Applied to Hazardous Waste Incinerator Evaluations, An 
Introduction For the Permit Writer. U.S. Environmental Protection 
Agency, Research Triangle Park, NC. (Docket No. A-88-04, II-J-10)
    119. Environmental Protection Agency, 1989. U.S. EPA Office of 
Toxic Substances Graphical Exposure Modeling System (GEMS) User's 
Guide and GAMS Version 3.0 User's Guide (DRAFT). Prepared under 
Contract No. 68-02-0481 for the U.S. Environmental Protection 
Agency, Washington, D.C. (Docket No. A-88-04, II-J-5a and II-J-13)
    120. Federal Emergency Management Agency, 1989. Handbook of 
Chemical Hazard Analysis Procedures. Available on request by writing 
to: Federal Emergency Management Agency, Publications Office, 500 C 
Street, S.W., Washington, D.C. 20472.
    121. Environmental Protection Agency, 1987. Technical Guidance 
for Hazards Analysis: Emergency Planning for Extremely Hazardous 
Substances. Available on request by telephone: 1-800-535-0202.
    122. Environmental Protection Agency, 1988. Superfund Exposure 
Assessment Manual. EPA-540/1-88-001, OSWER Directive 9285.5-1. 
Office of Remedial Response, Washington, D.C. 20460. (NTIS No. PB 
89-135859)
    123. Environmental Protection Agency, 1989. Incineration of 
Sewage Sludge; Technical Support Document. Office of Water 
Regulations and Standards, Washington, D.C. 20460. (NTIS No. PB 89-
136592)
    124. Environmental Protection Agency, 1989. Sludge Incineration 
Modeling (SIM) System User's Guide (Draft). Office of Pesticides and 
Toxic Substances, Exposure Evaluation Division, Washington, D.C. 
20460. (NTIS No. PB 89-138762)
    125. Environmental Protection Agency, 1989. Risk Assessment 
Guidance for Superfund. Volume I: Human Health Evaluation Manual 
Part A. (Interim Final). OSWER Directive 9285.7-01a. Office of Solid 
Waste and Emergency Response, Washington, D.C. 20460.
    126. Environmental Protection Agency, 1986. User's Manual for 
the Human Exposure Model (HEM). EPA Publication No. EPA-450/5-86-
001. Office of Air Quality Planning and Standards, Research Triangle 
Park, NC. 27711.

[[Page 41865]]

    127. Environmental Protection Agency, 1992. A Tiered Modeling 
Approach for Assessing the Risks Due to Sources of Hazardous Air 
Pollutants. EPA Publication No. EPA-450/4-92-001. Environmental 
Protection Agency, Research Triangle Park, NC. (NTIS No. PB 92-
164748)
    128. Environmental Protection Agency, 1992. Toxic Modeling 
System Short-term (TOXST) User's Guide. EPA Publication No. EPA-450/
4-92-002. Environmental Protection Agency, Research Triangle Park, 
NC.
    129. Environmental Protection Agency, 1992. Toxic Modeling 
System Long-term (TOXLT) User's Guide. EPA Publication No. EPA-450/
4-92-003. Environmental Protection Agency, Research Triangle Park, 
NC.
    130. 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. (NTIS No. PB 90-213893)
    131. Environmental Protection Agency, 1993. Guidance on the 
Application of Refined Models for Air Toxics Releases. EPA 
Publication No. EPA-450/4-91-007. Environmental Protection Agency, 
Research Triangle Park, NC. (NTIS No. PB 91-190983)
    132. Perry, R.H. and Chilton, C.H., 1973. Chemical Engineers' 
Handbook, Fifth Edition, McGraw-Hill Book Company, New York, NY.
    133. Environmental Protection Agency, 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)
    134. 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 No. PB 87-175519)
    135. Environmental Protection Agency, 1996. Meteorological 
Processor for Regulatory Models (MPRM) User's Guide. EPA Publication 
No. EPA-454/B-96-002. U.S. Environmental Protection Agency, Research 
Triangle Park, NC. (NTIS No. PB 96-180518)
    136. Bowen, B.M., J.M. Dewart and A.I. Chen, 1983. Stability 
Class Determination: A Comparison for One Site. Proceedings, Sixth 
Symposium on Turbulence and Diffusion. American Meteorological 
Society, Boston, MA; pp. 211-214. (Docket No. A-92-65, II-A-7)
    137. Environmental Protection Agency, 1993. An Evaluation of a 
Solar Radiation/Delta-T (SRDT) Method for Estimating Pasquill-
Gifford (P-G) Stability Categories. EPA Publication No. EPA-454/R-
93-055. U.S. Environmental Protection Agency, Research Triangle 
Park, NC. (NTIS No. PB 94-113958)
    138. Environmental Protection Agency, 1993. PCRAMMET User's 
Guide. EPA Publication No. EPA-454/B-93-009. U.S. Environmental 
Protection Agency, Research Triangle Park, NC.
    139. American Society of Mechanical Engineers, 1979. Recommended 
Guide for the Prediction of Airborne Effluents, Third Edition. 
American Society of Mechanical Engineers, New York, NY.

13.0  Bibliography <SUP>i
---------------------------------------------------------------------------

    \i\ The documents listed here are major sources of supplemental 
information on the theory and application of mathematical air 
quality models.
---------------------------------------------------------------------------

    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 SF<INF>6 Tracer Data and SO<INF>2 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 SO<INF>2 in an urban setting. This entry results in no decay 
(infinite half-life) being calculated. For SO<INF>2 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:
    <bullet> 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. SO<INF>2 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 SO<INF>2 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 SO<INF>2 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:
    <bullet> Industrial source complexes;
    <bullet> Rural or urban areas;
    <bullet> Flat or rolling terrain;
    <bullet> Transport distances less than 50 kilometers;
    <bullet> 1-hour to annual averaging times; and
    <bullet> 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:
    <bullet> Program control parameters, source data, and receptor 
data;
    <bullet> Tables of hourly meteorological data for each specified 
day;
    <bullet> ``N''-day average concentration or total deposition 
calculated at each receptor for any desired source combinations;
    <bullet> Concentration or deposition values calculated for any 
desired source combinations at all receptors for any specified day 
or time period within the day;
    <bullet> 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 SF<INF>6 Tracer Data and SO<INF>2 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 (O<INF>3) concentrations under short-term, episodic 
conditions lasting one or two days resulting from emissions of 
oxides of nitrogen (NO<INF>x), 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 NO<INF>2 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, 
NO<INF>2 photolysis rates; and ozone surface uptake resistance along 
with associated gridded vegetation (scaling) factors.
c. Output
    Printed output includes:
    <bullet> Gridded instantaneous concentration fields at user-
specified time intervals for user-specified pollutants and grid 
levels;
    <bullet> 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 (O<INF>3) formation from oxides 
of nitrogen (NO<INF>x) 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<SUP>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 NO<INF>x-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 NO<INF>x 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 O<INF>3 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 NO<INF>x 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 NO<INF>2 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:
    <bullet> Cumulative effect of changes in aircraft operations, 
point source and mobile source emissions at airports or air bases;
    <bullet> Simple terrain;
    <bullet> Transport distances less than 50 kilometers; and
    <bullet> 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:
    <bullet> A monthly and yearly emission inventory report for each 
source entered; and
    <bullet> 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:
    <bullet> Elevated point sources;
    <bullet> Terrain elevations above stack top;
    <bullet> Rural or urban areas;
    <bullet> Transport distances less than 50 kilometers; and
    <bullet> 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.,
    <bullet> Input meteorological data from ``SURFACE'' and 
``PROFILE''
    <bullet> Stack data for each source
    <bullet> Terrain information
    <bullet> Receptor information
    <bullet> Source-receptor location (line printer map).
    In addition, if the case-study option is selected, the listing 
includes:
    <bullet> Meteorological variables at plume height
    <bullet> Geometrical relationships between the source and the 
hill
    <bullet> Plume characteristics at each receptor, i.e.,
    -> distance in along-flow and cross flow direction
    -> effective plume-receptor height difference
    -> effective <greek-s><INF>y & <greek-s><INF>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 (H<INF>c) 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 H<INF>c, and 
the Froude number above H<INF>c.

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:
    <bullet> Interpolating between observations above and below the 
plume height, or
    <bullet> 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 (H<INF>c); see ``Plume 
Behavior''.

j. Horizontal Dispersion

    Horizontal dispersion for stable/neutral conditions is related 
to the turbulence velocity scale for lateral fluctuations, 
<greek-s><INF>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., <greek-s><INF>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<SUP>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 
(SO<INF>2 and SO<INF>4<SUP>=), 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., SO<INF>2 and SO<INF>4<SUP>=).

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:
    <bullet> Listing of model input data;
    <bullet> Plume centerline elevation, mole fraction, 
concentration, density, and temperature at each downwind distance;
    <bullet> <greek-s><INF>y and <greek-s><INF>z values at each 
downwind distance;
    <bullet> 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);
    <bullet> 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/N<INF>2O<INF>4 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/NH<INF>3 Test Data. USAF Engineering and Services Laboratory, 
Final Report.

o. Operating Information

    The model requires either a VAX computer or an IBM<Register>--
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 NO<INF>2 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 SO<INF>2, NO<INF>x, TSP, fraction of NO<INF>x as 
NO<INF>2, 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 O<INF>3 
and NO<INF>x, deposition velocity of TSP, sulfate, nitrate, SO<INF>2 
and NO<INF>x, 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 <greek-L>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 SO<INF>2 and 
NO<INF>x emissions), primary TSP and NO<INF>2 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: 
<a href="/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="add4deccedfff99b9b83cec2c0">[email protected]</a>

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 
<greek-D><greek-EE>/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 
<greek-D><greek-EE>/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 (SO<INF>2, 
SO<INF>4, NO<INF>X, HNO<INF>3, NO<INF>3). 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: SO<INF>2, SO<INF>4, NO<INF>x, HNO<INF>3, NO<INF>3.

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:
    <bullet> Location, surface roughness estimates, and altitude 
contours.
    <bullet> 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:
    <bullet> A curvilinear mesh or a chopped Cartesian mesh is 
generated automatically or manually;
    <bullet> Thermal and gravity effects are simulated by full 
gravity (heavy gases), no gravity (well mixed light gases at ambient 
temperature), and Boussinesq approximation methods;
    <bullet> 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.
    <bullet> 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).
    <bullet> 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

    <bullet> 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.
    <bullet> Visibility: Predicts plume visibility and surface 
deposition of aerosol.
    <bullet> 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.
    <bullet> Ozone formation and dispersion: The photochemical model 
computes ozone formation and dispersion at street level in the 
presence of sunlight.
    <bullet> Roadway Pollutants: Accounts for heat and turbulence 
due to vehicular movement. Emissions are based on traffic volume and 
emission factors.
    <bullet> Odor Dispersion: Identifies odor sources for waste 
water plants.
    <bullet> 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 NO<INF>2 and particles 
separately as well as together to avoid cancellation of NO<INF>2 
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 SO<INF>2, NO<INF>X, 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 
(0.1-1.0<greek-m>m) and coarse (1.0-10.<greek-m>m) size modes; and 
deposition velocities for SO<INF>2, NO<INF>X, 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 NO<INF>X, NO<INF>2, O<INF>3, and SO<INF>2, 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, NO<INF>2, SO<INF>2, H<INF>2SO<INF>4, 
HNO<INF>3, O<INF>3, 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, NO<INF>2, O<INF>3, OH, O(<SUP>1D), 
SO<INF>2, HNO<INF>3, and H<INF>2SO<INF>4 is treated by means of nine 
reactions. Steady state approximations are used for radicals and for 
the NO/NO<INF>2/O<INF>3 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 <greek-s><INF>y 
and <greek-s><INF>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 <greek-s><INF>y and 
<greek-s><INF>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, NO<INF>2, O<INF>3, 
SO<INF>2, SO<INF>4, 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 NO<INF>X-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:
    <bullet> Tall stationary point sources located at a shoreline of 
any large body of water;
    <bullet> Rural or urban areas;
    <bullet> Flat terrain;
    <bullet> Transport distances less than 50 km;
    <bullet> 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

    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

    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 <SUP>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.
---------------------------------------------------------------------------

    <bullet> Urban areas <SUP>2
---------------------------------------------------------------------------

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

    <bullet> PSD Class I areas
    <bullet> Nonattainment areas \2\
    <bullet> Topographic features (terrain, lakes, river valleys, 
etc.) \2\
    <bullet> Other major existing sources \2\
    <bullet> Other major sources subject to PSD requirements
    <bullet> NWS meteorological observations (surface and upper air)
    <bullet> On-site/local meteorological observations (surface and 
upper air)
    <bullet> State/local/on-site air quality monitoring locations 
\2\
    <bullet> 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:
    <bullet> 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.
    <bullet> Population
    -> total
    -> density
    <bullet> 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)
    <bullet> Actual and allowable annual emission rates (g/s) and 
operating rates <SUP>3
---------------------------------------------------------------------------

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

    <bullet> Maximum design load short-term emission rate (g/s) \3\
    <bullet> 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)
    <bullet> Area source emissions (rates, size of area, height of 
area source)\3\
    <bullet> 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
    <bullet>  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.)
    <bullet> Anticipated growth changes
    4. Air quality monitoring data:
    <bullet> 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) <SUP>4
---------------------------------------------------------------------------

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

    <bullet> Comparison with standards
    <bullet> 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:
    <bullet> 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
    <bullet> 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)
    <bullet> Discussion of topographic/land use influences
    6. Air quality modeling analyses:
    <bullet> 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
    <bullet> Evaluate downwash if stack height is less than GEP
    <bullet> Define worst case meteorology
    <bullet> Determine background and document method
    --long-term
    --short-term
    <bullet> Provide topographic map(s) of receptor network with 
respect to location of all sources
    <bullet> Follow current guidance on selection of receptor sites 
for refined analyses
    <bullet> Include receptor terrain heights (if applicable) used 
in analyses
    <bullet> Compare model estimates with measurements considering 
the upper ends of the frequency distribution
    <bullet> Determine extent of significant impact; provide maps
    <bullet> 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:
    <bullet> NAAQS
    <bullet> PSD increments
    <bullet> Emission offset impacts if nonattainment
    8. Documentation and guidelines for modeling methodology:
    <bullet> 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: ________________  <SUP>1           ________________ <SUP>2           ________________ <SUP>2                                
                                                                                                                                                        
Concentration due to modeled        ....................<SUP>..  ....................<SUP>..  ....................<SUP>..  ....................<SUP>.  ....................<SUP>.
 source (<greek-m>g/m<SUP>3).                                                                                                                                
Background concentration (<greek-   .....<SUP>.................  .....<SUP>.................  .....<SUP>.................  .....<SUP>................  .....<SUP>................
 m>g/m<SUP>3).                                                                                                                                               
Total concentration (<greek-m>g/    .<SUP>.....................  .<SUP>.....................  .<SUP>.....................  .<SUP>....................  .<SUP>....................
 m<SUP>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.................  ......................  ......................  ......................  .....................  .....................
--------------------------------------------------------------------------------------------------------------------------------------------------------
 <SUP>1 Use separate sheet for each pollutant (SO<INF>2, PM-10, CO, NO<INF>X, HC, Pb, Hg, Asbestos, etc.).                                                             
<SUP>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: ________________  <SUP>1           ________________ <SUP>2           ________________ <SUP>2                                
                                                                                                                                                        
Concentration due to modeled        ....................<SUP>..  ....................<SUP>..  ....................<SUP>..  ....................<SUP>.  ....................<SUP>.
 source (<greek-m>g/m<SUP>3).                                                                                                                                
Background concentration (<greek-   .....<SUP>.................  .....<SUP>.................  .....<SUP>.................  .....<SUP>................  .....<SUP>................
 m>g/m<SUP>3).                                                                                                                                               
Total concentration (<greek-m>g/    .<SUP>.....................  .<SUP>.....................  .<SUP>.....................  .<SUP>....................  .<SUP>....................
 m<SUP>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.................  ......................  ......................  ......................  .....................  .....................
--------------------------------------------------------------------------------------------------------------------------------------------------------
<SUP>1 Use separate sheet for each pollutant (SO<INF>2, PM-10, CO, NO<INF>x, HC, Pb, Hg, Asbestos, etc.).                                                              
<SUP>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  .............<SUP>.........  .............<SUP>.........  .............<SUP>........  .............<SUP>........  .............<SUP>........
 (<greek-m>g/m<SUP>3).                                                                                                                                       
Background concentration (<greek-    .....<SUP>.................  .....<SUP>.................  .....<SUP>................  .....<SUP>................  .....<SUP>................
 m>g/m<SUP>3).                                                                                                                                               
Total concentration (<greek-m>g/m<SUP>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 (SO<INF>2, PM-10, CO, NO<INF>X, 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 <SUP>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


</pre><script data-cfasync="false" src="/cdn-cgi/scripts/d07b1474/cloudflare-static/email-decode.min.js"></script></body>
</html>