41838 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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.

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outdated models are removed from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.

SUPPLEMENTARY INFORMATION:

Background 1

The purpose of the Guideline is to

promote consistency in the use of

modeling within the air management

process. The Guideline provides model

users with a common basis for

estimating pollution concentrations,

assessing control strategies and

specifying emission limits; these

activities are regulated at 40 CFR

51.112, 51.117, 51.150, 51.160, 51.166,

and 51.21. The Guideline was originally

published in April 1978. It was

incorporated by reference in the

regulations for the Prevention of

Significant Deterioration of Air Quality

in June 1978. The Guideline was

subsequently revised in 1986, and later

updated with supplement A in 1987 and

supplement B in July 1993. The

revisions in supplement B included

techniques and guidance for situations

where specific procedures had not

previously been available, and also

improved several previously adopted

techniques. As mentioned before, the

Guideline was published as appendix W

to 40 CFR part 51 when supplement B

was promulgated.

During the public comment period for

supplement B, EPA received requests to

consider several additional new

modeling techniques and suggestions

for enhanced technical guidance.

However, because there was not

sufficient time for the public to review

the new techniques and technical

guidance before promulgation of

supplement B, the new models and

enhanced technical guidance could not

be included in the supplement B

rulemaking. Thus, in a subsequent

regulatory proposal, EPA proposed to

further revise the Guideline with

supplement C and sought public

comment on four specific items. After

reviewing and addressing public

comments, EPA promulgated the last

revision in August 1995.

Final Action

Today’s action republishes appendix

W to 40 CFR part 51 and, in large part,

is pursuant to an agreement between

EPA and the Office of the Federal

Register (OFR) to reorganize appendix

W to conform with normal CFR format

imposed by OFR. This reorganization

mainly involves the systematic

identification of paragraphs, in this case

using sequential letters of the alphabet.

As a practical matter, such a format

should facilitate the process by which

future revisions of appendix W are

made, in which reference to specific

paragraphs can be more easily made.

Because the appendices (A, B, and C) do

not inherently lend themselves to the

sequencing structure imposed on the

rest of appendix W, these appendices

are organized much as they have been

in the past. EPA has made an agreement

with OFR that, when future revisions

become necessary to appendix A or B,

the entire model description will be set

out in the amendatory instruction.

Likewise, appendix C would be set out

in its entirety.

Another element of this action

involves models that are listed in

appendix B (summaries of Alternative

Air quality Models) of appendix W,

which are available for use on a case-bycase 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, Randomwalk 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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 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.
41840 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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. § 51.112 is amended by revising

paragraph (a)(1) and the first sentence of

paragraph (a)(2) to read as follows:

§ 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. § 51.160 is amended by revising

paragraph (f)(1) and the first sentence of

paragraph (f)(2) to read as follows:

§ 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. § 51.166 is amended by revising

paragraph (l)(1) and the first sentence of

paragraph (l)(2) to read as follows:

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

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
41842 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41843

technical approach, prior to the actual

analyses, helps avoid misunderstandings

concerning the final results and may reduce

the later need for additional analyses. The

use of an air quality checklist, such as

presented in Appendix C, and the

preparation of a written protocol help to keep

misunderstandings at a minimum.

c. It should not be construed that the

preferred models identified here are to be

permanently used to the exclusion of all

others or that they are the only models

available for relating emissions to air quality.

The model that most accurately estimates

concentrations in the area of interest is

always sought. However, designation of

specific models is needed to promote

consistency in model selection and

application.

d. The 1980 solicitation of new or different

models from the technical community 7 and

the program whereby these models are

evaluated, established a means by which new

models are identified, reviewed and made

available in the Guideline. There is a

pressing need for the development of models

for a wide range of regulatory applications.

Refined models that more realistically

simulate the physical and chemical process

in the atmosphere and that more reliably

estimate pollutant concentrations are

required. Thus, the solicitation of models is

considered to be continuous.

3.1 Preferred Modeling Techniques

3.1.1 Discussion

a. EPA has developed approximately 10

models suitable for regulatory application.

More than 20 additional models were

submitted by private developers for possible

inclusion in the Guideline. These refined

models have all been organized into eight

categories of use: rural, urban industrial

complex, reactive pollutants, mobile sources,

complex terrain, visibility, and long range

transport. They are undergoing an intensive

evaluation by category. The evaluation

exercises 8 9 10 include statistical measures of

model performance in comparison with

measured air quality data as suggested by the

American Meteorological Society 11 and,

where possible, peer scientific reviews.12 13 l4

b. When a single model is found to perform

better than others in a given category, it is

recommended for application in that category

as a preferred model and listed in Appendix

A. If no one model is found to clearly

perform better through the evaluation

exercise, then the preferred model listed in

Appendix A is selected on the basis of other

factors such as past use, public familiarity,

cost or resource requirements, and

availability. No further evaluation of a

preferred model is required if the source

follows EPA recommendations specified for

the model in the Guideline. The models not

specifically recommended for use in a

particular category are summarized in

Appendix B. These models should be

compared with measured air quality data

when they are used for regulatory

applications consistent with

recommendations in Section 3.2.

c. The solicitation of new refined models

which are based on sounder scientific

principles and which more reliably estimate

pollutant concentrations is considered by

EPA to be continuous. Models that are

submitted in accordance with the provisions

outlined in the Federal Register notice of

March 1980 (45 FR 20157) 7 will be evaluated

as submitted. These requirements are:

i. The model must be computerized and

functioning in a common Fortran language

suitable for use on a variety of computer

systems.

ii. The model must be documented in a

user’s guide which identifies the

mathematics of the model, data requirements

and program operating characteristics at a

level of detail comparable to that available

for currently recommended models, e.g., the

Industrial Source Complex (ISC) model.

iii. The model must be accompanied by a

complete test data set including input

parameters and output results. The test data

must be included in the user’s guide as well

as provided in computer-readable form.

iv. The model must be useful to typical

users, e.g., State air pollution control

agencies, for specific air quality control

problems. Such users should be able to

operate the computer program(s) from

available documentation.

v. The model documentation must include

a comparison with air quality data or with

other well-established analytical techniques.

vi. The developer must be willing to make

the model available to users at reasonable

cost or make it available for public access

through the National Technical Information

Service; the model cannot be proprietary.

d. The evaluation process will include a

determination of technical merit, in

accordance with the above six items

including the practicality of the model for

use in ongoing regulatory programs. Each

model will also be subjected to a

performance evaluation for an appropriate

data base and to a peer scientific review.

Models for wide use (not just an isolated

case!) found to perform better, based on an

evaluation for the same data bases used to

evaluate models in Appendix A, will be

proposed for inclusion as preferred models in

future Guideline revisions.

3.1.2 Recommendations

a. Appendix A identifies refined models

that are preferred for use in regulatory

applications. If a model is required for a

particular application, the user should select

a model from Appendix A. These models

may be used without a formal demonstration

of applicability as long as they are used as

indicated in each model summary of

Appendix A. Further recommendations for

the application of these models to specific

source problems are found in subsequent

sections of the Guideline.

b. If changes are made to a preferred model

without affecting the concentration estimates,

the preferred status of the model is

unchanged. Examples of modifications that

do not affect concentrations are those made

to enable use of a different computer or those

that affect only the format or averaging time

of the model results. However, when any

changes are made, the Regional

Administrator should require a test case

example to demonstrate that the

concentration estimates are not affected.

c. A preferred model should be operated

with the options listed in Appendix A as

‘‘Recommendations for Regulatory Use.’’ If

other options are exercised, the model is no

longer ‘‘preferred.’’ Any other modification to

a preferred model that would result in a

change in the concentration estimates

likewise alters its status as a preferred model.

Use of the model must then be justified on

a case-by-case basis.

3.2 Use of Alternative Models

3.2.1 Discussion

a. Selection of the best techniques for each

individual air quality analysis is always

encouraged, but the selection should be done

in a consistent manner. A simple listing of

models in this guide cannot alone achieve

that consistency nor can it necessarily

provide the best model for all possible

situations. An EPA document, ‘‘Interim

Procedures for Evaluating Air Quality

Models’’,15 16 has been prepared to assist in

developing a consistent approach when

justifying the use of other than the preferred

modeling techniques recommended in this

guide. An alternative to be considered to the

performance measures contained in Chapter

3 of this document is set forth in another EPA

document ‘‘Protocol for Determining the Best

Performing Model’’.17 The procedures in

both documents provide a general framework

for objective decision-making on the

acceptability of an alternative model for a

given regulatory application. The documents

contain procedures for conducting both the

technical evaluation of the model and the

field test or performance evaluation.

b. This section discusses the use of

alternate modeling techniques and defines

three situations when alternative models may

be used.

3.2.2 Recommendations

a. Determination of acceptability of a

model is a Regional Office responsibility.

Where the Regional Administrator finds that

an alternative model is more appropriate

than a preferred model, that model may be

used subject to the recommendations below.

This finding will normally result from a

determination that (1) A preferred air quality

model is not appropriate for the particular

application; or (2) a more appropriate model

or analytical procedure is available and is

applicable.

b. An alternative model should be

evaluated from both a theoretical and a

performance perspective before it is selected

for use. There are three separate conditions

under which such a model will normally be

approved for use: (1) If a demonstration can

be made that the model produces

concentration estimates equivalent to the

estimates obtained using a preferred model;

(2) if a statistical performance evaluation has

been conducted using measured air quality

data and the results of that evaluation

indicate the alternative model performs

better for the application than a comparable

model in Appendix A; and (3) if there is no

preferred model for the specific application

but a refined model is needed to satisfy

regulatory requirements. Any one of these

three separate conditions may warrant use of

an alternative model. Some known
41844 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

aAnother 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.

alternative models that are applicable for

selected situations are contained in

Appendix B. However, inclusion there does

not infer any unique status relative to other

alternative models that are being or will be

developed in the future.

c. Equivalency is established by

demonstrating that the maximum or highest,

second highest concentrations are within 2

percent of the estimates obtained from the

preferred model. The option to show

equivalency is intended as a simple

demonstration of acceptability for an

alternative model that is so nearly identical

(or contains options that can make it

identical) to a preferred model that it can be

treated for practical purposes as the preferred

model. Two percent was selected as the basis

for equivalency since it is a rough

approximation of the fraction that PSD Class

I increments are of the NAAQS for SO2, i.e.,

the difference in concentrations that is

judged to be significant. However,

notwithstanding this demonstration, use of

models that are not equivalent may be used

when the conditions of paragraph e of this

section are satisfied.

d. The procedures and techniques for

determining the acceptability of a model for

an individual case based on superior

performance is contained in the document

entitled ‘‘Interim Procedures for Evaluating

Air Quality Models’’, 15 and should be

followed, as appropriate.a Preparation and

implementation of an evaluation protocol

which is acceptable to both control agencies

and regulated industry is an important

element in such an evaluation.

e. When no Appendix A model is

applicable to the modeling problem, an

alternative refined model may be used

provided that:

i. The model can be demonstrated to be

applicable to the problem on a theoretical

basis; and

ii. The data bases which are necessary to

perform the analysis are available and

adequate; and

iii. Performance evaluations of the model

in similar circumstances have shown that the

model is not biased toward underestimates;

or

iv. After consultation with the EPA

Regional Office, a second model is selected

as a baseline or reference point for

performance and the interim procedures 15

protocol 17 are then used to demonstrate that

the proposed model performs better than the

reference model.

3.3 Availability of Supplementary Modeling

Guidance

a. The Regional Administrator has the

authority to select models that are

appropriate for use in a given situation.

However, there is a need for assistance and

guidance in the selection process so that

fairness and consistency in modeling

decisions is fostered among the various

Regional Offices and the States. To satisfy

that need, EPA established the Model

Clearinghouse and also holds periodic

workshops with headquarters, Regional

Office and State modeling representatives.

3.3.1 The Model Clearinghouse

3.3.1.1 Discussion

a. The Model Clearinghouse is the single

EPA focal point for review of air quality

simulation models proposed for use in

specific regulatory applications. Details

concerning the Clearinghouse and its

operation are found in the document, ‘‘Model

Clearinghouse: Operational Plan.’’ 6 Three

primary functions of the Clearinghouse are:

i. Review of decisions proposed by EPA

Regional Offices on the use of modeling

techniques and data bases.

ii. Periodic visits to Regional Offices to

gather information pertinent to regulatory

model usage.

iii. Preparation of an annual report

summarizing activities of the Clearinghouse

including specific determinations made

during the course of the year.

3.3.1.2 Recommendations

a. The Regional Administrator may request

assistance from the Model Clearinghouse

after an initial evaluation and decision has

been reached concerning the application of a

model, analytical technique or data base in

a particular regulatory action. The

Clearinghouse may also consider and

evaluate the use of modeling techniques

submitted in support of any regulatory

action. Additional responsibilities are: (1)

Review proposed action for consistency with

agency policy; (2) determine technical

adequacy; and (3) make recommendations

concerning the technique or data base.

3.3.2 Regional Meteorologists Workshops

13.3.2.1 Discussion

a. EPA conducts an annual in-house

workshop for the purpose of mutual

discussion and problem resolution among

Regional Office modeling specialists, EPA

research modeling experts, EPA Headquarters

modeling and regulatory staff and

representatives from State modeling

programs. A summary of the issues resolved

at previous workshops was issued in 1981 as

‘‘Regional Workshops on Air Quality

Modeling: A Summary Report.’’ 17 That

report clarified procedures not specifically

defined in the 1978 version of the Guideline

and was issued to ensure the consistent

interpretation of model requirements from

Region to Region. Similar workshops for the

purpose of clarifying Guideline procedures or

providing detailed instructions for the use of

those procedures are anticipated in the

future.

3.3.2.2 Recommendations

a. The Regional Office should always be

consulted for information and guidance

concerning modeling methods and

interpretations of modeling guidance, and to

ensure that the air quality model user has

available the latest most up-to-date policy

and procedures.

4.0 Simple-Terrain Stationary Source

Models

4.1 Discussion

a. Simple terrain, as used in this section,

is considered to be an area where terrain

features are all lower in elevation than the

top of the stack of the source(s) in question.

The models recommended in this section are

generally used in the air quality impact

analysis of stationary sources for most

criteria pollutants. The averaging time of the

concentration estimates produced by these

models ranges from 1 hour to an annual

average.

b. Model evaluation exercises have been

conducted to determine the ‘‘best, most

appropriate point source model’’ for use in

simple terrain.8 12 However, no one model

has been found to be clearly superior. Based

on past use, public familiarity, and

availability, ISC is the recommended model

for a wide range of regulatory applications.

Similar determinations were made for the

other refined models that are identified in

section 4.2.

4.2 Recommendations

4.2.1 Screening Techniques

a. Point source screening techniques are an

acceptable approach to air quality analyses.

One such approach is contained in the EPA

document ‘‘Screening Procedures for

Estimating the Air Quality Impact of

Stationary Sources’’.18 A computerized

version of the screening technique, SCREEN,

is available.19 20 For the current version of

SCREEN, see 12.0 References.20

b. All screening procedures should be

adjusted to the site and problem at hand.

Close attention should be paid to whether the

area should be classified urban or rural in

accordance with Section 8.2.8. The

climatology of the area should be studied to

help define the worst-case meteorological

conditions. Agreement should be reached

between the model user and the reviewing

authority on the choice of the screening

model for each analysis, and on the input

data as well as the ultimate use of the results.

4.2.2 Refined Analytical Techniques

a. A brief description of preferred models

for refined applications is found in Appendix

A. Also listed in Appendix A are the model

input requirements, the standard options that

should be selected when running the

program, and output options.

b. When modeling for compliance with

short term NAAQS and PSD increments is of

primary concern, a short term model may

also be used to provide long term

concentration estimates. However, when

modeling sources for which long term

standards alone are applicable (e.g., lead),

then the long term models should be used.

The conversion from long term to short term

concentration averages by any transformation

technique is not acceptable in regulatory

applications.

5.0 Model Use in Complex Terrain

5.1 Discussion

a. For the purpose of the Guideline,

complex terrain is defined as terrain

exceeding the height of the stack being
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41845

modeled. Complex terrain dispersion models

are normally applied to stationary sources of

pollutants such as SO2 and particulates.

b. A major outcome from the EPA Complex

Terrain Model Development project has been

the publication of a refined dispersion model

(CTDM) suitable for regulatory application to

plume impaction assessments in complex

terrain.21 Although CTDM as originally

produced was only applicable to those hours

characterized as neutral or stable, a computer

code for all stability conditions,

CTDMPLUS,19 together with a user’s guide,22

and on-site meteorological and terrain data

processors,23 24 is now available. Moreover,

CTSCREEN,19 25 a version of CTDMPLUS that

does not require on-site meteorological data

inputs, is also available as a screening

technique.

c. The methods discussed in this section

should be considered in two categories: (1)

Screening techniques, and (2) the refined

dispersion model, CTDMPLUS, discussed

below and listed in Appendix A.

d. Continued improvements in ability to

accurately model plume dispersion in

complex terrain situations can be expected,

e.g., from research on lee side effects due to

terrain obstacles. New approaches to improve

the ability of models to realistically simulate

atmospheric physics, e.g., hybrid models

which incorporate an accurate wind field

analysis, will ultimately provide more

appropriate tools for analyses. Such hybrid

modeling techniques are also acceptable for

regulatory applications after the appropriate

demonstration and evaluation.15

5.2 Recommendations

a. Recommendations in this section apply

primarily to those situations where the

impaction of plumes on terrain at elevations

equal to or greater than the plume centerline

during stable atmospheric conditions are

determined to be the problem. If a violation

of any NAAQS or the controlling increment

is indicated by using any of the preferred

screening techniques, then a refined complex

terrain model may be used. Phenomena such

as fumigation, wind direction shear, lee-side

effects, building wake- or terrain-induced

downwash, deposition, chemical

transformation, variable plume trajectories,

and long range transport are not addressed by

the recommendations in this section.

b. Where site-specific data are used for

either screening or refined complex terrain

models, a data base of at least 1 full-year of

meteorological data is preferred. If more data

are available, they should be used.

Meteorological data used in the analysis

should be reviewed for both spatial and

temporal representativeness.

c. Placement of receptors requires very

careful attention when modeling in complex

terrain. Often the highest concentrations are

predicted to occur under very stable

conditions, when the plume is near, or

impinges on, the terrain. The plume under

such conditions may be quite narrow in the

vertical, so that even relatively small changes

in a receptor’s location may substantially

affect the predicted concentration. Receptors

within about a kilometer of the source may

be even more sensitive to location. Thus, a

dense array of receptors may be required in

some cases. In order to avoid excessively

large computer runs due to such a large array

of receptors, it is often desirable to model the

area twice. The first model run would use a

moderate number of receptors carefully

located over the area of interest. The second

model run would use a more dense array of

receptors in areas showing potential for high

concentrations, as indicated by the results of

the first model run.

d. When CTSCREEN or CTDMPLUS is

used, digitized contour data must be first

processed by the CTDM Terrain Processor 23

to provide hill shape parameters in a format

suitable for direct input to CTDMPLUS. Then

the user supplies receptors either through an

interactive program that is part of the model

or directly, by using a text editor; using both

methods to select receptors will generally be

necessary to assure that the maximum

concentrations are estimated by either model.

In cases where a terrain feature may ‘‘appear

to the plume’’ as smaller, multiple hills, it

may be necessary to model the terrain both

as a single feature and as multiple hills to

determine design concentrations.

e. The user is encouraged to confer with

the Regional Office if any unresolvable

problems are encountered with any screening

or refined analytical procedures, e.g.,

meteorological data, receptor siting, or terrain

contour processing issues.

5.2.1 Screening Techniques

a. Five preferred screening techniques are

currently available to aid in the evaluation of

concentrations due to plume impaction

during stable conditions: (1) for 24-hour

impacts, the Valley Screening Technique 19

as outlined in the Valley Model User’s

Guide; 26 (2) CTSCREEN,19 as outlined in the

CTSCREEN User’s Guide; 25 (3) COMPLEX

I; 19 (4) SHORTZ/LONGZ; 19 27 and (5) Rough

Terrain Dispersion Model (RTDM) 19 90 in its

prescribed mode described below. As

appropriate, any of these screening

techniques may be used consistent with the

needs, resources, and available data of the

user.

b. The Valley Model, COMPLEX I,

SHORTZ/LONGZ, and RTDM should be used

only to estimate concentrations at receptors

whose elevations are greater than or equal to

plume height. For receptors at or below stack

height, a simple terrain model should be

used (see Chapter 4). Receptors between

stack height and plume height present a

unique problem since none of the above

models were designed to handle receptors in

this narrow regime, the definition of which

will vary hourly as meteorological conditions

vary. CTSCREEN may be used to estimate

concentrations under all stability conditions

at all receptors located ‘‘on terrain’’ above

stack top, but has limited applicability in

multi-source situations. As a result, the

estimation of concentrations at receptors

between stack height and plume height

should be considered on a case-by-case basis

after consultation with the EPA Regional

Office; the most appropriate technique may

be a function of the actual source(s) and

terrain configuration unique to that

application. One technique that will

generally be acceptable, but is not necessarily

preferred for any specific application,

involves applying both a complex terrain

model (except for the Valley Model) and a

simple terrain model. The Valley Model

should not be used for any intermediate

terrain receptor. For each receptor between

stack height and plume height, an hour-byhour 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),
41846 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

standard deviation of horizontal and vertical

wind speeds (σv, σG5W), vertical potential

temperature gradient (dθ/dz), friction

velocity (ux), Monin-Obukhov length (L),

mixing height (zi) as a function of terrain

height, and wind directions for both neutral/

stable conditions and unstable convective

conditions. Table 5–1 contains the matrix of

meteorological variables that is used for each

CTSCREEN analysis. There are 96

combinations, including exceptions, for each

wind direction for the neutral/stable case,

and 108 combinations for the unstable case.

The specification of wind direction, however,

is handled internally, based on the source

and terrain geometry. The matrix was

developed from examination of the range of

meteorological variables associated with

maximum monitored concentrations from the

data bases used to evaluate the performance

of CTDMPLUS. Although CTSCREEN is

designed to address a single source scenario,

there are a number of options that can be

selected on a case-by-case basis to address

multi-source situations. However, the

Regional Office should be consulted, and

concurrence obtained, on the protocol for

modeling multiple sources with CTSCREEN

to ensure that the worst case is identified and

assessed. The maximum concentration

output from CTSCREEN represents a worstcase 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 221⁄2° 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 sourceplume-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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 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 caseby-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

σv (m/s) ............................................................................................. 0.3 0.75

σw (m/s) ............................................................................................ 0.08 0.15 0.30 0.75

DQ/Dz (K/m) ..................................................................................... 0.01 0.02 0.035

WD (Wind direction optimized internally for each meteorological combination)

Exceptions:

(1) If U ≤ 2 m/s and σv ≥ 0.3 m/s, then include σw = 0.04 m/s.

(2) If σw = 0.75 m/s and U ≥ 3.0 m/s, then DU/Dz is limited to ≤ 0.01 K/m.

(3) If U ≥ 4 m/s, then σw ≥ 0.15 m/s.

(4) σw ≤ σv

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

ux (m/s) ........................................................................................... 0.1 0.3 0.5

L (m) ............................................................................................... ¥10 ¥50 ¥90 DU/Dz (K/m) 0.030 (potential temperature gradient above zi)

zi (m) ............................................................................................... 0.5h 1.0h 1.5h

(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.
41848 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 height ................. See Section 5.2.1.4.

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, 0.14, 0.2, 0.3 (default).Wind profile exponents.

PR006 ............................ ICOEF ............................ 3 (default) ........................................... Briggs Rural/ASME139 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° sectors.

PR016 to 019; 021; and

024.

IY, IZ, IRVPTG,

IHVPTG; IEPS; IEMIS.

0 ......................................................... Hourly values of turbulence, vertical potential temperature gradient, wind speed profile exponents, and stack emissions are not used.

6.0 Models for Ozone, Carbon Monoxide

and Nitrogen Dioxide

6.1 Discussion

a. Models discussed in this section are

applicable to pollutants often associated with

mobile sources, e.g., ozone (O3), carbon

monoxide (CO) and nitrogen dioxide (NO2).

Where stationary sources of CO and NO2 are

of concern, the reader is referred to Sections

4 and 5

b. A control agency with jurisdiction over

areas with significant ozone problems and

which has sufficient resources and data to

use a photochemical dispersion model is

encouraged to do so. Experience with and

evaluations of the Urban Airshed Model

show it to be an acceptable, refined

approach, and better data bases are becoming

available that support the more sophisticated

analytical procedures. However, empirical

models (e.g., EKMA) fill the gap between

more sophisticated photochemical dispersion

models and proportional (rollback) modeling

techniques and may be the only applicable

procedure if the available data bases are

insufficient for refined dispersion modeling.

c. Models for assessing the impact of

carbon monoxide emissions are needed for a

number of different purposes, e.g., to

evaluate the effects of point sources,

congested intersections and highways, as

well as the cumulative effect on ambient CO

concentrations of all sources of CO in an

urban area.94 95

d. Nitrogen oxides are reactive and also an

important contribution to the photochemical

ozone problem. They are usually of most

concern in areas of high ozone

concentrations. Unless suitable

photochemical dispersion models are used,

assumptions regarding the conversion of NO

to NO2 are required when modeling. Sitespecific conversion factors may be

developed. If site-specific conversion factors

are not available or photochemical models

are not used, NO2 modeling should be

considered only a screening procedure.

6.2 Recommendations

6.2.1 Models for Ozone

a. The Urban Airshed Model (UAM)19 28 is

recommended for photochemical or reactive

pollutant modeling applications involving

entire urban areas. To ensure proper

execution of this numerical model, users

must satisfy the extensive input data

requirements for the model as listed in

Appendix A and the users guide. Users are

also referred to the ‘‘Guideline for Regulatory

Application of the Urban Airshed Model’’ 29

for additional data requirements and

procedures for operating this model.

b. The empirical model, City-specific

EKMA,19 30–33 has limited applicability for

urban ozone analyses. Model users should

consult the appropriate Regional Office on a

case-by-case basis concerning acceptability of

this modeling technique.

c. Appendix B contains some additional

models that may be applied on a case-by-case

basis for photochemical or reactive pollutant

modeling. Other photochemical models,

including multi-layered trajectory models,

that are available may be used if shown to

be appropriate. Most photochemical

dispersion models require emission data on

individual hydrocarbon species and may

require three dimensional meteorological

information on an hourly basis. Reasonably

sophisticated computer facilities are also

often required. Because the input data are not

universally available and studies to collect

such data are very resource intensive, there

are only limited evaluations of those models.

d. For those cases which involve

estimating the impact on ozone

concentrations due to stationary sources of

VOC and NOX, whether for permitting or

other regulatory cases, the model user should

consult the appropriate Regional Office on

the acceptability of the modeling technique.

e. Proportional (rollback/forward)

modeling is not an acceptable procedure for

evaluating ozone control strategies.

6.2.2 Models for Carbon Monoxide

a. For analyzing CO impacts at roadway

intersections, users should follow the

procedures in the ‘‘Guideline for Modeling

Carbon Monoxide from Roadway

Intersections’’.34 The recommended model

for such analyses is CAL3QHC.35 This model

combines CALINE3 (already in Appendix A)

with a traffic model to calculate delays and

queues that occur at signalized intersections.

In areas where the use of either TEXIN2 or

CALINE4 has previously been established, its

use may continue. The capability exists for

these intersection models to be used in either

a screening or refined mode. The screening

approach is described in reference 34; a

refined approach may be considered on a

case-by-case basis. The latest version of the

MOBILE (mobile source emission factor)

model should be used for emissions input to

intersection models.

b. For analyses of highways characterized

by uninterrupted traffic flows, CALINE3 is

recommended, with emissions input from the

latest version of the MOBILE model.

c. The recommended model for urban

areawide CO analyses is RAM or Urban

Airshed Model (UAM); see Appendix A.

Information on SIP development and

requirements for using these models can be

found in references 34, 96, 97 and 98.

d. Where point sources of CO are of

concern, they should be treated using the

screening and refined techniques described

in Section 4 or 5 of the Guideline.
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41849

6.2.3 Models for Nitrogen Dioxide (Annual

Average)

a. A tiered screening approach is

recommended to obtain annual average

estimates of NO2 from point sources for New

Source Review analysis, including PSD, and

for SIP planning purposes. This multi-tiered

approach is conceptually shown in Figure

6–1 and described in paragraphs b and c of

this section. Figure 6–1 is as follows:

FIGURE 6–1.—MULTI-TIERED SCREENING APPROACH FOR ESTIMATING ANNUAL NO2 CONCENTRATIONS FROM

POINT SOURCES

Tier 1: Assume Total Conversion of NO to

NO2 

Tier 2: Multiply Annual NOX Estimate by Empirically Derived NO2/NOX Ratio.

b. For Tier 1 (the initial screen), use an

appropriate Gaussian model from Appendix

A to estimate the maximum annual average

concentration and assume a total conversion

of NO to NO2. If the concentration exceeds

the NAAQS and/or PSD increments for NO2,

proceed to the 2nd level screen.

c. For Tier 2 (2nd level) screening analysis,

multiply the Tier 1 estimate(s) by an

empirically derived NO2/NOX value of 0.75

(annual national default).36 An annual NO2/

NOX ratio differing from 0.75 may be used if

it can be shown that such a ratio is based on

data likely to be representative of the

location(s) where maximum annual impact

from the individual source under review

occurs. In the case where several sources

contribute to consumption of a PSD

increment, a locally derived annual NO2/

NOX ratio should also be shown to be

representative of the location where the

maximum collective impact from the new

plus existing sources occurs.

d. In urban areas, a proportional model

may be used as a preliminary assessment to

evaluate control strategies to meet the

NAAQS for multiple minor sources, i.e.

minor point, area and mobile sources of NOX;

concentrations resulting from major point

sources should be estimated separately as

discussed above, then added to the impact of

the minor sources. An acceptable screening

technique for urban complexes is to assume

that all NOX is emitted in the form of NO2

and to use a model from Appendix A for

nonreactive pollutants to estimate NO2

concentrations. A more accurate estimate can

be obtained by: (1) Calculating the annual

average concentrations of NOX with an urban

model, and (2) converting these estimates to

NO2 concentrations using an empirically

derived annual NO2/NOX ratio. A value of

0.75 is recommended for this ratio. However,

a spatially averaged annual NO2/NOX ratio

may be determined from an existing air

quality monitoring network and used in lieu

of the 0.75 value if it is determined to be

representative of prevailing ratios in the

urban area by the reviewing agency. To

ensure use of appropriate locally derived

annual NO2/NOX ratios, monitoring data

under consideration should be limited to

those collected at monitors meeting siting

criteria defined in 40 CFR Part 58, Appendix

D as representative of ‘‘neighborhood’’,

‘‘urban’’, or ‘‘regional’’ scales. Furthermore,

the highest annual spatially averaged NO2/

NOX ratio from the most recent 3 years of

complete data should be used to foster

conservatism in estimated impacts.

e. To demonstrate compliance with NO2

PSD increments in urban areas, emissions

from major and minor sources should be

included in the modeling analysis. Point and

area source emissions should be modeled as

discussed above. If mobile source emissions

do not contribute to localized areas of high

ambient NO2 concentrations, they should be

modeled as area sources. When modeled as

area sources, mobile source emissions should

be assumed uniform over the entire highway

link and allocated to each area source grid

square based on the portion of highway link

within each grid square. If localized areas of

high concentrations are likely, then mobile

sources should be modeled as line sources

with the preferred model ISCLT.

f. More refined techniques to handle

special circumstances may be considered on

a case-by-case basis and agreement with the

reviewing authority should be obtained. Such

techniques should consider individual

quantities of NO and NO2 emissions,

atmospheric transport and dispersion, and

atmospheric transformation of NO to NO2.

Where they are available, site-specific data

on the conversion of NO to NO2 may be used.

Photochemical dispersion models, if used for

other pollutants in the area, may also be

applied to the NOX problem.

7.0 Other Model Requirements

7.1 Discussion

a. This section covers those cases where

specific techniques have been developed for

special regulatory programs. Most of the

programs have, or will have when fully

developed, separate guidance documents that

cover the program and a discussion of the

tools that are needed. The following

paragraphs reference those guidance

documents, when they are available. No

attempt has been made to provide a

comprehensive discussion of each topic since

the reference documents were designed to do

that. This section will undergo periodic

revision as new programs are added and new

techniques are developed.

b. Other Federal agencies have also

developed specific modeling approaches for

their own regulatory or other requirements.

An example of this is the three-volume

manual issued by the U. S. Department of

Housing and Urban Development, ‘‘Air

Quality Considerations in Residential

Planning.’’ 37 Although such regulatory

requirements and manuals may have come

about because of EPA rules or standards, the

implementation of such regulations and the

use of the modeling techniques is under the

jurisdiction of the agency issuing the manual

or directive.

c. The need to estimate impacts at

distances greater than 50km (the nominal

distance to which EPA considers most

Gaussian models applicable) is an important

one especially when considering the effects

from secondary pollutants. Unfortunately,

models submitted to EPA have not as yet

undergone sufficient field evaluation to be

recommended for general use. Existing data

bases from field studies at mesoscale and

long range transport distances are limited in

detail. This limitation is a result of the

expense to perform the field studies required

to verify and improve mesoscale and long

range transport models. Particularly

important and sparse are meteorological data

adequate for generating three dimensional

wind fields. Application of models to

complicated terrain compounds the

difficulty. EPA has completed limited

evaluation of several long range transport

(LRT) models against two sets of field data.

The evaluation results are discussed in the

document, ‘‘Evaluation of Short-Term LongRange Transport Models.’’ 99 100 For the time

being, long range and mesoscale transport

models must be evaluated for regulatory use

on a case-by-case basis.

d. There are several regulatory programs

for which air pathway analysis procedures

and modeling techniques have been

developed. For continuous emission releases,

ISC forms the basis of many analytical

techniques. EPA is continuing to evaluate the

performance of a number of proprietary and

public domain models for intermittent and

non-stack emission releases. Until EPA

completes its evaluation, it is premature to

recommend specific models for air pathway

analyses of intermittent and non-stack

releases in the Guideline.

e. Regional scale models are used by EPA

to develop and evaluate national policy and

assist State and local control agencies. Two

such models are the Regional Oxidant Model

(ROM) 101 102 103 and the Regional Acid

Deposition Model (RADM).104 Due to the

level of resources required to apply these

models, it is not envisioned that regional

scale models will be used directly in most

model applications.

7.2 Recommendations

7.2.1 Fugitive Dust/Fugitive Emissions

a. Fugitive dust usually refers to the dust

put into the atmosphere by the wind blowing

over plowed fields, dirt roads or desert or

sandy areas with little or no vegetation.

Reentrained dust is that which is put into the

air by reason of vehicles driving over dirt

roads (or dirty roads) and dusty areas. Such

sources can be characterized as line, area or

volume sources. Emission rates may be based

on site-specific data or values from the

general literature.

b. Fugitive emissions are usually defined

as emissions that come from an industrial

source complex. They include the emissions

resulting from the industrial process that are

not captured and vented through a stack but

may be released from various locations

within the complex. Where such fugitive

emissions can be properly specified, the ISC

model, with consideration of gravitational

settling and dry deposition, is the

recommended model. In some unique cases

a model developed specifically for the

situation may be needed.

c. Due to the difficult nature of

characterizing and modeling fugitive dust

and fugitive emissions, it is recommended

that the proposed procedure be cleared by

the appropriate Regional Office for each

specific situation before the modeling

exercise is begun.
41850 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

b § 51.300–307.

c § 51.300–307.

dThe EPA refined formula height is defined as H

+ 1.5L (see Reference 46).

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 §§ 51.166(c) and 52.21(c).

b. Screening techniques like those

identified in Section 4 are also applicable to

PM–10 and to large particles. It is

recommended that subjectively determined

values for ‘‘half-life’’ or pollutant decay not

be used as a surrogate for particle removal.

Conservative assumptions which do not

allow removal or transformation are

suggested for screening. Proportional models

(rollback/forward) may not be applied for

screening analysis, unless such techniques

are used in conjunction with receptor

modeling.

c. Refined models such as those in Section

4.0 are recommended for PM–10 and large

particles. However, where possible, particle

size, gas-to-particle formation, and their

effect on ambient concentrations may be

considered. For urban-wide refined analyses

CDM 2.0 (long term) or RAM (short term)

should be used. ISC is recommended for

point sources of small particles and for

source-specific analyses of complicated

sources. No model recommended for general

use at this time accounts for secondary

particulate formation or other

transformations in a manner suitable for SIP

control strategy demonstrations. Where

possible, the use of receptor

models 38 39 105 106 107 in conjunction with

dispersion models is encouraged to more

precisely characterize the emissions

inventory and to validate source specific

impacts calculated by the dispersion model.

A SIP development guideline,108 model

reconciliation guidance,106 and an example

model application 109 are available to assist in

PM–10 analyses and control strategy

development.

d. Under certain conditions, recommended

dispersion models are not available or

applicable. In such circumstances, the

modeling approach should be approved by

the appropriate Regional Office on a case-bycase 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 caseby-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 §§ 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 μg/

m3. 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 casespecific review by the appropriate Regional

Office may be required.

c. In modeling the effect of traditional line

sources (such as a specific roadway or

highway) on lead air quality, dispersion

models applied for other pollutants can be

used. Dispersion models such as CALINE3

have been widely used for modeling carbon

monoxide emissions from highways.

However, where deposition is of concern, the

line source treatment in ISC may be used.

Also, where there is a point source in the

middle of a substantial road network, the

lead concentrations that result from the road

network should be treated as background (see

Section 9.2); the point source and any nearby

major roadways should be modeled

separately using the ISC model.

d. To model an entire major urban area or

to model areas without significant sources of

lead emissions, as a minimum a proportional

(rollback) model may be used for air quality

analysis. The rollback philosophy assumes

that measured pollutant concentrations are

proportional to emissions. However, urban or

other dispersion models are encouraged in

these circumstances where the use of such

models is feasible.

e. For further information concerning the

use of models in the development of lead

implementation plans, the documents

‘‘Supplementary Guidelines for Lead

Implementation Plans,’’ 40 and ‘‘Updated

Information on Approval and Promulgation

of Lead Implementation Plans,’’ 41 should be

consulted.

7.2.4. Visibility

a. The visibility regulations as promulgated

in December 1980 b require consideration of

the effect of new sources on the visibility

values of Federal Class I areas. The state of

scientific knowledge concerning identifying,

monitoring, modeling, and controlling

visibility impairment is contained in an EPA

report ‘‘Protecting Visibility: An EPA Report

to Congress’’.42 In 1985, EPA promulgated

Federal Implementation Plans (FIPs) for

states without approved visibility provisions

in their SIPs. A monitoring plan was

established as part of the FIPs.c

b. Guidance and a screening model,

VISCREEN, is contained in the EPA

document ‘‘Workbook for Plume Visual

Impact Screening and Analysis (Revised).’’ 43

VISCREEN can be used to calculate the

potential impact of a plume of specified

emissions for specific transport and

dispersion conditions. If a more

comprehensive analysis is required, any

refined model should be selected in

consultation with the EPA Regional Office

and the appropriate Federal Land Manager

who is responsible for determining whether

there is an adverse effect by a plume on a

Class I area.

c. PLUVUE II, listed in Appendix B, may

be applied on a case-by-case basis when

refined plume visibility evaluations are

needed. Plume visibility models have been

evaluated against several data sets.44, 45

7.2.5 Good Engineering Practice Stack

Height

a. The use of stack height credit in excess

of Good Engineering Practice (GEP) stack

height or credit resulting from any other

dispersion technique is prohibited in the

development of emission limitations by

§§ 51.118 and 51.164. The definitions of GEP

stack height and dispersion technique are

contained in § 51.100. Methods and

procedures for making the appropriate stack

height calculations, determining stack height

credits and an example of applying those

techniques are found in references 46, 47, 48,

and 49.

b. If stacks for new or existing major

sources are found to be less than the height

defined by EPA’s refined formula for

determining GEP height, d then air quality

impacts associated with cavity or wake

effects due to the nearby building structures

should be determined. Detailed downwash

screening procedures 18 for both the cavity

and wake regions should be followed. If more

refined concentration estimates are required,

the Industrial Source Complex (ISC) model

contains algorithms for building wake

calculations and should be used. Fluid

modeling can provide a great deal of

additional information for evaluating and

describing the cavity and wake effects.

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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41851

selected for use should be determined in

consultation with the EPA Regional Office

and the appropriate Federal Land Manager

(FLM). While the ultimate decision on

whether a Class I area is adversely affected

is the responsibility of the permitting

authority, the FLM has an affirmative

responsibility to protect air quality related

values that may be affected.

b. If LRT is determined to be important,

then estimates utilizing an appropriate

refined model for receptors at distances

greater than 50 km should be obtained.

MESOPUFF II, listed in Appendix B, may be

applied on a case-by-case basis when LRT

estimates are needed. Additional information

on applying this model is contained in the

EPA document ‘‘A Modeling Protocol For

Applying MESOPUFF II to Long Range

Transport Problems’’.111

7.2.7 Modeling Guidance for Other

Governmental Programs

a. When using the models recommended or

discussed in the Guideline in support of

programmatic requirements not specifically

covered by EPA regulations, the model user

should consult the appropriate Federal or

State agency to ensure the proper application

and use of that model. For modeling

associated with PSD permit applications that

involve a Class I area, the appropriate Federal

Land Manager should be consulted on all

modeling questions.

b. The Offshore and Coastal Dispersion

(OCD) model 112 was developed by the

Minerals Management Service and is

recommended for estimating air quality

impact from offshore sources on onshore, flat

terrain areas. The OCD model is not

recommended for use in air quality impact

assessments for onshore sources. Sources

located on or just inland of a shoreline where

fumigation is expected should be treated in

accordance with Section 8.2.9.

c. The Emissions and Dispersion Modeling

System (EDMS) 113 was developed by the

Federal Aviation Administration and the

United States Air Force and is recommended

for air quality assessment of primary

pollutant impacts at airports or air bases.

Regulatory application of EDMS is intended

for estimating the cumulative effect of

changes in aircraft operations, point source,

and mobile source emissions on pollutant

concentrations. It is not intended for PSD,

SIP, or other regulatory air quality analyses

of point or mobile sources at or peripheral to

airport property that are independent of

changes in aircraft operations. If changes in

other than aircraft operations are associated

with analyses, a model recommended in

Chapter 4, 5, or 6 should be used.

7.2.8 Air Pathway Analyses (Air Toxics and

Hazardous Waste)

a. Modeling is becoming an increasingly

important tool for regulatory control agencies

to assess the air quality impact of releases of

toxics and hazardous waste materials.

Appropriate screening techniques 114 115 for

calculating ambient concentrations due to

various well-defined neutrally buoyant toxic/

hazardous pollutant releases are available.

b. Several regulatory programs within EPA

have developed modeling techniques and

guidance for conducting air pathway

analyses as noted in references 116–129. ISC

forms the basis of the modeling procedures

for air pathway analyses of many of these

regulatory programs and, where identified, is

appropriate for obtaining refined ambient

concentration estimates of neutrally buoyant

continuous air toxic releases from traditional

sources. Appendix A to the Guideline

contains additional models appropriate for

obtaining refined estimates of continuous air

toxic releases from traditional sources.

Appendix B contains models that may be

used on a case-by-case basis for obtaining

refined estimates of denser-than-air

intermittent gaseous releases, e.g.,

DEGADIS; 130 guidance for the use of such

models is also available.131

c. Many air toxics models require input of

chemical properties and/or chemical

engineering variables in order to

appropriately characterize the source

emissions prior to dispersion in the

atmosphere; reference 132 is one source of

helpful data. In addition, EPA has numerous

programs to determine emission factors and

other estimates of air toxic emissions. The

Regional Office should be consulted for

guidance on appropriate emission estimating

procedures and any uncertainties that may be

associated with them.

8.0 General Modeling Considerations

8.1 Discussion

a. This section contains recommendations

concerning a number of different issues not

explicitly covered in other sections of this

guide. The topics covered here are not

specific to any one program or modeling area

but are common to nearly all modeling

analyses.

8.2 Recommendations

8.2.1 Design Concentrations

8.2.1.1 Design Concentrations for Criteria

Pollutants With Deterministic Standards

a. An air quality analysis for SO2, CO, Pb,

and NO2 is required to determine if the

source will (1) Cause a violation of the

NAAQS, or (2) cause or contribute to air

quality deterioration greater than the

specified allowable PSD increment. For the

former, background concentration (see

Section 9.2) should be added to the estimated

impact of the source to determine the design

concentration. For the latter, the design

concentration includes impact from all

increment consuming sources.

b. If the air quality analyses are conducted

using the period of meteorological input data

recommended in Section 9.3.1.2 (e.g., 5 years

of NWS data or 1 year of site-specific data),

then the design concentration based on the

highest, second-highest short term

concentration or long term average,

whichever is controlling, should be used to

determine emission limitations to assess

compliance with the NAAQS and to

determine PSD increments.

c. When sufficient and representative data

exist for less than a 5-year period from a

nearby NWS site, or when on-site data have

been collected for less than a full continuous

year, or when it has been determined that the

on site data may not be temporally

representative, then the highest

concentration estimate should be considered

the design value. This is because the length

of the data record may be too short to assure

that the conditions producing worst-case

estimates have been adequately sampled. The

highest value is then a surrogate for the

concentration that is not to be exceeded more

than once per year (the wording of the

deterministic standards). Also, the highest

concentration should be used whenever

selected worst-case conditions are input to a

screening technique. This specifically applies

to the use of techniques such as outlined in

‘‘Screening Procedures for Estimating the Air

Quality Impact of Stationary Sources,

Revised’’.18 Specific guidance for CO may be

found in the ‘‘Guideline for Modeling Carbon

Monoxide from Roadway Intersections’’.34

d. If the controlling concentration is an

annual average value and multiple years of

data (on-site or NWS) are used, then the

design value is the highest of the annual

averages calculated for the individual years.

If the controlling concentration is a quarterly

average and multiple years are used, then the

highest individual quarterly average should

be considered the design value.

e. As long a period of record as possible

should be used in making estimates to

determine design values and PSD

increments. If more than 1 year of sitespecific 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
41852 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 σy and σz should

be determined from the same stability

category. ‘‘Split sigmas’’ in that instance are

not recommended.

d. Sector averaging, which eliminates the

σy term, is generally acceptable only to

determine long term averages, such as

seasonal or annual, and when the

meteorological input data are statistically

summarized as in the STAR summaries.

Sector averaging is, however, commonly

acceptable in complex terrain screening

methods.

8.2.5 Plume Rise

a. The plume rise methods of Briggs 56 57

are incorporated in the preferred models and

are recommended for use in all modeling

applications. No provisions in these models

are made for fumigation or multistack plume

rise enhancement or the handling of such

special plumes as flares; these problems

should be considered on a case-by-case basis.

b. Since there is insufficient information to

identify and quantify dispersion during the

transitional plume rise period, gradual plume

rise is not generally recommended for use.

There are two exceptions where the use of

gradual plume rise is appropriate: (1) In

complex terrain screening procedures to

determine close-in impacts; (2) when

calculating the effects of building wakes. The

building wake algorithm in the ISC model

incorporates and automatically (i.e.,

internally) exercises the gradual plume rise

calculations. If the building wake is

calculated to affect the plume for any hour,

gradual plume rise is also used in downwind

dispersion calculations to the distance of

final plume rise, after which final plume rise

is used.

c. Stack tip downwash generally occurs

with poorly constructed stacks and when the

ratio of the stack exit velocity to wind speed

is small. An algorithm developed by Briggs

(Hanna et al.) 57 is the recommended

technique for this situation and is found in

the point source preferred models.

d. Where aerodynamic downwash occurs

due to the adverse influence of nearby

structures, the algorithms included in the ISC

model 58 should be used.

8.2.6 Chemical Transformation

a. The chemical transformation of SO2

emitted from point sources or single

industrial plants in rural areas is generally

assumed to be relatively unimportant to the

estimation of maximum concentrations when

travel time is limited to a few hours.

However, in urban areas, where synergistic

effects among pollutants are of considerable

consequence, chemical transformation rates

may be of concern. In urban area

applications, a half-life of 4 hours 55 may be

applied to the analysis of SO2 emissions.

Calculations of transformation coefficients

from site-specific studies can be used to

define a ‘‘half-life’’ to be used in a Gaussian

model with any travel time, or in any

application, if appropriate documentation is

provided. Such conversion factors for

pollutant half-life should not be used with

screening analyses.

b. Complete conversion of NO to NO2

should be assumed for all travel time when

simple screening techniques are used to

model point source emissions of nitrogen

oxides. If a Gaussian model is used, and data

are available on seasonal variations in

maximum ozone concentrations, the Ozone

Limiting Method 36 is recommended. In

refined analyses, case-by case conversion

rates based on technical studies appropriate

to the site in question may be used. The use

of more sophisticated modeling techniques

should be justified for individual cases.

c. Use of models incorporating complex

chemical mechanisms should be considered

only on a case-by-case basis with proper

demonstration of applicability. These are

generally regional models not designed for

the evaluation of individual sources but used

primarily for region-wide evaluations.

Visibility models also incorporate chemical

transformation mechanisms which are an

integral part of the visibility model itself and

should be used in visibility assessments.

8.2.7 Gravitational Settling and Deposition

a. An ‘‘infinite half-life’’ should be used for

estimates of particle concentrations when

Gaussian models containing only exponential

decay terms for treating settling and

deposition are used.

b. Gravitational settling and deposition

may be directly included in a model if either

is a significant factor. One preferred model

(ISC) contains a settling and deposition

algorithm and is recommended for use when

particulate matter sources can be quantified

and settling and deposition are problems.

8.2.8 Urban/Rural Classification

a. The selection of either rural or urban

dispersion coefficients in a specific

application should follow one of the

procedures suggested by Irwin 59 and briefly

described below. These include a land use

classification procedure or a population

based procedure to determine whether the

character of an area is primarily urban or

rural.

b. Land Use Procedure: (1) Classify the

land use within the total area, Ao,

circumscribed by a 3km radius circle about

the source using the meteorological land use

typing scheme proposed by Auer 60; (2) if

land use types I1, I2, C1, R2, and R3 account

for 50 percent or more of Ao, use urban

dispersion coefficients; otherwise, use

appropriate rural dispersion coefficients.

c. Population Density Procedure: (1)

Compute the average population density, p ̄

per square kilometer with Ao as defined

above; (2) If p ̄ is greater than 750 people/km2,

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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41853

eMalfunctions 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.

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 groundlevel concentrations.

b. When stagnation periods such as these

are found to occur, they should be addressed

in the air quality modeling analysis.

WYNDvalley, listed in Appendix B, may be

applied on a case-by-case basis for stagnation

periods of 24 hours or longer in valley-type

situations. Caution should be exercised when

applying the model to elevated point sources.

Users should consult with the appropriate

Regional Office prior to regulatory

application of WYNDvalley.

8.2.11 Calibration of Models

a. Calibration of long term multi-source

models has been a widely used procedure

even though the limitations imposed by

statistical theory on the reliability of the

calibration process for long term estimates

are well known.61 In some cases, where a

more accurate model is not available,

calibration may be the best alternative for

improving the accuracy of the estimated

concentrations needed for control strategy

evaluations.

b. Calibration of short term models is not

common practice and is subject to much

greater error and misunderstanding. There

have been attempts by some to compare short

term estimates and measurements on an

event-by-event basis and then to calibrate a

model with results of that comparison. This

approach is severely limited by uncertainties

in both source and meteorological data and

therefore it is difficult to precisely estimate

the concentration at an exact location for a

specific increment of time. Such

uncertainties make calibration of short term

models of questionable benefit. Therefore,

short term model calibration is unacceptable.

9.0 Model Input Data

a. Data bases and related procedures for

estimating input parameters are an integral

part of the modeling procedure. The most

appropriate data available should always be

selected for use in modeling analyses.

Concentrations can vary widely depending

on the source data or meteorological data

used. Input data are a major source of

inconsistencies in any modeling analysis.

This section attempts to minimize the

uncertainty associated with data base

selection and use by identifying requirements

for data used in modeling. A checklist of

input data requirements for modeling

analyses is included as Appendix C. More

specific data requirements and the format

required for the individual models are

described in detail in the users’ guide for

each model.

9.1 Source Data

9.1.1 Discussion

a. Sources of pollutants can be classified as

point, line and area/volume sources. Point

sources are defined in terms of size and may

vary between regulatory programs. The line

sources most frequently considered are

roadways and streets along which there are

well-defined movements of motor vehicles,

but they may be lines of roof vents or stacks

such as in aluminum refineries. Area and

volume sources are often collections of a

multitude of minor sources with individually

small emissions that are impractical to

consider as separate point or line sources.

Large area sources are typically treated as a

grid network of square areas, with pollutant

emissions distributed uniformly within each

grid square.

b. Emission factors are compiled in an EPA

publication commonly known as AP–42 62;

an indication of the quality and amount of

data on which many of the factors are based

is also provided. Other information

concerning emissions is available in EPA

publications relating to specific source

categories. The Regional Office should be

consulted to determine appropriate source

definitions and for guidance concerning the

determination of emissions from and

techniques for modeling the various source

types.

9.1.2 Recommendations

a. For point source applications the load or

operating condition that causes maximum

ground-level concentrations should be

established. As a minimum, the source

should be modeled using the design capacity

(100 percent load). If a source operates at

greater than design capacity for periods that

could result in violations of the standards or

PSD increments, this load e should be

modeled. Where the source operates at

substantially less than design capacity, and

the changes in the stack parameters

associated with the operating conditions

could lead to higher ground level

concentrations, loads such as 50 percent and

75 percent of capacity should also be

modeled. A range of operating conditions

should be considered in screening analyses;

the load causing the highest concentration, in

addition to the design load, should be

included in refined modeling. For a power

plant, the following paragraphs b through h

of this section describe the typical kind of

data on source characteristics and operating

conditions that may be needed. Generally,

input data requirements for air quality

models necessitate the use of metric units;

where English units are common for

engineering usage, a conversion to metric is

required.

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, 106 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
41854 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 startup 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

Averaging time Emission limit (#/MMBtu) 2 × Operating level (MMBtu/hr) 2 × Operating factor (e.g., hr/yr,

hr/day)

Stationary Point Source(s) Subject to SIP Emission Limit(s) Evaluation for Compliance with Ambient Standards (Including Areawide

Demonstrations)

Annual & quarterly ............... Maximum allowable emission limit or federally enforceable permit limit.Actual or design capacity

(whichever is greater), or

federally enforceable permit condition.

Actual operating factor averaged over most recent 2

years.3

Short term ............................ Maximum allowable emission limit or federally enforceable permit limit.Actual or design capacity

(whichever is greater), or

federally enforceable permit condition 4.

Continuous operation, i.e.,

all hours of each time period under consideration

(for all hours 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 emission limit or federal enforceable permit limit.Annual level when actually

operating, averaged over

the most recent 2 years 3.

Actual operating factor averaged over the most recent 2 years.3

Short term ............................ Maximum allowable emission limit or federally enforceable permit limit.Annual level when actually

operating, averaged over

the most recent 2 years 3.

Continuous operation, i.e.,

all hours of each time period under consideration

(for all hours of the meteorological data base).5

1The 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. 2Terminology 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.

4Operating 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

Averaging time Emission limit (#/MMBtu) 1 × Operating level (MMBtu/hr) 1 × Operating factor (e.g., hr/yr,

hr/day)

Proposed Major New or Modified Source

Annual & quarterly ............... Maximum allowable emission limit or federally enforceable permit limit.Design capacity or federally

enforceable permit condition.

Continuous operation (i.e.,

8760 hours).2

Short term (≤ 24 hours) ....... Maximum allowable emission limit or federally enforceable permit limit.Design capacity or federally

enforceable permit condition.3

Continuous operation (i.e.,

all hours of each time period under consideration)

(for all hours of the meteorological data base).2
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41855

fFor purposes of PSD, the location of monitors as

well as data quality assurance procedures must

satisfy requirements listed in the PSD Monitoring

Guidelines. 63

TABLE 9–2.—POINT SOURCE MODEL INPUT DATA (EMISSIONS) FOR PSD NAAQS COMPLIANCE DEMONSTRATIONS—

Continued

Averaging time Emission limit (#/MMBtu) 1 × Operating level (MMBtu/hr) 1 × Operating factor (e.g., hr/yr,

hr/day)

Nearby Background Source(s) 4

Annual & quarterly ............... Maximum allowable emission limit or federally enforceable permit limit.Actual or design capacity

(whichever is greater), or

federally enforceable permit condition.

Actual operating factor averaged over the most recent 2 years.5 7

Short term (≤ 24 hours) ....... Maximum allowable emission limit or federally enforceable permit limit.Actual or design capacity

(whichever is greater), or

federally enforceable permit condition.3

Continuous operation (i.e.,

all hours of each time period under consideration)

(for all hours of the meteorological data base).2

Other Background Source(s) 6

Annual & quarterly ............... Maximum allowable emission limit or federally enforceable permit limit.Annual level when actually

operating, averaged over

the most recent 2 years.5

Actual operating factor averaged over the most recent 2 years.5 7

Short term (≤ 24 hours) ....... Maximum allowable emission limit or federally enforceable permit limit.Annual level when actually

operating, averaged over

the most recent 2 years.5

Continuous operation (i.e.,

all hours of each time period under consideration)

(for all hours of the meteorological data base).2

1Terminology 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. 3Operating 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.

6Generally, the ambient impacts from non-nearby background sources can be represented by air quality data unless adequate data do not

exist. 7For those permitted sources not yet in operation or that have not established an appropriate factor, continuous operation (i.e., 8760 hours)

should be used.

9.2 Background Concentrations

9.2.1 Discussion

a. Background concentrations are an

essential part of the total air quality

concentration to be considered in

determining source impacts. Background air

quality includes pollutant concentrations due

to: (1) natural sources; (2) nearby sources

other than the one(s) currently under

consideration; and (3) unidentified sources.

b. Typically, air quality data should be

used to establish background concentrations

in the vicinity of the source(s) under

consideration. The monitoring network used

for background determinations should

conform to the same quality assurance and

other requirements as those networks

established for PSD purposes.63 An

appropriate data validation procedure should

be applied to the data prior to use.

c. If the source is not isolated, it may be

necessary to use a multi-source model to

establish the impact of nearby sources.

Background concentrations should be

determined for each critical (concentration)

averaging time.

9.2.2 Recommendations (Isolated Single

Source)

a. Two options (paragraph b or c of this

section) are available to determine the

background concentration near isolated

sources.

b. Use air quality data collected in the

vicinity of the source to determine the

background concentration for the averaging

times of concern.f Determine the mean

background concentration at each monitor by

excluding values when the source in

question is impacting the monitor. The mean

annual background is the average of the

annual concentrations so determined at each

monitor. For shorter averaging periods, the

meteorological conditions accompanying the

concentrations of concern should be

identified. Concentrations for meteorological

conditions of concern, at monitors not

impacted by the source in question, should

be averaged for each separate averaging time

to determine the average background value.

Monitoring sites inside a 90° 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.

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
41856 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 sitespecific 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 longterm 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). Sitespecific 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.
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 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 (ŒÆ) measurements

for use in estimating P–G stability categories

using the solar radiation/delta-T (SRDT)

methodology (see Stability Categories) should

be obtained using two matched thermometers

or a reliable thermocouple system to achieve

adequate accuracy.

f. Siting, probe placement, and operation of

>T systems should be based on guidance

found in Chapter 3 of reference 66, and such

guidance should be followed when obtaining

vertical temperature gradient data for use in

plume rise estimates or in determining the

critical dividing streamline height.

g. Wind Measurements. For refined

modeling applications in simple terrain

situations, if a source has a stack below

100m, select the stack top height as the wind

measurement height for characterization of

plume dilution and transport. For sources

with stacks extending above 100m, a 100m

tower is suggested unless the stack top is

significantly above 100m (i.e., ≥200m). In

cases with stack tops ≥200m, remote sensing

may be a feasible alternative. In some cases,

collection of stack top wind speed may be

impractical or incompatible with the input

requirements of the model to be used. In such

cases, the Regional Office should be

consulted to determine the appropriate

measurement height.

h. For refined modeling applications in

complex terrain, multiple level (typically

three or more) measurements of wind speed

and direction, temperature and turbulence

(wind fluctuation statistics) are required.

Such measurements should be obtained up to

the representative plume height(s) of interest

(i.e., the plume height(s) under those

conditions important to the determination of

the design concentration). The representative

plume height(s) of interest should be

determined using an appropriate complex

terrain screening procedure (e.g., CTSCREEN)

and should be documented in the

monitoring/modeling protocol. The necessary

meteorological measurements should be

obtained from an appropriately sited

meteorological tower augmented by SODAR

if the representative plume height(s) of

interest exceed 100m. The meteorological

tower need not exceed the lesser of the

representative plume height of interest (the

highest plume height if there is more than

one plume height of interest) or 100m.

i. In general, the wind speed used in

determining plume rise is defined as the

wind speed at stack top.

j. Specifications for wind measuring

instruments and systems are contained in the

‘‘On-Site Meteorological Program Guidance

for Regulatory Modeling Applications’’.66

k. Stability Categories. The P–G stability

categories, as originally defined, couple nearsurface 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 σE and σA

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 σE and σA 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.

PCRAMMET138 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 determinations).

1-hr.

Transport direction .................... 1-hr.

Dilution wind speed ................... 1-hr.

Turbulence measurements (σE

and σA) for use in stability

determinations.

1-hr.1

1To minimize meander effects in σA when

wind conditions are light and/or variable, determine the hourly average σ value from four

sequential 15-minute σ’s according to the following formula:

σ

σ σ σ σ

1

15

2

15

2

15

2

15

2

4 -hr = + + +

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
41858 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

average. For annual averages, the sum of all

valid hourly concentrations is divided by the

number of non-calm hours during the year.

A post-processor computer program,

CALMPRO 73 has been prepared following

these instructions and has been coded in

RAM and ISC.

b. The recommendations in paragraph a of

this section apply to the use of calms for

short term averages and do not apply to the

determination of long term averages using

‘‘STAR’’ data summaries. Calms should

continue to be included in the preparation of

‘‘STAR’’ summaries. A treatment for calms

and very light winds is built into the software

that produces the ‘‘STAR’’ summaries.

c. Stagnant conditions, including extended

periods of calms, often produce high

concentrations over wide areas for relatively

long averaging periods. The standard short

term Gaussian models are often not

applicable to such situations. When

stagnation conditions are of concern, other

modeling techniques should be considered

on a case-by-case basis (see also Section

8.2.10).

d. When used in Gaussian models,

measured on-site wind speeds of less than 1

m/s but higher than the response threshold

of the instrument should be input as 1 m/s;

the corresponding wind direction should also

be input. Observations below the response

threshold of the instrument are also set to 1

m/s but the wind direction from the previous

hour is used. If the wind speed or direction

can not be determined, that hour should be

treated as missing and short term averages

should then be calculated as described in

paragraph a of this section.

10.0 Accuracy and Uncertainty of Models

10.1 Discussion

a. Increasing reliance has been placed on

concentration estimates from models as the

primary basis for regulatory decisions

concerning source permits and emission

control requirements. In many situations,

such as review of a proposed source, no

practical alternative exists. Therefore, there is

an obvious need to know how accurate

models really are and how any uncertainty in

the estimates affects regulatory decisions.

EPA recognizes the need for incorporating

such information and has sponsored

workshops 11 74 on model accuracy, the

possible ways to quantify accuracy, and on

considerations in the incorporation of model

accuracy and uncertainty in the regulatory

process. The Second (EPA) Conference on

Air Quality Modeling, August 1982,75 was

devoted to that subject.

10.1.1 Overview of Model Uncertainty

a. Dispersion models generally attempt to

estimate concentrations at specific sites that

really represent an ensemble average of

numerous repetitions of the same event. The

event is characterized by measured or

‘‘known’’ conditions that are input to the

models, e.g., wind speed, mixed layer height,

surface heat flux, emission characteristics,

etc. However, in addition to the known

conditions, there are unmeasured or

unknown variations in the conditions of this

event, e.g., unresolved details of the

atmospheric flow such as the turbulent

velocity field. These unknown conditions

may vary among repetitions of the event. As

a result, deviations in observed

concentrations from their ensemble average,

and from the concentrations estimated by the

model, are likely to occur even though the

known conditions are fixed. Even with a

perfect model that predicts the correct

ensemble average, there are likely to be

deviations from the observed concentrations

in individual repetitions of the event, due to

variations in the unknown conditions. The

statistics of these concentration residuals are

termed ‘‘inherent’’ uncertainty. Available

evidence suggests that this source of

uncertainty alone may be responsible for a

typical range of variation in concentrations of

as much as ±50 percent.76

b. Moreover, there is ‘‘reducible’’

uncertainty 77 associated with the model and

its input conditions; neither models nor data

bases are perfect. Reducible uncertainties are

caused by: (1) Uncertainties in the input

values of the known conditions—emission

characteristics and meteorological data; (2)

errors in the measured concentrations which

are used to compute the concentration

residuals; and (3) inadequate model physics

and formulation. The ‘‘reducible’’

uncertainties can be minimized through

better (more accurate and more

representative) measurements and better

model physics.

c. To use the terminology correctly,

reference to model accuracy should be

limited to that portion of reducible

uncertainty which deals with the physics and

the formulation of the model. The accuracy

of the model is normally determined by an

evaluation procedure which involves the

comparison of model concentration estimates

with measured air quality data.78 The

statement of accuracy is based on statistical

tests or performance measures such as bias,

noise, correlation, etc.11 However,

information that allows a distinction between

contributions of the various elements of

inherent and reducible uncertainty is only

now beginning to emerge. As a result most

discussions of the accuracy of models make

no quantitative distinction between (1)

Limitations of the model versus (2)

limitations of the data base and of knowledge

concerning atmospheric variability. The

reader should be aware that statements on

model accuracy and uncertainty may imply

the need for improvements in model

performance that even the ‘‘perfect’’ model

could not satisfy.

10.1.2 Studies of Model Accuracy

a. A number of studies 79 80 have been

conducted to examine model accuracy,

particularly with respect to the reliability of

short-term concentrations required for

ambient standard and increment evaluations.

The results of these studies are not

surprising. Basically, they confirm what

leading atmospheric scientists have said for

some time: (1) Models are more reliable for

estimating longer time-averaged

concentrations than for estimating short-term

concentrations at specific locations; and (2)

the models are reasonably reliable in

estimating the magnitude of highest

concentrations occurring sometime,

somewhere within an area. For example,

errors in highest estimated concentrations of

±10 to 40 percent are found to be typical,81

i.e., certainly well within the often quoted

factor-of-two accuracy that has long been

recognized for these models. However,

estimates of concentrations that occur at a

specific time and site, are poorly correlated

with actually observed concentrations and

are much less reliable.

b. As noted in paragraph a of this section,

poor correlations between paired

concentrations at fixed stations may be due

to ‘‘reducible’’ uncertainties in knowledge of

the precise plume location and to

unquantified inherent uncertainties. For

example, Pasquill 82 estimates that, apart

from data input errors, maximum groundlevel 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 DecisionMaking

a. The accuracy of model estimates varies

with the model used, the type of application,

and site-specific characteristics. Thus, it is

desirable to quantify the accuracy or

uncertainty associated with concentration

estimates used in decision-making.

Communications between modelers and

decision-makers must be fostered and further

developed. Communications concerning

concentration estimates currently exist in

most cases, but the communications dealing

with the accuracy of models and its meaning

to the decision-maker are limited by the lack

of a technical basis for quantifying and

directly including uncertainty in decisions.

Procedures for quantifying and interpreting

uncertainty in the practical application of

such concepts are only beginning to evolve;

much study is still required.74 75 77

b. In all applications of models an effort is

encouraged to identify the reliability of the

model estimates for that particular area and

to determine the magnitude and sources of

error associated with the use of the model.

The analyst is responsible for recognizing

and quantifying limitations in the accuracy,

precision and sensitivity of the procedure.

Information that might be useful to the

decision-maker in recognizing the

seriousness of potential air quality violations

includes such model accuracy estimates as

accuracy of peak predictions, bias, noise,

correlation, frequency distribution, spatial

extent of high concentration, etc. Both space/

time pairing of estimates and measurements

and unpaired comparisons are

recommended. Emphasis should be on the

highest concentrations and the averaging

times of the standards or increments of

concern. Where possible, confidence

intervals about the statistical values should

be provided. However, while such

information can be provided by the modeler

to the decision-maker, it is unclear how this

information should be used to make an air
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 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 decisionmaking, EPA has developed and is

continuing to study procedures for

determining the accuracy of models,

quantifying the uncertainty, and expressing

confidence levels in decisions that are made

concerning emissions controls.83 84 However,

work in this area involves ‘‘breaking new

ground’’ with slow and sporadic progress

likely. As a result, it may be necessary to

continue using the ‘‘best estimate’’ until

sufficient technical progress has been made

to meaningfully implement such concepts

dealing with uncertainty.

10.1.4 Evaluation of Models

a. A number of actions are being taken to

ensure that the best model is used correctly

for each regulatory application and that a

model is not arbitrarily imposed. First, the

Guideline clearly recommends the most

appropriate model be used in each case.

Preferred models, based on a number of

factors, are identified for many uses. General

guidance on using alternatives to the

preferred models is also provided. Second,

all the models in eight categories (i.e., rural,

urban, industrial complex, reactive

pollutants, mobile source, complex terrain,

visibility and long range transport) that are

candidates for inclusion in the Guideline are

being subjected to a systematic performance

evaluation and a peer scientific review.85 The

same data bases are being used to evaluate all

models within each of eight categories.

Statistical performance measures, including

measures of difference (or residuals) such as

bias, variance of difference and gross

variability of the difference, and measures of

correlation such as time, space, and time and

space combined as recommended by the

AMS Woods Hole Workshop,11 are being

followed. The results of the scientific review

are being incorporated in the Guideline and

will be the basis for future revision.12 13

Third, more specific information has been

provided for justifying the site specific use of

alternative models in the documents ‘‘Interim

Procedures for Evaluating Air Quality

Models’’,15 and the ‘‘Protocol for

Determining the Best Performing Model’’.17

Together these documents provide methods

that allow a judgment to be made as to what

models are most appropriate for a specific

application. For the present, performance

and the theoretical evaluation of models are

being used as an indirect means to quantify

one element of uncertainty in air pollution

regulatory decisions.

b. In addition to performance evaluation of

models, sensitivity analyses are encouraged

since they can provide additional

information on the effect of inaccuracies in

the data bases and on the uncertainty in

model estimates. Sensitivity analyses can aid

in determining the effect of inaccuracies of

variations or uncertainties in the data bases

on the range of likely concentrations. Such

information may be used to determine source

impact and to evaluate control strategies.

Where possible, information from such

sensitivity analyses should be made available

to the decision-maker with an appropriate

interpretation of the effect on the critical

concentrations.

10.2 Recommendations

a. No specific guidance on the

consideration of model uncertainty in

decision-making is being given at this time.

There is incomplete technical information on

measures of model uncertainty that are most

relevant to the decision-maker. It is not clear

how a decisionmaker could use such

information, particularly given limitations of

the Clean Air Act. As procedures for

considering uncertainty develop and become

implementable, this guidance will be

changed and expanded. For the present,

continued use of the ‘‘best estimate’’ is

acceptable and is consistent with Clean Air

Act requirements.

11.0 Regulatory Application of Models

11.1 Discussion

a. Procedures with respect to the review

and analysis of air quality modeling and data

analyses in support of SIP revisions, PSD

permitting or other regulatory requirements

need a certain amount of standardization to

ensure consistency in the depth and

comprehensiveness of both the review and

the analysis itself. This section recommends

procedures that permit some degree of

standardization while at the same time

allowing the flexibility needed to assure the

technically best analysis for each regulatory

application.

b. Dispersion model estimates, especially

with the support of measured air quality

data, are the preferred basis for air quality

demonstrations. Nevertheless, there are

instances where the performance of

recommended dispersion modeling

techniques, by comparison with observed air

quality data, may be shown to be less than

acceptable. Also, there may be no

recommended modeling procedure suitable

for the situation. In these instances, emission

limitations may be established solely on the

basis of observed air quality data as would

be applied to a modeling analysis. The same

care should be given to the analyses of the

air quality data as would be applied to a

modeling analysis.

c. The current NAAQS for SO2 and CO are

both stated in terms of a concentration not to

be exceeded more than once a year. There is

only an annual standard for NO2 and a

quarterly standard for Pb. The PM–10 and

ozone standards permit the exceedance of a

concentration on an average of not more than

once a year; the convention is to average over

a 3-year period.5 86 103 This represents a

change from a deterministic to a more

statistical form of the standard and permits

some consideration to be given to unusual

circumstances. The NAAQS are subjected to

extensive review and possible revision every

5 years.

d. This section discusses general

requirements for concentration estimates and

identifies the relationship to emission limits.

The recommendations in section 11.2 apply

to: (1) revisions of State Implementation

Plans; (2) the review of new sources and the

prevention of significant deterioration (PSD);

and (3) analyses of the emissions trades

(‘‘bubbles’’).

11.2 Recommendations

11.2.1 Analysis Requirements

a. Every effort should be made by the

Regional Office to meet with all parties

involved in either a SIP revision or a PSD

permit application prior to the start of any

work on such a project. During this meeting,

a protocol should be established between the

preparing and reviewing parties to define the

procedures to be followed, the data to be

collected, the model to be used, and the

analysis of the source and concentration data.

An example of requirements for such an

effort is contained in the Air Quality

Analysis Checklist included here as

Appendix C. This checklist suggests the level

of detail required to assess the air quality

resulting from the proposed action. Special

cases may require additional data collection

or analysis and this should be determined

and agreed upon at this preapplication

meeting. The protocol should be written and

agreed upon by the parties concerned,

although a formal legal document is not

intended. Changes in such a protocol are

often required as the data collection and

analysis progresses. However, the protocol

establishes a common understanding of the

requirements.

b. An air quality analysis should begin

with a screening model to determine the

potential of the proposed source or control

strategy to violate the PSD increment or

NAAQS. It is recommended that the

screening techniques found in ‘‘Screening

Procedures for Estimating the Air Quality

Impact of Stationary Sources’’ 18 be used for

point source analyses. Screening procedures

for area source analysis are discussed in

‘‘Applying Atmospheric Simulation Models

to Air Quality Maintenance Areas’’.87 For

mobile source impact assessments the

‘‘Guideline for Modeling Carbon Monoxide

from Roadway Intersections’’ 34 is available.

c. If the concentration estimates from

screening techniques indicate that the PSD

increment or NAAQS may be approached or

exceeded, then a more refined modeling

analysis is appropriate and the model user

should select a model according to

recommendations in Sections 4.0–8.0. In

some instances, no refined technique may be

specified in this guide for the situation. The

model user is then encouraged to submit a

model developed specifically for the case at

hand. If that is not possible, a screening

technique may supply the needed results.

d. Regional Offices should require permit

applicants to incorporate the pollutant

contributions of all sources into their

analysis. Where necessary this may include

emissions associated with growth in the area

of impact of the new or modified source’s

impact. PSD air quality assessments should

consider the amount of the allowable air

quality increment that has already been

granted to any other sources. Therefore, the

most recent source applicant should model
41860 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

the existing or permitted sources in addition

to the one currently under consideration.

This would permit the use of newly acquired

data or improved modeling techniques if

such have become available since the last

source was permitted. When remodeling, the

worst case used in the previous modeling

analysis should be one set of conditions

modeled in the new analysis. All sources

should be modeled for each set of

meteorological conditions selected and for all

receptor sites used in the previous

applications as well as new sites specific to

the new source.

11.2.2 Use of Measured Data in Lieu of

Model Estimates

a. Modeling is the preferred method for

determining emission limitations for both

new and existing sources. When a preferred

model is available, model results alone

(including background) are sufficient.

Monitoring will normally not be accepted as

the sole basis for emission limitation

determination in flat terrain areas. In some

instances when the modeling technique

available is only a screening technique, the

addition of air quality data to the analysis

may lend credence to model results.

b. There are circumstances where there is

no applicable model, and measured data may

need to be used. Examples of such situations

are: (1) complex terrain locations; (2) land/

water interface areas; and (3) urban locations

with a large fraction of particulate emissions

from nontraditional sources. However, only

in the case of an existing source should

monitoring data alone be a basis for emission

limits. In addition, the following items

should be considered prior to the acceptance

of the measured data:

i. Does a monitoring network exist for the

pollutants and averaging times of concern?

ii. Has the monitoring network been

designed to locate points of maximum

concentration?

iii. Do the monitoring network and the data

reduction and storage procedures meet EPA

monitoring and quality assurance

requirements?

iv. Do the data set and the analysis allow

impact of the most important individual

sources to be identified if more than one

source or emission point is involved?

v. Is at least one full year of valid ambient

data available?

vi. Can it be demonstrated through the

comparison of monitored data with model

results that available models are not

applicable?

c. The number of monitors required is a

function of the problem being considered.

The source configuration, terrain

configuration, and meteorological variations

all have an impact on number and placement

of monitors. Decisions can only be made on

a case-by-case basis. The Interim Procedures

for Evaluating Air Quality Models 15 should

be used in establishing criteria for

demonstrating that a model is not applicable.

d. Sources should obtain approval from the

Regional Office or reviewing authority for the

monitoring network prior to the start of

monitoring. A monitoring protocol agreed to

by all concerned parties is highly desirable.

The design of the network, the number, type

and location of the monitors, the sampling

period, averaging time as well as the need for

meteorological monitoring or the use of

mobile sampling or plume tracking

techniques, should all be specified in the

protocol and agreed upon prior to start-up of

the network.

11.2.3 Emission Limits

11.2.3.1 Design Concentrations

a. Emission limits should be based on

concentration estimates for the averaging

time that results in the most stringent control

requirements. The concentration used in

specifying emission limits is called the

design value or design concentration and is

a sum of the concentration contributed by the

source and the background concentration.

b. To determine the averaging time for the

design value, the most restrictive National

Ambient Air Quality Standard (NAAQS)

should be identified by calculating, for each

averaging time, the ratio of the applicable

NAAQS (S)¥ background (B) to the

predicted concentration (P) (i.e., (S¥B)/P).

The averaging time with the lowest ratio

identifies the most restrictive standard. If the

annual average is the most restrictive, the

highest estimated annual average

concentration from one or a number of years

of data is the design value. When short term

standards are most restrictive, it may be

necessary to consider a broader range of

concentrations than the highest value. For

example, for pollutants such as SO2, the

highest, second-highest concentration is the

design value. For pollutants with statistically

based NAAQS, the design value is found by

determining the more restrictive of: (1) the

short-term concentration that is not expected

to be exceeded more than once per year over

the period specified in the standard, or (2)

the long-term concentration that is not

expected to exceed the long-term NAAQS.

Determination of design values for PM–10 is

presented in more detail in the ‘‘PM–10 SIP

Development Guideline’’.108

c. When the highest, second-highest

concentration is used in assessing potential

violations of a short term NAAQS, criteria

that are identified in ‘‘Guideline for

Interpretation of Air Quality Standards’’88

should be followed. This guidance specifies

that a violation of a short term standard

occurs at a site when the standard is

exceeded a second time. Thus, emission

limits that protect standards for averaging

times of 24 hours or less are appropriately

based on the highest, second-highest

estimated concentration plus a background

concentration which can reasonably be

assumed to occur with the concentration.

11.2.3.2 NAAQS Analyses for New or

Modified Sources

a. For new or modified sources predicted

to have a significant ambient impact 63 and to

be located in areas designated attainment or

unclassifiable for the SO2, Pb, NO2, or CO

NAAQS, the demonstration as to whether the

source will cause or contribute to an air

quality violation should be based on: (1) the

highest estimated annual average

concentration determined from annual

averages of individual years; or (2) the

highest, second-highest estimated

concentration for averaging times of 24-hours

or less; and (3) the significance of the spatial

and temporal contribution to any modeled

violation. For Pb, the highest estimated

concentration based on an individual

calendar quarter averaging period should be

used. Background concentrations should be

added to the estimated impact of the source.

The most restrictive standard should be used

in all cases to assess the threat of an air

quality violation. For new or modified

sources predicted to have a significant

ambient impact 63 in areas designated

attainment or unclassifiable for the PM–10

NAAQS, the demonstration of whether or not

the source will cause or contribute to an air

quality violation should be based on

sufficient data to show whether: (1) the

projected 24-hour average concentrations

will exceed the 24-hour NAAQS more than

once per year, on average; (2) the expected

(i.e., average) annual mean concentration will

exceed the annual NAAQS; and (3) the

source contributes significantly, in a

temporal and spatial sense, to any modeled

violation.

11.2.3.3 PSD Air Quality Increments and

Impacts

a. The allowable PSD increments for

criteria pollutants are established by

regulation and cited in § 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 incrementconsuming sources are involved in the

calculation, the use of a refined model with

at least 1 year of on-site or 5 years of off-site

NWS data is normally required. In such

cases, sequential modeling must demonstrate

that the allowable increments are not

exceeded temporally and spatially, i.e., for all

receptors for each time period throughout the

year(s) (time period means the appropriate

PSD averaging time, e.g., 3-hour, 24-hour,

etc.).

c. The PSD regulations require an

estimation of the SO2, particulate matter, and

NO2 impact on any Class I area. Normally,

Gaussian models should not be applied at

distances greater than can be accommodated

by the steady state assumptions inherent in

such models. The maximum distance for

refined Gaussian model application for

regulatory purposes is generally considered

to be 50km. Beyond the 50km range,

screening techniques may be used to

determine if more refined modeling is

needed. If refined models are needed, long

range transport models should be considered

in accordance with Section 7.2.6. As

previously noted in Sections 3.0 and 7.0, the

need to involve the Federal Land Manager in

decisions on potential air quality impacts,
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41861

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.

hSome EPA references, e.g., model user’s guides,

etc., are periodically revised. Users are referred to

the SCRAM BBS19 to download updates or

addenda; see Section A.0 of this appendix.

particularly in relation to PSD Class I areas,

cannot be overemphasized.

11.2.3.4 Emissions Trading Policy (Bubbles)

a. EPA’s final Emissions Trading Policy,

commonly referred to as the ‘‘bubble policy,’’

was published in the Federal Register in

1986.89 Principles contained in the policy

should be used to evaluate ambient impacts

of emission trading activities.

b. Emission increases and decreases within

the bubble should result in ambient air

quality equivalence. Two levels of analysis

are defined for establishing this equivalence.

In a Level I analysis the source configuration

and setting must meet certain limitations

(defined in the policy) that ensure ambient

equivalence; no modeling is required. In a

Level II analysis a modeling demonstration of

ambient equivalence is required but only the

sources involved in the emissions trade are

modeled. The resulting ambient estimates of

net increases/decreases are compared to a set

of significance levels to determine if the

bubble can be approved. A Level II analysis

requires the use of a refined model and the

most recent readily available full year of

representative meteorological data.

Sequential modeling must demonstrate that

the significance levels are met temporally

and spatially, i.e., for all receptors for each

time period throughout the year (time period

means the appropriate NAAQS averaging

time, e.g., 3-hour, 24-hour, etc.).

c. For those bubbles that cannot meet the

Level I or Level II requirements, the

Emissions Trading Policy allows for a Level

III analysis. A Level III analysis, from a

modeling standpoint, is generally equivalent

to the requirements for a standard SIP

revision where all sources (and background)

are considered and the estimates are

compared to the NAAQS as in Section

11.2.3.2.

d. The Emissions Trading Policy allows

States to adopt generic regulations for

processing bubbles. The modeling

procedures recommended in the Guideline

apply to such generic regulations. However,

an added requirement is that the modeling

procedures contained in any generic

regulation must be replicable such that there

is no doubt as to how each individual bubble

will be modeled. In general this means that

the models, the data bases and the

procedures for applying the model must be

defined in the regulation. The consequences

of the replicability requirement are that

bubbles for sources located in complex

terrain and certain industrial sources where

judgments must be made on source

characterization cannot be handled

generically.

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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
41864 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 ShortTerm 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.
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41865

iThe documents listed here are major sources of

supplemental information on the theory and

application of mathematical air quality models.

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 i

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.
41866 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 postprocessor 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.
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41867

e. Pollutant Types

BLP may be used to model primary

pollutants. This model does not treat settling

and deposition.

f. Source-Receptor Relationship

BLP treats up to 50 point sources, 10

parallel line sources, and 100 receptors

arbitrarily located.

User-input topographic elevation is

applied for each stack and each receptor.

g. Plume Behavior

BLP uses plume rise formulas of Schulman

and Scire (1980).

Vertical potential temperature gradients of

0.02 Kelvin per meter for E stability and

0.035 Kelvin per meter are used for stable

plume rise calculations. An option for user

input values is included.

Transitional rise is used for line sources.

Option to suppress the use of transitional

plume rise for point sources is included.

The building downwash algorithm of

Schulman and Scire (1980) is used.

h. Horizontal Winds

Constant, uniform (steady-state) wind is

assumed for an hour.

Straight line plume transport is assumed to

all downwind distances.

Wind speeds profile exponents of 0.10,

0.15, 0.20, 0.25, 0.30, and 0.30 are used for

stability classes A through F, respectively.

An option for user-defined values and an

option to suppress the use of the wind speed

profile feature are included.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero.

j. Horizontal Dispersion

Rural dispersion coefficients are from

Turner (1969), with no adjustment made for

variations in surface roughness or averaging

time.

Six stability classes are used.

k. Vertical Dispersion

Rural dispersion coefficients are from

Turner (1969), with no adjustment made for

variations in surface roughness.

Six stability classes are used.

Mixing height is accounted for with

multiple reflections until the vertical plume

standard deviation equals 1.6 times the

mixing height; uniform mixing is assumed

beyond that point.

Perfect reflection at the ground is assumed.

l. Chemical Transformation

Chemical transformations are treated using

linear decay. Decay rate is input by the user.

m. Physical Removal

Physical removal is not explicitly treated.

n. Evaluation Studies

Schulman, L.L. and J.S. Scire, 1980.

Buoyant Line and Point Source (BLP)

Dispersion Model User’s Guide, P–7304B.

Environmental Research and Technology,

Inc., Concord, MA.

Scire, J.S. and L.L. Schulman, 1981.

Evaluation of the BLP and ISC Models with

SF6 Tracer Data and SO2 Measurements at

Aluminum Reduction Plants. APCA

Specialty Conference on Dispersion

Modeling for Complex Sources, St. Louis,

MO.

A.2 CALINE3

Reference

Benson, Paul E., 1979. CALINE3—A

Versatile Dispersion Model for Predicting Air

Pollutant Levels Near Highways and Arterial

Streets. Interim Report, Report Number

FHWA/CA/TL–79/23. Federal Highway

Administration, Washington, D.C. (NTIS No.

PB 80–220841)

Availability

The CALINE3 model is available on

diskette (as PB 95–502712) from NTIS. The

source code and user’s guide are also

available on the Support Center for

Regulatory Models Bulletin Board System

(see Section A.0).

Abstract

CALINE3 can be used to estimate the

concentrations of nonreactive pollutants from

highway traffic. This steady-state Gaussian

model can be applied to determine air

pollution concentrations at receptor locations

downwind of ‘‘at-grade,’’ ‘‘fill,’’ ‘‘bridge,’’

and ‘‘cut section’’ highways located in

relatively uncomplicated terrain. The model

is applicable for any wind direction, highway

orientation, and receptor location. The model

has adjustments for averaging time and

surface roughness, and can handle up to 20

links and 20 receptors. It also contains an

algorithm for deposition and settling velocity

so that particulate concentrations can be

predicted.

a. Recommendations for Regulatory Use

CALINE–3 is appropriate for the following

applications:

Highway (line) sources;

Urban or rural areas;

Simple terrain;

Transport distances less than 50

kilometers; and

One-hour to 24-hour averaging times.

b. Input Requirements

Source data: up to 20 highway links

classed as ‘‘at-grade,’’ ‘‘fill’’ ‘‘bridge,’’ or

‘‘depressed’’; coordinates of link end points;

traffic volume; emission factor; source height;

and mixing zone width.

Meteorological data: wind speed, wind

angle (measured in degrees clockwise from

the Y axis), stability class, mixing height,

ambient (background to the highway)

concentration of pollutant.

Receptor data: coordinates and height

above ground for each receptor. c.

c. Output

Printed output includes concentration at

each receptor for the specified meteorological

condition.

d. Type of Model

CALINE–3 is a Gaussian plume model.

e. Pollutant Types

CALINE–3 may be used to model primary

pollutants.

f. Source-Receptor Relationship

Up to 20 highway links are treated.

CALINE–3 applies user input location and

emission rate for each link. User-input

receptor locations are applied.

g. Plume Behavior

Plume rise is not treated.

h. Horizontal Winds

User-input hourly wind speed and

direction are applied.

Constant, uniform (steady-state) wind is

assumed for an hour.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero.

j. Horizontal Dispersion

Six stability classes are used.

Rural dispersion coefficients from Turner

(1969) are used, with adjustment for

roughness length and averaging time.

Initial traffic-induced dispersion is

handled implicitly by plume size parameters.

k. Vertical Dispersion

Six stability classes are used.

Empirical dispersion coefficients from

Benson (1979) are used including an

adjustment for roughness length.

Initial traffic-induced dispersion is

handled implicitly by plume size parameters.

Adjustment for averaging time is included.

l. Chemical Transformation

Not treated.

m. Physical Removal

Optional deposition calculations are

included.

n. Evaluation Studies

Bemis, G.R. et al., 1977. Air Pollution and

Roadway Location, Design, and Operation—

Project Overview. FHWA–CA–TL–7080–77–

25, Federal Highway Administration,

Washington, D.C.

Cadle, S.H. et al., 1976. Results of the

General Motors Sulfate Dispersion

Experiment, GMR–2107. General Motors

Research Laboratories, Warren, MI.

Dabberdt, W.F., 1975. Studies of Air

Quality on and Near Highways, Project 2761.

Stanford Research Institute, Menlo Park, CA.

A.3 Climatological Dispersion Model (CDM

2.0)

Reference

Irwin, J.S., T. Chico and J. Catalano, 1985.

CDM 2.0—Climatological Dispersion

Model—User’s Guide. U.S. Environmental

Protection Agency, Research Triangle Park,

NC. (NTIS No. PB 86–136546)

Availability

The source code and user’s guide is

available on the Support Center for

Regulatory Models Bulletin Board System.

The computer code is also available on

diskette (as PB 90–500406) from the National

Technical Information Service (see Section

A.0).
41868 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

Abstract

CDM is a climatological steady-state

Gaussian plume model for determining longterm (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, buoyancyinduced dispersion (BID), and the

appropriate wind profile exponents.

Enter ‘‘0’’ for pollutant half-life for all

pollutants except for SO2 in an urban setting.

This entry results in no decay (infinite halflife) being calculated. For SO2 in an urban

setting, the pollutant half-life (in hours)

should be set to 4.0.

b. Input Requirements

Source data: location, average emissions

rates and heights of emissions for point and

area sources. Point source data requirements

also include stack gas temperature, stack gas

exit velocity, and stack inside diameter for

plume rise calculations for point sources.

Meteorological data: stability wind rose

(STAR deck day/night version), average

mixing height and wind speed in each

stability category, and average air

temperature.

Receptor data: cartesian coordinates of

each receptor.

c. Output

Printed output includes:

Average concentrations for the period of

the stability wind rose data (arithmetic mean

only) at each receptor, and

Optional point and area concentration rose

for each receptor.

d. Type of Model

CDM is a climatological Gaussian plume

model.

e. Pollutant Types

CDM may be used to model primary

pollutants. Settling and deposition are not

treated.

f. Source-Receptor Relationship

CDM applies user-specified locations for

all point sources and receptors.

Area sources are input as multiples of a

user-defined unit area source grid size.

User specified release heights are applied

for individual point sources and the area

source grid.

Actual separation between each sourcereceptor pair is used.

The user may select a single height at or

above ground level that applies to all

receptors.

No terrain differences between source and

receptor are treated.

g. Plume Behavior

CDM uses Briggs (1969, 1971, 1975) plume

rise equations. Optionally a plume rise-wind

speed product may be input for each point

source.

Stack tip downwash equation from Briggs

(1974) is preferred for regulatory use. The

Bjorklund and Bowers (1982) equation is also

included.

No plume rise is calculated for area

sources.

Does not treat fumigation or building

downwash.

h. Horizontal Winds

Wind data are input as a stability wind

rose (joint frequency distribution of 16 wind

directions, 6 wind classes, and 5 stability

classes).

Wind speed profile exponents for the

urban case (Irwin, 1979; EPA, 1980) are used,

assuming the anemometer height is at 10.0

meters.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero.

j. Horizontal Dispersion

Pollutants are assumed evenly distributed

across a 22.5 or 10.0 degree sector.

k. Vertical Dispersion

There are seven vertical dispersion

parameter schemes, but the following is

recommended for regulatory applications:

• Briggs-urban (Gifford, 1976).

Mixing height has no effect until

dispersion coefficient equals 0.8 times the

mixing height; uniform vertical mixing is

assumed beyond that point.

Buoyancy-induced dispersion (Pasquill,

1976) is included as an option. Perfect

reflection is assumed at the ground.

l. Chemical Transformation

Chemical transformations are treated using

exponential decay. Half-life is input by the

user.

m. Physical Removal

Physical removal is not explicitly treated.

n. Evaluation Studies

Busse, A.D. and J.R. Zimmerman, 1973.

User’s Guide for the Climatological

Dispersion Model—Appendix E. EPA

Publication No. EPA/R4–73–024. Office of

Research and Development, Research

Triangle Park, NC.

Irwin, J.S. and T.M. Brown, 1985. A

Sensitivity Analysis of the Treatment of Area

Sources by the Climatological Dispersion

Model. Journal of Air Pollution Control

Association, 35: 359–364.

Londergan, R., D. Minott, D. Wachter and

R. Fizz, 1983. Evaluation of Urban Air

Quality Simulation Models, EPA Publication

No. EPA–450/4–83–020. U.S. Environmental

Protection Agency, Research Triangle Park,

NC.

Zimmerman, J.R., 1971. Some Preliminary

Results of Modeling from the Air Pollution

Study of Ankara, Turkey, Proceedings of the

Second Meeting of the Expert Panel on Air

Pollution Modeling, NATO Committee on the

Challenges of Modern Society, Paris, France.

Zimmerman, J.R., 1972. The NATO/CCMS

Air Pollution Study of St. Louis, Missouri.

Presented at the Third Meeting of the Expert

Panel on Air Pollution Modeling, NATO

Committee on the Challenges of Modern

Society, Paris, France.

A.4 Gaussian-Plume Multiple Source Air

Quality Algorithm (RAM)

Reference

Turner, D.B. and J.H. Novak, 1978. User’s

Guide for RAM. Publication No. EPA–600/8–

78–016, Vol. a and b. U.S. Environmental

Protection Agency, Research Triangle Park,

NC. (NTIS Nos. PB 294791 and PB 294792)

Catalano, J.A., D.B. Turner and H. Novak,

1987. User’s Guide for RAM—Second

Edition. U.S. Environmental Protection

Agency, Research Triangle Park, NC.

Availability

The source code and user’s guide is

available on the Support Center for

Regulatory Models Bulletin Board System.

The computer code is also available on

diskette (as PB 90–500315) from the National

Technical Information Service (see Section

A.0).

Abstract

RAM is a steady-state Gaussian plume

model for estimating concentrations of

relatively stable pollutants, for averaging

times from an hour to a day, from point and

area sources in a rural or urban setting. Level

terrain is assumed. Calculations are

performed for each hour.

a. Recommendations for Regulatory Use

RAM is appropriate for the following

applications:

Point and area sources;

Urban areas;

Flat terrain;

Transport distances less than 50

kilometers; and

One hour to one year averaging times.

The following options should be selected

for regulatory applications:

Set the regulatory ‘‘default option’’ to

automatically select stack tip downwash,

final plume rise, buoyancy-induced

dispersion (BID), the new treatment for

calms, the appropriate wind profile

exponents, and the appropriate value for

pollutant half-life.

b. Input Requirements

Source data: point sources require location,

emission rate, physical stack height, stack gas

exit velocity, stack inside diameter and stack

gas temperature. Area sources require

location, size, emission rate, and height of

emissions.

Meteorological data: hourly surface

weather data from the preprocessor program

RAMMET which provides hourly stability

class, wind direction, wind speed,

temperature, and mixing height. Actual

anemometer height (a single value) is also

required.

Receptor data: coordinates of each

receptor. Options for automatic placement of
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 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 sourcereceptor pair is used.

All receptors are assumed to be at the same

height at or above ground level.

No terrain differences between source and

receptor are accounted for.

g. Plume Behavior

RAM uses Briggs (1969, 1971, 1975) plume

rise equations for final rise.

Stack tip downwash equation from Briggs

(1974) is used.

A user supplied fraction of the area source

height is treated as the physical height. The

remainder is assumed to be plume rise for a

5 meter per second wind speed, and to be

inversely proportional to wind speed.

Fumigation and building downwash are

not treated.

h. Horizontal Winds

Constant, uniform (steady state) wind is

assumed for an hour.

Straight line plume transport is assumed to

all downwind distances.

Separate wind speed profile exponents

(Irwin, 1979; EPA, 1980) for urban cases are

used.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero.

j. Horizontal Dispersion

Urban dispersion coefficients from Briggs

(Gifford, 1976) are used.

Buoyancy-induced dispersion (Pasquill,

1976) is included.

Six stability classes are used.

k. Vertical Dispersion

Urban dispersion coefficients from Briggs

(Gifford, 1976) are used.

Buoyancy-induced dispersion (Pasquill,

1976) is included.

Six stability classes are used.

Mixing height is accounted for with

multiple reflections until the vertical plume

standard deviation equals 1.6 times the

mixing height; uniform vertical mixing is

assumed beyond that point.

Perfect reflection is assumed at the ground.

l. Chemical Transformation

Chemical transformations are treated using

exponential decay. Half-life is input by the

user.

m. Physical Removal

Physical removal is not explicitly treated.

n. Evaluation Studies

Ellis, H., P. Lou, and G. Dalzell, 1980.

Comparison Study of Measured and

Predicted Concentrations with the RAM

Model at Two Power Plants Along Lake Erie.

Second Joint Conference on Applications of

Air Pollution Meteorology, New Orleans, LA.

Environmental Research and Technology,

1980. SO2 Monitoring and RAM (Urban)

Model Comparison Study in Summit County,

Ohio. Document P–3618–152, Environmental

Research & Technology, Inc., Concord, MA.

Guldberg, P.H. and C.W. Kern, 1978. A

Comparison Validation of the RAM and

PTMTP Models for Short-Term

Concentrations in Two Urban Areas. Journal

of Air Pollution Control Association, 28:

907–910.

Hodanbosi, R.R. and L.K. Peters, 1981.

Evaluation of RAM Model for Cleveland,

Ohio. Journal of Air Pollution Control

Association, 31: 253–255.

Kennedy, K.H., R.D. Siegel and M.P.

Steinberg, 1981. Case-Specific Evaluation of

the RAM Atmospheric Dispersion Model in

an Urban Area. 74th Annual Meeting of the

American Institute of Chemical Engineers,

New Orleans, LA.

Kummier, R.H., B. Cho, G. Roginski, R.

Sinha and A. Greenburg, 1979. A

Comparative Validation of the RAM and

Modified SAI Models for Short Term SO2

Concentrations in Detroit. Journal of Air

Pollution Control Association, 29: 720–723.

Londergan, R.J., N.E. Bowne, D.R. Murray,

H. Borenstein and J. Mangano, 1980. An

Evaluation of Short-Term Air Quality Models

Using Tracer Study Data. Report No. 4333,

American Petroleum Institute, Washington,

D.C.

Londergan, R., D. Minott, D. Wackter and

R. Fizz, 1983. Evaluation of Urban Air

Quality Simulation Models. EPA Publication

No. EPA–450/4–83–020. U.S. Environmental

Protection Agency, Research Triangle Park,

NC.

Morgenstern, P., M.J. Geraghty, and A.

McKnight, 1979. A Comparative Study of the

RAM (Urban) and RAMR (Rural) Models for

Short-term SO2 Concentrations in

Metropolitan Indianapolis. 72nd Annual

Meeting of the Air Pollution Control

Association, Cincinnati, OH.

Ruff, R.E., 1980. Evaluation of the RAM

Using the RAPS Data Base. Contract 68–02–

2770, SRI International, Menlo Park, CA.

A.5 Industrial Source Complex Model

(ISC3)

Reference

Environmental Protection Agency, 1995.

User’s Guide for the Industrial Source

Complex (ISC3) Dispersion Models, Volumes

1 and 2. EPA Publication Nos. EPA–454/B–

95–003a & b. Environmental Protection

Agency, Research Triangle Park, NC. (NTIS

Nos. PB 95–222741 and PB 95–222758,

respectively)

Availability

The model code is available on the Support

Center for Regulatory Air Models Bulletin

Board System. ISCST3 (as PB 96–502000)

and ISCLT3 (PB 96–502018) are also

available on diskette from the National

Technical Information Service (see Section

A.0).

Abstract

The ISC3 model is a steady-state Gaussian

plume model which can be used to assess

pollutant concentrations from a wide variety

of sources associated with an industrial

source complex. This model can account for

the following: settling and dry deposition of

particles; downwash; area, line and volume

sources; plume rise as a function of

downwind distance; separation of point

sources; and limited terrain adjustment. ISC3

operates in both long-term and short-term

modes.

a. Recommendations for Regulatory Use

ISC3 is appropriate for the following

applications:

• Industrial source complexes;

• Rural or urban areas;

• Flat or rolling terrain;

• Transport distances less than 50

kilometers;

• 1-hour to annual averaging times; and

• Continuous toxic air emissions.

The following options should be selected

for regulatory applications: For short term or

long term modeling, set the regulatory

‘‘default option’’; i.e., use the keyword

DFAULT, which automatically selects stack

tip downwash, final plume rise, buoyancy

induced dispersion (BID), the vertical

potential temperature gradient, a treatment

for calms, the appropriate wind profile

exponents, the appropriate value for

pollutant half-life, and a revised building

wake effects algorithm; set the ‘‘rural option’’

(use the keyword RURAL) or ‘‘urban option’’

(use the keyword URBAN); and set the

‘‘concentration option’’ (use the keyword

CONC).

b. Input Requirements

Source data: location, emission rate,

physical stack height, stack gas exit velocity,

stack inside diameter, and stack gas

temperature. Optional inputs include source

elevation, building dimensions, particle size

distribution with corresponding settling

velocities, and surface reflection coefficients.

Meteorological data: ISCST3 requires

hourly surface weather data from the

preprocessor program RAMMET, which

provides hourly stability class, wind

direction, wind speed, temperature, and

mixing height. For ISCLT3, input includes
41870 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

stability wind rose (STAR deck), average

afternoon mixing height, average morning

mixing height, and average air temperature.

Receptor data: coordinates and optional

ground elevation for each receptor.

c. Output

Printed output options include:

• Program control parameters, source data,

and receptor data;

• Tables of hourly meteorological data for

each specified day;

• ‘‘N’’-day average concentration or total

deposition calculated at each receptor for any

desired source combinations;

• Concentration or deposition values

calculated for any desired source

combinations at all receptors for any

specified day or time period within the day;

• Tables of highest and second highest

concentration or deposition values calculated

at each receptor for each specified time

period during a(n) ‘‘N’’-day period for any

desired source combinations, and tables of

the maximum 50 concentration or deposition

values calculated for any desired source

combinations for each specified time period.

d. Type of Model

ISC3 is a Gaussian plume model. It has

been revised to perform a double integration

of the Gaussian plume kernel for area

sources.

e. Pollutant Types

ISC3 may be used to model primary

pollutants and continuous releases of toxic

and hazardous waste pollutants. Settling and

deposition are treated.

f. Source-Receptor Relationships

ISC3 applies user-specified locations for

point, line, area and volume sources, and

user-specified receptor locations or receptor

rings.

User input topographic evaluation for each

receptor is used. Elevations above stack top

are reduced to the stack top elevation, i.e.,

‘‘terrain chopping’’.

User input height above ground level may

be used when necessary to simulate impact

at elevated or ‘‘flag pole’’ receptors, e.g., on

buildings.

Actual separation between each sourcereceptor pair is used.

g. Plume Behavior

ISC3 uses Briggs (1969, 1971, 1975) plume

rise equations for final rise.

Stack tip downwash equation from Briggs

(1974) is used.

Revised building wake effects algorithm is

used. For stacks higher than building height

plus one-half the lesser of the building height

or building width, the building wake

algorithm of Huber and Snyder (1976) is

used. For lower stacks, the building wake

algorithm of Schulman and Scire (Schulman

and Hanna, 1986) is used, but stack tip

downwash and BID are not used.

For rolling terrain (terrain not above stack

height), plume centerline is horizontal at

height of final rise above source.

Fumigation is not treated.

h. Horizontal Winds

Constant, uniform (steady-state) wind is

assumed for each hour.

Straight line plume transport is assumed to

all downwind distances.

Separate wind speed profile exponents

(Irwin, 1979; EPA, 1980) for both rural and

urban cases are used.

An optional treatment for calm winds is

included for short term modeling.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero.

j. Horizontal Dispersion

Rural dispersion coefficients from Turner

(1969) are used, with no adjustments for

surface roughness or averaging time.

Urban dispersion coefficients from Briggs

(Gifford, 1976) are used.

Buoyancy induced dispersion (Pasquill,

1976) is included.

Six stability classes are used.

k. Vertical Dispersion

Rural dispersion coefficients from Turner

(1969) are used, with no adjustments for

surface roughness.

Urban dispersion coefficients from Briggs

(Gifford, 1976) are used.

Buoyancy induced dispersion (Pasquill,

1976) is included.

Six stability classes are used.

Mixing height is accounted for with

multiple reflections until the vertical plume

standard deviation equals 1.6 times the

mixing height; uniform vertical mixing is

assumed beyond that point.

Perfect reflection is assumed at the ground.

l. Chemical Transformation

Chemical transformations are treated using

exponential decay. Time constant is input by

the user.

m. Physical Removal

Dry deposition effects for particles are

treated using a resistance formulation in

which the deposition velocity is the sum of

the resistances to pollutant transfer within

the surface layer of the atmosphere, plus a

gravitational settling term (EPA, 1994), based

on the modified surface depletion scheme of

Horst (1983).

n. Evaluation Studies

Bowers, J.F. and A.J. Anderson, 1981. An

Evaluation Study for the Industrial Source

Complex (ISC) Dispersion Model, EPA

Publication No. EPA–450/4–81–002. U.S.

Environmental Protection Agency, Research

Triangle Park, NC.

Bowers, J.F., A.J. Anderson and W.R.

Hargraves, 1982. Tests of the Industrial

Source Complex (ISC) Dispersion Model at

the Armco Middletown, Ohio Steel Mill. EPA

Publication No. EPA–450/4–82–006. U.S.

Environmental Protection Agency, Research

Triangle Park, NC.

Environmental Protection Agency, 1992.

Comparison of a Revised Area Source

Algorithm for the Industrial Source Complex

Short Term Model and Wind Tunnel Data.

EPA Publication No. EPA–454/R–92–014.

U.S. Environmental Protection Agency,

Research Triangle Park, NC. (NTIS No. PB

93–226751)

Environmental Protection Agency, 1992.

Sensitivity Analysis of a Revised Area Source

Algorithm for the Industrial Source Complex

Short Term Model. EPA Publication No.

EPA–454/R–92–015. U.S. Environmental

Protection Agency, Research Triangle Park,

NC. (NTIS No. PB 93–226769)

Environmental Protection Agency, 1992.

Development and Evaluation of a Revised

Area Source Algorithm for the Industrial

source complex Long Term Model. EPA

Publication No. EPA–454/R–92–016. U.S.

Environmental Protection Agency, Research

Triangle Park, NC. (NTIS No. PB 93–226777)

Environmental Protection Agency, 1994.

Development and Testing of a Dry Deposition

Algorithm (Revised). EPA Publication No.

EPA–454/R–94–015. U.S. Environmental

Protection Agency, Research Triangle Park,

NC. (NTIS No. PB 94–183100)

Scire, J.S. and L.L. Schulman, 1981.

Evaluation of the BLP and ISC Models with

SF6 Tracer Data and SO2 Measurements at

Aluminum Reduction Plants. Air Pollution

Control Association Specialty Conference on

Dispersion Modeling for Complex Sources,

St. Louis, MO.

Schulman, L.L. and S.R. Hanna, 1986.

Evaluation of Downwash Modification to the

Industrial Source Complex Model. Journal of

the Air Pollution Control Association, 36:

258–264.

A.6 Urban Airshed Model (UAM)

Reference

Environmental Protection Agency, 1990.

User’s Guide for the Urban Airshed Model,

Volume I–VIII. EPA Publication Nos. EPA–

450/4–90–007a–c, d(R), e-g, and EPA–454/B–

93–004, respectively. U.S. Environmental

Protection Agency, Research Triangle Park,

NC (NTIS Nos. PB 91–131227, PB 91–131235,

PB 91–131243, PB 93–122380, PB 91–

131268, PB 92–145382, and PB 92–224849,

respectively, for Vols. I–VII).

Availability

The model code is available on the Support

Center for Regulatory Air Models Bulletin

Board System (see Section A.0).

Abstract

UAM is an urban scale, three dimensional,

grid type numerical simulation model. The

model incorporates a condensed

photochemical kinetics mechanism for urban

atmospheres. The UAM is designed for

computing ozone (O3) concentrations under

short-term, episodic conditions lasting one or

two days resulting from emissions of oxides

of nitrogen (NOx), volatile organic

compounds (VOC), and carbon monoxide

(CO). The model treats urban VOC emissions

as their carbon-bond surrogates.

a. Recommendations for Regulatory Use

UAM is appropriate for the following

applications: urban areas having significant

ozone attainment problems and one hour

averaging times.

UAM has many options but no specific

recommendations can be made at this time

on all options. The reviewing agency should

be consulted on selection of options to be

used in regulatory applications.

b. Input Requirements

Source data: gridded, hourly emissions of

PAR, OLE, ETH, XYL, TOL, ALD2, FORM,
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41871

ISOR, ETOTH, MEOH, CO, NO, and NO2 for

low-level sources. For major elevated point

sources, hourly emissions, stack height, stack

diameter, exit velocity, and exit temperature.

Meteorological data: hourly, gridded,

divergence free, u and v wind components

for each vertical level; hourly gridded mixing

heights and surface temperatures; hourly

exposure class; hourly vertical potential

temperature gradient above and below the

mixing height; hourly surface atmospheric

pressure; hourly water mixing ratio; and

gridded surface roughness lengths.

Air quality data: concentration of all

carbon bond 4 species at the beginning of the

simulation for each grid cell; and hourly

concentrations of each pollutant at each level

along the inflow boundaries and top

boundary of the modeling region.

Other data requirements are: hourly mixed

layer average, NO2 photolysis rates; and

ozone surface uptake resistance along with

associated gridded vegetation (scaling)

factors.

c. Output

Printed output includes:

• Gridded instantaneous concentration

fields at user-specified time intervals for

user-specified pollutants and grid levels;

• Gridded time-average concentration

fields for user-specified time intervals,

pollutants, and grid levels.

d. Type of Model

UAM is a three dimensional, numerical,

photochemical grid model.

e. Pollutant Types

UAM may be used to model ozone (O3)

formation from oxides of nitrogen (NOx) and

volatile organic compound (VOC) emissions.

f. Source-Receptor Relationship

Low-level area and point source emissions

are specified within each surface grid cell.

Emissions from major point sources are

placed within cells aloft in accordance with

calculated effective plume heights.

Hourly average concentrations of each

pollutant are calculated for all grid cells at

each vertical level.

g. Plume Behavior

Plume rise is calculated for major point

sources using relationships recommended by

Briggs (1971).

h. Horizontal Winds

See Input Requirements.

i. Vertical Wind Speed

Calculated at each vertical grid cell

interface from the mass continuity

relationship using the input gridded

horizontal wind field.

j. Horizontal Dispersion

Horizontal eddy diffusivity is set to a user

specified constant value (nominally 50 m2/s).

k. Vertical Dispersion

Vertical eddy diffusivities for unstable and

neutral conditions calculated using

relationships of Lamb et al. (1977); for stable

conditions, the relationship of Businger and

Arya (1974) is employed. Stability class,

friction velocity, and Monin-Obukhov length

determined using procedure of Liu et al.

(1976).

l. Chemical Transformation

UAM employs a simplified version of the

Carbon-Bond IV Mechanism (CBM–IV)

developed by Gery et al. (1988) employing

various steady state approximations. The

CBM–IV mechanism incorporated in UAM

utilizes an updated simulation of PAN

chemistry that includes a peroxy-peroxy

radical termination reaction, significant

when the atmosphere is NOx-limited (Gery et

al., 1989). The current CBM–IV mechanism

accommodates 34 species and 82 reactions.

m. Physical Removal

Dry deposition of ozone and other

pollutant species are calculated. Vegetation

(scaling) factors are applied to the reference

surface uptake resistance of each species

depending on land use type.

n. Evaluation Studies

Builtjes, P.J.H., K.D. van der Hurt and S.D.

Reynolds, 1982. Evaluation of the

Performance of a Photochemical Dispersion

Model in Practical Applications. 13th

International Technical Meeting on Air

Pollution Modeling and Its Application, Ile

des Embiez, France.

Cole, H.S., D.E. Layland, G.K. Moss and

C.F. Newberry, 1983. The St. Louis Ozone

Modeling Project. EPA Publication No. EPA–

450/4–83–019. U.S. Environmental

Protection Agency, Research Triangle Park,

NC.

Dennis, R.L., M.W. Downton and R.S. Keil,

1983. Evaluation of Performance Measures

for an Urban Photochemical Model. EPA

Publication No. EPA–450/4–83–021. U.S.

Environmental Protection Agency, Research

Triangle Park, NC.

Haney, J.L. and T.N. Braverman, 1985.

Evaluation and Application of the Urban

Airshed Model in the Philadelphia Air

Quality Control Region. EPA Publication No.

EPA–450/4–85–003. U.S. Environmental

Protection Agency, Research Triangle Park,

NC.

Layland, D.E. and H.S. Cole, 1983. A

Review of Recent Applications of the SAI

Urban Airshed Model. EPA Publication No.

EPA–450/4–84–004. U.S. Environmental

Protection Agency, Research Triangle Park,

NC.

Layland, D.E., S.D. Reynolds, H. Hogo and

W.R. Oliver, 1983. Demonstration of

Photochemical Grid Model Usage for Ozone

Control Assessment. 76th Annual Meeting of

the Air Pollution Control Association,

Atlanta, GA.

Morris, R.E. et al., 1990. Urban Airshed

Model Study of Five Cities. EPA Publication

No. EPA–450/4–90–006a-g. U.S.

Environmental Protection Agency, Research

Triangle Park, NC.

Reynolds, S.D., H. Hogo, W.R. Oliver and

L.E. Reid, 1982. Application of the SAI

Airshed Model to the Tulsa Metropolitan

Area, SAI No. 82004. Systems Applications,

Inc., San Rafael, CA.

Schere, K.L. and J.H. Shreffler, 1982. Final

Evaluation of Urban-Scale Photochemical Air

Quality Simulation Models. EPA Publication

No. EPA–600/3–82–094. U.S. Environmental

Protection Agency, Research Triangle Park,

NC.

Seigneur C., T.W. Tesche, C.E. Reid, P.M.

Roth, W.R. Oliver and J.C. Cassmassi, 1981.

The Sensitivity of Complex Photochemical

Model Estimates to Detail In Input

Information, Appendix A—A Compilation of

Simulation Results. EPA Publication No.

EPA–450/4–81–031b. U.S. Environmental

Protection Agency, Research Triangle Park,

NC.

South Coast Air Quality Management

District, 1989. Air Quality Management

Plan—Appendix V–R (Urban Airshed Model

Performance Evaluation). El Monte, CA.

Stern, R. and B. Scherer, 1982. Simulation

of a Photochemical Smog Episode in the

Rhine-Ruhr Area with a Three Dimensional

Grid Model. 13th International Technical

Meeting on Air Pollution Modeling and Its

Application, Ile des Embiez, France.

Tesche, T.W., C. Seigneur, L.E. Reid, P.M.

Roth, W.R. Oliver and J.C. Cassmassi, 1981.

The Sensitivity of Complex Photochemical

Model Estimates to Detail in Input

Information. EPA Publication No. EPA–450/

4–81–031a. U.S. Environmental Protection

Agency, Research Triangle Park, NC.

Tesche, T.W., W.R. Oliver, H. Hogo, P.

Saxeena and J.L. Haney, 1983. Volume IV—

Assessment of NOx Emission Control

Requirements in the South Coast Air Basin—

Appendix A. Performance Evaluation of the

Systems Applications Airshed Model for the

26–27 June 1974 O3 Episode in the South

Coast Air Basin, SYSAPP 83/037. Systems

Applications, Inc., San Rafael, CA.

Tesche, T.W., W.R. Oliver, H. Hogo, P.

Saxeena and J.L. Haney, 1983. Volume IV—

Assessment of NOx Emission Control

Requirements in the South Coast Air Basin—

Appendix B. Performance Evaluation of the

Systems Applications Airshed Model for the

7–8 November 1978 NO2 Episode in the

South Coast Air Basin, SYSAPP 83/038.

Systems Applications, Inc., San Rafael, CA.

Tesche, T.W., 1988. Accuracy of Ozone Air

Quality Models. Journal of Environmental

Engineering, 114(4): 739–752.

A.7 Offshore and Coastal Dispersion Model

(OCD)

Reference

DiCristofaro, D.C. and S.R. Hanna, 1989.

OCD: The Offshore and Coastal Dispersion

Model, Version 4. Volume I: User’s Guide,

and Volume II: Appendices. Sigma Research

Corporation, Westford, MA. (NTIS Nos. PB

93–144384 and PB 93–144392)

Availability

This model code is available on the

Support Center for Regulatory Air Models

Bulletin Board System and also on diskette

(as PB 91–505230) from the National

Technical Information Service (see Section

A.0).

Technical Contact

Minerals Management Service, Attn: Mr.

Dirk Herkhof, Parkway Atrium Building, 381

Elden Street, Herndon, VA 22070–4817,

Phone: (703) 787–1735.

Abstract

OCD is a straight-line Gaussian model

developed to determine the impact of

offshore emissions from point, area or line

sources on the air quality of coastal regions.

OCD incorporates overwater plume transport
41872 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 nonpermanent 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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41873

Force Bases—Model Application and

Background.’’ FAA Report No. FAA–EE–88–

5; USAF Report No. ESL–TR–88–55, Federal

Aviation Administration, 800 Independence

Avenue, S.W., Washington, D.C. 20591.

(NTIS No. ADA 199794)

Availability

EDMS is available for $40 from: Federal

Aviation Administration, Attn: Ms. Diana

Liang, AEE–120, 800 Independence Avenue,

S.W., Washington, D.C. 20591, Phone: (202)

267–3494.

Abstract

EDMS is a combined emissions/dispersion

model for assessing pollution at civilian

airports and military air bases. This model,

which was jointly developed by the Federal

Aviation Administration (FAA) and the

United States Air Force (USAF), produces an

emission inventory of all airport sources and

calculates concentrations produced by these

sources at specified receptors. The system

stores emission factors for fixed sources such

as fuel storage tanks and incinerators and

also for mobile sources such as automobiles

or aircraft. EDMS incorporates an emissions

model to calculate an emission inventory for

each airport source and a dispersion model,

the Graphical Input Microcomputer Model

(GIMM) (Segal, 1983) to calculate pollutant

concentrations produced by these sources at

specified receptors. The GIMM, which

processes point, area, and line sources, also

incorporates a special meteorological

preprocessor for processing up to one year of

National Climatic Data Center (NCDC) hourly

data. The model operates in both a screening

and refined mode, accepting up to 170

sources and 10 receptors.

a. Recommendations for Regulatory Use

EDMS is appropriate for the following

applications:

• Cumulative effect of changes in aircraft

operations, point source and mobile source

emissions at airports or air bases;

• Simple terrain;

• Transport distances less than 50

kilometers; and

• 1-hour to annual averaging times.

b. Input Requirements

All data are entered through a ‘‘runtime’’

version of the Condor data base which is an

integral part of EDMS. Typical entry items

are source and receptor coordinates, percent

cold starts, vehicles per hour, etc. Some point

sources, such as heating plants, require stack

height, stack diameter, and effluent

temperature inputs.

Wind speed, wind direction, hourly

temperature, and Pasquill-Gifford stability

category (P–G) are the meteorological inputs.

They can be entered manually through the

EDMS data entry screens or automatically

through the processing of previously loaded

NCDC hourly data.

c. Output

Printed outputs consist of:

• A monthly and yearly emission

inventory report for each source entered; and

• A concentration summing report for up

to 8760 hours (one year) of data.

d. Type of Model

For its emissions inventory calculations,

EDMS uses algorithms consistent with the

EPA Compilation of Air Pollutant Emission

Factors, AP–42. For its dispersion

calculations, EDMS uses the GIMM model

which is described in reports FAA–EE–88–4

and FAA–EE–88–5, referenced above. GIMM

uses a Gaussian plume algorithm.

e. Pollutant Types

EDMS inventories and calculates the

dispersion of carbon monoxide, nitrogen

oxides, sulphur oxides, hydrocarbons, and

suspended particles.

f. Source-Receptor Relationship

Up to 170 sources and 10 receptors can be

treated simultaneously. Area sources are

treated as a series of lines that are positioned

perpendicular to the wind.

Line sources (roadways, runways) are

modeled as a series of points. Terrain

elevation differences between sources and

receptors are neglected.

Receptors are assumed to be at ground

level.

g. Plume Behavior

Plume rise is calculated for all point

sources (heating plants, incinerators, etc.)

using Briggs plume rise equations (Catalano,

1986; Briggs, 1969; Briggs, 1971; Briggs,

1972).

Building and stack tip downwash effects

are not treated.

Roadway dispersion employs a

modification to the Gaussian plume

algorithms as suggested by Rao and Keenan

(1980) to account for close-in vehicleinduced 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.
41874 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

a. Recommendation for Regulatory Use

CTDMPLUS is appropriate for the

following applications:

• Elevated point sources;

• Terrain elevations above stack top;

• Rural or urban areas;

• Transport distances less than 50

kilometers; and

• One hour to annual averaging times

when used with a post-processor program

such as CHAVG.

b. Input Requirements

Source data: For each source, user supplies

source location, height, stack diameter, stack

exit velocity, stack exit temperature, and

emission rate; if variable emissions are

appropriate, the user supplies hourly values

for emission rate, stack exit velocity, and

stack exit temperature.

Meteorological data: the user must supply

hourly averaged values of wind, temperature

and turbulence data for creation of the basic

meteorological data file (‘‘PROFILE’’).

Meteorological preprocessors then create a

SURFACE data file (hourly values of mixed

layer heights, surface friction velocity,

Monin-Obukhov length and surface

roughness length) and a RAWINsonde data

file (upper air measurements of pressure,

temperature, wind direction, and wind

speed).

Receptor data: receptor names (up to 400)

and coordinates, and hill number (each

receptor must have a hill number assigned).

Terrain data: user inputs digitized contour

information to the terrain preprocessor which

creates the TERRAIN data file (for up to 25

hills).

c. Output

When CTDMPLUS is run, it produces a

concentration file, in either binary or text

format (user’s choice), and a list file

containing a verification of model inputs, i.e.,

• Input meteorological data from

‘‘SURFACE’’ and ‘‘PROFILE’’

• Stack data for each source

• Terrain information

• Receptor information

• Source-receptor location (line printer

map).

In addition, if the case-study option is

selected, the listing includes:

• Meteorological variables at plume height

• Geometrical relationships between the

source and the hill

• Plume characteristics at each receptor,

i.e.,

¥> distance in along-flow and cross flow

direction

¥> effective plume-receptor height

difference

¥> effective σy & σz 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 nonreactive, 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°, so that the

linearized equation of motion for Boussinesq

flow are applicable. Receptors upwind of the

impingement point, or those associated with

any of the hills in the modeling domain,

require separate treatment.

g. Plume Behavior

As in CTDM, the basic plume rise

algorithms are based on Briggs’ (1975)

recommendations.

A central feature of CTDMPLUS for

neutral/stable conditions is its use of a

critical dividing-streamline height (Hc) to

separate the flow in the vicinity of a hill into

two separate layers. The plume component in

the upper layer has sufficient kinetic energy

to pass over the top of the hill while

streamlines in the lower portion are

constrained to flow in a horizontal plane

around the hill. Two separate components of

CTDMPLUS compute ground-level

concentrations resulting from plume material

in each of these flows.

The model calculates on an hourly (or

appropriate steady averaging period) basis

how the plume trajectory (and, in stable/

neutral conditions, the shape) is deformed by

each hill. Hourly profiles of wind and

temperature measurements are used by

CTDMPLUS to compute plume rise, plume

penetration (a formulation is included to

handle penetration into elevated stable

layers, based on Briggs (1984)), convective

scaling parameters, the value of Hc, and the

Froude number above Hc.

h. Horizontal Winds

CTDMPLUS does not simulate calm

meteorological conditions. Both scalar and

vector wind speed observations can be read

by the model. If vector wind speed is

unavailable, it is calculated from the scalar

wind speed. The assignment of wind speed

(either vector or scalar) at plume height is

done by either:

• Interpolating between observations

above and below the plume height, or

• Extrapolating (within the surface layer)

from the nearest measurement height to the

plume height.

i. Vertical Wind Speed

Vertical flow is treated for the plume

component above the critical dividing

streamline height (Hc); see ‘‘Plume

Behavior’’.

j. Horizontal Dispersion

Horizontal dispersion for stable/neutral

conditions is related to the turbulence

velocity scale for lateral fluctuations, σv, 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.,

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

System19 (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 31⁄2’’ 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.
41876 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 threedimensional meteorological and emission

input, the receptor locations, the plume rise

formulas, the sigma formulas, etc. Without

explicit user’s specifications, standard

default values are assumed.

AVACTA II provides both concentration

fields on the user specified receptor points,

and dry/wet deposition patterns throughout

the domain. The model is particularly

oriented to the simulation of the dynamics

and transformation of sulfur species (SO2 and

SO4

=), but can handle virtually any pair of

primary-secondary pollutants.

a. Recommendations for Regulatory Use

AVACTA II can be used if it can be

demonstrated to estimate concentrations

equivalent to those provided by the preferred

model for a given application. AVACTA II

must be executed in the equivalent mode.

AVACTA II can be used on a case-by-case

basis in lieu of a preferred model if it can be

demonstrated, using the criteria in Section

3.2, that AVACTA II is more appropriate for

the specific application. In this case the

model options/modes which are most

appropriate for the application should be

used.

b. Input Requirements

A time-varying input is required at each

computational step. Only those data which

have changed need to be input by the user.

Source data requirements are: Coordinates,

emission rates of primary and secondary

pollutants, initial plume sigmas (for nonpoint 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 highestsecond-highest concentration values. Both

partial and total concentrations are provided.

d. Type of Model

AVACTA II is Gaussian segment/puff

model.

e. Pollutant Types

AVACTA II can handle any couple of

primary-secondary pollutants (e.g., SO2 and

SO4

=).

f. Source Receptor Relationship

The AVACTA II approach maintains the

basic Gaussian formulation, but allows a

numerical simulation of both nonstationary

and nonhomogeneous meteorological

conditions. The emitted pollutant material is

divided into a sequence of ‘‘elements,’’ either

segments or puffs, which are connected

together but whose dynamics are a function

of the local meteorological conditions. Since

the meteorological parameters vary with time

and space, each element evolves according to

the different meteorological conditions

encountered along its trajectory.

AVACTA II calculates the partial

contribution of each source in each receptor

during each interval. The partial

concentration is the sum of the contribution

of all existing puffs, plus that of the closest

segment.

g. Plume Behavior

The user can select the following plume

rise formulas:

Briggs (1969, 1971, 1972)

CONCAWE (Briggs, 1975)

Lucas-Moore (Briggs, 1975)

User’s function, i.e., a subroutine supplied

by the user

With cold plumes, the program uses a

special routine for the computation of the jet

plume rise. The user can also select several

computational options that control plume

behavior in complex terrain and its total/

partial reflections.

h. Horizontal Winds

A 3D mass-consistent wind field is

optionally generated.

i. Vertical Wind Speed

A 3D mass-consistent wind field is

optionally generated.

j. Horizontal Dispersion

During each step, the sigmas of each

element are increased. The user can select

the following sigma functions:

Pasquill-Gifford-Turner (in the functional

form specified by Green et al., 1980)

Brookhaven (Gifford, 1975)

Briggs, open country (Gifford, 1975)

Briggs, urban, i.e., McElroy-Pooler (Gifford,

1975)

Irwin (1979a)

LO–LOCAT (MacCready et al., 1974)

User-specified function, by points

User-specified function, with a user’s

subroutine

The virtual distance/age concept is used

for incrementing the sigmas at each time

step.

k. Vertical Dispersion

During each step, the sigmas of each

element are increased. The user can select

the following sigma functions:

Pasquill-Gifford-Turner (in the functional

form specified by Green et al., 1980)

Brookhaven (Gifford, 1975)

Briggs, open country (Gifford, 1975)

Briggs, urban, i.e., McElroy-Pooler (Gifford,

1975)

LO–LOCAT (MacCready et al., 1974)

User-specified function, with a user’s

subroutine

The virtual distance/age concept is used

for incrementing the sigmas at each time

step.

l. Chemical Transformation

First order chemical reactions (primary-tosecondary 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 upwardlydirected 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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41877

data or convert units of input values.

Chemical property data must be input by the

user. Such data for a few selected species are

available within the model. Additional data

may be added to this data base by the user.

Source data requirements are: emission

rate and release duration; emission chemical

and physical properties (molecular weight,

density vs. concentration profile in the case

of aerosol releases, and contaminant heat

capacity in the case of a nonisothermal gas

release; stack parameters (i.e., diameter,

elevation above ground level, temperature at

release point).

Meteorological data requirements are: wind

speed at designated height above ground,

ambient temperature and pressure, surface

roughness, relative humidity, and ground

surface temperature (which in most cases can

be adequately approximated by the ambient

temperature).

Receptor data requirements are: averaging

time of interest, above-ground height of

receptors, and maximum distance between

receptors (since the model computes

downwind receptor distances to optimize

model performance, this parameter is used

only for nominal control of the output listing,

and is of secondary importance). No indoor

concentrations are calculated by the model.

c. Output

Printed output includes in tabular form:

• Listing of model input data;

• Plume centerline elevation, mole

fraction, concentration, density, and

temperature at each downwind distance;

• σy and σz values at each downwind

distance;

• Off-centerline distances to 2 specified

concentration values at a specified receptor

height at each downwind distance (these

values can be used to draw concentration

isopleths after model execution);

• Concentration vs. time histories for

finite-duration releases (if specified by user).

The output print file is automatically saved

and must be sent to the appropriate printer

by the user after program execution.

No graphical output is generated by the

current version of this program.

d. Type of Model

DEGADIS estimates plume rise and

dispersion for vertically-upward jet releases

using mass and momentum balances with air

entrainment based on laboratory and fieldscale 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 nonreactive 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 lowmomentum 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 densitystratified fluids.

l. Chemical Transformation

Not specifically treated.

m. Physical Removal

Not treated.

n. Evaluation Studies

Spicer, T.O. and J.A. Havens, 1986.

Development of Vapor Dispersion Models for

Nonneutrally Buoyant Gas Mixtures—

Analysis of USAF/N2O4 Test Data. USAF

Engineering and Services Laboratory, Final

Report ESL–TR–86–24.

Spicer, T.O. and J.A. Havens, 1988.

Development of Vapor Dispersion Models for

Nonneutrally Buoyant Gas Mixtures—

Analysis of TFI/NH3 Test Data. USAF

Engineering and Services Laboratory, Final

Report.

o. Operating Information

The model requires either a VAX computer

or an IBM—compatible PC for its execution.

The model currently does not require

supporting software. A FORTRAN compiler

is required to generate program executables

in the VAX computing environment. PC

executables are provided within the source

code; however, a PC FORTRAN compiler

may be used to tailor a PC executable to the

user’s PC environment.

B.3 ERT Visibility Model

Reference

ENSR Consulting and Engineering, 1990.

ERT Visibility Model: Version 4; Technical

Description and User’s Guide. Document

M2020–003. ENSR Consulting and

Engineering, 35 Nagog Park, Acton, MA

01720.

Availability

The user’s guide and model code on

diskette are available as a package (as PB 96–

501978) from the National Technical

Information Service (see Section B.0).

Abstract

The ERT Visibility Model is a Gaussian

dispersion model designed to estimate

visibility impairment for arbitrary lines of

sight due to isolated point source emissions

by simulating gas-to-particle conversion, dry

deposition, NO to NO2 conversion and linear

radiative transfer.

a. Recommendations for Regulatory Use

There is no specific recommendation at the

present time. The ERT Visibility Model may

be used on a case-by-case basis.

b. Input Requirements

Source data requirements are: stack height,

stack temperature, emissions of SO2, NOx,

TSP, fraction of NOx as NO2, fraction of TSP

which is carbonaceous, exit velocity, and exit

radius.

Meteorological data requirements are:

hourly ambient temperature, mixing depth,

wind speed at stack height, stability class,

potential temperature gradient, and wind

direction.

Receptor data requirements are: observer

coordinates with respect to source, latitude,

longitude, time zone, date, time of day,

elevation, relative humidity, background

visual range, line-of-sight azimuth and

elevation angle, inclination angle of the

observed object, distance from observer to

object, object and surface reflectivity, number

and spacing of integral receptor points along

line of sight.

Other data requirements are: ambient

concentrations of O3 and NOx, deposition

velocity of TSP, sulfate, nitrate, SO2 and NOx,

first-order transformation rate for sulfate and

nitrate.

c. Output

Printed output includes both summary and

detailed results as follows: Summary output:

Page 1—site, observer and object parameters;

Page 2—optical pollutants and associated

extinction coefficients; Page 3—plume model

input parameters; Page 4—total calculated

visual range reduction, and each pollutant’s

contribution; Page 5—calculated plume

contrast, object contrast and object contrast

degradation at the 550nm wavelength; Page

6—calculated blue/red ratio and ΛE
41878 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

(U*V*W*) values for both sky and object

discoloration.

Detailed output: phase functions for each

pollutant in four wavelengths (400, 450, 550,

650nm), concentrations for each pollutant

along sight path, solar geometry contrast

parameters at all wavelengths, intensities,

tristimulus values and chromaticity

coordinates for views of the object, sun,

background sky and plume.

d. Type of Model

ERT Visibility model is a Gaussian plume

model for estimating visibility impairment.

e. Pollutant Types

Optical activity of sulfate, nitrate (derived

from SO2 and NOx emissions), primary TSP

and NO2 is simulated.

f. Source Receptor Relationship

Single source and hour is simulated.

Unlimited number of lines-of-sight

(receptors) is permitted per model run.

g. Plume Behavior

Briggs (1971) plume rise equations for final

rise are used.

h. Horizontal Wind Field

A single wind speed and direction is

specified for each case study. The wind is

assumed to be spatially uniform.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero.

j. Horizontal Dispersion

Rural dispersion coefficients from Turner

(1969) are used.

k. Vertical Dispersion

Rural dispersion coefficients from Turner

(1969) are used. Mixing height is accounted

for with multiple reflection handled by

summation of series near the source, and

Fourier representation farther downwind.

l. Chemical Transformation

First order transformations of sulfates and

nitrates are used.

m. Physical Removal

Dry deposition is treated by the source

depletion method.

n. Evaluation Studies

Seigneur, C., R.W. Bergstrom and A.B.

Hudischewskyj, 1982. Evaluation of the EPA

PLUVUE Model and the ERT Visibility

Model Based on the 1979 VISTTA Data Base.

EPA Publication No. EPA–450/4–82–008.

U.S. Environmental Protection Agency,

Research Triangle Park, NC.

White, W.H., C. Seigneur, D.W. Heinold,

M.W. Eltgroth, L.W. Richards, P.T. Roberts,

P.S. Bhardwaja, W.D. Conner and W.E.

Wilson, Jr., 1985. Predicting the Visibility of

Chimney Plumes: An Inter-comparison of

Four Models with Observations at a WellControlled 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, multicompound 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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41879

dispersion downwind of a transition point.

HEGADAS simulates the ground-level heavygas dispersion.

e. Pollutant Types

HGSYSTEM may be used to model nonreactive chemicals or hydrogen fluoride.

f. Source-Receptor Relationships

HGSYSTEM estimates the expected area of

exposure to concentrations above userspecified 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 crosssection 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 groundlevel 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 crosswind 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 verticallyreleased 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 postprocessors 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: ysa@RT66.com

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 threedimensional 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
41880 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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-bycase 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 nonGaussian.

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

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 userselected 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.
41882 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

Receptor locations are restricted to 36

azimuths (every 10 degrees) and five userspecified 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 ∆Η/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 ∆Η/3.5).

l. Chemical Transformation

Not treated.

m. Physical Removal

Not treated.

n. Evaluation Studies

Londergan, R., D. Minott, D. Wackter, T.

Kincaid and D. Bonitata, 1983. Evaluation of

Rural Air Quality Simulation Models,

Appendix G: Statistical Tables for PPSP. EPA

Publication No. EPA–450/4–83–003.

Environmental Protection Agency, Research

Triangle Park, NC.

Weil, J.C. and R.P. Brower, 1982. The

Maryland PPSP dispersion model for tall

stacks. Ref. No. PPSP MP–36. Prepared for

Maryland Department of Natural Resources.

Prepared by Environmental Center, Martin

Marietta Corporation, Baltimore, Maryland.

(NTIS No. PB 82–219155)

B.8 Mesoscale Puff Model (MESOPUFF II)

Reference

Scire, J.S., F.W. Lurmann, A. Bass and S.R.

Hanna, 1984. User’s Guide to the Mesopuff

II Model and Related Processor Programs.

EPA Publication No. EPA–600/8–84–013.

U.S. Environmental Protection Agency,

Research Triangle Park, NC. (NTIS No. PB

84–181775)

A Modeling Protocol for Applying

MESOPUFF II to Long Range Transport

Problems, 1992. EPA Publication No. EPA–

454/R–92–021. U.S. Environmental

Protection Agency, Research Triangle Park,

NC.

Availability

This model code is available on the

Support Center for Regulatory Air Models

Bulletin Board System and also on diskette

(as PB 93–500247) from the National

Technical Information Service (see Section

B.0).

Abstract

MESOPUFF II is a short term, regional

scale puff model designed to calculate

concentrations of up to 5 pollutant species

(SO2, SO4, NOX, HNO3, NO3). Transport, puff

growth, chemical transformation, and wet

and dry deposition are accounted for in the

model.

a. Recommendations for Regulatory Use

There is no specific recommendation at the

present time. The model may be used on a

case-by-case basis.

b. Input Requirements

Required input data include four types: (1)

input control parameters and selected

technical options, (2) hourly surface

meteorological data and twice daily upper air

measurements, hourly precipitation data are

optional, (3) surface land use classification

information, (4) source and emissions data.

Data from up to 25 surface National

Weather Service stations and up to 10 upper

air stations may be considered. Spatially

variable fields at hour intervals of winds,

mixing height, stability class, and relevant

turbulence parameters are derived by

MESOPAC II, the meteorological

preprocessor program described in the User

Guide.

Source and emission data for up to 25

point sources and/or up to 5 area sources can

be included. Required information are:

location in grid coordinates, stack height, exit

velocity and temperature, and emission rates

for the pollutant to be modeled.

Receptor data requirements: up to a 40×40

grid may be used and non-gridded receptor

locations may be considered.

c. Output

Line printer output includes: all input

parameters, optionally selected arrays of

ground-level concentrations of pollutant

species at specified time intervals.

Line printer contour plots output from

MESOFILE II post-processor program.

Computer readable output of concentration

array to disk/tape for each hour.

d. Type of Model

MESOPUFF II is a Gaussian puff

superposition model.

e. Pollutant Types

Up to five pollutant species may be

modeled simultaneously and include: SO2,

SO4, NOx, HNO3, NO3.

f. Source-Receptor Relationship

Up to 25 point sources and/or up to 5 area

sources are permitted.

g. Plume Behavior

Briggs (1975) plume rise equations are

used, including plume penetration with

buoyancy flux computed in the model.

Fumigation of puffs is considered and may

produce immediate mixing or multiple

reflection calculations at user option.

h. Horizontal Winds

Gridded wind fields are computed for 2

layers; boundary layer and above the mixed

layer. Upper air rawinsonde data and hourly

surface winds are used to obtain spatially

variable u,v component fields at hourly

intervals. The gridded fields are computed by

interpolation between stations in the

MESOPAC II preprocessor.

i. Vertical Wind Speed

Vertical winds are assumed to be zero.

j. Horizontal Dispersion

Incremental puff growth is computed over

discrete time steps with horizontal growth

parameters determined from power law

equations fit to sigma y curves of Turner out

to 100km. At distances greater than 100km,

puff growth is determined by the rate given

by Heffter (1965).

Puff growth is a function of stability class

and changes in stability are treated.

Optionally, user input plume growth

coefficients may be considered.

k. Vertical Dispersion

For puffs emitted at an effective stack

height which is less than the mixing height,

uniform mixing of the pollutant within the

mixed layer is performed. For puffs centered

above the mixing height, no effect at the

ground occurs.

l. Chemical Transformation

Hourly chemical rate constants are

computed from empirical expressions

derived from photochemical model

simulations.

m. Physical Removal

Dry deposition is treated with a resistance

method.

Wet removal may be considered if hourly

precipitation data are input.

n. Evaluation Studies

Results of tests for some model parameters

are discussed in:

Scire, J.S., F.W. Lurmann, A. Bass and S.R.

Hanna, 1984. Development of the

MESOPUFF II Dispersion Model. EPA

Publication No. EPA–600/3–84–057. U.S.

Environmental Protection Agency, Research

Triangle Park, NC.
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 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 ShortTerm 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-,
41884 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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, stabilitydependent 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 winddirection shear gradients.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero.

j. Horizontal Dispersion

Rural dispersion coefficients from Turner

(1969) are used.

Six stability classes are used.

k. Vertical Dispersion

Rural dispersion coefficients from Turner

(1969) are used.

Six stability classes are used.

An optional test for plume height above

mixing height before terrain adjustment is

included.

l. Chemical Transformation

Chemical transformations are treated using

exponential decay. Half-life is input by the

user.

m. Physical Removal

Physical removal is treated using

exponential decay. Half-life is input by the

user.

n. Evaluation Studies

Londergan, R., D. Minott, D. Wackter, T.

Kincaid and D. Bonitata, 1983. Evaluation of

Rural Air Quality Simulation Models. EPA

Publication No. EPA–450/4–83–003. U.S.

Environmental Protection Agency, Research

Triangle Park, NC.

B.11 PANACHE

Reference

Transoft Group, 1994. User’s Guide of

Fluidyn-PANACHE, a Three-Dimensional

Deterministic Simulation of Pollutants

Dispersion Model for Complex Terrain; Cary,

North Carolina.

Availability

For a cost to be negotiated with the model

developer, the computer code is available

from: Transoft US, Inc., 818 Reedy Creek

Road, Cary, NC 27513–3307; Phone: (919)

380–7500, Fax: (919) 380–7592.

Abstract

PANACHE is an Eulerian (and Lagrangian

for particulate matter), 3-dimensional finite

volume fluid mechanics code designed to

simulate continuous and short-term pollution

dispersion in the atmosphere, in simple or

complex terrain. For single or multiple

sources, pollutant emissions from stack,

point, area, volume, general sources and

distant sources are treated. The model

automatically treats obstacles, effects of

vegetation and water bodies, the effects of

vertical temperature stratification on the

wind and diffusion fields, and turbulent

shear flows caused by atmospheric boundary

layer or terrain effects. The code solves

Navier Stokes equations in a curvilinear

mesh espousing the terrain and obstacles. A

2nd order resolution helps keep the number

of cells limited in case of shearing flow. An

initial wind field is computed by using a

Lagrangian multiplier to interpolate wind

data collected on site. The mesh generator,

the solver and the numerical schemes have

been adopted for atmospheric flows with or

without chemical reactions. The model code

operates on any workstation or IBM—

compatible PC (486 or higher). Gaussian and

puff modes are available in PANACHE for

fast, preliminary simulation.

a. Recommendations for Regulatory Use

On a case-by-case basis, PANACHE may be

appropriate for the following types of

situations: industrial or urban zone on a flat

or complex terrain, transport distance from a

few meters to 50km, continuous releases with

hourly, monthly or annual averaging times,

chemically reactive or non-reactive gases or

particulate emissions for stationary or

roadway sources.

b. Input Requirements

Data may be input directly from an

external source (e.g., GIS file) or

interactively. The model provides the option

to use default values when input parameters

are unavailable.

PANACHE user environment integrates the

pre- and post-processor with the solver. The

calculations can be done interactively or in

batch mode. An inverse scheme is provided

to estimate missing data from a few measured

values of the wind.

Terrain data requirements:

• Location, surface roughness estimates,

and altitude contours.

• Location and dimensions of obstacles,

forests, fields, and water bodies.

Source data requirements:

For all types of sources, the exit

temperature and plume mass flow rates and

concentration of each of the pollutants are

required. External sources require mass flow

rate. For roadways, estimated traffic volume

and vehicular emissions are required.

Meteorological data requirements:

Hourly stability class, wind direction,

wind speed, temperature, cloud cover,

humidity, and mixing height data with lapse

rate below and above it.

Primary meteorological variables available

from the National Weather Service can be

processed using PCRAMMET (see Section

9.3.3.2 of Appendix W) to an input file.

Data required at the domain boundary:

Wind profile (uniform, log or power law),

depending on the terrain conditions (e.g.,

residential area, forest, sea, etc.).

Chemical source data requirements:

A database of selected species with specific

heats and molecular weights can be extended

by the user. For heavy gases the database

includes a compressibility coefficients table.

Solar reflection:

For natural convection simulation with

low wind on a sunny day, approximate

values of temperature for fields, forests, water

bodies, shadows and their variations with the

time of the day are determined automatically.

c. Output

Printed output option: pollutant

concentration at receptor points, and listing

of input data (terrain, chemical, weather, and

source data) with turbulence and precision

control data.

Graphical output includes: In 3-

dimensional perspective or in any crosswind,

downwind or horizontal plane: wind

velocity, pollutant concentration, 3-

dimensional isosurface. The profile of

concentration can be obtained along any line

on the terrain. The concentration contours

can be either instantaneous or time integrated

for the emission from a source or a source

combination. A special utility is included to

help prepare a report or a video animation.

The user can select images, put in

annotations, or do animation.

d. Type of Model

The model uses an Eulerian (and

Lagrangian for particulate matter) 3-

dimensional finite volume model solving full

Navier-Stokes equations. The numerical

diffusion is low with appropriate turbulence

models for building wakes. A second order

resolution may be sought to limit the

diffusion. Gaussian and puff modes are

available. The numerical scheme is self

adaptive for the following situations:

• A curvilinear mesh or a chopped

Cartesian mesh is generated automatically or

manually;

• Thermal and gravity effects are

simulated by full gravity (heavy gases), no

gravity (well mixed light gases at ambient

temperature), and Boussinesq approximation

methods;

• K-diff, K-e or a boundary layer

turbulence models are used for turbulence

calculations. The flow behind obstacles such
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41885

as buildings, is calculated by using a

modified K-e.

• For heavy gases, a 3-dimensional heat

conduction from the ground and a

stratification model for heat exchange from

the atmosphere are used (with anisotropic

turbulence).

• If local wind data are available, an initial

wind field with terrain effects can be

computed using a Lagrangian multiplier,

which substantially reduces computation

time.

e. Pollutant Types

• Scavenging, Acid Rain: A module for

water droplets traveling through a plume

considers the absorption and de-absorption

effects of the pollutants by the droplet.

Evaporation and chemical reactions with

gases are also taken into account.

• Visibility: Predicts plume visibility and

surface deposition of aerosol.

• Particulate matter: Calculates settling

and dry deposition of particles based on a

Probability Density Function (PDF) of their

diameters. The exchange of mass, momentum

and heat between particles and gas is treated

with implicit coupling procedures.

• Ozone formation and dispersion: The

photochemical model computes ozone

formation and dispersion at street level in the

presence of sunlight.

• Roadway Pollutants: Accounts for heat

and turbulence due to vehicular movement.

Emissions are based on traffic volume and

emission factors.

• Odor Dispersion: Identifies odor sources

for waste water plants.

• Radon Dispersion: Simulates natural

radon accumulation in valleys and mine

environments.

PANACHE may also be used in emergency

planning and management for episodic

emissions, and fire and soot spread in

forested and urban areas or from combustible

pools.

f. Source-Receptor Relationship

Simultaneous use of multiple kinds of

sources at user defined locations. Any

number of user defined receptors can identify

pollutants from each source individually.

g. Plume Behavior

The options influencing the behavior are

full gravity, Boussinesq approximation or no

gravity.

h. Horizontal Winds

Horizontal wind speed approximations are

made only at the boundaries based on

National Weather Service data. Inside the

domain of interest, full Navier-Stokes

resolution with natural viscosity is used for

3-dimensional terrain and temperature

dependent wind field calculation.

i. Vertical Wind Speed

Vertical wind speed approximations are

made only at the boundaries based on

National Weather Service data. The domain

of interest is treated as for horizontal winds.

j. Horizontal Dispersion

Diffusion is calculated using appropriate

turbulence models. A 2nd order solution for

shearing flow can be sought when the

number of meshes is limited between

obstacles.

k. Vertical Dispersion

Dispersion by full gravity unless

Boussinesq approximation or no gravity

requested. Vertical dispersion is treated as

above for horizontal dispersion.

l. Chemical Transformation

PANCHEM, an atmospheric chemistry

module for chemical reactions, is available.

Photochemical reactions are used for

tropospheric ozone calculations.

m. Physical Removal

Physical removal is treated using dry

deposition coefficients

n. Evaluation Studies

Goldwire, H.C. Jr, T.G. McRae, G.W.

Johnson, D.L. Hipple, R.P. Koopman, J.W.

McClure, L.K. Morris and R.T. Cederhall,

1985. Desert Tortoise Series Data Report:

1983 Pressurized Ammonia Spills. UCID

20562, Lawrence Livermore National

Laboratory; Livermore, California.

Green, S.R., 1992. Modeling Turbulent Air

Flow in a Stand of Widely Spaced Trees, The

PHOENICS Journal of Computational Fluid

Dynamics and Its Applications, 5: 294–312.

Gryning, S.E. and E. Lyck, 1984.

Atmospheric Dispersion from Elevated

Sources in an Urban Area: Comparison

Between Tracer Experiments and Model

Calculations. Journal of Climate and Applied

Meteorology, 23: 651–660.

Havens, J., T. Spicer, H. Walker and T.

Williams, 1995. Validation of Mathematical

Models Using Wind-Tunnel Data Sets for

Dense Gas Dispersion in the Presence of

Obstacles. University of Arkansas, 8th

International Symposium-Loss Prevention

and Safety Promotion in the Process

Industries; Antwerp, Belgium.

McQuaid, J. (ed), 1985. Heavy Gas

Dispersion Trials at Thorney Island. Proc. of

a Symposium held at the University of

Sheffield, Great Britain.

Pavitskiy, N.Y., A.A. Yakuskin and S.V.

Zhubrin, 1993. Vehicular Exhaust Dispersion

Around Group of Buildings. The PHOENICS

Journal of Computational Fluid Dynamics

and Its Applications, 6: 270–285.

Tripathi, S., 1994. Evaluation of FluidynPANACHE 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 nonhorizontal views through the plume with a

sky viewing background; horizontal views

through the plume with white, gray and

black viewing backgrounds; and horizontal

views along the axis of the plume with a sky

viewing background.

a. Recommendations for Regulatory Use

The Plume Visibility Model (PLUVUE II)

may be used on a case-by-case basis as a third

level screening model. When applying

PLUVUE II, the following precautions should

be taken:

1. Treat the optical effects of NO2 and

particles separately as well as together to

avoid cancellation of NO2 absorption with

particle scattering.

2. Examine the visual impact of the plume

in 0.1 (or 0), 0.5, and 1.0 times the expected

level of particulate matter in the background

air.

3. Examine the visual impact of the plume

over the full range of observer-plume sun

angles.

4. The user should consult the appropriate

Federal Land Manager when using PLUVUE

II to assess visibility impacts in a Class I area.

b. Input Requirements

Source data requirements are: location and

elevation; emission rates of SO2, NOX, and

particulates; flue gas flow rate, exit velocity,

and exit temperature; flue gas oxygen

content; properties (including density, mass

median and standard geometric deviation of

radius) of the emitted aerosols in the

accumulation (0.1–1.0μm) and coarse (1.0–

10.μm) size modes; and deposition velocities

for SO2, NOX, coarse mode aerosol, and

accumulations mode aerosol.

Meteorological data requirements are:

stability class, wind direction (for an

observer-based run), wind speed, lapse rate,

air temperature, relative humidity, and

mixing height.

Other data requirements are: ambient

background concentrations of NOX, NO2, O3,

and SO2, and background visual range of

sulfate and nitrate concentrations.

Receptor (observer) data requirements are:

location, terrain elevation at points along

plume trajectory, white, gray, and black

viewing backgrounds, the distance from the

observer to the terrain observed behind the

plume.

c. Output

Printed output includes plume

concentrations and visual effects at specified

downwind distances for calculated or

specified lines of sight.

d. Type of Model

PLUVUE II is a Gaussian plume model.

Visibility impairment is quantified once the
41886 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

spectral light intensity has been calculated

for the specific lines of sight. Visibility

impairment includes visual range reduction,

plume contrast, relative coloration of a plume

to its viewing background, and plume

perceptibility due to its contrast and color

with respect to a viewing background.

e. Pollutant Types

PLUVUE II treats NO, NO2, SO2, H2SO4,

HNO3, O3, primary and secondary particles to

calculate effects on visibility.

f. Source Receptor Relationship

For performing the optics calculations at

selected points along the plume trajectory,

PLUVUE II has two modes: plume based and

observer based calculations. The major

difference is the orientation of the viewer to

the source and the plume.

g. Plume Behavior

Briggs (1969, 1971, 1972) final plume rise

equations are used.

h. Horizontal Winds

User-specified wind speed (and direction

for an observer-based run) are assumed

constant for the calculation.

i. Vertical Wind Speed

Vertical wind speed is assumed equal to

zero.

j. Horizontal Dispersion

Constant, uniform (steady-state) wind is

assumed for each hour. Straight line plume

transport is assumed to all downwind

distances.

k. Vertical Dispersion

Rural dispersion coefficients from Turner

(1969) are used, with no adjustment for

surface roughness. Six stability classes are

used.

l. Chemical Transformation

The chemistry of NO, NO2, O3, OH, O(1D),

SO2, HNO3, and H2SO4 is treated by means

of nine reactions. Steady state

approximations are used for radicals and for

the NO/NO2/O3 reactions.

m. Physical Removal

Dry deposition of gaseous and particulate

pollutants is treated using deposition

velocities.

n. Evaluation Studies

Bergstrom, R.W., C. Seigneur, B.L. Babson,

H.Y. Holman and M.A. Wojcik, 1981.

Comparison of the Observed and Predicted

Visual Effects Caused by Power Plant

Plumes. Atmospheric Environment, 15:

2135–2150.

Bergstrom, R.W., Seigneur, C.D. Johnson

and L.W. Richards, 1984. Measurements and

Simulations of the Visual Effects of

Particulate Plumes. Atmospheric

Environment, 18(10): 2231–2244.

Seigneur, C., R.W. Bergstrom and A.B.

Hudischewskyj, 1982. Evaluation of the EPA

PLUVUE Model and the ERT Visibility

Model Based on the 1979 VISTTA Data Base.

EPA Publication No. EPA–450/4–82–008.

U.S. Environmental Protection Agency,

Research Triangle Park, NC.

White, W.H., C. Seigneur, D.W. Heinold,

M.W. Eltgroth, L.W. Richards, P.T. Roberts,

P.S. Bhardwaja, W.D. Conner and W.E.

Wilson, Jr, 1985. Predicting the Visibility of

Chimney Plumes: An Inter-comparison of

Four Models with Observations at a WellControlled 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 gradienttransfer 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 σy

and σz; 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 σy

and σz, 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 nonreactive 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.
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 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 caseby-case basis.

b. Input Requirements

Source data requirements are: emission

rates, name, and molecular weight of each

species of pollutant emitted; ambient

pressure, ambient temperature, stack height,

stack diameter, stack exit velocity, stack gas

temperature, and location.

Meteorological data requirements are: wind

speeds, plume widths or stability classes,

photolytic rate constants, and plume depths

or stability classes.

Receptor data requirements are: downwind

distances or travel times at which

calculations are to be made.

Initial concentration of all species is

required, and the specification of downwind

ambient concentrations to be entrained by

the plume is optional.

c. Output

Short-term concentrations of primary and

secondary pollutants at either user specified

time increments, or user specified downwind

distances.

d. Type of Model

Reactive Gaussian plume model.

e. Pollutant Types

Currently, using the Carbon Bond

Mechanism (CBM–IV), 34 species are

simulated (82 reactions), including NO, NO2,

O3, SO2, SO4, five categories of reactive

hydrocarbons, secondary nitrogen

compounds, organic aerosols, and radical

species.

f. Source-Receptor Relationships

Single point source.

Single area or volume source.

Multiple sources can be simulated if they

are lined up along the wind trajectory.

Predicted concentrations are obtained at a

user specified time increment, or at user

specified downwind distances.

g. Plume Behavior

Briggs (1971) plume rise equations are

used.

h. Horizontal Winds

User specifies wind speeds as a function of

time.

i. Vertical Wind Speed

Not treated.

j. Horizontal Dispersion

User specified plume widths, or user may

specify stability and widths will be

computed using Turner (1969).

k. Vertical Dispersion

User specified plume depths, or user may

specify stability in which case depths will be

calculated using Turner (1969). Note that

vertical uniformity in plume concentration is

assumed.

l. Chemical Transformation

RPM–IV has the flexibility of using any

user input chemical kinetic mechanism.

Currently it is run using the chemistry of the

Carbon Bond Mechanism, CBM–IV (Gery et

al., 1988). The CBM–IV mechanism, as

incorporated in RPM–IV, utilizes an updated

simulation of PAN chemistry that includes a

peroxy-peroxy radical termination reaction,

significant when the atmosphere is NOXlimited (Gery et al., 1989). As stated above,

the current CBM–IV mechanism

accommodates 34 species and 82 reactions

focusing primarily on hydrocarbon/nitrogen

oxides and ozone photochemistry.

m. Physical Removal

Not treated.

n. Evaluation Studies

Stewart, D.A. and M–K Liu, 1981.

Development and Application of a Reactive

Plume Model. Atmospheric Environment, 15:

2377–2393.

B.15 Shoreline Dispersion Model (SDM)

Reference

PEI Associates, 1988. User’s Guide to

SDM–A Shoreline Dispersion Model. EPA

Publication No. EPA–450/4–88–017. U.S.

Environmental Protection Agency, Research

Triangle Park, NC. (NTIS No. PB 89–164305)

Availability

The model code is available on the Support

Center for Regulatory Air Models Bulletin

Board System (see Section B.0).

Abstract

SDM is a hybrid multi-point Gaussian

dispersion model that calculates source

impact for those hours during the year when

fumigation events are expected using a

special fumigation algorithm and the MPTER

regulatory model for the remaining hours (see

Appendix A).

a. Recommendations for Regulatory Use

SDM may be used on a case-by-case basis

for the following applications:

• Tall stationary point sources located at a

shoreline of any large body of water;

• Rural or urban areas;

• Flat terrain;

• Transport distances less than 50 km;

• 1-hour to 1-year averaging times.

b. Input Requirements

Source data: location, emission rate,

physical stack height, stack gas exit velocity,

stack inside diameter, stack gas temperature

and shoreline coordinates.

Meteorological data: hourly values of mean

wind speed within the Thermal Internal

Boundary Layer (TIBL) and at stack height;

mean potential temperature over land and

over water; over water lapse rate; and surface

sensible heat flux. In addition to these

meteorological data, SDM access standard

NWS surface and upper air meteorological

data through the RAMMET preprocessor.

Receptor data: coordinates for each

receptor.

c. Output

Printed output includes the MPTER model

output as well as: special shoreline
41888 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 userspecified 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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 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 halfhourly) 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 (halfhourly) 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 usersupplied 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 windroad angle. Three stability classes are used:

unstable, neutral and stable. The parameters

take into account the effect of trafficgenerated 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, PCbased, 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
41890 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

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 crosswindaveraged 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 liquidvapor 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 AirQuality 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-bycase 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

condition).

Default.

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
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 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.
41892 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

1The ‘‘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.

2Within 50km or distance to which source has a

significant impact, whichever is less.

3Particulate emissions should be specified as a

function of particulate diameter and density ranges.

4See footnote 2 of this Appendix C.

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

CHECKLIST1

1. Source location map(s) showing location

with respect to:

• Urban areas 2

• PSD Class I areas

• Nonattainment areas 2

• Topographic features (terrain, lakes, river

valleys, etc.) 2

• Other major existing sources 2

• Other major sources subject to PSD

requirements

• NWS meteorological observations

(surface and upper air)

• On-site/local meteorological

observations (surface and upper air)

• State/local/on-site air quality monitoring

locations 2

• Plant layout on a topographic map

covering a 1km radius of the source with

information sufficient to determine GEP stack

heights

2. Information on urban/rural

characteristics:

• Land use within 3km of source classified

according to Auer (1978): Correlation of land

use and cover with meteorological anomalies.

Journal of Applied Meteorology, 17: 636–643.

• Population

¥> total

¥> density

• Based on current guidance determination

of whether the area should be addressed

using urban or rural modeling methodology

3. Emission inventory and operating/

design parameters for major sources within

region of significant impact of proposed site

(same as required for applicant)

• Actual and allowable annual emission

rates (g/s) and operating rates 3

• Maximum design load short-term

emission rate (g/s) 3

• Associated emissions/stack

characteristics as a function of load for

maximum, average, and nominal operating

conditions if stack height is less than GEP or

located in complex terrain. Screening

analyses as footnoted above or detailed

analyses, if necessary, must be employed to

determine the constraining load condition

(e.g., 50%, 75%, or 100% load) to be relied

upon in the short-term modeling analysis.

—location (UTM’s)

—height of stack (m) and grade level above

MSL

—stack exit diameter (m)

—exit velocity (m/s)

—exit temperature (°K)

• Area source emissions (rates, size of area,

height of area source)3

• Location and dimensions of buildings

(plant layout drawing)

—to determine GEP stack height

—to determine potential building

downwash considerations for stack heights

less than GEP

• Associated parameters

—boiler size (megawatts, pounds/hr.

steam, fuel consumption, etc.)

—boiler parameters (% excess air, boiler

type, type of firing, etc.)

—operating conditions (pollutant content

in fuel, hours of operation, capacity factor, %

load for winter, summer, etc.)

—pollutant control equipment parameters

(design efficiency, operation record, e.g., can

it be bypassed?, etc.)

• Anticipated growth changes

4. Air quality monitoring data:

• Summary of existing observations for

latest five years (including any additional

quality assured measured data which can be

obtained from any state or local agency or

company) 4

• Comparison with standards

• Discussion of background due to

uninventoried sources and contributions

from outside the inventoried area and

description of the method used for

determination of background (should be

consistent with the Guideline)

5. Meteorological data:

• Five consecutive years of the most recent

representative sequential hourly National

Weather Service (NWS) data, or one or more

years of hourly sequential on-site data

• Discussion of meteorological conditions

observed (as applied or modified for the sitespecific area, i.e., identify possible variations

due to difference between the monitoring site

and the specific site of the source)

• Discussion of topographic/land use

influences

6. Air quality modeling analyses:

• Model each individual year for which

data are available with a recommended

model or model demonstrated to be

acceptable on a case-by-case basis

—urban dispersion coefficients for urban

areas

—rural dispersion coefficients for rural

areas

• Evaluate downwash if stack height is less

than GEP

• Define worst case meteorology

• Determine background and document

method

—long-term

—short-term

• Provide topographic map(s) of receptor

network with respect to location of all

sources

• Follow current guidance on selection of

receptor sites for refined analyses

• Include receptor terrain heights (if

applicable) used in analyses

• Compare model estimates with

measurements considering the upper ends of

the frequency distribution

• Determine extent of significant impact;

provide maps

• Define areas of maximum and highest,

second-highest impacts due to applicant

source (refer to format suggested in Air

Quality Summary Tables)

¥> long-term

¥> short-term

7. Comparison with acceptable air quality

levels:

• NAAQS

• PSD increments

• Emission offset impacts if nonattainment

8. Documentation and guidelines for

modeling methodology:

• Follow guidance documents

¥> Appendix W to 40 CFR Part 51

¥> ‘‘Screening Procedures for Estimating

the Air Quality Impact of Stationary Sources,

Revised’’ (EPA–450/R–92–019), 1992

¥> ‘‘Guideline for Determination of Good

Engineering Practice Stack Height (Technical

Support Document for the Stack Height

Regulations)’’ (EPA–450/4–80–023R), 1985

¥> ‘‘Ambient Monitoring Guidelines for

PSD’’ (EPA–450/4–87–007), 1987

¥> Applicable sections of 40 CFR Parts 51

and 52.
Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations 41893

AIR QUALITY SUMMARY—FOR NEW SOURCE ALONE

Highest Highest

2d high Highest Highest2d high Annual

Pollutant: llllllll 1 llllllll 2 llllllll 2

Concentration due to modeled source

(μg/m3).

Background concentration (μg/m3) ......

Total concentration (μg/m3) .................

Receptor distance (km) (or UTM

easting).

Receptor direction (°) (or UTM

northing).

Receptor elevation (m) ........................

Wind speed (m/s) .................................

Wind direction (°) .................................

Mixing depth (m) ..................................

Temperature (°K) .................................

Stability .................................................

Day/month/year of occurrence .............

Surface air data from ...........................

Surface station elevation (m) ...............

Anemometer height above local

ground level (m).

Upper air data from ..............................

Period of record analyzed ....................

Model used ..........................................

Recommended model ..........................

1 Use separate sheet for each pollutant (SO2, PM–10, CO, NOX, HC, Pb, Hg, Asbestos, etc.). 2 List all appropriate averaging periods (1-hr, 3-hr, 8-hr, 24-hr, 30-day, 90-day, etc.) for which an air quality standard exists.

AIR QUALITY SUMMARY—FOR ALL NEW SOURCES

Highest Highest 2nd high Highest Highest 2nd high Annual

Pollutant: llllllll 1 llllllll 2 llllllll 2

Concentration due to modeled source

(μg/m3).

Background concentration (μg/m3) ......

Total concentration (μg/m3) .................

Receptor distance (km) (or UTM

easting).

Receptor direction (°) (or UTM

northing).

Receptor elevation (m) ........................

Wind speed (m/s) .................................

Wind direction (°) .................................

Mixing depth (m) ..................................

Temperature (°K) .................................

Stability .................................................

Day/month/year of occurrence .............

Surface air data from ...........................

Surface station elevation (m) ...............

Anemometer height above local

ground level (m).

Upper air data from ..............................

Period of record analyzed ....................

Model used ..........................................

Recommended model ..........................

1 Use separate sheet for each pollutant (SO2, PM–10, CO, NOx, HC, Pb, Hg, Asbestos, etc.). 2 List all appropriate averaging periods (l-hr, 3-hr, 8-hr, 24-hr, 30-day, 90-day, etc.) for which an air quality standard exists.

AIR QUALITY SUMMARY—FOR ALL SOURCES

Highest Highest 2nd high Highest Highest 2nd high Annual

Pollutant:llllllll1 llllllll2 llllllll2

Concentration due to modeled source

(μg/m3).

.............................. .............................. .............................. ..............................

Background concentration (μg/m3) ...... .............................. .............................. .............................. ..............................

Total concentration (μg/m3) ................. .............................. .............................. .............................. ..............................
41894 Federal Register / Vol. 61, No. 156 / Monday, August 12, 1996 / Rules and Regulations

AIR QUALITY SUMMARY—FOR ALL SOURCES—Continued

Highest Highest 2nd high Highest Highest 2nd high Annual

Receptor distance (km) (or UTM

easting).

.............................. .............................. .............................. ..............................

Receptor direction (°) (or UTM

northing).

.............................. .............................. .............................. ..............................

Receptor elevation (m) ........................ .............................. .............................. .............................. ..............................

Wind speed (m/s) ................................. .............................. .............................. .............................. ..............................

Wind direction (°) ................................. .............................. .............................. .............................. ..............................

Mixing depth (m) .................................. .............................. .............................. .............................. ..............................

Temperature (°K) ................................. .............................. .............................. .............................. ..............................

Stability ................................................. .............................. .............................. .............................. ..............................

Day/month/year of occurrence ............. .............................. .............................. .............................. ..............................

Surface air data from ........................... .............................. .............................. .............................. ..............................

Surface station elevation (m) ............... .............................. .............................. .............................. ..............................

Anemometer height above local

ground level (m).

.............................. .............................. .............................. ..............................

Upper air data from .............................. .............................. .............................. .............................. ..............................

Period of record analyzed .................... .............................. .............................. .............................. ..............................

Model used .......................................... .............................. .............................. .............................. ..............................

Recommended model .......................... .............................. .............................. .............................. ..............................

1 Use separate sheet for each pollutant (SO2, PM–10, CO, NOX, HC, Pb, Hg, Asbestos, etc.) 2 List all appropriate averaging periods (1-hr, 3-hr, 8-hr, 24-hr, 30-day, 90-day, etc.) for which an air quality standard exists.

STACK PARAMETERS FOR ANNUAL MODELING

Stack No. Serving

Emission rate

for each

pollutant

(g/s)

Stack

exit diameter

(m)

Stack

exit velocity

(m/s)

Stack

exit temperature

(°K)

Physical

height

Stack

(m)

GEP

stack ht.

(m)

Stack

base

elevation

(m)

Building dimensions (m)

Height Width Length

STACK PARAMETERS FOR SHORT-TERM MODELING 1

Stack No. Serving

Emission rate

for each

pollutant

(g/s)

Stack

exit diameter

(m)

Stack

exit velocity

(m/s)

Stack

exit temperature

(°K)

Physical

height

Stack

(m)

GEP

stack ht.

(m)

Stack

base

elevation

(m)

Building dimensions (m)

Height Width Length

1Separate 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. § 52.21 is amended by revising

paragraph (l)(1) and the first sentence of

paragraph (l)(2) to read as follows:

§ 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