Requirements for Preparation, Adoption, and Submittal of Implementation Plans

Note: EPA no longer updates this information, but it may be useful as a reference or resource.

[Federal Register: August 12, 1996 (Volume 61, Number 156)] [Rules and Regulations]
[Page 41837-41894]
>From the Federal Register Online via GPO Access [wais.access.gpo.gov]

[[Page 41837]]

Part II

Environmental Protection Agency

40 CFR Parts 51 and 52

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

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ENVIRONMENTAL PROTECTION AGENCY

40 CFR Parts 51 and 52

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

Requirements for Preparation, Adoption, and Submittal of Implementation Plans

AGENCY: Environmental Protection Agency (EPA).

ACTION: Direct final rule.

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6th Conference on Air Quality Modeling (August 1995) are added to Guideline appendix B for case-by-case use; several outdated models are removed from appendix B. Appendix A models considered to be ``obsolete'' (i.e., CRSTER & MPTER, replaced by ISC3) are removed, as is Table 4-1.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

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

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

FOR FURTHER INFORMATION CONTACT: Joseph A. Tikvart, Leader, Air Quality Modeling Group (MD-14), Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711; telephone (919) 541-5561 or C. Thomas Coulter, telephone (919) 541- 0832.

\1\ In reviewing this preamble, note that appendix W (Guideline) itself contains several appendices which are mentioned. Appendix A is the repository for preferred models, while appendix B is the repository for alternate models justified for use on a case-by-case basis.

SUPPLEMENTARY INFORMATION:

Background 1

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

Final Action

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

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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.

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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

The authority citation for part 51 continues to read as follows: Authority: 42 U.S.C. 7401-7671q.
2. Sec. 51.112 is amended by revising paragraph (a)(1) and the first sentence of paragraph (a)(2) to read as follows:
Sec. 51.112 Demonstration of adequacy.
(a) * * *
(1) The adequacy of a control strategy shall be demonstrated by means of applicable air quality models, data bases, and other requirements specified in appendix W of this part (Guideline on Air Quality Models).
(2) Where an air quality model specified in appendix W of this part (Guideline on Air Quality Models) is inappropriate, the model may be modified or another model substituted. * * *
- * * * *
- Sec. 51.160 is amended by revising paragraph (f)(1) and the first sentence of paragraph (f)(2) to read as follows:
Sec. 51.160 Legally enforceable procedures.
- * * * *
 (f) * * *
 (1) All applications of air quality modeling involved in this subpart shall be based on the applicable models, data bases, and other requirements specified in appendix W of this part (Guideline on Air Quality Models).
 (2) Where an air quality model specified in appendix W of this part (Guideline on Air Quality Models) is inappropriate, the model may be modified or another model substituted. * * *
- * * * *
- Sec. 51.166 is amended by revising paragraph (l)(1) and the first sentence of paragraph (l)(2) to read as follows:
Sec. 51.166 Prevention of significant deterioration of air quality.
- * * * *
 - * *
 (1) All applications of air quality modeling involved in this subpart shall be based on the applicable models, data bases, and other requirements specified in appendix W of this part (Guideline on Air Quality Models).
 (2) Where an air quality model specified in appendix W of this part (Guideline on Air Quality Models) is inappropriate, the model may be modified or another model substituted. * * *
- * * * *
- Appendix W to Part 51 revised to read as follows:
Appendix W to Part 51--Guideline on Air Quality Models
Preface
a. Industry and control agencies have long expressed a need for consistency in the application of air quality models for regulatory purposes. In the 1977 Clean Air Act, Congress mandated such consistency and encouraged the standardization of model applications. The Guideline on Air Quality Models (hereafter, Guideline) was first published in April 1978 to satisfy these requirements by specifying models and providing guidance for their use. The Guideline provides a common basis for estimating the air quality concentrations used in assessing control strategies and developing emission limits.
b. The continuing development of new air quality models in response to regulatory requirements and the expanded requirements for models to cover even more complex problems have emphasized the need for periodic review and update of guidance on these techniques. Four primary on-going activities provide direct input to revisions of the Guideline. The first is a series of annual EPA workshops conducted for the purpose of ensuring consistency and providing clarification in the application of models. The second activity, directed toward the improvement of modeling procedures, is the cooperative agreement that EPA has with the scientific community represented by the American Meteorological Society. This agreement provides scientific assessment of procedures and proposed techniques and sponsors workshops on key technical issues. The third activity is the solicitation and review of new models from the technical and user community. In the March 27, 1980 Federal Register, a procedure was outlined for the submittal to EPA of privately developed models. After extensive evaluation and scientific review, these models, as well as those made available by EPA, are considered for recognition in the Guideline. The fourth activity is the extensive on-going research efforts by EPA and others in air quality and meteorological modeling.
c. Based primarily on these four activities, this document embodies all revisions to the Guideline Although the text has been revised from the original 1978 guide, the present content and topics are similar. As necessary, new sections and topics are included. EPA does not make changes to the guidance on a predetermined schedule, but rather on an as needed basis. EPA believes that revisions of the Guideline should be timely and responsive to user needs and should involve public participation to the greatest possible extent. All future changes to the guidance will be proposed and finalized in the Federal Register. Information on the current status of modeling guidance can always be obtained from EPA's Regional Offices.
Table of Contents
List of Tables
1.0 Introduction
2.0 Overview of Model Use
2.1 Suitability of Models
2.2 Classes of Models
2.3 Levels of Sophistication of Models 3.0 Recommended Air Quality Models
3.1 Preferred Modeling Techniques
3.1.1 Discussion
3.1.2 Recommendations
3.2 Use of Alternative Models
3.2.1 Discussion
3.2.2 Recommendations
3.3 Availability of Supplementary Modeling Guidance 3.3.1 The Model Clearinghouse
3.3.2 Regional Meteorologists Workshops 4.0 Simple-Terrain Stationary Source Models 4.1 Discussion
4.2 Recommendations
4.2.1 Screening Techniques
4.2.2 Refined Analytical Techniques 5.0 Model Use in Complex Terrain
5.1 Discussion
5.2 Recommendations
5.2.1 Screening Techniques
5.2.2 Refined Analytical Techniques 6.0 Models for Ozone, Carbon Monoxide and Nitrogen Dioxide 6.1 Discussion
6.2 Recommendations
6.2.1 Models for Ozone
6.2.2 Models for Carbon Monoxide
6.2.3 Models for Nitrogen Dioxide (Annual Average) 7.0 Other Model Requirements
7.1 Discussion
7.2 Recommendations
7.2.1 Fugitive Dust/Fugitive Emissions 7.2.2 Particulate Matter
7.2.3 Lead
7.2.4 Visibility
7.2.5 Good Engineering Practice Stack Height 7.2.6 Long Range Transport (LRT) (i.e., beyond 50km) 7.2.7 Modeling Guidance for Other Governmental Programs 7.2.8 Air Pathway Analyses (Air Toxics and Hazardous Waste) 8.0 General Modeling Considerations
8.1 Discussion
8.2 Recommendations
8.2.1 Design Concentrations
8.2.2 Critical Receptor Sites
8.2.3 Dispersion Coefficients
8.2.4 Stability Categories
8.2.5 Plume Rise
8.2.6 Chemical Transformation
8.2.7 Gravitational Settling and Deposition 8.2.8 Urban/Rural Classification
8.2.9 Fumigation
8.2.10 Stagnation
8.2.11 Calibration of Models
9.0 Model Input Data
9.1 Source Data
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9.1.1 Discussion
9.1.2 Recommendations
9.2 Background Concentrations
9.2.1 Discussion
9.2.2 Recommendations (Isolated Single Source) 9.2.3 Recommendations (Multi-Source Areas) 9.3 Meteorological Input Data
9.3.1 Length of Record of Meteorological Data 9.3.2 National Weather Service Data 9.3.3 Site-Specific Data
9.3.4 Treatment of Calms
10.0 Accuracy and Uncertainty of Models 10.1 Discussion
10.1.1 Overview of Model Uncertainty 10.1.2 Studies of Model Accuracy
10.1.3 Use of Uncertainty in Decision-Making 10.1.4 Evaluation of Models
10.2 Recommendations
11.0 Regulatory Application of Models
11.1 Discussion
11.2 Recommendations
11.2.1 Analysis Requirements
11.2.2 Use of Measured Data in Lieu of Model Estimates 11.2.3 Emission Limits
12.0 References
13.0 Bibliography
14.0 Glossary of Terms
Appendix A to Appendix W of Part 51--Summaries of Preferred Air Quality Models
Appendix B to Appendix W of Part 51--Summaries of Alternative Air Quality Models
Appendix C to Appendix W of Part 51--Example Air Quality Analysis Checklist
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 List of Tables 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Table No. Title 
```
5-1a...................................... Neutral/Stable
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Meteorological Matrix for 
 CTSCREEN. 
```
5-1b...................................... Unstable/Convective
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Meteorological Matrix for 
 CTSCREEN. 
```
5-2....................................... Preferred Options for the
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 SHORTZ/LONGZ Computer Codes
 When Used in a Screening 
 Mode. 
```
5-3....................................... Preferred Options for the
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 RTDM Computer Code When 
 Used in a Screening Mode. 
```
9-1....................................... Model Emission Input Data
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 for Point Sources. 
```
9-2....................................... Point Source Model Input
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Data (Emissions) for PSD 
 NAAQS Compliance 
 Demonstrations. 
```
9-3....................................... Averaging Times for SiteSpecific
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 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
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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
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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 computerreadable 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
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alternative models that are applicable for selected situations are contained in Appendix B. However, inclusion there does not infer any unique status relative to other alternative models that are being or will be developed in the future.
c. Equivalency is established by demonstrating that the maximum or highest, second highest concentrations are within 2 percent of the estimates obtained from the preferred model. The option to show equivalency is intended as a simple demonstration of acceptability for an alternative model that is so nearly identical (or contains options that can make it identical) to a preferred model that it can be treated for practical purposes as the preferred model. Two percent was selected as the basis for equivalency since it is a rough approximation of the fraction that PSD Class I increments are of the NAAQS for SO<INF>2, i.e., the difference in concentrations that is judged to be significant. However, notwithstanding this demonstration, use of models that are not equivalent may be used when the conditions of paragraph e of this section are satisfied. d. The procedures and techniques for determining the acceptability of a model for an individual case based on superior performance is contained in the document entitled ``Interim Procedures for Evaluating Air Quality Models'', \15\ and should be followed, as appropriate.a Preparation and implementation of an evaluation protocol which is acceptable to both control agencies and regulated industry is an important element in such an evaluation.
\a\ Another EPA document, ``Protocol for Determining the Best Performing Model'', \17\ contains advanced statistical techniques for determining which model performs better than other competing models. In many cases, this protocol should be considered by users of the ``Interim Procedures for Evaluating Air Quality Models'' in preference to the material currently in Chapter 3 of that document.
e. When no Appendix A model is applicable to the modeling problem, an alternative refined model may be used provided that: i. The model can be demonstrated to be applicable to the problem on a theoretical basis; and
ii. The data bases which are necessary to perform the analysis are available and adequate; and
iii. Performance evaluations of the model in similar circumstances have shown that the model is not biased toward underestimates; or
iv. After consultation with the EPA Regional Office, a second model is selected as a baseline or reference point for performance and the interim procedures \15\ protocol \17\ are then used to demonstrate that the proposed model performs better than the reference model.
3.3 Availability of Supplementary Modeling Guidance
a. The Regional Administrator has the authority to select models that are appropriate for use in a given situation. However, there is a need for assistance and guidance in the selection process so that fairness and consistency in modeling decisions is fostered among the various Regional Offices and the States. To satisfy that need, EPA established the Model Clearinghouse and also holds periodic workshops with headquarters, Regional Office and State modeling representatives.
3.3.1 The Model Clearinghouse
3.3.1.1 Discussion
a. The Model Clearinghouse is the single EPA focal point for review of air quality simulation models proposed for use in specific regulatory applications. Details concerning the Clearinghouse and its operation are found in the document, ``Model Clearinghouse: Operational Plan.'' \6\ Three primary functions of the Clearinghouse are:
i. Review of decisions proposed by EPA Regional Offices on the use of modeling techniques and data bases. ii. Periodic visits to Regional Offices to gather information pertinent to regulatory model usage.
iii. Preparation of an annual report summarizing activities of the Clearinghouse including specific determinations made during the course of the year.
3.3.1.2 Recommendations
a. The Regional Administrator may request assistance from the Model Clearinghouse after an initial evaluation and decision has been reached concerning the application of a model, analytical technique or data base in a particular regulatory action. The Clearinghouse may also consider and evaluate the use of modeling techniques submitted in support of any regulatory action. Additional responsibilities are: (1) Review proposed action for consistency with agency policy; (2) determine technical adequacy; and (3) make recommendations concerning the technique or data base.
3.3.2 Regional Meteorologists Workshops
13.3.2.1 Discussion
a. EPA conducts an annual in-house workshop for the purpose of mutual discussion and problem resolution among Regional Office modeling specialists, EPA research modeling experts, EPA Headquarters modeling and regulatory staff and representatives from State modeling programs. A summary of the issues resolved at previous workshops was issued in 1981 as ``Regional Workshops on Air Quality Modeling: A Summary Report.'' \17\ That report clarified procedures not specifically defined in the 1978 version of the Guideline and was issued to ensure the consistent interpretation of model requirements from Region to Region. Similar workshops for the purpose of clarifying Guideline procedures or providing detailed instructions for the use of those procedures are anticipated in the future.
3.3.2.2 Recommendations
a. The Regional Office should always be consulted for information and guidance concerning modeling methods and interpretations of modeling guidance, and to ensure that the air quality model user has available the latest most up-to-date policy and procedures.
4.0 Simple-Terrain Stationary Source Models
4.1 Discussion
a. Simple terrain, as used in this section, is considered to be an area where terrain features are all lower in elevation than the top of the stack of the source(s) in question. The models recommended in this section are generally used in the air quality impact analysis of stationary sources for most criteria pollutants. The averaging time of the concentration estimates produced by these models ranges from 1 hour to an annual average. b. Model evaluation exercises have been conducted to determine the ``best, most appropriate point source model'' for use in simple terrain.8 12 However, no one model has been found to be clearly superior. Based on past use, public familiarity, and availability, ISC is the recommended model for a wide range of regulatory applications. Similar determinations were made for the other refined models that are identified in section 4.2.
4.2 Recommendations
4.2.1 Screening Techniques
a. Point source screening techniques are an acceptable approach to air quality analyses. One such approach is contained in the EPA document ``Screening Procedures for Estimating the Air Quality Impact of Stationary Sources''.\18\ A computerized version of the screening technique, SCREEN, is available.19 20 For the current version of SCREEN, see 12.0 References.\20\ b. All screening procedures should be adjusted to the site and problem at hand. Close attention should be paid to whether the area should be classified urban or rural in accordance with Section 8.2.8. The climatology of the area should be studied to help define the worst-case meteorological conditions. Agreement should be reached between the model user and the reviewing authority on the choice of the screening model for each analysis, and on the input data as well as the ultimate use of the results.
4.2.2 Refined Analytical Techniques
a. A brief description of preferred models for refined applications is found in Appendix A. Also listed in Appendix A are the model input requirements, the standard options that should be selected when running the program, and output options. b. When modeling for compliance with short term NAAQS and PSD increments is of primary concern, a short term model may also be used to provide long term concentration estimates. However, when modeling sources for which long term standards alone are applicable (e.g., lead), then the long term models should be used. The conversion from long term to short term concentration averages by any transformation technique is not acceptable in regulatory applications.
5.0 Model Use in Complex Terrain
5.1 Discussion
a. For the purpose of the Guideline, complex terrain is defined as terrain exceeding the height of the stack being
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modeled. Complex terrain dispersion models are normally applied to stationary sources of pollutants such as SO<INF>2 and particulates. b. A major outcome from the EPA Complex Terrain Model Development project has been the publication of a refined dispersion model (CTDM) suitable for regulatory application to plume impaction assessments in complex terrain.\21\ Although CTDM as originally produced was only applicable to those hours characterized as neutral or stable, a computer code for all stability conditions, CTDMPLUS,\19\ together with a user's guide,\22\ and on-site meteorological and terrain data processors,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 terraininduced downwash, deposition, chemical transformation, variable plume trajectories, and long range transport are not addressed by the recommendations in this section.
b. Where site-specific data are used for either screening or refined complex terrain models, a data base of at least 1 full-year of meteorological data is preferred. If more data are available, they should be used. Meteorological data used in the analysis should be reviewed for both spatial and temporal representativeness. c. Placement of receptors requires very careful attention when modeling in complex terrain. Often the highest concentrations are predicted to occur under very stable conditions, when the plume is near, or impinges on, the terrain. The plume under such conditions may be quite narrow in the vertical, so that even relatively small changes in a receptor's location may substantially affect the predicted concentration. Receptors within about a kilometer of the source may be even more sensitive to location. Thus, a dense array of receptors may be required in some cases. In order to avoid excessively large computer runs due to such a large array of receptors, it is often desirable to model the area twice. The first model run would use a moderate number of receptors carefully located over the area of interest. The second model run would use a more dense array of receptors in areas showing potential for high concentrations, as indicated by the results of the first model run. d. When CTSCREEN or CTDMPLUS is used, digitized contour data must be first processed by the CTDM Terrain Processor \23\ to provide hill shape parameters in a format suitable for direct input to CTDMPLUS. Then the user supplies receptors either through an interactive program that is part of the model or directly, by using a text editor; using both methods to select receptors will generally be necessary to assure that the maximum concentrations are estimated by either model. In cases where a terrain feature may ``appear to the plume'' as smaller, multiple hills, it may be necessary to model the terrain both as a single feature and as multiple hills to determine design concentrations.
e. The user is encouraged to confer with the Regional Office if any unresolvable problems are encountered with any screening or refined analytical procedures, e.g., meteorological data, receptor siting, or terrain contour processing issues.
5.2.1 Screening Techniques
a. Five preferred screening techniques are currently available to aid in the evaluation of concentrations due to plume impaction during stable conditions: (1) for 24-hour impacts, the Valley Screening Technique \19\ as outlined in the Valley Model User's Guide; \26\ (2) CTSCREEN,\19\ as outlined in the CTSCREEN User's Guide; \25\ (3) COMPLEX I; \19\ (4) SHORTZ/LONGZ; 19 27 and (5) Rough Terrain Dispersion Model (RTDM) 19 90 in its prescribed mode described below. As appropriate, any of these screening techniques may be used consistent with the needs, resources, and available data of the user.
b. The Valley Model, COMPLEX I, SHORTZ/LONGZ, and RTDM should be used only to estimate concentrations at receptors whose elevations are greater than or equal to plume height. For receptors at or below stack height, a simple terrain model should be used (see Chapter 4). Receptors between stack height and plume height present a unique problem since none of the above models were designed to handle receptors in this narrow regime, the definition of which will vary hourly as meteorological conditions vary. CTSCREEN may be used to estimate concentrations under all stability conditions at all receptors located ``on terrain'' above stack top, but has limited applicability in multi-source situations. As a result, the estimation of concentrations at receptors between stack height and plume height should be considered on a case-by-case basis after consultation with the EPA Regional Office; the most appropriate technique may be a function of the actual source(s) and terrain configuration unique to that application. One technique that will generally be acceptable, but is not necessarily preferred for any specific application, involves applying both a complex terrain model (except for the Valley Model) and a simple terrain model. The Valley Model should not be used for any intermediate terrain receptor. For each receptor between stack height and plume height, an hour-by-hour comparison of the concentration estimates from both models is made. The higher of the two modeled concentrations should be chosen to represent the impact at that receptor for that hour, and then used to compute the concentration for the appropriate averaging time(s). For the simple terrain models, terrain may have to be ``chopped off'' at stack height, since these models are frequently limited to receptors no greater than stack height.
5.2.1.1 Valley Screening Technique
a. The Valley Screening Technique may be used to determine 24- hour averages. This technique uses the Valley Model with the following worst-case assumptions for rural areas: (1) P-G stability ``F''; (2) wind speed of 2.5 m/s; and (3) 6 hours of occurrence. For urban areas the stability should be changed to ``P-G stability E.'' b. When using the Valley Screening Technique to obtain 24-hour average concentrations the following apply: (1) multiple sources should be treated individually and the concentrations for each wind direction summed; (2) only one wind direction should be used (see User's Guide,\26\ page 2-15) even if individual runs are made for each source; (3) for buoyant sources, the BID option may be used, and the option to use the 2.6 stable plume rise factor should be selected; (4) if plume impaction is likely on any elevated terrain closer to the source than the distance from the source to the final plume rise, then the transitional (or gradual) plume rise option for stable conditions should be selected.
c. The standard polar receptor grid found in the Valley Model User's Guide may not be sufficiently dense for all analyses if only one geographical scale factor is used. The user should choose an additional set of receptors at appropriate downwind distances whose elevations are equal to plume height minus 10 meters. Alternatively, the user may exercise the ``Valley equivalent'' option in COMPLEX I or SCREEN and note the comments above on the placement of receptors in complex terrain models.
d. When using the ``Valley equivalent'' option in COMPLEX I, set the wind profile exponents (PL) to 0.0, respectively, for all six stability classes.
5.2.1.2 CTSCREEN
a. CTSCREEN may be used to obtain conservative, yet realistic, worst-case estimates for receptors located on terrain above stack height. CTSCREEN accounts for the three-dimensional nature of plume and terrain interaction and requires detailed terrain data representative of the modeling domain. The model description and user's instructions are contained in the user's guide.\25\ The terrain data must be digitized in the same manner as for CTDMPLUS and a terrain processor is available.\23\ A discussion of the model's performance characteristics is provided in a technical paper.\91\ CTSCREEN is designed to execute a fixed matrix of meteorological values for wind speed (u),
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standard deviation of horizontal and vertical wind speeds (<greek-s><INF>v, <greek-s>G5w), vertical potential temperature gradient (d<greek-th>/dz), friction velocity (u<INF>x), MoninObukhov length (L), mixing height (z<INF>i) as a function of terrain height, and wind directions for both neutral/stable conditions and unstable convective conditions. Table 5-1 contains the matrix of meteorological variables that is used for each CTSCREEN analysis. There are 96 combinations, including exceptions, for each wind direction for the neutral/stable case, and 108 combinations for the unstable case. The specification of wind direction, however, is handled internally, based on the source and terrain geometry. The matrix was developed from examination of the range of meteorological variables associated with maximum monitored concentrations from the data bases used to evaluate the performance of CTDMPLUS. Although CTSCREEN is designed to address a single source scenario, there are a number of options that can be selected on a case-by-case basis to address multi-source situations. However, the Regional Office should be consulted, and concurrence obtained, on the protocol for modeling multiple sources with CTSCREEN to ensure that the worst case is identified and assessed. The maximum concentration output from CTSCREEN represents a worst-case 1-hour concentration. Time-scaling factors of 0.7 for 3-hour, 0.15 for 24-hour and 0.03 for annual concentration averages are applied internally by CTSCREEN to the highest 1-hour concentration calculated by the model.
5.2.1.3 COMPLEX I
a. If the area is rural, COMPLEX I may be used to estimate concentrations for all averaging times. COMPLEX I is a modification of the MPTER model that incorporates the plume impaction algorithm of the Valley Model.\19\ It is a multiple-source screening technique that accepts hourly meteorological data as input. The output is the same as the normal MPTER output. When using COMPLEX I the following options should be selected: (1) Set terrain adjustment IOPT (1)=1; (2) set buoyancy induced dispersion IOPT (4)=1; (3) set IOPT (25)=1; (4) set the terrain adjustment values to 0.5, 0.5, 0.5 0.5, 0.0, 0.0, (respectively for six stability classes); and (5) set Z MIN=10. b. When using the ``Valley equivalent'' option (only) in COMPLEX I, set the wind profile exponents (PL) to 0.0, respectively, for all six stability classes. For all other regulatory uses of COMPLEX I, set the wind profile exponents to the values used in the simple terrain models, i.e., 0.07, 0.07, 0.10, 0.15, 0.35, and 0.55, respectively, for rural modeling.
c. Gradual plume rise should be used to estimate concentrations at nearby elevated receptors, if plume impaction is likely on any elevated terrain closer to the source than the distance from the source to the final plume rise (see Section 8.2.5).
5.2.1.4 SHORTZ/LONGZ
a. If the source is located in an urbanized (Section 8.2.8) complex terrain valley, then the suggested screening technique is SHORTZ for short-term averages or LONGZ for long-term averages. SHORTZ and LONGZ may be used as screening techniques in these complex terrain applications without demonstration and evaluation. Application of these models in other than urbanized valley situations will require the same evaluation and demonstration procedures as are required for all Appendix B models. b. Both SHORTZ and LONGZ have a number of options. When using these models as screening techniques for urbanized valley applications, the options listed in Table 5-2 should be selected.
5.2.1.5 RTDM (Screening Mode)
a. RTDM with the options specified in Table 5-3 may be used as a screening technique in rural complex terrain situations without demonstration and evaluation.
b. The RTDM screening technique can provide a more refined concentration estimate if on-site wind speed and direction characteristic of plume dilution and transport are used as input to the model. In complex terrain, these winds can seldom be estimated accurately from the standard surface (10m level) measurements. Therefore, in order to increase confidence in model estimates, EPA recommends that wind data input to RTDM should be based on fixed measurements at stack top height. For stacks greater than 100m, the measurement height may be limited to 100m in height relative to stack base. However, for very tall stacks, see guidance in Section 9.3.3.2. This recommendation is broadened to include wind data representative of plume transport height where such data are derived from measurements taken with remote sensing devices such as SODAR. The data from both fixed and remote measurements should meet quality assurance and recovery rate requirements. The user should also be aware that RTDM in the screening mode accepts the input of measured wind speeds at only one height. The default values for the wind speed profile exponents shown in Table 5-3 are used in the model to determine the wind speed at other heights. RTDM uses wind speed at stack top to calculate the plume rise and the critical dividing streamline height, and the wind speed at plume transport level to calculate dilution. RTDM treats wind direction as constant with height.
c. RTDM makes use of the ``critical dividing streamline'' concept and thus treats plume interactions with terrain quite differently from other models such as SHORTZ and COMPLEX I. The plume height relative to the critical dividing streamline determines whether the plume impacts the terrain, or is lifted up and over the terrain. The receptor spacing to identify maximum impact concentrations is quite critical depending on the location of the plume in the vertical. Analysis of the expected plume height relative to the height of the critical dividing streamline should be performed for differing meteorological conditions in order to help develop an appropriate array of receptors. Then it is advisable to model the area twice according to the suggestions in Section 5.2.
5.2.1.6 Restrictions
a. For screening analyses using the Valley Screening Technique, COMPLEX I or RTDM, a sector greater than 22\1/2\ deg. should not be allowed. Full ground reflection should always be used in the Valley Screening Technique and COMPLEX I.
5.2.2 Refined Analytical Techniques
a. When the results of the screening analysis demonstrate a possible violation of NAAQS or the controlling PSD increments, a more refined analysis may need to be conducted. b. The Complex Terrain Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLUS) is a refined air quality model that is preferred for use in all stability conditions for complex terrain applications. CTDMPLUS is a sequential model that requires five input files: (1) General program specifications; (2) a terrain data file; (3) a receptor file; (4) a surface meteorological data file; and (5) a user created meteorological profile data file. Two optional input files consist of hourly emissions parameters and a file containing upper air data from rawinsonde data files, e.g., a National Climatic Data Center TD-6201 file, unless there are no hours categorized as unstable in the record. The model description and user instructions are contained in Volume 1 of the User's Guide.\22\ Separate publications \23\ \24\ describe the terrain preprocessor system and the meteorological preprocessor program. In Part I of a technical article \92\ is a discussion of the model and its preprocessors; the model's performance characteristics are discussed in Part II of the same article.\93\ The size of the CTDMPLUS executable file on a personal computer is approximately 360K bytes. The model produces hourly average concentrations of stable pollutants, i.e., chemical transformation or decay of species and settling/deposition are not simulated. To obtain concentration averages corresponding to the NAAQS, e.g., 3- or 24-hour, or annual averages, the user must execute a postprocessor program such as CHAVG.\19\ CTDMPLUS is applicable to all receptors on terrain elevations above stack top. However, the model contains no algorithms for simulating building downwash or the mixing or recirculation found in cavity zones in the lee of a hill. The path taken by a plume through an array of hills cannot be simulated. CTDMPLUS does not explicitly simulate calm meteorological periods, and for those situations the user should follow the guidance in Section 9.3.4. The user should follow the recommendations in the User's Guide under General Program Specifications for: (1) Selecting mixed layer heights, (2) setting minimum scalar wind speed to 1 m/s, and (3) scaling wind direction with height. Close coordination with the Regional Office is essential to insure a consistent, technically sound application of this model.
c. The performance of CTDMPLUS is greatly improved by the use of meteorological data from several levels up to plume height. However, due to the vast range of source-plume-hill geometries possible in complex terrain, detailed requirements for meteorological monitoring in support of refined analyses using CTDMPLUS should be determined on a case-by-case basis. The following general guidance should be considered in the development of a meteorological monitoring protocol for regulatory applications of CTDMPLUS and
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reviewed in detail by the Regional Office before initiating any monitoring. As appropriate, the On-Site Meteorological Program Guidance document \66\ should be consulted for specific guidance on siting requirements for meteorological towers, selection and exposure of sensors, etc. As more experience is gained with the model in a variety of circumstances, more specific guidance may be developed.
d. Site specific meteorological data are critical to dispersion modeling in complex terrain and, consequently, the meteorological requirements are more demanding than for simple terrain. Generally, three different meteorological files (referred to as surface, profile, and rawin files) are needed to run CTDMPLUS in a regulatory mode.
e. The surface file is created by the meteorological preprocessor (METPRO) \24\ based on on-site measurements or estimates of solar and/or net radiation, cloud cover and ceiling, and the mixed layer height. These data are used in METPRO to calculate the various surface layer scaling parameters (roughness length, friction velocity, and Monin-Obukhov length) which are needed to run the model. All of the user inputs required for the surface file are based either on surface observations or on measurements at or below 10m.
f. The profile data file is prepared by the user with on-site measurements (from at least three levels) of wind speed, wind direction, turbulence, and potential temperature. These measurements should be obtained up to the representative plume height(s) of interest (i.e., the plume height(s) under those conditions important to the determination of the design concentration). The representative plume height(s) of interest should be determined using an appropriate complex terrain screening procedure (e.g., CTSCREEN) and should be documented in the monitoring/modeling protocol. The necessary meteorological measurements should be obtained from an appropriately sited meteorological tower augmented by SODAR if the representative plume height(s) of interest exceed 100m. The meteorological tower need not exceed the lesser of the representative plume height of interest (the highest plume height if there is more than one plume height of interest) or 100m. g. Locating towers on nearby terrain to obtain stack height or plume height measurements for use in profiles by CTDMPLUS should be avoided unless it can clearly be demonstrated that such measurements would be representative of conditions affecting the plume. h. The rawin file is created by a second meteorological preprocessor (READ62) \24\ based on NWS (National Weather Service) upper air data. The rawin file is used in CTDMPLUS to calculate vertical potential temperature gradients for use in estimating plume penetration in unstable conditions. The representativeness of the off-site NWS upper air data should be evaluated on a case-by-case basis.
i. In the absence of an appropriate refined model, screening results may need to be used to determine air quality impact and/or emission limits.
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Table 5-1a.--Neutral/Stable Meteorological Matrix for CTSCREEN 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Variable Specific values 
 
```
U (m/s).................................... 1.0 2.0 3.0 4.0 5.0
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
<greek-s><INF>v (m/s)........................... 0.3 0.75 
```
<greek-s><INF>w (m/s)........................... 0.08 0.15 0.30 0.75
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
DQ/Dz (K/m)................................ 0.01 0.02 0.035 
WD 
(4) (Wind direction optimized internally 
 for each meteorological combination) 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
Exceptions: 
```
(1) If U <ls-thn-eq> 2 m/s and <greek-s><INF>v <gr-thn-eq> 0.3 m/s, then include <greek-s><INF>w = 0.04 m/s. (2) If <greek-s><INF>w = 0.75 m/s and U <gr-thn-eq> 3.0 m/s, then DU/Dz is limited to <ls-thn-eq> 0.01 K/m.
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
(3) If U <gr-thn-eq> 4 m/s, then <greek-s><INF>w <gr-thn-eq> 0.15 m/s. 
(4) <greek-s><INF>w <ls-thn-eq> <greek-s><INF>v 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Table 5-1b.--Unstable/Convective Meteorological Matrix for CTSCREEN 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Variable Specific values 
 
```
U (m/s)................................... 1.0 2.0 3.0 4.0 5.0
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
u<INF>x (m/s).................................. 0.1 0.3 0.5 
L (m)..................................... -10 -50 -90 
DU/<INF>Dz (K/m) 0.030 
(3) (potential temperature gradient above 
 z<INF>i) 
z<INF>i (m).................................... 0.5h 1.0h 1.5h 
 
(2) (where h = terrain height) 
```
Table 5-2.--Preferred Options for the SHORTZ/LONGZ Computer Codes When Used in a Screening Mode
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
Option Selection 
 
```
I Switch 9................................ .......................................... If using NWS data, set =
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 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
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 entrainment 
 coefficient). 
```
GAMMA 2................................... .......................................... Always default to
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 ``stable''. 
```
XRY....................................... .......................................... Set = 0 (50m rectilinear
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 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.
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
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
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 given in Table 2-2 of 
 User's Instructions; if
 site-specific data are 
 available, see Regional
 Office for advice. 
```
[[Page 41848]]
Table 5-3.--Preferred Options for the RTDM Computer Code When Used in a Screening Mode
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Parameter Variable Value Remarks 
```
PR001-003.......................... SCALE................. ...................... Scale factors assuming
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 horizontal distance is in 
 kilometers, vertical 
 distance is in feet, and 
 wind speed is in meters 
 per second. 
```
PR004.............................. ZWIND1................ Wind measurement See Section 5.2.1.4.
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 height. 
 ZWIND2................ Not used.............. Height of second 
 anemometer. 
 IDILUT................ 1..................... Dilution wind speed scaled 
 to plume height. 
 ZA.................... 0 (default)........... Anemometer-terrain height 
 above stack base. 
```
PR005.............................. EXPON................. 0.09, 0.11, 0.12, Wind profile exponents.
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 0.14, 0.2, 0.3 
 (default). 
```
PR006.............................. ICOEF................. 3 (default)........... Briggs Rural/ASME \139\
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 dispersion parameters. 
```
PR009.............................. IPPP.................. 0 (default)........... Partial plume penetration;
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 not used. 
```
PR010.............................. IBUOY................. 1 (default)........... Buoyancy-enhanced
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 dispersion is used. 
 ALPHA................. 3.162 (default)....... Buoyancy-enhanced 
 dispersion coefficient. 
```
PR011.............................. IDMX.................. 1 (default)........... Unlimited mixing height for
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 stable conditions. 
```
PR012.............................. ITRANS................ 1 (default)........... Transitional plume rise is
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 used. 
```
PR013.............................. TERCOR................ 6*0.5 (default)....... Plume patch correction
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 factors. 
```
PR014.............................. RVPTG................. 0.02, 0.035 (default). Vertical potential
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 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
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 is used. 
```
PR023.............................. IHORIZ................ 2 (default)........... Sector averaging.
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 SECTOR................ 6*22.5 (default)...... Using 22.5 deg. sectors. 
```
PR016 to 019; 021; and 024......... IY, IZ, IRVPTG, 0..................... Hourly values of
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 IHVPTG; IEPS; IEMIS. turbulence, vertical 
 potential temperature 
 gradient, wind speed 
 profile exponents, and 
 stack emissions are not 
 used. 
```
6.0 Models for Ozone, Carbon Monoxide and Nitrogen Dioxide
6.1 Discussion
a. Models discussed in this section are applicable to pollutants often associated with mobile sources, e.g., ozone (O<INF>3), carbon monoxide (CO) and nitrogen dioxide (NO<INF>2). Where stationary sources of CO and NO<INF>2 are of concern, the reader is referred to Sections 4 and 5
b. A control agency with jurisdiction over areas with significant ozone problems and which has sufficient resources and data to use a photochemical dispersion model is encouraged to do so. Experience with and evaluations of the Urban Airshed Model show it to be an acceptable, refined approach, and better data bases are becoming available that support the more sophisticated analytical procedures. However, empirical models (e.g., EKMA) fill the gap between more sophisticated photochemical dispersion models and proportional (rollback) modeling techniques and may be the only applicable procedure if the available data bases are insufficient for refined dispersion modeling.
c. Models for assessing the impact of carbon monoxide emissions are needed for a number of different purposes, e.g., to evaluate the effects of point sources, congested intersections and highways, as well as the cumulative effect on ambient CO concentrations of all sources of CO in an urban area.94 95 d. Nitrogen oxides are reactive and also an important contribution to the photochemical ozone problem. They are usually of most concern in areas of high ozone concentrations. Unless suitable photochemical dispersion models are used, assumptions regarding the conversion of NO to NO<INF>2 are required when modeling. Sitespecific conversion factors may be developed. If site-specific conversion factors are not available or photochemical models are not used, NO<INF>2 modeling should be considered only a screening procedure.
6.2 Recommendations
6.2.1 Models for Ozone
a. The Urban Airshed Model (UAM)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 multilayered trajectory models, that are available may be used if shown to be appropriate. Most photochemical dispersion models require emission data on individual hydrocarbon species and may require three dimensional meteorological information on an hourly basis. Reasonably sophisticated computer facilities are also often required. Because the input data are not universally available and studies to collect such data are very resource intensive, there are only limited evaluations of those models. d. For those cases which involve estimating the impact on ozone concentrations due to stationary sources of VOC and NO<INF>X, whether for permitting or other regulatory cases, the model user should consult the appropriate Regional Office on the acceptability of the modeling technique.
e. Proportional (rollback/forward) modeling is not an acceptable procedure for evaluating ozone control strategies.
6.2.2 Models for Carbon Monoxide
a. For analyzing CO impacts at roadway intersections, users should follow the procedures in the ``Guideline for Modeling Carbon Monoxide from Roadway Intersections''.\34\ The recommended model for such analyses is CAL3QHC.\35\ This model combines CALINE3 (already in Appendix A) with a traffic model to calculate delays and queues that occur at signalized intersections. In areas where the use of either TEXIN2 or CALINE4 has previously been established, its use may continue. The capability exists for these intersection models to be used in either a screening or refined mode. The screening approach is described in reference 34; a refined approach may be considered on a case-by-case basis. The latest version of the MOBILE (mobile source emission factor) model should be used for emissions input to intersection models.
b. For analyses of highways characterized by uninterrupted traffic flows, CALINE3 is recommended, with emissions input from the latest version of the MOBILE model.
c. The recommended model for urban areawide CO analyses is RAM or Urban Airshed Model (UAM); see Appendix A. Information on SIP development and requirements for using these models can be found in references 34, 96, 97 and 98.
d. Where point sources of CO are of concern, they should be treated using the screening and refined techniques described in Section 4 or 5 of the Guideline.
[[Page 41849]]
6.2.3 Models for Nitrogen Dioxide (Annual Average)
a. A tiered screening approach is recommended to obtain annual average estimates of NO<INF>2 from point sources for New Source Review analysis, including PSD, and for SIP planning purposes. This multi-tiered approach is conceptually shown in Figure 6-1 and described in paragraphs b and c of this section. Figure 6-1 is as follows:
Figure 6-1.--Multi-tiered Screening Approach for Estimating Annual NO<INF>2
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Concentrations From Point Sources 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
Tier 1: Assume Total Conversion of NO to NO<INF>2 
```
Tier 2: Multiply Annual NO<INF>X Estimate by Empirically Derived NO<INF>2/NO<INF>X
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Ratio. 
```
b. For Tier 1 (the initial screen), use an appropriate Gaussian model from Appendix A to estimate the maximum annual average concentration and assume a total conversion of NO to NO<INF>2. If the concentration exceeds the NAAQS and/or PSD increments for NO<INF>2, proceed to the 2nd level screen. c. For Tier 2 (2nd level) screening analysis, multiply the Tier 1 estimate(s) by an empirically derived NO<INF>2/NO<INF>X value of 0.75 (annual national default).36 An annual NO<INF>2/NO<INF>X ratio differing from 0.75 may be used if it can be shown that such a ratio is based on data likely to be representative of the location(s) where maximum annual impact from the individual source under review occurs. In the case where several sources contribute to consumption of a PSD increment, a locally derived annual NO<INF>2/ NO<INF>X ratio should also be shown to be representative of the location where the maximum collective impact from the new plus existing sources occurs.
d. In urban areas, a proportional model may be used as a preliminary assessment to evaluate control strategies to meet the NAAQS for multiple minor sources, i.e. minor point, area and mobile sources of NO<INF>X; concentrations resulting from major point sources should be estimated separately as discussed above, then added to the impact of the minor sources. An acceptable screening technique for urban complexes is to assume that all NO<INF>X is emitted in the form of NO<INF>2 and to use a model from Appendix A for nonreactive pollutants to estimate NO<INF>2 concentrations. A more accurate estimate can be obtained by: (1) Calculating the annual average concentrations of NO<INF>X with an urban model, and (2) converting these estimates to NO<INF>2 concentrations using an empirically derived annual NO<INF>2/NO<INF>X ratio. A value of 0.75 is recommended for this ratio. However, a spatially averaged annual NO<INF>2/NO<INF>X ratio may be determined from an existing air quality monitoring network and used in lieu of the 0.75 value if it is determined to be representative of prevailing ratios in the urban area by the reviewing agency. To ensure use of appropriate locally derived annual NO<INF>2/NO<INF>X ratios, monitoring data under consideration should be limited to those collected at monitors meeting siting criteria defined in 40 CFR Part 58, Appendix D as representative of ``neighborhood'', ``urban'', or ``regional'' scales. Furthermore, the highest annual spatially averaged NO<INF>2/ NO<INF>X ratio from the most recent 3 years of complete data should be used to foster conservatism in estimated impacts. e. To demonstrate compliance with NO<INF>2 PSD increments in urban areas, emissions from major and minor sources should be included in the modeling analysis. Point and area source emissions should be modeled as discussed above. If mobile source emissions do not contribute to localized areas of high ambient NO<INF>2 concentrations, they should be modeled as area sources. When modeled as area sources, mobile source emissions should be assumed uniform over the entire highway link and allocated to each area source grid square based on the portion of highway link within each grid square. If localized areas of high concentrations are likely, then mobile sources should be modeled as line sources with the preferred model ISCLT.
f. More refined techniques to handle special circumstances may be considered on a case-by-case basis and agreement with the reviewing authority should be obtained. Such techniques should consider individual quantities of NO and NO<INF>2 emissions, atmospheric transport and dispersion, and atmospheric transformation of NO to NO<INF>2. Where they are available, site-specific data on the conversion of NO to NO<INF>2 may be used. Photochemical dispersion models, if used for other pollutants in the area, may also be applied to the NO<INF>X problem.
7.0 Other Model Requirements
7.1 Discussion
a. This section covers those cases where specific techniques have been developed for special regulatory programs. Most of the programs have, or will have when fully developed, separate guidance documents that cover the program and a discussion of the tools that are needed. The following paragraphs reference those guidance documents, when they are available. No attempt has been made to provide a comprehensive discussion of each topic since the reference documents were designed to do that. This section will undergo periodic revision as new programs are added and new techniques are developed.
b. Other Federal agencies have also developed specific modeling approaches for their own regulatory or other requirements. An example of this is the three-volume manual issued by the U. S. Department of Housing and Urban Development, ``Air Quality Considerations in Residential Planning.'' \37\ Although such regulatory requirements and manuals may have come about because of EPA rules or standards, the implementation of such regulations and the use of the modeling techniques is under the jurisdiction of the agency issuing the manual or directive. c. The need to estimate impacts at distances greater than 50km (the nominal distance to which EPA considers most Gaussian models applicable) is an important one especially when considering the effects from secondary pollutants. Unfortunately, models submitted to EPA have not as yet undergone sufficient field evaluation to be recommended for general use. Existing data bases from field studies at mesoscale and long range transport distances are limited in detail. This limitation is a result of the expense to perform the field studies required to verify and improve mesoscale and long range transport models. Particularly important and sparse are meteorological data adequate for generating three dimensional wind fields. Application of models to complicated terrain compounds the difficulty. EPA has completed limited evaluation of several long range transport (LRT) models against two sets of field data. The evaluation results are discussed in the document, ``Evaluation of Short-Term Long-Range Transport Models.'' 99 100 For the time being, long range and mesoscale transport models must be evaluated for regulatory use on a case-by-case basis. d. There are several regulatory programs for which air pathway analysis procedures and modeling techniques have been developed. For continuous emission releases, ISC forms the basis of many analytical techniques. EPA is continuing to evaluate the performance of a number of proprietary and public domain models for intermittent and non-stack emission releases. Until EPA completes its evaluation, it is premature to recommend specific models for air pathway analyses of intermittent and non-stack releases in the Guideline. e. Regional scale models are used by EPA to develop and evaluate national policy and assist State and local control agencies. Two such models are the Regional Oxidant Model (ROM) 101 102 103 and the Regional Acid Deposition Model (RADM).\104\ Due to the level of resources required to apply these models, it is not envisioned that regional scale models will be used directly in most model applications.
7.2 Recommendations
7.2.1 Fugitive Dust/Fugitive Emissions
a. Fugitive dust usually refers to the dust put into the atmosphere by the wind blowing over plowed fields, dirt roads or desert or sandy areas with little or no vegetation. Reentrained dust is that which is put into the air by reason of vehicles driving over dirt roads (or dirty roads) and dusty areas. Such sources can be characterized as line, area or volume sources. Emission rates may be based on site-specific data or values from the general literature. b. Fugitive emissions are usually defined as emissions that come from an industrial source complex. They include the emissions resulting from the industrial process that are not captured and vented through a stack but may be released from various locations within the complex. Where such fugitive emissions can be properly specified, the ISC model, with consideration of gravitational settling and dry deposition, is the recommended model. In some unique cases a model developed specifically for the situation may be needed.
c. Due to the difficult nature of characterizing and modeling fugitive dust and fugitive emissions, it is recommended that the proposed procedure be cleared by the appropriate Regional Office for each specific situation before the modeling exercise is begun.
[[Page 41850]]
7.2.2 Particulate Matter
a. The particulate matter NAAQS, promulgated on July 1, 1987 (52 FR 24634), includes only particles with an aerodynamic diameter less than or equal to a nominal 10 micrometers (PM-10). EPA promulgated regulations for PSD increments measured as PM-10 on June 3, 1993 (58 FR 31621), which are codified at Secs. 51.166(c) and 52.21(c). b. Screening techniques like those identified in Section 4 are also applicable to PM-10 and to large particles. It is recommended that subjectively determined values for ``half-life'' or pollutant decay not be used as a surrogate for particle removal. Conservative assumptions which do not allow removal or transformation are suggested for screening. Proportional models (rollback/forward) may not be applied for screening analysis, unless such techniques are used in conjunction with receptor modeling. c. Refined models such as those in Section 4.0 are recommended for PM-10 and large particles. However, where possible, particle size, gas-to-particle formation, and their effect on ambient concentrations may be considered. For urban-wide refined analyses CDM 2.0 (long term) or RAM (short term) should be used. ISC is recommended for point sources of small particles and for sourcespecific analyses of complicated sources. No model recommended for general use at this time accounts for secondary particulate formation or other transformations in a manner suitable for SIP control strategy demonstrations. Where possible, the use of receptor models 38 39 105 106 107 in conjunction with dispersion models is encouraged to more precisely characterize the emissions inventory and to validate source specific impacts calculated by the dispersion model. A SIP development guideline,108 model reconciliation guidance,106 and an example model application 109 are available to assist in PM-10 analyses and control strategy development.
d. Under certain conditions, recommended dispersion models are not available or applicable. In such circumstances, the modeling approach should be approved by the appropriate Regional Office on a case-by-case basis. For example, where there is no recommended air quality model and area sources are a predominant component of PM-10, an attainment demonstration may be based on rollback of the apportionment derived from two reconciled receptor models, if the strategy provides a conservative demonstration of attainment. At this time, analyses involving model calculations for distances beyond 50km and under stagnation conditions should also be justified on a case-by-case basis (see Sections 7.2.6 and 8.2.10). e. As an aid to assessing the impact on ambient air quality of particulate matter generated from prescribed burning activities, reference 110 is available.
7.2.3 Lead
a. The air quality analyses required for lead implementation plans are given in Secs. 51.83, 51.84 and 51.85. Sections 51.83 and 51.85 require the use of a modified rollback model as a minimum to demonstrate attainment of the lead air quality standard but the use of a dispersion model is the preferred approach. Section 51.83 requires the analysis of an entire urban area if the measured lead concentration in the urbanized area exceeds a quarterly (three month) average of 4.0 <greek-m>g/m\3\. Section 51.84 requires the use of a dispersion model to demonstrate attainment of the lead air quality standard around specified lead point sources. For other areas reporting a violation of the lead standard, Section 51.85 requires an analysis of the area in the vicinity of the monitor reporting the violation. The NAAQS for lead is a quarterly (three month) average, thus requiring the use of modeling techniques that can provide long-term concentration estimates. b. The SIP should contain an air quality analysis to determine the maximum quarterly lead concentration resulting from major lead point sources, such as smelters, gasoline additive plants, etc. For these applications the ISC model is preferred, since the model can account for deposition of particles and the impact of fugitive emissions. If the source is located in complicated terrain or is subject to unusual climatic conditions, a case-specific review by the appropriate Regional Office may be required. c. In modeling the effect of traditional line sources (such as a specific roadway or highway) on lead air quality, dispersion models applied for other pollutants can be used. Dispersion models such as CALINE3 have been widely used for modeling carbon monoxide emissions from highways. However, where deposition is of concern, the line source treatment in ISC may be used. Also, where there is a point source in the middle of a substantial road network, the lead concentrations that result from the road network should be treated as background (see Section 9.2); the point source and any nearby major roadways should be modeled separately using the ISC model. d. To model an entire major urban area or to model areas without significant sources of lead emissions, as a minimum a proportional (rollback) model may be used for air quality analysis. The rollback philosophy assumes that measured pollutant concentrations are proportional to emissions. However, urban or other dispersion models are encouraged in these circumstances where the use of such models is feasible.
e. For further information concerning the use of models in the development of lead implementation plans, the documents ``Supplementary Guidelines for Lead Implementation Plans,'' \40\ and ``Updated Information on Approval and Promulgation of Lead Implementation Plans,'' \41\ should be consulted. 7.2.4. Visibility
a. The visibility regulations as promulgated in December 1980 b require consideration of the effect of new sources on the visibility values of Federal Class I areas. The state of scientific knowledge concerning identifying, monitoring, modeling, and controlling visibility impairment is contained in an EPA report ``Protecting Visibility: An EPA Report to Congress''.\42\ In 1985, EPA promulgated Federal Implementation Plans (FIPs) for states without approved visibility provisions in their SIPs. A monitoring plan was established as part of the FIPs.c

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

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

\d\ The EPA refined formula height is defined as H + 1.5L (see Reference 46).

7.2.6 Long Range Transport (LRT) (i.e., beyond 50km) a. Section 165(e) of the Clean Air Act requires that suspected significant impacts on PSD Class I areas be determined. However, 50km is the useful distance to which most Gaussian models are considered accurate for setting emission limits. Since in many cases PSD analyses may show that Class I areas may be threatened at distances greater than 50km from new sources, some procedure is needed to (1) determine if a significant impact will occur, and (2) identify the model to be used in setting an emission limit if the Class I increments are threatened (models for this purpose should be approved for use on a case-by-case basis as required in Section 3.2). This procedure and the models
[[Page 41851]]
selected for use should be determined in consultation with the EPA Regional Office and the appropriate Federal Land Manager (FLM). While the ultimate decision on whether a Class I area is adversely affected is the responsibility of the permitting authority, the FLM has an affirmative responsibility to protect air quality related values that may be affected.
b. If LRT is determined to be important, then estimates utilizing an appropriate refined model for receptors at distances greater than 50 km should be obtained. MESOPUFF II, listed in Appendix B, may be applied on a case-by-case basis when LRT estimates are needed. Additional information on applying this model is contained in the EPA document ``A Modeling Protocol For Applying MESOPUFF II to Long Range Transport Problems''.\111\
7.2.7 Modeling Guidance for Other Governmental Programs
a. When using the models recommended or discussed in the Guideline in support of programmatic requirements not specifically covered by EPA regulations, the model user should consult the appropriate Federal or State agency to ensure the proper application and use of that model. For modeling associated with PSD permit applications that involve a Class I area, the appropriate Federal Land Manager should be consulted on all modeling questions. b. The Offshore and Coastal Dispersion (OCD) model \112\ was developed by the Minerals Management Service and is recommended for estimating air quality impact from offshore sources on onshore, flat terrain areas. The OCD model is not recommended for use in air quality impact assessments for onshore sources. Sources located on or just inland of a shoreline where fumigation is expected should be treated in accordance with Section 8.2.9. c. The Emissions and Dispersion Modeling System (EDMS) \113\ was developed by the Federal Aviation Administration and the United States Air Force and is recommended for air quality assessment of primary pollutant impacts at airports or air bases. Regulatory application of EDMS is intended for estimating the cumulative effect of changes in aircraft operations, point source, and mobile source emissions on pollutant concentrations. It is not intended for PSD, SIP, or other regulatory air quality analyses of point or mobile sources at or peripheral to airport property that are independent of changes in aircraft operations. If changes in other than aircraft operations are associated with analyses, a model recommended in Chapter 4, 5, or 6 should be used.
7.2.8 Air Pathway Analyses (Air Toxics and Hazardous Waste)
a. Modeling is becoming an increasingly important tool for regulatory control agencies to assess the air quality impact of releases of toxics and hazardous waste materials. Appropriate screening techniques \114\ \115\ for calculating ambient concentrations due to various well-defined neutrally buoyant toxic/ hazardous pollutant releases are available. b. Several regulatory programs within EPA have developed modeling techniques and guidance for conducting air pathway analyses as noted in references 116-129. ISC forms the basis of the modeling procedures for air pathway analyses of many of these regulatory programs and, where identified, is appropriate for obtaining refined ambient concentration estimates of neutrally buoyant continuous air toxic releases from traditional sources. Appendix A to the Guideline contains additional models appropriate for obtaining refined estimates of continuous air toxic releases from traditional sources. Appendix B contains models that may be used on a case-by-case basis for obtaining refined estimates of denser-than-air intermittent gaseous releases, e.g., DEGADIS; \130\ guidance for the use of such models is also available.\131\
c. Many air toxics models require input of chemical properties and/or chemical engineering variables in order to appropriately characterize the source emissions prior to dispersion in the atmosphere; reference 132 is one source of helpful data. In addition, EPA has numerous programs to determine emission factors and other estimates of air toxic emissions. The Regional Office should be consulted for guidance on appropriate emission estimating procedures and any uncertainties that may be associated with them.
8.0 General Modeling Considerations
8.1 Discussion
a. This section contains recommendations concerning a number of different issues not explicitly covered in other sections of this guide. The topics covered here are not specific to any one program or modeling area but are common to nearly all modeling analyses.
8.2 Recommendations
8.2.1 Design Concentrations
8.2.1.1 Design Concentrations for Criteria Pollutants With Deterministic Standards
a. An air quality analysis for SO<INF>2, CO, Pb, and NO<INF>2 is required to determine if the source will (1) Cause a violation of the NAAQS, or (2) cause or contribute to air quality deterioration greater than the specified allowable PSD increment. For the former, background concentration (see Section 9.2) should be added to the estimated impact of the source to determine the design concentration. For the latter, the design concentration includes impact from all increment consuming sources. b. If the air quality analyses are conducted using the period of meteorological input data recommended in Section 9.3.1.2 (e.g., 5 years of NWS data or 1 year of site-specific data), then the design concentration based on the highest, second-highest short term concentration or long term average, whichever is controlling, should be used to determine emission limitations to assess compliance with the NAAQS and to determine PSD increments. c. When sufficient and representative data exist for less than a 5-year period from a nearby NWS site, or when on-site data have been collected for less than a full continuous year, or when it has been determined that the on site data may not be temporally representative, then the highest concentration estimate should be considered the design value. This is because the length of the data record may be too short to assure that the conditions producing worst-case estimates have been adequately sampled. The highest value is then a surrogate for the concentration that is not to be exceeded more than once per year (the wording of the deterministic standards). Also, the highest concentration should be used whenever selected worst-case conditions are input to a screening technique. This specifically applies to the use of techniques such as outlined in ``Screening Procedures for Estimating the Air Quality Impact of Stationary Sources, Revised''.\18\ Specific guidance for CO may be found in the ``Guideline for Modeling Carbon Monoxide from Roadway Intersections''.\34\
d. If the controlling concentration is an annual average value and multiple years of data (on-site or NWS) are used, then the design value is the highest of the annual averages calculated for the individual years. If the controlling concentration is a quarterly average and multiple years are used, then the highest individual quarterly average should be considered the design value. e. As long a period of record as possible should be used in making estimates to determine design values and PSD increments. If more than 1 year of site-specific data is available, it should be used.
8.2.1.2 Design Concentrations for Criteria Pollutants With Expected Exceedance Standards
a. Specific instructions for the determination of design concentrations for criteria pollutants with expected exceedance standards, ozone and PM-10, are contained in special guidance documents for the preparation of SIPs for those pollutants.\86\ \108\ For all SIP revisions the user should check with the Regional Office to obtain the most recent guidance documents and policy memoranda concerning the pollutant in question.
8.2.2 Critical Receptor Sites
a. Receptor sites for refined modeling should be utilized in sufficient detail to estimate the highest concentrations and possible violations of a NAAQS or a PSD increment. In designing a receptor network, the emphasis should be placed on receptor resolution and location, not total number of receptors. The selection of receptor sites should be a case-by-case determination taking into consideration the topography, the climatology, monitor sites, and the results of the initial screening procedure. For large sources (those equivalent to a 500MW power plant) and where violations of the NAAQS or PSD increment are likely, 360 receptors for a polar coordinate grid system and 400 receptors for a rectangular grid system, where the distance from the source to the farthest receptor is 10km, are usually adequate to identify areas of high concentration. Additional receptors may be needed in the high concentration location if greater resolution is indicated by terrain or source factors.
8.2.3 Dispersion Coefficients
a. Gaussian models used in most applications should employ dispersion
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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, PasquillGifford 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 caseby -case basis.
c. For a given model application where stability categories are the basis for selecting dispersion coefficients, both <greek-s><INF>y and <greek-s><INF>z should be determined from the same stability category. ``Split sigmas'' in that instance are not recommended.
d. Sector averaging, which eliminates the <greek-s><INF>y term, is generally acceptable only to determine long term averages, such as seasonal or annual, and when the meteorological input data are statistically summarized as in the STAR summaries. Sector averaging is, however, commonly acceptable in complex terrain screening methods.
8.2.5 Plume Rise
a. The plume rise methods of Briggs \56\ \57\ are incorporated in the preferred models and are recommended for use in all modeling applications. No provisions in these models are made for fumigation or multistack plume rise enhancement or the handling of such special plumes as flares; these problems should be considered on a case-bycase 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 SO<INF>2 emitted from point sources or single industrial plants in rural areas is generally assumed to be relatively unimportant to the estimation of maximum concentrations when travel time is limited to a few hours. However, in urban areas, where synergistic effects among pollutants are of considerable consequence, chemical transformation rates may be of concern. In urban area applications, a half-life of 4 hours \55\ may be applied to the analysis of SO<INF>2 emissions. Calculations of transformation coefficients from site-specific studies can be used to define a ``half-life'' to be used in a Gaussian model with any travel time, or in any application, if appropriate documentation is provided. Such conversion factors for pollutant half-life should not be used with screening analyses.
b. Complete conversion of NO to NO<INF>2 should be assumed for all travel time when simple screening techniques are used to model point source emissions of nitrogen oxides. If a Gaussian model is used, and data are available on seasonal variations in maximum ozone concentrations, the Ozone Limiting Method \36\ is recommended. In refined analyses, case-by case conversion rates based on technical studies appropriate to the site in question may be used. The use of more sophisticated modeling techniques should be justified for individual cases.
c. Use of models incorporating complex chemical mechanisms should be considered only on a case-by-case basis with proper demonstration of applicability. These are generally regional models not designed for the evaluation of individual sources but used primarily for region-wide evaluations. Visibility models also incorporate chemical transformation mechanisms which are an integral part of the visibility model itself and should be used in visibility assessments.
8.2.7 Gravitational Settling and Deposition
a. An ``infinite half-life'' should be used for estimates of particle concentrations when Gaussian models containing only exponential decay terms for treating settling and deposition are used.
b. Gravitational settling and deposition may be directly included in a model if either is a significant factor. One preferred model (ISC) contains a settling and deposition algorithm and is recommended for use when particulate matter sources can be quantified and settling and deposition are problems.
8.2.8 Urban/Rural Classification
a. The selection of either rural or urban dispersion coefficients in a specific application should follow one of the procedures suggested by Irwin \59\ and briefly described below. These include a land use classification procedure or a population based procedure to determine whether the character of an area is primarily urban or rural.
b. Land Use Procedure: (1) Classify the land use within the total area, A<INF>o, circumscribed by a 3km radius circle about the source using the meteorological land use typing scheme proposed by Auer \60\; (2) if land use types I1, I2, C1, R2, and R3 account for 50 percent or more of A<INF>o, use urban dispersion coefficients; otherwise, use appropriate rural dispersion coefficients. c. Population Density Procedure: (1) Compute the average population density, p per square kilometer with A<INF>o as defined above; (2) If p is greater than 750 people/km\2\, use urban dispersion coefficients; otherwise use appropriate rural dispersion coefficients.
d. Of the two methods, the land use procedure is considered more definitive. Population density should be used with caution and should not be applied to highly industrialized areas where the population density may be low and thus a rural classification would be indicated, but the area is sufficiently built-up so that the urban land use criteria would be satisfied. In this case, the classification should already be ``urban'' and urban dispersion parameters should be used.
e. Sources located in an area defined as urban should be modeled using urban dispersion parameters. Sources located in areas defined as rural should be modeled using the rural dispersion parameters. For analyses of whole urban complexes, the entire area should be modeled as an urban region if most of the sources are located in areas classified as urban.
8.2.9 Fumigation
a. Fumigation occurs when a plume (or multiple plumes) is emitted into a stable layer of air and that layer is subsequently mixed to the ground either through convective transfer of heat from the surface or because of advection to less stable surroundings. Fumigation may cause excessively high concentrations but is usually rather short-lived at a given receptor. There are no recommended refined techniques to model this phenomenon. There are, however, screening procedures (see ``Screening Procedures for Estimating the Air Quality Impact of Stationary Sources'' \18\) that may be used to approximate the concentrations. Considerable care should be exercised in using the results obtained from the screening techniques.
b. Fumigation is also an important phenomenon on and near the shoreline of bodies of water. This can affect both individual plumes and area-wide emissions. When fumigation conditions are expected to occur from a source or sources with tall
[[Page 41853]]
stacks located on or just inland of a shoreline, this should be addressed in the air quality modeling analysis. The Shoreline Dispersion Model (SDM) listed in Appendix B may be applied on a case-by-case basis when air quality estimates under shoreline fumigation conditions are needed.\133\ Information on the results of EPA's evaluation of this model together with other coastal fumigation models may be found in reference 134. Selection of the appropriate model for applications where shoreline fumigation is of concern should be determined in consultation with the Regional Office.
8.2.10 Stagnation
a. Stagnation conditions are characterized by calm or very low wind speeds, and variable wind directions. These stagnant meteorological conditions may persist for several hours to several days. During stagnation conditions, the dispersion of air pollutants, especially those from low-level emissions sources, tends to be minimized, potentially leading to relatively high ground-level concentrations.
b. When stagnation periods such as these are found to occur, they should be addressed in the air quality modeling analysis. WYNDvalley, listed in Appendix B, may be applied on a case-by-case basis for stagnation periods of 24 hours or longer in valley-type situations. Caution should be exercised when applying the model to elevated point sources. Users should consult with the appropriate Regional Office prior to regulatory application of WYNDvalley.
8.2.11 Calibration of Models
a. Calibration of long term multi-source models has been a widely used procedure even though the limitations imposed by statistical theory on the reliability of the calibration process for long term estimates are well known.\61\ In some cases, where a more accurate model is not available, calibration may be the best alternative for improving the accuracy of the estimated concentrations needed for control strategy evaluations. b. Calibration of short term models is not common practice and is subject to much greater error and misunderstanding. There have been attempts by some to compare short term estimates and measurements on an event-by-event basis and then to calibrate a model with results of that comparison. This approach is severely limited by uncertainties in both source and meteorological data and therefore it is difficult to precisely estimate the concentration at an exact location for a specific increment of time. Such uncertainties make calibration of short term models of questionable benefit. Therefore, short term model calibration is unacceptable.
9.0 Model Input Data
a. Data bases and related procedures for estimating input parameters are an integral part of the modeling procedure. The most appropriate data available should always be selected for use in modeling analyses. Concentrations can vary widely depending on the source data or meteorological data used. Input data are a major source of inconsistencies in any modeling analysis. This section attempts to minimize the uncertainty associated with data base selection and use by identifying requirements for data used in modeling. A checklist of input data requirements for modeling analyses is included as Appendix C. More specific data requirements and the format required for the individual models are described in detail in the users' guide for each model.
9.1 Source Data
9.1.1 Discussion
a. Sources of pollutants can be classified as point, line and area/volume sources. Point sources are defined in terms of size and may vary between regulatory programs. The line sources most frequently considered are roadways and streets along which there are well-defined movements of motor vehicles, but they may be lines of roof vents or stacks such as in aluminum refineries. Area and volume sources are often collections of a multitude of minor sources with individually small emissions that are impractical to consider as separate point or line sources. Large area sources are typically treated as a grid network of square areas, with pollutant emissions distributed uniformly within each grid square. b. Emission factors are compiled in an EPA publication commonly known as AP-42 \62\; an indication of the quality and amount of data on which many of the factors are based is also provided. Other information concerning emissions is available in EPA publications relating to specific source categories. The Regional Office should be consulted to determine appropriate source definitions and for guidance concerning the determination of emissions from and techniques for modeling the various source types.
9.1.2 Recommendations
a. For point source applications the load or operating condition that causes maximum ground-level concentrations should be established. As a minimum, the source should be modeled using the design capacity (100 percent load). If a source operates at greater than design capacity for periods that could result in violations of the standards or PSD increments, this load e should be modeled. Where the source operates at substantially less than design capacity, and the changes in the stack parameters associated with the operating conditions could lead to higher ground level concentrations, loads such as 50 percent and 75 percent of capacity should also be modeled. A range of operating conditions should be considered in screening analyses; the load causing the highest concentration, in addition to the design load, should be included in refined modeling. For a power plant, the following paragraphs b through h of this section describe the typical kind of data on source characteristics and operating conditions that may be needed. Generally, input data requirements for air quality models necessitate the use of metric units; where English units are common for engineering usage, a conversion to metric is required.

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

b. Plant layout. The connection scheme between boilers and stacks, and the distance and direction between stacks, building parameters (length, width, height, location and orientation relative to stacks) for plant structures which house boilers, control equipment, and surrounding buildings within a distance of approximately five stack heights.
c. Stack parameters. For all stacks, the stack height and inside diameter (meters), and the temperature (K) and volume flow rate (actual cubic meters per second) or exit gas velocity (meters per second) for operation at 100 percent, 75 percent and 50 percent load.
d. Boiler size. For all boilers, the associated megawatts, 10\6\ BTU/hr, and pounds of steam per hour, and the design and/or actual fuel consumption rate for 100 percent load for coal (tons/hour), oil (barrels/hour), and natural gas (thousand cubic feet/hour). e. Boiler parameters. For all boilers, the percent excess air used, the boiler type (e.g., wet bottom, cyclone, etc.), and the type of firing (e.g., pulverized coal, front firing, etc.). f. Operating conditions. For all boilers, the type, amount and pollutant contents of fuel, the total hours of boiler operation and the boiler capacity factor during the year, and the percent load for peak conditions.
g. Pollution control equipment parameters. For each boiler served and each pollutant affected, the type of emission control equipment, the year of its installation, its design efficiency and mass emission rate, the data of the last test and the tested efficiency, the number of hours of operation during the latest year, and the best engineering estimate of its projected efficiency if used in conjunction with coal combustion; data for any anticipated modifications or additions.
h. Data for new boilers or stacks. For all new boilers and stacks under construction and for all planned modifications to existing boilers or stacks, the scheduled date of completion, and the data or best estimates available for paragraphs b through g of this section above following completion of construction or modification.
i. In stationary point source applications for compliance with short term ambient standards, SIP control strategies should be tested using the emission input shown on Table 9-1. When using a refined model, sources should be modeled sequentially with these loads for every hour of the year. To evaluate SIPs for compliance with quarterly and annual standards, emission input data shown in Table 9-1 should again be used. Emissions from area sources should generally be based on annual average conditions. The source input information in each model user's guide should be carefully consulted and the checklist in Appendix C should also be consulted for other possible emission data that could be helpful. PSD NAAQS compliance demonstrations should follow the emission input data shown in Table 9-2. For purposes of emissions trading, new
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source review and demonstrations, refer to current EPA policy and guidance to establish input data.
j. Line source modeling of streets and highways requires data on the width of the roadway and the median strip, the types and amounts of pollutant emissions, the number of lanes, the emissions from each lane and the height of emissions. The location of the ends of the straight roadway segments should be specified by appropriate grid coordinates. Detailed information and data requirements for modeling mobile sources of pollution are provided in the user's manuals for each of the models applicable to mobile sources. k. The impact of growth on emissions should be considered in all modeling analyses covering existing sources. Increases in emissions due to planned expansion or planned fuel switches should be identified. Increases in emissions at individual sources that may be associated with a general industrial/commercial/residential expansion in multi-source urban areas should also be treated. For new sources the impact of growth on emissions should generally be considered for the period prior to the start-up date for the source. Such changes in emissions should treat increased area source emissions, changes in existing point source emissions which were not subject to preconstruction review, and emissions due to sources with permits to construct that have not yet started operation.
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Table 9-1.-- Model Emission Input Data for Point Sources \1\ 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Operating factor
```
Averaging time Emission limit (#/ x Operating level x (e.g., hr/yr, hr/
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 MMBtu) \2\ (MMBtu/hr) \2\ day) 
```
Stationary Point Source(s) Subject to SIP Emission Limit(s) Evaluation for Compliance with Ambient Standards
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 (Including Areawide Demonstrations) 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
```
Annual & quarterly............. Maximum allowable Actual or design Actual operating
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or capacity factor averaged 
 federally (whichever is over most recent
 enforceable greater), or 2 years.\3\ 
 permit limit. federally 
 enforceable 
 permit condition. 
```
Short term..................... Maximum allowable Actual or design Continuous
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or capacity operation, i.e.,
 federally (whichever is all hours of 
 enforceable greater), or each time period
 permit limit. federally under 
 enforceable consideration 
 permit condition (for all hours 
 \4\. of the 
 meteorological 
 data base).\5\ 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Nearby Background Source(s) 
 
 Same input requirements as for stationary point source(s) above. 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
 Other Background Source(s) 
 
 If modeled (see Section 9.2.3), input data requirements are defined below. 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
```
Annual & quarterly............. Maximum allowable Annual level when Actual operating
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or actually factor averaged 
 federal operating, over the most 
 enforceable averaged over the recent 2 
 permit limit. most recent 2 years.\3\ 
 years \3\. 
```
Short term..................... Maximum allowable Annual level when Continuous
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or actually operation, i.e.,
 federally operating, all hours of 
 enforceable averaged over the each time period
 permit limit. most recent 2 under 
 years \3\. consideration 
 (for all hours 
 of the 
 meteorological 
 data base).\5\ 
```
\1\ The model input data requirements shown on this table apply to stationary source control strategies for STATE IMPLEMENTATION PLANS. For purposes of emissions trading, new source review, or prevention of significant deterioration, other model input criteria may apply. Refer to the policy and guidance for these programs to
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 establish the input data. 
```
\2\ Terminology applicable to fuel burning sources; analogous terminology (e.g., #/throughput) may be used for
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 other types of sources. 
\3\ Unless it is determined that this period is not representative. 
```
\4\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 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 nonoperating
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 time periods.) 
```
Table 9-2.--Point Source Model Input Data (Emissions) for PSD NAAQS Compliance Demonstrations

Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Operating factor
```
Averaging time Emission limit (#/ x Operating level x (e.g., hr/yr, hr/
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 MMBtu) \1\ (MMBtu/hr) \1\ day) 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Proposed Major New or Modified Source 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
```
Annual & quarterly............. Maximum allowable Design capacity or Continuous
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or federally operation (i.e.,
 federally enforceable 8760 hours).\2\ 
 enforceable permit condition. 
 permit limit. 
```
Short term ( 24 hours)......... Maximum allowable Design capacity or Continuous
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or federally operation (i.e.,
 federally enforceable all hours of 
 enforceable permit each time period
 permit limit. condition.\3\ under 
 consideration) 
 (for all hours 
 of the 
 meteorological 
 data base).\2\ 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
```
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Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
 Nearby Background Source(s) \4\ 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
```
Annual & quarterly............. Maximum allowable Actual or design Actual operating
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or capacity factor averaged 
 federally (whichever is over the most 
 enforceable greater), or recent 2 years.5
 permit limit. federally 7 
 enforceable 
 permit condition. 
```
Short term ( 24 hours)......... Maximum allowable Actual or design Continuous
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or capacity operation (i.e.,
 federally (whichever is all hours of 
 enforceable greater), or each time period
 permit limit. federally under 
 enforceable consideration) 
 permit (for all hours 
 condition.\3\ of the 
 meteorological 
 data base).\2\ 
 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
 Other Background Source(s) \6\ 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 
```
Annual & quarterly............. Maximum allowable Annual level when Actual operating
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or actually factor averaged 
 federally operating, over the most 
 enforceable averaged over the recent 2 years.5
 permit limit. most recent 2 7 
 years.\5\ 
```
Short term ( 24 hours)......... Maximum allowable Annual level when Continuous
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 emission limit or actually operation (i.e.,
 federally operating, all hours of 
 enforceable averaged over the each time period
 permit limit. most recent 2 under 
 years.\5\ consideration) 
 (for all hours 
 of the 
 meteorological 
 data base).\2\ 
```
\1\ Terminology applicable to fuel burning sources; analogous terminology (e.g., #/throughput) may be used for
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 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 nonoperating
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 time periods. 
```
\3\ Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 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.
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
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\5\ Unless it is determined that this period is not representative. 
```
\6\ Generally, the ambient impacts from non-nearby background sources can be represented by air quality data
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
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 unless adequate data do not exist. 
```
\7\ For those permitted sources not yet in operation or that have not established an appropriate factor,
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
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 continuous operation (i.e., 8760 hours) should be used. 
```
9.2 Background Concentrations
9.2.1 Discussion
a. Background concentrations are an essential part of the total air quality concentration to be considered in determining source impacts. Background air quality includes pollutant concentrations due to: (1) natural sources; (2) nearby sources other than the one(s) currently under consideration; and (3) unidentified sources. b. Typically, air quality data should be used to establish background concentrations in the vicinity of the source(s) under consideration. The monitoring network used for background determinations should conform to the same quality assurance and other requirements as those networks established for PSD purposes.\63\ An appropriate data validation procedure should be applied to the data prior to use.
c. If the source is not isolated, it may be necessary to use a multi-source model to establish the impact of nearby sources. Background concentrations should be determined for each critical (concentration) averaging time.
9.2.2 Recommendations (Isolated Single Source)
a. Two options (paragraph b or c of this section) are available to determine the background concentration near isolated sources. b. Use air quality data collected in the vicinity of the source to determine the background concentration for the averaging times of concern.f Determine the mean background concentration at each monitor by excluding values when the source in question is impacting the monitor. The mean annual background is the average of the annual concentrations so determined at each monitor. For shorter averaging periods, the meteorological conditions accompanying the concentrations of concern should be identified. Concentrations for meteorological conditions of concern, at monitors not impacted by the source in question, should be averaged for each separate averaging time to determine the average background value. Monitoring sites inside a 90 deg. sector downwind of the source may be used to determine the area of impact. One hour concentrations may be added and averaged to determine longer averaging periods.

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

c. If there are no monitors located in the vicinity of the source, a ``regional site'' may be used to determine background. A ``regional site'' is one that is located away from the area of interest but is impacted by similar natural and distant man-made sources.
9.2.3 Recommendations (Multi-Source Areas)
a. In multi-source areas, two components of background should be determined.
b. Nearby Sources: All sources expected to cause a significant concentration gradient in the vicinity of the source or sources under consideration for emission limit(s) should be explicitly modeled. For evaluation for compliance with the short term and annual ambient standards, the nearby sources should be modeled using the emission input data shown in Table 9-1 or 9-2. The number of such sources is expected to be small except in unusual situations. The nearby source inventory should be determined in consultation with the reviewing authority. It is envisioned that the nearby sources and the sources under consideration will be evaluated together using an appropriate Appendix A model. c. The impact of the nearby sources should be examined at locations where interactions between the plume of the point source under consideration and those of nearby sources (plus natural background) can occur. Significant locations include: (1) the area of
[[Page 41856]]
maximum impact of the point source; (2) the area of maximum impact of nearby sources; and (3) the area where all sources combine to cause maximum impact. These locations may be identified through trial and error analyses.
d. Other Sources: That portion of the background attributable to all other sources (e.g., natural sources, minor sources and distant major sources) should be determined by the procedures found in Section 9.2.2 or by application of a model using Table 9-1 or 9-2.
9.3 Meteorological Input Data
a. The meteorological data used as input to a dispersion model should be selected on the basis of spatial and climatological (temporal) representativeness as well as the ability of the individual parameters selected to characterize the transport and dispersion conditions in the area of concern. The representativeness of the data is dependent on: (1) the proximity of the meteorological monitoring site to the area under consideration; (2) the complexity of the terrain; (3) the exposure of the meteorological monitoring site; and (4) the period of time during which data are collected. The spatial representativeness of the data can be adversely affected by large distances between the source and receptors of interest and the complex topographic characteristics of the area. Temporal representativeness is a function of the year-to-year variations in weather conditions.
b. Model input data are normally obtained either from the National Weather Service or as part of an on-site measurement program. Local universities, Federal Aviation Administration (FAA), military stations, industry and pollution control agencies may also be sources of such data. Some recommendations for the use of each type of data are included in this section 9.3.
9.3.1 Length of Record of Meteorological Data
9.3.1.1 Discussion
a. The model user should acquire enough meteorological data to ensure that worst-case meteorological conditions are adequately represented in the model results. The trend toward statistically based standards suggests a need for all meteorological conditions to be adequately represented in the data set selected for model input. The number of years of record needed to obtain a stable distribution of conditions depends on the variable being measured and has been estimated by Landsberg and Jacobs \64\ for various parameters. Although that study indicates in excess of 10 years may be required to achieve stability in the frequency distributions of some meteorological variables, such long periods are not reasonable for model input data. This is due in part to the fact that hourly data in model input format are frequently not available for such periods and that hourly calculations of concentration for long periods are prohibitively expensive. A recent study \65\ compared various periods from a 17-year data set to determine the minimum number of years of data needed to approximate the concentrations modeled with a 17-year period of meteorological data from one station. This study indicated that the variability of model estimates due to the meteorological data input was adequately reduced if a 5-year period of record of meteorological input was used.
9.3.1.2 Recommendations
a. Five years of representative meteorological data should be used when estimating concentrations with an air quality model. Consecutive years from the most recent, readily available 5-year period are preferred. The meteorological data may be data collected either onsite or at the nearest National Weather Service (NWS) station. If the source is large, e.g., a 500MW power plant, the use of 5 years of NWS meteorological data or at least 1 year of 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 long-term averages; however, concentration estimates based on the two separate input data sets may not necessarily agree. b. Although most NWS measurements are made at a standard height of 10 meters, the actual anemometer height should be used as input to the preferred model.
c. National Weather Service wind directions are reported to the nearest 10 degrees. A specific set of randomly generated numbers has been developed for use with the preferred EPA models and should be used to ensure a lack of bias in wind direction assignments within the models.
d. Data from universities, FAA, military stations, industry and pollution control agencies may be used if such data are equivalent in accuracy and detail to the NWS data.
9.3.3 Site-Specific Data
9.3.3.1 Discussion
a. Spatial or geographical representativeness is best achieved by collection of all of the needed model input data at the actual site of the source(s). Site-specific measured data are therefore preferred as model input, provided appropriate instrumentation and quality assurance procedures are followed and that the data collected are representative (free from undue local or ``micro'' influences) and compatible with the input requirements of the model to be used. However, direct measurements of all the needed model input parameters may not be possible. This section discusses suggestions for the collection and use of on-site data. Since the methods outlined in this section are still being tested, comparison of the model parameters derived using these site-specific data should be compared at least on a spot-check basis, with parameters derived from more conventional observations.
9.3.3.2 Recommendations: Site-specific Data Collection
a. The document ``On-Site Meteorological Program Guidance for Regulatory Modeling Applications''\66\ provides recommendations on the collection and use of on-site meteorological data. Recommendations on characteristics, siting, and exposure of meteorological instruments and on data recording, processing, completeness requirements, reporting, and archiving are also included. This publication should be used as a supplement to the limited guidance on these subjects now found in the ``Ambient Monitoring Guidelines for Prevention of Significant Deterioration''.\63\ Detailed information on quality assurance is provided in the ``Quality Assurance Handbook for Air Pollution Measurement Systems: Volume IV''.\67\ As a minimum, site-specific measurements of ambient air temperature, transport wind speed and direction, and the parameters to determine Pasquill-Gifford (P-G) stability categories should be available in meteorological data sets to be used in modeling. Care should be taken to ensure that meteorological instruments are located to provide representative characterization of pollutant transport between sources and receptors of interest. The Regional Office will determine the appropriateness of the measurement locations. b. All site-specific data should be reduced to hourly averages. Table 9-3 lists the wind related parameters and the averaging time requirements.
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c. Solar Radiation Measurements. Total solar radiation should be measured with a reliable pyranometer, sited and operated in accordance with established on-site meteorological guidance. \66\ d. Temperature Measurements. Temperature measurements should be made at standard shelter height (2m) in accordance with established on-site meteorological guidance. \66\
e. Temperature Difference Measurements. Temperature difference (<bd/h-l-arr>all) measurements for use in estimating P-G stability categories using the solar radiation/delta-T (SRDT) methodology (see Stability Categories) should be obtained using two matched thermometers or a reliable thermocouple system to achieve adequate accuracy.
f. Siting, probe placement, and operation of <triangle>T systems should be based on guidance found in Chapter 3 of reference 66, and such guidance should be followed when obtaining vertical temperature gradient data for use in plume rise estimates or in determining the critical dividing streamline height.
g. Wind Measurements. For refined modeling applications in simple terrain situations, if a source has a stack below 100m, select the stack top height as the wind measurement height for characterization of plume dilution and transport. For sources with stacks extending above 100m, a 100m tower is suggested unless the stack top is significantly above 100m (i.e., <gr-thn-eq>200m). In cases with stack tops <gr-thn-eq>200m, remote sensing may be a feasible alternative. In some cases, collection of stack top wind speed may be impractical or incompatible with the input requirements of the model to be used. In such cases, the Regional Office should be consulted to determine the appropriate measurement height. h. For refined modeling applications in complex terrain, multiple level (typically three or more) measurements of wind speed and direction, temperature and turbulence (wind fluctuation statistics) are required. Such measurements should be obtained up to the representative plume height(s) of interest (i.e., the plume height(s) under those conditions important to the determination of the design concentration). The representative plume height(s) of interest should be determined using an appropriate complex terrain screening procedure (e.g., CTSCREEN) and should be documented in the monitoring/modeling protocol. The necessary meteorological measurements should be obtained from an appropriately sited meteorological tower augmented by SODAR if the representative plume height(s) of interest exceed 100m. The meteorological tower need not exceed the lesser of the representative plume height of interest (the highest plume height if there is more than one plume height of interest) or 100m.
i. In general, the wind speed used in determining plume rise is defined as the wind speed at stack top. j. Specifications for wind measuring instruments and systems are contained in the ``On-Site Meteorological Program Guidance for Regulatory Modeling Applications''.\66\ k. Stability Categories. The P-G stability categories, as originally defined, couple near-surface measurements of wind speed with subjectively determined insolation assessments based on hourly cloud cover and ceiling height observations. The wind speed measurements are made at or near 10m. The insolation rate is typically assessed using observations of cloud cover and ceiling height based on criteria outlined by Turner.\50\ It is recommended that the P-G stability category be estimated using the Turner method with site-specific wind speed measured at or near 10m and representative cloud cover and ceiling height. Implementation of the Turner method, as well as considerations in determining representativeness of cloud cover and ceiling height in cases for which site-specific cloud observations are unavailable, may be found in Section 6 of reference 66. In the absence of requisite data to implement the Turner method, the SRDT method or wind fluctuation statistics (i.e., the <greek-s><INF>E and <greek-s><INF>A methods) may be used.
l. The SRDT method, described in Section 6.4.4.2 of reference 66, is modified slightly from that published by Bowen et al. (1983) \136\ and has been evaluated with three on-site data bases.\137\ The two methods of stability classification which use wind fluctuation statistics, the <greek-s><INF>E and <greek-s><INF>A methods, are also described in detail in Section 6.4.4 of reference 66 (note applicable tables in Section 6). For additional information on the wind fluctuation methods, see references 68-72. m. Hours in the record having missing data should be treated according to an established data substitution protocol and after valid data retrieval requirements have been met. Such protocols are usually part of the approved monitoring program plan. Data substitution guidance is provided in Section 5.3 of reference 66. n. Meteorological Data Processors. The following meteorological preprocessors are recommended by EPA: RAMMET, PCRAMMET, STAR, PCSTAR, MPRM,\135\ and METPRO.\24\ RAMMET is the recommended meteorological preprocessor for use in applications employing hourly NWS data. The RAMMET format is the standard data input format used in sequential Gaussian models recommended by EPA. PCRAMMET \138\ is the PC equivalent of the mainframe version (RAMMET). STAR is the recommended preprocessor for use in applications employing joint frequency distributions (wind direction and wind speed by stability class) based on NWS data. PCSTAR is the PC equivalent of the mainframe version (STAR). MPRM is the recommended preprocessor for use in applications employing on-site meteorological data. The latest version (MPRM 1.3) has been configured to implement the SRDT method for estimating P-G stability categories. MPRM is a general purpose meteorological data preprocessor which supports regulatory models requiring RAMMET formatted data and STAR formatted data. In addition to on-site data, MPRM provides equivalent processing of NWS data. METPRO is the required meteorological data preprocessor for use with CTDMPLUS. All of the above mentioned data preprocessors are available for downloading from the SCRAM BBS.\19\
Table 9-3.--Averaging Times for Site-Specific Wind and Turbulence
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Measurements 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 Parameter Averaging time 
```
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
Surface wind speed (for use in stability 1-hr. 
 determinations). 
Transport direction....................... 1-hr. 
Dilution wind speed....................... 1-hr. 
Turbulence measurements (<greek-s><INF>E and 1-hr.\1\ 
 <greek-s><INF>A) for use in stability 
 determinations. 
```
\1\ To minimize meander effects in <greek-s><INF>A when wind conditions are light and/or variable, determine the hourly average <greek-s> value from four sequential 15-minute <greek-s>'s according to the following
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
```
 formula: 
```
[GRAPHIC] [TIFF OMITTED] TR12AU96.000
9.3.4 Treatment of Calms
9.3.4.1 Discussion
a. Treatment of calm or light and variable wind poses a special problem in model applications since Gaussian models assume that concentration is inversely proportional to wind speed. Furthermore, concentrations become unrealistically large when wind speeds less than 1 m/s are input to the model. A procedure has been developed for use with NWS data to prevent the occurrence of overly conservative concentration estimates during periods of calms. This procedure acknowledges that a Gaussian plume model does not apply during calm conditions and that our knowledge of plume behavior and wind patterns during these conditions does not, at present, permit the development of a better technique. Therefore, the procedure disregards hours which are identified as calm. The hour is treated as missing and a convention for handling missing hours is recommended.
b. Preprocessed meteorological data input to most Appendix A EPA models substitute a 1.00 m/s wind speed and the previous direction for the calm hour. The new treatment of calms in those models attempts to identify the original calm cases by checking for a 1.00 m/s wind speed coincident with a wind direction equal to the previous hour's wind direction. Such cases are then treated in a prescribed manner when estimating short term concentrations.
9.3.4.2 Recommendations
a. Hourly concentrations calculated with Gaussian models using calms should not be considered valid; the wind and concentration estimates for these hours should be disregarded and considered to be missing. Critical concentrations for 3-, 8-, and 24-hour averages should be calculated by dividing the sum of the hourly concentration for the period by the number of valid or non-missing hours. If the total number of valid hours is less than 18 for 24-hour averages, less than 6 for 8-hour averages or less than 3 for 3-hour averages, the total concentration should be divided by 18 for the 24-hour average, 6 for the 8-hour average and 3 for the 3-hour
[[Page 41858]]
average. For annual averages, the sum of all valid hourly concentrations is divided by the number of non-calm hours during the year. A post-processor computer program, CALMPRO \73\ has been prepared following these instructions and has been coded in RAM and ISC.
b. The recommendations in paragraph a of this section apply to the use of calms for short term averages and do not apply to the determination of long term averages using ``STAR'' data summaries. Calms should continue to be included in the preparation of ``STAR'' summaries. A treatment for calms and very light winds is built into the software that produces the ``STAR'' summaries. c. Stagnant conditions, including extended periods of calms, often produce high concentrations over wide areas for relatively long averaging periods. The standard short term Gaussian models are often not applicable to such situations. When stagnation conditions are of concern, other modeling techniques should be considered on a case-by-case basis (see also Section 8.2.10). d. When used in Gaussian models, measured on-site wind speeds of less than 1 m/s but higher than the response threshold of the instrument should be input as 1 m/s; the corresponding wind direction should also be input. Observations below the response threshold of the instrument are also set to 1 m/s but the wind direction from the previous hour is used. If the wind speed or direction can not be determined, that hour should be treated as missing and short term averages should then be calculated as described in paragraph a of this section.
10.0 Accuracy and Uncertainty of Models
10.1 Discussion
a. Increasing reliance has been placed on concentration estimates from models as the primary basis for regulatory decisions concerning source permits and emission control requirements. In many situations, such as review of a proposed source, no practical alternative exists. Therefore, there is an obvious need to know how accurate models really are and how any uncertainty in the estimates affects regulatory decisions. EPA recognizes the need for incorporating such information and has sponsored workshops 11 74 on model accuracy, the possible ways to quantify accuracy, and on considerations in the incorporation of model accuracy and uncertainty in the regulatory process. The Second (EPA) Conference on Air Quality Modeling, August 1982,75 was devoted to that subject.
10.1.1 Overview of Model Uncertainty
a. Dispersion models generally attempt to estimate concentrations at specific sites that really represent an ensemble average of numerous repetitions of the same event. The event is characterized by measured or ``known'' conditions that are input to the models, e.g., wind speed, mixed layer height, surface heat flux, emission characteristics, etc. However, in addition to the known conditions, there are unmeasured or unknown variations in the conditions of this event, e.g., unresolved details of the atmospheric flow such as the turbulent velocity field. These unknown conditions may vary among repetitions of the event. As a result, deviations in observed concentrations from their ensemble average, and from the concentrations estimated by the model, are likely to occur even though the known conditions are fixed. Even with a perfect model that predicts the correct ensemble average, there are likely to be deviations from the observed concentrations in individual repetitions of the event, due to variations in the unknown conditions. The statistics of these concentration residuals are termed ``inherent'' uncertainty. Available evidence suggests that this source of uncertainty alone may be responsible for a typical range of variation in concentrations of as much as <plus-minus>50 percent.\76\
b. Moreover, there is ``reducible'' uncertainty \77\ associated with the model and its input conditions; neither models nor data bases are perfect. Reducible uncertainties are caused by: (1) Uncertainties in the input values of the known conditions--emission characteristics and meteorological data; (2) errors in the measured concentrations which are used to compute the concentration residuals; and (3) inadequate model physics and formulation. The ``reducible'' uncertainties can be minimized through better (more accurate and more representative) measurements and better model physics.
c. To use the terminology correctly, reference to model accuracy should be limited to that portion of reducible uncertainty which deals with the physics and the formulation of the model. The accuracy of the model is normally determined by an evaluation procedure which involves the comparison of model concentration estimates with measured air quality data.\78\ The statement of accuracy is based on statistical tests or performance measures such as bias, noise, correlation, etc.\11\ However, information that allows a distinction between contributions of the various elements of inherent and reducible uncertainty is only now beginning to emerge. As a result most discussions of the accuracy of models make no quantitative distinction between (1) Limitations of the model versus (2) limitations of the data base and of knowledge concerning atmospheric variability. The reader should be aware that statements on model accuracy and uncertainty may imply the need for improvements in model performance that even the ``perfect'' model could not satisfy.
10.1.2 Studies of Model Accuracy
a. A number of studies 79 80 have been conducted to examine model accuracy, particularly with respect to the reliability of short-term concentrations required for ambient standard and increment evaluations. The results of these studies are not surprising. Basically, they confirm what leading atmospheric scientists have said for some time: (1) Models are more reliable for estimating longer time-averaged concentrations than for estimating short-term concentrations at specific locations; and (2) the models are reasonably reliable in estimating the magnitude of highest concentrations occurring sometime, somewhere within an area. For example, errors in highest estimated concentrations of <plus-minus>10 to 40 percent are found to be typical,\81\ i.e., certainly well within the often quoted factor-of-two accuracy that has long been recognized for these models. However, estimates of concentrations that occur at a specific time and site, are poorly correlated with actually observed concentrations and are much less reliable.
b. As noted in paragraph a of this section, poor correlations between paired concentrations at fixed stations may be due to ``reducible'' uncertainties in knowledge of the precise plume location and to unquantified inherent uncertainties. For example, Pasquill \82\ estimates that, apart from data input errors, maximum ground-level concentrations at a given hour for a point source in flat terrain could be in error by 50 percent due to these uncertainties. Uncertainty of five to 10 degrees in the measured wind direction, which transports the plume, can result in concentration errors of 20 to 70 percent for a particular time and location, depending on stability and station location. Such uncertainties do not indicate that an estimated concentration does not occur, only that the precise time and locations are in doubt.
10.1.3 Use of Uncertainty in Decision-Making
a. The accuracy of model estimates varies with the model used, the type of application, and site-specific characteristics. Thus, it is desirable to quantify the accuracy or uncertainty associated with concentration estimates used in decision-making. Communications between modelers and decision-makers must be fostered and further developed. Communications concerning concentration estimates currently exist in most cases, but the communications dealing with the accuracy of models and its meaning to the decision-maker are limited by the lack of a technical basis for quantifying and directly including uncertainty in decisions. Procedures for quantifying and interpreting uncertainty in the practical application of such concepts are only beginning to evolve; much study is still required.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
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pollution control decision. Given a range of possible outcomes, it is easiest and tends to ensure consistency if the decision-maker confines his judgment to use of the ``best estimate'' provided by the modeler (i.e., the design concentration estimated by a model recommended in the Guideline or an alternate model of known accuracy). This is an indication of the practical limitations imposed by current abilities of the technical community. c. To improve the basis for decision-making, EPA has developed and is continuing to study procedures for determining the accuracy of models, quantifying the uncertainty, and expressing confidence levels in decisions that are made concerning emissions controls.83 84 However, work in this area involves ``breaking new ground'' with slow and sporadic progress likely. As a result, it may be necessary to continue using the ``best estimate'' until sufficient technical progress has been made to meaningfully implement such concepts dealing with uncertainty.
10.1.4 Evaluation of Models
a. A number of actions are being taken to ensure that the best model is used correctly for each regulatory application and that a model is not arbitrarily imposed. First, the Guideline clearly recommends the most appropriate model be used in each case. Preferred models, based on a number of factors, are identified for many uses. General guidance on using alternatives to the preferred models is also provided. Second, all the models in eight categories (i.e., rural, urban, industrial complex, reactive pollutants, mobile source, complex terrain, visibility and long range transport) that are candidates for inclusion in the Guideline are being subjected to a systematic performance evaluation and a peer scientific review.\85\ The same data bases are being used to evaluate all models within each of eight categories. Statistical performance measures, including measures of difference (or residuals) such as bias, variance of difference and gross variability of the difference, and measures of correlation such as time, space, and time and space combined as recommended by the AMS Woods Hole Workshop,\11\ are being followed. The results of the scientific review are being incorporated in the Guideline and will be the basis for future revision.12 13 Third, more specific information has been provided for justifying the site specific use of alternative models in the documents ``Interim Procedures for Evaluating Air Quality Models'',\15\ and the ``Protocol for Determining the Best Performing Model''.\17\ Together these documents provide methods that allow a judgment to be made as to what models are most appropriate for a specific application. For the present, performance and the theoretical evaluation of models are being used as an indirect means to quantify one element of uncertainty in air pollution regulatory decisions.
b. In addition to performance evaluation of models, sensitivity analyses are encouraged since they can provide additional information on the effect of inaccuracies in the data bases and on the uncertainty in model estimates. Sensitivity analyses can aid in determining the effect of inaccuracies of variations or uncertainties in the data bases on the range of likely concentrations. Such information may be used to determine source impact and to evaluate control strategies. Where possible, information from such sensitivity analyses should be made available to the decision-maker with an appropriate interpretation of the effect on the critical concentrations.
10.2 Recommendations
a. No specific guidance on the consideration of model uncertainty in decision-making is being given at this time. There is incomplete technical information on measures of model uncertainty that are most relevant to the decision-maker. It is not clear how a decisionmaker could use such information, particularly given limitations of the Clean Air Act. As procedures for considering uncertainty develop and become implementable, this guidance will be changed and expanded. For the present, continued use of the ``best estimate'' is acceptable and is consistent with Clean Air Act requirements.
11.0 Regulatory Application of Models
11.1 Discussion
a. Procedures with respect to the review and analysis of air quality modeling and data analyses in support of SIP revisions, PSD permitting or other regulatory requirements need a certain amount of standardization to ensure consistency in the depth and comprehensiveness of both the review and the analysis itself. This section recommends procedures that permit some degree of standardization while at the same time allowing the flexibility needed to assure the technically best analysis for each regulatory application.
b. Dispersion model estimates, especially with the support of measured air quality data, are the preferred basis for air quality demonstrations. Nevertheless, there are instances where the performance of recommended dispersion modeling techniques, by comparison with observed air quality data, may be shown to be less than acceptable. Also, there may be no recommended modeling procedure suitable for the situation. In these instances, emission limitations may be established solely on the basis of observed air quality data as would be applied to a modeling analysis. The same care should be given to the analyses of the air quality data as would be applied to a modeling analysis. c. The current NAAQS for SO<INF>2 and CO are both stated in terms of a concentration not to be exceeded more than once a year. There is only an annual standard for NO<INF>2 and a quarterly standard for Pb. The PM-10 and ozone standards permit the exceedance of a concentration on an average of not more than once a year; the convention is to average over a 3-year period.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
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the existing or permitted sources in addition to the one currently under consideration. This would permit the use of newly acquired data or improved modeling techniques if such have become available since the last source was permitted. When remodeling, the worst case used in the previous modeling analysis should be one set of conditions modeled in the new analysis. All sources should be modeled for each set of meteorological conditions selected and for all receptor sites used in the previous applications as well as new sites specific to the new source.
11.2.2 Use of Measured Data in Lieu of Model Estimates
a. Modeling is the preferred method for determining emission limitations for both new and existing sources. When a preferred model is available, model results alone (including background) are sufficient. Monitoring will normally not be accepted as the sole basis for emission limitation determination in flat terrain areas. In some instances when the modeling technique available is only a screening technique, the addition of air quality data to the analysis may lend credence to model results. b. There are circumstances where there is no applicable model, and measured data may need to be used. Examples of such situations are: (1) complex terrain locations; (2) land/water interface areas; and (3) urban locations with a large fraction of particulate emissions from nontraditional sources. However, only in the case of an existing source should monitoring data alone be a basis for emission limits. In addition, the following items should be considered prior to the acceptance of the measured data: i. Does a monitoring network exist for the pollutants and averaging times of concern?
ii. Has the monitoring network been designed to locate points of maximum concentration?
iii. Do the monitoring network and the data reduction and storage procedures meet EPA monitoring and quality assurance requirements?
iv. Do the data set and the analysis allow impact of the most important individual sources to be identified if more than one source or emission point is involved?
v. Is at least one full year of valid ambient data available? vi. Can it be demonstrated through the comparison of monitored data with model results that available models are not applicable? c. The number of monitors required is a function of the problem being considered. The source configuration, terrain configuration, and meteorological variations all have an impact on number and placement of monitors. Decisions can only be made on a case-by-case basis. The Interim Procedures for Evaluating Air Quality Models \15\ should be used in establishing criteria for demonstrating that a model is not applicable.
d. Sources should obtain approval from the Regional Office or reviewing authority for the monitoring network prior to the start of monitoring. A monitoring protocol agreed to by all concerned parties is highly desirable. The design of the network, the number, type and location of the monitors, the sampling period, averaging time as well as the need for meteorological monitoring or the use of mobile sampling or plume tracking techniques, should all be specified in the protocol and agreed upon prior to start-up of the network.
11.2.3 Emission Limits
11.2.3.1 Design Concentrations
a. Emission limits should be based on concentration estimates for the averaging time that results in the most stringent control requirements. The concentration used in specifying emission limits is called the design value or design concentration and is a sum of the concentration contributed by the source and the background concentration.
b. To determine the averaging time for the design value, the most restrictive National Ambient Air Quality Standard (NAAQS) should be identified by calculating, for each averaging time, the ratio of the applicable NAAQS (S)- background (B) to the predicted concentration (P) (i.e., (S-B)/P). The averaging time with the lowest ratio identifies the most restrictive standard. If the annual average is the most restrictive, the highest estimated annual average concentration from one or a number of years of data is the design value. When short term standards are most restrictive, it may be necessary to consider a broader range of concentrations than the highest value. For example, for pollutants such as SO<INF>2, the highest, second-highest concentration is the design value. For pollutants with statistically based NAAQS, the design value is found by determining the more restrictive of: (1) the short-term concentration that is not expected to be exceeded more than once per year over the period specified in the standard, or (2) the long-term concentration that is not expected to exceed the long-term NAAQS. Determination of design values for PM-10 is presented in more detail in the ``PM-10 SIP Development Guideline''.\108\ c. When the highest, second-highest concentration is used in assessing potential violations of a short term NAAQS, criteria that are identified in ``Guideline for Interpretation of Air Quality Standards''88 should be followed. This guidance specifies that a violation of a short term standard occurs at a site when the standard is exceeded a second time. Thus, emission limits that protect standards for averaging times of 24 hours or less are appropriately based on the highest, second-highest estimated concentration plus a background concentration which can reasonably be assumed to occur with the concentration.
11.2.3.2 NAAQS Analyses for New or Modified Sources
a. For new or modified sources predicted to have a significant ambient impact \63\ and to be located in areas designated attainment or unclassifiable for the SO<INF>2, Pb, NO<INF>2, or CO NAAQS, the demonstration as to whether the source will cause or contribute to an air quality violation should be based on: (1) the highest estimated annual average concentration determined from annual averages of individual years; or (2) the highest, second-highest estimated concentration for averaging times of 24-hours or less; and (3) the significance of the spatial and temporal contribution to any modeled violation. For Pb, the highest estimated concentration based on an individual calendar quarter averaging period should be used. Background concentrations should be added to the estimated impact of the source. The most restrictive standard should be used in all cases to assess the threat of an air quality violation. For new or modified sources predicted to have a significant ambient impact \63\ in areas designated attainment or unclassifiable for the PM-10 NAAQS, the demonstration of whether or not the source will cause or contribute to an air quality violation should be based on sufficient data to show whether: (1) the projected 24-hour average concentrations will exceed the 24-hour NAAQS more than once per year, on average; (2) the expected (i.e., average) annual mean concentration will exceed the annual NAAQS; and (3) the source contributes significantly, in a temporal and spatial sense, to any modeled violation.
11.2.3.3 PSD Air Quality Increments and Impacts
a. The allowable PSD increments for criteria pollutants are established by regulation and cited in Sec. 51.166. These maximum allowable increases in pollutant concentrations may be exceeded once per year at each site, except for the annual increment that may not be exceeded. The highest, second-highest increase in estimated concentrations for the short term averages as determined by a model should be less than or equal to the permitted increment. The modeled annual averages should not exceed the increment. b. Screening techniques defined in Sections 4.0 and 5.0 can sometimes be used to estimate short term incremental concentrations for the first new source that triggers the baseline in a given area. However, when multiple increment-consuming sources are involved in the calculation, the use of a refined model with at least 1 year of on-site or 5 years of off-site NWS data is normally required. In such cases, sequential modeling must demonstrate that the allowable increments are not exceeded temporally and spatially, i.e., for all receptors for each time period throughout the year(s) (time period means the appropriate PSD averaging time, e.g., 3-hour, 24-hour, etc.).
c. The PSD regulations require an estimation of the SO<INF>2, particulate matter, and NO<INF>2 impact on any Class I area. Normally, Gaussian models should not be applied at distances greater than can be accommodated by the steady state assumptions inherent in such models. The maximum distance for refined Gaussian model application for regulatory purposes is generally considered to be 50km. Beyond the 50km range, screening techniques may be used to determine if more refined modeling is needed. If refined models are needed, long range transport models should be considered in accordance with Section 7.2.6. As previously noted in Sections 3.0 and 7.0, the need to involve the Federal Land Manager in decisions on potential air quality impacts,
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particularly in relation to PSD Class I areas, cannot be overemphasized.
11.2.3.4 Emissions Trading Policy (Bubbles)
a. EPA's final Emissions Trading Policy, commonly referred to as the ``bubble policy,'' was published in the Federal Register in 1986.89 Principles contained in the policy should be used to evaluate ambient impacts of emission trading activities. b. Emission increases and decreases within the bubble should result in ambient air quality equivalence. Two levels of analysis are defined for establishing this equivalence. In a Level I analysis the source configuration and setting must meet certain limitations (defined in the policy) that ensure ambient equivalence; no modeling is required. In a Level II analysis a modeling demonstration of ambient equivalence is required but only the sources involved in the emissions trade are modeled. The resulting ambient estimates of net increases/decreases are compared to a set of significance levels to determine if the bubble can be approved. A Level II analysis requires the use of a refined model and the most recent readily available full year of representative meteorological data. Sequential modeling must demonstrate that the significance levels are met temporally and spatially, i.e., for all receptors for each time period throughout the year (time period means the appropriate NAAQS averaging time, e.g., 3-hour, 24-hour, etc.). c. For those bubbles that cannot meet the Level I or Level II requirements, the Emissions Trading Policy allows for a Level III analysis. A Level III analysis, from a modeling standpoint, is generally equivalent to the requirements for a standard SIP revision where all sources (and background) are considered and the estimates are compared to the NAAQS as in Section 11.2.3.2. d. The Emissions Trading Policy allows States to adopt generic regulations for processing bubbles. The modeling procedures recommended in the Guideline apply to such generic regulations. However, an added requirement is that the modeling procedures contained in any generic regulation must be replicable such that there is no doubt as to how each individual bubble will be modeled. In general this means that the models, the data bases and the procedures for applying the model must be defined in the regulation. The consequences of the replicability requirement are that bubbles for sources located in complex terrain and certain industrial sources where judgments must be made on source characterization cannot be handled generically.
12.0 References g h

\g\ Documents not available in the open literature or from the National Technical Information Service (NTIS) have been placed in Docket No. A-80-46 or A-88-04. Item Numbers for documents placed in the Docket are shown at the end of the reference. \h\ Some EPA references, e.g., model user's guides, etc., are periodically revised. Users are referred to the SCRAM BBS19 to download updates or addenda; see Section A.0 of this appendix.
1. Code of Federal Regulations (Title 40, Part 51): Protection of the Environment; Requirements for Preparation, Adoption, and Submittal of Implementation Plans.
2. Environmental Protection Agency, 1977. Guidelines for the Regional Evaluation of State and Local New Source Review Program. EPA Publication No. EPA-450/2-77-027. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB-275053)
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4. Environmental Protection Agency, 1981. Guideline for Fluid Modeling of Atmospheric Diffusion. EPA Publication No. EPA-600/8-81-
5. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 81-201410)
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9. Londergan, R.J., D.H. Minott, D.J. Wackter, T. Kincaid and D. Bonitata, 1982. Evaluation of Rural Air Quality Simulation Models. EPA Publication No. EPA-450/4-82-020. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 83-182758)
10. Londergan, R.J., D.H. Minott, D.J. Wackter and R.R. Fizz,
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27. User's Guide to the Complex Terrain Dispersion Model Plus Algorithms for Unstable Situations (CTDMPLUS) Volume 1; Model Description and User Instructions. EPA Publication No. EPA-600/8-89-
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29. Mills, M.T., R.J. Paine, E.A. Insley and B.A. Egan, 1987. The Complex Terrain Dispersion Model Terrain Preprocessor System-- User's Guide and Program Description. EPA Publication No. EPA-600/8- 88-003. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 88-162094)
30. Paine, R.J., 1987. User's Guide to the CTDM Meteorological Preprocessor Program. EPA Publication No. EPA-600/8-88-004. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 88-162102)
31. Perry, S.G., D.J. Burns and A.J. Cimorelli, 1990. User's Guide to CTDMPLUS: Volume 2. The Screening Mode (CTSCREEN). EPA Publication No. EPA-600/8-90-087. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 91-136564)
32. Burt, E.W., 1977. Valley Model User's Guide. EPA Publication No. EPA-450/2-77-018. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB-274054)
33. Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for the SHORTZ and [[Page 41862]]
 LONGZ Computer Programs, Volumes I and II. EPA Publication No. EPA- 903/9-82-004a and b. U.S. Environmental Protection Agency Region III, Philadelphia, PA. (NTIS Nos. PB 83-146092 and PB 83-146100) 28. Environmental Protection Agency, 1990. User's Guide for the Urban Airshed Model, Volume I-VIII. EPA Publication Nos. EPA-450/4- 90-007a-c, d(R), e-g; EPA-454/B-93-004, respectively. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS Nos. PB 91-131227, PB 91-131235, PB 91-131243, PB 93-122380, PB 91- 131268, PB 92-145382, and PB 92-224849, respectively, for Vols. IVII)
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 69. Mitchell, Jr., A.E. and K.O. Timbre, 1979. Atmospheric Stability Class from Horizontal Wind Fluctuation. Presented at 72nd Annual Meeting of Air Pollution Control Association, Cincinnati, OH; June 24-29, 1979. (Docket No. A-80-46, II-P-9) 70. Nuclear Regulatory Commission, 1972. Meteorological Programs in Support of Nuclear Power Plants. Regulatory Guide 1.23 (DRAFT). U.S. Nuclear Regulatory Commission, Office of Standards Development, Washington, DC. (Docket No. A-80-46, II-P-11) 71. Smedman-Hogstrom, A. and V. Hogstrom, 1978. A Practical Method for Determining Wind Frequency Distributions for the Lowest 200m from Routine Meteorological Data. Journal of Applied Meteorology, 17(7): 942-954.
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 73. Environmental Protection Agency, 1984. Calms Processor (CALMPRO) User's Guide. EPA Publication No. EPA-901/9-84-001. U.S. Environmental Protection Agency, Region I, Boston, MA. (NTIS No. PB 84-229467)
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 75. Environmental Protection Agency, 1981. Proceedings of the Second Conference on Air Quality Modeling, Washington, DC. U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket No. A-80-46, II-M-16)
 76. Hanna, S.R., 1982. Natural Variability of Observed Hourly SO<INF>2 and CO Concentrations in St. Louis. Atmospheric Environment, 16(6): 1435-1440.
 77. Fox, D.G., 1983. Uncertainty in Air Quality Modeling. Bulletin of the American Meteorological Society, 65(1): 27-36. 78. Bowne, N.E., 1981. Validation and Performance Criteria for Air Quality Models. Appendix F in Air Quality Modeling and the Clean Air Act: Recommendations to EPA on Dispersion Modeling for Regulatory Applications. American Meteorological Society, Boston, MA; pp. 159-171. (Docket No. A-80-46, II-A-106) 79. Bowne, N.E. and R.J. Londergan, 1983. Overview, Results, and Conclusions for the EPRI Plume Model Validation and Development Project: Plains Site. EPRI EA-3074. Electric Power Research Institute, Palo Alto, CA.
 80. Moore, G.E., T.E. Stoeckenius and D.A. Stewart, 1982. A Survey of Statistical Measures of Model Performance and Accuracy for Several Air Quality Models. EPA Publication No. EPA-450/4-83-001. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 83-260810)
 81. Rhoads, R.G., 1981. Accuracy of Air Quality Models. Staff Report. U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket No. A-80-46, II-G-6)
 82. Pasquill, F., 1974. Atmospheric Diffusion, 2nd Edition. John Wiley and Sons, New York, NY; 479 pp.
 83. Austin, B.S., T.E. Stoeckenius, M.C. Dudik and T.S. Stocking, 1988. User's Guide to the Expected Exceedances System. Systems Applications, Inc., San Rafael, CA. Prepared under Contract No. 68-02-4352 Option I for the U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket No. A-88-04, II-I-3) 84. Thrall, A.D., T.E. Stoeckenius and C.S. Burton, 1985. A Method for Calculating Dispersion Modeling Uncertainty Applied to the Regulation of an Emission Source. Systems Applications, Inc., San Rafael, CA. Prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket No. A-80-46, IV-G-1) 85. Environmental Protection Agency, 1981. Plan for Evaluating Model Performance. Staff Report. U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket No. A-80-46, II-G-6) 86. Environmental Protection Agency, 1979. Guideline for the Interpretation of Ozone Air Quality Standards. EPA Publication No. EPA 450/4-79-003. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB-292271) 87. Environmental Protection Agency, 1974. Guidelines for Air Quality Maintenance Planning and Analysis, Volume 12: Applying Atmospheric Simulation Models to Air Quality Maintenance Areas. EPA Publication No. EPA-450/4-74-013. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB-237750) 88. Environmental Protection Agency, 1977. Guidelines for Interpretation of Air Quality Standards (Revised). OAQPS No. 1.2- 008. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 81-196420)
 89. Environmental Protection Agency, 1986. Emissions Trading Policy Statement; General Principles for Creation, Banking, and Use of Emission Reduction Credits. Federal Register, 51: 43814-43860. 90. Environmental Research and Technology, 1987. User's Guide to the Rough Terrain Diffusion Model (RTDM), Rev. 3.20. ERT Document No. P-D535-585. Environmental Research and Technology, Inc., Concord, MA. (NTIS No. PB 88-171467)
 91. Burns, D.J., S.G. Perry and A.J. Cimorelli, 1991. An Advanced Screening Model for Complex Terrain Applications. Paper presented at the 7th Joint Conference on Applications of Air Pollution Meteorology (cosponsored by the American Meteorological Society and the Air & Waste Management Association), January 13-18, 1991, New Orleans, LA.
 92. Perry, S.G., 1992. CTDMPLUS: A Dispersion Model for Sources near Complex Topography. Part I: Technical Formulations. Journal of Applied Meteorology, 31(7): 633-645.
 93. Paumier, J.O., S.G. Perry and D.J. Burns, 1992. CTDMPLUS: A Dispersion Model for Sources near Complex Topography. Part II: Performance Characteristics. Journal of Applied Meteorology, 31(7): 646-660.
 94. Environmental Protection Agency, 1986. Evaluation of Mobile Source Air Quality Simulation Models. EPA Publication No. EPA-450/4- 86-002. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 86-167293)
 95. Shannon, J.D., 1987. Mobile Source Modeling Review. A report prepared under a
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 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 ShortTerm Long-Range Transport Models, Volumes I and II. EPA Publication Nos. EPA-450/4-86-016a and b. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS Nos. PB 87-142337 and PB 87- 142345)
 100. McNider, R.T., 1987. Review of Short-Term Long-Range Models. A report prepared under a cooperative agreement with the Environmental Protection Agency. (Docket No. A-88-04, II-I-10) 101. Lamb, R.G., 1983. A Regional Scale (1,000km) Model of Photochemical Air Pollution, Part I--Theoretical Formulation, Part II--Input Processor Network Design, and Part III--Tests of Numerical Algorithms. EPA Publication Nos. EPA-600/3-83-035, EPA-600/3-84-085, and EPA-600/3-85-037. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS Nos. PB 83-207688, PB 84-232651, and PB 85- 203818, respectively)
 102. Young, J.O., M. Aissa, T.L. Boehm et al., 1989. Development of the Regional Oxidant Model, Version 2.1. EPA Publication No. EPA- 600/3-89-044. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 89-194252) 103. Environmental Protection Agency, 1991. The Regional Oxidant Model (ROM) User's Guide. Part 1: The ROM Preprocessors. EPA Publication No. EPA-600/8-90-083a (NTIS No. PB 91-171926); Part 2: The ROM Processor Network. EPA Publication No. EPA-600/8-90-083b (NTIS No. PB 91-171934); Part 3: The Core Model. EPA Publication No. EPA-600/8-90-083c (NTIS No. PB 91-171942). U.S. Environmental Protection Agency, Research Triangle Park, NC. 104. Chang, J.S., R.A. Brost, I.S.A. Isaksen, S. Madronich, P. Middleton, W.R. Stockwell and C.J. Waleck, 1987. A Three-Dimensional Eulerian Acid Deposition Model: Physical Concepts and Formulation. Journal of Geophysical Research, 92(D12): 14681-14700. 105. Environmental Protection Agency, 1987. Protocol for Applying and Validating the CMB. U.S. Environmental Protection Agency. EPA Publication No. EPA-450/4-87-010. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 87- 206496)
 106. Environmental Protection Agency, 1987. Protocol for Reconciling Differences Among Receptor and Dispersion Models. EPA Publication No. EPA-450/4-87-008. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 87-206504) 107. Environmental Protection Agency, 1988. Chemical Mass Balance Model Diagnostics. EPA Publication No. EPA-450/4-88-005. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 88-208319)
 108. Environmental Protection Agency, 1987. PM-10 SIP Development Guideline. EPA Publication No. EPA-450/2-86-001. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 87-206488)
 109. Environmental Protection Agency, 1987. Example Modeling to Illustrate SIP Development for The PM-10 NAAQS. EPA Publication No. EPA-450/4-87-012. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 87-205191) 110. Sestak, M.L. and A.R. Riebau, 1988. SASEM Simple Approach Smoke Estimation Model. U.S. Bureau of Land Management, Technical Note 382. BLM/YA/PT-88/003 + 7000. Available from Printed Materials Distribution Section, BLM Service Center (SC-658B), Denver, CO 80225-0047. (NTIS No. PB 90-185653)
 111. Environmental Protection Agency, 1992. A Modeling Protocol for Applying Mesopuff II to Long Range Transport Problems. EPA Publication No. EPA-454/R-92-021. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 112. DiCristofaro, D.C. and S.R. Hanna, 1989. The Offshore and Coastal Dispersion (OCD) Model, Volume I: User's Guide, Volume II: Appendices. Version 4 Prepared for Minerals Management Services by Sigma Research Corporation, Westford, MA. (Docket No. A-88-04, II-DA -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. FAAEE -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-
 1. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 27711. [[Page 41865]]
 127. Environmental Protection Agency, 1992. A Tiered Modeling Approach for Assessing the Risks Due to Sources of Hazardous Air Pollutants. EPA Publication No. EPA-450/4-92-001. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 92- 164748)
 128. Environmental Protection Agency, 1992. Toxic Modeling System Short-term (TOXST) User's Guide. EPA Publication No. EPA-450/ 4-92-002. Environmental Protection Agency, Research Triangle Park, NC.
 129. Environmental Protection Agency, 1992. Toxic Modeling System Long-term (TOXLT) User's Guide. EPA Publication No. EPA-450/ 4-92-003. Environmental Protection Agency, Research Triangle Park, NC.
 130. Environmental Protection Agency, 1989. User's Guide for the DEGADIS 2.1 Dense Gas Dispersion Model. EPA Publication No. EPA-450/ 4-89-019. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 90-213893)
 131. Environmental Protection Agency, 1993. Guidance on the Application of Refined Models for Air Toxics Releases. EPA Publication No. EPA-450/4-91-007. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 91-190983) 132. Perry, R.H. and Chilton, C.H., 1973. Chemical Engineers' Handbook, Fifth Edition, McGraw-Hill Book Company, New York, NY. 133. Environmental Protection Agency, 1988. User's Guide to SDM--A Shoreline Dispersion Model. EPA Publication No. EPA-450/4-88- 017. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 89-164305)
 134. Environmental Protection Agency, 1987. Analysis and Evaluation of Statistical Coastal Fumigation Models. EPA Publication No. EPA-450/4-87-002. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 87-175519) 135. Environmental Protection Agency, 1996. Meteorological Processor for Regulatory Models (MPRM) User's Guide. EPA Publication No. EPA-454/B-96-002. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 96-180518) 136. Bowen, B.M., J.M. Dewart and A.I. Chen, 1983. Stability Class Determination: A Comparison for One Site. Proceedings, Sixth Symposium on Turbulence and Diffusion. American Meteorological Society, Boston, MA; pp. 211-214. (Docket No. A-92-65, II-A-7) 137. Environmental Protection Agency, 1993. An Evaluation of a Solar Radiation/Delta-T (SRDT) Method for Estimating PasquillGifford (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
 
 \i\ The documents listed here are major sources of supplemental information on the theory and application of mathematical air quality models.
 
 American Meteorological Society, 1971-1985. Symposia on Turbulence, Diffusion, and Air Pollution (1st-7th), Boston, MA. American Meteorological Society, 1977-1984. Joint Conferences on Applications of Air Pollution Meteorology (1st-4th). Sponsored by the American Meteorological Society and the Air Pollution Control Association, Boston, MA.
 American Meteorological Society, 1978. Accuracy of Dispersion Models. Bulletin of the American Meteorological Society, 59(8): 1025-1026.
 American Meteorological Society, 1981. Air Quality Modeling and the Clean Air Act: Recommendations to EPA on Dispersion Modeling for Regulatory Applications, Boston, MA.
 Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission Critical Review Series, Oak Ridge National Laboratory, Oak Ridge, TN.
 Dickerson, W.H. and P.H. Gudiksen, 1980. ASCOT FY 79 Program Report. Report UCRL--52899, ASCOT 80-1. Lawrence Livermore National Laboratory, Livermore, CA.
 Drake, R.L. and S.M. Barrager, 1979. Mathematical Models for Atmospheric Pollutants. EPRI EA-1131. Electric Power Research Institute, Palo Alto, CA.
 Environmental Protection Agency, 1978. Workbook for Comparison of Air Quality Models. EPA Publication No. EPA-450/2-78-028a and b. U.S. Environmental Protection Agency, Research Triangle Park, NC. Fox, D.G., and J.E. Fairobent, 1981. NCAQ Panel Examines Uses and Limitations of Air Quality Models. Bulletin of the American Meteorological Society, 62(2): 218-221. Gifford, F.A., 1976. Turbulent Diffusion Typing Schemes: A Review. Nuclear Safety, 17(1): 68-86.
 Gudiksen, P.H., and M.H. Dickerson, Eds., Executive Summary: Atmospheric Studies in Complex Terrain Technical Progress Report FY- 1979 Through FY-1983. Lawrence Livermore National Laboratory, Livermore, CA. (Docket Reference No. II-I-103). Hales, J.M., 1976. Tall Stacks and the Atmospheric Environment. EPA Publication No. EPA-450/3-76-007. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Hanna, S.R., G.A. Briggs, J. Deardorff, B.A. Egan, G.A. Gifford and F. Pasquill, 1977. AMS Workshop on Stability Classification Schemes And Sigma Curves--Summary of Recommendations. Bulletin of the American Meteorological Society, 58(12): 1305-1309. Hanna, S.R., G.A. Briggs and R.P. Hosker, Jr., 1982. Handbook on Atmospheric Diffusion. Technical Information Center, U.S. Department of Energy, Washington, D.C.
 Haugen, D.A., Workshop Coordinator, 1975. Lectures on Air Pollution and Environmental Impact Analyses. Sponsored by the American Meteorological Society, Boston, MA. Hoffnagle, G.F., M.E. Smith, T.V. Crawford and T.J. Lockhart, 1981. On-site Meteorological Instrumentation Requirements to Characterize Diffusion from Point Sources--A Workshop, 15-17 January 1980, Raleigh, NC. Bulletin of the American Meteorological Society, 62(2): 255-261.
 McMahon, R.A. and P.J. Denison, 1979. Empirical Atmospheric Deposition Parameters--A Survey. Atmospheric Environment, 13: 571- 585.
 McRae, G.J., J.A. Leone and J.H. Seinfeld, 1983. Evaluation of Chemical Reaction Mechanisms for Photochemical Smog. Part I: Mechanism Descriptions and Documentation. EPA Publication No. EPA- 600/3/83-086. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Pasquill, F. and F.B. Smith, 1983. Atmospheric Diffusion, 3rd Edition. Ellis Horwood Limited, Chichester, West Sussex, England, 438 pp.
 Randerson, D., Ed., 1984. Atmospheric Science and Power Production. DOE/TIC 2760l. Office of Scientific and Technical Information, U.S. Department of Energy, Oak Ridge, TN. Roberts, J.J., Ed., 1977. Report to U.S. EPA of the Specialists' Conference on the EPA Modeling Guideline. U.S. Environmental Protection Agency, Research Triangle Park, NC. Smith, M.E., Ed., 1973. Recommended Guide for the Prediction of the Dispersion of Airborne Effluents. The American Society of Mechanical Engineers, New York, NY.
 Stern, A.C., Ed., 1976. Air Pollution, Third Edition, Volume I: Air Pollutants, Their Transformation and Transport. Academic Press, New York, NY.
 Turner, D.B., 1979. Atmospheric Dispersion Modeling: A Critical Review. Journal of the Air Pollution Control Association, 29(5): 502-519.
 Whiteman, C.D. and K.J. Allwine, 1982. Green River Ambient Model Assessment Program FY-1982 Progress Report. PNL-4520. Pacific Northwest Laboratory, Richland, WA.
 14.0 Glossary of Terms
 Air quality. Ambient pollutant concentrations and their temporal and spatial distribution.
 Algorithm. A specific mathematical calculation procedure. A model may contain several algorithms.
 Background. Ambient pollutant concentrations due to: (1) Natural sources;
 (2) Nearby sources other than the one(s) currently under consideration; and
 (3) Unidentified sources.
 Calibrate. An objective adjustment using measured air quality data (e.g., an adjustment based on least-squares linear regression). Calm. For purposes of air quality modeling, calm is used to define the situation when the wind is indeterminate with regard to speed or direction.
 Complex terrain. Terrain exceeding the height of the stack being modeled.
 Computer code. A set of statements that comprise a computer program.
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 Evaluate. To appraise the performance and accuracy of a model based on a comparison of concentration estimates with observed air quality data.
 Fluid modeling. Modeling conducted in a wind tunnel or water channel to quantitatively evaluate the influence of buildings and/or terrain on pollutant concentrations.
 Fugitive dust. Dust discharged to the atmosphere in an unconfined flow stream such as that from unpaved roads, storage piles and heavy construction operations. Model. A quantitative or mathematical representation or simulation which attempts to describe the characteristics or relationships of physical events.
 Preferred model. A refined model that is recommended for a specific type of regulatory application. Receptor. A location at which ambient air quality is measured or estimated.
 Receptor models. Procedures that examine an ambient monitor sample of particulate matter and the conditions of its collection to infer the types or relative mix of sources impacting on it during collection.
 Refined model. An analytical technique that provides a detailed treatment of physical and chemical atmospheric processes and requires detailed and precise input data. Specialized estimates are calculated that are useful for evaluating source impact relative to air quality standards and allowable increments. The estimates are more accurate than those obtained from conservative screening techniques.
 Rollback. A simple model that assumes that if emissions from each source affecting a given receptor are decreased by the same percentage, ambient air quality concentrations decrease proportionately.
 Screening technique. A relatively simple analysis technique to determine if a given source is likely to pose a threat to air quality. Concentration estimates from screening techniques are conservative.
 Simple terrain. An area where terrain features are all lower in elevation than the top of the stack of the source.
 Appendix A to Appendix W of part 51--Summaries of Preferred Air Quality Models
 Table of Contents
 A.0 Introduction and Availability
 A.1 Buoyant Line and Point Source Dispersion Model (BLP) A.2 Caline3
 A.3 Climatological Dispersion Model (CDM 2.0) A.4 Gaussian-Plume Multiple Source Air Quality Algorithm (RAM) A.5 Industrial Source Complex Model (ISC3) A.6 Urban Airshed Model (UAM)
 A.7 Offshore and Coastal Dispersion Model (OCD) A.8 Emissions and Dispersion Modeling System (EDMS) A.9 Complex Terrain Dispersion Model Plus Algorithms For Unstable Situations (CTDMPLUS)
 A.REF References
 A.0 Introduction and Availability
 This appendix summarizes key features of refined air quality models preferred for specific regulatory applications. For each model, information is provided on availability, approximate cost, regulatory use, data input, output format and options, simulation of atmospheric physics, and accuracy. These models may be used without a formal demonstration of applicability provided they satisfy the recommendations for regulatory use; not all options in the models are necessarily recommended for regulatory use. Many of these models have been subjected to a performance evaluation using comparisons with observed air quality data. A summary of such comparisons for models contained in this appendix is included in Moore et al. (1982). Where possible, several of the models contained herein have been subjected to evaluation exercises, including (1) statistical performance tests recommended by the American Meteorological Society and (2) peer scientific reviews. The models in this appendix have been selected on the basis of the results of the model evaluations, experience with previous use, familiarity of the model to various air quality programs, and the costs and resource requirements for use. All models and user's documentation in this appendix are available from: Computer Products, National Technical Information Service (NTIS), U.S. Department of Commerce, Springfield, VA 22161, Phone: (703) 487-4650. In addition, model codes and selected, abridged user's guides are available from the Support Center for Regulatory Air Models Bulletin Board System \19\ (SCRAM BBS), telephone (919) 541-5742. The SCRAM BBS is an electronic bulletin board system designed to be user friendly and accessible from anywhere in the country. Model users with personal computers are encouraged to use the SCRAM BBS to download current model codes and text files.
 A.1 Buoyant Line and Point Source Dispersion Model (BLP)
 Reference
 Schulman, Lloyd L. and Joseph S. Scire, 1980. Buoyant Line and Point Source (BLP) Dispersion Model User's Guide. Document P-7304B. Environmental Research and Technology, Inc., Concord, MA. (NTIS No. PB 81-164642)
 Availability
 The computer code is available on the Support Center for Regulatory Models Bulletin Board System and also on diskette (as PB 90-500281) from the National Technical Information Service (see Section A.0).
 Abstract
 BLP is a Gaussian plume dispersion model designed to handle unique modeling problems associated with aluminum reduction plants, and other industrial sources where plume rise and downwash effects from stationary line sources are important.
 a. Recommendations for Regulatory Use
 The BLP model is appropriate for the following applications: Aluminum reduction plants which contain buoyant, elevated line sources;
 Rural areas;
 Transport distances less than 50 kilometers; Simple terrain; and
 One hour to one year averaging times. The following options should be selected for regulatory applications:
 Rural (IRU=1) mixing height option; Default (no selection) for plume rise wind shear (LSHEAR), transitional point source plume rise (LTRANS), vertical potential temperature gradient (DTHTA), vertical wind speed power law profile exponents (PEXP), maximum variation in number of stability classes per hour (IDELS), pollutant decay (DECFAC), the constant in Briggs' stable plume rise equation (CONST2), constant in Briggs' neutral plume rise equation (CONST3), convergence criterion for the line source calculations (CRIT), and maximum iterations allowed for line source calculations (MAXIT); and
 Terrain option (TERAN) set equal to 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 For other applications, BLP can be used if it can be demonstrated to give the same estimates as a recommended model for the same application, and will subsequently be executed in that mode.
 BLP can be used on a case-by-case basis with specific options not available in a recommended model if it can be demonstrated, using the criteria in Section 3.2, that the model is more appropriate for a specific application.
 b. Input Requirements
 Source data: point sources require stack location, elevation of stack base, physical stack height, stack inside diameter, stack gas exit velocity, stack gas exit temperature, and pollutant emission rate. Line sources require coordinates of the end points of the line, release height, emission rate, average line source width, average building width, average spacing between buildings, and average line source buoyancy parameter. Meteorological data: hourly surface weather data from punched cards or from the preprocessor program RAMMET which provides hourly stability class, wind direction, wind speed, temperature, and mixing height.
 Receptor data: locations and elevations of receptors, or location and size of receptor grid or request automatically generated receptor grid.
 c. Output
 Printed output (from a separate post-processor program) includes:
 Total concentration or, optionally, source contribution analysis; monthly and annual frequency distributions for 1-, 3-, and 24-hour average concentrations; tables of 1-, 3-, and 24-hour average concentrations at each receptor; table of the annual (or length of run) average concentrations at each receptor; Five highest 1-, 3-, and 24-hour average concentrations at each receptor; and
 Fifty highest 1-, 3-, and 24-hour concentrations over the receptor field.
 d. Type of Model
 BLP is a gaussian plume model.
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 e. Pollutant Types
 BLP may be used to model primary pollutants. This model does not treat settling and deposition.
 f. Source-Receptor Relationship
 BLP treats up to 50 point sources, 10 parallel line sources, and 100 receptors arbitrarily located.
 User-input topographic elevation is applied for each stack and each receptor.
 g. Plume Behavior
 BLP uses plume rise formulas of Schulman and Scire (1980). Vertical potential temperature gradients of 0.02 Kelvin per meter for E stability and 0.035 Kelvin per meter are used for stable plume rise calculations. An option for user input values is included.
 Transitional rise is used for line sources. Option to suppress the use of transitional plume rise for point sources is included.
 The building downwash algorithm of Schulman and Scire (1980) is used.
 h. Horizontal Winds
 Constant, uniform (steady-state) wind is assumed for an hour. Straight line plume transport is assumed to all downwind distances.
 Wind speeds profile exponents of 0.10, 0.15, 0.20, 0.25, 0.30, and 0.30 are used for stability classes A through F, respectively. An option for user-defined values and an option to suppress the use of the wind speed profile feature are included.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Rural dispersion coefficients are from Turner (1969), with no adjustment made for variations in surface roughness or averaging time.
 Six stability classes are used.
 k. Vertical Dispersion
 Rural dispersion coefficients are from Turner (1969), with no adjustment made for variations in surface roughness. Six stability classes are used.
 Mixing height is accounted for with multiple reflections until the vertical plume standard deviation equals 1.6 times the mixing height; uniform mixing is assumed beyond that point. Perfect reflection at the ground is assumed.
 l. Chemical Transformation
 Chemical transformations are treated using linear decay. Decay rate is input by the user.
 m. Physical Removal
 Physical removal is not explicitly treated.
 n. Evaluation Studies
 Schulman, L.L. and J.S. Scire, 1980. Buoyant Line and Point Source (BLP) Dispersion Model User's Guide, P-7304B. Environmental Research and Technology, Inc., Concord, MA. Scire, J.S. and L.L. Schulman, 1981. Evaluation of the BLP and ISC Models with SF<INF>6 Tracer Data and SO<INF>2 Measurements at Aluminum Reduction Plants. APCA Specialty Conference on Dispersion Modeling for Complex Sources, St. Louis, MO.
 A.2 CALINE3
 Reference
 Benson, Paul E., 1979. CALINE3--A Versatile Dispersion Model for Predicting Air Pollutant Levels Near Highways and Arterial Streets. Interim Report, Report Number FHWA/CA/TL-79/23. Federal Highway Administration, Washington, D.C. (NTIS No. PB 80-220841)
 Availability
 The CALINE3 model is available on diskette (as PB 95-502712) from NTIS. The source code and user's guide are also available on the Support Center for Regulatory Models Bulletin Board System (see Section A.0).
 Abstract
 CALINE3 can be used to estimate the concentrations of nonreactive pollutants from highway traffic. This steady-state Gaussian model can be applied to determine air pollution concentrations at receptor locations downwind of ``at-grade,'' ``fill,'' ``bridge,'' and ``cut section'' highways located in relatively uncomplicated terrain. The model is applicable for any wind direction, highway orientation, and receptor location. The model has adjustments for averaging time and surface roughness, and can handle up to 20 links and 20 receptors. It also contains an algorithm for deposition and settling velocity so that particulate concentrations can be predicted.
 a. Recommendations for Regulatory Use
 CALINE-3 is appropriate for the following applications: Highway (line) sources;
 Urban or rural areas;
 Simple terrain;
 Transport distances less than 50 kilometers; and One-hour to 24-hour averaging times.
 b. Input Requirements
 Source data: up to 20 highway links classed as ``at-grade,'' ``fill'' ``bridge,'' or ``depressed''; coordinates of link end points; traffic volume; emission factor; source height; and mixing zone width.
 Meteorological data: wind speed, wind angle (measured in degrees clockwise from the Y axis), stability class, mixing height, ambient (background to the highway) concentration of pollutant. Receptor data: coordinates and height above ground for each receptor. c.
 c. Output
 Printed output includes concentration at each receptor for the specified meteorological condition.
 d. Type of Model
 CALINE-3 is a Gaussian plume model.
 e. Pollutant Types
 CALINE-3 may be used to model primary pollutants.
 f. Source-Receptor Relationship
 Up to 20 highway links are treated. CALINE-3 applies user input location and emission rate for each link. User-input receptor locations are applied.
 g. Plume Behavior
 Plume rise is not treated.
 h. Horizontal Winds
 User-input hourly wind speed and direction are applied. Constant, uniform (steady-state) wind is assumed for an hour.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Six stability classes are used.
 Rural dispersion coefficients from Turner (1969) are used, with adjustment for roughness length and averaging time. Initial traffic-induced dispersion is handled implicitly by plume size parameters.
 k. Vertical Dispersion
 Six stability classes are used.
 Empirical dispersion coefficients from Benson (1979) are used including an adjustment for roughness length. Initial traffic-induced dispersion is handled implicitly by plume size parameters.
 Adjustment for averaging time is included.
 l. Chemical Transformation
 Not treated.
 m. Physical Removal
 Optional deposition calculations are included.
 n. Evaluation Studies
 Bemis, G.R. et al., 1977. Air Pollution and Roadway Location, Design, and Operation--Project Overview. FHWA-CA-TL-7080-77-25, Federal Highway Administration, Washington, D.C. Cadle, S.H. et al., 1976. Results of the General Motors Sulfate Dispersion Experiment, GMR-2107. General Motors Research Laboratories, Warren, MI.
 Dabberdt, W.F., 1975. Studies of Air Quality on and Near Highways, Project 2761. Stanford Research Institute, Menlo Park, CA.
 A.3 Climatological Dispersion Model (CDM 2.0)
 Reference
 Irwin, J.S., T. Chico and J. Catalano, 1985. CDM 2.0-- Climatological Dispersion Model--User's Guide. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 86- 136546)
 Availability
 The source code and user's guide is available on the Support Center for Regulatory Models Bulletin Board System. The computer code is also available on diskette (as PB 90-500406) from the National Technical Information Service (see Section A.0).
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 Abstract
 CDM is a climatological steady-state Gaussian plume model for determining long-term (seasonal or annual) arithmetic average pollutant concentrations at any ground-level receptor in an urban area.
 a. Recommendations for Regulatory Use
 CDM is appropriate for the following applications: Point and area sources;
 Urban areas;
 Flat terrain;
 Transport distances less than 50 kilometers; Long term averages over one month to one year or longer. The following option should be selected for regulatory applications:
 Set the regulatory ``default option'' (NDEF=1) which automatically selects stack tip downwash, final plume rise, buoyancy-induced dispersion (BID), and the appropriate wind profile exponents.
 Enter ``0'' for pollutant half-life for all pollutants except for SO<INF>2 in an urban setting. This entry results in no decay (infinite half-life) being calculated. For SO<INF>2 in an urban setting, the pollutant half-life (in hours) should be set to 4.0.
 b. Input Requirements
 Source data: location, average emissions rates and heights of emissions for point and area sources. Point source data requirements also include stack gas temperature, stack gas exit velocity, and stack inside diameter for plume rise calculations for point sources. Meteorological data: stability wind rose (STAR deck day/night version), average mixing height and wind speed in each stability category, and average air temperature.
 Receptor data: cartesian coordinates of each receptor.
 c. Output
 Printed output includes:
 Average concentrations for the period of the stability wind rose data (arithmetic mean only) at each receptor, and Optional point and area concentration rose for each receptor.
 d. Type of Model
 CDM is a climatological Gaussian plume model.
 e. Pollutant Types
 CDM may be used to model primary pollutants. Settling and deposition are not treated.
 f. Source-Receptor Relationship
 CDM applies user-specified locations for all point sources and receptors.
 Area sources are input as multiples of a user-defined unit area source grid size.
 User specified release heights are applied for individual point sources and the area source grid.
 Actual separation between each source-receptor pair is used. The user may select a single height at or above ground level that applies to all receptors.
 No terrain differences between source and receptor are treated.
 g. Plume Behavior
 CDM uses Briggs (1969, 1971, 1975) plume rise equations. Optionally a plume rise-wind speed product may be input for each point source.
 Stack tip downwash equation from Briggs (1974) is preferred for regulatory use. The Bjorklund and Bowers (1982) equation is also included.
 No plume rise is calculated for area sources. Does not treat fumigation or building downwash.
 h. Horizontal Winds
 Wind data are input as a stability wind rose (joint frequency distribution of 16 wind directions, 6 wind classes, and 5 stability classes).
 Wind speed profile exponents for the urban case (Irwin, 1979; EPA, 1980) are used, assuming the anemometer height is at 10.0 meters.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Pollutants are assumed evenly distributed across a 22.5 or 10.0 degree sector.
 k. Vertical Dispersion
 There are seven vertical dispersion parameter schemes, but the following is recommended for regulatory applications: <bullet> Briggs-urban (Gifford, 1976). Mixing height has no effect until dispersion coefficient equals 0.8 times the mixing height; uniform vertical mixing is assumed beyond that point.
 Buoyancy-induced dispersion (Pasquill, 1976) is included as an option. Perfect reflection is assumed at the ground.
 l. Chemical Transformation
 Chemical transformations are treated using exponential decay. Half-life is input by the user.
 m. Physical Removal
 Physical removal is not explicitly treated.
 n. Evaluation Studies
 Busse, A.D. and J.R. Zimmerman, 1973. User's Guide for the Climatological Dispersion Model--Appendix E. EPA Publication No. EPA/R4-73-024. Office of Research and Development, Research Triangle Park, NC.
 Irwin, J.S. and T.M. Brown, 1985. A Sensitivity Analysis of the Treatment of Area Sources by the Climatological Dispersion Model. Journal of Air Pollution Control Association, 35: 359-364. Londergan, R., D. Minott, D. Wachter and R. Fizz, 1983. Evaluation of Urban Air Quality Simulation Models, EPA Publication No. EPA-450/4-83-020. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Zimmerman, J.R., 1971. Some Preliminary Results of Modeling from the Air Pollution Study of Ankara, Turkey, Proceedings of the Second Meeting of the Expert Panel on Air Pollution Modeling, NATO Committee on the Challenges of Modern Society, Paris, France. Zimmerman, J.R., 1972. The NATO/CCMS Air Pollution Study of St. Louis, Missouri. Presented at the Third Meeting of the Expert Panel on Air Pollution Modeling, NATO Committee on the Challenges of Modern Society, Paris, France.
 A.4 Gaussian-Plume Multiple Source Air Quality Algorithm (RAM)
 Reference
 Turner, D.B. and J.H. Novak, 1978. User's Guide for RAM. Publication No. EPA-600/8-78-016, Vol. a and b. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS Nos. PB 294791 and PB 294792)
 Catalano, J.A., D.B. Turner and H. Novak, 1987. User's Guide for RAM--Second Edition. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Availability
 The source code and user's guide is available on the Support Center for Regulatory Models Bulletin Board System. The computer code is also available on diskette (as PB 90-500315) from the National Technical Information Service (see Section A.0).
 Abstract
 RAM is a steady-state Gaussian plume model for estimating concentrations of relatively stable pollutants, for averaging times from an hour to a day, from point and area sources in a rural or urban setting. Level terrain is assumed. Calculations are performed for each hour.
 a. Recommendations for Regulatory Use
 RAM is appropriate for the following applications: Point and area sources;
 Urban areas;
 Flat terrain;
 Transport distances less than 50 kilometers; and One hour to one year averaging times. The following options should be selected for regulatory applications:
 Set the regulatory ``default option'' to automatically select stack tip downwash, final plume rise, buoyancy-induced dispersion (BID), the new treatment for calms, the appropriate wind profile exponents, and the appropriate value for pollutant half-life.
 b. Input Requirements
 Source data: point sources require location, emission rate, physical stack height, stack gas exit velocity, stack inside diameter and stack gas temperature. Area sources require location, size, emission rate, and height of emissions. Meteorological data: hourly surface weather data from the preprocessor program RAMMET which provides hourly stability class, wind direction, wind speed, temperature, and mixing height. Actual anemometer height (a single value) is also required. Receptor data: coordinates of each receptor. Options for automatic placement of
 [[Page 41869]]
 receptors near expected concentration maxima, and a gridded receptor array are included.
 c. Output
 Printed output optionally includes: One to 24-hour and annual average concentrations at each receptor,
 Limited individual source contribution list, and Highest through fifth highest concentrations at each receptor for period, with the highest and high, second-high values flagged.
 d. Type of Model
 RAM is a Gaussian plume model.
 e. Pollutant Types
 RAM may be used to model primary pollutants. Settling and deposition are not treated.
 f. Source-Receptor Relationship
 RAM applies user-specified locations for all point sources and receptors. Area sources are input as multiples of a user-defined unit area source grid size.
 User specified stack heights are applied for individual point sources.
 Up to 3 effective release heights may be specified for the area sources. Area source release heights are assumed to be appropriate for a 5 meter per second wind and to be inversely proportional to wind speed.
 Actual separation between each source-receptor pair is used. All receptors are assumed to be at the same height at or above ground level.
 No terrain differences between source and receptor are accounted for.
 g. Plume Behavior
 RAM uses Briggs (1969, 1971, 1975) plume rise equations for final rise.
 Stack tip downwash equation from Briggs (1974) is used. A user supplied fraction of the area source height is treated as the physical height. The remainder is assumed to be plume rise for a 5 meter per second wind speed, and to be inversely proportional to wind speed.
 Fumigation and building downwash are not treated.
 h. Horizontal Winds
 Constant, uniform (steady state) wind is assumed for an hour. Straight line plume transport is assumed to all downwind distances.
 Separate wind speed profile exponents (Irwin, 1979; EPA, 1980) for urban cases are used.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Urban dispersion coefficients from Briggs (Gifford, 1976) are used.
 Buoyancy-induced dispersion (Pasquill, 1976) is included. Six stability classes are used.
 k. Vertical Dispersion
 Urban dispersion coefficients from Briggs (Gifford, 1976) are used.
 Buoyancy-induced dispersion (Pasquill, 1976) is included. Six stability classes are used.
 Mixing height is accounted for with multiple reflections until the vertical plume standard deviation equals 1.6 times the mixing height; uniform vertical mixing is assumed beyond that point. Perfect reflection is assumed at the ground.
 l. Chemical Transformation
 Chemical transformations are treated using exponential decay. Half-life is input by the user.
 m. Physical Removal
 Physical removal is not explicitly treated.
 n. Evaluation Studies
 Ellis, H., P. Lou, and G. Dalzell, 1980. Comparison Study of Measured and Predicted Concentrations with the RAM Model at Two Power Plants Along Lake Erie. Second Joint Conference on Applications of Air Pollution Meteorology, New Orleans, LA. Environmental Research and Technology, 1980. SO<INF>2 Monitoring and RAM (Urban) Model Comparison Study in Summit County, Ohio. Document P-3618-152, Environmental Research & Technology, Inc., Concord, MA.
 Guldberg, P.H. and C.W. Kern, 1978. A Comparison Validation of the RAM and PTMTP Models for Short-Term Concentrations in Two Urban Areas. Journal of Air Pollution Control Association, 28: 907-910. Hodanbosi, R.R. and L.K. Peters, 1981. Evaluation of RAM Model for Cleveland, Ohio. Journal of Air Pollution Control Association, 31: 253-255.
 Kennedy, K.H., R.D. Siegel and M.P. Steinberg, 1981. CaseSpecific Evaluation of the RAM Atmospheric Dispersion Model in an Urban Area. 74th Annual Meeting of the American Institute of Chemical Engineers, New Orleans, LA.
 Kummier, R.H., B. Cho, G. Roginski, R. Sinha and A. Greenburg, 1979. A Comparative Validation of the RAM and Modified SAI Models for Short Term SO<INF>2 Concentrations in Detroit. Journal of Air Pollution Control Association, 29: 720-723. Londergan, R.J., N.E. Bowne, D.R. Murray, H. Borenstein and J. Mangano, 1980. An Evaluation of Short-Term Air Quality Models Using Tracer Study Data. Report No. 4333, American Petroleum Institute, Washington, D.C.
 Londergan, R., D. Minott, D. Wackter and R. Fizz, 1983. Evaluation of Urban Air Quality Simulation Models. EPA Publication No. EPA-450/4-83-020. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Morgenstern, P., M.J. Geraghty, and A. McKnight, 1979. A Comparative Study of the RAM (Urban) and RAMR (Rural) Models for Short-term SO<INF>2 Concentrations in Metropolitan Indianapolis. 72nd Annual Meeting of the Air Pollution Control Association, Cincinnati, OH.
 Ruff, R.E., 1980. Evaluation of the RAM Using the RAPS Data Base. Contract 68-02-2770, SRI International, Menlo Park, CA.
 A.5 Industrial Source Complex Model (ISC3)
 Reference
 Environmental Protection Agency, 1995. User's Guide for the Industrial Source Complex (ISC3) Dispersion Models, Volumes 1 and 2. EPA Publication Nos. EPA-454/B-95-003a & b. Environmental Protection Agency, Research Triangle Park, NC. (NTIS Nos. PB 95-222741 and PB 95-222758, respectively)
 Availability
 The model code is available on the Support Center for Regulatory Air Models Bulletin Board System. ISCST3 (as PB 96-502000) and ISCLT3 (PB 96-502018) are also available on diskette from the National Technical Information Service (see Section A.0).
 Abstract
 The ISC3 model is a steady-state Gaussian plume model which can be used to assess pollutant concentrations from a wide variety of sources associated with an industrial source complex. This model can account for the following: settling and dry deposition of particles; downwash; area, line and volume sources; plume rise as a function of downwind distance; separation of point sources; and limited terrain adjustment. ISC3 operates in both long-term and short-term modes.
 a. Recommendations for Regulatory Use
 ISC3 is appropriate for the following applications: <bullet> Industrial source complexes; <bullet> Rural or urban areas; <bullet> Flat or rolling terrain; <bullet> Transport distances less than 50 kilometers; <bullet> 1-hour to annual averaging times; and <bullet> Continuous toxic air emissions. The following options should be selected for regulatory applications: For short term or long term modeling, set the regulatory ``default option''; i.e., use the keyword DFAULT, which automatically selects stack tip downwash, final plume rise, buoyancy induced dispersion (BID), the vertical potential temperature gradient, a treatment for calms, the appropriate wind profile exponents, the appropriate value for pollutant half-life, and a revised building wake effects algorithm; set the ``rural option'' (use the keyword RURAL) or ``urban option'' (use the keyword URBAN); and set the ``concentration option'' (use the keyword CONC).
 b. Input Requirements
 Source data: location, emission rate, physical stack height, stack gas exit velocity, stack inside diameter, and stack gas temperature. Optional inputs include source elevation, building dimensions, particle size distribution with corresponding settling velocities, and surface reflection coefficients. Meteorological data: ISCST3 requires hourly surface weather data from the preprocessor program RAMMET, which provides hourly stability class, wind direction, wind speed, temperature, and mixing height. For ISCLT3, input includes
 [[Page 41870]]
 stability wind rose (STAR deck), average afternoon mixing height, average morning mixing height, and average air temperature. Receptor data: coordinates and optional ground elevation for each receptor.
 c. Output
 Printed output options include:
 <bullet> Program control parameters, source data, and receptor data;
 <bullet> Tables of hourly meteorological data for each specified day;
 <bullet> ``N''-day average concentration or total deposition calculated at each receptor for any desired source combinations; <bullet> Concentration or deposition values calculated for any desired source combinations at all receptors for any specified day or time period within the day;
 <bullet> Tables of highest and second highest concentration or deposition values calculated at each receptor for each specified time period during a(n) ``N''-day period for any desired source combinations, and tables of the maximum 50 concentration or deposition values calculated for any desired source combinations for each specified time period.
 d. Type of Model
 ISC3 is a Gaussian plume model. It has been revised to perform a double integration of the Gaussian plume kernel for area sources.
 e. Pollutant Types
 ISC3 may be used to model primary pollutants and continuous releases of toxic and hazardous waste pollutants. Settling and deposition are treated.
 f. Source-Receptor Relationships
 ISC3 applies user-specified locations for point, line, area and volume sources, and user-specified receptor locations or receptor rings.
 User input topographic evaluation for each receptor is used. Elevations above stack top are reduced to the stack top elevation, i.e., ``terrain chopping''.
 User input height above ground level may be used when necessary to simulate impact at elevated or ``flag pole'' receptors, e.g., on buildings.
 Actual separation between each source-receptor pair is used.
 g. Plume Behavior
 ISC3 uses Briggs (1969, 1971, 1975) plume rise equations for final rise.
 Stack tip downwash equation from Briggs (1974) is used. Revised building wake effects algorithm is used. For stacks higher than building height plus one-half the lesser of the building height or building width, the building wake algorithm of Huber and Snyder (1976) is used. For lower stacks, the building wake algorithm of Schulman and Scire (Schulman and Hanna, 1986) is used, but stack tip downwash and BID are not used.
 For rolling terrain (terrain not above stack height), plume centerline is horizontal at height of final rise above source. Fumigation is not treated.
 h. Horizontal Winds
 Constant, uniform (steady-state) wind is assumed for each hour. Straight line plume transport is assumed to all downwind distances.
 Separate wind speed profile exponents (Irwin, 1979; EPA, 1980) for both rural and urban cases are used. An optional treatment for calm winds is included for short term modeling.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Rural dispersion coefficients from Turner (1969) are used, with no adjustments for surface roughness or averaging time. Urban dispersion coefficients from Briggs (Gifford, 1976) are used.
 Buoyancy induced dispersion (Pasquill, 1976) is included. Six stability classes are used.
 k. Vertical Dispersion
 Rural dispersion coefficients from Turner (1969) are used, with no adjustments for surface roughness.
 Urban dispersion coefficients from Briggs (Gifford, 1976) are used.
 Buoyancy induced dispersion (Pasquill, 1976) is included. Six stability classes are used.
 Mixing height is accounted for with multiple reflections until the vertical plume standard deviation equals 1.6 times the mixing height; uniform vertical mixing is assumed beyond that point. Perfect reflection is assumed at the ground.
 l. Chemical Transformation
 Chemical transformations are treated using exponential decay. Time constant is input by the user.
 m. Physical Removal
 Dry deposition effects for particles are treated using a resistance formulation in which the deposition velocity is the sum of the resistances to pollutant transfer within the surface layer of the atmosphere, plus a gravitational settling term (EPA, 1994), based on the modified surface depletion scheme of Horst (1983).
 n. Evaluation Studies
 Bowers, J.F. and A.J. Anderson, 1981. An Evaluation Study for the Industrial Source Complex (ISC) Dispersion Model, EPA Publication No. EPA-450/4-81-002. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Bowers, J.F., A.J. Anderson and W.R. Hargraves, 1982. Tests of the Industrial Source Complex (ISC) Dispersion Model at the Armco Middletown, Ohio Steel Mill. EPA Publication No. EPA-450/4-82-006. U.S. Environmental Protection Agency, Research Triangle Park, NC. Environmental Protection Agency, 1992. Comparison of a Revised Area Source Algorithm for the Industrial Source Complex Short Term Model and Wind Tunnel Data. EPA Publication No. EPA-454/R-92-014. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 93-226751)
 Environmental Protection Agency, 1992. Sensitivity Analysis of a Revised Area Source Algorithm for the Industrial Source Complex Short Term Model. EPA Publication No. EPA-454/R-92-015. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 93-226769)
 Environmental Protection Agency, 1992. Development and Evaluation of a Revised Area Source Algorithm for the Industrial source complex Long Term Model. EPA Publication No. EPA-454/R-92- 016. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 93-226777)
 Environmental Protection Agency, 1994. Development and Testing of a Dry Deposition Algorithm (Revised). EPA Publication No. EPA- 454/R-94-015. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 94-183100) Scire, J.S. and L.L. Schulman, 1981. Evaluation of the BLP and ISC Models with SF<INF>6 Tracer Data and SO<INF>2 Measurements at Aluminum Reduction Plants. Air Pollution Control Association Specialty Conference on Dispersion Modeling for Complex Sources, St. Louis, MO.
 Schulman, L.L. and S.R. Hanna, 1986. Evaluation of Downwash Modification to the Industrial Source Complex Model. Journal of the Air Pollution Control Association, 36: 258-264.
 A.6 Urban Airshed Model (UAM)
 Reference
 Environmental Protection Agency, 1990. User's Guide for the Urban Airshed Model, Volume I-VIII. EPA Publication Nos. EPA-450/4- 90-007a-c, d(R), e-g, and EPA-454/B-93-004, respectively. U.S. Environmental Protection Agency, Research Triangle Park, NC (NTIS Nos. PB 91-131227, PB 91-131235, PB 91-131243, PB 93-122380, PB 91- 131268, PB 92-145382, and PB 92-224849, respectively, for Vols. IVII).
 Availability
 The model code is available on the Support Center for Regulatory Air Models Bulletin Board System (see Section A.0).
 Abstract
 UAM is an urban scale, three dimensional, grid type numerical simulation model. The model incorporates a condensed photochemical kinetics mechanism for urban atmospheres. The UAM is designed for computing ozone (O<INF>3) concentrations under short-term, episodic conditions lasting one or two days resulting from emissions of oxides of nitrogen (NO<INF>x), volatile organic compounds (VOC), and carbon monoxide (CO). The model treats urban VOC emissions as their carbon-bond surrogates.
 a. Recommendations for Regulatory Use
 UAM is appropriate for the following applications: urban areas having significant ozone attainment problems and one hour averaging times.
 UAM has many options but no specific recommendations can be made at this time on all options. The reviewing agency should be consulted on selection of options to be used in regulatory applications.
 b. Input Requirements
 Source data: gridded, hourly emissions of PAR, OLE, ETH, XYL, TOL, ALD2, FORM,
 [[Page 41871]]
 ISOR, ETOTH, MEOH, CO, NO, and NO<INF>2 for low-level sources. For major elevated point sources, hourly emissions, stack height, stack diameter, exit velocity, and exit temperature. Meteorological data: hourly, gridded, divergence free, u and v wind components for each vertical level; hourly gridded mixing heights and surface temperatures; hourly exposure class; hourly vertical potential temperature gradient above and below the mixing height; hourly surface atmospheric pressure; hourly water mixing ratio; and gridded surface roughness lengths. Air quality data: concentration of all carbon bond 4 species at the beginning of the simulation for each grid cell; and hourly concentrations of each pollutant at each level along the inflow boundaries and top boundary of the modeling region. Other data requirements are: hourly mixed layer average, NO<INF>2 photolysis rates; and ozone surface uptake resistance along with associated gridded vegetation (scaling) factors. c. Output
 Printed output includes:
 <bullet> Gridded instantaneous concentration fields at userspecified time intervals for user-specified pollutants and grid levels;
 <bullet> Gridded time-average concentration fields for userspecified time intervals, pollutants, and grid levels. d. Type of Model
 UAM is a three dimensional, numerical, photochemical grid model. e. Pollutant Types
 UAM may be used to model ozone (O<INF>3) formation from oxides of nitrogen (NO<INF>x) and volatile organic compound (VOC) emissions.
 f. Source-Receptor Relationship
 Low-level area and point source emissions are specified within each surface grid cell. Emissions from major point sources are placed within cells aloft in accordance with calculated effective plume heights.
 Hourly average concentrations of each pollutant are calculated for all grid cells at each vertical level. g. Plume Behavior
 Plume rise is calculated for major point sources using relationships recommended by Briggs (1971). h. Horizontal Winds
 See Input Requirements.
 i. Vertical Wind Speed
 Calculated at each vertical grid cell interface from the mass continuity relationship using the input gridded horizontal wind field.
 j. Horizontal Dispersion
 Horizontal eddy diffusivity is set to a user specified constant value (nominally 50 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 NO<INF>x-limited (Gery et al., 1989). The current CBMIV mechanism accommodates 34 species and 82 reactions.
 m. Physical Removal
 Dry deposition of ozone and other pollutant species are calculated. Vegetation (scaling) factors are applied to the reference surface uptake resistance of each species depending on land use type.
 n. Evaluation Studies
 Builtjes, P.J.H., K.D. van der Hurt and S.D. Reynolds, 1982. Evaluation of the Performance of a Photochemical Dispersion Model in Practical Applications. 13th International Technical Meeting on Air Pollution Modeling and Its Application, Ile des Embiez, France. Cole, H.S., D.E. Layland, G.K. Moss and C.F. Newberry, 1983. The St. Louis Ozone Modeling Project. EPA Publication No. EPA-450/4-83- 019. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Dennis, R.L., M.W. Downton and R.S. Keil, 1983. Evaluation of Performance Measures for an Urban Photochemical Model. EPA Publication No. EPA-450/4-83-021. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Haney, J.L. and T.N. Braverman, 1985. Evaluation and Application of the Urban Airshed Model in the Philadelphia Air Quality Control Region. EPA Publication No. EPA-450/4-85-003. U.S. Environmental Protection Agency, Research Triangle Park, NC. Layland, D.E. and H.S. Cole, 1983. A Review of Recent Applications of the SAI Urban Airshed Model. EPA Publication No. EPA-450/4-84-004. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Layland, D.E., S.D. Reynolds, H. Hogo and W.R. Oliver, 1983. Demonstration of Photochemical Grid Model Usage for Ozone Control Assessment. 76th Annual Meeting of the Air Pollution Control Association, Atlanta, GA.
 Morris, R.E. et al., 1990. Urban Airshed Model Study of Five Cities. EPA Publication No. EPA-450/4-90-006a-g. U.S. Environmental Protection Agency, Research Triangle Park, NC. Reynolds, S.D., H. Hogo, W.R. Oliver and L.E. Reid, 1982. Application of the SAI Airshed Model to the Tulsa Metropolitan Area, SAI No. 82004. Systems Applications, Inc., San Rafael, CA. Schere, K.L. and J.H. Shreffler, 1982. Final Evaluation of Urban-Scale Photochemical Air Quality Simulation Models. EPA Publication No. EPA-600/3-82-094. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Seigneur C., T.W. Tesche, C.E. Reid, P.M. Roth, W.R. Oliver and J.C. Cassmassi, 1981. The Sensitivity of Complex Photochemical Model Estimates to Detail In Input Information, Appendix A--A Compilation of Simulation Results. EPA Publication No. EPA-450/4-81-031b. U.S. Environmental Protection Agency, Research Triangle Park, NC. South Coast Air Quality Management District, 1989. Air Quality Management Plan--Appendix V-R (Urban Airshed Model Performance Evaluation). El Monte, CA.
 Stern, R. and B. Scherer, 1982. Simulation of a Photochemical Smog Episode in the Rhine-Ruhr Area with a Three Dimensional Grid Model. 13th International Technical Meeting on Air Pollution Modeling and Its Application, Ile des Embiez, France. Tesche, T.W., C. Seigneur, L.E. Reid, P.M. Roth, W.R. Oliver and J.C. Cassmassi, 1981. The Sensitivity of Complex Photochemical Model Estimates to Detail in Input Information. EPA Publication No. EPA- 450/4-81-031a. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Tesche, T.W., W.R. Oliver, H. Hogo, P. Saxeena and J.L. Haney, 1983. Volume IV--Assessment of NO<INF>x Emission Control Requirements in the South Coast Air Basin--Appendix A. Performance Evaluation of the Systems Applications Airshed Model for the 26-27 June 1974 O<INF>3 Episode in the South Coast Air Basin, SYSAPP 83/ 037. Systems Applications, Inc., San Rafael, CA. Tesche, T.W., W.R. Oliver, H. Hogo, P. Saxeena and J.L. Haney, 1983. Volume IV--Assessment of NO<INF>x Emission Control Requirements in the South Coast Air Basin--Appendix B. Performance Evaluation of the Systems Applications Airshed Model for the 7-8 November 1978 NO<INF>2 Episode in the South Coast Air Basin, SYSAPP 83/038. Systems Applications, Inc., San Rafael, CA. Tesche, T.W., 1988. Accuracy of Ozone Air Quality Models. Journal of Environmental Engineering, 114(4): 739-752. A.7 Offshore and Coastal Dispersion Model (OCD) Reference
 DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and Coastal Dispersion Model, Version 4. Volume I: User's Guide, and Volume II: Appendices. Sigma Research Corporation, Westford, MA. (NTIS Nos. PB 93-144384 and PB 93-144392) Availability
 This model code is available on the Support Center for Regulatory Air Models Bulletin Board System and also on diskette (as PB 91-505230) from the National Technical Information Service (see Section A.0).
 Technical Contact
 Minerals Management Service, Attn: Mr. Dirk Herkhof, Parkway Atrium Building, 381 Elden Street, Herndon, VA 22070-4817, Phone: (703) 787-1735.
 Abstract
 OCD is a straight-line Gaussian model developed to determine the impact of offshore emissions from point, area or line sources on the air quality of coastal regions. OCD incorporates overwater plume transport
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 and dispersion as well as changes that occur as the plume crosses the shoreline. Hourly meteorological data are needed from both offshore and onshore locations. These include water surface temperature, overwater air temperature, mixing height, and relative humidity.
 Some of the key features include platform building downwash, partial plume penetration into elevated inversions, direct use of turbulence intensities for plume dispersion, interaction with the overland internal boundary layer, and continuous shoreline fumigation.
 a. Recommendations for Regulatory Use
 OCD has been recommended for use by the Minerals Management Service for emissions located on the Outer Continental Shelf (50 FR 12248; 28 March 1985). OCD is applicable for overwater sources where onshore receptors are below the lowest source height. Where onshore receptors are above the lowest source height, offshore plume transport and dispersion may be modeled on a case-by-case basis in consultation with the EPA Regional Office.
 b. Input Requirements
 Source data: point, area or line source location, pollutant emission rate, building height, stack height, stack gas temperature, stack inside diameter, stack gas exit velocity, stack angle from vertical, elevation of stack base above water surface and gridded specification of the land/water surfaces. As an option, emission rate, stack gas exit velocity and temperature can be varied hourly. Meteorological data (over water): wind direction, wind speed, mixing height, relative humidity, air temperature, water surface temperature, vertical wind direction shear (optional), vertical temperature gradient (optional), turbulence intensities (optional). Meteorological data (over land): wind direction, wind speed, temperature, stability class, mixing height. Receptor data: location, height above local ground-level, ground-level elevation above the water surface.
 c. Output
 All input options, specification of sources, receptors and land/ Water map including locations of sources and receptors. Summary tables of five highest concentrations at each receptor for each averaging period, and average concentration for entire run period at each receptor.
 Optional case study printout with hourly plume and receptor characteristics. Optional table of annual impact assessment from non-permanent activities.
 Concentration files written to disk or tape can be used by ANALYSIS postprocessor to produce the highest concentrations for each receptor, the cumulative frequency distributions for each receptor, the tabulation of all concentrations exceeding a given threshold, and the manipulation of hourly concentration files.
 d. Type of Model
 OCD is a Gaussian plume model constructed on the framework of the MPTER model.
 e. Pollutant Types
 OCD may be used to model primary pollutants. Settling and deposition are not treated.
 f. Source-Receptor Relationship
 Up to 250 point sources, 5 area sources, or 1 line source and 180 receptors may be used.
 Receptors and sources are allowed at any location. The coastal configuration is determined by a grid of up to 3600 rectangles. Each element of the grid is designated as either land or water to identify the coastline.
 g. Plume Behavior
 As in MPTER, the basic plume rise algorithms are based on Briggs' recommendations.
 Momentum rise includes consideration of the stack angle from the vertical.
 The effect of drilling platforms, ships, or any overwater obstructions near the source are used to decrease plume rise using a revised platform downwash algorithm based on laboratory experiments. Partial plume penetration of elevated inversions is included using the suggestions of Briggs (1975) and Weil and Brower (1984). Continuous shoreline fumigation is parametrized using the Turner method where complete vertical mixing through the thermal internal boundary layer (TIBL) occurs as soon as the plume intercepts the TIBL.
 h. Horizontal Winds
 Constant, uniform wind is assumed for each hour. Overwater wind speed can be estimated from overland wind speed using relationship of Hsu (1981).
 Wind speed profiles are estimated using similarity theory (Businger, 1973). Surface layer fluxes for these formulas are calculated from bulk aerodynamic methods.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Lateral turbulence intensity is recommended as a direct estimate of horizontal dispersion. If lateral turbulence intensity is not available, it is estimated from boundary layer theory. For wind speeds less than 8 m/s, lateral turbulence intensity is assumed inversely proportional to wind speed.
 Horizontal dispersion may be enhanced because of obstructions near the source. A virtual source technique is used to simulate the initial plume dilution due to downwash. Formulas recommended by Pasquill (1976) are used to calculate buoyant plume enhancement and wind direction shear enhancement. At the water/land interface, the change to overland dispersion rates is modeled using a virtual source. The overland dispersion rates can be calculated from either lateral turbulence intensity or Pasquill-Gifford curves. The change is implemented where the plume intercepts the rising internal boundary layer.
 k. Vertical Dispersion
 Observed vertical turbulence intensity is not recommended as a direct estimate of vertical dispersion. Turbulence intensity should be estimated from boundary layer theory as default in the model. For very stable conditions, vertical dispersion is also a function of lapse rate.
 Vertical dispersion may be enhanced because of obstructions near the source. A virtual source technique is used to simulate the initial plume dilution due to downwash. Formulas recommended by Pasquill (1976) are used to calculate buoyant plume enhancement.
 At the water/land interface, the change to overland dispersion rates is modeled using a virtual source. The overland dispersion rates can be calculated from either vertical turbulence intensity or the Pasquill-Gifford coefficients. The change is implemented where the plume intercepts the rising internal boundary layer.
 l. Chemical Transformation
 Chemical transformations are treated using exponential decay. Different rates can be specified by month and by day or night.
 m. Physical Removal
 Physical removal is also treated using exponential decay.
 n. Evaluation Studies
 DiCristofaro, D.C. and S.R. Hanna, 1989. OCD: The Offshore and Coastal Dispersion Model. Volume I: User's Guide. Sigma Research Corporation, Westford, MA.
 Hanna, S.R., L.L. Schulman, R.J. Paine and J.E. Pleim, 1984. The Offshore and Coastal Dispersion (OCD) Model User's Guide, Revised. OCS Study, MMS 84-0069. Environmental Research & Technology, Inc., Concord, MA. (NTIS No. PB 86-159803)
 Hanna, S.R., L.L. Schulman, R.J. Paine, J.E. Pleim and M. Baer, 1985. Development and Evaluation of the Offshore and Coastal Dispersion (OCD) Model. Journal of the Air Pollution Control Association, 35: 1039-1047.
 Hanna, S.R. and D.C. DiCristofaro, 1988. Development and Evaluation of the OCD/API Model. Final Report, API Pub. 4461, American Petroleum Institute, Washington, D.C.
 A.8 Emissions and Dispersion Modeling System (EDMS)
 Reference
 Segal, H.M., 1991. ``EDMS--Microcomputer Pollution Model for Civilian Airports and Air Force Bases: User's Guide.'' FAA Report No. FAA-EE-91-3; USAF Report No. ESL-TR-91-31, Federal Aviation Administration, 800 Independence Avenue, S.W., Washington, D.C. 20591. (NTIS No. ADA 240528)
 Segal, H.M. and Hamilton, P.L., 1988. ``A Microcomputer Pollution Model for Civilian Airports and Air Force Bases--Model Description.'' FAA Report No. FAA-EE-88-4; USAF Report No. ESL-TR- 88-53, Federal Aviation Administration, 800 Independence Avenue, S.W., Washington, D.C. 20591. (NTIS No. ADA 199003) Segal, H.M., 1988. ``A Microcomputer Pollution Model for Civilian Airports and Air
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 Force Bases--Model Application and Background.'' FAA Report No. FAAEE -88-5; USAF Report No. ESL-TR-88-55, Federal Aviation Administration, 800 Independence Avenue, S.W., Washington, D.C. 20591. (NTIS No. ADA 199794)
 Availability
 EDMS is available for $40 from: Federal Aviation Administration, Attn: Ms. Diana Liang, AEE-120, 800 Independence Avenue, S.W., Washington, D.C. 20591, Phone: (202) 267-3494.
 Abstract
 EDMS is a combined emissions/dispersion model for assessing pollution at civilian airports and military air bases. This model, which was jointly developed by the Federal Aviation Administration (FAA) and the United States Air Force (USAF), produces an emission inventory of all airport sources and calculates concentrations produced by these sources at specified receptors. The system stores emission factors for fixed sources such as fuel storage tanks and incinerators and also for mobile sources such as automobiles or aircraft. EDMS incorporates an emissions model to calculate an emission inventory for each airport source and a dispersion model, the Graphical Input Microcomputer Model (GIMM) (Segal, 1983) to calculate pollutant concentrations produced by these sources at specified receptors. The GIMM, which processes point, area, and line sources, also incorporates a special meteorological preprocessor for processing up to one year of National Climatic Data Center (NCDC) hourly data. The model operates in both a screening and refined mode, accepting up to 170 sources and 10 receptors.
 a. Recommendations for Regulatory Use
 EDMS is appropriate for the following applications: <bullet> Cumulative effect of changes in aircraft operations, point source and mobile source emissions at airports or air bases; <bullet> Simple terrain;
 <bullet> Transport distances less than 50 kilometers; and <bullet> 1-hour to annual averaging times.
 b. Input Requirements
 All data are entered through a ``runtime'' version of the Condor data base which is an integral part of EDMS. Typical entry items are source and receptor coordinates, percent cold starts, vehicles per hour, etc. Some point sources, such as heating plants, require stack height, stack diameter, and effluent temperature inputs. Wind speed, wind direction, hourly temperature, and PasquillGifford stability category (P-G) are the meteorological inputs. They can be entered manually through the EDMS data entry screens or automatically through the processing of previously loaded NCDC hourly data.
 c. Output
 Printed outputs consist of:
 <bullet> A monthly and yearly emission inventory report for each source entered; and
 <bullet> A concentration summing report for up to 8760 hours (one year) of data.
 d. Type of Model
 For its emissions inventory calculations, EDMS uses algorithms consistent with the EPA Compilation of Air Pollutant Emission Factors, AP-42. For its dispersion calculations, EDMS uses the GIMM model which is described in reports FAA-EE-88-4 and FAA-EE-88-5, referenced above. GIMM uses a Gaussian plume algorithm.
 e. Pollutant Types
 EDMS inventories and calculates the dispersion of carbon monoxide, nitrogen oxides, sulphur oxides, hydrocarbons, and suspended particles.
 f. Source-Receptor Relationship
 Up to 170 sources and 10 receptors can be treated simultaneously. Area sources are treated as a series of lines that are positioned perpendicular to the wind. Line sources (roadways, runways) are modeled as a series of points. Terrain elevation differences between sources and receptors are neglected.
 Receptors are assumed to be at ground level.
 g. Plume Behavior
 Plume rise is calculated for all point sources (heating plants, incinerators, etc.) using Briggs plume rise equations (Catalano, 1986; Briggs, 1969; Briggs, 1971; Briggs, 1972). Building and stack tip downwash effects are not treated. Roadway dispersion employs a modification to the Gaussian plume algorithms as suggested by Rao and Keenan (1980) to account for close-in vehicle-induced turbulence.
 h. Horizontal Winds
 Steady state winds are assumed for each hour. Winds are assumed to be constant with altitude.
 Winds are entered manually by the user or automatically by reading previously loaded NCC annual data files.
 i. Vertical Wind Speed
 Vertical wind speed is assumed to be zero.
 j. Horizontal Dispersion
 Four stability classes are used (P-G classes B through E). Horizontal dispersion coefficients are computed using a table look-up and linear interpolation scheme. Coefficients are based on Pasquill (1976) as adapted by Petersen (1980). A modified coefficient table is used to account for trafficenhanced 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 trafficenhanced 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.
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 a. Recommendation for Regulatory Use
 CTDMPLUS is appropriate for the following applications: <bullet> Elevated point sources; <bullet> Terrain elevations above stack top; <bullet> Rural or urban areas; <bullet> Transport distances less than 50 kilometers; and <bullet> One hour to annual averaging times when used with a post-processor program such as CHAVG.
 b. Input Requirements
 Source data: For each source, user supplies source location, height, stack diameter, stack exit velocity, stack exit temperature, and emission rate; if variable emissions are appropriate, the user supplies hourly values for emission rate, stack exit velocity, and stack exit temperature.
 Meteorological data: the user must supply hourly averaged values of wind, temperature and turbulence data for creation of the basic meteorological data file (``PROFILE''). Meteorological preprocessors then create a SURFACE data file (hourly values of mixed layer heights, surface friction velocity, Monin-Obukhov length and surface roughness length) and a RAWINsonde data file (upper air measurements of pressure, temperature, wind direction, and wind speed). Receptor data: receptor names (up to 400) and coordinates, and hill number (each receptor must have a hill number assigned). Terrain data: user inputs digitized contour information to the terrain preprocessor which creates the TERRAIN data file (for up to 25 hills).
 c. Output
 When CTDMPLUS is run, it produces a concentration file, in either binary or text format (user's choice), and a list file containing a verification of model inputs, i.e., <bullet> Input meteorological data from ``SURFACE'' and ``PROFILE''
 <bullet> Stack data for each source <bullet> Terrain information
 <bullet> Receptor information <bullet> Source-receptor location (line printer map). In addition, if the case-study option is selected, the listing includes:
 <bullet> Meteorological variables at plume height <bullet> Geometrical relationships between the source and the hill
 <bullet> Plume characteristics at each receptor, i.e., -> distance in along-flow and cross flow direction -> effective plume-receptor height difference -> effective <greek-s><INF>y & <greek-s><INF>z values, both flat terrain and hill induced (the difference shows the effect of the hill)
 -> concentration components due to WRAP, LIFT and FLAT. If the user selects the TOPN option, a summary table of the top 4 concentrations at each receptor is given. If the ISOR option is selected, a source contribution table for every hour will be printed.
 A separate disk file of predicted (1-hour only) concentrations (``CONC'') is written if the user chooses this option. Three forms of output are possible:
 (1) A binary file of concentrations, one value for each receptor in the hourly sequence as run;
 (2) A text file of concentrations, one value for each receptor in the hourly sequence as run; or
 (3) A text file as described above, but with a listing of receptor information (names, positions, hill number) at the beginning of the file.
 Hourly information provided to these files besides the concentrations themselves includes the year, month, day, and hour information as well as the receptor number with the highest concentration.
 d. Type of Model
 CTDMPLUS is a refined steady-state, point source plume model for use in all stability conditions for complex terrain applications.
 e. Pollutant Types
 CTDMPLUS may be used to model non-reactive, primary pollutants.
 f. Source-Receptor Relationship
 Up to 40 point sources, 400 receptors and 25 hills may be used. Receptors and sources are allowed at any location. Hill slopes are assumed not to exceed 15 deg., so that the linearized equation of motion for Boussinesq flow are applicable. Receptors upwind of the impingement point, or those associated with any of the hills in the modeling domain, require separate treatment.
 g. Plume Behavior
 As in CTDM, the basic plume rise algorithms are based on Briggs' (1975) recommendations.
 A central feature of CTDMPLUS for neutral/stable conditions is its use of a critical dividing-streamline height (H<INF>c) to separate the flow in the vicinity of a hill into two separate layers. The plume component in the upper layer has sufficient kinetic energy to pass over the top of the hill while streamlines in the lower portion are constrained to flow in a horizontal plane around the hill. Two separate components of CTDMPLUS compute groundlevel concentrations resulting from plume material in each of these flows.
 The model calculates on an hourly (or appropriate steady averaging period) basis how the plume trajectory (and, in stable/ neutral conditions, the shape) is deformed by each hill. Hourly profiles of wind and temperature measurements are used by CTDMPLUS to compute plume rise, plume penetration (a formulation is included to handle penetration into elevated stable layers, based on Briggs (1984)), convective scaling parameters, the value of H<INF>c, and the Froude number above H<INF>c.
 h. Horizontal Winds
 CTDMPLUS does not simulate calm meteorological conditions. Both scalar and vector wind speed observations can be read by the model. If vector wind speed is unavailable, it is calculated from the scalar wind speed. The assignment of wind speed (either vector or scalar) at plume height is done by either: <bullet> Interpolating between observations above and below the plume height, or
 <bullet> Extrapolating (within the surface layer) from the nearest measurement height to the plume height.
 i. Vertical Wind Speed
 Vertical flow is treated for the plume component above the critical dividing streamline height (H<INF>c); see ``Plume Behavior''.
 j. Horizontal Dispersion
 Horizontal dispersion for stable/neutral conditions is related to the turbulence velocity scale for lateral fluctuations, <greek-s><INF>v, for which a minimum value of 0.2 m/s is used. Convective scaling formulations are used to estimate horizontal dispersion for unstable conditions.
 k. Vertical Dispersion
 Direct estimates of vertical dispersion for stable/neutral conditions are based on observed vertical turbulence intensity, e.g., <greek-s><INF>w (standard deviation of the vertical velocity fluctuation). In simulating unstable (convective) conditions, CTDMPLUS relies on a skewed, bi-Gaussian probability density function (PDF) description of the vertical velocities to estimate the vertical distribution of pollutant concentration.
 l. Chemical Transformation
 Chemical transformation is not treated by CTDMPLUS.
 m. Physical Removal
 Physical removal is not treated by CTDMPLUS (complete reflection at the ground/hill surface is assumed).
 n. Evaluation Studies
 Burns, D.J., L.H. Adams and S.G. Perry, 1990. Testing and Evaluation of the CTDMPLUS Dispersion Model: Daytime Convective Conditions. Environmental Protection Agency, Research Triangle Park, NC.
 Paumier, J.O., S.G. Perry and D.J. Burns, 1990. An Analysis of CTDMPLUS Model Predictions with the Lovett Power Plant Data Base. Environmental Protection Agency, Research Triangle Park, NC. Paumier, J.O., S.G. Perry and D.J. Burns, 1992. CTDMPLUS: A Dispersion Model for Sources near Complex Topography. Part II: Performance Characteristics. Journal of Applied Meteorology, 31(7): 646-660.
 A. REF References
 Benson, P.E., 1979. CALINE3--A Versatile Dispersion Model for Predicting Air Pollution Levels Near Highways and Arterial Streets. Interim Report, Report Number FHWA/CA/TL-79/23. Federal Highway Administration, Washington, D.C.
 Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission Critical Review Series, Oak Ridge National Laboratory, Oak Ridge, TN. (NTIS No. TID-25075)
 Briggs, G.A., 1971. Some Recent Analyses of Plume Rise Observations. Proceedings of the Second International Clean Air Congress, edited by H.M. Englund and W.T. Berry. Academic Press, New York, NY.
 Briggs, G.A., 1974. Diffusion Estimation for Small Emissions. USAEC Report ATDL-106. U.S. Atomic Energy Commission, Oak Ridge, TN.
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 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 212f 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 3\1/2\'' diskette of the FORTRAN coding and the user's guide are available at a cost of $3,500 (non-profit organization) or $5,000 (other organizations) from: AeroVironment, Inc., 222 Huntington Drive, Monrovia, CA 91016, Phone: (818) 357-9983.
 [[Page 41876]]
 Abstract
 The AVACTA II model is a Gaussian model in which atmospheric dispersion phenomena are described by the evolution of plume elements, either segments or puffs. The model can be applied for short time (e.g., one day) simulations in both transport and calm conditions.
 The user is given flexibility in defining the computational domain, the three-dimensional meteorological and emission input, the receptor locations, the plume rise formulas, the sigma formulas, etc. Without explicit user's specifications, standard default values are assumed.
 AVACTA II provides both concentration fields on the user specified receptor points, and dry/wet deposition patterns throughout the domain. The model is particularly oriented to the simulation of the dynamics and transformation of sulfur species (SO<INF>2 and SO<INF>4=), 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 highest-second-highest concentration values. Both partial and total concentrations are provided.
 d. Type of Model
 AVACTA II is Gaussian segment/puff model.
 e. Pollutant Types
 AVACTA II can handle any couple of primary-secondary pollutants (e.g., SO<INF>2 and SO<INF>4=).
 f. Source Receptor Relationship
 The AVACTA II approach maintains the basic Gaussian formulation, but allows a numerical simulation of both nonstationary and nonhomogeneous meteorological conditions. The emitted pollutant material is divided into a sequence of ``elements,'' either segments or puffs, which are connected together but whose dynamics are a function of the local meteorological conditions. Since the meteorological parameters vary with time and space, each element evolves according to the different meteorological conditions encountered along its trajectory.
 AVACTA II calculates the partial contribution of each source in each receptor during each interval. The partial concentration is the sum of the contribution of all existing puffs, plus that of the closest segment.
 g. Plume Behavior
 The user can select the following plume rise formulas: Briggs (1969, 1971, 1972)
 CONCAWE (Briggs, 1975)
 Lucas-Moore (Briggs, 1975)
 User's function, i.e., a subroutine supplied by the user With cold plumes, the program uses a special routine for the computation of the jet plume rise. The user can also select several computational options that control plume behavior in complex terrain and its total/partial reflections.
 h. Horizontal Winds
 A 3D mass-consistent wind field is optionally generated.
 i. Vertical Wind Speed
 A 3D mass-consistent wind field is optionally generated.
 j. Horizontal Dispersion
 During each step, the sigmas of each element are increased. The user can select the following sigma functions: Pasquill-Gifford-Turner (in the functional form specified by Green et al., 1980)
 Brookhaven (Gifford, 1975)
 Briggs, open country (Gifford, 1975) Briggs, urban, i.e., McElroy-Pooler (Gifford, 1975) Irwin (1979a)
 LO-LOCAT (MacCready et al., 1974)
 User-specified function, by points
 User-specified function, with a user's subroutine The virtual distance/age concept is used for incrementing the sigmas at each time step.
 k. Vertical Dispersion
 During each step, the sigmas of each element are increased. The user can select the following sigma functions: Pasquill-Gifford-Turner (in the functional form specified by Green et al., 1980)
 Brookhaven (Gifford, 1975)
 Briggs, open country (Gifford, 1975) Briggs, urban, i.e., McElroy-Pooler (Gifford, 1975) LO-LOCAT (MacCready et al., 1974)
 User-specified function, with a user's subroutine The virtual distance/age concept is used for incrementing the sigmas at each time step.
 l. Chemical Transformation
 First order chemical reactions (primary-to-secondary pollutant)
 m. Physical Removal
 First order dry and wet deposition schemes
 n. Evaluation Studies
 Zannetti P., G. Carboni and A. Ceriani, 1985. AVACTA II Model Simulations of Worst-Case Air Pollution Scenarios in Northern Italy. 15th International Technical Meeting on Air Pollution Modeling and Its Application, St. Louis, Missouri, April 15-19.
 B.2 Dense Gas Dispersion Model (DEGADIS)
 Reference
 Environmental Protection Agency, 1989. User's Guide for the DEGADIS 2.1--Dense Gas Dispersion Model. EPA Publication No. EPA- 450/4-89-019. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711. (NTIS No. PB 90-213893)
 Availability
 The model code is only available on the Support Center for Regulatory Air Models Bulletin Board System (see Section B.0).
 Abstract
 DEGADIS 2.1 is a mathematical dispersion model that can be used to model the transport of toxic chemical releases into the atmosphere. Its range of applicability includes continuous, instantaneous, finite duration, and time-variant releases; negatively-buoyant and neutrally-buoyant releases; ground-level, low-momentum area releases; ground-level or elevated 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
 [[Page 41877]]
 data or convert units of input values. Chemical property data must be input by the user. Such data for a few selected species are available within the model. Additional data may be added to this data base by the user.
 Source data requirements are: emission rate and release duration; emission chemical and physical properties (molecular weight, density vs. concentration profile in the case of aerosol releases, and contaminant heat capacity in the case of a nonisothermal gas release; stack parameters (i.e., diameter, elevation above ground level, temperature at release point). Meteorological data requirements are: wind speed at designated height above ground, ambient temperature and pressure, surface roughness, relative humidity, and ground surface temperature (which in most cases can be adequately approximated by the ambient temperature).
 Receptor data requirements are: averaging time of interest, above-ground height of receptors, and maximum distance between receptors (since the model computes downwind receptor distances to optimize model performance, this parameter is used only for nominal control of the output listing, and is of secondary importance). No indoor concentrations are calculated by the model.
 c. Output
 Printed output includes in tabular form: <bullet> Listing of model input data; <bullet> Plume centerline elevation, mole fraction, concentration, density, and temperature at each downwind distance; <bullet> <greek-s><INF>y and <greek-s><INF>z values at each downwind distance;
 <bullet> Off-centerline distances to 2 specified concentration values at a specified receptor height at each downwind distance (these values can be used to draw concentration isopleths after model execution);
 <bullet> Concentration vs. time histories for finite-duration releases (if specified by user).
 The output print file is automatically saved and must be sent to the appropriate printer by the user after program execution. No graphical output is generated by the current version of this program.
 d. Type of Model
 DEGADIS estimates plume rise and dispersion for verticallyupward jet releases using mass and momentum balances with air entrainment based on laboratory and field-scale data. These balances assume Gaussian similarity profiles for velocity, density, and concentration within the jet. Ground-level denser-than-air phenomena is treated using a power law concentration distribution profile in the vertical and a hybrid top hat-Gaussian concentration distribution profile in the horizontal. A power law specification is used for the vertical wind profile. Ground-level cloud slumping phenomena and air entrainment are based on laboratory measurements and field-scale observations.
 e. Pollutant Types
 Neutrally- or negatively-buoyant gases and aerosols. Pollutants are assumed to be non-reactive and non-depositing.
 f. Source-Receptor Relationships
 Only one source can be modeled at a time. There is no limitation to the number of receptors; the downwind receptor distances are internally-calculated by the model. The DEGADIS calculation is carried out until the plume centerline concentration is 50below the lowest concentration level specified by the user.
 The model contains no modules for source calculations or release characterization.
 g. Plume Behavior
 Jet/plume trajectory is estimated from mass and momentum balance equations. Surrounding terrain is assumed to be flat, and stack tip downwash, building wake effects, and fumigation are not treated.
 h. Horizontal Winds
 Constant logarithmic velocity profile which accounts for stability and surface roughness is used. The wind speed profile exponent is determined from a least squares fit of the logarithmic profile from ground level to the wind speed reference height. Calm winds can be simulated for ground-level low-momentum releases.
 Along-wind dispersion of transient releases is treated using the methods of Colenbrander (1980) and Beals (1971).
 i. Vertical Wind Speed
 Not treated.
 j. Horizontal Dispersion
 When the plume centerline is above ground level, horizontal dispersion coefficients are based upon Turner (1969) and Slade (1968) with adjustments made for averaging time and plume density. When the plume centerline is at ground level, horizontal dispersion also accounts for entrainment due to gravity currents as parameterized from laboratory experiments.
 k. Vertical Dispersion
 When the plume centerline is above ground level, vertical dispersion coefficients are based upon Turner (1969) and Slade (1968). Perfect ground reflection is applied. In the ground-level dense-gas regime, vertical dispersion is also based upon results from laboratory experiments in 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/N<INF>2O<INF>4 Test Data. USAF Engineering and Services Laboratory, Final Report ESL-TR-86-24.
 Spicer, T.O. and J.A. Havens, 1988. Development of Vapor Dispersion Models for Nonneutrally Buoyant Gas Mixtures--Analysis of TFI/NH<INF>3 Test Data. USAF Engineering and Services Laboratory, Final Report.
 o. Operating Information
 The model requires either a VAX computer or an IBM<Register>-- compatible PC for its execution. The model currently does not require supporting software. A FORTRAN compiler is required to generate program executables in the VAX computing environment. PC executables are provided within the source code; however, a PC FORTRAN compiler may be used to tailor a PC executable to the user's PC environment.
 B.3 ERT Visibility Model
 Reference
 ENSR Consulting and Engineering, 1990. ERT Visibility Model: Version 4; Technical Description and User's Guide. Document M2020- 003. ENSR Consulting and Engineering, 35 Nagog Park, Acton, MA 01720.
 Availability
 The user's guide and model code on diskette are available as a package (as PB 96-501978) from the National Technical Information Service (see Section B.0).
 Abstract
 The ERT Visibility Model is a Gaussian dispersion model designed to estimate visibility impairment for arbitrary lines of sight due to isolated point source emissions by simulating gas-to-particle conversion, dry deposition, NO to NO<INF>2 conversion and linear radiative transfer.
 a. Recommendations for Regulatory Use
 There is no specific recommendation at the present time. The ERT Visibility Model may be used on a case-by-case basis.
 b. Input Requirements
 Source data requirements are: stack height, stack temperature, emissions of SO<INF>2, NO<INF>x, TSP, fraction of NO<INF>x as NO<INF>2, fraction of TSP which is carbonaceous, exit velocity, and exit radius.
 Meteorological data requirements are: hourly ambient temperature, mixing depth, wind speed at stack height, stability class, potential temperature gradient, and wind direction. Receptor data requirements are: observer coordinates with respect to source, latitude, longitude, time zone, date, time of day, elevation, relative humidity, background visual range, line-ofsight azimuth and elevation angle, inclination angle of the observed object, distance from observer to object, object and surface reflectivity, number and spacing of integral receptor points along line of sight.
 Other data requirements are: ambient concentrations of O<INF>3 and NO<INF>x, deposition velocity of TSP, sulfate, nitrate, SO<INF>2 and NO<INF>x, first-order transformation rate for sulfate and nitrate.
 c. Output
 Printed output includes both summary and detailed results as follows: Summary output: Page 1--site, observer and object parameters; Page 2--optical pollutants and associated extinction coefficients; Page 3--plume model input parameters; Page 4--total calculated visual range reduction, and each pollutant's contribution; Page 5--calculated plume contrast, object contrast and object contrast degradation at the 550nm wavelength; Page 6-- calculated blue/red ratio and <greek-L>E
 [[Page 41878]]
 (U*V*W*) values for both sky and object discoloration. Detailed output: phase functions for each pollutant in four wavelengths (400, 450, 550, 650nm), concentrations for each pollutant along sight path, solar geometry contrast parameters at all wavelengths, intensities, tristimulus values and chromaticity coordinates for views of the object, sun, background sky and plume.
 d. Type of Model
 ERT Visibility model is a Gaussian plume model for estimating visibility impairment.
 e. Pollutant Types
 Optical activity of sulfate, nitrate (derived from SO<INF>2 and NO<INF>x emissions), primary TSP and NO<INF>2 is simulated.
 f. Source Receptor Relationship
 Single source and hour is simulated. Unlimited number of linesof -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 Intercomparison of Four Models with Observations at a Well-Controlled Power Plant. Atmospheric Environment, 19: 515-528.
 B.4 HGSYSTEM
 (Dispersion Models for Ideal Gases and Hydrogen Fluoride)
 Reference
 Post, L. (ed.), 1994. HGSYSTEM 3.0 Technical Reference Manual. Shell Research Limited, Thornton Research Centre, Chester, United Kingdom. (TNER 94.059)
 Post, L., 1994. HGSYSTEM 3.0 User's Manual. Shell Research Limited, Thornton Research Centre, Chester, United Kingdom. (TNER 94.059)
 Availability
 The PC-DOS version of the HGSYSTEM software (HGSYSTEM: Version 3.0, Programs for modeling the dispersion of ideal gas and hydrogen fluoride releases, executable programs and source code can be installed from diskettes. These diskettes and all documentation are available as a package from API [(202) 682-8340] or from NTIS as PB 96-501960 (see Section B.0).
 Technical Contacts
 Doug N. Blewitt, AMOCO Corporation, 1670 Broadway/MC 2018, Denver, CO, 80201, (303) 830-5312.
 Howard J. Feldman, American Petroleum Institute, 1220 L Street Northwest, Washington, DC 20005, (202) 682-8340.
 Abstract
 HGSYSTEM is a PC-based software package consisting of mathematical models for estimating of one or more consecutive phases between spillage and near-field and far-field dispersion of a pollutant. The pollutant can be either a two-phase, multi-compound mixture of non-reactive compounds or hydrogen fluoride (HF) with chemical reactions. The individual models are: Database program:
 DATAPROP Generates physical properties used in other HGSYSTEM models
 Source term models:
 SPILL Transient liquid release from a pressurized vessel HFSPILL SPILL version specifically for HF LPOOL Evaporating multi-compound liquid pool model Near-field dispersion models:
 AEROPLUME High-momentum jet dispersion model HFPLUME AEROPLUME version specifically for HF HEGABOX Dispersion of instantaneous heavy gas releases Far-field dispersion models:
 HEGADAS(S,T) Heavy gas dispersion (steady-state and transient version)
 PGPLUME Passive Gaussian dispersion Utility programs:
 HFFLASH Flashing of HF from pressurized vessel POSTHS/POSTHT Post-processing of HEGADAS(S,T) results PROFILE Post-processor for concentration contours of airborne plumes
 GET2COL Utility for data retrieval
 The models assume flat, unobstructed terrain. HGSYSTEM can be used to model steady-state, finite-duration, instantaneous and time dependent releases, depending on the individual model used. The models can be run consecutively, with relevant data being passed on from one model to the next using link files. The models can be run in batch mode or using an iterative utility program.
 a. Recommendations for Regulatory Use
 HGSYSTEM can be used as a refined model to estimate short-term ambient concentrations. For toxic chemical releases (non-reactive chemicals or hydrogen fluoride; 1-hour or less averaging times) the expected area of exposure to concentrations above specified threshold values can be determined. For flammable non-reactive gases it can be used to determine the area in which the cloud may ignite.
 b. Input Requirements
 HFSPILL input data: reservoir data (temperature, pressure, volume, HF mass, mass-fraction water), pipe-exit diameter and ambient pressure.
 EVAP input data: spill rate, liquid properties, and evaporation rate (boiling pool) or ambient data (non-boiling pool). HFPLUME and PLUME input data: reservoir characteristics, pollutant parameters, pipe/release data, ambient conditions, surface roughness and stability class.
 HEGADAS input data: ambient conditions, pollutant parameters, pool data or data at transition point, surface roughness, stability class and averaging time.
 PGPLUME input data: link data provided by HFPLUME and the averaging time.
 c. Output
 The HGSYSTEM models contain three post-processor programs which can be used to extract modeling results for graphical display by external software packages. GET2COL can be used to extract data from the model output files. HSPOST can be used to develop isopleths, extract any 2 parameters for plotting and correct for finite release duration. HTPOST can be used to produce time history plots. HFSPILL output data: reservoir mass, spill rate, and other reservoir variables as a function of time. For HF liquid, HFSPILL generates link data to HFPLUME for the initial phase of choked liquid flow (flashing jet), and link data to EVAP for the subsequent phase of unchoked liquid flow (evaporating liquid pool). EVAP output data: pool dimensions, pool evaporation rate, pool mass and other pool variables for steady state conditions or as a function of time. EVAP generates link data to the dispersion model HEGADAS (pool dimensions and pool evaporation rate). HFPLUME and PLUME output data: plume variables (concentration, width, centroid height, temperature, velocity, etc.) as a function of downwind distance.
 HEGADAS output data: concentration variables and temperature as a function of downwind distance and (for transient case) time. PGPLUME output data: concentration as a function of downwind distance, cross-wind distance and height.
 d. Type of Model
 HGSYSTEM is made up of four types of dispersion models. HFPLUME and PLUME simulate the near-field dispersion and PGPLUME simulates the passive-gas
 [[Page 41879]]
 dispersion downwind of a transition point. HEGADAS simulates the ground-level heavy-gas dispersion.
 e. Pollutant Types
 HGSYSTEM may be used to model non-reactive chemicals or hydrogen fluoride.
 f. Source-Receptor Relationships
 HGSYSTEM estimates the expected area of exposure to concentrations above user-specified threshold values. By imposing conservation of mass, momentum and energy the concentration, density, speed and temperature are evaluated as a function of downwind distance.
 g. Plume Behavior
 HFPLUME and PLUME: (1) are steady-state models assuming a tophat profile with cross-section averaged plume variables; and (2) the momentum equation is taken into account for horizontal ambient shear, gravity, ground collision, gravity-slumping pressure forces and ground-surface drag.
 HEGADAS: assumes the heavy cloud to move with the ambient wind speed, and adopts a power-law fit of the ambient wind speed for the velocity profile.
 PGPLUME: simulates the passive-gas dispersion downwind of a transition point from HFPLUME or PLUME for steady-state and finite duration releases.
 h. Horizontal Winds
 A power law fit of the ambient wind speed is used.
 i. Vertical Wind Speed
 Not treated.
 j. Horizontal Dispersion
 HFPLUME and PLUME: Plume dilution is caused by air entrainment resulting from high plume speeds, trailing vortices in wake of falling plume (before touchdown), ambient turbulence and density stratification. Plume dispersion is assumed to be steady and momentum-dominated, and effects of downwind diffusion and wind meander (averaging time) are not taken into account. HEGADAS: This model adopts a concentration similarity profile expressed in terms of an unknown center-line ground-level concentration and unknown vertical/cross-wind dispersion parameters. These quantities are determined from a number of basic equations describing gas-mass conservation, air entrainment (empirical law describing vertical top-entrainment in terms of global Richardson number), cross-wind gravity spreading (initial gravity spreading followed by gravity-current collapse) and cross-wind diffusion (Briggs formula).
 PGPLUME: This model assumes a Gaussian concentration profile in which the cross-wind and vertical dispersion coefficients are determined by empirical expressions. All unknown parameters in this profile are determined by imposing appropriate matching criteria at the transition point.
 k. Vertical Dispersion
 See description above.
 l. Chemical Transformation
 Not treated.
 m. Physical Removal
 Not treated.
 n. Evaluation Studies
 PLUME has been validated against field data for releases of liquified propane, and wind tunnel data for buoyant and 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 post-processors and hard copies of user manuals (User's Manual, Maintenance Manual, Operations Manual, Maintenance Interface Manual, Topo Manual, and 3-Dimensional Plume Manual) are available from YSA Corporation, Rt. 4 Box 81-A, Santa Fe, NM 87501; Phone: (505) 989-7351; Fax: (505) 989-7965; e-mail: 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 three-dimensional meteorological models on desktop workstations. The present versions of the programs are running on super computers and workstations. GUIs are available on Sun Microsystems and Silicon Graphics workstations. The modeling system can also run on a laptop workstation which makes it possible to run the programs in the field or away from the office. As technology continues to advance, a version of HOTMAC/RAPTAD suitable for PC-based platforms will be considered for release by YSA. HOTMAC, Higher Order Turbulence Model for Atmospheric Circulation, is a mesoscale weather prediction model that forecasts wind, temperature, humidity, and atmospheric turbulence distributions over complex surface conditions. HOTMAC has options to include non-hydrostatic pressure computation, nested grids, land-use distributions, cloud, fog, and precipitation physics. HOTMAC can interface with tower, rawinsonde, and large-scale weather data using a four-dimensional data assimilation method. RAPTAD, Random Puff Transport and Diffusion, is a Lagrangian random puff model that is used to forecast transport and diffusion of airborne materials over complex terrain. Concentrations are computed by summing the concentration of each puff at the receptor location. The random puff method is equivalent to the random particle method with a Gaussian kernel for particle distribution. The advantage of the puff method is the accuracy and speed of computation. The particle method requires the release of a large number of particles which could be computationally expensive. The puff method requires the release of a much less number of puffs, typically \1/10\ to \1/100\ of the number of particles required by the particle method. The averaging time for concentration estimates is variable from 5 minutes to 15 minutes for each receptor. In addition to the concentration computation at the receptor sites, RAPTAD computes and graphically displays hourly concentration contours at the ground level. RAPTAD is applicable to point and area sources. The meteorological data produced from HOTMAC are used as input to RAPTAD. RAPTAD can forecast concentration distributions for neutrally buoyant gas, buoyant gas and denser-than-air gas. The models are significantly advanced in both
 [[Page 41880]]
 their model physics and in their operational procedures. GUIs are provided to help the user prepare input files, run programs, and display the modeled results graphically in three dimensions.
 a. Recommendation for Regulatory Use
 There are no specific recommendations at the present time. The HOTMAC/RAPTAD modeling system may be used on a case-by-case basis.
 b. Input Requirements
 Meteorological Data: The modeling system is significantly different from the majority of regulatory models in terms of how meteorological data are provided and used in concentration simulations. Regulatory models use the wind data which are obtained directly from measurements or analyzed by using a simple constraint such as a mass conservation equation. Thus, the accuracy of the computation will depend significantly on the quantity and quality of the wind data. This approach is acceptable as long as the study area is flat and the simulation period is short. As the regulations become more stringent and more realistic surface conditions are required, a significantly large volume of meteorological data is required which could become very expensive. An alternative approach is to augment the measurements with predicted values from a mesoscale meteorological model. This is the approach we have taken here. This approach has several advantages over the conventional method. First, concentration computations use the model forecast wind while the conventional method extrapolates the observed winds. Extrapolation of wind data over complex terrain and for an extended period of time quickly loses its accuracy. Secondly, the number of stations for upper air soundings is typically limited from none to at most a few stations in the study area. The corresponding number in a mesoscale model is the number of grid points in the horizontal plane which is typically 50 X 50. Consequently, concentration distributions using model forecasted winds would be much more accurate than those obtained by using winds which were extrapolated from the limited number of measurements. HOTMAC requires meteorological data for initialization and to provide boundary conditions if the boundary conditions change significantly with time. The minimum amount of data required to run HOTMAC is wind and potential temperature profiles at a single station. HOTMAC forecasts wind and turbulence distributions in the boundary layer through a set of model equations for solar radiation, heat energy balance at the ground, conservation of momentum, conservation of internal energy, and conservation of mass. Terrain Data: HOTMAC and RAPTAD use the digitized terrain data from the U.S. Geological Survey and the Defense Mapping Agency. Extraction of terrain data is greatly simplified by using YSA's GUI software called Topo. The user specifies the latitudes and longitudes of the southwest and northeast corner points of the study area. Then, Topo extracts the digitized elevation data within the area specified and converts from the latitudes and longitudes to the UTM (Universal Transverse Mercator) coordinates for up to three nested grids.
 Emission Data: Emission data requirements are emission rate, stack height, stack diameter, stack location, stack gas exit velocity, and stack buoyancy.
 Receptor Data: Receptor data requirements are names, location coordinates, and desired averaging time for concentration estimates, which is variable from 5 to 15 minutes.
 c. Output
 HOTMAC outputs include hourly winds, temperatures, and turbulence variables at every grid point. Ancillary codes graphically display vertical profiles of wind, temperature, and turbulence variables at selected locations and wind vector distributions at specified heights above the ground. These codes also produce graphic files of wind direction projected on vertical cross sections.
 RAPTAD outputs include hourly values of surface concentration, time variations of mean and standard deviation of concentrations at selected locations, and coordinates of puff center locations. Ancillary codes produce color contour plots of surface concentration, time variations of mean concentrations and ratios of standard deviation to mean value at selected locations, and concentration distributions in the vertical cross sections. The averaging time of concentration at a receptor location is variable from 5 to 15 minutes. Color contour plots of surface concentration can be animated on the monitor to review time variations of high concentration areas.
 d. Type of Model
 HOTMAC is a 3-dimensional Eulerian model for weather forecasting, and RAPTAD is a 3-dimensional Lagrangian random puff model for pollutant transport and diffusion.
 e. Pollutant types
 RAPTAD may be used to model any inert pollutants, including dense and buoyant gases.
 f. Source-Receptor Relationship
 Up to six point or area sources are specified and up to 50 sampling locations are selected. Source and receptor heights are specified by the user.
 g. Plume Behavior
 Neutrally buoyant plumes are transported by mean and turbulence winds that are modeled by HOTMAC. Non-neutrally buoyant plume equations are based on Van Dop (1992). In general, plumes are 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.
 [[Page 41881]]
 a. Recommendations for Regulatory Use
 LONGZ can be used if it can be demonstrated to estimate concentrations equivalent to those provided by the preferred model for a given application. LONGZ must be executed in the equivalent mode.
 LONGZ can be used on a case-by-case basis in lieu of a preferred model if it can be demonstrated, using the criteria in Section 3.2 of Appendix W, that LONGZ is more appropriate for the specific application. In this case the model options/modes which are most appropriate for the application should be used.
 b. Input Requirements
 Source data requirements are: for point, building or area sources, location, elevation, total emission rate (optionally classified by gravitational settling velocity) and decay coefficient; for stack sources, stack height, effluent temperature, effluent exit velocity, stack radius (inner), emission rate, and ground elevation (optional); for building sources, height, length and width, and orientation; for area sources, characteristic vertical dimension, and length, width and orientation. Meteorological data requirements are: wind speed and measurement height, wind profile exponents, wind direction standard deviations (turbulent intensities), mixing height, air temperature, vertical potential temperature gradient.
 Receptor data requirements are: coordinates, ground elevation.
 c. Output
 Printed output includes total concentration due to emissions from user-specified source groups, including the combined emissions from all sources (with optional allowance for depletion by deposition).
 d. Type of Model
 LONGZ is a climatological Gaussian plume model.
 e. Pollutant Types
 LONGZ may be used to model primary pollutants. Settling and deposition are treated.
 f. Source-Receptor Relationships
 LONGZ applies user specified locations for sources and receptors. Receptors are assumed to be at ground level.
 g. Plume Behavior
 Plume rise equations of Bjorklund and Bowers (1982) are used. Stack tip downwash (Bjorklund and Bowers, 1982) is included. All plumes move horizontally and will fully intercept elevated terrain.
 Plumes above mixing height are ignored. Perfect reflection at mixing height is assumed for plumes below the mixing height.
 Plume rise is limited when the mean wind at stack height approaches or exceeds stack exit velocity. Perfect reflection at ground is assumed for pollutants with no settling velocity.
 Zero reflection at ground is assumed for pollutants with finite settling velocity.
 LONGZ does not simulate fumigation. Tilted plume is used for pollutants with settling velocity specified.
 Buoyancy-induced dispersion is treated (Briggs, 1972).
 h. Horizontal Winds
 Wind field is homogeneous and steady-state. Wind speed profile exponents are functions of both stability class and wind speed. Default values are specified in Bjorklund and Bowers (1982).
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Pollutants are initially uniformly distributed within each wind direction sector. A smoothing function is then used to remove discontinuities at sector boundaries.
 k. Vertical Dispersion
 Vertical dispersion is derived from input vertical turbulent intensities using adjustments to plume height and rate of plume growth with downwind distance specified in Bjorklund and Bowers (1982).
 l. Chemical Transformation
 Chemical transformations are treated using exponential decay. Time constant is input by the user.
 m. Physical Removal
 Gravitational settling and dry deposition of particulates are treated.
 n. Evaluation Studies
 Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for the SHORTZ and LONGZ Computer Programs, Volume I and II. EPA Publication No. EPA-903/9-82-004. U.S. Environmental Protection Agency, Region III, Philadelphia, PA.
 B.7 Maryland Power Plant Siting Program (PPSP) Model
 Reference
 Brower, R., 1982. The Maryland Power Plant Siting Program (PPSP) Air Quality Model User's Guide. Ref. No. PPSP-MP-38. Prepared for Maryland Department of Natural Resources by Environmental Center, Martin Marietta Corporation, Baltimore, MD. (NTIS No. PB 82-238387) Weil, J.C. and R.P. Brower, 1982. The Maryland PPSP Dispersion Model for Tall Stacks. Ref. No. PPSP-MP-36. Prepared for Maryland Department of Natural Resources by Environmental Center, Martin Marietta Corporation, Baltimore, MD. (NTIS No. PB 82-219155)
 Availability
 The model code and test data are available on diskette for a nominal cost to defray shipping and handling charges from: Mr. Roger Brower, Versar, Inc., 9200 Rumsey Road, Columbia, MD 21045; Phone: (410) 964-9299.
 Abstract
 PPSP is a Gaussian dispersion model applicable to tall stacks in either rural or urban areas, but in terrain that is essentially flat (on a scale large compared to the ground roughness elements). The PPSP model follows the same general formulation and computer coding as CRSTER, also a Gaussian model, but it differs in four major ways. The differences are in the scientific formulation of specific ingredients or ``sub-models'' to the Gaussian model, and are based on recent theoretical improvements as well as supporting experimental data. The differences are: (1) stability during daytime is based on convective scaling instead of the Turner criteria; (2) Briggs' dispersion curves for elevated sources are used; (3) Briggs plume rise formulas for convective conditions are included; and (4) plume penetration of elevated stable layers is given by Briggs' (1984) model.
 a. Recommendations for Regulatory Use
 PPSP can be used if it can be demonstrated to estimate concentrations equivalent to those provided by the preferred model for a given application. PPSP must be executed in the equivalent mode.
 PPSP can be used on a case-by-case basis in lieu of a preferred model if it can be demonstrated, using the criteria in Section 3.2 of Appendix W, that PPSP is more appropriate for the specific application. In this case the model options/modes which are most appropriate for the application should be used.
 b. Input Requirements
 Source data requirements are: emission rate (monthly rates optional), physical stack height, stack gas exit velocity, stack inside diameter, stack gas temperature. Meteorological data requirements are: hourly surface weather data from the EPA meteorological preprocessor program. Preprocessor output includes hourly stability class, wind direction, wind speed, temperature, and mixing height. Actual anemometer height (a single value) is also required. Wind speed profile exponents (one for each stability class) are required if on-site data are input. Receptor data requirements are: distance of each of the five receptor rings.
 c. Output
 Printed output includes:
 Highest and second highest concentrations for the year at each receptor for averaging times of 1, 3, and 24-hours, plus a 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.
 [[Page 41882]]
 Receptor locations are restricted to 36 azimuths (every 10 degrees) and five user-specified radial distances.
 g. Plume Behavior
 Briggs (1975) final rise formulas for buoyant plumes are used. Momentum rise is not considered.
 Transitional or distance-dependent plume rise is not modeled. Penetration (complete, partial, or zero) of elevated inversions is treated with Briggs (1984) model; ground-level concentrations are dependent on degree of plume penetration.
 h. Horizontal Winds
 Wind speeds are corrected for release height based on power law variation, with different exponents for different stability classes and variable reference height (7 meters is default). Wind speed power law exponents are 0.10, 0.15, 0.20, 0.25, 0.30, and 0.30 for stability classes A through F, respectively. Constant, uniform (steady-state) wind assumed within each hour.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Rural dispersion parameters are Briggs (Gifford, 1975), with stability class defined by u/w* during daytime, and by the method of Turner (1964) at night.
 Urban dispersion is treated by changing all stable cases to stability class D.
 Buoyancy-induced dispersion (Pasquill, 1976) is included (using <greek-D><greek-EE>/3.5).
 k. Vertical Dispersion
 Rural dispersion parameters are Briggs (Gifford, 1975), with stability class defined by u/w* during daytime, and by the method of Turner (1964).
 Urban dispersion is treated by changing all stable cases to stability class D.
 Buoyancy-induced dispersion (Pasquill, 1976) is included (using <greek-D><greek-EE>/3.5).
 l. Chemical Transformation
 Not treated.
 m. Physical Removal
 Not treated.
 n. Evaluation Studies
 Londergan, R., D. Minott, D. Wackter, T. Kincaid and D. Bonitata, 1983. Evaluation of Rural Air Quality Simulation Models, Appendix G: Statistical Tables for PPSP. EPA Publication No. EPA- 450/4-83-003. Environmental Protection Agency, Research Triangle Park, NC.
 Weil, J.C. and R.P. Brower, 1982. The Maryland PPSP dispersion model for tall stacks. Ref. No. PPSP MP-36. Prepared for Maryland Department of Natural Resources. Prepared by Environmental Center, Martin Marietta Corporation, Baltimore, Maryland. (NTIS No. PB 82- 219155)
 B.8 Mesoscale Puff Model (MESOPUFF II)
 Reference
 Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984. User's Guide to the Mesopuff II Model and Related Processor Programs. EPA Publication No. EPA-600/8-84-013. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 84-181775) A Modeling Protocol for Applying MESOPUFF II to Long Range Transport Problems, 1992. EPA Publication No. EPA-454/R-92-021. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Availability
 This model code is available on the Support Center for Regulatory Air Models Bulletin Board System and also on diskette (as PB 93-500247) from the National Technical Information Service (see Section B.0).
 Abstract
 MESOPUFF II is a short term, regional scale puff model designed to calculate concentrations of up to 5 pollutant species (SO<INF>2, SO<INF>4, NO<INF>X, HNO<INF>3, NO<INF>3). Transport, puff growth, chemical transformation, and wet and dry deposition are accounted for in the model.
 a. Recommendations for Regulatory Use
 There is no specific recommendation at the present time. The model may be used on a case-by-case basis.
 b. Input Requirements
 Required input data include four types: (1) input control parameters and selected technical options, (2) hourly surface meteorological data and twice daily upper air measurements, hourly precipitation data are optional, (3) surface land use classification information, (4) source and emissions data. Data from up to 25 surface National Weather Service stations and up to 10 upper air stations may be considered. Spatially variable fields at hour intervals of winds, mixing height, stability class, and relevant turbulence parameters are derived by MESOPAC II, the meteorological preprocessor program described in the User Guide. Source and emission data for up to 25 point sources and/or up to 5 area sources can be included. Required information are: location in grid coordinates, stack height, exit velocity and temperature, and emission rates for the pollutant to be modeled. Receptor data requirements: up to a 40 x 40 grid may be used and non-gridded receptor locations may be considered.
 c. Output
 Line printer output includes: all input parameters, optionally selected arrays of ground-level concentrations of pollutant species at specified time intervals.
 Line printer contour plots output from MESOFILE II postprocessor program. Computer readable output of concentration array to disk/tape for each hour.
 d. Type of Model
 MESOPUFF II is a Gaussian puff superposition model.
 e. Pollutant Types
 Up to five pollutant species may be modeled simultaneously and include: SO<INF>2, SO<INF>4, NO<INF>x, HNO<INF>3, NO<INF>3.
 f. Source-Receptor Relationship
 Up to 25 point sources and/or up to 5 area sources are permitted.
 g. Plume Behavior
 Briggs (1975) plume rise equations are used, including plume penetration with buoyancy flux computed in the model. Fumigation of puffs is considered and may produce immediate mixing or multiple reflection calculations at user option.
 h. Horizontal Winds
 Gridded wind fields are computed for 2 layers; boundary layer and above the mixed layer. Upper air rawinsonde data and hourly surface winds are used to obtain spatially variable u,v component fields at hourly intervals. The gridded fields are computed by interpolation between stations in the MESOPAC II preprocessor.
 i. Vertical Wind Speed
 Vertical winds are assumed to be zero.
 j. Horizontal Dispersion
 Incremental puff growth is computed over discrete time steps with horizontal growth parameters determined from power law equations fit to sigma y curves of Turner out to 100km. At distances greater than 100km, puff growth is determined by the rate given by Heffter (1965).
 Puff growth is a function of stability class and changes in stability are treated. Optionally, user input plume growth coefficients may be considered.
 k. Vertical Dispersion
 For puffs emitted at an effective stack height which is less than the mixing height, uniform mixing of the pollutant within the mixed layer is performed. For puffs centered above the mixing height, no effect at the ground occurs.
 l. Chemical Transformation
 Hourly chemical rate constants are computed from empirical expressions derived from photochemical model simulations.
 m. Physical Removal
 Dry deposition is treated with a resistance method. Wet removal may be considered if hourly precipitation data are input.
 n. Evaluation Studies
 Results of tests for some model parameters are discussed in: Scire, J.S., F.W. Lurmann, A. Bass and S.R. Hanna, 1984. Development of the MESOPUFF II Dispersion Model. EPA Publication No. EPA-600/3-84-057. U.S. Environmental Protection Agency, Research Triangle Park, NC.
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 B.9 Mesoscale Transport Diffusion and Deposition Model for Industrial Sources (MTDDIS)
 Reference
 Wang, I.T. and T.L. Waldron, 1980. User's Guide for MTDDIS Mesoscale Transport, Diffusion, and Deposition Model for Industrial Sources. EMSC6062.1UR(R2). Combustion Engineering, Newbury Park, CA.
 Availability
 A diskette copy of the FORTRAN coding and the user's guide are available for a cost of $100 from: Dr. I. T. Wang, Environmental Modeling & Analysis, 2219 E. Thousand Oaks Blvd., Suite 435, Thousand Oaks, CA 91362.
 Abstract
 MTDDIS is a variable-trajectory Gaussian puff model applicable to long-range transport of point source emissions over level or rolling terrain. The model can be used to determine 3-hour maximum and 24-hour average concentrations of relatively nonreactive pollutants from up to 10 separate stacks.
 a. Recommendations for Regulatory Use
 There is no specific recommendation at the present time. The MTDDIS Model may be used on a case-by-case basis.
 b. Input Requirements
 Source data requirements are: emission rate, physical stack height, stack gas exit velocity, stack inside diameter, stack gas temperature, and location.
 Meteorological data requirements are: hourly surface weather data, from up to 10 stations, including cloud ceiling, wind direction, wind speed, temperature, opaque cloud cover and precipitation. For long-range applications, user-analyzed daily mixing heights are recommended. If these are not available, the NWS daily mixing heights will be used by the program. A single upper air sounding station for the region is assumed. For each model run, air trajectories are generated for a 48-hour period, and therefore, the afternoon mixing height of the day before and the mixing heights of the day after are also required by the model as input, in order to generate hourly mixing heights for the modeled period. Receptor data requirements are: up to three user-specified rectangular grids.
 c. Output
 Printed output includes:
 Tabulations of hourly meteorological parameters include both input surface observations and calculated hourly stability classes and mixing heights for each station;
 Printed air trajectories for the two consecutive 24-hour periods for air parcels generated 4 hours apart starting at 0000 LST; and 3-hour maximum and 24-hour average grid concentrations over user-specified rectangular grids are output for the second 24-hour period.
 d. Type of Model
 MTDDIS is a Gaussian puff model.
 e. Pollutant Types
 MTDDIS can be used to model primary pollutants. Dry deposition is treated. Exponential decay can account for some reactions.
 f. Source-Receptor Relationship
 MTDDIS treats up to 10 point sources. Up to three rectangular receptor grids may be specified by the user.
 g. Plume Behavior
 Briggs (1971, 1972) plume rise formulas are used. If plume height exceeds mixing height, ground level concentration is assumed zero.
 Fumigation and downwash are not treated.
 h. Horizontal Winds
 Wind speeds and wind directions at each station are first corrected for release height. Speed conversions are based on power law variation and direction conversions are based on linear height dependence as recommended by Irwin (1979b). Converted wind speeds and wind directions are then weighted according to the algorithms of Heffter (1980) to calculate the effective transport wind speed and direction.
 i. Vertical Wind Field
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Transport-time-dependent dispersion coefficients from Heffter (1980) are used.
 k. Vertical Dispersion
 Transport-time-dependent dispersion coefficients from Heffter (1980) are used.
 l. Chemical Transformation
 Chemical transformations are treated using exponential decay. Half-life is input by the user.
 m. Physical Removal
 Dry deposition is treated. User input deposition velocity is required.
 Wet deposition is treated. User input hourly precipitation rate and precipitation layer depth or cloud ceiling height are required.
 n. Evaluation Studies
 Carhart, R.A., A.J. Policastro, M. Wastag and L. Coke, 1989. Evaluation of Eight Short-Term Long-Range Transport Models Using Field Data. Atmospheric Environment, 23: 85-105.
 B.10 Multi-Source (SCSTER) Model
 Reference
 Malik, M.H. and B. Baldwin, 1980. Program Documentation for Multi-Source (SCSTER) Model. Program Documentation EN7408SS. Southern Company Services, Inc., Technical Engineering Systems, 64 Perimeter Center East, Atlanta, GA.
 Availability
 The SCSTER model and user's manual are available at no charge on a limited basis through Southern Company Services. The computer code may be provided on a diskette. Requests should be directed to: Mr. Stanley S. Vasa, Senior Environmental Specialist, Southern Company Services, P.O. Box 2625, Birmingham, AL 35202.
 Abstract
 SCSTER is a modified version of the EPA CRSTER model. The primary distinctions of SCSTER are its capability to consider multiple sources that are not necessarily collocated, its enhanced receptor specifications, its variable plume height terrain adjustment procedures and plume distortion from directional wind shear.
 a. Recommendations for Regulatory Use
 SCSTER can be used if it can be demonstrated to estimate concentrations equivalent to those provided by the preferred model for a given application. SCSTER must be executed in the equivalent mode.
 SCSTER can be used on a case-by-case basis in lieu of a preferred model if it can be demonstrated, using the criteria in Section 3.2 of Appendix W, that SCSTER is more appropriate for the specific application. In this case the model options/modes which are most appropriate for the application should be used.
 b. Input Requirements
 Source data requirements are: emission rate, stack gas exit velocity, stack gas temperature, stack exit diameter, physical stack height, elevation of stack base, and coordinates of stack location. The variable emission data can be monthly or annual averages. Meteorological data requirements are: hourly surface weather data from the EPA meteorological preprocessor program. Preprocessor output includes hourly stability class wind direction, wind speed, temperature, and mixing height. Actual anemometer height (a single value) is optional. Wind speed profile exponents (one for each stability class) are optional.
 Receptor data requirements are: cartesian coordinates and elevations of individual receptors; distances of receptor rings, with elevation of each receptor; receptor grid networks, with elevation of each receptor.
 Any combination of the three receptor input types may be used to consider up to 600 receptor locations.
 c. Output
 Printed output includes:
 Highest and second highest concentrations for the year at each receptor for averaging times of 1-, 3-, and 24-hours, a userselected averaging time which may be 2-12 hours, and a 50 high table for 1-, 3-, and 24-hours;
 Annual arithmetic average at each receptor; and the highest 1- hour and 24-hour concentrations over the receptor field for each day considered.
 Optional tables of source contributions of individual point sources at up to 20 receptor locations for each averaging period; Optional magnetic tape output in either binary or fixed block format includes:
 All 1-hour concentrations.
 Optional card/disk output includes for each receptor: Receptor coordinates; receptor elevation; highest and highest, second-highest, 1-, 3-,
 [[Page 41884]]
 and 24-hour concentrations; and annual average concentration.
 d. Type of Model
 SCSTER is a Gaussian plume model.
 e. Pollutant Types
 SCSTER may be used to model primary pollutants. Settling and deposition are not treated.
 f. Source-Receptor Relationship
 SCSTER can handle up to 60 separate stacks at varying locations and up to 600 receptors, including up to 15 receptor rings. User input topographic elevation for each receptor is used.
 g. Plume Behavior
 SCSTER uses Briggs (1969, 1971, 1972) final plume rise formulas. Transitional plume rise is optional. SCSTER contains options to incorporate wind directional shear with a plume distortion method described in Appendix A of the User's Guide.
 SCSTER provides four terrain adjustments including the CRSTER full terrain height adjustment and a user-input, stability-dependent plume path coefficient adjustment for receptors above stack height.
 h. Horizontal Winds
 Wind speeds are corrected for release height based on power law exponents from DeMarrais (1959), different exponents for different stability classes; default reference height of 7m. Default exponents are 0.10, 0.15, 0.20, 0.25, 0.30, and 0.30 for stability classes A through F, respectively.
 Steady-state wind is assumed within a given hour. Optional consideration of plume distortion due to user-input, stability-dependent wind-direction shear gradients.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Rural dispersion coefficients from Turner (1969) are used. Six stability classes are used.
 k. Vertical Dispersion
 Rural dispersion coefficients from Turner (1969) are used. Six stability classes are used.
 An optional test for plume height above mixing height before terrain adjustment is included.
 l. Chemical Transformation
 Chemical transformations are treated using exponential decay. Half-life is input by the user.
 m. Physical Removal
 Physical removal is treated using exponential decay. Half-life is input by the user.
 n. Evaluation Studies
 Londergan, R., D. Minott, D. Wackter, T. Kincaid and D. Bonitata, 1983. Evaluation of Rural Air Quality Simulation Models. EPA Publication No. EPA-450/4-83-003. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 B.11 PANACHE
 Reference
 Transoft Group, 1994. User's Guide of Fluidyn-PANACHE, a ThreeDimensional Deterministic Simulation of Pollutants Dispersion Model for Complex Terrain; Cary, North Carolina.
 Availability
 For a cost to be negotiated with the model developer, the computer code is available from: Transoft US, Inc., 818 Reedy Creek Road, Cary, NC 27513-3307; Phone: (919) 380-7500, Fax: (919) 380- 7592.
 Abstract
 PANACHE is an Eulerian (and Lagrangian for particulate matter), 3-dimensional finite volume fluid mechanics code designed to simulate continuous and short-term pollution dispersion in the atmosphere, in simple or complex terrain. For single or multiple sources, pollutant emissions from stack, point, area, volume, general sources and distant sources are treated. The model automatically treats obstacles, effects of vegetation and water bodies, the effects of vertical temperature stratification on the wind and diffusion fields, and turbulent shear flows caused by atmospheric boundary layer or terrain effects. The code solves Navier Stokes equations in a curvilinear mesh espousing the terrain and obstacles. A 2nd order resolution helps keep the number of cells limited in case of shearing flow. An initial wind field is computed by using a Lagrangian multiplier to interpolate wind data collected on site. The mesh generator, the solver and the numerical schemes have been adopted for atmospheric flows with or without chemical reactions. The model code operates on any workstation or IBM-- compatible PC (486 or higher). Gaussian and puff modes are available in PANACHE for fast, preliminary simulation.
 a. Recommendations for Regulatory Use
 On a case-by-case basis, PANACHE may be appropriate for the following types of situations: industrial or urban zone on a flat or complex terrain, transport distance from a few meters to 50km, continuous releases with hourly, monthly or annual averaging times, chemically reactive or non-reactive gases or particulate emissions for stationary or roadway sources.
 b. Input Requirements
 Data may be input directly from an external source (e.g., GIS file) or interactively. The model provides the option to use default values when input parameters are unavailable. PANACHE user environment integrates the pre- and post-processor with the solver. The calculations can be done interactively or in batch mode. An inverse scheme is provided to estimate missing data from a few measured values of the wind. Terrain data requirements:
 <bullet> Location, surface roughness estimates, and altitude contours.
 <bullet> Location and dimensions of obstacles, forests, fields, and water bodies.
 Source data requirements:
 For all types of sources, the exit temperature and plume mass flow rates and concentration of each of the pollutants are required. External sources require mass flow rate. For roadways, estimated traffic volume and vehicular emissions are required. Meteorological data requirements:
 Hourly stability class, wind direction, wind speed, temperature, cloud cover, humidity, and mixing height data with lapse rate below and above it.
 Primary meteorological variables available from the National Weather Service can be processed using PCRAMMET (see Section 9.3.3.2 of Appendix W) to an input file.
 Data required at the domain boundary: Wind profile (uniform, log or power law), depending on the terrain conditions (e.g., residential area, forest, sea, etc.). Chemical source data requirements:
 A database of selected species with specific heats and molecular weights can be extended by the user. For heavy gases the database includes a compressibility coefficients table. Solar reflection:
 For natural convection simulation with low wind on a sunny day, approximate values of temperature for fields, forests, water bodies, shadows and their variations with the time of the day are determined automatically.
 c. Output
 Printed output option: pollutant concentration at receptor points, and listing of input data (terrain, chemical, weather, and source data) with turbulence and precision control data. Graphical output includes: In 3-dimensional perspective or in any crosswind, downwind or horizontal plane: wind velocity, pollutant concentration, 3-dimensional isosurface. The profile of concentration can be obtained along any line on the terrain. The concentration contours can be either instantaneous or time integrated for the emission from a source or a source combination. A special utility is included to help prepare a report or a video animation. The user can select images, put in annotations, or do animation.
 d. Type of Model
 The model uses an Eulerian (and Lagrangian for particulate matter) 3-dimensional finite volume model solving full Navier-Stokes equations. The numerical diffusion is low with appropriate turbulence models for building wakes. A second order resolution may be sought to limit the diffusion. Gaussian and puff modes are available. The numerical scheme is self adaptive for the following situations:
 <bullet> A curvilinear mesh or a chopped Cartesian mesh is generated automatically or manually;
 <bullet> Thermal and gravity effects are simulated by full gravity (heavy gases), no gravity (well mixed light gases at ambient temperature), and Boussinesq approximation methods; <bullet> K-diff, K-e or a boundary layer turbulence models are used for turbulence calculations. The flow behind obstacles such
 [[Page 41885]]
 as buildings, is calculated by using a modified K-e. <bullet> For heavy gases, a 3-dimensional heat conduction from the ground and a stratification model for heat exchange from the atmosphere are used (with anisotropic turbulence). <bullet> If local wind data are available, an initial wind field with terrain effects can be computed using a Lagrangian multiplier, which substantially reduces computation time.
 e. Pollutant Types
 <bullet> Scavenging, Acid Rain: A module for water droplets traveling through a plume considers the absorption and de-absorption effects of the pollutants by the droplet. Evaporation and chemical reactions with gases are also taken into account. <bullet> Visibility: Predicts plume visibility and surface deposition of aerosol.
 <bullet> Particulate matter: Calculates settling and dry deposition of particles based on a Probability Density Function (PDF) of their diameters. The exchange of mass, momentum and heat between particles and gas is treated with implicit coupling procedures.
 <bullet> Ozone formation and dispersion: The photochemical model computes ozone formation and dispersion at street level in the presence of sunlight.
 <bullet> Roadway Pollutants: Accounts for heat and turbulence due to vehicular movement. Emissions are based on traffic volume and emission factors.
 <bullet> Odor Dispersion: Identifies odor sources for waste water plants.
 <bullet> Radon Dispersion: Simulates natural radon accumulation in valleys and mine environments.
 PANACHE may also be used in emergency planning and management for episodic emissions, and fire and soot spread in forested and urban areas or from combustible pools.
 f. Source-Receptor Relationship
 Simultaneous use of multiple kinds of sources at user defined locations. Any number of user defined receptors can identify pollutants from each source individually.
 g. Plume Behavior
 The options influencing the behavior are full gravity, Boussinesq approximation or no gravity.
 h. Horizontal Winds
 Horizontal wind speed approximations are made only at the boundaries based on National Weather Service data. Inside the domain of interest, full Navier-Stokes resolution with natural viscosity is used for 3-dimensional terrain and temperature dependent wind field calculation.
 i. Vertical Wind Speed
 Vertical wind speed approximations are made only at the boundaries based on National Weather Service data. The domain of interest is treated as for horizontal winds.
 j. Horizontal Dispersion
 Diffusion is calculated using appropriate turbulence models. A 2nd order solution for shearing flow can be sought when the number of meshes is limited between obstacles.
 k. Vertical Dispersion
 Dispersion by full gravity unless Boussinesq approximation or no gravity requested. Vertical dispersion is treated as above for horizontal dispersion.
 l. Chemical Transformation
 PANCHEM, an atmospheric chemistry module for chemical reactions, is available. Photochemical reactions are used for tropospheric ozone calculations.
 m. Physical Removal
 Physical removal is treated using dry deposition coefficients
 n. Evaluation Studies
 Goldwire, H.C. Jr, T.G. McRae, G.W. Johnson, D.L. Hipple, R.P. Koopman, J.W. McClure, L.K. Morris and R.T. Cederhall, 1985. Desert Tortoise Series Data Report: 1983 Pressurized Ammonia Spills. UCID 20562, Lawrence Livermore National Laboratory; Livermore, California.
 Green, S.R., 1992. Modeling Turbulent Air Flow in a Stand of Widely Spaced Trees, The PHOENICS Journal of Computational Fluid Dynamics and Its Applications, 5: 294-312. Gryning, S.E. and E. Lyck, 1984. Atmospheric Dispersion from Elevated Sources in an Urban Area: Comparison Between Tracer Experiments and Model Calculations. Journal of Climate and Applied Meteorology, 23: 651-660.
 Havens, J., T. Spicer, H. Walker and T. Williams, 1995. Validation of Mathematical Models Using Wind-Tunnel Data Sets for Dense Gas Dispersion in the Presence of Obstacles. University of Arkansas, 8th International Symposium-Loss Prevention and Safety Promotion in the Process Industries; Antwerp, Belgium. McQuaid, J. (ed), 1985. Heavy Gas Dispersion Trials at Thorney Island. Proc. of a Symposium held at the University of Sheffield, Great Britain.
 Pavitskiy, N.Y., A.A. Yakuskin and S.V. Zhubrin, 1993. Vehicular Exhaust Dispersion Around Group of Buildings. The PHOENICS Journal of Computational Fluid Dynamics and Its Applications, 6: 270-285. Tripathi, S., 1994. Evaluation of Fluidyn-PANACHE on Heavy Gas Dispersion Test Case. Seminar on Evaluation of Models of Heavy Gas Dispersion Organized by European Commission; Mol, Belgium.
 B.12 Plume Visibility Model (PLUVUE II)
 Reference
 Environmental Protection Agency, 1992. User's Manual for the Plume Visibility Model, PLUVUE II (Revised). EPA Publication No. EPA-454/B-92-008, (NTIS PB93-188233). U.S. Environmental Protection Agency, Research Triangle Park, NC.
 Availability
 This model code is available on the Support Center for Regulatory Air Models Bulletin Board System and also on diskette (as PB 90-500778) from the National Technical Information Service (see Section B.0).
 Abstract
 The Plume Visibility Model (PLUVUE II) is used for estimating visual range reduction and atmospheric discoloration caused by plumes consisting of primary particles, nitrogen oxides and sulfur oxides emitted from a single emission source. PLUVUE II uses Gaussian formulations to predict transport and dispersion. The model includes chemical reactions, optical effects and surface deposition. Four types of optics calculations are made: horizontal and 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-bycase basis as a third level screening model. When applying PLUVUE II, the following precautions should be taken:
 1. Treat the optical effects of NO<INF>2 and particles separately as well as together to avoid cancellation of NO<INF>2 absorption with particle scattering.
 2. Examine the visual impact of the plume in 0.1 (or 0), 0.5, and 1.0 times the expected level of particulate matter in the background air.
 3. Examine the visual impact of the plume over the full range of observer-plume sun angles.
 4. The user should consult the appropriate Federal Land Manager when using PLUVUE II to assess visibility impacts in a Class I area. b. Input Requirements
 Source data requirements are: location and elevation; emission rates of SO<INF>2, NO<INF>X, and particulates; flue gas flow rate, exit velocity, and exit temperature; flue gas oxygen content; properties (including density, mass median and standard geometric deviation of radius) of the emitted aerosols in the accumulation (0.1-1.0<greek-m>m) and coarse (1.0-10.<greek-m>m) size modes; and deposition velocities for SO<INF>2, NO<INF>X, coarse mode aerosol, and accumulations mode aerosol.
 Meteorological data requirements are: stability class, wind direction (for an observer-based run), wind speed, lapse rate, air temperature, relative humidity, and mixing height. Other data requirements are: ambient background concentrations of NO<INF>X, NO<INF>2, O<INF>3, and SO<INF>2, and background visual range of sulfate and nitrate concentrations. Receptor (observer) data requirements are: location, terrain elevation at points along plume trajectory, white, gray, and black viewing backgrounds, the distance from the observer to the terrain observed behind the plume.
 c. Output
 Printed output includes plume concentrations and visual effects at specified downwind distances for calculated or specified lines of sight.
 d. Type of Model
 PLUVUE II is a Gaussian plume model. Visibility impairment is quantified once the
 [[Page 41886]]
 spectral light intensity has been calculated for the specific lines of sight. Visibility impairment includes visual range reduction, plume contrast, relative coloration of a plume to its viewing background, and plume perceptibility due to its contrast and color with respect to a viewing background.
 e. Pollutant Types
 PLUVUE II treats NO, NO<INF>2, SO<INF>2, H<INF>2SO<INF>4, HNO<INF>3, O<INF>3, primary and secondary particles to calculate effects on visibility.
 f. Source Receptor Relationship
 For performing the optics calculations at selected points along the plume trajectory, PLUVUE II has two modes: plume based and observer based calculations. The major difference is the orientation of the viewer to the source and the plume.
 g. Plume Behavior
 Briggs (1969, 1971, 1972) final plume rise equations are used.
 h. Horizontal Winds
 User-specified wind speed (and direction for an observer-based run) are assumed constant for the calculation.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 Constant, uniform (steady-state) wind is assumed for each hour. Straight line plume transport is assumed to all downwind distances.
 k. Vertical Dispersion
 Rural dispersion coefficients from Turner (1969) are used, with no adjustment for surface roughness. Six stability classes are used.
 l. Chemical Transformation
 The chemistry of NO, NO<INF>2, O<INF>3, OH, O(1D), SO<INF>2, HNO<INF>3, and H<INF>2SO<INF>4 is treated by means of nine reactions. Steady state approximations are used for radicals and for the NO/NO<INF>2/O<INF>3 reactions.
 m. Physical Removal
 Dry deposition of gaseous and particulate pollutants is treated using deposition velocities.
 n. Evaluation Studies
 Bergstrom, R.W., C. Seigneur, B.L. Babson, H.Y. Holman and M.A. Wojcik, 1981. Comparison of the Observed and Predicted Visual Effects Caused by Power Plant Plumes. Atmospheric Environment, 15: 2135-2150.
 Bergstrom, R.W., Seigneur, C.D. Johnson and L.W. Richards, 1984. Measurements and Simulations of the Visual Effects of Particulate Plumes. Atmospheric Environment, 18(10): 2231-2244. Seigneur, C., R.W. Bergstrom and A.B. Hudischewskyj, 1982. Evaluation of the EPA PLUVUE Model and the ERT Visibility Model Based on the 1979 VISTTA Data Base. EPA Publication No. EPA-450/4- 82-008. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 White, W.H., C. Seigneur, D.W. Heinold, M.W. Eltgroth, L.W. Richards, P.T. Roberts, P.S. Bhardwaja, W.D. Conner and W.E. Wilson, Jr, 1985. Predicting the Visibility of Chimney Plumes: An Intercomparison of Four Models with Observations at a Well-Controlled Power Plant. Atmospheric Environment, 19: 515-528.
 B.13 Point, Area, Line Source Algorithm (PAL-DS)
 Reference
 Petersen, W.B, 1978. User's Guide for PAL--A Gaussian-Plume Algorithm for Point, Area, and Line Sources. EPA Publication No. EPA-600/4-78-013. Office of Research and Development, Research Triangle Park, NC. (NTIS No. PB 281306) Rao, K.S. and H.F. Snodgrass, 1982. PAL-DS Model: The PAL Model Including Deposition and Sedimentation. EPA Publication No. EPA-600/ 8-82-023. Office of Research and Development, Research Triangle Park, NC. (NTIS No. PB 83-117739)
 Availability
 The computer code is available on diskette (as PB 90-500802) from the National Technical Information Service (see Section B.0).
 Abstract
 PAL-DS is an acronym for this point, area, and line source algorithm and is a method of estimating short-term dispersion using Gaussian-plume steady-state assumptions. The algorithm can be used for estimating concentrations of non-reactive pollutants at 99 receptors for averaging times of 1 to 24 hours, and for a limited number of point, area, and line sources (99 of each type). This algorithm is not intended for application to entire urban areas but is intended, rather, to assess the impact on air quality, on scales of tens to hundreds of meters, of portions of urban areas such as shopping centers, large parking areas, and airports. Level terrain is assumed. The Gaussian point source equation estimates concentrations from point sources after determining the effective height of emission and the upwind and crosswind distance of the source from the receptor. Numerical integration of the Gaussian point source equation is used to determine concentrations from the four types of line sources. Subroutines are included that estimate concentrations for multiple lane line and curved path sources, special line sources (line sources with endpoints at different heights above ground), and special curved path sources. Integration over the area source, which includes edge effects from the source region, is done by considering finite line sources perpendicular to the wind at intervals upwind from the receptor. The crosswind integration is done analytically; integration upwind is done numerically by successive approximations. The PAL-DS model utilizes Gaussian plume-type diffusiondeposition 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 <greek-s><INF>y and <greek-s><INF>z; area sources--source strength, size of area source, coordinates of S.W. corner, and height of area source; and line sources--source strength, number of lanes, height of source, coordinates of end points, initial <greek-s><INF>y and <greek-s><INF>z, width of line source, and width of median. Diurnal variations in emissions are permitted. When applicable, the settling velocity and deposition velocity are also permitted. Meteorological data: wind profile exponents, anemometer height, wind direction and speed, stability class, mixing height, air temperature, and hourly variations in emission rate. Receptor data: receptor coordinates.
 c. Output
 Printed output includes:
 Hourly concentration and deposition flux for each source type at each receptor; and
 Average concentration for up to 24 hours for each source type at each receptor.
 d. Type of Model
 PAL-DS is a Gaussian plume model.
 e. Pollutant Types
 PAL-DS may be used to model non-reactive pollutants.
 f. Source-Receptor Relationships
 Up to 99 sources of each of 6 source types: point, area, and 4 types of line sources.
 Source and receptor coordinates are uniquely defined. Unique stack height for each source. Coordinates of receptor locations are user defined.
 g. Plume Behavior
 Briggs final plume rise equations are used. Fumigation and downwash are not treated. If plume height exceeds mixing height, concentrations are assumed equal to zero.
 Surface concentrations are set to zero when the plume centerline exceeds mixing height.
 h. Horizontal Winds
 User-supplied hourly wind data are used.
 [[Page 41887]]
 Constant, uniform (steady-state) wind is assumed within each hour. Wind is assumed to increase with height.
 i. Vertical Wind Speeds
 Assumed equal to zero.
 j. Horizontal Dispersion
 Rural dispersion coefficients from Turner (1969) are used with no adjustments made for surface roughness. Six stability classes are used.
 Dispersion coefficients (Pasquill-Gifford) are assumed based on a 3cm roughness height.
 k. Vertical Dispersion
 Six stability classes are used.
 Rural dispersion coefficients from Turner (1969) are used; no further adjustments are made for variation in surface roughness, transport or averaging time.
 Multiple reflection is handled by summation of series until the vertical standard deviation equals 1.6 times mixing height. Uniform vertical mixing is assumed thereafter.
 l. Chemical Transformation
 Not treated.
 m. Physical Removal
 PAL-DS can treat deposition of both gaseous and suspended particulates in the plume since gravitational settling and dry deposition of the particles are explicitly accounted for.
 n. Evaluation Studies
 None Cited.
 B.14 Reactive Plume Model (RPM-IV)
 Reference
 Environmental Protection Agency, 1993. Reactive Plume Model IV (RPM-IV) User's Guide. EPA Publication No. EPA-454/B-93-012. U.S. Environmental Protection Agency (ESRL), Research Triangle Park, NC. (NTIS No. PB 93-217412)
 Availability
 The above report and model computer code are available on the Support Center for Regulatory Air Models Bulletin Board System. The model code is also available on diskette (as PB 96-502026) from the National Technical Information Service (see Section B.0).
 Abstract
 The Reactive Plume Model, RPM-IV, is a computerized model used for estimating short-term concentrations of primary and secondary reactive pollutants resulting from single or, in some special cases, multiple sources if they are aligned with the mean wind direction. The model is capable of simulating the complex interaction of plume dispersion and non-linear photochemistry. If Carbon Mechanism IV (CBM-IV) is used, emissions must be disaggregated into carbon bond classes prior to model application. The model can be run on a mainframe computer, workstation, or IBM-compatible PC with at least 2 megabytes of memory. A major feature of RPM-IV is its ability to interface with input and output files from EPA's Regional Oxidant Model (ROM) and Urban Airshed Model (UAM) to provide an internally consistent set of modeled ambient concentrations for various pollutant species.
 a. Recommendations for Regulatory Use
 There is no specific recommendation at the present time. RPM-IV may be used on a case-by-case basis.
 b. Input Requirements
 Source data requirements are: emission rates, name, and molecular weight of each species of pollutant emitted; ambient pressure, ambient temperature, stack height, stack diameter, stack exit velocity, stack gas temperature, and location. Meteorological data requirements are: wind speeds, plume widths or stability classes, photolytic rate constants, and plume depths or stability classes.
 Receptor data requirements are: downwind distances or travel times at which calculations are to be made. Initial concentration of all species is required, and the specification of downwind ambient concentrations to be entrained by the plume is optional.
 c. Output
 Short-term concentrations of primary and secondary pollutants at either user specified time increments, or user specified downwind distances.
 d. Type of Model
 Reactive Gaussian plume model.
 e. Pollutant Types
 Currently, using the Carbon Bond Mechanism (CBM-IV), 34 species are simulated (82 reactions), including NO, NO<INF>2, O<INF>3, SO<INF>2, SO<INF>4, five categories of reactive hydrocarbons, secondary nitrogen compounds, organic aerosols, and radical species.
 f. Source-Receptor Relationships
 Single point source.
 Single area or volume source.
 Multiple sources can be simulated if they are lined up along the wind trajectory.
 Predicted concentrations are obtained at a user specified time increment, or at user specified downwind distances.
 g. Plume Behavior
 Briggs (1971) plume rise equations are used.
 h. Horizontal Winds
 User specifies wind speeds as a function of time.
 i. Vertical Wind Speed
 Not treated.
 j. Horizontal Dispersion
 User specified plume widths, or user may specify stability and widths will be computed using Turner (1969).
 k. Vertical Dispersion
 User specified plume depths, or user may specify stability in which case depths will be calculated using Turner (1969). Note that vertical uniformity in plume concentration is assumed.
 l. Chemical Transformation
 RPM-IV has the flexibility of using any user input chemical kinetic mechanism. Currently it is run using the chemistry of the Carbon Bond Mechanism, CBM-IV (Gery et al., 1988). The CBM-IV mechanism, as incorporated in RPM-IV, utilizes an updated simulation of PAN chemistry that includes a peroxy-peroxy radical termination reaction, significant when the atmosphere is NO<INF>X-limited (Gery et al., 1989). As stated above, the current CBM-IV mechanism accommodates 34 species and 82 reactions focusing primarily on hydrocarbon/nitrogen oxides and ozone photochemistry.
 m. Physical Removal
 Not treated.
 n. Evaluation Studies
 Stewart, D.A. and M-K Liu, 1981. Development and Application of a Reactive Plume Model. Atmospheric Environment, 15: 2377-2393.
 B.15 Shoreline Dispersion Model (SDM)
 Reference
 PEI Associates, 1988. User's Guide to SDM-A Shoreline Dispersion Model. EPA Publication No. EPA-450/4-88-017. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 89- 164305)
 Availability
 The model code is available on the Support Center for Regulatory Air Models Bulletin Board System (see Section B.0).
 Abstract
 SDM is a hybrid multi-point Gaussian dispersion model that calculates source impact for those hours during the year when fumigation events are expected using a special fumigation algorithm and the MPTER regulatory model for the remaining hours (see Appendix A).
 a. Recommendations for Regulatory Use
 SDM may be used on a case-by-case basis for the following applications:
 <bullet> Tall stationary point sources located at a shoreline of any large body of water;
 <bullet> Rural or urban areas; <bullet> Flat terrain;
 <bullet> Transport distances less than 50 km; <bullet> 1-hour to 1-year averaging times.
 b. Input Requirements
 Source data: location, emission rate, physical stack height, stack gas exit velocity, stack inside diameter, stack gas temperature and shoreline coordinates.
 Meteorological data: hourly values of mean wind speed within the Thermal Internal Boundary Layer (TIBL) and at stack height; mean potential temperature over land and over water; over water lapse rate; and surface sensible heat flux. In addition to these meteorological data, SDM access standard NWS surface and upper air meteorological data through the RAMMET preprocessor. Receptor data: coordinates for each receptor.
 c. Output
 Printed output includes the MPTER model output as well as: special shoreline
 [[Page 41888]]
 fumigation applicability report for each day and source; high-five tables on the standard output with ``F'' designation next to the concentration if that averaging period includes a fumigation event.
 d. Type of Model
 SDM is hybrid Gaussian model.
 e. Pollutant Types
 SDM may be used to model primary pollutants. Settling and deposition are not treated.
 f. Source-Receptor Relationships
 SDM applies user-specified locations of stationary point sources and receptors. User input stack height, shoreline orientation and source characteristics for each source. No topographic elevation is input; flat terrain is assumed.
 g. Plume Behavior
 SDM uses Briggs (1975) plume rise for final rise. SDM does not treat stack tip or building downwash.
 h. Horizontal Winds
 Constant, uniform (steady-state) wind is assumed for an hour. Straight line plume transport is assumed to all downwind distances. Separate wind speed profile exponents (EPA, 1980) for both rural and urban cases are assumed.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Horizontal Dispersion
 For the fumigation algorithm coefficients based on Misra (1980) and Misra and McMillan (1980) are used for plume transport in stable air above TIBL and based on Lamb (1978) for transport in the unstable air below the TIBL. An effective horizontal dispersion coefficient based on Misra and Onlock (1982) is used. For nonfumigation periods, algorithms contained in the MPTER model are used (see Appendix A).
 k. Vertical Dispersion
 For the fumigation algorithm, coefficients based on Misra (1980) and Misra and McMillan (1980) are used.
 l. Chemical Transformation
 Chemical transformation is not included in the fumigation algorithm.
 m. Physical Removal
 Physical removal is not explicitly treated.
 n. Evaluation Studies
 Environmental Protection Agency, 1987. Analysis and Evaluation of Statistical Coastal Fumigation Models. EPA Publication No. EPA- 450/4-87-002. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS PB 87-175519)
 B.16 SHORTZ
 Reference
 Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for the SHORTZ and LONGZ Computer Programs, Volumes I and II. EPA Publication No. EPA-903/9-82-004a and b. U.S. Environmental Protection Agency, Region III, Philadelphia, PA.
 Availability
 The computer code is available on the Support Center for Regulatory Air Models Bulletin Board System and on diskette (as PB 96-501986) from the National Technical Information Service (see Section B.0).
 Abstract
 SHORTZ utilizes the steady state bivariate Gaussian plume formulation for both urban and rural areas in flat or complex terrain to calculate ground-level ambient air concentrations. The model can calculate 1-hour, 2-hour, 3-hour etc. average concentrations due to emissions from stacks, buildings and area sources for up to 300 arbitrarily placed sources. The output consists of total concentration at each receptor due to emissions from each user-specified source or group of sources, including all sources. If the option for gravitational settling is invoked, analysis cannot be accomplished in complex terrain without violating mass continuity.
 a. Recommendations for Regulatory Use
 SHORTZ can be used if it can be demonstrated to estimate concentrations equivalent to those provided by the preferred model for a given application. SHORTZ must be executed in the equivalent mode.
 SHORTZ can be used on a case-by-case basis in lieu of a preferred model if it can be demonstrated, using the criteria in Section 3.2, that SHORTZ is more appropriate for the specific application. In this case the model options/modes which are most appropriate for the application should be used.
 b. Input Requirements
 Source data requirements are: for point, building or area sources, location, elevation, total emission rate (optionally classified by gravitational settling velocity) and decay coefficient; for stack sources, stack height, effluent temperature, effluent exit velocity, stack radius (inner), actual volumetric flow rate, and ground elevation (optional); for building sources, height, length and width, and orientation; for area sources, characteristic vertical dimension, and length, width and orientation. Meteorological data requirements are: wind speed and measurement height, wind profile exponents, wind direction, standard deviations of vertical and horizontal wind directions, (i.e., vertical and lateral turbulent intensities), mixing height, air temperature, and vertical potential temperature gradient. Receptor data requirements are: coordinates, ground elevation.
 c. Output
 Printed output includes total concentration due to emissions from user-specified source groups, including the combined emissions from all sources (with optional allowance for depletion by deposition).
 d. Type of Model
 SHORTZ is a Gaussian plume model.
 e. Pollutant Types
 SHORTZ may be used to model primary pollutants. Settling and deposition of particulates are treated.
 f. Source-Receptor Relationships
 User specified locations for sources and receptors are used. Receptors are assumed to be at ground level.
 g. Plume Behavior
 Plume rise equations of Bjorklund and Bowers (1982) are used. Stack tip downwash (Bjorklund and Bowers, 1982) is included. All plumes move horizontally and will fully intercept elevated terrain.
 Plumes above mixing height are ignored. Perfect reflection at mixing height is assumed for plumes below the mixing height.
 Plume rise is limited when the mean wind at stack height approaches or exceeds stack exit velocity. Perfect reflection at ground is assumed for pollutants with no settling velocity.
 Zero reflection at ground is assumed for pollutants with finite settling velocity.
 Tilted plume is used for pollutants with settling velocity specified. Buoyancy-induced dispersion (Briggs, 1972) is included.
 h. Horizontal Winds
 Winds are assumed homogeneous and steady-state. Wind speed profile exponents are functions of both stability class and wind speed. Default values are specified in Bjorklund and Bowers (1982).
 i. Vertical Wind Speed
 Vertical winds are assumed equal to zero.
 j. Horizontal Dispersion
 Horizontal plume size is derived from input lateral turbulent intensities using adjustments to plume height, and rate of plume growth with downwind distance specified in Bjorklund and Bowers (1982).
 k. Vertical Dispersion
 Vertical plume size is derived from input vertical turbulent intensities using adjustments to plume height and rate of plume growth with downwind distance specified in Bjorklund and Bowers (1982).
 l. Chemical Transformation
 Chemical transformations are treated using exponential decay. Time constant is input by the user.
 m. Physical Removal
 Settling and deposition of particulates are treated.
 n. Evaluation Studies
 Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for the SHORTZ and LONGZ Computer Programs. EPA Publication No. EPA-903/ 9-82-004. EPA Environmental Protection Agency, Region III, Philadelphia, PA.
 Wackter, D. and R. Londergan, 1984. Evaluation of Complex Terrain Air Quality Simulation Models. EPA Publication No. EPA-450/ 4-84-017. U.S. Environmental
 [[Page 41889]]
 Protection Agency, Research Triangle Park, NC.
 B.17 Simple Line-Source Model
 Reference
 Chock, D.P., 1980. User's Guide for the Simple Line-Source Model for Vehicle Exhaust Dispersion Near a Road. Ford Research Laboratory, Dearborn, MI.
 Availability
 Copies of the above reference are available without charge from: Dr. D.P. Chock, Ford Research Laboratory, P.O. Box 2053; MD-3083, Dearborn, MI 48121-2053. The short model algorithm is contained in the User's Guide.
 Abstract
 The Simple Line-Source Model is a simple steady-state Gaussian plume model which can be used to determine hourly (or half-hourly) averages of exhaust concentrations within 100m from a roadway on a relatively flat terrain. The model allows for plume rise due to the heated exhaust, which can be important when the crossroad wind is very low. The model also utilizes a new set of vertical dispersion parameters which reflects the influence of traffic-induced turbulence.
 a. Recommendations for Regulatory Use
 The Simple Line-Source Model can be used if it can be demonstrated to estimate concentrations equivalent to those provided by the preferred model for a given application. The model must be executed in the equivalent mode.
 The Simple Line-Source Model can be used on a case-by-case basis in lieu of a preferred model if it can be demonstrated, using criteria in Section 3.2, that it is more appropriate for the specific application. In this case the model options/modes which are most appropriate for the application should be used.
 b. Input Requirements
 Source data requirements are: emission rate per unit length per lane, the number of lanes on each road, distances from lane centers to the receptor, source and receptor heights. Meteorological data requirements are: buoyancy flux, ambient stability condition, ambient wind and its direction relative to the road.
 Receptor data requirements are: distance and height above ground.
 c. Output
 Printed output includes hourly or (half-hourly) concentrations at the receptor due to exhaust emission from a road (or a system of roads by summing the results from repeated model applications).
 d. Type of Model
 The Simple Line-Source Model is a Gaussian plume model.
 e. Pollutant Types
 The Simple Line-Source Model can be used to model primary pollutants. Settling and deposition are not treated.
 f. Source-Receptor Relationship
 The Simple Line-Source Model treats arbitrary location of line sources and receptors.
 g. Plume Behavior
 Plume-rise formula adequate for a heated line source is used.
 h. Horizontal Winds
 The Simple Line-Source Model uses user-supplied hourly (or halfhourly) ambient wind speed and direction. The wind measurements are from a height of 5 to 10m.
 i. Vertical Wind Speed
 Vertical wind speed is assumed equal to zero.
 j. Dispersion Parameters
 Horizontal dispersion parameter is not used.
 k. Vertical Dispersion
 A vertical dispersion parameter is used which is a function of stability and wind-road angle. Three stability classes are used: unstable, neutral and stable. The parameters take into account the effect of traffic-generated turbulence (Chock, 1980).
 l. Chemical Transformation
 Not treated.
 m. Physical Removal
 Not treated.
 n. Evaluation Studies
 Chock, D.P., 1978. A Simple Line-Source Model for Dispersion Near Roadways. Atmospheric Environment, 12: 823-829. Sistla, G., P. Samson, M. Keenan and S.T. Rao, 1979. A Study of Pollutant Dispersion Near Highways. Atmospheric Environment, 13: 669-685.
 B.18 SLAB
 Reference:
 Ermak, D.L., 1990. User's Manual for SLAB: An Atmospheric Dispersion Model for Denser-than-Air Releases (UCRL-MA-105607), Lawrence Livermore National Laboratory.
 Availability
 The computer code can be obtained from: Energy Science and Technology Center, P.O. Box 1020, Oak Ridge, TN 37830, Phone (615) 576-2606.
 The User's Manual (as DE 91-008443) can be obtained from the National Technical Information Service. The computer code is also available on the Support Center for Regulatory Air Models Bulletin Board System (Public Upload/ Download Area; see Section B.0.)
 Abstract
 The SLAB model is a computer model, PC-based, that simulates the atmospheric dispersion of denser-than-air releases. The types of releases treated by the model include a ground-level evaporating pool, an elevated horizontal jet, a stack or elevated vertical jet and an instantaneous volume source. All sources except the evaporating pool may be characterized as aerosols. Only one type of release can be processed in any individual simulation. Also, the model simulates only one set of meteorological conditions; therefore direct application of the model over time periods longer than one or two hours is not recommended.
 a. Recommendations for use
 The SLAB model should be used as a refined model to estimate spatial and temporal distribution of short-term ambient concentration (e.g., 1-hour or less averaging times) and the expected area of exposure to concentrations above specified threshold values for toxic chemical releases where the release is suspected to be denser than the ambient air.
 b. Input Requirements
 The SLAB model is executed in the batch mode. Data are input directly from an external input file. There are 29 input parameters required to run each simulation. These parameters are divided into 5 categories by the user's guide: source type, source properties, spill properties, field properties, and meteorological parameters. The model is not designed to accept real-time meteorological data or convert units of input values. Chemical property data are not available within the model and must be input by the user. Some chemical and physical property data are available in the user's guide.
 Source type is chosen as one of the following: evaporating pool release, horizontal jet release, vertical jet or stack release, or instantaneous or short duration evaporating pool release. Source property data requirements are physical and chemical properties (molecular weight, vapor heat capacity at constant pressure; boiling point; latent heat of vaporization; liquid heat capacity; liquid density; saturation pressure constants), and initial liquid mass fraction in the release. Spill properties include: source temperature, emission rate, source dimensions, instantaneous source mass, release duration, and elevation above ground level.
 Required field properties are: desired concentration averaging time, maximum downwind distance (to stop the calculation), and four separate heights at which the concentration calculations are to be made.
 Meteorological parameter requirements are: ambient measurement height, ambient wind speed at designated ambient measurement height, ambient temperature, surface roughness, relative humidity, atmospheric stability class, and inverse Monin-Obukhov length (optional, only used as an input parameter when stability class is unknown).
 c. Output
 No graphical output is generated by the current version of this program. The output print file is automatically saved and must be sent to the appropriate printer by the user after program execution. Printed output includes in tabular form: Listing of model input data;
 Instantaneous spatially-averaged cloud parameters--time, downwind distance, magnitude of peak concentration, cloud dimensions (including length for puff-type
 [[Page 41890]]
 simulations), volume (or mole) and mass fractions, downwind velocity, vapor mass fraction, density, temperature, cloud velocity, vapor fraction, water content, gravity flow velocities, and entrainment velocities;
 Time-averaged cloud parameters--parameters which may be used externally to calculate time-averaged concentrations at any location within the simulation domain (tabulated as functions of downwind distance);
 Time-averaged concentration values at plume centerline and at five off-centerline distances (off-centerline distances are multiples of the effective cloud half-width, which varies as a function of downwind distance) at four user-specified heights and at the height of the plume centerline.
 d. Type of Model
 As described by Ermak (1989), transport and dispersion are calculated by solving the conservation equations for mass, species, energy, and momentum, with the cloud being modeled as either a steady-state plume, a transient puff, or a combination of both, depending on the duration of the release. In the steady-state plume mode, the crosswind-averaged conservation equations are solved and all variables depend only on the downwind distance. In the transient puff mode, the volume-averaged conservation equations are solved, and all variables depend only on the downwind travel time of the puff center of mass. Time is related to downwind distance by the height-averaged ambient wind speed. The basic conservation equations are solved via a numerical integration scheme in space and time.
 e. Pollutant Types
 Pollutants are assumed to be non-reactive and non-depositing dense gases or liquid-vapor mixtures (aerosols). Surface heat transfer and water vapor flux are also included in the model.
 f. Source-Receptor Relationships
 Only one source can be modeled at a time. There is no limitation to the number of receptors; the downwind receptor distances are internally-calculated by the model. The SLAB calculation is carried out up to the user-specified maximum downwind distance.
 The model contains submodels for the source characterization of evaporating pools, elevated vertical or horizontal jets, and instantaneous volume sources.
 g. Plume Behavior
 Plume trajectory and dispersion is based on crosswind-averaged mass, species, energy, and momentum balance equations. Surrounding terrain is assumed to be flat and of uniform surface roughness. No obstacle or building effects are taken into account.
 h. Horizontal Winds
 A power law approximation of the logarithmic velocity profile which accounts for stability and surface roughness is used.
 i. Vertical Wind Speed
 Not treated.
 j. Vertical Dispersion
 The crosswind dispersion parameters are calculated from formulas reported by Morgan et al. (1983), which are based on experimental data from several sources. The formulas account for entrainment due to atmospheric turbulence, surface friction, thermal convection due to ground heating, differential motion between the air and the cloud, and damping due to stable density stratification within the cloud.
 k. Horizontal Dispersion
 The horizontal dispersion parameters are calculated from formulas similar to those described for vertical dispersion, also from the work of Morgan et al. (1983).
 l. Chemical Transformation
 The thermodynamics of the mixing of the dense gas or aerosol with ambient air (including water vapor) are treated. The relationship between the vapor and liquid fractions within the cloud is treated using the local thermodynamic equilibrium approximation. Reactions of released chemicals with water or ambient air are not treated.
 m. Physical Removal
 Not treated.
 n. Evaluation Studies
 Blewitt, D.N., J.F. Yohn and D.L. Ermak, 1987. An Evaluation of SLAB and DEGADIS Heavy Gas Dispersion Models Using the HF Spill Test Data. Proceedings, AIChE International Conference on Vapor Cloud Modeling, Boston, MA, November, pp. 56-80. Ermak, D.L., S.T. Chan, D.L. Morgan and L.K. Morris, 1982. A Comparison of Dense Gas Dispersion Model Simulations with Burro Series LNG Spill Test Results. J. Haz. Matls., 6: 129-160. Zapert, J.G., R.J. Londergan and H. Thistle, 1991. Evaluation of Dense Gas Simulation Models. EPA Publication No. EPA-450/4-90-018. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 B.19 WYNDvalley Model
 Reference
 Harrison, Halstead, 1992. ``A User's Guide to WYNDvalley 3.11, an Eulerian-Grid Air-Quality Dispersion Model with Versatile Boundaries, Sources, and Winds,'' WYNDsoft Inc., Mercer Island, WA.
 Availability
 Copies of the user's guide and the executable model computer codes are available at a cost of $295.00 from: WYNDsoft, Incorporated, 6333 77th Avenue, Mercer Island, WA 98040, Phone: (206) 232-1819.
 Abstract
 WYNDvalley 3.11 is a multi-layer (up to five vertical layers) Eulerian grid dispersion model that permits users flexibility in defining borders around the areas to be modeled, the boundary conditions at these borders, the intensities and locations of emissions sources, and the winds and diffusivities that affect the dispersion of atmospheric pollutants. The model's output includes gridded contour plots of pollutant concentrations for the highest brief episodes (during any single time step), the highest and second-highest 24-hour averages, averaged dry and wet deposition fluxes, and a colored ``movie'' showing evolving dispersal of pollutant concentrations, together with temporal plots of the concentrations at specified receptor sites and statistical inference of the probabilities that standards will be exceeded at those sites. WYNDvalley is implemented on IBM compatible microcomputers, with interactive data input and color graphics display.
 a. Recommendations for Regulatory Use
 WYNDvalley may be used on a case-by-case basis to estimate concentrations during valley stagnation periods of 24 hours or longer. Recommended inputs are listed below.
 
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 Variable Recommended value
 
 Horizontal cell dimension................. 250 to 500 meters.
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 Vertical layers........................... 3 to 5.
 Layer depth............................... 50 to 100 meters. Background (internal to model)............ Zero (background should be
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 added externally to model estimates). Lateral meander velocity.................. Default. Diffusivities............................. Default. Ventilation parameter (upper boundary Default. condition).
 Dry deposition velocity................... Zero (site-specific). Washout ratio............................. Zero (site-specific).
 
 b. Input Requirements
 Input data, including model options, modeling domain boundaries, boundary conditions, receptor locations, source locations, and emission rates, may be entered interactively, or through existing template files from a previous run. Meteorological data, including wind speeds, wind directions, rain rates (optionally, for wet deposition calculations), and time of day and year, may be of arbitrary time increment (usually an hour) and are entered into the model through an external meteorological data file. Optionally, users may specify diffusivities and upper boundary conditions for each time increment. Source emission rates may be constant or modulated on a daily, weekly, and/or seasonal basis.
 c. Output
 Output from WYNDvalley includes gridded contour maps of the highest pollutant concentrations at each time step and the highest and second-highest 24-hour average concentrations. Output also includes the deposition patterns for wet, dry, and total fluxes of the pollutants to the surface, integrated over the simulation period. A running ``movie'' of the concentration patterns is displayed on the screen (with optional printout) as they evolve during the simulation. Output files include tables of daily-averaged pollutant concentrations at
 [[Page 41891]]
 every modeled grid cell, and of hourly concentrations at up to eight specified receptors. Statistical analyses are performed on the hourly and daily data to estimate the probabilities that specified levels will be exceeded more than once during an arbitrary number of days with similar weather.
 d. Type of Model
 WYNDvalley is a three dimensional Eulerian grid model.
 e. Pollutant Types
 WYNDvalley may be used to model any inert pollutant.
 f. Source-Receptor Relationships
 Source and receptors may be located anywhere within the userdefined modeling domain. All point and area sources, or portions of an area source, within a given grid cell are summed to define a representative emission rate for that cell. Concentrations are calculated for each and every grid cell in the modeling domain. Up to eight grid cells may be selected as receptors, for which time histories of concentration and deposition fluxes are determined, and probabilities of exceedance are calculated.
 g. Plume Behavior
 Emissions for buoyant point sources are placed by the user in a grid cell which best reflects the expected effective plume height during stagnation conditions. Five vertical layers are available to the user.
 h. Horizontal Winds
 During each time step in the model, the winds are assumed to be uniform throughout the modeling domain. Numerical diffusion is minimized in the advection algorithm. To account for terrain effects on winds and dispersion, an ad hoc algorithm is employed in the model to distribute concentrations near boundaries.
 i. Vertical Wind Speed
 Winds are assumed to be constant with height.
 j. Horizontal Dispersion
 Horizontal eddy diffusion coefficients may be entered explicitly by the user at every time step. Alternatively, a default algorithm may be invoked to estimate these coefficients from the wind velocities and their variances.
 k. Vertical Dispersion
 Vertical eddy diffusion coefficients and a top-of-model boundary condition may be entered explicitly by the user at every time step. Alternatively, a default algorithm may be invoked to estimate these coefficients from the horizontal wind velocities and their variances, and from an empirical time-of-day correction derived from temperature gradient measurements and Monin-Obukhov similarities.
 l. Chemical Transformation
 Chemical transformation is not explicitly treated by WYNDvalley.
 m. Physical Removal
 WYNDvalley optionally simulates both wet and dry deposition. Dry deposition is proportional to concentration in the lowest layer, while wet deposition is proportional to rain rate and concentration in each layer. Appropriate coefficients (deposition velocities and washout ratios) are input by the user.
 n. Evaluation Studies
 Harrison, H., G. Pade, C. Bowman and R. Wilson, 1990. Air Quality During Stagnations: A Comparison of RAM and WYNDvalley with PM-10 Measurements at Five Sites. Journal of the Air & Waste Management Association, 40: 47-52.
 Maykut, N. et al., 1990. Evaluation of the Atmospheric Deposition of Toxic Contaminants to Puget Sound. State of Washington, Puget Sound Water Quality Authority, Seattle, WA. Yoshida, C., 1990. A Comparison of WYNDvalley Versions 2.12 and 3.0 with PM-10 Measurements in Six Cities in the Pacific Northwest. Lane Regional Air Pollution Authority, Springfield, OR.
 B. REF References
 Beals, G.A., 1971. A Guide to Local Dispersion of Air Pollutants. Air Weather Service Technical Report #214 (April 1971). Bjorklund, J.R. and J.F. Bowers, 1982. User's Instructions for the SHORTZ and LONGZ Computer Programs. EPA Publication No. EPA-903/ 9-82-004a and b. U.S. Environmental Protection Agency, Region III, Philadelphia, PA.
 Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission Critical Review Series, Oak Ridge National Laboratory, Oak Ridge, TN. (NTIS No. TID-25075)
 Briggs, G.A., 1971. Some Recent Analyses of Plume Rise Observations. Proceedings of the Second International Clean Air Congress, edited by H.M. Englund and W.T. Berry. Academic Press, New York, NY.
 Briggs, G.A., 1972. Discussion on Chimney Plumes in Neutral and Stable Surroundings. Atmospheric Environment, 6: 507-510. Briggs, G.A., 1974. Diffusion Estimation for Small Emissions. USAEC Report ATDL-106. U.S. Atomic Energy Commission, Oak Ridge, TN. Briggs, G.A., 1975. Plume Rise Predictions. Lectures on Air Pollution and Environmental Impact Analyses. American Meteorological Society, Boston, MA, pp. 59-111.
 Briggs, G.A., 1984. Plume Rise and Buoyancy Effects. Atmospheric Science and Power Production, Darryl Randerson (Ed.). DOE Report DOE/TIC-27601, Technical Information Center, Oak Ridge, TN. (NTIS No. DE84005177)
 Carpenter, S.B., T.L. Montgomery, J.M. Leavitt, W.C. Colbaugh and F.W. Thomas, 1971. Principal Plume Dispersion Models: TVA Power Plants. Journal of Air Pollution Control Association, 21: 491-495. Chock, D.P., 1980. User's Guide for the Simple Line-Source Model for Vehicle Exhaust Dispersion Near a Road. Environmental Science Department, General Motors Research Laboratories, Warren, MI. Colenbrander, G.W., 1980. A Mathematical Model for the Transient Behavior of Dense Vapor Clouds, 3rd International Symposium on Loss Prevention and Safety Promotion in the Process Industries, Basel, Switzerland.
 DeMarrais, G.A., 1959. Wind Speed Profiles at Brookhaven National Laboratory. Journal of Applied Meteorology, 16: 181-189. Ermak, D.L., 1989. A Description of the SLAB Model, presented at JANNAF Safety and Environmental Protection Subcommittee Meeting, San Antonio, TX, April, 1989.
 Gery, M.W., G.Z. Whitten and J.P. Killus, 1988. Development and Testing of CBM-IV for Urban and Regional Modeling. EPA Publication No. EPA-600/3-88-012. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 88-180039) Gery, M.W., G.Z. Whitten, J.P. Killus and M.C. Dodge, 1989. A Photochemical Kinetics Mechanism for Urban and Regional Scale Computer Modeling. Journal of Geophysical Research, 94: 12,925- 12,956.
 Gifford, F.A. and S.R. Hanna, 1970. Urban Air Pollution Modeling. Proceedings of the Second International Clean Air Congress, Academic Press, Washington, D.C.; pp. 140-1151. Gifford, F.A., 1975. Atmospheric Dispersion Models for Environmental Pollution Applications. Lectures on Air Pollution and Environmental Impact Analyses. American Meteorological Society, Boston, MA.
 Green, A.E., Singhal R.P. and R. Venkateswar, 1980. Analytical Extensions of the Gaussian Plume Model. Journal of the Air Pollution Control Association, 30: 773-776.
 Heffter, J.L., 1965. The Variations of Horizontal Diffusion Parameters with Time for Travel Periods of One Hour or Longer. Journal of Applied Meteorology, 4: 153-156. Heffter, J.L., 1980. Air Resources Laboratories Atmospheric Transport and Dispersion Model (ARL-ATAD). NOAA Technical Memorandum ERL ARL-81. Air Resources Laboratories, Silver Spring, MD. Irwin, J.S., 1979a. Estimating Plume Dispersion--A Recommended Generalized Scheme. Fourth Symposium on Turbulence, Diffusion and Air Pollution, Reno, Nevada.
 Irwin, J.S., 1979b. A Theoretical Variation of the Wind Profile Power-Law Exponent as a Function of Surface Roughness and Stability. Atmospheric Environment, 13: 191-194.
 MacCready, P.B., Baboolal, L.B. and P.B. Lissaman, 1974. Diffusion and Turbulence Aloft Over Complex Terrain. Preprint Volume, AMS Symposium on Atmospheric Diffusion and Air Pollution, Santa Barbara, CA. American Meteorological Society, Boston, MA. Moore, G.E., T.E. Stoeckenius and D.A. Stewart, 1982. A Survey of Statistical Measures of Model Performance and Accuracy for Several Air Quality Models. EPA Publication No. EPA-450/4-83-001. U.S. Environmental Protection Agency, Research Triangle Park, NC. Morgan, D.L., Jr., L.K. Morris and D.L. Ermak, 1983. SLAB: A Time-Dependent Computer Model for the Dispersion of Heavy Gas Released in the Atmosphere, UCRL-53383, Lawrence Livermore National Laboratory, Livermore, CA.
 Pasquill, F., 1976. Atmospheric Dispersion Parameters in Gaussian Plume Modeling, Part II. EPA Publication No. EPA-600/4-76- 030b. U.S. Environmental Protection Agency, Research Triangle Park, NC.
 [[Page 41892]]
 Slade, D.H., 1968. Meteorology and Atomic Energy, U.S. Atomic Energy Commission, 445 pp. (NTIS No. TID-24190) Turner, D.B., 1964. A Diffusion Model of An Urban Area. Journal of Applied Meteorology, 3: 83-91.
 Turner, D.B., 1969. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999-AP-26. U.S. Environmental Protection Agency, Research Triangle Park, NC. Van Dop, H., 1992. Buoyant Plume Rise in a Lagrangian Frame Work. Atmospheric Environment, 26A: 1335-1346.
 Appendix C to Appendix W of Part 51--Example Air Quality Analysis Checklist
 C.0 Introduction
 This checklist recommends a standardized set of data and a standard basic level of analysis needed for PSD applications and SIP revisions. The checklist implies a level of detail required to assess both PSD increments and the NAAQS. Individual cases may require more or less information and the Regional Meteorologist should be consulted at an early stage in the development of a data base for a modeling analysis.
 At pre-application meetings between source owner and reviewing authority, this checklist should prove useful in developing a consensus on the data base, modeling techniques and overall technical approach prior to the actual analyses. Such agreement will help avoid misunderstandings concerning the final results and may reduce the later need for additional analyses.
 EXAMPLE AIR QUALITY ANALYSIS CHECKLIST 1
 
 Source location map(s) showing location with respect to:
 \1\ The ``Screening Procedures for Estimating the Air Quality Impact of Stationary Sources, Revised'', October 1992 (EPA-450/R-92- 019), should be used as a screening tool to determine whether modeling analyses are required. Screening procedures should be refined by the user to be site/problem specific.
 
 <bullet> Urban areas 2
 
 \2\ Within 50km or distance to which source has a significant impact, whichever is less.
 
 <bullet> PSD Class I areas
 <bullet> Nonattainment areas \2\ <bullet> Topographic features (terrain, lakes, river valleys, etc.) \2\
 <bullet> Other major existing sources \2\ <bullet> Other major sources subject to PSD requirements <bullet> NWS meteorological observations (surface and upper air) <bullet> On-site/local meteorological observations (surface and upper air)
 <bullet> State/local/on-site air quality monitoring locations \2\
 <bullet> Plant layout on a topographic map covering a 1km radius of the source with information sufficient to determine GEP stack heights
 2. Information on urban/rural characteristics: <bullet> Land use within 3km of source classified according to Auer (1978): Correlation of land use and cover with meteorological anomalies. Journal of Applied Meteorology, 17: 636-643. <bullet> Population
 -> total
 -> density
 <bullet> Based on current guidance determination of whether the area should be addressed using urban or rural modeling methodology 3. Emission inventory and operating/design parameters for major sources within region of significant impact of proposed site (same as required for applicant)
 <bullet> Actual and allowable annual emission rates (g/s) and operating rates 3
 
 \3\ Particulate emissions should be specified as a function of particulate diameter and density ranges.
 
 <bullet> Maximum design load short-term emission rate (g/s) \3\ <bullet> Associated emissions/stack characteristics as a function of load for maximum, average, and nominal operating conditions if stack height is less than GEP or located in complex terrain. Screening analyses as footnoted above or detailed analyses, if necessary, must be employed to determine the constraining load condition (e.g., 50, 75, or 10012ad) to be relied upon in the short-term modeling analysis.
 --location (UTM's)
 --height of stack (m) and grade level above MSL --stack exit diameter (m)
 --exit velocity (m/s)
 --exit temperature ( deg.K)
 <bullet> Area source emissions (rates, size of area, height of area source)\3\
 <bullet> Location and dimensions of buildings (plant layout drawing)
 --to determine GEP stack height
 --to determine potential building downwash considerations for stack heights less than GEP
 <bullet> Associated parameters --boiler size (megawatts, pounds/hr. steam, fuel consumption, etc.)
 --boiler parameters ( 2.121996e-313xcess air, boiler type, type of firing, etc.)
 --operating conditions (pollutant content in fuel, hours of operation, capacity factor, 12ad for winter, summer, etc.) --pollutant control equipment parameters (design efficiency, operation record, e.g., can it be bypassed?, etc.) <bullet> Anticipated growth changes 4. Air quality monitoring data:
 <bullet> Summary of existing observations for latest five years (including any additional quality assured measured data which can be obtained from any state or local agency or company) 4
 
 \4\ See footnote 2 of this Appendix C.
 
 <bullet> Comparison with standards <bullet> Discussion of background due to uninventoried sources and contributions from outside the inventoried area and description of the method used for determination of background (should be consistent with the Guideline)
 5. Meteorological data:
 <bullet> Five consecutive years of the most recent representative sequential hourly National Weather Service (NWS) data, or one or more years of hourly sequential on-site data <bullet> Discussion of meteorological conditions observed (as applied or modified for the site-specific area, i.e., identify possible variations due to difference between the monitoring site and the specific site of the source)
 <bullet> Discussion of topographic/land use influences 6. Air quality modeling analyses:
 <bullet> Model each individual year for which data are available with a recommended model or model demonstrated to be acceptable on a case-by-case basis
 --urban dispersion coefficients for urban areas --rural dispersion coefficients for rural areas <bullet> Evaluate downwash if stack height is less than GEP <bullet> Define worst case meteorology <bullet> Determine background and document method --long-term
 --short-term
 <bullet> Provide topographic map(s) of receptor network with respect to location of all sources
 <bullet> Follow current guidance on selection of receptor sites for refined analyses
 <bullet> Include receptor terrain heights (if applicable) used in analyses
 <bullet> Compare model estimates with measurements considering the upper ends of the frequency distribution <bullet> Determine extent of significant impact; provide maps <bullet> Define areas of maximum and highest, second-highest impacts due to applicant source (refer to format suggested in Air Quality Summary Tables)
 -> long-term
 -> short-term
 7. Comparison with acceptable air quality levels: <bullet> NAAQS
 <bullet> PSD increments
 <bullet> Emission offset impacts if nonattainment 8. Documentation and guidelines for modeling methodology: <bullet> Follow guidance documents -> Appendix W to 40 CFR Part 51
 -> ``Screening Procedures for Estimating the Air Quality Impact of Stationary Sources, Revised'' (EPA-450/R-92-019), 1992 -> ``Guideline for Determination of Good Engineering Practice Stack Height (Technical Support Document for the Stack Height Regulations)'' (EPA-450/4-80-023R), 1985 -> ``Ambient Monitoring Guidelines for PSD'' (EPA-450/4-87-007), 1987
 -> Applicable sections of 40 CFR Parts 51 and 52.
 [[Page 41893]]
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 Air Quality Summary--For New Source Alone
 
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 Highest Highest 2d high Highest Highest 2d high Annual
 
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 Pollutant: ________________ 1 ________________ 2 ________________ 2
 Concentration due to modeled ...................... ...................... ...................... ..................... .....................
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 source (<greek-m>g/m3).
 Background concentration (<greek- ...................... ...................... ...................... ..................... .....................
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 m>g/m3).
 Total concentration (<greek-m>g/ ...................... ...................... ...................... ..................... .....................
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 m3).
 Receptor distance (km) (or UTM ...................... ...................... ...................... ..................... .....................
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 easting).
 Receptor direction ( deg.) (or UTM ...................... ...................... ...................... ..................... .....................
 Note: EPA no longer updates this information, but it may be useful as a reference or resource.
 
 northing).