Vol. 56 No. 137 Wednesday, July 17, 1991  p 32688

>>>>  This article, FR94, is divided into five files.  This is File D:  Technical Amendments; Guideline on Air Quality Models (Revised) of Appendix X to Part 266, through Appendix A.REF to Appendix X of Part 266 - Summaries of Preferred Air Quality Models.  <<<<

Appendix X to Part 266-Guideline On Air Quality Models (Revised)

[EPA DOCUMENT NUMBER EPA-450/2-78-027R]

Preface

	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 was first published in April 1978 to satisfy these requirements by specifying models and providing guidance for their use. This guideline provides a common basis for estimating the air quality concentrations used in assessing control strategies and developing emission limits.

	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 this modeling 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 this guideline. The fourth activity is the extensive on-going research efforts by EPA and others in air quality and meteorological modeling.

	Based primarily on these four activities, this document embodies revisions to the "Guideline on Air Quality Models." Although the text has been revised from the 1978 guide, the present content and topics are similar. As necessary, new sections and topics are included. A new format has also been adopted in an attempt to lessen the time required to incorporate changes. The looseleaf notebook format allows future changes to be made on a page-by-page basis. Changes will not be scheduled, but announcements of proposed changes will be made in the Federal Register as needed. EPA believes that revisions to this guideline should be timely and responsive to user needs and should involve public participation to the greatest possible extent. Information on the current status of modeling guidance can always be obtained from EPA's Regional Offices.

	This revised guideline was promulgated in September 1986 (51 FR 32l76-32179) and, with further revisions known as supplement A, in January 1988 (53 FR 392-396).

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

	3.2 Use of Alternative Models

	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 Nitrogem 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 (beyond 50 km)

	7.2.7 Modeling Guidance for Other Governmental Programs

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

	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-Summaries of Preferred Air Quality Models

	Appendix B-Summaries of Alternative Air Quality Models

	Appendix C-Example Air Quality Analysis Checklist

List of Tables

	 		 Table No. and title        Page

4-1 	Preferred Models for Selected Applications in Simple Terrain

5-1	Preferred Options for the SHORTZ/LONGZ Computer Codes When Used in a Screening Mode.

5-2	Preferred Options for the RTDM Computer Code When Used in a  Screening Mode.

9-1	Model Emission Input Data for Point Sources

9-2	Averaging Times for Site-Specific Wind and Turbulence Measurements.

9-3	Wind Fluctuation Criteria for Estimating Pasquill Stability  Categories.

9-4	Nighttime P-G Stability Categories Based on FA from Table 9-3

1.0 Introduction

	This 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. It 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.

	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.

	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.

	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.

	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.

	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.

	All changes to this guideline must follow rulemaking requirements since the guideline has been incorporated by reference in the PSD regulations. Changes will be proposed and noticed in the Federal Register. Ample opportunity for public comment will be provided for each proposed change and public hearings scheduled if requested. Published, final changes will be made available through the National Technical Information Service (NTIS).

	A wide range of topics on modeling and data bases are discussed in the remainder of this guideline. Where specific recommendations are made, the recommendations are typed in a single-spaced format. 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 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.

	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

	Before attempting to implement the guidance contained in this document, 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

	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.

	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.
	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.

	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

	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 CRSTER 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.

	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 this guideline.

	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

	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.

	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.

	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

	This section recommends refined modeling techniques that are preferredfor 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.

	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 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.

	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.

	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

	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. (12,13,14)

	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 this 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.

	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: 1. The model must be computerized and functioning in a common Fortran language suitable for use on a variety of computer systems.

	2. 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 Single Source [CRSTER] Model.

	3. The model must be accompanied by a complete test data set including input parameters and output results. The test data must be included in the user's guide as well as provided in computer-readable form.

	4. 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.

	5. The model documentation must include a comparison with air quality data or with other well-established analytical techniques.

	6. 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.

	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

	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 that appendix. 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 this guideline.

	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.

	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

	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," (l5, l6) 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. These procedures provide a general framework for objective decision-making on the acceptability of an alternative model for a given regulatory application. The document contains procedures for conducting both the technical evaluation of the model and the field test or performance evaluation. An example problem that focuses on the design and execution of the protocol for conducting a field performance evaluation is also included in that document.

	This section discusses the use of alternate modeling techniques and defines three situations when alternative models may be used.

3.2.2 Recommendations

	Determination of acceptability of a model is a Regional Office responsibility. Where the Regional Administrator or reviewing authority 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.

	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 alternative models known to be available to the public 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 for the future.

	Equivalency is established by demonstrating that the maximum or highest, second highest concentrations are within two percent of the estimates obtained from the preferred model. The option to show equivalency is intended as a simple demonstration of acceptability for an alternative model that is so nearly identical (or contains options that can make it identical) to a preferred model that it can be treated for practical purposes as the preferred model. Two percent was selected as the basis for equivalency since it is a rough approximation of the fraction that PSD Class I increments are of the NAAQS for SO2, i.e., the difference in concentrations that is judged to be significant. However, this demonstration is not intended to preclude the use of models that are not equivalent. They may be used when one of two other conditions identified below are satisfied.

	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. 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.

	When no appendix A model is applicable to the modeling problem, an alternative refined model may be used provided that:

	1. The model can be demonstrated to be applicable to the problem on a theoretical basis, and

	2. the data bases which are necessary to perform the analysis are available and adequate, and

	3a. performance evaluations of the model in similar circumstances have shown that the model is not biased toward underestimates (examples of such circumstances include long range transport and shoreline fumigation), or

	3b. after consultation with the EPA Regional Office, a second model is selected as a baseline or reference point for performance and the interim procedures (l5) are then used to demonstrate that the proposed model performs better than the reference model (an example of such circumstances includes complex terrain).

3.3 Availability of Supplementary Modeling Guidance

	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.

	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:

	(1) Review of decisions proposed by EPA Regional Offices on the use of modeling techniques and data bases.

	(2) Periodic visits to Regional Offices to gather information pertinent to regulatory model usage.

	(3) 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.

	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

	3.3.2.1 Discussion.

	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 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.

	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

	Simple terrain, as used here, 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.

	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. Thus, based on past use, public familiarity, and availability CRSTER remains the recommended model for rural, simple terrain, single point source applications. Similar determinations were made for the other refined models that are identified in the following sections.

4.2 Recommendations.

4.2.1 Screening Techniques

	The EPA document "Guidelines for Air Quality Maintenance Planning and Analysis, Volume 10R: Procedures for Evaluating Air Quality Impact of New Stationary Sources" 18 contains screening procedures that should be used if the source is in simple terrain. A computerized version of the Volume 10R screening technique for use in simple terrain (urban and rural) is available in UNAMAP" 19 as PTPLU-2.

	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

	Table 4-1 lists preferred models for selected applications. These preferred models should be used for the sources, land use categories and averaging times indicated in the table. A brief description of each of these models is found in appendix A. Also listed in that appendix are the model input requirements, the standard options that should be selected when running the program and output options.

	When modeling for compliance with short term NAAQS and PSD increments is of primary concern, the short term models listed in Table 4-1 may also be used to provide long term concentration estimates. When modeling for 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.

Table 4-1.- Preferred Models for Selected Applications in Simple Terrain

Land Use
Model1

Short Term (1-24 hours):

Single Source
Rural
CRSTER

Urban
RAM
Multiple Source
Rural
MPTR

Urban
RAM
Complicated Sources2
Rural/Urban
ISCST
Buoyant Industrial Line Sources
Rural
BLP
Long Term (monthly, seasonal or annual):

Single Source
Rural
CRSTER

Urban
RAM
Multiple Source
Rural
MPTER

Urban
CDM 2.0 or RAM3
Complicated Sources2
Rural/Urban
ISCLT
Buoyant Industrial Line Sources
Rural
BLP

1Several of these models contain options which allow them to be interchanged. For example, ISCST can be substituted for CRSTER and equivalent, if not identical, concentration estimates obtained. Similarily, for a point source application, MPTER with urban option can be substituted for RAM. Where a substitution is convenient to the user and equivalent estimates are assured, it may be made. The models as listed here reflect the applications for which they were originally intended.
2Complicated sources are sources with special problems such as aerodynamic downwash, particle deposition, volume and area sources, etc.
3If only a few sources in an urban area are to be modeled, RAM should be used.

5.0 Model Use in Complex Terrain

5.1 Discussion

	For the purpose of this guideline, complex terrain is defined as terrain exceeding the height of the stack being modeled. Complex terrain dispersion models are normally applied to stationary sources of pollutants such as SO2 and particulates.

	Although the need for refined complex terrain dispersion models has been acknowledged for several years, adequate refined models have not been developed. The lack of detailed, descriptive data bases and basic knowledge concerning the behavior of atmospheric variables in the vicinity of complex terrain presents a considerable obstacle to the solution of the problem and the development of refined models.

	A workshop (20) of invited complex terrain experts was held by the American Meteorological Society as a part of the AMS-EPA Cooperative Agreement in May of 1983. Several major complex terrain problems were identified at this workshop; among them were: (1) Valley stagnation, (2) valley fumigation, (3) downwash on the leeside of terrain obstacles; and (4) the identification of conditions under which plume impaction can occur.

	A first step toward the solution of two of these problems has been taken in the multi-year EPA Complex Terrain Model Development project. (21,22,23,24) One product of this project is expected to be a model suitable for regulatory application to plume impaction problems in complex terrain. In addition, insight into the leeside effects problem is also anticipated. Completion of the project is not expected before late 1987. Preliminary results have identified at least two concepts that have important implications for the regulatory application of models in complex terrain and will require further detailed study and evaluation. First, plume impaction resulting in high concentrations was observed to occur during the field study as well as in supporting fluid modeling studies. (21) Further, the occurrence of impaction was linked to a "critical streamline" that separates flow around an obstacle from flow over an obstacle. Second, high concentrations were also observed to occur in the lee of the obstacle and were of sufficient magnitude to indicate that this phenomenon should be considered, if appropriate, in the determination of source impacts. (22)

	To date most projects have been designed to identify plume behavior in complex terrain and to define the meteorological variables influencing that behavior. Until such time as it is possible to develop and evaluate a model hased on the quantification of the meteorological and plume parameters identified in these studies, existing algorithms adapted to site-specific complex terrain situations are all that are available. The methods discussed in this section should be considered screening, or "refined" screening, techniques and not refined dispersion models.

5.2 Recommendations

	The following recommendations apply primarily to the 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. The evaluation of other concentrations should be considered after consultation with the Regional Office. However, limited guidance on calculation of concentrations between stack height and plume centerline is provided.

	Models developed for specific uses in complex terrain will be considered on a case-by-case basis after a suitable demonstration of their technical merits and an evaluation using measured on-site data following the procedures in "Interim Procedures for the Evaluation of Air Quality Models." (15) Since the location of plume centerline is as important a concern in complex terrain as dispersion rates, it should be noted that the dispersion models combined with a wind field analysis model should be superior to an assumption of straight-line plume travel. Such hybrid modeling techniques are also acceptable, after the appropriate demonstration and evaluation.

5.2.1 Screening Techniques

	In the absence of an approved case-specific, refined, complex terrain model, four screening techniques are currently available to aid in the evaluation of concentrations due to plume impaction during stable conditions: the Valley Screening Technique as outlined in the Valley Model's User's Guide, (19, 25) COMPLEX I, (19) SHORTZ/LONGZ, (26) and the Rough Terrain Dispersion Model (RTDM) (91) in its prescribed mode described below. These methods should be used only to calculate concentrations at receptors whose elevations are greater than or equal to plume height. Receptors below stack height should be modeled using a preferred simple terrain model (see chapter 4). Receptors between stack height and plume height should be modeled with both complex terrain and simple terrain models and the highest concentration used. (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.)

	If a violation of any NAAQS or the controlling increment is indicated by using the Valley Screening Technique, a second-or third-level screening technique may be used. A site-specific data base of at least one full year of meteorological data is preferred for use with either the second- or third-level screening technique. If more data are available, they should be used. Meteorological data used in the analysis should be reviewed for both spatial and temporal representativeness.

	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 a change in a receptor to a location where the terrain is as little as 25 meters or so higher or lower may make a substantial change in the predicted concentration. Receptors within about a kilometer of the source may be even more sensitive to location. Thus, a very 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.

	5.2.1.1 Initial Screening Technique.

	The initial screen to determine 24-hour averages is the Valley Screening Technique. 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."

	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, (25) 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.

	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 and note the comments above on the placement of receptors in complex terrain models.

	5.2.1.2 Second-Level Screening Technique (Rural).

	If the area is rural, the suggested second-level screening technique is COMPLEX I for all averaging times. COMPLEX I is a modification of the MPTER model that incorporates the plume impaction algorithm of the Valley Model. 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 6 stability classes); and (5) set Z MIN = 10.

	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).

	5.2.1.3 Second-Level Screening Technique (Urban).

	If the source is located in an urbanized (section 8.2.8) complex terrain valley, then the suggested second-level 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.)

	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 5-1 should be selected.

	5.2.1.4 Third Level Screening Technique (Rural).

	If a violation of any NAAQS or the controlling increment is indicated by using the second-level screening technique, a third-level screening technique may be used for rural applications. RTDM with the options specified in Table 5-2 may be used as a screening technique in rural complex terrain situations without demonstration and evaluation.

	The RTDM1 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 5-2 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.
1The RTDM model is available as part of Change 3 to UNAMAP Version 6.
	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. It is suggested that an analysis of the expected plume height relative to the height of the critical dividing streamline 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 5.2.1.

	5.2.1.5 Restrictions.

	For screening analyses using the Valley Screening Technique, Complex I or RTDM, a sector greater than 22 1/2E 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

	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. Since there are no refined techniques currently recommended for complex terrain applications, any refined model used should be applied in accordance with section 3.2. In particular, use of the "Interim Procedures for Evaluating Air Quality Models" (15) and a second model to serve as a baseline or reference point for the comparison should be used in a demonstration of applicability. New approaches to improve the ability of models to realistically simulate atmospheric physics, for example hybrid models which incorporate an accurate wind field analysis, will ultimately provide more appropriate tools for analyses.

	In the absence of an appropriate refined model, screening results may need to be used to determine air quality impact and/or emission limits.

Table 5-1.-Preferred Options for the SHORTZ/LONGZ Computer Codes When Used in a Screening Mode

Option
Selection

I Switch 9
If using NWS data, set = 0. If using site-
specific data, check with the Regional Office.
I Switch 17
Set = 1 (urban option).
GAMMA 1
Use default values (0.6 entrainment coefficient).
GAMMA 2
Always default to stable.
XRY
Set = 0 (50 m rectilinear expansion distance).
NS, VS, FRQ (SHORTZ)
Do not use. (Applicable only in flat
(particle size, etc.)
terrain).
NUS, VS, FRQ (LONGZ)

(particle size, etc.)

ALPHA
Select 0.9.
SIGEPU (dispersion parameters)
Use Cramer curves (default).
SIGAPU (dispersion parameters)
If site-specific turbulence data are

available, see the Regional Office for

advice.
P (wind profile)
Select default values given in table 2-2 of

User's Instructions. If site-specific data

are available, see the Regional Office for

advice.

Table 5-2.-Preferred Options for the RTDM Computer Code When Used in a Screening Mode

Parameter
Variable
Value
Remarks

PR00l-003
SCALE

Scale factors assuming horizontal distance is in kilometers, vertical distance is in feet, and wind speed is in meters per second.
PR004
ZWINDl
Wind Measurement
Height
See section 5.2.1.4.

ZWIND2
Not used
Height of second anemometer.

IDILUT
1
Dilution wind speed scaled to plume height.

ZA
0 (default)
Anemometer-terrain height above stack base.
PR005
EXPON
.09, .11, .12, .14,
.2, .3 (default)
Wind profile exponents.
PR006
ICOEF
3 (default)
Briggs Rural/ASME (1979) dispersion parameters.
PR009
IPPP
0 (default)
Partial plume penetration, not used.
PR010
IBUOY
1 (default)
Buoyancy-enhanced dispersion is used.

ALPHA
3.162 (default)
Buoyancy-enhanced dispersion coefficient.
PR011
IDMX
1 (default)
Unlimited mixing height for stable conditions.
PR012
ITRANS
1 (default)
Transitional plume rise is used.
PR013
TERCOR
6*0.5 (default)
Plume path correction factors.
PR014
RVPTG
0.02, 0.035 (default)
Vertical potential temperature gradient values for stabilities E and F.
PR015
ITIPD
1
Stack-tip downwash is used.
PR020
ISHEAR
0 (default)
Wind shear, not used.
PR022
IREFL
1 (default)
Partial surface reflection is used.
PR023
IH0RIZ
2 (default)
Sector averaging.

SECTOR
6*22.5 (default)
Using 22.5E sectors.
PR0l6 to 019; 021; and 024.
IY, IZ, IRVPTG,
IHVPTG; IEPS; IEMIS
0
Hourly values of turbulence, vertical potential temperature gradient, wind speed profile exponents, and stack emissions are not used.

6.0 Models for Ozone, Carbon Monoxide and Nitrogen Dioxide

6.1 Discussion.

	Models discussed in this section are applicable to pollutants often associated with mobile sources, e.g., ozone (03), carbon monoxide (CO) and nitrogen dioxide (NO2). Where stationary sources of CO and NO2 are of concern, the reader is referred to sections 4 and 5.

	A control agency whose jurisdiction contains areas with significant ozone problems and who 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. 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 model 5 and proportional (rollback) modeling techniques and may be the only applicable procedure if the data bases available are insufficient for refined dispersion modeling.

	Carbon monoxide is generally considered to be a problem only in specific areas with high numbers of vehicles or slow moving traffic. For that reason, frequently only "hot spots" or project level analyses are needed in SIP revisions.

	Nitrogen oxides are reactive and also an important contribution to the photochemical ozone problem. They are usually of most concern in areas of high ozone concentrations. Unless suitable photochemical dispersion models are used, assumptions regarding the conversion of NO to NO2 are required when modeling. Site-specific conversion factors may be developed. If site-specific conversion factors are not available or photochemical models are not used, NO2 modeling should be considered only a screening procedure.

6.2 Recommendations

6.2.1 Models for Ozone.

	The Urban Airshed Model (27) 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 Applying the Airshed Model to Urban Areas" (28) for further information on data base requirements, kinds of tasks involved in the model application, and the overall level of resources required.

	The empirical model, City-specific EKMA (29,30,31,32,33) is an acceptable approach for urban ozone applications.

	Appendix B contains some additional models that may be applied on a case-by-case basis for photochemical or reactive pollutant modeling. Other photochemical models, including multi-layered trajectory models, that are available may be used if shown to be appropriate. Most photochemical dispersion models require emission data on individual hydrocarbon species and may require three dimensional meteorological information on an hourly basis. Reasonably sophisticated computer facilities are also often required. Because the input data are not universally available and studies to collect such data are very resource intensive, there are only limited evaluations of those models.

	Proportional (rollback/forward) modeling is no longer an acceptable procedure for evaluating ozone control strategies.

	6.2.2 Models for Carbon Monoxide.

	Carbon monoxide modeling for the development of SIP-required control strategies should follow the guidance provided in the "Carbon Monoxide Hot Spot Guidelines" (34) or in Volume 9 of the "Guidelines for Air Quality Maintenance Planning and Analysis." (35) These volumes provide screening techniques for locating and quantifying worst case carbon monoxide concentrations, and for establishing background values; they also provide methods for assessing carbon monoxide concentrations at multiple locations across the urban area. If results from screening techniques or measured carbon monoxide levels in an urban area are clearly well below the standards and expected to remain below the standard, or it can be demonstrated that the Federal Motor Vehicle Control Program will provide the needed CO reductions, then urban area-wide strategies may be evaluated using a modified rollback or proportional model approach.

	Project analysis of mobile source emissions of carbon monoxide should first include an analysis using the screening techniques referenced above. If concentrations using these techniques exceed the NAAQS, then refined techniques are needed to determine compliance with the standards. CALINE3 (see appendix A) is the preferred model for use when refined analyses are required. For free flow sources, the latest version of mobile source emission factors are required for input to CALINE3, and for interrupted flow sources (i.e., signalyzed intersections), procedures to calculate modal emission factors as contained in Worksheet 2 of the "Guidelines for Air Quality Maintenance Planning and Analysis, Volume 9" (35) are recommended.

	Situations that require the use of refined techniques on an urban-wide basis should be considered on a case-by-case basis. If a suitable model is available and the data and technical competence required for its use are available, then such a model should be considered.

	Where point sources of CO are of concern, they should be modeled using the screening and preferred techniques of sections 4 or 5.

	6.2.3 Models for Nitrogen Dioxide (Annual Average).

	A three-tiered screening approach is recommended to obtain annual average estimates of NO2 from point sources:

	a. Initial screen: Use an appropriate Gaussian model from Appendix A to estimate the maximum annual average concentration and assume a total conversion of NO to NO2. If the concentration exceeds the NAAQS for NO2, proceed to the 2nd level screen.

	b. 2nd level screen: Apply the Ozone Limiting Method (36) to the annual NOx estimate obtained in (a) above using a representative average annual ozone concentration. If the result is still greater than the NAAQS, the more refined Ozone Limiting Method in the 3rd level screen should be applied.

	c. 3rd level screen: Apply the Ozone Limiting Method separately for each hour of the year or multi-year period. Use representative hourly NO2 background and ozone levels in the calculations.

	In urban areas, a proportional model may be used as a preliminary assessment to evaluate control strategies for multiple sources (mobile and area) of NOx; concentrations resulting from major point sources should be estimated separately as discussed above, then added to the impact of area sources. An acceptable screening technique for urban complexes is to assume that all NOx is emitted in the form of NO2 and to use a model from Appendix A for nonreactive pollutants to estimate NO2 concentrations. A more accurate estimate can be obtained by (1) calculating the annual average concentrations of NOx with an urban model, and (2) converting these estimates to NO2 concentrations based on a spatially averaged NO2/NOx annual ratio determined from an existing air quality monitoring network.

	In situations where there are sufficient hydrocarbons available to significantly enhance the rate of NO to NO2 conversion, the assumptions implicit in the Ozone Limiting Procedure may not be appropriate. More refined techniques should be considered on a case-by-case basis and agreement with the reviewing authority should be obtained. Such techniques should consider individual quantities of NO and NO2 emissions, atmospheric transport and dispersion, and atmospheric transformation of NO to NO2. Where it is available site-specific data on the conversion of NO to NO2 may be used. Photochemical dispersion models, if used for other pollutants in the area, may also be applied to the NOx problem.

7.0 Other Model Requirements

7.1 Discussion

	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.

	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.

	The need to estimate impacts at distances greater than 50 km (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.

	A current EPA agreement with Argonne National Laboratory, scheduled for completion In FY 1986, will result in the development of evaluation procedures for long range transport models. Models submitted to EPA will be tested with currently available data bases using these procedures. Similar research in this area is also being performed by others in EPA and other organizations. For the time being, however, long range and mesoscale transport models must be evaluated for regulatory use on a case-by-case basis.

7.2 Recommendations

	7.2.1 Fugitive Dust/Fugitive Emissions.

	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.

	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.

	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.

7.2.2 Particulate Matter.

	Currently a proposed NAAQS for particulate matter includes provisions both for particles in the size range less than 10 micrometers (PM10) and for Total Suspended Particulates (TSP). State Implementation Plans will be developed by States to attain and maintain this new standard when the standard is promulgated.

	Screening techniques like those identified in section 4 are also applicable to PM10 and to large particles (TSP). 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.

	Refined models such as those in section 4 are recommended for both PM10 and TSP. 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 or RAM should be used. CRSTER and MPTER are recommended for point sources of small particles. For source-specific analyses of complicated sources, the ISC model is preferred. 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) 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.

	For those cases where no recommended technique is available or applicable, modeling approaches should be approved by the appropriate Regional Office on a case-by-case basis. At this time analyses involving model calculations for distances beyond 50 km should also be justified on a case-by-case basis (see section 7.2.6).

	7.2.3 Lead.

	The air quality analyses required for lead implementation plans are given in §§ 51.83, 51.84 and 51.85 of 40 CFR part 51. Sections 51.83 and 51.85 require the use of a modified rollback model as a minimum to demonstrate attainment of the lead air quality standard but the use of a dispersion model is the preferred approach. Section 51.83 requires the analysis of an entire urban area if the measured lead concentration in the urbanized area exceeds a quarterly (three month) average of 4.0 :g/m3 . Section 51.84 requires the use of a dispersion model to demonstrate attainment of the lead air quality standard around specified lead point sources. For other areas reporting a violation of the lead standard, § 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.

	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.

	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 and APRAC-3 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.

	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.

	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.

	The visibility regulations as promulgated in December 19801 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) At the present time, "although information derived from modeling and monitoring can, in some cases, aid the States in development and implementation of the visibility program,"2 the States are not currently required to establish monitoring networks or perform modeling analyses. However, a monitoring strategy is required. As additional knowledge is gained, guidance on "plume blight" and regional scale models will be provided, as appropriate.
	145 FR 80084.
240 CFR 51.300-307

	References 43, 44, and 45 may also be useful when visibility evaluations are needed. Appendix B contains two models developed for application to visibility problems.

	7.2.5 Good Engineering Practice Stack Height.

	The use of stack height credit in excess of Good Engineering Practice (GEP) stack height is prohibited in the development of emission limitations by 40 CFR 51.12 and 40 CFR 51.18. The definition of GEP stack height is contained in 40 CFR 51.1. 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.

	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,1 then air quality impacts associated with cavity or wake effects due to the nearby building structures should be determined. Detailed downwash screening procedures (17) 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.
1The EPA refined formula height is defined as H+l.5L (refer to reference 46).

	7.2.6 Long Range Transport (beyond 50 km).

	Section 165(e) of the Clean Air Act requires that suspected significant impacts on PSD Class I areas be determined. However, the useful distance to which most Gaussian models are considered accurate for setting emission limits is 50 km. Since in many cases Class I areas may be threatened at distances greater than 50 km 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 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.

	LRT models for use beyond 50 km and for other than PSD purposes also should be selected on a case-by-case basis. Normally, use of these models will require an acceptable demonstration of applicability and an evaluation of model performance if possible (See section 3.2).

	7.2.7 Modeling Guidance for Other Governmental Programs

	When using the models recommended or discussed in this 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.

	The Offshore and Coastal Dispersion (OCD) model (92) 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.

8.0 General Modeling Considerations

8.1 Discussion

	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 S02, Particulate Matter, Lead, and N02.

	An air quality analysis 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.

	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 one 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.

	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 "Procedures for Evaluating Air Quality Impact of New Stationary Sources." (18)

	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.

	As long a period of record as possible should be used in making estimates to determine design values and PSD increments. If more than one year of site-specific data is available, it should be used.

	8.2.1.2 Design Concentrations for Criteria Pollutants with Expected Exceedance Standards.

	Specific instructions for the determination of design concentrations for criteria pollutants with expected exceedance standards are contained in special guidance documents for the preparation of State Implementation Plans for those pollutants. 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

	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 500 MW 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 10 km, 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

	Gaussian models used in most applications should employ dispersion coefficients consistent with those contained in the preferred models in appendix A. Factors such as averaging time, urban/rural surroundings, and type of source (point vs. line) may dictate the selection of specific coefficients. Generally, coefficients used in appendix A models are identical to, or at least based on, Pasquill-Gifford coefficients (50) in rural areas and McElroy-Pooler (51) coefficients in urban areas.

	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.

	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

	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.

	Procedures to determine Pasquill stability categories from other than NWS data are found in section 9.3. Any other method to determine Pasquill stability categories must be justified on a case-by-case basis.

	For a given model application where stability categories are the basis for selecting dispersion coefficients, both Fy and Fz should be determined from the same stability category. "Split sigmas" in that instance are not recommended.

	Sector averaging, which eliminates the Fy 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

	The plume rise methods of Briggs (56, 57) are incorporated in the preferred models and are recommended for use in all modeling applications. No provisions in these models are made for fumigation or multistack plume rise enhancement or the handling of such special plumes as flares; these problems should be considered on a case-by-case basis.

	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 impact; (2) when calculating the effects of building wakes. The building wake algorithm in the ISC model incorporates 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.

	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.

	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

	The chemical transformation of SO2 emitted from point sources or single industrial plants in rural areas is generally assumed to be relatively unimportant to the estimation of maximum concentrations when travel time is limited to a few hours. However, in urban area, where synergistic effects among pollutants are of considerable consequence, chemical transformation rates may be of concern. In urban area applications, a half-life of 4 hours (55) may be applied to the analysis of SO2 emissions. Calculations of transformation coefficients from site-specific studies can be used to define a "half-life" to be used in a Gaussian model with any travel time, or in any application, if appropriate documentation is provided. Such conversion factors for pollutant half-life should not be used with screening analyses.

	Complete conversion of NO to NO2 should be assumed for all travel time when simple screening techniques are used to model point source emissions of nitrogen oxides. If a Gaussian model is used, and data are available on seasonable 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.

	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

	An "infinite half-life" should be used for estimates of total suspended particulate concentrations when Gaussian models containing only exponential decay terms for treating settling and deposition are used.

	Gravitational settling and deposition may be directly included in a model if either is a significant factor. At least one preferred model (ISC) contains settling and deposition algorithms and is recommended for use when particulate matter sources can be quantified and settling and deposition are problems.

8.2.8 Urban/Rural Classification

	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.

	Land Use Procedure: (1) Classify the land use within the total area, Ao, circumscribed by a 3 km radius circle about the source using the meteorological land use typing scheme proposed by Auer (60); (2) if land use types I1, I2, C1, R2, and R3 account for 50 percent or more of Ao, use urban dispersion coefficients; otherwise, use appropriate rural dispersion coefficients.

	Population Density Procedure: (1) Compute the average population density, p per square kilometer with Ao as defined above; (2) If p is greater than 750 people/km2, use urban dispersion coefficients; otherwise use appropriate rural dispersion coefficients.

	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.

	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

	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 "Guidelines for Air Quality Maintenance Planning and Analysis Volume 10R: Procedures for Evaluating Air Quality Impact of New Stationary Sources") (18) that may be used to approximate the concentrations. Considerable care should be exercised in the use of the results obtained from the screening techniques.

	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. Although models have been developed to address this problem, the evaluations so far do not permit the recommendation of any specific technique.

	The Regional Office should be contacted to determine the appropriate model for applications where fumigation is of concern.

8.2.10 Stagnation

	Although both short and long term periods of very light winds are important in the identification of worst-case conditions, the models identified in this guideline cannot adequately simulate such conditions. If stagnation conditions are determined to be important to the analysis, then techniques specific to the situation and location must be developed. Such techniques might include empirical models or box models. Assistance from the appropriate Regional Office should be obtained prior to embarking on the development of such a procedure.

8.2.11 Calibration of Models

	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.

	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

	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

	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.

	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

	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 load1 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. The following example for a power plant is typical of the 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. 	1Malfunctions 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.

	a. 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.

	b. 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.

	c. Boiler size. For all boilers, the associated megawatts, 106 BTU/hr, and pounds of steam per hour, and the design and/or actual fuel consumption rate for 100 percent load for coal (tons/hour), oil (barrels/hour), and natural gas (thousand cubic feet/hour).

	d. 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.).

	e. 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.

	f. 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 date 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.

	g. Data for new bollers 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 items (a) through (f) above following completion of construction or modification.

	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 SIP's for compliance with quarterly and annual standards, emission input data shown on 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.

	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.

	The impact of growth on emissions should be considered in all modeling analyses covering existing sources. Increases in emissions due to planned expansion or planned fuel switches should be identified. Increases in emissions at individual sources that may be associated with a general industrial/commercial/residential expansion in multi-source urban areas should also be treated. For new sources the impact of growth on emissions should generally be considered for the period prior to the start-up date for the source. Such changes in emissions should treat increased area source emissions, changes in existing point source emissions which were not subject to preconstruction review, and emissions due to sources with permits to construct that have not yet started operation.

Table 9-1.-Model Emission Input Data for Point Sources1

Emission limit (#/MMBtu)2
X
Operating level (MMBtu/hr)2
X
Operating factor (e.g. hr/yr, hr/day)

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

Averaging time, Annual & quarterly.
Maximum allowable emission limit or federally enforceable permit limit

Actual or design capacity (whichever is greater), or federally enforceable permit condition.

Actual operating factor averaged over most recent 2 years.3
Short term
Maximum allowable emission limit or federally  enforceable permit limit.

Actual or design capacity (whichever is greater), or federally enforceable permit condition4.

Continuous operation, i.e., all hours of each time period under consideration (for all hours of the meteorological data base).5

	

Nearby Background Source(s) Same input requirements as for stationary point source(s) above.

	

Other Background Source(s): If modeled (see section 9.2.3), input data requirements are defined below.

Averaging time, Annual & quarterly
Maximum allowable emission limit or federally enforceable permit limit.

Annual level when actually operating, averaged over the most recent 2 years.3

Actual operating factor averaged over most recent 2 years3
Short term
Maximum allowable emission limit or federally enforceable permit limit.

Annual level when actually operating, averaged over the most recent 2 years.3

Continuous operation, i.e., all hours of each time period under consideration (for all hours of the meteorological data base).5

1The model input data requirements shown on this table apply to stationary source control strategies for STATE IMPLEMENTATION PLANS. For purposes of emissions trading, new source review, or prevention of significant deterioration, other model input criteria may apply. Refer to the policy and guidance for these programs to establish the input data.
2Terminology applicable to fuel burning sources; analogous terminology, e.g., #/throughput may be used for other type of sources.
3Unless it is determined that this period is not representative.
4Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing the highest concentration.
5If 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 a.m. to 4 p.m. each day, only these hours will be modeled with emissions from the source. Modeled emissions should not be averaged across nonoperating time periods.)

9.2 Background Concentrations

9.2.1 Discussion

	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.

	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.

	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)

	Two options are available to determine background near isolated sources.

	Option One: Use air quality data collected in the vicinity of the source to determine the background concentration for the averaging times of concern.1 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 90E 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.
1For purposes of PSD, the location of monitors as well as data quality assurance procedures must satisfy requirements listed in the PSD Monitoring Guidelines. (63)

	Option Two: 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)

	In multi-source areas two components of background should be determined.

	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. The number of such sources is expected to be small except in unusual situations. The nearby source inventory should be determined in consultation withthe local air pollution control agency. It is envisioned that the nearby sources and the sources under consideration will be evaluated together using an appropriate appendix A model.

	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 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.

	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 either by the procedures found in section 9.2.2 or by application of a model using Table 9-1.

9.3 Meteoro1ogical lnput Data

	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.

	Model input data are normally obtained either from the National Weather Service or as part of an on-site measurement program. Local universities, 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.

9.3.1 Length of Record of Meteorological Data

	9.3.1.1 Discussion.

	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.

	Five years of representative meteorologica1 data should be used when estimating concentrations with an air qua1ity 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. 1f the source is large, e.g., a 500 MW power plant, the use of 5 years of NWS meteorological data or at least 1 year of site-specific data is required.
	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.

	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.

	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.

	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.

	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.

	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.

	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.

	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.

	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

	Guidance provided in the "Ambient Montoring Guidelines for Prevention of Significant Deterioration (PSD)" (63) should be used for the establishment of special monitoring networks for PSD and other air quality modeling analyses. That guidance includes requirements and specifications for both pollutant and meteorological monitoring. Additional information is available in the EPA quality assurance handbooks and site selection guidance documents published on a pollutant-by-pollutant basis (see the Air Programs Report and Guidelines Index EPA-450/2-82-016). Volume IV of the series of reports "Quality Assurance Handbook for Air Pollution Measurement Systems" (66) contains such information for meteorological measurements. As a minimum, site-specific measurements of ambient air temperature, transport wind speed and direction, and the parameters to determine Pasquill-Gifford stability categories should be available in meteorological data sets to be used in modeling. Care should be taken to ensure that monitors are located to represent the area of concern and that they are not influenced by very localized effects. Site-specific data for model applications should cover as long a period of measurement as is possible to ensure adequate representation of "worst-case" meteorology. The Regional Office will determine the appropriateness of the measurement locations.

	All site-specific data should be reduced to hourly averages. Table 9-2 lists the wind related parameters and the averaging time requirements.

Temperature Measurements

	Temperature measurements should be made at standard shelter height in accordance with the guidance referenced above.

Wind Measurements

	In addition to surface wind measurements, the transport wind direction should be measured at an elevation as close as possible to the plume height. To approximate this, if a source has a stack below 100 m, select the stack top height as the transport wind measurement height. For sourcees with stacks extending above 100 m, a 100 m tower is suggested unless the stack top is significantly above 100 meters (200 m or more). In cases with stacks 200 m or above, the Regional Office should determine the appropriate measurement height on a case-by-case basis. Remote sensing may be a feasible alternative. The dilution wind speed used in determining plume rise and also used in the Gaussian dispersion equation is, by convention, defined as the wind speed at stack top.

	For routine tower measurements and surface measurements the wind speed should be measured using an anemometer and the wind direction measured using a horizontal vane. Specifications for wind measuring instruments and monitoring systems are contained in the "Ambient Air Monitoring Guidelines for Prevention of Significant Deterioration (PSD)" (63) and in the quality assurance handbook on meteorological measurements (66). Irwin (67) provides additional guidance for processing wind data.

Stability Categories

	The Pasquill-Gifford (P-G) stability categories, as originally defined, couple near-surface measurements of wind speed with subjectively determined insolation assessements based on hourly cloud cover and ceiling observations. The wind speed measurements are made at or near 10 m. The insolation rate is typically assessed using the cloud cover and ceiling height criteria outlined by Turner (50). Often the cloud cover data are not available in site-specific data sets. In the absence of such observations, it is recommended that the P-G stability category be estimated using Table 9-3. This table requires FE, the standard deviation of the vertical wind direction fluctuations. If the surface roughness of the area surrounding the source is different from the 15 cm roughness length upon which the table is based, an adjustment may be made as indicated in the second footnote of Table 9-3. FE is computed from direct measurements of the elevation angle of the vertical wind directions.

	If measurements of elevation angle are not available, FE may be determined using the transform:

FE =	Fw/u,

where:

FE =	the standard deviation of the vertical wind direction fluctuations over a one-hour period.
Fw =	the standard deviation of the vertical wind speed fluctuations over a one-hour period.
u =	the average horizontal wind speed for a one-hour period.

	Since both Fw and u are in meters per second, G6sE is in radians. To use FE in Table 9-3, FE must be converted to degrees. It is recommended that a vertically mounted propeller anemometer be used to measure the vertical wind speed fluctuations. The instrument should meet the specifications given in the Ambient Monitoring Guidelines referenced above. Compute Fw directly each hour using at least 360 values based on a recommended readout interval of up to 10 seconds. If FE is computed using the output of the anemometer by other than direct application of the formula for a variance, the method should be demonstrated to be equivalent to direct computation. Both the vertical wind speed fluctuations and the horizontal wind speed should be measured at the same level. Moreover, these measurements should be made at a height of 10 m for use in estimating the P-G stability category. Where trees or land use preclude measurements as low as 10 m, measurements should be made at a height above the obstructions.

	If on-site measurements of either FE or Fw are not available, stability categories may be determined using the horizontal wind direction flucuation, FA, as outlined by Irwin (68). Irwin includes the Mitchell and Timbre (69) method that uses categories of FA (70) listed in Table 9-3, as an initial estimate of the P-G stability category. This relationship is considered adequate for daytime use. During the nighttime (one hour prior to sunset to one hour after sunrise), the adjustments given in Table 9-4 should be applied to these categories. As with FE an hourly average FA may be adjusted for surface roughness by multiplying the table values of FA by a factor based on the average surface roughness length determined within 1 to 3 km of the source. The need for such adjustments should be determined on a case-by-case basis.

	Wind direction meander may, at times, lead to an erroneous determination of P-G stability category based on FA. To minimize wind direction meander contributions, FA may be determined for each of four 15-minute periods in an hour. However, 360 samples are needed during each 15-minute period. To obtain the FA for stability determinations in these situations, take the square root of one-quarter of the sum of the squares of the four 15-minute FA's, as illustrated in the footnote to Table 9-2. While this approach is acceptable for determining stability, FA's calculated in this manner are not likely to be suitable for input to models under development that are designed to accept on-site hourly F's based on 60-minute periods.

	There has not been a widespread use of FE and FA to determine P-G categories. As mentioned in the footnotes to Table 9-3, the techniques outlined have not been extensively tested. The criteria listed in Table 9-3, are for FE and FA values at 10 m. For best results, the FE and FA values should be for heights near the surface as close to 10 m as practicable. Obstacles and large roughness elements may preclude measurements as low as 10 m. If circumstances preclude measurements below 30 m, the Regional Meteorologist should be consulted to determine the appropriate measurements to be taken on a case-by-case basis. The criteria listed in Tables 9-3 and 9-4 result from studies conducted in relatively flat terrain in rather ideal circumstances. For routine applications where conditions are often less than ideal, it is recommended that a temporary program be initiated at each site to spot-check the stability class estimates. Irwin's method using FE or FA should be compared with P-G stability class estimates using on-site wind speed and subjective assessments of the insolation based on ceiling height and cloud cover. The Regional Meteorologist should be consulted when using the spot-check results to refine and adjust the preliminary criteria outlined in Tables 9-3 and 9-4.

	In summary, when on-site data sets are being used, Pasquill-Gifford stability categories should be determined from one of the following schemes listed in the order of preference:

	(1) Turner's 1964 method (54) using site-specific data which include cloud cover, ceiling height and surface (~10 m) wind speeds.

	(2) FE from site-specific measurements and Table 9-3 (FE may be determined from elevation angle measurements or may be estimated from measurements of Fw according to the transform: FE = Fw/u (see page 9-17)).

	(3) FA from site-specific measurements and Tables 9-3 and 9-4.

	(4) Turner's 1964 method using site-specific wind speed with cloud cover and ceiling height from a nearby NWS site.

Table 9-2.-Averaging Times for Site-Specific Wind and Turbulence Measurements

Parameter
Averaging time

Surface wind speed (for use in stability determinations).
1-hr
Transport direction
1-hr
Dilution wind speed
1-hr
Turbulence measurements (FE and FA) for use in stability determinations.
1-hr1

1To minimize meander effects in F1 when wind conditions are light and/or variable, determine the hourly average F's from four 15-minute F's according to the following formula:

>>>>  See the accompanying hardcopy volume for non-machine-readable data that appears at this point. <<<<

>>>>  See the accompanying hardcopy volume for non-machine-readable data that appears at this point. <<<<

Table 9-4.-Nighttime1 P-G Stability Categories Based on FA from Table 9-3

If the FA
Stability
Category is
And the Wind Speed
at 10 m is m/s
Then the Pasquill
Stability Category
is

A
<2.9
F

2.9 to 3.6
E

$3.6
D
B
<2.4
F

2.4 to 3.0
E

$3.0
D
C
<2.4
E

$2.4
D
D
wind speed not considered.
D
E
wind speed not considered.2
E
F
wind speed not considered.3
F

Adapted from Irwin, J. 1980 68.
1Nighttime is considered to be from 1 hour prior to sunset to 1 hour after sunrise.
2The original Mitchell and Timbre (69) table had no wind speed restrictions; However, the original Pasquill criteria suggest that for wind speeds greater than 5 m/s, neutral conditions should be used.
3The original Mitchell and Timbre (69) table had no wind speed restrictions; however, the original Pasquill criteria suggest that for wind speeds greater than or equal to 5 m/s, the D category would be appropriate, and for wind speeds between 3 m/s and 5 m/s, the E category should be used.

9.3.4 Treatment of Calms

	9.3.4.1 Discussion.

	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.

	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.

	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 nonmissing 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 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 hardwired in the following models: RAM, ISC, MPTER and CRSTER.

	The recommendations above 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.

	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)

	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 above.

10.0 Accuracy and Uncertainty of Models

10.1 Discussion

	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

	Dispersion models generally attempt to estimate concentrations at specific sites that really represent an ensemble average of numerous repetitions of the same event. The event is characterized by measured or "known" conditions that are input to the models, e.g., wind speed, mixed layer height, surface heat flux, emission characteristics, etc. However, in addition to the known conditions, there are unmeasured or unknown variations in the conditions of this event, e.g., unresolved details of the atmospheric flow such as the turbulent velocity field. These unknown conditions, may vary among repetitions of the event. As a result, deviations in observed concentrations from their ensemble average, and from the concentrations estimated by the model, are likely to occur even though the known conditions are fixed. Even with a perfect model that predicts the correct ensemble average, there are likely to be deviations from the observed concentrations in individual repetitions of the event, due to variations in the unknown conditions. The statistics of these concentration residuals are termed "inherent" uncertainty. Available evidence suggests that this source of uncertainty alone may be responsible for a typical range of variation in concentrations of as much as ± 50 percent. (76)

	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 condition-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.

	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 number of studies (79, 80) have been conducted to examine model accuracy, particularly with respect to the reliability of short-term concentrations required for ambient standard and increment evaluations. The results of these studies are not surprising. Basically, they confirm what leading atmospheric scientists have said for some time: (1) Models are more reliable for estimating longer time-averaged concentrations than for estimating short-term concentrations at specific locations; and (2) the models are reasonably reliable in estimating the magnitude of highest concentrations occurring sometime, somewhere within an area. For example, errors in highest estimated concentrations of ± 10 to 40 percent are found to be typical (81) i.e., certainly well within the often-quoted factor-of-two accuracy that has long been recognized for these models. However, estimates of concentrations that occur at a specific time and site, are poorly correlated with actually observed concentrations and are much less reliable.

	As noted above, 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

	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)

	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 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 this guideline or an alternate model of known accuracy). This is an indication of the practical limitations imposed by current abilities of the technical community.

	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 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, this guideline clearly recommends that 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 this 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 this 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 document "Interim Procedures for Evaluating Air Quality Models." (15) This document provides a method, following recommendations of the Woods Hole Workshop, that allows 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.

	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 decisionmaker with an appropriate interpretation of the effect on the critical concentrations.

10.2 Recommendations

	No specific guidance on the consideration of model uncertainty in decisionmaking is being given at this time. There is incomplete technical information on measures of model uncertainty that are most relevant to the decisionmaker. 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 CAA requirements.

11.0 Regulatory Application of Models

11.1 Discussion

	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.

	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. The same care should be given to the analysis of the air quality data as would be applied to a modeling analysis.

	The current NAAQS for SO2, TSP, and CO are all stated in terms of a concentration not to be exceeded more than once a year. There is only an annual standard for NO2. The ozone standard was revised in 1979 and that standard permits the exceedance of a concentration on an average of not more than once a year, averaged over a 3-year period. (5, 86) 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.

	This section discusses general requirements for concentration estimates and identifies the relationship to emission limits. The following recommendations 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.

	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.

	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 the NAAQS. It is recommended that the screening techniques found in "Procedures for Evaluating Air Quality Impact of New 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)

	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, 5, 6 or 7. 1n 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.

	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. The most recent source applicant should be allowed the prerogative to remodel 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.

	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.

	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:

	a. Does a monitoring network exist for the pollutants and averaging times of concern;

	b. Has the monitoring network been designed to locate points of maximum concentration;

	c. Do the monitoring network and the data reduction and storage procedures meet EPA monitoring and quality assurance requirements;

	d. 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;

	e. Is at least one full year of valid ambient data available; and

	f. Can it be demonstrated through the comparison of monitored data with model results that available models are not applicable?

	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 Procedure for Evaluating Air Quality Models (15) should be used in establishing criteria for demonstrating that a model is not applicable.

	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.

	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.

	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) minus background (B) to the predicted concentration (P) (i.e., (S-B)/P). The averaging time with the lowest ratio identifies the most restrictive standard. If the annual average is the most restrictive, the highest estimated annual average concentration from one or a number of years of data is the design value. When short term standards are most restrictive, it may be necessary to consider a broader range of concentrations than the highest value. For example, for pollutants such as SO2, the highest, second-highest concentration is the design value. For pollutants with statistically based NAAQS, the design value is found by determining the value that is not expected to be exceeded more than once per year over the period specified in the standard.

	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 guideline 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 Air Quality Standards.

	For new or modified sources to be located in areas where the SO2, TSP, lead, NO2, or CO NAAQS are being attained, the determination of whether or not 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. For lead, 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.

	11.2.3.3 PSD Air Quality Increments and Impacts.

	The allowable PSD increments for criteria pollutants are established by regulation and cited in 40 CFR 51.24. 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.

	Screening techniques defined in sections 4 and 5 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 one year of on-site or five 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.).

	The PSD regulations require an estimation of the SO2 and TSP 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 50 km. Beyond the 50 km 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 and 7, the need to involve the Federal Land Manager in decisions on potential air quality impacts, particularly in relation to PSD Class I areas, cannot be overemphasized.

	11.2.3.4 Emissions Trading Policy (Bubbles).

	EPA's Emissions Trading Policy, commonly referred to as the "bubble policy," was proposed in the Federal Register on April 7, 1982. (89) Until a final policy is promulgated, principles contained in the proposal should be used to evaluate trading activities which become ripe for decision. Certain technical clarifications of the policy, including procedures for modeling bubbles, were provided to the Regional Offices in February, 1983. (90)

	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 and clarification to 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 one 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.)

	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 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.

	The Emissions Trading Policy allows States to adopt generic regulations for processing bubbles. The modeling procedures recommended in this 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 References1
1Documents not available in the open literature or from the National Technical Information Service (NTIS) have been placed in Docket No. A-80-46. Docket Reference Numbers for documents placed in the docket are shown at the end of the reference.

	1. CFR title 40 part 51, 1982. Protection of the Environment; Requirements for Preparation, Adoption, and Submittal of Implementation Plans.

	2. Environmental Protection Agency, 1977. Guidelines for the Regional Evaluation of State and Local New Source Review Program. EPA Publication No. EPA-450/2-77-027. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 275053).

	3. Environmental Protection Agency, 1980. Prevention of Significant Deterioration Workshop Manual, 1980. EPA Publication No. EPA-450/2-80-081. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 81-136459).

	4. Environmental Protection Agency, 1981. Guideline for Fluid Modeling of Atmospheric Diffusion. EPA Publication No. EPA-600/8-81-009. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 81-201410).

	5. CFR title 40 part 50, 1982. Protection of the Environment; National Primary and Secondary Ambient Air Quality Standards.

	6. Environmental Protection Agency, 1981. Model Clearinghouse: Operational Plan. Staff Report. U. S. Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No. II-G-6).

	7. Environmental Protection Agency, 1980. Guidelines on Air Quality Models. Federal Register, 45(61):20157-20158.

	8. Londergan, R. J., D. H. Minott, D. J. Wackter, T. Kincaid and D. Bonitata, 1982. Evaluation of Rural Air Quality Simulation Models. EPA Publication No. EPA-450/4-83-003. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 83-182758).

	9. Londergan, R. J., D. H. Minott, D. J. Wackter and R. R. Fizz, 1983. Eva1uation of Urban Air Quality Simulation Models. EPA Publication No. EPA-450/4-83-020. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 84-241173).

	10. Londergan, R. J., and D. J. Wackter, 1984. Evaluation of Complex Terrain Air Quality Simulation Models. EPA Publication No. EPA-450/4-84-017. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-119485).

	11. Fox, D. G., 1981. Judging Air Quality Model Performance. Bulletin of the American Meteorological Society, 62(5):599-609.

	12. American Meteorological Society, 1983. Synthesis of the Rural Model Reviews. EPA Publication No. EPA-600/3-83-108.  U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 84-121037).

	13. American Meteorological Society, 1984. Review of the Attributes and Performance of Six Urban Diffusion Models. EPA Publication No. 600/S3-84-089. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 84-236850).

	14. White, F. D., Ed., and J. K. S. Ching, R. L. Dennis and W. H. Snyder, 1985. Summary of Complex Terrain Model Evaluation. EPA Publication No. EPA 600/3-85-060. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-236891).

	15. Environmental Protection Agency, 1984. Interim Procedures for Evaluating Air Quality Models (Revised). EPA Publication No. EPA-450/4-84-023. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-106060).

	16. Environmental Protection Agency, 1985. Interim Procedures for Evaluating Air Quality Models: Experience with Implementation. EPA Publication No. EPA 450/4-85-006. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-242477).

	17. Environmental Protection Agency, 1981. Regional Workshops on Air Quality Modeling: A Summary Report. EPA Publication No. EPA-450/4-82-015. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 83-150573).

	18. Environmental Protection Agency, 1977. Guidelines for Air Quality Maintenance Planning and Analysis, Volume 10R: Procedures for Evaluating Air Quality Impact of New Stationary Sources. EPA Publication No. EPA 450/4-77-001. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 274087).

	19. Environmental Protection Agency, 1986. User's Network for Applied Modeling of Air Pollution (UNAMAP), Version 6 (Computer Programs on Tape). National Technical Information Service, Springfield, VA. (NTIS No. PB 86-22236l).

	20. Egan, B. A., Editor, 1985. Dispersion in Complex Terrain: Report of a Workshop held at Keystone, Colorado, May 1983. EPA Publication No. EPA 600/9-85-031. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 86-122694/AS).

	21. Lavery, T. F., A. Bass, D. G. Strimaitis, A. Venkatram, B. R. Greene, P. J. Drivas and B. A. Egan, 1982. EPA Complex Terrain Model Development. First Milestone Report-1981. EPA Publication No. EPA-600/3-82-036. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 82-231713).

	22. Lavery, T. F., D. G. Strimaitis, A. Venkatram, B. R. Greene, S. R. Hanna, D. C. DiCristofaro and B. A. Egan, 1983. EPA Complex Terrain Model Development: Third Milestone Report-1983. EPA Publication No. EPA-600/3-83-101. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 84-155837).

	23. Strimaitis, D. G., D. C. DiCristofaro, B. R. Greene, R.J. Yamartino, A. Venkatram, D. A. Godden, P. Karamchandani, T. F. Lavery, B. A. Egan, 1985. EPA Complex Terrain Model Development. Fifth Milestone Report. EPA Publication No. EPA 600/3-85-069. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 86-167350).

	24. Schienmeier, F. A., 1984. Scientific Assessment Document on Status of Complex Terrain Dispersion Models for EPA Regulatory Applications. EPA Publication No. EPA 600/3-84-103. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-138584).

	25. 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).
	26. Bjorklund, J. R. and J. F. Bowers, 1982. Users' 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, Middle Atlantic Region III, Philadelphia, PA. (NTIS Nos. PB 83-146092 and PB 83-146100).

	27. Ames, J., T. C. Meyers, L. E. Reid, D. Whitney, S. H. Golding, S. R. Hayes, and S. D. Reynolds, 1984. SAI Airshed Model Operations Manuals. EPA Publication No. EPA 600/8-85-007a and 007b. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS Nos. PB 85-191567 and PB 85-191568).

	28. Environmental Protection Agency, 1980. Guideline for Applying the Airshed Model to Urban Areas. LPA Publication No. EPA 450/4-80-020, U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 81-200529).

	29. Environmental Protection Agency, 1981. Guideline for Use of City-Specific EKMA (Empirical Kinetic Modeling Approach) in Preparing Ozone SIP's. EPA Publication No. EPA-450/4-80-027. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 81-118739).

	30. Whitten, G. Z. and H. Hogo, 1978. User's Manual for Kinetics Model and Ozone Isopleth Plotting Package. EPA Publication No. EPA 600/8-78-014a. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 286248).

	31. Environmental Protection Agency, 1981. State Implementation Plans; Approval of 1982 Ozone and Carbon Monoxide Plan Revisions for Areas Needing an Attainment Date Extension; and Approved Ozone Modeling Techniques; Final Policy and Proposed Rulemaking. Federa1 Register, 46(14): 7182-7192.

	32. Environmental Protection Agency, 1981. Guideline for Use of City-Specific EKMA in Preparing Ozone SIP's; Guideline Availability. Federal Register, 46(77): 22906-22907. 

	33. Environmental Protection Agency. 1984. Guideline for Using the Carbon Bond Mechanism in City-Specific EKMA. EPA Publication No. EPA-450/4-84-005 U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 84-198910).

	34. Midurski, T.P., 1978. Carbon Monoxide Hot Spot Guidelines, Volumes I-III. EPA Publication No. EPA-450/3-78-033, 034, 035 U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS Nos. PB 82-238072, PB 82-266214, and PB 82-266222).

	35. Environmental Protection Agency, 1978. Guidelines for Air Quality Maintenance Planning and Analysis, Volume 9 (Revised): Evaluating Indirect Sources. EPA Publication No. EPA-450/4-78-001. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 288206).

	36. Cole, H.S. and J.E. Summerhays, 1979. A Review of Techniques Available for Estimation of Short-Term N02 Concentrations. Journal of the Air Pollution Control Association, 29(8):812-817.

	37. U.S. Department of Housing and Urban Development, 1980. Air Quality Considerations in Residential Planning. U.S. Superintendent of Documents, Washington, DC. (GPO Order No. 023-000-00577-8, 023-000-00576-0, 023-000-00575-1).

	38. Environmental Protection Agency, 1981. Receptor Model Technical Series, Volume 1-Overview of Receptor Model Application to Particulate Source Apportionment. EPA Publication No. EPA-450/4-81-016A. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 82-139429).

	39. Pace, T.G., 1982. The Role of Receptor Models for Revised Particulate Matter Standards. A Specialty Conference on: Receptor Models Applied to Contemporary Pollution Problems. Air Pollution Control Association, Pittsburgh, PA, pp. 18-28. (Docket Reference No. II-P-10).

	40. Environmental Protection Agency, 1978. Supplementary Guidelines for Lead Implementation Plans. EPA Publication No. EPA-450/2-78-038. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 82-232737).

	41. Environmental Protection Agency, 1983. Updated Information on Approval and Promulgation of Lead Implementation Plans (Draft). U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No. II-B-38).

	42. Environmental Protection Agency, 1979. Protecting Visibility: An EPA Report to Congress. EPA Publication No. EPA-450/5-79-008. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 80-220320).

	43. Environmental Protection Agency, 1978. The Development of Mathematical Models for the Prediction of Anthropogenic Visibility Impairment, Vol. III: Case Studies for Selected Scenarios. EPA Publication No. EPA-450/3-78-110C. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 293121).

	44. Environmental Protection Agency, 1980. Workbook for Estimating Visibility Impairment. EPA Publication No. EPA 450/4-80-031. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 81-157885).

	45. Environmental Protection Agency, 1980. Interim Guidance for Visibility Monitoring. EPA Publication No. EPA 450/2-80-082. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 81-157760).

	46. Environmental Protection Agency, 1985. Guideline for Determination of Good Engineering Practice Stack Height (Technical Support Document for the Stack Height Regulations), Revised. EPA Publication No. EPA 450/4-80-023R. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-225241).

	47. Environmental Protection Agency, 1981. Guideline for Use of Fluid Modeling to Determine Good Engineering Practice (GEP) Stack Height. EPA Publication No. EPA-450/4-81-003. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 82-145327).

	48. Lawson, Jr., R.E. and W.H. Snyder, 1983. Determination of Good Engineering Practice Stack Height: A Demonstration Study for a Power Plant. EPA Publication No. EPA 600/3-83-024. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 83-207407).

	49. Snyder, W.H., and R.E. Lawson, Jr., 1985. Fluid Modeling Demonstration of Good Engineering-Practice Stack Height in Complex Terrain. EPA Publication No. EPA-600/3-85-022. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-203107).

	50. Turner, D.B., 1969. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999-AP-26. U.S. Department of Health, Education and Welfare, Public Health Service, Cincinnati, OH. (NTIS No. PB 191482).

	51. McElroy, J.L. and F. Pooler, Jr., 1968. St. Louis Dispersion Study, Volume II-Analysis. National Air Pollution Control Administration Publication No. AP-53, U.S. Department of Health Education and Welfare, Public Health Service, Arlington, VA. (NTIS No. PB 190255).

	52. Irwin, J. S., 1983. Estimating Plume Dispersion-A Comparison of Several Sigma Schemes. Journal of Climate and Applied Meteorology, 22:92-114.
	53. Briggs, G.A. and F.S. Binkowski, 1985. Research on Diffusion in Atmospheric Boundary Layers: A Position Paper on Status and Needs. EPA Publication No. EPA/600/3-85-072. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 86-122587).

	54. Pasquill, F., 1976. Atmospheric Dispersion Parameters in Gaussian Plume Modeling, Part II. Possible Requirements for Change in the Turner Workbook Values. EPA Publication No. EPA-600/4/76-030b. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 258036/3BA).

	55. Turner, D. B., 1964. A Diffusion Model for an Urban Area. Journal of Applied Meteorology, 3:83-91.

	56. Briggs, G.A., 1975. Plume Rise Predictions. Chapter 3 in Lectures on Air Pollution and Environmental Impact Analyses.
American Meteorological Society, Boston, MA, pp. 59-111.

	57. Hanna, S.R., G.A. Briggs and R.P. Hosker, Jr., 1982. Plume Rise. Chapter 2 in Handbook on Atmospheric Diffusion. Technical Information Center, U.S. Department of Energy, Washington, DC, pp. 11-24. DOE/TIC-11223 (DE 82002045).

	58. Environmental Protection Agency, 1986. Industrial Source Complex (ISC) Dispersion Model User's Guide, Second Edition, Volumes 1 and 2. EPA Publication No. EPA-450/4-86-005a and 005b. U.S. Environmental Protection Agency, Research Triangle Park, NC.

	59. Irwin, J.S., (undated). Proposed Criteria for Selection of Urban Versus Rural Dispersion Coefficients. Staff Report. Meteorology and Assessment Division, U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No. II-B-8).

	60. Auer, Jr., A.H., 1978. Correlation of Land Use and Cover with Meteorological Anomalies. Journal of Applied Meteorology, 17:636-643.

	61. Brier, G.W., 1973. Validity of the Air Quality Display Model Calibration Procedure. EPA Publication No. EPA-R4-73-017. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 218716).

	62. Environmental Protection Agency, 1985. Compilation of Air Pollutant Emission Factors, Volume I: Stationary Point and Area Sources, Fourth Edition. EPA Publication No. AP-42. U.S. Environmental Protection Agency, Research Triangle Park, NC. (GPO Order No. 055-000-00251-7).

	63. Environmental Protection Agency, 1980. Ambient Air Monitoring Guidelines for Prevention of Significant Deterioration (PSD). EPA Publication No. EPA-450/4-80-012. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 81-153231).

	64. Landsberg, H.E. and W.C. Jacobs, 1951. Compendium of Meteorology. American Meteorological Society, Boston, MA, pp 976-992.

	65. Burton, C.S., T.E. Stoeckenius and J.P. Nordin, 1983. The Temporal Representativeness of Short-term Meteorological Data Sets: Implications for Air Quality Impact Assessments, Systems Applications Inc., San Rafael, CA. (Docket Reference No. II-G-11).

	66. Finkelstein, P.L., D.A. Mazzarella, T.J. Lockhart, W.J. King and J.H. White, 1983. Quality Assurance Handbook for Air Pollution Measurement Systems, Volume IV: Meteorological Measurements. EPA Publication No. EPA-600/4-82-060. U.S.
Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No. II-B-36).

	67. Irwin, J.S., 1980. Dispersion Estimate Suggestion #9: Processing of Wind Data. U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No. II-B-33).

	68. Irwin, J.S., 1980. Dispersion Estimate Suggestion #8: Estimation of Pasquill Stability Categories. U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No.II-B-10).

	69. Mitchell, Jr., A.E. and K.O. Timbre, 1979. Atmospheric Stability Class from Horizontal Wind Fluctuation. Presented at 72nd Annual Meeting of Air Pollution Control Association, Cincinnati, OH, June 24-29, 1979. (Docket Reference No. 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 Reference No. 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):942-953.

	72. Smith, T.B. and S.M. Howard, 1972. Methodology for Treating Diffusivity. MRI 72 FR-1030. Meteorology Research, Incorporated, Altadena, CA. (Docket Reference No. II-P-8).

	73. Environmental Protection Agency, 1984. Calms Processor (CALMPRO) User's Guide. EPA Publication No. EPA-901/9-84-001. U.S. Environmental Protection Agency, Region I, Boston, MA. (NTIS No. PB 84-229467). (Computer program available from NTIS: No. PB 84-229475).

	74. Burton, C.S., 1981. The Role of Atmospheric Models in Regulatory Decision-Making: Summary Report. Systems Applications, Inc., San Rafael, CA. Prepared under contract No. 68-01-5845 for U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No. II-M-6).

	75. Environmental Protection Agency, 1981. Proceedings of the Second Conference on Air Quality Modeling, Washington, DC, U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No. II-M-16).

	76. Hanna, S.R., 1982. Natural Variability of Observed Hourly SO2 and CO Concentrations in St. Louis. Atmospheric Environment, 16:1435-1440.

	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 Reference No. 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 Reference No. II-G-6).

	82. Pasquill, F., 1974. Atmospheric Diffusion, 2nd Edition. John Wiley and Sons, New York, NY, 479 pp.

	83. Hillyer, M.J. and C.S. Burton, 1980. The ExEx Methods: Incorporating Variability in Sulfur Dioxide Emissions Into Power Plant Impact Assessment. Systems Applications, Inc., San Rafael, CA. Prepared under Contract No. 68-01-3957 for Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No. II-B-37).

	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 Reference No. IV-G-I).

	85. Environmental Protection Agency, 1981. Plan for Evaluating Model Performance. Staff Report. U.S. Environmental Protection Agency, Research Triangle Park, NC. (Docket Reference No. 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, 1982. Emissions Trading Policy Statement; General Principles for Creation, Banking, and Use of Emission Reduction Credits. Federal Register, 47(67):15076-15086.

	90. Meyer, S., 1983. Memorandum of February 17 to Regional Office Air Management Division Directors, Emissions Trading Policy Technical Clarifications. Office of Air, Noise and Radiation, U.S. Environmental Protection Agency, Washington, DC. (Docket Reference No. II-B-34).

	91. 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. (Docket Reference No. IV-D-5).

	92. 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 and Technology, Inc., Concord, MA. (NTIS PB 86-159803).

13.0 Bibliography1
1The 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.

	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. 

	Randerson, D., Ed., 1984. Atmospheric Science and Power Production. DOE/TIC-27601. Office of Scientific and Technical Information, U.S. Department of Energy, Oak Ridge, TN.

	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.

	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 X of Part 266-Summaries of Preferred Air Quality Models

Table of Contents

A.0 Introduction

A.1 Buoyant line and point source dispersion model (BLP)

A.2 Caline 3

A.3 Climatological dispersion model (CDM 2.0)

A.4 Gaussian-Plume multiple source air quality algorithm (RAM)

A.5 Industrial source complex model (ISC)

A.6 Multiple point Gaussian dispersion algorithm with terrain adjustment (MPTER)

A.7 Single source (CRSTER) model

A.8 Urban airshed model (UAM)

A.9 Offshore and coastal dispersion model (OCD)

A.REF References

A.0 Introduction

	This appendix summarizes key features of refined air quality models preferred for specific regulatory app1ications. For each model, information is provided on availability, approximate cost in 19861, 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. The models are listed by name in alphabetical order.
1All models except the Urban Airshed Model are available on UNAMAP (Version 6) from NTIS at a price consistent with the previous version of UNAMAP.
	Each of these models has been subjected to a performance evaluation using comparisons with observed air quality data. A summary of such comparisons for all models contained in this appendix is included in "A Survey of Statistical Measures of Model Performance and Accuracy for Several Air Quality Models," EPA-450/4-83-001. 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.

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 PB 81-164642)

Availability

	This model is available as part of UNAMAP (Version 6). The computer code is available on magnetic tape from: Computer Products, National Technical Information Service, U.S. Department of Commerce, Springfield, Virginia 22161, phone (703) 487-4650.

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 mode1 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), con-vergence 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.

	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.

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 typographic 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 .02 Kelvin per meter for E stability and .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 .10, .15, .20, .25, .30, and .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 16 times the mixing height; uniform mixing is assumed beyond that point.

	Perfect reflection at the ground is assumed.

1. Chemical Transformation

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

m. Physical Removal

	Physical removal is not explicitly treated.

n. Evaluation Studies

	Schulman, L. L., and J. S. Scire, 1980. Buoyant Line and Point Source (BLP) Dispersion Model User's Guide, P-7304B. Environmental Research and Technology, Inc., Concord, MA.

	Scire, J. S., and L. L. Schulman, 1981. Evaluation of the BLP and ISC Models with SF6 Tracer Data and SO2 Measurements at Aluminum Reduction Plants. APCA Specialty Conference on Dispersion Modeling for Complex Sources, St. Louis, MO.

A.2 Caline3

Reference

	Benson, Paul E. 1979. CALINE3-A Versatile Dispersion Model for Predicting Air Pollutant Levels Near Highways and Arterial Streets. Interim Report, Report Number FHWA/CA/TL-79/23. Federal Highway Administration, Washington, DC (NTIS PB80-220841).
Availability

	The CALINE3 model computer tape is available from NTIS as PB80-220833. The model is also available from the California Department of Transportation (manual free of charge and approximately $50 for the computer tape). Requests should be directed to: Mr. Marlin Beckwith, Chief, Office of Computer Systems, California Department of Transportation, 1120 N. Street, Sacramento, California 95814.

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 hours 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. 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. Vertica1 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.

1. 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, DC. 

	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 Operation Model (CDM 2.0)

References

	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, N.C. (NTIS PB86-136546)

Availability

	This model is available as part of UNAMAP (Version 6). The computer code is available on magnetic tape from: Computer Products, National Technical Information Service, U.S. Department of Commerce, Springfield, Virginia 22161, phone (703) 487-4650.

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 distancesless than 50 kilometers;

	Long term averages over one month to one year or longer.

	The following option should be selected for regulatory applications:

	Set the regulatory "default option" (NDEF = 1) which automatically selects stack tip downwash, final plume rise, buoyancy-induced dispersion (BID), and the appropriate wind profile exponents.

	Enter "0" for pollutant half-life for all pollutants except for SO2 in an urban setting. This entry results in no decay (infinite half-life) being calculated. For SO2 in an urban setting, the pollutant half-life (in hours) should be set to 4.0.

b. Input Requirements

	Source data: Location, average emissions rates and heights of emissions for point and area sources. Point source data requirements also include stack gas temperature, stack gas exit velocity, and stack inside diameter for plume rise calculations for point sources.

	Meteorological data: Stability wind rose (STAR deck day/night version), average mixing height and wind speed in each stability category, and average air temperature.

	Receptor data: cartesian coordinates of each receptor.

c. Output

	Printed output includes:

	Average concentrations for the period of the stability wind rose data (arithmetic mean only) at each receptor, and

	Optional point and area concentration rose for each receptor.

d. Type of Model
	CDM is a climatological Gaussian plume model.

e. Pollutant Types

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

f. Source-Receptor Relationship

	CDM applies user-specified locations for all point sources and receptors.

	Area sources are input as multiples of a user-defined unit area source grid size.

	User specified release heights are applied for individual point sources and the area source grid.

	Actual separation between each source-receptor pair is used.

	The user may select a sing1e 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 (EPA, 1980) are used, assuming the anemometer height is at 10.0 meters.

i. Vertical Wind Speed

	Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

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

k. Vertical Dispersion

	There are seven vertical dispersion parameter schemes, but the following is recommended for regulatory applications: Briggs-urban (Gifford, 1976).

	Mixing height has no effect until dispersion coefficient equals 0.8 times the mixing height; uniform vertical mixing is assumed beyond that point.

	Buoyancy-induced disperion (Pasquill, 1976) is included as an option.

	Perfect reflection is assumed at the ground.
1. 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

	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

	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.

	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)

References:

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

Reference:

	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. (Distributed as part of UNAMAP, Version 6, Documentation)

Availability:

	This model is available as part of UNAMAP (Version 6). The computer code is available on magnetic tape from: Computer Products National Technical Information Service : U. S. Department of Commerce, Springfield, Virginia 22161 Phone (703) 487-4650

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), a treatment for calms, the appropriate wind profile exponents, and the appropriate value for pollutant half-life.

b. lnput 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 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 (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 sizing is assumed beyond that point.

	Perfect reflection is assumed at the ground.

l. Chemical Transformation

	Chemica1 transformations are treated using exponential decay. Half-life is input by the user.

m. Physical Removal

	Physical removal is not explicitly treated.

n. Evaluation Studies

	Ellis, H., P. Lou, and G. Dalzell, 1980. Comparison Study of Measured and Predicted Concentrations with the RAM Model at Two Power Plants Along Lake Erie, Second Joint Conference on Applications of Air Pollution Meteorology, New Orleans, LA. 

	Environmental Research and Technology, 1980. SO2 Monitoring
and RAM (Urban) Model Comparison Study in Summit County, Ohio. Document P-3618-152, Envirommental Research & Technology. Inc.. Concord, MA, 1980.

	Guldberg, P. H., and C. W. Kern, 1978. A Comparison Validation of the RAM and PTMTP Models for Short-Term Concentrations in Two Urban Areas, Journal of Air Pollution Control Association, 28:907-910.

	Hodanbosi, R. R., and L. K. Peters, 1981. Evaluation of RAM Model for Cleveland, Ohio," Journal of Air Pollution Control Association, 31:253-255,

	Kennedy, K. H., R. D. Siegel, and M. P. Steinberg, 1981. Case-Specific Evaluation of the RAM Atmospheric Dispersion Model in an Urban Area, 74th Annual Meeting of the American Institute of Chemical Engineers, New Orleans, LA.

	Kummier, R. H.. B. Cho, G. Roginski, R. Sinha and A. Greenburg. 1979. A Comparative Validation of the RAM and Modified SAI Modes for Short-Term 502 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, DC.

	Morgenstern, P., M. J. Geraghty, and A. McKnight, 1979. A Comparative Study of the RAM (Urban) and RAMR (Rural) Models for Short-term SO2 Concentrations in Metropolitan Indianapolis. 72nd Annual Meeting of the Air Pollution Control Association, Cincinnati, OH.

	Ruff, R. E, 1980. Evaluation of the RAM Using the RAPS Data Base, Contract 68-02-2770, SRI International, Menlo Park, CA.

	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.

A.5 Industrial Source Complex Model (ISC)

Reference

	Environmental Protection Agency, 1986. Industrial Source Complex (ISC) Dispersion Model User's Guide, Second Edition, Volumes 1 and 2. Publication Nos. EPA-450/4-86-005a, and -005b. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS PB86 234259 and PB86 234267).

	Environmental Protection Agency, 1987. Industrial Source Complex (ISC) Dispersion Model. Addendum to the User's Guide. U.S. Environmental Protection Agency, Research Triangle Park, NC.

Availability

	This model is available as part of UNAMAP (Version 6). The computer code is available on magnetic tape from: Computer Products, National Technical Information Service, U.S. Department of Commerce, Springfield, Virginia 22161, Phone (703) 487-4650.

Abstract

	The ISC 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 particulates; downwash; area, line and volume sources; plume rise as a function of downwind distance; separation of point sources; and limited terrain adjustment. It operates in both long-term and short-term modes.

a. Recommendations for Regulatory Use

	ISC is appropriate for the following applications:

	Industrial source complexes;

	Rural or urban areas;

	Flat or rolling terrain;

	Transport distances less than 50 kilometers; and

	One hour to annual averaging times.

	The following options should be selected for regulatory applications:

	For short term modeling, set the regulatory "default option" (ISW(28) = 1), 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 rural option (ISW(20) = 0) or urban option (ISW(20) = 3); and set the concentration option (ISW(1) = 1).

	For long term modeling, set the regulatory "default option" (ISW(22) = 0), which automatically selects stack tip downwash, final plume rise, buoyancy-induced dispersion (BID), the vertical potential temperature gradient, the appropriate wind profile exponents, and the appropriate value for pollutant half-life, and a revised building wake effects algorithm; set rural option (ISW(9) = 3) or urban option (ISW(9) = 4); and set the concentration option (ISW(1) = 1).

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: ISCST requires hourly surface weather data from the preprocessor program RAMMET, which provides hourly stability class, wind direction, wind speed, temperature, and mixing height. For ISCLT, input includes stability wind rose (STAR deck), average afternoon mixing height, average morning mixing height, and average air temperature.

	Receptor data: coordinates and optional ground elevation for each receptor.

c. Output

	Printed output options include:

	Program control parameters, source data and receptor data;

	Tables of hourly meteorological data for each specified day;

	"N"-day average concentration or total deposition calculated at each receptor for any desired combinations of sources;

	Concentration or deposition values calculated for any desired combinations of sources at all receptors for any specified day or time period within the day;

	Tables of highest and second-highest concentration or deposition values calculated at each receptor for each specified time period during an "N"-day period for any desired combinations of sources; and tables of the maximum 50 concentration or deposition values;

	Calculated for any desired combinations of sources for each specified time period.

d. Type of Model

	ISC is a Gaussian plume model.

e. Pollutant Types

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

f. Source-Receptor Relationships

	ISC applies user-specified locations for point, line, area and volume sources, and user-specified receptor locations or receptor rings.

	User input topographic elevation 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

	ISC 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 (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

	Settling and dry deposition of particulates are treated.

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.

	Scire, J. S., and L. L. Schulman, 1981. Evaluation of the BLP and ISC Models with SF6 Tracer Data and S02 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 Modifications to the Industrial Source Complex Model. Journal of the Air Pollution Control Association, 36:258-264.

A.6 Multiple Point Gaussian Dispersion Algorithm with Terrain Adjustment (MPTER)

Reference

	Pierce, Thomas D. and D. Bruce Turner, 1980. User's Guide for MPTER. EPA Publication No. EPA-600/8-80-016. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB-80-197361).

	Chico, T. and J.A. Catalano, 1986. Addendum to the User's Guide for MPTER. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711. (Distributed as part of UNAMAP, Version 6, Documentation)

Availability

	This model is available as part of UNAMAP (Version 6). The computer code is available on magnetic tape from: Computer Products, National Technical Information Service, U.S. Department of Commerce, Springfield, Virginia 22161, Phone (703) 487-4650.

Abstract

	MPTER is a Multiple Point Source Algorithm. This algorithm is useful for estimating air quality concentrations of relatively non-reactive pollutants. Hourly estimates are made using the Gaussian steady state model.

a. Recommendations for Regulatory Use

	MPTER is appropriate for the following applications:

	Point sources;

	Rural or urban areas;

	Flat or rolling terrain (no terrain above stack height);

	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" (IOPT(25) = 1) to automatically select stack tip downwash, final plume rise, buoyancy-induced dispersion (BID), a treatment for calms, the appropriate wind profile exponents, and the appropriate value for pollutant half-life.

b. Input Requirements

	Source data: location, emission rate, physical stack height, stack gas exit velocity, stack inside diameter, stack gas temperature, and optional ground level elevation.

	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 and optional ground elevation for each receptor.

c. Output

	Printed output includes:

	One to 24-hour and annual average concentrations at each receptor;

	Highest through fifth highest concentrations at each receptor for period, with the highest and high, second-high values flagged; and

	Limited source contribution table.

d. Type of Model

	MPTER is a Gaussian plume model.

e. Pollutant Types

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

f. Source-Receptor Relationship

	MPTER applies user-specified locations of point sources and receptors.

	User input stack height and source characteristics for each source are used.

	User input topographic elevation for each receptor is used.

g. Plume Behavior

	MPTER uses Briggs (1969, 1971, 1975) plume rise equations for final rise.

	Stack tip downwash equation from Briggs (1974) is used.

	For rolling terrain (terrain not above stack height), plume centerline is horizontal at height of final rise above the source.

	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 (EPA, 1980) for both rural and urban cases are used.

i. Vertical Wind Speed

	Vertical speed is assumed equal to zero.

j. Horizontal Dispersion

	Rural dispersion coefficients from Turner (1969) are used with no adjustments made for variations in surface roughness or averaging times.

	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 made for variations in 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. Half-life is input by the user.

m. Physical Removal

	Physical removal is not explicitly treated.

n. Evaluation Studies

	No specific studies for MPTER because regulatory editions of CRSTER and MPTER are equivalent. Studies for CRSTER are relevant to MPTER as well (See page A-32).

A.7 Single Source (CRSTER) Model

Reference

	Environmental Protection Agency, 1977. User's Manual for Single Source (CRSTER) Model. EPA Publication No. EPA-450/2-77-013. U.S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 271360).

	Catalano, J.A., 1986. Single Source (CRSTER) Model. Addendum to the User's Manual. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711. (Distributed as part of UNAMAP, Version 6, Documentation)

Availability

	This model is available as part of UNAMAP (Version 6). The computer code is available on magnetic tape from: Computer Products, National Technical Information Service, U.S. Department of Commerce, Springfield, Virginia 22161, phone (703) 487-4650.

Abstract

	CRSTER is a steady state, Gaussian dispersion model designed to calculate concentrations from point sources at a single location in either a rural or urban setting. Highest and high-second high concentrations are calculated at each receptor for 1-hour, 3-hour, 24-hour, and annual averaging time.

a. Recommendations for Regulatory Use

	CRSTER is appropriate for the following applications:

	Single point sources;

	Rural or urban areas;

	Transport distances less than 50 kilometers; and

	Flat or rolling terrain (no terrain above stack height).
	The following options should be selected for regulatory applications:

	Set the regulatory "default option" which automatically selects stack tip downwash, final plume rise, buoyancy-induced dispersion (BID), a treatment for calms, the appropriate wind profile exponents, and the appropriate value for pollutant half-life.

b. Input Requirements

	Source data: Emission rate, physical stack height, stack gas exit velocity, stack inside diameter, and stack gas temperature.

	Meteorological data: Hourly surface weather data from the preprocessor program RAMMET. Preprocessor output includes hourly stability class wind direction, wind speed, temperature, and mixing height. Actual anemometer height (a single value) is also required.

	Receptor data: require distance of each of the five receptor rings.

c. Output

	Printed output includes:

	Highest and second highest concentrations for the year at each receptor for averaging times of 1, 3, and 24-hours, plus a user-selected averaging time which may be 2, 4, 6, 8, or 12 hours;

	Annual arithmetic average at each receptor;

	For each day, the highest 1-hour and 24-hour concentrations over the receptor field; and

	Option for source contributions to concentrations at selected receptors.

d. Type of Model

	CRSTER is a Gaussian plume model.

e. Pollutant Types

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

f. Source-Receptor Relationship

	CRSTER treats up to 19 point sources, no area sources.

	All point sources are assumed collocated.

	User input stack height is used for each source.

	User input topographic elevation is used for each receptor, but must be below top of stack or program will terminate execution.

	Receptors are assumed at ground level.

g. Plume Behavior

	CRSTER uses Briggs (1969, 1971, 1975) plume rise equations for final rise.

	Stack tip downwash equation from Briggs (1974) is used.

	For rolling terrain (terrain not above stack height), plume centerline is horizontal at height of final rise above the source.
	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 set of wind speed profile exponents (EPA, 1980) for both rural and urban cases are used.

i. Vertical Wind Speed

	Vertical wind speed is assumed equal to zero.

j. Horizontal Dispersion

	Rural dispersion coefficients from Turner (1969) are used in CRSTER with no adjustments made for variations in surface roughness or averaging times.

	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 made for surface roughness.

	Urban dispersion coefficients from Briggs (Gifford, 1975) 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 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

	Klug, W., 1974. Dispersion from Tall Stacks. Fifth NATO/CCMS International Technical Meeting on Air Pollution Modeling, Denmark.

	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. 3. American Petroleum Institute, Washington, DC.

	Mills, M.T., R. Caiazza, D.D. Hergert, and D.A. Lynn, 1981. Evaluation of Point Source Dispersion Models. EPA Publication No. EPA-450/4-81-032. U.S. Environmental Protection Agency, Research Triangle Park, NC.

	Mills, M.T., and F.A. Record, 1975. Comprehensive Analysis of Time-Concentration Relationships and the Validation of a Single Source Dispersion Model. EPA Publication No. EPA-450/3-75-083. U.S. Environmental Protection Agency, Research Triangle Park, NC.

	Mills, M.T., and R.W. Stern, 1975. Model Validation and Time-Concentration Analysis of Three Power Plants. EPA Publication No. EPA-450/3-76-002. U.S. Environmental Protection Agency, Research Triangle Park, NC.

	Londergan, R., D. Minott, D. Wackter, T. Kincaid, and B. Bonitata, 1983. Evaluation of Rural Air Quality Simulation Models. EPA Publication No. EPA-450/4-83-033. U.S. Environmental Protection Agency, Research Triangle Park, NC.

	TRC-Environmental Consultants, Inc., 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.

A.8 Urban Airshed Model (UAM)

References

	Ames, J., T. C. Myers, L. E. Reid, D. C. Whitney, S. H. Golding, S.R. Hayes, and S. D. Reynolds, 1985. SAI Airshed Model Operations Manuals-Volume I-User's Manual. EPA Publication No. EPA-600/8-85-007a. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-191567).

	Ames, J. S., R. Hayes, T. C. Myers, and D. C. Whitney, 1985. SAI Airshed Model Operations Manuals-Volume II-Systems Manual. EPA Publication No. EPA-600/8-85-007b. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTIS No. PB 85-191575).

	Environmental Protection Agency, 1980. Guideline for Applying the Airshed Model to Urban Areas. Publication No. EPA 450/4-80-020. U. S. Environmental Protection Agency, Research Triangle Park, NC. (NTlS No. PB 8l-200529).

Availability

	The computer code is available on magnetic tape from: Computer Products, National Technical Information Service, U.S. Department of Commerce, Springfield, Virginia 22161, phone (703) 487-4650.

Abstract

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

a. Recommendations for Regulatory Use

	UAM is appropriate for the following applications: Single urban areas having significant ozone attainment problems in the absence of interurban emission transport; 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. At the present time, the following options should be selected for regulatory applications:

	Omit SO2 and AEROSOLS from the SPECIES packet for the CHEMPARAM file;
	Set ROADWAY flag to FALSE in the SIMULATION packet for the SIM-CONTROL file; and

	Set surface layer height to zero in the REGION packet for the AIRQUALITY, BOUNDARY, DIFFBREAK, METSCALARS, PTSOURCE,
REGIONTOP, TEMPERATUR, TERRAIN, TOPCONC, and WIND files.

b. Input Requirements

	Source data: Gridded, hourly emissions of PAR, OLE, ETH, ARO, CARB, NO, and NO2 for low-level sources. CO is optional. 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; hourly gridded 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 O3, NO, NO2, PAR, OLE, ETH, ARO, CARB, PAN, and CO at the beginning of the simulation for each grid cell; and hourly concentrations of each pollutant at each level along the inflow boundaries and top boundary of the modeling region.

	Other data requirements are: Hourly mixed layer average, NO2 photolysis rates; and ozone surface uptake resistance along with associated gridded vegetation (scaling) factors.

c. Output

	Printed output includes: Gridded instantaneous concentration fields at user-specified time intervals for user-specified pollutants and grid levels; Gridded time average concentration fields for user-specified time intervals, pollutants, and grid levels.

d. Type of Model

	UAM is a-three dimensional, numerical, photochemical grid model.

e. Pollutant Types

	UAM may be used to model ozone (O3) formation from oxides of nitrogen (NOx) and volatile organic compound (VOC) emissions.

f. Source-Receptor Relationship

	Low-level area and point source emissions are specified within each surface grid cell.

	Up to 500 major point sources are allowed.

	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).

1. Chemical Transformation

	UAM employs a simplified version of the Carbon-Bond II Mechanism (CBM-II) developed by Whitten, Killus, and Hogo (1980) employing various steady-state approximations. CBM-II is further simplified during nighttime hours to improve computational efficiency. CBM-II utilizes five carbon-bond species (PAR-single bonded carbon atoms; OLE-terminal double bonded carbon atoms; ETH-ethylene; ARO-alkylated aromatic rings; and CARB-aldehydes, ketones, and surrogate carbonyls) which serve as surrogates for the large variety of emitted organic compounds in the urban atmosphere.

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.

	Reynolds, S.D., H. Hogo, W.R. Oliver, 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 450/4-8l-03lb. U.S. Environmental Protection Agency, Research Triangle Park, NC.

	Stern, R. and B.Scherer, 1982. Simulation of a Photochemical Smog Episode in the Rhine-Ruhr Area with a Three Dimensional Grid Model. 13th International Technical Meeting on Air Pollution Modeling and Its Application, Ile des Embiez, France.

	Tesche, T.W., C. Seigneur, L.E. Reid, P.M. Roth, W.R. Oliver, and J.C. Cassmassi, 1981. The Sensitivity of Complex Photochemical Model Estimates to Detail In Input Information. EPA Publication No. EPA 450/4-81-031a. U.S. Environmental Protection Agency, Research Triangle Park, NC.

	Tesche, T.W., W.R. Oliver, H. Hogo, P. Saxeena and J.L. Haney, 1983. Volume IV-Assessment of NOx Emission Control Requirements in the South Coast Air Basin-Appendix A. Performance Evaluation of the Systems Applications Airshed Model for the 26-27 June 1974 O3 Episode in the South Coast Air Basin, SYSAPP 83/037. Systems Applications, Inc., San Rafael, CA.

	Tesche, T.W., W.R. Oliver, H. Hogo, P. Saxeena and J.L. Haney, 1983. Volume IV-Assessment of NOx Emission Control Requirements in the South Coast Air Basin-Appendix B. Performance Evaluation of the Systems Applications Airshed Model for the 7-8 November 1978 NO2 Episode in the South Coast Air Basin, SYSAPP 83/038. Systems Applications, Inc., San Rafael, CA.

A.9 Offshore and Coastal Dispersion Model (OCD)

Reference

	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 and Technology, Inc., Concord, MA. (NTIS PB 86-159803)

Availability

	The above user's guide is available for $40.95 from NTIS. The computer tape is available from NTIS as number PB85-246106 at a cost of $800.

Technical Contact

	Minerals Management Service, 12203 Sunrise Valley Drive, Mail Stop 644, Reston, VA 22091, ATTN: Mitchell Baer.

Abstract

	OCD is a straight-line Gaussian model developed to determine the impact of offshore emissions from point sources on the air quality of coastal regions. OCD incorporates overwater plume transport 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 and overwater air temperature 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 (Federal Register 50, l2248, 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 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 (overwater): 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). For all meteorological input variables, hourly data are preferred to climatological values.

	Meteorological data (overland): 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.

	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 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 following the approach of the BLP model.

	Partial plume penetration of elevated inversions is included using the suggestions of Briggs (1975) and Weil and Brower (1984).

	If overwater conditions are stable and overland conditions unstable, the Deardorff-Willis (1982) fumigation model is used to simulate the entrainment of the plume in the rising thermal internal boundary layer. The fumigation calculations are used only if the concentrations are lower than those resulting from the change to overland dispersion coefficients at the water/land interface.

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 10 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, as in the BLP model, 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 the Turner (1969) coefficients. The change is implemented where the plume intercepts the rising internal boundary layer.

k. Vertical Dispersion

	Vertical turbulence intensity is recommended as a direct estimate of vertical dispersion. If not available, turbulence intensity is estimated from boundary layer theory. 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, as in the BLP model, 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 Turner (1969) 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

	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

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 FHKA/CA/TL-79/23. Federal Highway Administration, Washington, DC.

	Briggs, G.A., 1969. Plume Rise. U.S. Atomic Energy Commission Critical Review Series, Oak Ridge National Laboratory, Oak Ridge, TN. NTIS 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.

	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,b. U.S. Environmental Protection Agency, Region III, Philadelphia, PA.

	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.

	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.

	Gifford, F.A., Jr. 1976. Turbulent Diffusion Typing Schemes-A Review. Nuclear Safety, 17:68-86.

	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.

	Larsen, R.I., 1971. A Mathematical Model for Relating Air Quality Measurements to Air Quality Standards. Office of Air Programs Publication No. AP-89. U.S. Environmental Protection Agency, Research Triangle Park, NC.

	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.

	McElroy, J.L. and F. Pooler, Jr., 1968. St. Louis Dispersion Study Volume II-Analysis. NAPCA Publication No. AP-53. U.S. Environmental Protection Agency, Research Triangle Park, NC. 

	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.

	Turner, D.B., 1969. Workbook of Atmospheric Dispersion Estimates. PHS Publication No. 999-26. U.S. Environmental Protection Agency, Research Triangle Park, NC.

	Whitten, G.Z., J.P. Killus, and H. Hogo, 1980. Modeling of Simulated Photochemical Smog with Kinetic Mechanisms. Volume 1. Final Report. EPA Publication No. EPA 600/3-80-028a. U.S. Environmental Protection Agency, Research Triangle Park, NC. 

	Briggs, G.A., 1975. Plume Rise Predictions. Lectures on Air Pollution and Environmental Impact Analyses. American Meteorological Society, Boston, MA, pp. 59-111.

	Businger, J.A., 1973. Turbulence Transfer in the Atmospheric Surface Layer. Workshop in Micrometeorology. American Meteorological Society, Boston, MA, pp. 67-100.

	Deardorff, J.W. and G.E. Willis, 1982. Ground Level Concentrations Due to Fumigation into an Entraining Mixing Layer. Atmospheric Environment, 16:1159-1170.

	Hsu, S.A., 1981. Models for Estimating Offshore Winds from Onshore Meteorological Measurements. Boundary Layer Meteorology,
20:341-352.

	Schulman, L.L., S.R. Hanna, and D.W. Heinold, 1985. Evaluation of Proposed Downwash Modifications to the Industrial Source Complex Model. ERT Document P-B810-012. Prepared for American Petroleum Institute.

	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.

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