40 cfr ch. i (7–1–99 edition) pt. 51, app. w

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40 CFR Ch. I (7–1–99 Edition)Pt. 51, App. W

APPENDIX W TO PART 51—GUIDELINE ONAIR QUALITY MODELS

PREFACE

a. Industry and control agencies have longexpressed a need for consistency in the appli-cation of air quality models for regulatorypurposes. In the 1977 Clean Air Act, Congressmandated such consistency and encouragedthe standardization of model applications.The Guideline on Air Quality Models (here-after, Guideline) was first published in April1978 to satisfy these requirements by speci-fying models and providing guidance fortheir use. The Guideline provides a commonbasis for estimating the air quality con-centrations used in assessing control strate-gies and developing emission limits.

b. The continuing development of new airquality models in response to regulatory re-quirements and the expanded requirementsfor models to cover even more complex prob-lems have emphasized the need for periodicreview and update of guidance on these tech-niques. Four primary on-going activities pro-vide direct input to revisions of the Guide-line. The first is a series of annual EPAworkshops conducted for the purpose of en-suring consistency and providing clarifica-tion in the application of models. The secondactivity, directed toward the improvementof modeling procedures, is the cooperativeagreement that EPA has with the scientificcommunity represented by the AmericanMeteorological Society. This agreement pro-vides scientific assessment of procedures andproposed techniques and sponsors workshopson key technical issues. The third activity isthe solicitation and review of new modelsfrom the technical and user community. Inthe March 27, 1980 FEDERAL REGISTER, a pro-cedure was outlined for the submittal toEPA of privately developed models. After ex-tensive evaluation and scientific review,these models, as well as those made avail-able by EPA, are considered for recognitionin the Guideline. The fourth activity is theextensive on-going research efforts by EPAand others in air quality and meteorologicalmodeling.

c. Based primarily on these four activities,this document embodies all revisions to theGuideline Although the text has been revisedfrom the original 1978 guide, the present con-tent and topics are similar. As necessary,new sections and topics are included. EPAdoes not make changes to the guidance on apredetermined schedule, but rather on an asneeded basis. EPA believes that revisions ofthe Guideline should be timely and respon-sive to user needs and should involve publicparticipation to the greatest possible extent.All future changes to the guidance will be

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Environmental Protection Agency Pt. 51, App. W

proposed and finalized in the FEDERAL REG-ISTER. Information on the current status ofmodeling guidance can always be obtainedfrom EPA’s Regional Offices.

TABLE OF CONTENTS

List of Tables

1.0 Introduction2.0 Overview of Model Use

2.1 Suitability of Models2.2 Classes of Models2.3 Levels of Sophistication of Models

3.0 Recommended Air Quality Models3.1 Preferred Modeling Techniques3.1.1 Discussion3.1.2 Recommendations3.2 Use of Alternative Models3.2.1 Discussion3.2.2 Recommendations3.3 Availability of Supplementary Mod-

eling Guidance3.3.1 The Model Clearinghouse3.3.2 Regional Meteorologists Workshops

4.0 Simple-Terrain Stationary Source Models4.1 Discussion4.2 Recommendations4.2.1 Screening Techniques4.2.2 Refined Analytical Techniques

5.0 Model Use in Complex Terrain5.1 Discussion5.2 Recommendations5.2.1 Screening Techniques5.2.2 Refined Analytical Techniques

6.0 Models for Ozone, Carbon Monoxide andNitrogen Dioxide

6.1 Discussion6.2 Recommendations6.2.1 Models for Ozone6.2.2 Models for Carbon Monoxide6.2.3 Models for Nitrogen Dioxide (Annual

Average)7.0 Other Model Requirements

7.1 Discussion7.2 Recommendations7.2.1 Fugitive Dust/Fugitive Emissions7.2.2 Particulate Matter7.2.3 Lead7.2.4 Visibility7.2.5 Good Engineering Practice Stack

Height7.2.6 Long Range Transport (LRT) (i.e., be-

yond 50km)7.2.7 Modeling Guidance for Other Govern-

mental Programs7.2.8 Air Pathway Analyses (Air Toxics and

Hazardous Waste)8.0 General Modeling Considerations

8.1 Discussion8.2 Recommendations8.2.1 Design Concentrations8.2.2 Critical Receptor Sites8.2.3 Dispersion Coefficients8.2.4 Stability Categories8.2.5 Plume Rise8.2.6 Chemical Transformation8.2.7 Gravitational Settling and Deposition

8.2.8 Urban/Rural Classification8.2.9 Fumigation8.2.10 Stagnation8.2.11 Calibration of Models

9.0 Model Input Data9.1 Source Data9.1.1 Discussion9.1.2 Recommendations9.2 Background Concentrations9.2.1 Discussion9.2.2 Recommendations (Isolated Single

Source)9.2.3 Recommendations (Multi-Source

Areas)9.3 Meteorological Input Data9.3.1 Length of Record of Meteorological

Data9.3.2 National Weather Service Data9.3.3 Site-Specific Data9.3.4 Treatment of Calms

10.0 Accuracy and Uncertainty of Models10.1 Discussion10.1.1 Overview of Model Uncertainty10.1.2 Studies of Model Accuracy10.1.3 Use of Uncertainty in Decision-Mak-

ing10.1.4 Evaluation of Models10.2 Recommendations

11.0 Regulatory Application of Models11.1 Discussion11.2 Recommendations11.2.1 Analysis Requirements11.2.2 Use of Measured Data in Lieu of

Model Estimates11.2.3 Emission Limits

12.0 References13.0 Bibliography14.0 Glossary of TermsAPPENDIX A TO APPENDIX W OF PART 51—

SUMMARIES OF PREFERRED AIR QUALITYMODELS

APPENDIX B TO APPENDIX W OF PART 51—SUMMARIES OF ALTERNATIVE AIR QUALITYMODELS

APPENDIX C TO APPENDIX W OF PART 51—EX-AMPLE AIR QUALITY ANALYSIS CHECKLIST

LIST OF TABLES

TableNo. Title

5–1a Neutral/Stable Meteorological Matrix for CTSCREEN.5–1b Unstable/Convective Meteorological Matrix for

CTSCREEN.5–2 Preferred Options for the SHORTZ/LONGZ Com-

puter Codes When Used in a Screening Mode.5–3 Preferred Options for the RTDM Computer Code

When Used in a Screening Mode.9–1 Model Emission Input Data for Point Sources.9–2 Point Source Model Input Data (Emissions) for PSD

NAAQS Compliance Demonstrations.9–3 Averaging Times for Site-Specific Wind and Turbu-

lence Measurements.

1.0 INTRODUCTION

a. The Guideline recommends air qualitymodeling techniques that should be applied


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to State Implementation Plan (SIP) 1 revi-sions for existing sources and to new sourcereviews,2 including prevention of significantdeterioration (PSD).3 It is intended for useby EPA Regional Offices in judging the ade-quacy of modeling analyses performed byEPA, State and local agencies and by indus-try. The guidance is appropriate for use byother Federal agencies and by State agencieswith air quality and land management re-sponsibilities. The Guideline serves to iden-tify, for all interested parties, those tech-niques and data bases EPA considers accept-able. The guide is not intended to be a com-pendium of modeling techniques. Rather, itshould serve as a basis by which air qualitymanagers, supported by sound scientificjudgment, have a common measure of ac-ceptable technical analysis.

b. Due to limitations in the spatial andtemporal coverage of air quality measure-ments, monitoring data normally are notsufficient as the sole basis for demonstratingthe adequacy of emission limits for existingsources. Also, the impacts of new sourcesthat do not yet exist can only be determinedthrough modeling. Thus, models, whileuniquely filling one program need, have be-come a primary analytical tool in most airquality assessments. Air quality measure-ments though can be used in a complemen-tary manner to dispersion models, with dueregard for the strengths and weaknesses ofboth analysis techniques. Measurements areparticularly useful in assessing the accuracyof model estimates. The use of air qualitymeasurements alone however could be pref-erable, as detailed in a later section of thisdocument, when models are found to be un-acceptable and monitoring data with suffi-cient spatial and temporal coverage areavailable.

c. It would be advantageous to categorizethe various regulatory programs and toapply a designated model to each proposedsource needing analysis under a given pro-gram. However, the diversity of the nation’stopography and climate, and variations insource configurations and operating charac-teristics dictate against a strict modeling‘‘cookbook.’’ There is no one model capableof properly addressing all conceivable situa-tions even within a broad category such aspoint sources. Meteorological phenomena as-sociated with threats to air quality stand-ards are rarely amenable to a single mathe-matical treatment; thus, case-by-case anal-ysis and judgment are frequently required.As modeling efforts become more complex, itis increasingly important that they be di-rected by highly competent individuals witha broad range of experience and knowledge inair quality meteorology. Further, theyshould be coordinated closely with special-ists in emissions characteristics, air moni-toring and data processing. The judgment of

experienced meteorologists and analysts isessential.

d. The model that most accurately esti-mates concentrations in the area of interestis always sought. However, it is clear fromthe needs expressed by the States and EPARegional Offices, by many industries andtrade associations, and also by the delibera-tions of Congress, that consistency in the se-lection and application of models and databases should also be sought, even in case-by-case analyses. Consistency ensures that airquality control agencies and the general pub-lic have a common basis for estimating pol-lutant concentrations, assessing controlstrategies and specifying emission limits.Such consistency is not, however, promotedat the expense of model and data base accu-racy. This guide provides a consistent basisfor selection of the most accurate modelsand data bases for use in air quality assess-ments.

e. Recommendations are made in thisguide concerning air quality models, databases, requirements for concentration esti-mates, the use of measured data in lieu ofmodel estimates, and model evaluation pro-cedures. Models are identified for some spe-cific applications. The guidance providedhere should be followed in all air qualityanalyses relative to State ImplementationPlans and in analyses required by EPA,State and local agency air programs. TheEPA may approve the use of another tech-nique that can be demonstrated to be moreappropriate than those recommended in thisguide. This is discussed at greater length insection 3.0. In all cases, the model applied toa given situation should be the one that pro-vides the most accurate representation of at-mospheric transport, dispersion, and chem-ical transformations in the area of interest.However, to ensure consistency, deviationsfrom this guide should be carefully docu-mented and fully supported.

f. From time to time situations arise re-quiring clarification of the intent of theguidance on a specific topic. Periodic work-shops are held with the EPA Regional Mete-orologists to ensure consistency in modelingguidance and to promote the use of more ac-curate air quality models and data bases.The workshops serve to provide further ex-planations of Guideline requirements to theRegional Offices and workshop reports areissued with this clarifying information. Inaddition, findings from on-going researchprograms, new model submittals, or resultsfrom model evaluations and applications arecontinuously evaluated. Based on this infor-mation changes in the guidance may be indi-cated.

g. All changes to the Guideline must followrulemaking requirements since the Guide-line is codified in this appendix W of part 51.EPA will promulgate proposed and finalrules in the FEDERAL REGISTER to amend this


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appendix W. Ample opportunity for publiccomment will be provided for each proposedchange and public hearings scheduled if re-quested.

h. A wide range of topics on modeling anddata bases are discussed in the Guideline.Chapter 2 gives an overview of models andtheir appropriate use. Chapter 3 provides spe-cific guidance on the use of ‘‘preferred’’ airquality models and on the selection of alter-native techniques. Chapters 4 through 7 pro-vide recommendations on modeling tech-niques for application to simple-terrain sta-tionary source problems, complex terrainproblems, and mobile source problems. Spe-cific modeling requirements for selected reg-ulatory issues are also addressed. Chapter 8discusses issues common to many modelinganalyses, including acceptable model compo-nents. Chapter 9 makes recommendations fordata inputs to models including source, me-teorological and background air qualitydata. Chapter 10 covers the uncertainty inmodel estimates and how that informationcan be useful to the regulatory decision-maker. The last chapter summarizes how es-timates and measurements of air quality areused in assessing source impact and in evalu-ating control strategies.

i. This appendix W itself contains three ap-pendices: A, B, and C. Thus, when referenceis made to ‘‘Appendix A’’, it refers to appen-dix A to this appendix W. Appendices B andC are referenced in the same way.

j. Appendix A contains summaries of re-fined air quality models that are ‘‘preferred’’for specific applications; both EPA modelsand models developed by others are included.Appendix B contains summaries of other re-fined models that may be considered with acase-specific justification. Appendix C con-tains a checklist of requirements for an airquality analysis.

2.0 OVERVIEW OF MODEL USE

a. Before attempting to implement theguidance contained in this appendix, thereader should be aware of certain general in-formation concerning air quality models andtheir use. Such information is provided inthis section.

2.1 Suitability of Models

a. The extent to which a specific air qual-ity model is suitable for the evaluation ofsource impact depends upon several factors.These include: (1) The meteorological andtopographic complexities of the area; (2) thelevel of detail and accuracy needed for theanalysis; (3) the technical competence ofthose undertaking such simulation mod-eling; (4) the resources available; and (5) thedetail and accuracy of the data base, i.e.,emissions inventory, meteorological data,and air quality data. Appropriate datashould be available before any attempt is

made to apply a model. A model that re-quires detailed, precise, input data shouldnot be used when such data are unavailable.However, assuming the data are adequate,the greater the detail with which a modelconsiders the spatial and temporal vari-ations in emissions and meteorological con-ditions, the greater the ability to evaluatethe source impact and to distinguish the ef-fects of various control strategies.

b. Air quality models have been appliedwith the most accuracy or the least degree ofuncertainty to simulations of long termaverages in areas with relatively simple to-pography. Areas subject to major topo-graphic influences experience meteorologicalcomplexities that are extremely difficult tosimulate. Although models are available forsuch circumstances, they are frequently sitespecific and resource intensive. In the ab-sence of a model capable of simulating suchcomplexities, only a preliminary approxima-tion may be feasible until such time as bet-ter models and data bases become available.

c. Models are highly specialized tools.Competent and experienced personnel are anessential prerequisite to the successful appli-cation of simulation models. The need forspecialists is critical when the more sophis-ticated models are used or the area being in-vestigated has complicated meteorologicalor topographic features. A model applied im-properly, or with inappropriately chosendata, can lead to serious misjudgments re-garding the source impact or the effective-ness of a control strategy.

d. The resource demands generated by useof air quality models vary widely dependingon the specific application. The resources re-quired depend on the nature of the model andits complexity, the detail of the data base,the difficulty of the application, and theamount and level of expertise required. Thecosts of manpower and computational facili-ties may also be important factors in the se-lection and use of a model for a specific anal-ysis. However, it should be recognized thatunder some sets of physical circumstancesand accuracy requirements, no presentmodel may be appropriate. Thus, consider-ation of these factors should not lead to se-lection of an inappropriate model.

2.2 Classes of Models

a. The air quality modeling procedures dis-cussed in this guide can be categorized intofour generic classes: Gaussian, numerical,statistical or empirical, and physical. Withinthese classes, especially Gaussian and nu-merical models, a large number of individual‘‘computational algorithms’’ may exist, eachwith its own specific applications. Whileeach of the algorithms may have the samegeneric basis, e.g., Gaussian, it is acceptedpractice to refer to them individually asmodels. For example, the Industrial SourceComplex (ISC) model and the RAM model are


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commonly referred to as individual models.In fact, they are both variations of a basicGaussian model. In many cases the only realdifference between models within the dif-ferent classes is the degree of detail consid-ered in the input or output data.

b. Gaussian models are the most widelyused techniques for estimating the impact ofnonreactive pollutants. Numerical modelsmay be more appropriate than Gaussianmodels for area source urban applicationsthat involve reactive pollutants, but they re-quire much more extensive input data basesand resources and therefore are not as widelyapplied. Statistical or empirical techniquesare frequently employed in situations whereincomplete scientific understanding of thephysical and chemical processes or lack ofthe required data bases make the use of aGaussian or numerical model impractical.Various specific models in these three ge-neric types are discussed in the Guideline.

c. Physical modeling, the fourth generictype, involves the use of wind tunnel orother fluid modeling facilities. This class ofmodeling is a complex process requiring ahigh level of technical expertise, as well asaccess to the necessary facilities. Neverthe-less, physical modeling may be useful forcomplex flow situations, such as building,terrain or stack downwash conditions, plumeimpact on elevated terrain, diffusion in anurban environment, or diffusion in complexterrain. It is particularly applicable to suchsituations for a source or group of sources ina geographic area limited to a few squarekilometers. If physical modeling is availableand its applicability demonstrated, it may bethe best technique. A discussion of physicalmodeling is beyond the scope of this guide.The EPA publication ‘‘Guideline for FluidModeling of Atmospheric Diffusion,’’4 pro-vides information on fluid modeling applica-tions and the limitations of that method.

2.3 Levels of Sophistication of Models

a. In addition to the various classes ofmodels, there are two levels of sophistica-tion. The first level consists of general, rel-atively simple estimation techniques thatprovide conservative estimates of the airquality impact of a specific source, or sourcecategory. These are screening techniques orscreening models. The purpose of such tech-niques is to eliminate the need of furthermore detailed modeling for those sourcesthat clearly will not cause or contribute toambient concentrations in excess of eitherthe National Ambient Air Quality Standards(NAAQS) 5 or the allowable prevention of sig-nificant deterioration (PSD) concentrationincrements.3 If a screening technique indi-cates that the concentration contributed bythe source exceeds the PSD increment or theincrement remaining to just meet the

NAAQS, then the second level of more so-phisticated models should be applied.

b. The second level consists of those ana-lytical techniques that provide more de-tailed treatment of physical and chemicalatmospheric processes, require more detailedand precise input data, and provide more spe-cialized concentration estimates. As a resultthey provide a more refined and, at leasttheoretically, a more accurate estimate ofsource impact and the effectiveness of con-trol strategies. These are referred to as re-fined models.

c. The use of screening techniques followedby a more refined analysis is always desir-able, however there are situations where thescreening techniques are practically andtechnically the only viable option for esti-mating source impact. In such cases, an at-tempt should be made to acquire or improvethe necessary data bases and to develop ap-propriate analytical techniques.

3.0 RECOMMENDED AIR QUALITY MODELS

a. This section recommends refined mod-eling techniques that are preferred for use inregulatory air quality programs. The statusof models developed by EPA, as well as thosesubmitted to EPA for review and possible in-clusion in this guidance, is discussed. Thesection also addresses the selection of mod-els for individual cases and provides rec-ommendations for situations where the pre-ferred models are not applicable. Two addi-tional sources of modeling guidance, theModel Clearinghouse 6 and periodic RegionalMeteorologists’ workshops, are also brieflydiscussed here.

b. In all regulatory analyses, especially ifother than preferred models are selected foruse, early discussions among Regional Officestaff, State and local control agencies, in-dustry representatives, and where appro-priate, the Federal Land Manager, are in-valuable and are encouraged. Agreement onthe data base to be used, modeling tech-niques to be applied and the overall tech-nical approach, prior to the actual analyses,helps avoid misunderstandings concerningthe final results and may reduce the laterneed for additional analyses. The use of anair quality checklist, such as presented inappendix C, and the preparation of a writtenprotocol help to keep misunderstandings at aminimum.

c. It should not be construed that the pre-ferred models identified here are to be per-manently used to the exclusion of all othersor that they are the only models availablefor relating emissions to air quality. Themodel that most accurately estimates con-centrations in the area of interest is alwayssought. However, designation of specificmodels is needed to promote consistency inmodel selection and application.

d. The 1980 solicitation of new or differentmodels from the technical community 7 and


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the program whereby these models are evalu-ated, established a means by which new mod-els are identified, reviewed and made avail-able in the Guideline. There is a pressingneed for the development of models for awide range of regulatory applications. Re-fined models that more realistically simu-late the physical and chemical process in theatmosphere and that more reliably estimatepollutant concentrations are required. Thus,the solicitation of models is considered to becontinuous.

3.1 Preferred Modeling Techniques

3.1.1 Discussion

a. EPA has developed approximately 10models suitable for regulatory application.More than 20 additional models were sub-mitted by private developers for possible in-clusion in the Guideline. These refined mod-els have all been organized into eight cat-egories of use: rural, urban industrial com-plex, reactive pollutants, mobile sources,complex terrain, visibility, and long rangetransport. They are undergoing an intensiveevaluation by category. The evaluation exer-cises 8 9 10 include statistical measures ofmodel performance in comparison withmeasured air quality data as suggested bythe American Meteorological Society 11 and,where possible, peer scientific reviews.12 13 l4

b. When a single model is found to performbetter than others in a given category, it isrecommended for application in that cat-egory as a preferred model and listed in ap-pendix A. If no one model is found to clearlyperform better through the evaluation exer-cise, then the preferred model listed in ap-pendix A is selected on the basis of other fac-tors such as past use, public familiarity, costor resource requirements, and availability.No further evaluation of a preferred model isrequired if the source follows EPA rec-ommendations specified for the model in theGuideline. The models not specifically rec-ommended for use in a particular categoryare summarized in appendix B. These modelsshould be compared with measured air qual-ity data when they are used for regulatoryapplications consistent with recommenda-tions in section 3.2.

c. The solicitation of new refined modelswhich are based on sounder scientific prin-ciples and which more reliably estimate pol-lutant concentrations is considered by EPAto be continuous. Models that are submittedin accordance with the provisions outlined inthe FEDERAL REGISTER notice of March 1980(45 FR 20157) 7 will be evaluated as submitted.These requirements are:

i. The model must be computerized andfunctioning in a common Fortran languagesuitable for use on a variety of computer sys-tems.

ii. The model must be documented in auser’s guide which identifies the mathe-

matics of the model, data requirements andprogram operating characteristics at a levelof detail comparable to that available forcurrently recommended models, e.g., the In-dustrial Source Complex (ISC) model.

iii. The model must be accompanied by acomplete test data set including input pa-rameters and output results. The test datamust be included in the user’s guide as wellas provided in computer-readable form.

iv. The model must be useful to typicalusers, e.g., State air pollution control agen-cies, for specific air quality control prob-lems. Such users should be able to operatethe computer program(s) from available doc-umentation.

v. The model documentation must includea comparison with air quality data or withother well-established analytical techniques.

vi. The developer must be willing to makethe model available to users at reasonablecost or make it available for public accessthrough the National Technical InformationService; the model cannot be proprietary.

d. The evaluation process will include a de-termination of technical merit, in accord-ance with the above six items including thepracticality of the model for use in ongoingregulatory programs. Each model will alsobe subjected to a performance evaluation foran appropriate data base and to a peer sci-entific review. Models for wide use (not justan isolated case!) found to perform better,based on an evaluation for the same databases used to evaluate models in appendix A,will be proposed for inclusion as preferredmodels in future Guideline revisions.

3.1.2 Recommendations

a. Appendix A identifies refined modelsthat are preferred for use in regulatory ap-plications. If a model is required for a par-ticular application, the user should select amodel from appendix A. These models maybe used without a formal demonstration ofapplicability as long as they are used as indi-cated in each model summary of appendix A.Further recommendations for the applica-tion of these models to specific source prob-lems are found in subsequent sections of theGuideline.

b. If changes are made to a preferred modelwithout affecting the concentration esti-mates, the preferred status of the model isunchanged. Examples of modifications thatdo not affect concentrations are those madeto enable use of a different computer orthose that affect only the format or aver-aging time of the model results. However,when any changes are made, the RegionalAdministrator should require a test case ex-ample to demonstrate that the concentra-tion estimates are not affected.

c. A preferred model should be operatedwith the options listed in appendix A as‘‘Recommendations for Regulatory Use.’’ Ifother options are exercised, the model is no


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a Another EPA document, ‘‘Protocol for De-termining the Best Performing Model’’, 17

contains advanced statistical techniques fordetermining which model performs betterthan other competing models. In many cases,this protocol should be considered by usersof the ‘‘Interim Procedures for EvaluatingAir Quality Models’’ in preference to the ma-terial currently in Chapter 3 of that docu-ment.

longer ‘‘preferred.’’ Any other modificationto a preferred model that would result in achange in the concentration estimates like-wise alters its status as a preferred model.Use of the model must then be justified on acase-by-case basis.

3.2 Use of Alternative Models

3.2.1 Discussion

a. Selection of the best techniques for eachindividual air quality analysis is always en-couraged, but the selection should be done ina consistent manner. A simple listing ofmodels in this guide cannot alone achievethat consistency nor can it necessarily pro-vide the best model for all possible situa-tions. An EPA document, ‘‘Interim Proce-dures for Evaluating Air Quality Mod-els’’,15 16 has been prepared to assist in devel-oping a consistent approach when justifyingthe use of other than the preferred modelingtechniques recommended in this guide. Analternative to be considered to the perform-ance measures contained in Chapter 3 of thisdocument is set forth in another EPA docu-ment ‘‘Protocol for Determining the BestPerforming Model’’. 17 The procedures in bothdocuments provide a general framework forobjective decision-making on the accept-ability of an alternative model for a givenregulatory application. The documents con-tain procedures for conducting both thetechnical evaluation of the model and thefield test or performance evaluation.

b. This section discusses the use of alter-nate modeling techniques and defines threesituations when alternative models may beused.


a. Determination of acceptability of amodel is a Regional Office responsibility.Where the Regional Administrator finds thatan alternative model is more appropriatethan a preferred model, that model may beused subject to the recommendations below.This finding will normally result from a de-termination that (1) A preferred air qualitymodel is not appropriate for the particularapplication; or (2) a more appropriate modelor analytical procedure is available and isapplicable.

b. An alternative model should be evalu-ated from both a theoretical and a perform-ance perspective before it is selected for use.There are three separate conditions underwhich such a model will normally be ap-proved for use: (1) If a demonstration can bemade that the model produces concentrationestimates equivalent to the estimates ob-tained using a preferred model; (2) if a statis-tical performance evaluation has been con-ducted using measured air quality data andthe results of that evaluation indicate thealternative model performs better for the ap-plication than a comparable model in appen-

dix A; and (3) if there is no preferred modelfor the specific application but a refinedmodel is needed to satisfy regulatory re-quirements. Any one of these three separateconditions may warrant use of an alternativemodel. Some known alternative models thatare applicable for selected situations arecontained in appendix B. However, inclusionthere does not infer any unique status rel-ative to other alternative models that arebeing or will be developed in the future.

c. Equivalency is established by dem-onstrating that the maximum or highest,second highest concentrations are within 2percent of the estimates obtained from thepreferred model. The option to show equiva-lency is intended as a simple demonstrationof acceptability for an alternative modelthat is so nearly identical (or contains op-tions that can make it identical) to a pre-ferred model that it can be treated for prac-tical purposes as the preferred model. Twopercent was selected as the basis for equiva-lency since it is a rough approximation ofthe fraction that PSD Class I increments areof the NAAQS for SO2, i.e., the difference inconcentrations that is judged to be signifi-cant. However, notwithstanding this dem-onstration, use of models that are not equiv-alent may be used when the conditions ofparagraph e of this section are satisfied.

d. The procedures and techniques for deter-mining the acceptability of a model for anindividual case based on superior perform-ance is contained in the document entitled‘‘Interim Procedures for Evaluating AirQuality Models’’, 15 and should be followed,as appropriate.a Preparation and implemen-tation of an evaluation protocol which is ac-ceptable to both control agencies and regu-lated industry is an important element insuch an evaluation.

e. When no appendix A model is applicableto the modeling problem, an alternative re-fined model may be used provided that:

i. The model can be demonstrated to be ap-plicable to the problem on a theoreticalbasis; and

ii. The data bases which are necessary toperform the analysis are available and ade-quate; and

iii. Performance evaluations of the modelin similar circumstances have shown thatthe model is not biased toward underesti-mates; or


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iv. After consultation with the EPA Re-gional Office, a second model is selected as abaseline or reference point for performanceand the interim procedures 15 protocol 17 arethen used to demonstrate that the proposedmodel performs better than the referencemodel.

3.3 Availability of Supplementary ModelingGuidance

a. The Regional Administrator has the au-thority to select models that are appropriatefor use in a given situation. However, thereis a need for assistance and guidance in theselection process so that fairness and con-sistency in modeling decisions is fosteredamong the various Regional Offices and theStates. To satisfy that need, EPA estab-lished the Model Clearinghouse and alsoholds periodic workshops with headquarters,Regional Office and State modeling rep-resentatives.

3.3.1 The Model Clearinghouse

3.3.1.1 Discussion

a. The Model Clearinghouse is the singleEPA focal point for review of air qualitysimulation models proposed for use in spe-cific regulatory applications. Details con-cerning the Clearinghouse and its operationare found in the document, ‘‘Model Clearing-house: Operational Plan.’’ 6 Three primaryfunctions of the Clearinghouse are:

i. Review of decisions proposed by EPA Re-gional Offices on the use of modeling tech-niques and data bases.

ii. Periodic visits to Regional Offices togather information pertinent to regulatorymodel usage.

iii. Preparation of an annual report sum-marizing activities of the Clearinghouse in-cluding specific determinations made duringthe course of the year.

3.3.1.2 Recommendations

a. The Regional Administrator may re-quest assistance from the Model Clearing-house after an initial evaluation and deci-sion has been reached concerning the appli-cation of a model, analytical technique ordata base in a particular regulatory action.The Clearinghouse may also consider andevaluate the use of modeling techniques sub-mitted in support of any regulatory action.Additional responsibilities are: (1) Reviewproposed action for consistency with agencypolicy; (2) determine technical adequacy; and(3) make recommendations concerning thetechnique or data base.

3.3.2 Regional Meteorologists Workshops

3.3.2.1 Discussion

a. EPA conducts an annual in-house work-shop for the purpose of mutual discussion

and problem resolution among Regional Of-fice modeling specialists, EPA research mod-eling experts, EPA Headquarters modelingand regulatory staff and representativesfrom State modeling programs. A summaryof the issues resolved at previous workshopswas issued in 1981 as ‘‘Regional Workshopson Air Quality Modeling: A Summary Re-port.’’ 17 That report clarified procedures notspecifically defined in the 1978 version of theGuideline and was issued to ensure the con-sistent interpretation of model requirementsfrom Region to Region. Similar workshopsfor the purpose of clarifying Guideline proce-dures or providing detailed instructions forthe use of those procedures are anticipatedin the future.


a. The Regional Office should always beconsulted for information and guidance con-cerning modeling methods and interpreta-tions of modeling guidance, and to ensurethat the air quality model user has availablethe latest most up-to-date policy and proce-dures.

4.0 SIMPLE-TERRAIN STATIONARY SOURCEMODELS

4.1 Discussion

a. Simple terrain, as used in this section, isconsidered to be an area where terrain fea-tures are all lower in elevation than the topof the stack of the source(s) in question. Themodels recommended in this section are gen-erally used in the air quality impact analysisof stationary sources for most criteria pol-lutants. The averaging time of the con-centration estimates produced by these mod-els ranges from 1 hour to an annual average.

b. Model evaluation exercises have beenconducted to determine the ‘‘best, most ap-propriate point source model’’ for use in sim-ple terrain.8 12 However, no one model hasbeen found to be clearly superior. Based onpast use, public familiarity, and availability,ISC is the recommended model for a widerange of regulatory applications. Similar de-terminations were made for the other refinedmodels that are identified in section 4.2.

4.2 Recommendations

4.2.1 Screening Techniques

a. Point source screening techniques are anacceptable approach to air quality analyses.One such approach is contained in the EPAdocument ‘‘Screening Procedures for Esti-mating the Air Quality Impact of StationarySources’’. 18 A computerized version of thescreening technique, SCREEN, is avail-able.19 20 For the current version of SCREEN,see 12.0 References. 20

b. All screening procedures should be ad-justed to the site and problem at hand. Closeattention should be paid to whether the area


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should be classified urban or rural in accord-ance with section 8.2.8. The climatology ofthe area should be studied to help define theworst-case meteorological conditions. Agree-ment should be reached between the modeluser and the reviewing authority on thechoice of the screening model for each anal-ysis, and on the input data as well as the ul-timate use of the results.

4.2.2 Refined Analytical Techniques

a. A brief description of preferred modelsfor refined applications is found in appendixA. Also listed in appendix A are the modelinput requirements, the standard optionsthat should be selected when running theprogram, and output options.

b. When modeling for compliance withshort term NAAQS and PSD increments is ofprimary concern, a short term model mayalso be used to provide long term concentra-tion estimates. However, when modelingsources for which long term standards aloneare applicable (e.g., lead), then the long termmodels should be used. The conversion fromlong term to short term concentration aver-ages by any transformation technique is notacceptable in regulatory applications.

5.0 MODEL USE IN COMPLEX TERRAIN

5.1 Discussion

a. For the purpose of the Guideline, com-plex terrain is defined as terrain exceedingthe height of the stack being modeled. Com-plex terrain dispersion models are normallyapplied to stationary sources of pollutantssuch as SO2 and particulates.

b. A major outcome from the EPA ComplexTerrain Model Development project has beenthe publication of a refined dispersion model(CTDM) suitable for regulatory applicationto plume impaction assessments in complexterrain. 21 Although CTDM as originally pro-duced was only applicable to those hourscharacterized as neutral or stable, a com-puter code for all stability conditions,CTDMPLUS, 19 together with a user’sguide, 22 and on-site meteorological and ter-rain data processors,23 24 is now available.Moreover, CTSCREEN,19 25 a version ofCTDMPLUS that does not require on-sitemeteorological data inputs, is also availableas a screening technique.

c. The methods discussed in this sectionshould be considered in two categories: (1)Screening techniques, and (2) the refined dis-persion model, CTDMPLUS, discussed belowand listed in appendix A.

d. Continued improvements in ability toaccurately model plume dispersion in com-plex terrain situations can be expected, e.g.,from research on lee side effects due to ter-rain obstacles. New approaches to improvethe ability of models to realistically simu-late atmospheric physics, e.g., hybrid modelswhich incorporate an accurate wind field

analysis, will ultimately provide more ap-propriate tools for analyses. Such hybridmodeling techniques are also acceptable forregulatory applications after the appropriatedemonstration and evaluation. 15

5.2 Recommendations

a. Recommendations in this section applyprimarily to those situations where the im-paction of plumes on terrain at elevationsequal to or greater than the plume center-line during stable atmospheric conditionsare determined to be the problem. If a viola-tion of any NAAQS or the controlling incre-ment is indicated by using any of the pre-ferred screening techniques, then a refinedcomplex terrain model may be used. Phe-nomena such as fumigation, wind directionshear, lee-side effects, building wake- or ter-rain-induced downwash, deposition, chemicaltransformation, variable plume trajectories,and long range transport are not addressedby the recommendations in this section.

b. Where site-specific data are used for ei-ther screening or refined complex terrainmodels, a data base of at least 1 full-year ofmeteorological data is preferred. If moredata are available, they should be used. Me-teorological data used in the analysis shouldbe reviewed for both spatial and temporalrepresentativeness.

c. Placement of receptors requires verycareful attention when modeling in complexterrain. Often the highest concentrations arepredicted to occur under very stable condi-tions, when the plume is near, or impingeson, the terrain. The plume under such condi-tions may be quite narrow in the vertical, sothat even relatively small changes in a re-ceptor’s location may substantially affectthe predicted concentration. Receptors with-in about a kilometer of the source may beeven more sensitive to location. Thus, adense array of receptors may be required insome cases. In order to avoid excessivelylarge computer runs due to such a largearray of receptors, it is often desirable tomodel the area twice. The first model runwould use a moderate number of receptorscarefully located over the area of interest.The second model run would use a moredense array of receptors in areas showing po-tential for high concentrations, as indicatedby the results of the first model run.

d. When CTSCREEN or CTDMPLUS isused, digitized contour data must be firstprocessed by the CTDM Terrain Processor 23

to provide hill shape parameters in a formatsuitable for direct input to CTDMPLUS.Then the user supplies receptors eitherthrough an interactive program that is partof the model or directly, by using a text edi-tor; using both methods to select receptorswill generally be necessary to assure thatthe maximum concentrations are estimatedby either model. In cases where a terrain fea-ture may ‘‘appear to the plume’’ as smaller,


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multiple hills, it may be necessary to modelthe terrain both as a single feature and asmultiple hills to determine design con-centrations.

e. The user is encouraged to confer withthe Regional Office if any unresolvable prob-lems are encountered with any screening orrefined analytical procedures, e.g., meteoro-logical data, receptor siting, or terrain con-tour processing issues.

5.2.1 Screening Techniques

a. Five preferred screening techniques arecurrently available to aid in the evaluationof concentrations due to plume impactionduring stable conditions: (1) for 24-hour im-pacts, the Valley Screening Technique 19 asoutlined in the Valley Model User’s Guide; 26

(2) CTSCREEN,19 as outlined in theCTSCREEN User’s Guide; 25 (3) COMPLEXI; 19 (4) SHORTZ/LONGZ; 19 27 and (5) RoughTerrain Dispersion Model (RTDM) 19 90 in itsprescribed mode described below. As appro-priate, any of these screening techniquesmay be used consistent with the needs, re-sources, and available data of the user.

b. The Valley Model, COMPLEX I,SHORTZ/LONGZ, and RTDM should be usedonly to estimate concentrations at receptorswhose elevations are greater than or equal toplume height. For receptors at or belowstack height, a simple terrain model shouldbe used (see Chapter 4). Receptors betweenstack height and plume height present aunique problem since none of the above mod-els were designed to handle receptors in thisnarrow regime, the definition of which willvary hourly as meteorological conditionsvary. CTSCREEN may be used to estimateconcentrations under all stability conditionsat all receptors located ‘‘on terrain’’ abovestack top, but has limited applicability inmulti-source situations. As a result, the esti-mation of concentrations at receptors be-tween stack height and plume height shouldbe considered on a case-by-case basis afterconsultation with the EPA Regional Office;the most appropriate technique may be afunction of the actual source(s) and terrainconfiguration unique to that application.One technique that will generally be accept-able, but is not necessarily preferred for anyspecific application, involves applying both acomplex terrain model (except for the ValleyModel) and a simple terrain model. The Val-ley Model should not be used for any inter-mediate terrain receptor. For each receptorbetween stack height and plume height, anhour-by-hour comparison of the concentra-tion estimates from both models is made.The higher of the two modeled concentra-tions should be chosen to represent the im-pact at that receptor for that hour, and thenused to compute the concentration for theappropriate averaging time(s). For the sim-ple terrain models, terrain may have to be

‘‘chopped off’’ at stack height, since thesemodels are frequently limited to receptorsno greater than stack height.

5.2.1.1 Valley Screening Technique

a. The Valley Screening Technique may beused to determine 24-hour averages. Thistechnique uses the Valley Model with thefollowing worst-case assumptions for ruralareas: (1) P–G stability ‘‘F’’; (2) wind speed of2.5 m/s; and (3) 6 hours of occurrence. Forurban areas the stability should be changedto ‘‘P–G stability E.’’

b. When using the Valley Screening Tech-nique to obtain 24-hour average concentra-tions the following apply: (1) multiplesources should be treated individually andthe concentrations for each wind directionsummed; (2) only one wind direction shouldbe used (see User’s Guide,26 page 2–15) even ifindividual runs are made for each source; (3)for buoyant sources, the BID option may beused, and the option to use the 2.6 stableplume rise factor should be selected; (4) ifplume impaction is likely on any elevatedterrain closer to the source than the dis-tance from the source to the final plumerise, then the transitional (or gradual) plumerise option for stable conditions should be se-lected.

c. The standard polar receptor grid foundin the Valley Model User’s Guide may not besufficiently dense for all analyses if only onegeographical scale factor is used. The usershould choose an additional set of receptorsat appropriate downwind distances whoseelevations are equal to plume height minus10 meters. Alternatively, the user may exer-cise the ‘‘Valley equivalent’’ option in COM-PLEX I or SCREEN and note the commentsabove on the placement of receptors in com-plex terrain models.

d. When using the ‘‘Valley equivalent’’ op-tion in COMPLEX I, set the wind profile ex-ponents (PL) to 0.0, respectively, for all sixstability classes.

5.2.1.2 CTSCREEN

a. CTSCREEN may be used to obtain con-servative, yet realistic, worst-case estimatesfor receptors located on terrain above stackheight. CTSCREEN accounts for the three-dimensional nature of plume and terraininteraction and requires detailed terraindata representative of the modeling domain.The model description and user’s instruc-tions are contained in the user’s guide. 25 Theterrain data must be digitized in the samemanner as for CTDMPLUS and a terrainprocessor is available. 23 A discussion of themodel’s performance characteristics is pro-vided in a technical paper. 91 CTSCREEN isdesigned to execute a fixed matrix of mete-orological values for wind speed (u), standarddeviation of horizontal and vertical wind


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speeds (σv, σG5w), vertical potential tempera-ture gradient (dθ/dz), friction velocity (ux),Monin-Obukhov length (L), mixing height (zi)as a function of terrain height, and wind di-rections for both neutral/stable conditionsand unstable convective conditions. Table 5–1 contains the matrix of meteorological vari-ables that is used for each CTSCREEN anal-ysis. There are 96 combinations, includingexceptions, for each wind direction for theneutral/stable case, and 108 combinations forthe unstable case. The specification of winddirection, however, is handled internally,based on the source and terrain geometry.The matrix was developed from examinationof the range of meteorological variables as-sociated with maximum monitored con-centrations from the data bases used toevaluate the performance of CTDMPLUS. Al-though CTSCREEN is designed to address asingle source scenario, there are a number ofoptions that can be selected on a case-by-case basis to address multi-source situations.However, the Regional Office should be con-sulted, and concurrence obtained, on the pro-tocol for modeling multiple sources withCTSCREEN to ensure that the worst case isidentified and assessed. The maximum con-centration output from CTSCREEN rep-resents a worst-case 1-hour concentration.Time-scaling factors of 0.7 for 3-hour, 0.15 for24-hour and 0.03 for annual concentrationaverages are applied internally byCTSCREEN to the highest 1-hour concentra-tion calculated by the model.

5.2.1.3 COMPLEX I

a. If the area is rural, COMPLEX I may beused to estimate concentrations for all aver-aging times. COMPLEX I is a modification ofthe MPTER model that incorporates theplume impaction algorithm of the ValleyModel. 19 It is a multiple-source screeningtechnique that accepts hourly meteorolog-ical data as input. The output is the same asthe normal MPTER output. When usingCOMPLEX I the following options should beselected: (1) Set terrain adjustment IOPT(1)=1; (2) set buoyancy induced dispersionIOPT (4)=1; (3) set IOPT (25)=1; (4) set theterrain adjustment values to 0.5, 0.5, 0.5 0.5,0.0, 0.0, (respectively for six stability class-es); and (5) set Z MIN=10.

b. When using the ‘‘Valley equivalent’’ op-tion (only) in COMPLEX I, set the wind pro-file exponents (PL) to 0.0, respectively, forall six stability classes. For all other regu-latory uses of COMPLEX I, set the wind pro-file exponents to the values used in the sim-ple terrain models, i.e., 0.07, 0.07, 0.10, 0.15,0.35, and 0.55, respectively, for rural mod-eling.

c. Gradual plume rise should be used to es-timate concentrations at nearby elevated re-ceptors, if plume impaction is likely on anyelevated terrain closer to the source than

the distance from the source to the finalplume rise (see section 8.2.5).

5.2.1.4 SHORTZ/LONGZ

a. If the source is located in an urbanized(Section 8.2.8) complex terrain valley, thenthe suggested screening technique isSHORTZ for short-term averages or LONGZfor long-term averages. SHORTZ and LONGZmay be used as screening techniques in thesecomplex terrain applications without dem-onstration and evaluation. Application ofthese models in other than urbanized valleysituations will require the same evaluationand demonstration procedures as are re-quired for all appendix B models.

b. Both SHORTZ and LONGZ have a num-ber of options. When using these models asscreening techniques for urbanized valley ap-plications, the options listed in table 5–2should be selected.

5.2.1.5 RTDM (Screening Mode)

a. RTDM with the options specified intable 5–3 may be used as a screening tech-nique in rural complex terrain situationswithout demonstration and evaluation.

b. The RTDM screening technique can pro-vide a more refined concentration estimateif on-site wind speed and direction char-acteristic of plume dilution and transportare used as input to the model. In complexterrain, these winds can seldom be estimatedaccurately from the standard surface (10mlevel) measurements. Therefore, in order toincrease confidence in model estimates, EPArecommends that wind data input to RTDMshould be based on fixed measurements atstack top height. For stacks greater than100m, the measurement height may be lim-ited to 100m in height relative to stack base.However, for very tall stacks, see guidancein section 9.3.3.2. This recommendation isbroadened to include wind data representa-tive of plume transport height where suchdata are derived from measurements takenwith remote sensing devices such as SODAR.The data from both fixed and remote meas-urements should meet quality assurance andrecovery rate requirements. The user shouldalso be aware that RTDM in the screeningmode accepts the input of measured windspeeds at only one height. The default valuesfor the wind speed profile exponents shownin table 5–3 are used in the model to deter-mine the wind speed at other heights. RTDMuses wind speed at stack top to calculate theplume rise and the critical dividing stream-line height, and the wind speed at plumetransport level to calculate dilution. RTDMtreats wind direction as constant withheight.

c. RTDM makes use of the ‘‘critical divid-ing streamline’’ concept and thus treatsplume interactions with terrain quite dif-ferently from other models such as SHORTZ


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and COMPLEX I. The plume height relativeto the critical dividing streamline deter-mines whether the plume impacts the ter-rain, or is lifted up and over the terrain. Thereceptor spacing to identify maximum im-pact concentrations is quite critical depend-ing on the location of the plume in thevertical. Analysis of the expected plumeheight relative to the height of the criticaldividing streamline should be performed fordiffering meteorological conditions in orderto help develop an appropriate array of re-ceptors. Then it is advisable to model thearea twice according to the suggestions insection 5.2.

5.2.1.6 Restrictions

a. For screening analyses using the ValleyScreening Technique, COMPLEX I or RTDM,a sector greater than 221⁄2° should not be al-lowed. Full ground reflection should alwaysbe used in the Valley Screening Techniqueand COMPLEX I.

5.2.2 Refined Analytical Techniques

a. When the results of the screening anal-ysis demonstrate a possible violation ofNAAQS or the controlling PSD increments, amore refined analysis may need to be con-ducted.

b. The Complex Terrain Dispersion ModelPlus Algorithms for Unstable Situations(CTDMPLUS) is a refined air quality modelthat is preferred for use in all stability con-ditions for complex terrain applications.CTDMPLUS is a sequential model that re-quires five input files: (1) General programspecifications; (2) a terrain data file; (3) a re-ceptor file; (4) a surface meteorological datafile; and (5) a user created meteorologicalprofile data file. Two optional input filesconsist of hourly emissions parameters and afile containing upper air data from rawin-sonde data files, e.g., a National ClimaticData Center TD–6201 file, unless there are nohours categorized as unstable in the record.The model description and user instructionsare contained in Volume 1 of the User’sGuide. 22 Separate publications 23 24 describethe terrain preprocessor system and the me-teorological preprocessor program. In Part Iof a technical article 92 is a discussion of themodel and its preprocessors; the model’s per-formance characteristics are discussed inPart II of the same article.93 The size of theCTDMPLUS executable file on a personalcomputer is approximately 360K bytes. Themodel produces hourly average concentra-tions of stable pollutants, i.e., chemicaltransformation or decay of species and set-tling/deposition are not simulated. To obtainconcentration averages corresponding to theNAAQS, e.g., 3- or 24-hour, or annual aver-ages, the user must execute a postprocessorprogram such as CHAVG. 19 CTDMPLUS isapplicable to all receptors on terrain ele-

vations above stack top. However, the modelcontains no algorithms for simulating build-ing downwash or the mixing or recirculationfound in cavity zones in the lee of a hill. Thepath taken by a plume through an array ofhills cannot be simulated. CTDMPLUS doesnot explicitly simulate calm meteorologicalperiods, and for those situations the usershould follow the guidance in section 9.3.4.The user should follow the recommendationsin the User’s Guide under General ProgramSpecifications for: (1) Selecting mixed layerheights, (2) setting minimum scalar windspeed to 1 m/s, and (3) scaling wind directionwith height. Close coordination with the Re-gional Office is essential to insure a con-sistent, technically sound application of thismodel.

c. The performance of CTDMPLUS isgreatly improved by the use of meteorolog-ical data from several levels up to plumeheight. However, due to the vast range ofsource-plume-hill geometries possible incomplex terrain, detailed requirements formeteorological monitoring in support of re-fined analyses using CTDMPLUS should bedetermined on a case-by-case basis. The fol-lowing general guidance should be consid-ered in the development of a meteorologicalmonitoring protocol for regulatory applica-tions of CTDMPLUS and reviewed in detailby the Regional Office before initiating anymonitoring. As appropriate, the On-Site Me-teorological Program Guidance document 66

should be consulted for specific guidance onsiting requirements for meteorological tow-ers, selection and exposure of sensors, etc. Asmore experience is gained with the model ina variety of circumstances, more specificguidance may be developed.

d. Site specific meteorological data arecritical to dispersion modeling in complexterrain and, consequently, the meteorolog-ical requirements are more demanding thanfor simple terrain. Generally, three differentmeteorological files (referred to as surface,profile, and rawin files) are needed to runCTDMPLUS in a regulatory mode.

e. The surface file is created by the mete-orological preprocessor (METPRO) 24 basedon on-site measurements or estimates ofsolar and/or net radiation, cloud cover andceiling, and the mixed layer height. Thesedata are used in METPRO to calculate thevarious surface layer scaling parameters(roughness length, friction velocity, andMonin-Obukhov length) which are needed torun the model. All of the user inputs re-quired for the surface file are based either onsurface observations or on measurements ator below 10m.

f. The profile data file is prepared by theuser with on-site measurements (from atleast three levels) of wind speed, wind direc-tion, turbulence, and potential temperature.These measurements should be obtained up


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to the representative plume height(s) of in-terest (i.e., the plume height(s) under thoseconditions important to the determinationof the design concentration). The representa-tive plume height(s) of interest should be de-termined using an appropriate complex ter-rain screening procedure (e.g., CTSCREEN)and should be documented in the monitoring/modeling protocol. The necessary meteoro-logical measurements should be obtainedfrom an appropriately sited meteorologicaltower augmented by SODAR if the represent-ative plume height(s) of interest exceed100m. The meteorological tower need not ex-ceed the lesser of the representative plumeheight of interest (the highest plume heightif there is more than one plume height of in-terest) or 100m.

g. Locating towers on nearby terrain to ob-tain stack height or plume height measure-

ments for use in profiles by CTDMPLUSshould be avoided unless it can clearly bedemonstrated that such measurementswould be representative of conditions affect-ing the plume.

h. The rawin file is created by a second me-teorological preprocessor (READ62) 24 basedon NWS (National Weather Service) upperair data. The rawin file is used inCTDMPLUS to calculate vertical potentialtemperature gradients for use in estimatingplume penetration in unstable conditions.The representativeness of the off-site NWSupper air data should be evaluated on a case-by-case basis.

i. In the absence of an appropriate refinedmodel, screening results may need to be usedto determine air quality impact and/or emis-sion limits.

TABLE 5–1A—NEUTRAL/STABLE METEOROLOGICAL MATRIX FOR CTSCREEN

Variable Specific values

U (m/s) .................................................................. 1.0 2.0 3.0 4.0 5.0σv (m/s) ................................................................. 0.3 0.75σw (m/s) ................................................................. 0.08 0.15 0.30 0.75DQ/Dz (K/m) .......................................................... 0.01 0.02 0.035WD (Wind direction optimized internally for each meteorological combination)

Exceptions:(1) If U ≤ 2 m/s and σv ≥ 0.3 m/s, then include σw = 0.04 m/s.(2) If σw = 0.75 m/s and U ≥ 3.0 m/s, then DU/Dz is limited to ≤ 0.01 K/m.(3) If U ≥ 4 m/s, then σw ≥ 0.15 m/s.(4) σw ≤ σv

TABLE 5–1B—UNSTABLE/CONVECTIVE METEOROLOGICAL MATRIX FOR CTSCREEN

Variable Specific values

U (m/s) ................................................................ 1.0 2.0 3.0 4.0 5.0ux (m/s) ................................................................ 0.1 0.3 0.5L (m) .................................................................... ¥10 ¥50 ¥90DU/Dz(K/m) 0.030 (potential temperature gradient above zi)zi (m) ................................................................... 0.5h 1.0h 1.5h

(where h = terrain height)

TABLE 5–2—PREFERRED OPTIONS FOR THE SHORTZ/LONGZ COMPUTER CODES WHEN USED IN ASCREENING MODE

Option Selection

I Switch 9 ............................... ................................................ If using NWS data, set = 0, If using site-specific data, checkwith the Regional Office.

I Switch 17 ............................. ................................................ Set = 1 (urban option).GAMMA 1 .............................. ................................................ Use default values (0.6 entrainment coefficient).GAMMA 2 .............................. ................................................ Always default to ‘‘stable’’.XRY ........................................ ................................................ Set = 0 (50m rectilinear expansion distance).NS, VS, FRQ (SHORTZ)

(particle size, etc.) Do not use (applicable only in flat terrain).NUS, VS, FRQ (LONGZ)ALPHA ................................... ................................................ Select 0.9.SIGEPU

(dispersion parameters) ........ Use Cramer curves (default); if site-specific turbulence data areavailable, see Regional Office for advice.

SIGAPUP (wind profile) ....................... ................................................ Select default values given in table 2–2 of User’s Instructions; if

site-specific data are available, see Regional Office for ad-vice.


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TABLE 5–3—PREFERRED OPTIONS FOR THE RTDM COMPUTER CODE WHEN USED IN A SCREENINGMODE

Parameter Variable Value Remarks

PR001–003 .................. SCALE ................ ....................................................... Scale factors assuming horizontal distance isin kilometers, vertical distance is in feet,and wind speed is in meters per second.

PR004 .......................... ZWIND1 .............. Wind measurement height ........... See section 5.2.1.4.ZWIND2 .............. Not used ....................................... Height of second anemometer.IDILUT ................ 1 ................................................... Dilution wind speed scaled to plume height.ZA ....................... 0 (default) ..................................... Anemometer-terrain height above stack

base.PR005 .......................... EXPON ............... 0.09, 0.11, 0.12, 0.14, 0.2, 0.3

(default).Wind profile exponents.

PR006 .......................... ICOEF ................. 3 (default) ..................................... Briggs Rural/ASME 139 dispersion param-eters.

PR009 .......................... IPPP .................... 0 (default) ..................................... Partial plume penetration; not used.PR010 .......................... IBUOY ................. 1 (default) ..................................... Buoyancy-enhanced dispersion is used.

ALPHA ................ 3.162 (default) .............................. Buoyancy-enhanced dispersion coefficient.PR011 .......................... IDMX ................... 1 (default) ..................................... Unlimited mixing height for stable conditions.PR012 .......................... ITRANS ............... 1 (default) ..................................... Transitional plume rise is used.PR013 .......................... TERCOR ............. 6*0.5 (default) ............................... Plume patch correction factors.PR014 .......................... RVPTG ............... 0.02, 0.035 (default) ..................... Vertical potential temperature gradient values

for stabilities E and F.PR015 .......................... ITIPD ................... 1 ................................................... Stack-tip downwash is used.PR020 .......................... ISHEAR .............. 0 (default) ..................................... Wind shear; not used.PR022 .......................... IREFL .................. 1 (default) ..................................... Partial surface reflection is used.PR023 .......................... IHORIZ ................ 2 (default) ..................................... Sector averaging.

SECTOR ............. 6*22.5 (default) ............................. Using 22.5° sectors.PR016 to 019; 021; and

024.IY, IZ, IRVPTG,

IHVPTG; IEPS;IEMIS.

0 ................................................... Hourly values of turbulence, vertical potentialtemperature gradient, wind speed profileexponents, and stack emissions are notused.

6.0 MODELS FOR OZONE, CARBON MONOXIDEAND NITROGEN DIOXIDE

6.1 Discussion

a. Models discussed in this section are ap-plicable to pollutants often associated withmobile sources, e.g., ozone (O3), carbon mon-oxide (CO) and nitrogen dioxide (NO2). Wherestationary sources of CO and NO2 are of con-cern, the reader is referred to sections 4 and5

b. A control agency with jurisdiction overareas with significant ozone problems andwhich has sufficient resources and data touse a photochemical dispersion model is en-couraged to do so. Experience with and eval-uations of the Urban Airshed Model show itto be an acceptable, refined approach, andbetter data bases are becoming availablethat support the more sophisticated analyt-ical procedures. However, empirical models(e.g., EKMA) fill the gap between more so-phisticated photochemical dispersion modelsand proportional (rollback) modeling tech-niques and may be the only applicable proce-dure if the available data bases are insuffi-cient for refined dispersion modeling.

c. Models for assessing the impact of car-bon monoxide emissions are needed for anumber of different purposes, e.g., to evalu-ate the effects of point sources, congestedintersections and highways, as well as thecumulative effect on ambient CO concentra-

tions of all sources of CO in an urbanarea.94 95

d. Nitrogen oxides are reactive and also animportant contribution to the photo-chemical ozone problem. They are usually ofmost concern in areas of high ozone con-centrations. Unless suitable photochemicaldispersion models are used, assumptions re-garding the conversion of NO to NO2 are re-quired when modeling. Site-specific conver-sion factors may be developed. If site-specificconversion factors are not available or pho-tochemical models are not used, NO2 mod-eling should be considered only a screeningprocedure.

6.2 Recommendations

6.2.1 Models for Ozone

a. The Urban Airshed Model (UAM)19 28 isrecommended for photochemical or reactivepollutant modeling applications involvingentire urban areas. To ensure proper execu-tion of this numerical model, users must sat-isfy the extensive input data requirementsfor the model as listed in appendix A and theusers guide. Users are also referred to the‘‘Guideline for Regulatory Application of theUrban Airshed Model’’ 29 for additional datarequirements and procedures for operatingthis model.

b. The empirical model, City-specificEKMA,19 30–33 has limited applicability for


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urban ozone analyses. Model users shouldconsult the appropriate Regional Office on acase-by-case basis concerning acceptabilityof this modeling technique.

c. Appendix B contains some additionalmodels that may be applied on a case-by-case basis for photochemical or reactive pol-lutant modeling. Other photochemical mod-els, including multi-layered trajectory mod-els, that are available may be used if shownto be appropriate. Most photochemical dis-persion models require emission data on in-dividual hydrocarbon species and may re-quire three dimensional meteorological in-formation on an hourly basis. Reasonably so-phisticated computer facilities are also oftenrequired. Because the input data are not uni-versally available and studies to collect suchdata are very resource intensive, there areonly limited evaluations of those models.

d. For those cases which involve esti-mating the impact on ozone concentrationsdue to stationary sources of VOC and NOX,

whether for permitting or other regulatorycases, the model user should consult the ap-propriate Regional Office on the accept-ability of the modeling technique.

e. Proportional (rollback/forward) mod-eling is not an acceptable procedure for eval-uating ozone control strategies.

6.2.2 Models for Carbon Monoxide

a. For analyzing CO impacts at roadwayintersections, users should follow the proce-dures in the ‘‘Guideline for Modeling CarbonMonoxide from Roadway Intersections’’. 34

The recommended model for such analyses isCAL3QHC. 35 This model combines CALINE3(already in appendix A) with a traffic modelto calculate delays and queues that occur atsignalized intersections. In areas where theuse of either TEXIN2 or CALINE4 has pre-viously been established, its use may con-tinue. The capability exists for these inter-section models to be used in either a screen-ing or refined mode. The screening approachis described in reference 34; a refined ap-proach may be considered on a case-by-casebasis. The latest version of the MOBILE (mo-bile source emission factor) model should beused for emissions input to intersection mod-els.

b. For analyses of highways characterizedby uninterrupted traffic flows, CALINE3 isrecommended, with emissions input from thelatest version of the MOBILE model.

c. The recommended model for urbanareawide CO analyses is RAM or UrbanAirshed Model (UAM); see appendix A. Infor-mation on SIP development and require-ments for using these models can be found inreferences 34, 96, 97 and 98.

d. Where point sources of CO are of con-cern, they should be treated using thescreening and refined techniques described insection 4 or 5 of the Guideline.

6.2.3 Models for Nitrogen Dioxide (AnnualAverage)

a. A tiered screening approach is rec-ommended to obtain annual average esti-mates of NO2 from point sources for NewSource Review analysis, including PSD, andfor SIP planning purposes. This multi-tieredapproach is conceptually shown in Figure 6–1 and described in paragraphs b and c of thissection. Figure 6–1 is as follows:

FIGURE 6–1—MULTI-TIERED SCREENING AP-PROACH FOR ESTIMATING ANNUAL NO2 CON-CENTRATIONS FROM POINT SOURCES

Tier 1: Assume Total Conversion of NO toNO2

↓

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

b. For Tier 1 (the initial screen), use an ap-propriate Gaussian model from appendix Ato estimate the maximum annual averageconcentration and assume a total conversionof NO to NO2. If the concentration exceedsthe NAAQS and/or PSD increments for NO2,

proceed to the 2nd level screen.c. For Tier 2 (2nd level) screening analysis,

multiply the Tier 1 estimate(s) by an empiri-cally derived NO2/NOX value of 0.75 (annualnational default).36 An annual NO2/NOX ratiodiffering from 0.75 may be used if it can beshown that such a ratio is based on datalikely to be representative of the location(s)where maximum annual impact from the in-dividual source under review occurs. In thecase where several sources contribute to con-sumption of a PSD increment, a locally de-rived annual NO2/NOX ratio should also beshown to be representative of the locationwhere the maximum collective impact fromthe new plus existing sources occurs.

d. In urban areas, a proportional modelmay be used as a preliminary assessment toevaluate control strategies to meet theNAAQS for multiple minor sources, i.e.minor point, area and mobile sources of NOX;concentrations resulting from major pointsources should be estimated separately asdiscussed above, then added to the impact ofthe minor sources. An acceptable screeningtechnique for urban complexes is to assumethat all NOX is emitted in the form of NO2

and to use a model from appendix A for non-reactive pollutants to estimate NO2 con-centrations. A more accurate estimate canbe obtained by: (1) Calculating the annualaverage concentrations of NOX with an urbanmodel, and (2) converting these estimates toNO2 concentrations using an empirically de-rived annual NO2/NOX ratio. A value of 0.75 isrecommended for this ratio. However, a spa-tially averaged annual NO2/NOX ratio may bedetermined from an existing air qualitymonitoring network and used in lieu of the


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0.75 value if it is determined to be represent-ative of prevailing ratios in the urban areaby the reviewing agency. To ensure use ofappropriate locally derived annual NO2/NOX

ratios, monitoring data under considerationshould be limited to those collected at mon-itors meeting siting criteria defined in 40CFR part 58, appendix D as representative of‘‘neighborhood’’, ‘‘urban’’, or ‘‘regional’’scales. Furthermore, the highest annual spa-tially averaged NO2/NOX ratio from the mostrecent 3 years of complete data should beused to foster conservatism in estimated im-pacts.

e. To demonstrate compliance with NO2

PSD increments in urban areas, emissionsfrom major and minor sources should be in-cluded in the modeling analysis. Point andarea source emissions should be modeled asdiscussed above. If mobile source emissionsdo not contribute to localized areas of highambient NO2 concentrations, they should bemodeled as area sources. When modeled asarea sources, mobile source emissions shouldbe assumed uniform over the entire highwaylink and allocated to each area source gridsquare based on the portion of highway linkwithin each grid square. If localized areas ofhigh concentrations are likely, then mobilesources should be modeled as line sourceswith the preferred model ISCLT.

f. More refined techniques to handle spe-cial circumstances may be considered on acase-by-case basis and agreement with thereviewing authority should be obtained.Such techniques should consider individualquantities of NO and NO2 emissions, atmos-pheric transport and dispersion, and atmos-pheric transformation of NO to NO2. Wherethey are available, site-specific data on theconversion of NO to NO2 may be used. Photo-chemical dispersion models, if used for otherpollutants in the area, may also be appliedto the NOX problem.

7.0 OTHER MODEL REQUIREMENTS

7.1 Discussion

a. This section covers those cases wherespecific techniques have been developed forspecial regulatory programs. Most of theprograms have, or will have when fully de-veloped, separate guidance documents thatcover the program and a discussion of thetools that are needed. The following para-graphs reference those guidance documents,when they are available. No attempt hasbeen made to provide a comprehensive dis-cussion of each topic since the reference doc-uments were designed to do that. This sec-tion will undergo periodic revision as newprograms are added and new techniques aredeveloped.

b. Other Federal agencies have also devel-oped specific modeling approaches for theirown regulatory or other requirements. Anexample of this is the three-volume manual

issued by the U. S. Department of Housingand Urban Development, ‘‘Air Quality Con-siderations in Residential Planning.’’ 37 Al-though such regulatory requirements andmanuals may have come about because ofEPA rules or standards, the implementationof such regulations and the use of the mod-eling techniques is under the jurisdiction ofthe agency issuing the manual or directive.

c. The need to estimate impacts at dis-tances greater than 50km (the nominal dis-tance to which EPA considers most Gaussianmodels applicable) is an important one espe-cially when considering the effects from sec-ondary pollutants. Unfortunately, modelssubmitted to EPA have not as yet undergonesufficient field evaluation to be rec-ommended for general use. Existing databases from field studies at mesoscale andlong range transport distances are limited indetail. This limitation is a result of the ex-pense to perform the field studies required toverify and improve mesoscale and long rangetransport models. Particularly importantand sparse are meteorological data adequatefor generating three dimensional wind fields.Application of models to complicated terraincompounds the difficulty. EPA has com-pleted limited evaluation of several longrange transport (LRT) models against twosets of field data. The evaluation results arediscussed in the document, ‘‘Evaluation ofShort-Term Long-Range Transport Mod-els.’’ 99 100 For the time being, long range andmesoscale transport models must be evalu-ated for regulatory use on a case-by-casebasis.

d. There are several regulatory programsfor which air pathway analysis proceduresand modeling techniques have been devel-oped. For continuous emission releases, ISCforms the basis of many analytical tech-niques. EPA is continuing to evaluate theperformance of a number of proprietary andpublic domain models for intermittent andnon-stack emission releases. Until EPA com-pletes its evaluation, it is premature to rec-ommend specific models for air pathwayanalyses of intermittent and non-stack re-leases in the Guideline.

e. Regional scale models are used by EPAto develop and evaluate national policy andassist State and local control agencies. Twosuch models are the Regional Oxidant Model(ROM) 101 102 103 and the Regional Acid Deposi-tion Model (RADM). 104 Due to the level of re-sources required to apply these models, it isnot envisioned that regional scale modelswill be used directly in most model applica-tions.

7.2 Recommendations

7.2.1 Fugitive Dust/Fugitive Emissions

a. Fugitive dust usually refers to the dustput into the atmosphere by the wind blowingover plowed fields, dirt roads or desert or


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sandy areas with little or no vegetation. Re-entrained dust is that which is put into theair by reason of vehicles driving over dirtroads (or dirty roads) and dusty areas. Suchsources can be characterized as line, area orvolume sources. Emission rates may be basedon site-specific data or values from the gen-eral literature.

b. Fugitive emissions are usually definedas emissions that come from an industrialsource complex. They include the emissionsresulting from the industrial process thatare not captured and vented through a stackbut may be released from various locationswithin the complex. Where such fugitiveemissions can be properly specified, the ISCmodel, with consideration of gravitationalsettling and dry deposition, is the rec-ommended model. In some unique cases amodel developed specifically for the situa-tion may be needed.

c. Due to the difficult nature of character-izing and modeling fugitive dust and fugitiveemissions, it is recommended that the pro-posed procedure be cleared by the appro-priate Regional Office for each specific situa-tion before the modeling exercise is begun.

7.2.2 Particulate Matter

a. The particulate matter NAAQS, promul-gated on July 1, 1987 (52 FR 24634), includesonly particles with an aerodynamic diameterless than or equal to a nominal 10 microm-eters (PM–10). EPA promulgated regulationsfor PSD increments measured as PM–10 onJune 3, 1993 (58 FR 31621), which are codifiedat §§ 51.166(c) and 52.21(c).

b. Screening techniques like those identi-fied in section 4 are also applicable to PM–10and to large particles. It is recommendedthat subjectively determined values for‘‘half-life’’ or pollutant decay not be used asa surrogate for particle removal. Conserv-ative assumptions which do not allow re-moval or transformation are suggested forscreening. Proportional models (rollback/for-ward) may not be applied for screening anal-ysis, unless such techniques are used in con-junction with receptor modeling.

c. Refined models such as those in section4.0 are recommended for PM–10 and largeparticles. However, where possible, particlesize, gas-to-particle formation, and their ef-fect on ambient concentrations may be con-sidered. For urban-wide refined analysesCDM 2.0 (long term) or RAM (short term)should be used. ISC is recommended for pointsources of small particles and for source-spe-cific analyses of complicated sources. Nomodel recommended for general use at thistime accounts for secondary particulate for-mation or other transformations in a man-ner suitable for SIP control strategy dem-onstrations. Where possible, the use of recep-tor models 38 39 105 106 107 in conjunction withdispersion models is encouraged to more pre-cisely characterize the emissions inventory

and to validate source specific impacts cal-culated by the dispersion model. A SIP de-velopment guideline,108 model reconciliationguidance,106 and an example model applica-tion 109 are available to assist in PM–10 anal-yses and control strategy development.

d. Under certain conditions, recommendeddispersion models are not available or appli-cable. In such circumstances, the modelingapproach should be approved by the appro-priate Regional Office on a case-by-casebasis. For example, where there is no rec-ommended air quality model and areasources are a predominant component ofPM–10, an attainment demonstration may bebased on rollback of the apportionment de-rived from two reconciled receptor models, ifthe strategy provides a conservative dem-onstration of attainment. At this time, anal-yses involving model calculations for dis-tances beyond 50km and under stagnationconditions should also be justified on a case-by-case basis (see sections 7.2.6 and 8.2.10).

e. As an aid to assessing the impact on am-bient air quality of particulate matter gen-erated from prescribed burning activities,reference 110 is available.

7.2.3 Lead

a. The air quality analyses required forlead implementation plans are given in§§ 51.83, 51.84 and 51.85. Sections 51.83 and51.85 require the use of a modified rollbackmodel as a minimum to demonstrate attain-ment of the lead air quality standard but theuse of a dispersion model is the preferred ap-proach. Section 51.83 requires the analysis ofan entire urban area if the measured leadconcentration in the urbanized area exceedsa quarterly (three month) average of 4.0 µg/m3. Section 51.84 requires the use of a disper-sion model to demonstrate attainment of thelead air quality standard around specifiedlead point sources. For other areas reportinga violation of the lead standard, § 51.85 re-quires an analysis of the area in the vicinityof the monitor reporting the violation. TheNAAQS for lead is a quarterly (three month)average, thus requiring the use of modelingtechniques that can provide long-term con-centration estimates.

b. The SIP should contain an air qualityanalysis to determine the maximum quar-terly lead concentration resulting frommajor lead point sources, such as smelters,gasoline additive plants, etc. For these appli-cations the ISC model is preferred, since themodel can account for deposition of particlesand the impact of fugitive emissions. If thesource is located in complicated terrain or issubject to unusual climatic conditions, acase-specific review by the appropriate Re-gional Office may be required.

c. In modeling the effect of traditional linesources (such as a specific roadway or high-way) on lead air quality, dispersion models


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b § 51.300–307.c § 51.300–307.

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

applied for other pollutants can be used. Dis-persion models such as CALINE3 have beenwidely used for modeling carbon monoxideemissions from highways. However, wheredeposition is of concern, the line sourcetreatment in ISC may be used. Also, wherethere is a point source in the middle of a sub-stantial road network, the lead concentra-tions that result from the road networkshould be treated as background (see section9.2); the point source and any nearby majorroadways should be modeled separately usingthe ISC model.

d. To model an entire major urban area orto model areas without significant sources oflead emissions, as a minimum a proportional(rollback) model may be used for air qualityanalysis. The rollback philosophy assumesthat measured pollutant concentrations areproportional to emissions. However, urban orother dispersion models are encouraged inthese circumstances where the use of suchmodels is feasible.

e. For further information concerning theuse of models in the development of lead im-plementation plans, the documents ‘‘Supple-mentary Guidelines for Lead Implementa-tion Plans,’’ 40 and ‘‘Updated Information onApproval and Promulgation of Lead Imple-mentation Plans,’’ 41 should be consulted.

7.2.4. Visibility

a. The visibility regulations as promul-gated in December 1980 b require consider-ation of the effect of new sources on the visi-bility values of Federal Class I areas. Thestate of scientific knowledge concerningidentifying, monitoring, modeling, and con-trolling visibility impairment is containedin an EPA report ‘‘Protecting Visibility: AnEPA Report to Congress’’.42 In 1985, EPA pro-mulgated Federal Implementation Plans(FIPs) for States without approved visibilityprovisions in their SIPs. A monitoring planwas established as part of the FIPs.c

b. Guidance and a screening model,VISCREEN, is contained in the EPA docu-ment ‘‘Workbook for Plume Visual ImpactScreening and Analysis (Revised).’’ 43

VISCREEN can be used to calculate the po-tential impact of a plume of specified emis-sions for specific transport and dispersionconditions. If a more comprehensive analysisis required, any refined model should be se-lected in consultation with the EPA Re-gional Office and the appropriate FederalLand Manager who is responsible for deter-mining whether there is an adverse effect bya plume on a Class I area.

c. PLUVUE II, listed in appendix B, may beapplied on a case-by-case basis when refinedplume visibility evaluations are needed.

Plume visibility models have been evaluatedagainst several data sets.44, 45

7.2.5 Good Engineering Practice StackHeight

a. The use of stack height credit in excessof Good Engineering Practice (GEP) stackheight or credit resulting from any other dis-persion technique is prohibited in the devel-opment of emission limitations by §§ 51.118and 51.164. The definitions of GEP stackheight and dispersion technique are con-tained in § 51.100. Methods and procedures formaking the appropriate stack height cal-culations, determining stack height creditsand an example of applying those techniquesare found in references 46, 47, 48, and 49.

b. If stacks for new or existing majorsources are found to be less than the heightdefined by EPA’s refined formula for deter-mining GEP height, d then air quality im-pacts associated with cavity or wake effectsdue to the nearby building structures shouldbe determined. Detailed downwash screeningprocedures 18 for both the cavity and wakeregions should be followed. If more refinedconcentration estimates are required, the In-dustrial Source Complex (ISC) model con-tains algorithms for building wake calcula-tions and should be used. Fluid modeling canprovide a great deal of additional informa-tion for evaluating and describing the cavityand wake effects.

7.2.6 Long Range Transport (LRT) (i.e.,beyond 50km)

a. Section 165(e) of the Clean Air Act re-quires that suspected significant impacts onPSD Class I areas be determined. However,50km is the useful distance to which mostGaussian models are considered accurate forsetting emission limits. Since in many casesPSD analyses may show that Class I areasmay be threatened at distances greater than50km from new sources, some procedure isneeded to (1) determine if a significant im-pact will occur, and (2) identify the model tobe used in setting an emission limit if theClass I increments are threatened (modelsfor this purpose should be approved for useon a case-by-case basis as required in section3.2). This procedure and the models selectedfor use should be determined in consultationwith the EPA Regional Office and the appro-priate Federal Land Manager (FLM). Whilethe ultimate decision on whether a Class Iarea is adversely affected is the responsi-bility of the permitting authority, the FLMhas an affirmative responsibility to protectair quality related values that may be af-fected.


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b. If LRT is determined to be important,then estimates utilizing an appropriate re-fined model for receptors at distances great-er than 50 km should be obtained.MESOPUFF II, listed in appendix B, may beapplied on a case-by-case basis when LRT es-timates are needed. Additional informationon applying this model is contained in theEPA document ‘‘A Modeling Protocol ForApplying MESOPUFF II to Long RangeTransport Problems’’. 111

7.2.7 Modeling Guidance for OtherGovernmental Programs

a. When using the models recommended ordiscussed in the Guideline in support of pro-grammatic requirements not specificallycovered by EPA regulations, the model usershould consult the appropriate Federal orState agency to ensure the proper applica-tion and use of that model. For modeling as-sociated with PSD permit applications thatinvolve a Class I area, the appropriate Fed-eral Land Manager should be consulted onall modeling questions.

b. The Offshore and Coastal Dispersion(OCD) model 112 was developed by the Min-erals Management Service and is rec-ommended for estimating air quality impactfrom offshore sources on onshore, flat ter-rain areas. The OCD model is not rec-ommended for use in air quality impact as-sessments for onshore sources. Sources lo-cated on or just inland of a shoreline wherefumigation is expected should be treated inaccordance with section 8.2.9.

c. The Emissions and Dispersion ModelingSystem (EDMS) 113 was developed by the Fed-eral Aviation Administration and the UnitedStates Air Force and is recommended for airquality assessment of primary pollutant im-pacts at airports or air bases. Regulatory ap-plication of EDMS is intended for estimatingthe cumulative effect of changes in aircraftoperations, point source, and mobile sourceemissions on pollutant concentrations. It isnot intended for PSD, SIP, or other regu-latory air quality analyses of point or mobilesources at or peripheral to airport propertythat are independent of changes in aircraftoperations. If changes in other than aircraftoperations are associated with analyses, amodel recommended in Chapter 4, 5, or 6should be used.

7.2.8 Air Pathway Analyses (Air Toxics andHazardous Waste)

a. Modeling is becoming an increasinglyimportant tool for regulatory control agen-cies to assess the air quality impact of re-leases of toxics and hazardous waste mate-rials. Appropriate screening techniques 114 115

for calculating ambient concentrations dueto various well-defined neutrally buoyanttoxic/hazardous pollutant releases are avail-able.

b. Several regulatory programs withinEPA have developed modeling techniquesand guidance for conducting air pathwayanalyses as noted in references 116–129. ISCforms the basis of the modeling proceduresfor air pathway analyses of many of theseregulatory programs and, where identified, isappropriate for obtaining refined ambientconcentration estimates of neutrally buoy-ant continuous air toxic releases from tradi-tional sources. Appendix A to the Guidelinecontains additional models appropriate forobtaining refined estimates of continuous airtoxic releases from traditional sources. Ap-pendix B contains models that may be usedon a case-by-case basis for obtaining refinedestimates of denser-than-air intermittentgaseous releases, e.g., DEGADIS; 130 guidancefor the use of such models is also avail-able. 131

c. Many air toxics models require input ofchemical properties and/or chemical engi-neering variables in order to appropriatelycharacterize the source emissions prior todispersion in the atmosphere; reference 132 isone source of helpful data. In addition, EPAhas numerous programs to determine emis-sion factors and other estimates of air toxicemissions. The Regional Office should beconsulted for guidance on appropriate emis-sion estimating procedures and any uncer-tainties that may be associated with them.

8.0 GENERAL MODELING CONSIDERATIONS

8.1 Discussion

a. This section contains recommendationsconcerning a number of different issues notexplicitly covered in other sections of thisguide. The topics covered here are not spe-cific to any one program or modeling areabut are common to nearly all modeling anal-yses.

8.2 Recommendations

8.2.1 Design Concentrations

8.2.1.1 Design Concentrations for CriteriaPollutants With Deterministic Standards

a. An air quality analysis for SO2, CO, Pb,and NO2 is required to determine if thesource will (1) Cause a violation of theNAAQS, or (2) cause or contribute to airquality deterioration greater than the speci-fied allowable PSD increment. For theformer, background concentration (see sec-tion 9.2) should be added to the estimatedimpact of the source to determine the designconcentration. For the latter, the designconcentration includes impact from all in-crement consuming sources.

b. If the air quality analyses are conductedusing the period of meteorological input datarecommended in section 9.3.1.2 (e.g., 5 yearsof NWS data or 1 year of site-specific data),then the design concentration based on the


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highest, second-highest short term con-centration or long term average, whicheveris controlling, should be used to determineemission limitations to assess compliancewith the NAAQS and to determine PSD in-crements.

c. When sufficient and representative dataexist for less than a 5-year period from anearby NWS site, or when on-site data havebeen collected for less than a full continuousyear, or when it has been determined thatthe on site data may not be temporally rep-resentative, then the highest concentrationestimate should be considered the designvalue. This is because the length of the datarecord may be too short to assure that theconditions producing worst-case estimateshave been adequately sampled. The highestvalue is then a surrogate for the concentra-tion that is not to be exceeded more thanonce per year (the wording of the deter-ministic standards). Also, the highest con-centration should be used whenever selectedworst-case conditions are input to a screen-ing technique. This specifically applies tothe use of techniques such as outlined in‘‘Screening Procedures for Estimating theAir Quality Impact of Stationary Sources,Revised’’. 18 Specific guidance for CO may befound in the ‘‘Guideline for Modeling CarbonMonoxide from Roadway Intersections’’. 34

d. If the controlling concentration is anannual average value and multiple years ofdata (on-site or NWS) are used, then the de-sign value is the highest of the annual aver-ages calculated for the individual years. Ifthe controlling concentration is a quarterlyaverage and multiple years are used, thenthe highest individual quarterly averageshould be considered the design value.

e. As long a period of record as possibleshould be used in making estimates to deter-mine design values and PSD increments. Ifmore than 1 year of site-specific data isavailable, it should be used.

8.2.1.2 Design Concentrations for CriteriaPollutants With Expected ExceedanceStandards

a. Specific instructions for the determina-tion of design concentrations for criteria pol-lutants with expected exceedance standards,ozone and PM–10, are contained in specialguidance documents for the preparation ofSIPs for those pollutants. 86 108 For all SIP re-visions the user should check with the Re-gional Office to obtain the most recent guid-ance documents and policy memoranda con-cerning the pollutant in question.

8.2.2 Critical Receptor Sites

a. Receptor sites for refined modelingshould be utilized in sufficient detail to esti-mate the highest concentrations and possibleviolations of a NAAQS or a PSD increment.In designing a receptor network, the empha-

sis should be placed on receptor resolutionand location, not total number of receptors.The selection of receptor sites should be acase-by-case determination taking into con-sideration the topography, the climatology,monitor sites, and the results of theinitialscreening procedure. For large sources(those equivalent to a 500MW power plant)and where violations of the NAAQS or PSDincrement are likely, 360 receptors for apolar coordinate grid system and 400 recep-tors for a rectangular grid system, where thedistance from the source to the farthest re-ceptor is 10km, are usually adequate to iden-tify areas of high concentration. Additionalreceptors may be needed in the high con-centration location if greater resolution isindicated by terrain or source factors.

8.2.3 Dispersion Coefficients

a. Gaussian models used in most applica-tions should employ dispersion coefficientsconsistent with those contained in the pre-ferred models in appendix A. Factors such asaveraging time, urban/rural surroundings,and type of source (point vs. line) may dic-tate the selection of specific coefficients.Generally, coefficients used in appendix Amodels are identical to, or at least based on,Pasquill-Gifford coefficients 50 in rural areasand McElroy-Pooler 51 coefficients in urbanareas.

b. Research is continuing toward the devel-opment of methods to determine dispersioncoefficients directly from measured or ob-served variables. 52 53 No method to date hasproved to be widely applicable. Thus, directmeasurement, as well as other dispersion co-efficients related to distance and stability,may be used in Gaussian modeling only if ademonstration can be made that such param-eters are more applicable and accurate forthe given situation than are algorithms con-tained in the preferred models.

c. Buoyancy-induced dispersion (BID), asidentified by Pasquill, 54 is included in thepreferred models and should be used wherebuoyant sources, e.g., those involving fuelcombustion, are involved.

8.2.4 Stability Categories

a. The Pasquill approach to classifying sta-bility is generally required in all preferredmodels (Appendix A). The Pasquill method,as modified by Turner, 55 was developed foruse with commonly observed meteorologicaldata from the National Weather Service andis based on cloud cover, insolation and windspeed.

b. Procedures to determine Pasquill sta-bility categories from other than NWS dataare found in subsection 9.3. Any other meth-od to determine Pasquill stability categoriesmust be justified on a case-by-case basis.

c. For a given model application where sta-bility categories are the basis for selecting


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dispersion coefficients, both σy and σz shouldbe determined from the same stability cat-egory. ‘‘Split sigmas’’ in that instance arenot recommended.

d. Sector averaging, which eliminates theσy term, is generally acceptable only to de-termine long term averages, such as seasonalor annual, and when the meteorologicalinput data are statistically summarized as inthe STAR summaries. Sector averaging is,however, commonly acceptable in complexterrain screening methods.

8.2.5 Plume Rise

a. The plume rise methods of Briggs 56 57 areincorporated in the preferred models and arerecommended for use in all modeling appli-cations. No provisions in these models aremade for fumigation or multistack plumerise enhancement or the handling of suchspecial plumes as flares; these problemsshould be considered on a case-by-case basis.

b. Since there is insufficient informationto identify and quantify dispersion duringthe transitional plume rise period, gradualplume rise is not generally recommended foruse. There are two exceptions where the useof gradual plume rise is appropriate: (1) Incomplex terrain screening procedures to de-termine close-in impacts; (2) when calcu-lating the effects of building wakes. Thebuilding wake algorithm in the ISC modelincorporates and automatically (i.e., inter-nally) exercises the gradual plume rise cal-culations. If the building wake is calculatedto affect the plume for any hour, gradualplume rise is also used in downwind disper-sion calculations to the distance of finalplume rise, after which final plume rise isused.

c. Stack tip downwash generally occurswith poorly constructed stacks and when theratio of the stack exit velocity to wind speedis small. An algorithm developed by Briggs(Hanna et al.) 57 is the recommended tech-nique for this situation and is found in thepoint source preferred models.

d. Where aerodynamic downwash occursdue to the adverse influence of nearby struc-tures, the algorithms included in the ISCmodel 58 should be used.

8.2.6 Chemical Transformation

a. The chemical transformation of SO2

emitted from point sources or single indus-trial plants in rural areas is generally as-sumed to be relatively unimportant to theestimation of maximum concentrationswhen travel time is limited to a few hours.However, in urban areas, where synergisticeffects among pollutants are of considerableconsequence, chemical transformation ratesmay be of concern. In urban area applica-tions, a half-life of 4 hours 55 may be appliedto the analysis of SO2 emissions. Calcula-tions of transformation coefficients from

site-specific studies can be used to define a‘‘half-life’’ to be used in a Gaussian modelwith 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 anal-yses.

b. Complete conversion of NO to NO2

should be assumed for all travel time whensimple screening techniques are used tomodel point source emissions of nitrogen ox-ides. If a Gaussian model is used, and dataare available on seasonal variations in max-imum ozone concentrations, the Ozone Lim-iting Method 36 is recommended. In refinedanalyses, case-by case conversion rates basedon technical studies appropriate to the sitein question may be used. The use of more so-phisticated modeling techniques should bejustified for individual cases.

c. Use of models incorporating complexchemical mechanisms should be consideredonly on a case-by-case basis with properdemonstration of applicability. These aregenerally regional models not designed forthe evaluation of individual sources but usedprimarily for region-wide evaluations. Visi-bility models also incorporate chemicaltransformation mechanisms which are an in-tegral part of the visibility model itself andshould be used in visibility assessments.

8.2.7 Gravitational Settling and Deposition

a. An ‘‘infinite half-life’’ should be used forestimates of particle concentrations whenGaussian models containing only expo-nential decay terms for treating settling anddeposition are used.

b. Gravitational settling and depositionmay be directly included in a model if eitheris a significant factor. One preferred model(ISC) contains a settling and deposition algo-rithm and is recommended for use when par-ticulate matter sources can be quantifiedand settling and deposition are problems.

8.2.8 Urban/Rural Classification

a. The selection of either rural or urbandispersion coefficients in a specific applica-tion should follow one of the procedures sug-gested by Irwin 59 and briefly describedbelow. These include a land use classifica-tion procedure or a population based proce-dure to determine whether the character ofan area is primarily urban or rural.

b. Land Use Procedure: (1) Classify theland use within the total area, Ao, cir-cumscribed by a 3km radius circle about thesource using the meteorological land usetyping scheme proposed by Auer 60; (2) if landuse types I1, I2, C1, R2, and R3 account for 50percent or more of Ao, use urban dispersioncoefficients; otherwise, use appropriate ruraldispersion coefficients.

c. Population Density Procedure: (1) Com-pute the average population density, p

¯per


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square kilometer with Ao as defined above;(2) If p

¯is greater than 750 people/km2, use

urban dispersion coefficients; otherwise useappropriate rural dispersion coefficients.

d. Of the two methods, the land use proce-dure is considered more definitive. Popu-lation density should be used with cautionand should not be applied to highly industri-alized areas where the population densitymay be low and thus a rural classificationwould be indicated, but the area is suffi-ciently built-up so that the urban land usecriteria would be satisfied. In this case, theclassification should already be ‘‘urban’’ andurban dispersion parameters should be used.

e. Sources located in an area defined asurban should be modeled using urban disper-sion parameters. Sources located in areas de-fined as rural should be modeled using therural dispersion parameters. For analyses ofwhole urban complexes, the entire areashould be modeled as an urban region if mostof the sources are located in areas classifiedas urban.

8.2.9 Fumigation

a. Fumigation occurs when a plume (ormultiple plumes) is emitted into a stablelayer of air and that layer is subsequentlymixed to the ground either through convec-tive transfer of heat from the surface or be-cause of advection to less stable sur-roundings. Fumigation may cause exces-sively high concentrations but is usuallyrather short-lived at a given receptor. Thereare no recommended refined techniques tomodel this phenomenon. There are, however,screening procedures (see ‘‘Screening Proce-dures for Estimating the Air Quality Impactof Stationary Sources’’ 18) that may be usedto approximate the concentrations. Consid-erable care should be exercised in using theresults obtained from the screening tech-niques.

b. Fumigation is also an important phe-nomenon on and near the shoreline of bodiesof water. This can affect both individualplumes and area-wide emissions. When fumi-gation conditions are expected to occur froma source or sources with tall stacks locatedon or just inland of a shoreline, this shouldbe addressed in the air quality modelinganalysis. The Shoreline Dispersion Model(SDM) listed in appendix B may be appliedon a case-by-case basis when air quality esti-mates under shoreline fumigation conditionsare needed.133 Information on the results ofEPA’s evaluation of this model togetherwith other coastal fumigation models maybe found in reference 134. Selection of the ap-propriate model for applications whereshoreline fumigation is of concern should bedetermined in consultation with the Re-gional Office.

8.2.10 Stagnation

a. Stagnation conditions are characterizedby calm or very low wind speeds, and vari-able wind directions. These stagnant mete-orological conditions may persist for severalhours to several days. During stagnationconditions, the dispersion of air pollutants,especially those from low-level emissionssources, tends to be minimized, potentiallyleading to relatively high ground-level con-centrations.

b. When stagnation periods such as theseare found to occur, they should be addressedin the air quality modeling analysis.WYNDvalley, listed in appendix B, may beapplied on a case-by-case basis for stagna-tion periods of 24 hours or longer in valley-type situations. Caution should be exercisedwhen applying the model to elevated pointsources. Users should consult with the appro-priate Regional Office prior to regulatory ap-plication of WYNDvalley.

8.2.11 Calibration of Models

a. Calibration of long term multi-sourcemodels has been a widely used procedureeven though the limitations imposed by sta-tistical theory on the reliability of the cali-bration process for long term estimates arewell known. 61 In some cases, where a moreaccurate model is not available, calibrationmay be the best alternative for improvingthe accuracy of the estimated concentra-tions needed for control strategy evalua-tions.

b. Calibration of short term models is notcommon practice and is subject to muchgreater error and misunderstanding. Therehave been attempts by some to compareshort term estimates and measurements onan event-by-event basis and then to calibratea model with results of that comparison.This approach is severely limited by uncer-tainties in both source and meteorologicaldata and therefore it is difficult to preciselyestimate the concentration at an exact loca-tion for a specific increment of time. Suchuncertainties make calibration of short termmodels of questionable benefit. Therefore,short term model calibration is unaccept-able.

9.0 MODEL INPUT DATA

a. Data bases and related procedures for es-timating input parameters are an integralpart of the modeling procedure. The most ap-propriate data available should always be se-lected for use in modeling analyses. Con-centrations can vary widely depending onthe source data or meteorological data used.Input data are a major source of inconsist-encies in any modeling analysis. This sectionattempts to minimize the uncertainty asso-ciated with data base selection and use byidentifying requirements for data used in


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e Malfunctions which may result in excessemissions are not considered to be a normaloperating condition. They generally shouldnot be considered in determining allowableemissions. However, if the excess emissionsare the result of poor maintenance, carelessoperation, or other preventable conditions, itmay be necessary to consider them in deter-mining source impact.

modeling. A checklist of input data require-ments for modeling analyses is included asappendix C. More specific data requirementsand the format required for the individualmodels are described in detail in the users’guide for each model.

9.1 Source Data

9.1.1 Discussion

a. Sources of pollutants can be classified aspoint, line and area/volume sources. Pointsources are defined in terms of size and mayvary between regulatory programs. The linesources most frequently considered are road-ways and streets along which there are well-defined movements of motor vehicles, butthey may be lines of roof vents or stackssuch as in aluminum refineries. Area andvolume sources are often collections of amultitude of minor sources with individuallysmall emissions that are impractical to con-sider as separate point or line sources. Largearea sources are typically treated as a gridnetwork of square areas, with pollutantemissions distributed uniformly within eachgrid square.

b. Emission factors are compiled in an EPApublication commonly known as AP–42 62; anindication of the quality and amount of dataon which many of the factors are based isalso provided. Other information concerningemissions is available in EPA publicationsrelating to specific source categories. TheRegional Office should be consulted to deter-mine appropriate source definitions and forguidance concerning the determination ofemissions from and techniques for modelingthe various source types.


a. For point source applications the load oroperating condition that causes maximumground-level concentrations should be estab-lished. As a minimum, the source should bemodeled using the design capacity (100 per-cent load). If a source operates at greaterthan design capacity for periods that couldresult in violations of the standards or PSDincrements, this load e should be modeled.Where the source operates at substantiallyless than design capacity, and the changes inthe stack parameters associated with the op-erating conditions could lead to higherground level concentrations, loads such as 50percent and 75 percent of capacity should

also be modeled. A range of operating condi-tions should be considered in screening anal-yses; the load causing the highest concentra-tion, in addition to the design load, shouldbe included in refined modeling. For a powerplant, the following paragraphs b through hof this section describe the typical kind ofdata on source characteristics and operatingconditions that may be needed. Generally,input data requirements for air quality mod-els necessitate the use of metric units; whereEnglish units are common for engineeringusage, a conversion to metric is required.

b. Plant layout. The connection scheme be-tween boilers and stacks, and the distanceand direction between stacks, building pa-rameters (length, width, height, location andorientation relative to stacks) for plantstructures which house boilers, controlequipment, and surrounding buildings withina distance of approximately five stackheights.

c. Stack parameters. For all stacks, thestack height and inside diameter (meters),and the temperature (K) and volume flowrate (actual cubic meters per second) or exitgas velocity (meters per second) for oper-ation at 100 percent, 75 percent and 50 per-cent load.

d. Boiler size. For all boilers, the associ-ated megawatts, 106 BTU/hr, and pounds ofsteam per hour, and the design and/or actualfuel consumption rate for 100 percent loadfor coal (tons/hour), oil (barrels/hour), andnatural gas (thousand cubic feet/hour).

e. Boiler parameters. For all boilers, thepercent excess air used, the boiler type (e.g.,wet bottom, cyclone, etc.), and the type offiring (e.g., pulverized coal, front firing,etc.).

f. Operating conditions. For all boilers, thetype, amount and pollutant contents of fuel,the total hours of boiler operation and theboiler capacity factor during the year, andthe percent load for peak conditions.

g. Pollution control equipment param-eters. For each boiler served and each pollut-ant affected, the type of emission controlequipment, the year of its installation, itsdesign efficiency and mass emission rate, thedata of the last test and the tested effi-ciency, the number of hours of operationduring the latest year, and the best engineer-ing estimate of its projected efficiency ifused in conjunction with coal combustion;data for any anticipated modifications or ad-ditions.

h. Data for new boilers or stacks. For allnew boilers and stacks under constructionand for all planned modifications to existingboilers or stacks, the scheduled date of com-pletion, and the data or best estimates avail-able for paragraphs b through g of this sec-tion above following completion of construc-tion or modification.

i. In stationary point source applicationsfor compliance with short term ambient


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standards, SIP control strategies should betested using the emission input shown ontable 9–1. When using a refined model,sources should be modeled sequentially withthese loads for every hour of the year. Toevaluate SIPs for compliance with quarterlyand annual standards, emission input datashown in table 9–1 should again be used.Emissions from area sources should gen-erally be based on annual average condi-tions. The source input information in eachmodel user’s guide should be carefully con-sulted and the checklist in appendix C shouldalso be consulted for other possible emissiondata that could be helpful. PSD NAAQS com-pliance demonstrations should follow theemission input data shown in table 9–2. Forpurposes of emissions trading, new source re-view and demonstrations, refer to currentEPA policy and guidance to establish inputdata.

j. Line source modeling of streets and high-ways requires data on the width of the road-way and the median strip, the types andamounts of pollutant emissions, the numberof lanes, the emissions from each lane andthe height of emissions. The location of the

ends of the straight roadway segmentsshould be specified by appropriate grid co-ordinates. Detailed information and data re-quirements for modeling mobile sources ofpollution are provided in the user’s manualsfor each of the models applicable to mobilesources.

k. The impact of growth on emissionsshould be considered in all modeling anal-yses covering existing sources. Increases inemissions due to planned expansion orplanned fuel switches should be identified.Increases in emissions at individual sourcesthat may be associated with a general indus-trial/commercial/residential expansion inmulti-source urban areas should also betreated. For new sources the impact ofgrowth on emissions should generally be con-sidered for the period prior to the start-update for the source. Such changes in emis-sions should treat increased area sourceemissions, changes in existing point sourceemissions which were not subject topreconstruction review, and emissions due tosources with permits to construct that havenot yet started operation.

TABLE 9–1— MODEL EMISSION INPUT DATA FOR POINT SOURCES 1

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

hr/day)

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

Annual & quarterly .......... Maximum allowable emis-sion limit or federally en-forceable permit limit.

Actual or design capacity(whichever is greater), orfederally enforceable per-mit condition.

Actual operating factoraveraged over most re-cent 2 years.3

Short term ....................... Maximum allowable emis-sion limit or federally en-forceable permit limit.

Actual or design capacity(whichever is greater), orfederally enforceable per-mit condition 4.

Continuous operation, i.e.,all hours of each time pe-riod under consideration(for all hours of the mete-orological data base).5

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

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

Annual & quarterly .......... Maximum allowable emis-sion limit or Federal en-forceable permit limit.

Annual level when actuallyoperating, averaged overthe most recent 2years 3.

Actual operating factoraveraged over the mostrecent 2 years.3

Short term ....................... Maximum allowable emis-sion limit or federally en-forceable permit limit.

Annual level when actuallyoperating, averaged overthe most recent 2years 3.

Continuous operation, i.e.,all hours of each time pe-riod under consideration(for all hours of the mete-orological data base).5

1 The model input data requirements shown on this table apply to stationary source control strategies for STATE IMPLEMEN-TATION PLANS. For purposes of emissions trading, new source review, or prevention of significant deterioration, other modelinput criteria may apply. Refer to the policy and guidance for these programs to establish the input data.

2 Terminology applicable to fuel burning sources; analogous terminology (e.g., ⊥/throughput) may be used for other types ofsources.

3 Unless it is determined that this period is not representative.4 Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing the

highest concentration.5 If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24 hours) and the source operation is

constrained by a federally enforceable permit condition, an appropriate adjustment to the modeled emission rate may be made(e.g., if operation is only 8:00 a.m. to 4:00 p.m. each day, only these hours will be modeled with emissions from the source.Modeled emissions should not be averaged across non-operating time periods.)


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

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

hr/day)

Proposed Major New or Modified Source


Design capacity or federallyenforceable permit condi-tion.

Continuous operation (i.e.,8760 hours).2

Short term (≤ 24 hours) .. Maximum allowable emis-sion limit or federally en-forceable permit limit.

Design capacity or federallyenforceable permit condi-tion.3

Continuous operation (i.e.,all hours of each time pe-riod under consideration)(for all hours of the mete-orological data base).2

Nearby Background Source(s) 4


Actual or design capacity(whichever is greater), orfederally enforceable per-mit condition.

Actual operating factoraveraged over the mostrecent 2 years.5 7


Actual or design capacity(whichever is greater), orfederally enforceable per-mit condition.3


Other Background Source(s) 6


Annual level when actuallyoperating, averaged overthe most recent 2 years.5

Actual operating factoraveraged over the mostrecent 2 years.5 7


Annual level when actuallyoperating, averaged overthe most recent 2 years.5


1 Terminology applicable to fuel burning sources; analogous terminology (e.g., ⊥/throughput) may be used for other types ofsources.

2hnsp;If operation does not occur for all hours of the time period of consideration (e.g., 3 or 24 hours) and the source oper-ation is constrained by a federally enforceable permit condition, an appropriate adjustment to the modeled emission rate may bemade (e.g., if operation is only 8:00 a.m. to 4:00 p.m. each day, only these hours will be modeled with emissions from thesource. Modeled emissions should not be averaged across non-operating time periods.

3 Operating levels such as 50 percent and 75 percent of capacity should also be modeled to determine the load causing thehighest concentration.

4 Includes existing facility to which modification is proposed if the emissions from the existing facility will not be affected by themodification. Otherwise use the same parameters as for major modification.

5 Unless it is determined that this period is not representative.6 Generally, the ambient impacts from non-nearby background sources can be represented by air quality data unless adequate

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

8760 hours) should be used.

9.2 Background Concentrations

9.2.1 Discussion

a. Background concentrations are an es-sential part of the total air quality con-centration to be considered in determiningsource impacts. Background air quality in-cludes pollutant concentrations due to: (1)natural sources; (2) nearby sources otherthan the one(s) currently under consider-ation; and (3) unidentified sources.

b. Typically, air quality data should beused to establish background concentrationsin the vicinity of the source(s) under consid-eration. The monitoring network used forbackground determinations should conform

to the same quality assurance and other re-quirements as those networks established forPSD purposes. 63 An appropriate data valida-tion procedure should be applied to the dataprior to use.

c. If the source is not isolated, it may benecessary to use a multi-source model to es-tablish the impact of nearby sources. Back-ground concentrations should be determinedfor each critical (concentration) averagingtime.


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f For purposes of PSD, the location of mon-itors as well as data quality assurance proce-dures must satisfy requirements listed in thePSD Monitoring Guidelines. 63

9.2.2 Recommendations (Isolated SingleSource)

a. Two options (paragraph b or c of thissection) are available to determine the back-ground concentration near isolated sources.

b. Use air quality data collected in the vi-cinity of the source to determine the back-ground concentration for the averagingtimes of concern.f Determine the mean back-ground concentration at each monitor by ex-cluding values when the source in question isimpacting the monitor. The mean annualbackground is the average of the annual con-centrations so determined at each monitor.For shorter averaging periods, the meteoro-logical conditions accompanying the con-centrations of concern should be identified.Concentrations for meteorological condi-tions of concern, at monitors not impactedby the source in question, should be averagedfor each separate averaging time to deter-mine the average background value. Moni-toring sites inside a 90° sector downwind ofthe source may be used to determine thearea of impact. One hour concentrations maybe added and averaged to determine longeraveraging periods.

c. If there are no monitors located in thevicinity of the source, a ‘‘regional site’’ maybe used to determine background. A ‘‘re-gional site’’ is one that is located away fromthe area of interest but is impacted by simi-lar natural and distant man-made sources.

9.2.3 Recommendations (Multi-Source Areas)

a. In multi-source areas, two componentsof background should be determined.

b. Nearby Sources: All sources expected tocause a significant concentration gradient inthe vicinity of the source or sources underconsideration for emission limit(s) should beexplicitly modeled. For evaluation for com-pliance with the short term and annual am-bient standards, the nearby sources shouldbe modeled using the emission input datashown in table 9–1 or 9–2. The number of suchsources is expected to be small except in un-usual situations. The nearby source inven-tory should be determined in consultationwith the reviewing authority. It is envi-sioned that the nearby sources and thesources under consideration will be evalu-ated together using an appropriate appendixA model.

c. The impact of the nearby sources shouldbe examined at locations where interactionsbetween the plume of the point source underconsideration and those of nearby sources(plus natural background) can occur. Signifi-cant locations include: (1) the area of max-imum impact of the point source; (2) the area

of maximum impact of nearby sources; and(3) the area where all sources combine tocause maximum impact. These locationsmay be identified through trial and erroranalyses.

d. Other Sources: That portion of the back-ground attributable to all other sources (e.g.,natural sources, minor sources and distantmajor sources) should be determined by theprocedures found in section 9.2.2 or by appli-cation of a model using table 9–1 or 9–2.

9.3 Meteorological Input Data

a. The meteorological data used as input toa dispersion model should be selected on thebasis of spatial and climatological (tem-poral) representativeness as well as the abil-ity of the individual parameters selected tocharacterize the transport and dispersionconditions in the area of concern. The rep-resentativeness of the data is dependent on:(1) the proximity of the meteorological mon-itoring site to the area under consideration;(2) the complexity of the terrain; (3) the ex-posure of the meteorological monitoringsite; and (4) the period of time during whichdata are collected. The spatial representa-tiveness of the data can be adversely affectedby large distances between the source and re-ceptors of interest and the complex topo-graphic characteristics of the area. Tem-poral representativeness is a function of theyear-to-year variations in weather condi-tions.

b. Model input data are normally obtainedeither from the National Weather Service oras part of an on-site measurement program.Local universities, Federal Aviation Admin-istration (FAA), military stations, industryand pollution control agencies may also besources of such data. Some recommendationsfor the use of each type of data are includedin this section 9.3.

9.3.1 Length of Record of MeteorologicalData

9.3.1.1 Discussion

a. The model user should acquire enoughmeteorological data to ensure that worst-case meteorological conditions are ade-quately represented in the model results.The trend toward statistically based stand-ards suggests a need for all meteorologicalconditions to be adequately represented inthe data set selected for model input. Thenumber of years of record needed to obtain astable distribution of conditions depends onthe variable being measured and has been es-timated by Landsberg and Jacobs 64 for var-ious parameters. Although that study indi-cates in excess of 10 years may be required toachieve stability in the frequency distribu-tions of some meteorological variables, suchlong periods are not reasonable for modelinput data. This is due in part to the factthat hourly data in model input format are


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frequently not available for such periods andthat hourly calculations of concentration forlong periods are prohibitively expensive. Arecent study 65 compared various periodsfrom a 17-year data set to determine theminimum number of years of data needed toapproximate the concentrations modeledwith a 17-year period of meteorological datafrom one station. This study indicated thatthe variability of model estimates due to themeteorological data input was adequatelyreduced if a 5-year period of record of mete-orological input was used.


a. Five years of representative meteorolog-ical data should be used when estimatingconcentrations with an air quality model.Consecutive years from the most recent,readily available 5-year period are preferred.The meteorological data may be data col-lected either onsite or at the nearest Na-tional Weather Service (NWS) station. If thesource is large, e.g., a 500MW power plant,the use of 5 years of NWS meteorologicaldata or at least 1 year of site-specific data isrequired.

b. If one year or more, up to five years, ofsite-specific data is available, these data arepreferred for use in air quality analyses.Such data should have been subjected toquality assurance procedures as described insection 9.3.3.2.

c. For permitted sources whose emissionlimitations are based on a specific year ofmeteorological data that year should beadded to any longer period being used (e.g., 5years of NWS data) when modeling the facil-ity at a later time.

9.3.2 National Weather Service Data

9.3.2.1 Discussion

a. The National Weather Service (NWS)meteorological data are routinely availableand familiar to most model users. Althoughthe NWS does not provide direct measure-ments of all the needed dispersion modelinput variables, methods have been devel-oped and successfully used to translate thebasic NWS data to the needed model input.Direct measurements of model input param-eters have been made for limited model stud-ies and those methods and techniques are be-coming more widely applied; however, mostmodel applications still rely heavily on theNWS data.

b. There are two standard formats of theNWS data for use in air quality models. Theshort term models use the standard hourlyweather observations available from the Na-tional Climatic Data Center (NCDC). Theseobservations are then ‘‘preprocessed’’ beforethey can be used in the models. ‘‘STAR’’summaries are available from NCDC for longterm model use. These are joint frequencydistributions of wind speed, direction and P–

G stability category. They are used as directinput to models such as the long termversion of ISC. 58


a. The preferred short term models listedin appendix A all accept as input the NWSmeteorological data preprocessed into modelcompatible form. Long-term (monthly sea-sonal or annual) preferred models use NWS‘‘STAR’’ summaries. Summarized concentra-tion estimates from the short term modelsmay also be used to develop long-term aver-ages; however, concentration estimatesbased on the two separate input data setsmay not necessarily agree.

b. Although most NWS measurements aremade at a standard height of 10 meters, theactual anemometer height should be used asinput to the preferred model.

c. National Weather Service wind direc-tions are reported to the nearest 10 degrees.A specific set of randomly generated num-bers has been developed for use with the pre-ferred EPA models and should be used to en-sure a lack of bias in wind direction assign-ments within the models.

d. Data from universities, FAA, militarystations, industry and pollution controlagencies may be used if such data are equiva-lent in accuracy and detail to the NWS data.

9.3.3 Site-Specific Data

9.3.3.1 Discussion

a. Spatial or geographical representative-ness is best achieved by collection of all ofthe needed model input data at the actualsite of the source(s). Site-specific measureddata are therefore preferred as model input,provided appropriate instrumentation andquality assurance procedures are followedand that the data collected are representa-tive (free from undue local or ‘‘micro’’ influ-ences) and compatible with the input re-quirements of the model to be used. How-ever, direct measurements of all the neededmodel input parameters may not be possible.This section discusses suggestions for thecollection and use of on-site data. Since themethods outlined in this section are stillbeing tested, comparison of the model pa-rameters derived using these site-specificdata should be compared at least on a spot-check basis, with parameters derived frommore conventional observations.

9.3.3.2 Recommendations: Site-specific DataCollection

a. The document ‘‘On-Site MeteorologicalProgram Guidance for Regulatory ModelingApplications’’ 66 provides recommendations


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on the collection and use of on-site meteoro-logical data. Recommendations on charac-teristics, siting, and exposure of meteorolog-ical instruments and on data recording, proc-essing, completeness requirements, report-ing, and archiving are also included. Thispublication should be used as a supplementto the limited guidance on these subjectsnow found in the ‘‘Ambient MonitoringGuidelines for Prevention of Significant De-terioration’’. 63 Detailed information on qual-ity assurance is provided in the ‘‘Quality As-surance Handbook for Air Pollution Meas-urement Systems: Volume IV’’. 67 As a min-imum, site-specific measurements of ambi-ent air temperature, transport wind speedand direction, and the parameters to deter-mine Pasquill-Gifford (P–G) stability cat-egories should be available in meteorologicaldata sets to be used in modeling. Care shouldbe taken to ensure that meteorological in-struments are located to provide representa-tive characterization of pollutant transportbetween sources and receptors of interest.The Regional Office will determine the ap-propriateness of the measurement locations.

b. All site-specific data should be reducedto hourly averages. Table 9–3 lists the windrelated parameters and the averaging timerequirements.

c. Solar Radiation Measurements. Totalsolar radiation should be measured with a re-liable pyranometer, sited and operated in ac-cordance with established on-site meteoro-logical guidance. 66

d. Temperature Measurements. Tempera-ture measurements should be made at stand-ard shelter height (2m) in accordance withestablished on-site meteorological guid-ance. 66

e. Temperature Difference Measurements.Temperature difference (ŒÆ) measurementsfor use in estimating P–G stability cat-egories using the solar radiation/delta-T(SRDT) methodology (see Stability Cat-egories) should be obtained using twomatched thermometers or a reliable thermo-couple system to achieve adequate accuracy.

f. Siting, probe placement, and operationof >T systems should be based on guidancefound in Chapter 3 of reference 66, and suchguidance should be followed when obtainingvertical temperature gradient data for use inplume rise estimates or in determining thecritical dividing streamline height.

g. Wind Measurements. For refined mod-eling applications in simple terrain situa-tions, if a source has a stack below 100m, se-lect the stack top height as the wind meas-urement height for characterization ofplume dilution and transport. For sourceswith stacks extending above 100m, a 100mtower is suggested unless the stack top issignificantly above 100m (i.e., ≥200m). Incases with stack tops ≥200m, remote sensingmay be a feasible alternative. In some cases,collection of stack top wind speed may be

impractical or incompatible with the inputrequirements of the model to be used. Insuch cases, the Regional Office should beconsulted to determine the appropriatemeasurement height.

h. For refined modeling applications incomplex terrain, multiple level (typicallythree or more) measurements of wind speedand direction, temperature and turbulence(wind fluctuation statistics) are required.Such measurements should be obtained up tothe representative plume height(s) of inter-est (i.e., the plume height(s) under those con-ditions important to the determination ofthe design concentration). The representa-tive plume height(s) of interest should be de-termined using an appropriate complex ter-rain screening procedure (e.g., CTSCREEN)and should be documented in the monitoring/modeling protocol. The necessary meteoro-logical measurements should be obtainedfrom an appropriately sited meteorologicaltower augmented by SODAR if the represent-ative plume height(s) of interest exceed100m. The meteorological tower need not ex-ceed the lesser of the representative plumeheight of interest (the highest plume heightif there is more than one plume height of in-terest) or 100m.

i. In general, the wind speed used in deter-mining plume rise is defined as the windspeed at stack top.

j. Specifications for wind measuring in-struments and systems are contained in the‘‘On-Site Meteorological Program Guidancefor Regulatory Modeling Applications’’. 66

k. Stability Categories. The P–G stabilitycategories, as originally defined, couplenear-surface measurements of wind speedwith subjectively determined insolation as-sessments based on hourly cloud cover andceiling height observations. The wind speedmeasurements are made at or near 10m. Theinsolation rate is typically assessed usingobservations of cloud cover and ceilingheight based on criteria outlined by Turn-er. 50 It is recommended that the P–G sta-bility category be estimated using the Turn-er method with site-specific wind speedmeasured at or near 10m and representativecloud cover and ceiling height. Implementa-tion of the Turner method, as well as consid-erations in determining representativenessof cloud cover and ceiling height in cases forwhich site-specific cloud observations areunavailable, may be found in section 6 of ref-erence 66. In the absence of requisite data toimplement the Turner method, the SRDTmethod or wind fluctuation statistics (i.e.,the σE and σA methods) may be used.

l. The SRDT method, described in section6.4.4.2 of reference 66, is modified slightlyfrom that published by Bowen et al. (1983) 136

and has been evaluated with three on-sitedata bases. 137 The two methods of stabilityclassification which use wind fluctuation


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statistics, the σE and σA methods, are also de-scribed in detail in section 6.4.4 of reference66 (note applicable tables in section 6). Foradditional information on the wind fluctua-tion methods, see references 68–72.

m. Hours in the record having missing datashould be treated according to an establisheddata substitution protocol and after validdata retrieval requirements have been met.Such protocols are usually part of the ap-proved monitoring program plan. Data sub-stitution guidance is provided in section 5.3of reference 66.

n. Meteorological Data Processors. The fol-lowing meteorological preprocessors are rec-ommended by EPA: RAMMET, PCRAMMET,STAR, PCSTAR, MPRM, 135 and METPRO. 24

RAMMET is the recommended meteorolog-ical preprocessor for use in applications em-ploying hourly NWS data. The RAMMET for-mat is the standard data input format usedin sequential Gaussian models recommendedby EPA. PCRAMMET 138 is the PC equivalentof the mainframe version (RAMMET). STARis the recommended preprocessor for use inapplications employing joint frequency dis-tributions (wind direction and wind speed bystability class) based on NWS data. PCSTARis the PC equivalent of the mainframeversion (STAR). MPRM is the recommendedpreprocessor for use in applications employ-ing on-site meteorological data. The latestversion (MPRM 1.3) has been configured toimplement the SRDT method for estimatingP–G stability categories. MPRM is a generalpurpose meteorological data preprocessorwhich supports regulatory models requiringRAMMET formatted data and STAR for-matted data. In addition to on-site data,MPRM provides equivalent processing ofNWS data. METPRO is the required meteoro-logical data preprocessor for use withCTDMPLUS. All of the above mentioneddata preprocessors are available fordownloading from the SCRAM BBS. 19

TABLE 9–3—AVERAGING TIMES FOR SITE-SPE-CIFIC WIND AND TURBULENCE MEASUREMENTS

Parameter Averagingtime

Surface wind speed (for use in stability de-terminations).

1-hr.

Transport direction ...................................... 1-hr.Dilution wind speed ..................................... 1-hr.Turbulence measurements (σE and σA) for

use in stability determinations.1-hr.1

1 To minimize meander effects in σA when wind conditionsare light and/or variable, determine the hourly average σvalue from four sequential 15-minute σ’s according to the fol-lowing formula:

σσ σ σ σ

115

215

215

215

2

4-hr =+ + +

9.3.4 Treatment of Calms

9.3.4.1 Discussion

a. Treatment of calm or light and variablewind poses a special problem in model appli-cations since Gaussian models assume thatconcentration is inversely proportional towind speed. Furthermore, concentrations be-come unrealistically large when wind speedsless than 1 m/s are input to the model. A pro-cedure has been developed for use with NWSdata to prevent the occurrence of overly con-servative concentration estimates during pe-riods of calms. This procedure acknowledgesthat a Gaussian plume model does not applyduring calm conditions and that our knowl-edge of plume behavior and wind patternsduring these conditions does not, at present,permit the development of a better tech-nique. Therefore, the procedure disregardshours which are identified as calm. The houris treated as missing and a convention forhandling missing hours is recommended.

b. Preprocessed meteorological data inputto most appendix A EPA models substitute a1.00 m/s wind speed and the previous direc-tion for the calm hour. The new treatment ofcalms in those models attempts to identifythe original calm cases by checking for a 1.00m/s wind speed coincident with a wind direc-tion equal to the previous hour’s wind direc-tion. Such cases are then treated in a pre-scribed manner when estimating short termconcentrations.


a. Hourly concentrations calculated withGaussian models using calms should not beconsidered valid; the wind and concentrationestimates for these hours should be dis-regarded and considered to be missing. Crit-ical concentrations for 3-, 8-, and 24-houraverages should be calculated by dividingthe sum of the hourly concentration for theperiod by the number of valid or non-missinghours. If the total number of valid hours isless than 18 for 24-hour averages, less than 6for 8-hour averages or less than 3 for 3-houraverages, the total concentration should bedivided by 18 for the 24-hour average, 6 forthe 8-hour average and 3 for the 3-hour aver-age. For annual averages, the sum of allvalid hourly concentrations is divided by thenumber of non-calm hours during the year. Apost-processor computer program,CALMPRO 73 has been prepared followingthese instructions and has been coded inRAM and ISC.

b. The recommendations in paragraph a ofthis section apply to the use of calms forshort term averages and do not apply to thedetermination of long term averages using‘‘STAR’’ data summaries. Calms should con-tinue to be included in the preparation of‘‘STAR’’ summaries. A treatment for calms


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and very light winds is built into the soft-ware that produces the ‘‘STAR’’ summaries.

c. Stagnant conditions, including extendedperiods of calms, often produce high con-centrations over wide areas for relativelylong averaging periods. The standard shortterm Gaussian models are often not applica-ble to such situations. When stagnation con-ditions are of concern, other modeling tech-niques should be considered on a case-by-case basis (see also section 8.2.10).

d. When used in Gaussian models, meas-ured on-site wind speeds of less than 1 m/sbut higher than the response threshold of theinstrument should be input as 1 m/s; the cor-responding wind direction should also beinput. Observations below the responsethreshold of the instrument are also set to 1m/s but the wind direction from the previoushour is used. If the wind speed or directioncan not be determined, that hour should betreated as missing and short term averagesshould then be calculated as described inparagraph a of this section.

10.0 ACCURACY AND UNCERTAINTY OF MODELS

10.1 Discussion

a. Increasing reliance has been placed onconcentration estimates from models as theprimary basis for regulatory decisions con-cerning source permits and emission controlrequirements. In many situations, such asreview of a proposed source, no practical al-ternative exists. Therefore, there is an obvi-ous need to know how accurate models reallyare and how any uncertainty in the esti-mates affects regulatory decisions. EPA rec-ognizes the need for incorporating such in-formation and has sponsored workshops 11 74

on model accuracy, the possible ways toquantify accuracy, and on considerations inthe incorporation of model accuracy and un-certainty in the regulatory process. The Sec-ond (EPA) Conference on Air Quality Mod-eling, August 1982,75 was devoted to that sub-ject.

10.1.1 Overview of Model Uncertainty

a. Dispersion models generally attempt toestimate concentrations at specific sitesthat really represent an ensemble average ofnumerous repetitions of the same event. Theevent is characterized by measured or‘‘known’’ conditions that are input to themodels, e.g., wind speed, mixed layer height,surface heat flux, emission characteristics,etc. However, in addition to the known con-ditions, there are unmeasured or unknownvariations in the conditions of this event,e.g., unresolved details of the atmosphericflow such as the turbulent velocity field.These unknown conditions may vary amongrepetitions of the event. As a result, devi-ations in observed concentrations from theirensemble average, and from the concentra-tions estimated by the model, are likely to

occur even though the known conditions arefixed. Even with a perfect model that pre-dicts the correct ensemble average, there arelikely to be deviations from the observedconcentrations in individual repetitions ofthe event, due to variations in the unknownconditions. The statistics of these concentra-tion residuals are termed ‘‘inherent’’ uncer-tainty. Available evidence suggests that thissource of uncertainty alone may be respon-sible for a typical range of variation in con-centrations of as much as #50 percent. 76

b. Moreover, there is ‘‘reducible’’ uncer-tainty 77 associated with the model and itsinput conditions; neither models nor databases are perfect. Reducible uncertaintiesare caused by: (1) Uncertainties in the inputvalues of the known conditions—emissioncharacteristics and meteorological data; (2)errors in the measured concentrations whichare used to compute the concentration re-siduals; and (3) inadequate model physics andformulation. The ‘‘reducible’’ uncertaintiescan be minimized through better (more accu-rate and more representative) measurementsand better model physics.

c. To use the terminology correctly, ref-erence to model accuracy should be limitedto that portion of reducible uncertaintywhich deals with the physics and the formu-lation of the model. The accuracy of themodel is normally determined by an evalua-tion procedure which involves the compari-son of model concentration estimates withmeasured air quality data. 78 The statementof accuracy is based on statistical tests orperformance measures such as bias, noise,correlation, etc. 11 However, information thatallows a distinction between contributions ofthe various elements of inherent and reduc-ible uncertainty is only now beginning toemerge. As a result most discussions of theaccuracy of models make no quantitativedistinction between (1) Limitations of themodel versus (2) limitations of the data baseand of knowledge concerning atmosphericvariability. The reader should be aware thatstatements on model accuracy and uncer-tainty may imply the need for improvementsin model performance that even the ‘‘per-fect’’ model could not satisfy.

10.1.2 Studies of Model Accuracy

a. A number of studies 79 80 have been con-ducted to examine model accuracy, particu-larly with respect to the reliability of short-term concentrations required for ambientstandard and increment evaluations. The re-sults of these studies are not surprising. Ba-sically, they confirm what leading atmos-pheric scientists have said for some time: (1)Models are more reliable for estimatinglonger time-averaged concentrations thanfor estimating short-term concentrations atspecific locations; and (2) the models are rea-sonably reliable in estimating the magnitude


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of highest concentrations occurring some-time, somewhere within an area. For exam-ple, errors in highest estimated concentra-tions of #10 to 40 percent are found to be typ-ical, 81 i.e., certainly well within the oftenquoted factor-of-two accuracy that has longbeen recognized for these models. However,estimates of concentrations that occur at aspecific time and site, are poorly correlatedwith actually observed concentrations andare much less reliable.

b. As noted in paragraph a of this section,poor correlations between paired concentra-tions at fixed stations may be due to ‘‘reduc-ible’’ uncertainties in knowledge of the pre-cise plume location and to unquantified in-herent uncertainties. For example,Pasquill 82 estimates that, apart from datainput errors, maximum ground-level con-centrations at a given hour for a pointsource in flat terrain could be in error by 50percent due to these uncertainties. Uncer-tainty of five to 10 degrees in the measuredwind direction, which transports the plume,can result in concentration errors of 20 to 70percent for a particular time and location,depending on stability and station location.Such uncertainties do not indicate that anestimated concentration does not occur, onlythat the precise time and locations are indoubt.

10.1.3 Use of Uncertainty in Decision-Making

a. The accuracy of model estimates varieswith the model used, the type of application,and site-specific characteristics. Thus, it isdesirable to quantify the accuracy or uncer-tainty associated with concentration esti-mates used in decision-making. Communica-tions between modelers and decision-makersmust be fostered and further developed. Com-munications concerning concentration esti-mates currently exist in most cases, but thecommunications dealing with the accuracyof models and its meaning to the decision-maker are limited by the lack of a technicalbasis for quantifying and directly includinguncertainty in decisions. Procedures forquantifying and interpreting uncertainty inthe practical application of such conceptsare only beginning to evolve; much study isstill required.74 75 77

b. In all applications of models an effort isencouraged to identify the reliability of themodel estimates for that particular area andto determine the magnitude and sources oferror associated with the use of the model.The analyst is responsible for recognizingand quantifying limitations in the accuracy,precision and sensitivity of the procedure.Information that might be useful to the deci-sion-maker in recognizing the seriousness ofpotential air quality violations includes suchmodel accuracy estimates as accuracy ofpeak predictions, bias, noise, correlation,frequency distribution, spatial extent of high

concentration, etc. Both space/time pairingof estimates and measurements and unpairedcomparisons are recommended. Emphasisshould be on the highest concentrations andthe averaging times of the standards or in-crements of concern. Where possible, con-fidence intervals about the statistical valuesshould be provided. However, while such in-formation can be provided by the modeler tothe decision-maker, it is unclear how this in-formation should be used to make an air pol-lution control decision. Given a range of pos-sible outcomes, it is easiest and tends to en-sure consistency if the decision-maker con-fines his judgment to use of the ‘‘best esti-mate’’ provided by the modeler (i.e., the de-sign concentration estimated by a model rec-ommended in the Guideline or an alternatemodel of known accuracy). This is an indica-tion of the practical limitations imposed bycurrent abilities of the technical commu-nity.

c. To improve the basis for decision-mak-ing, EPA has developed and is continuing tostudy procedures for determining the accu-racy of models, quantifying the uncertainty,and expressing confidence levels in decisionsthat are made concerning emissions con-trols.83 84 However, work in this area involves‘‘breaking new ground’’ with slow and spo-radic progress likely. As a result, it may benecessary to continue using the ‘‘best esti-mate’’ until sufficient technical progress hasbeen made to meaningfully implement suchconcepts dealing with uncertainty.

10.1.4 Evaluation of Models

a. A number of actions are being taken toensure that the best model is used correctlyfor each regulatory application and that amodel is not arbitrarily imposed. First, theGuideline clearly recommends the most ap-propriate model be used in each case. Pre-ferred models, based on a number of factors,are identified for many uses. General guid-ance on using alternatives to the preferredmodels is also provided. Second, all the mod-els in eight categories (i.e., rural, urban, in-dustrial complex, reactive pollutants, mobilesource, complex terrain, visibility and longrange transport) that are candidates for in-clusion in the Guideline are being subjectedto a systematic performance evaluation anda peer scientific review. 85 The same databases are being used to evaluate all modelswithin each of eight categories. Statisticalperformance measures, including measuresof difference (or residuals) such as bias, vari-ance of difference and gross variability ofthe difference, and measures of correlationsuch as time, space, and time and space com-bined as recommended by the AMS WoodsHole Workshop, 11 are being followed. The re-sults of the scientific review are being incor-porated in the Guideline and will be the basisfor future revision.12 13 Third, more specific


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information has been provided for justifyingthe site specific use of alternative models inthe documents ‘‘Interim Procedures for Eval-uating Air Quality Models’’, 15 and the ‘‘Pro-tocol for Determining the Best PerformingModel’’. 17 Together these documents providemethods that allow a judgment to be madeas to what models are most appropriate for aspecific application. For the present, per-formance and the theoretical evaluation ofmodels are being used as an indirect meansto quantify one element of uncertainty in airpollution regulatory decisions.

b. In addition to performance evaluation ofmodels, sensitivity analyses are encouragedsince they can provide additional informa-tion on the effect of inaccuracies in the databases and on the uncertainty in model esti-mates. Sensitivity analyses can aid in deter-mining the effect of inaccuracies of vari-ations or uncertainties in the data bases onthe range of likely concentrations. Such in-formation may be used to determine sourceimpact and to evaluate control strategies.Where possible, information from such sensi-tivity analyses should be made available tothe decision-maker with an appropriate in-terpretation of the effect on the critical con-centrations.

10.2 Recommendations

a. No specific guidance on the consider-ation of model uncertainty in decision-mak-ing is being given at this time. There is in-complete technical information on measuresof model uncertainty that are most relevantto the decision-maker. It is not clear how adecisionmaker could use such information,particularly given limitations of the CleanAir Act. As procedures for considering uncer-tainty develop and become implementable,this guidance will be changed and expanded.For the present, continued use of the ‘‘bestestimate’’ is acceptable and is consistentwith Clean Air Act requirements.

11.0 REGULATORY APPLICATION OF MODELS

11.1 Discussion

a. Procedures with respect to the reviewand analysis of air quality modeling anddata analyses in support of SIP revisions,PSD permitting or other regulatory require-ments need a certain amount of standardiza-tion to ensure consistency in the depth andcomprehensiveness of both the review andthe analysis itself. This section recommendsprocedures that permit some degree of stand-ardization while at the same time allowingthe flexibility needed to assure the tech-nically best analysis for each regulatory ap-plication.

b. Dispersion model estimates, especiallywith the support of measured air qualitydata, are the preferred basis for air qualitydemonstrations. Nevertheless, there are in-stances where the performance of rec-

ommended dispersion modeling techniques,by comparison with observed air qualitydata, may be shown to be less than accept-able. Also, there may be no recommendedmodeling procedure suitable for the situa-tion. In these instances, emission limitationsmay be established solely on the basis of ob-served air quality data as would be appliedto a modeling analysis. The same care shouldbe given to the analyses of the air qualitydata as would be applied to a modeling anal-ysis.

c. The current NAAQS for SO2 and CO areboth stated in terms of a concentration notto be exceeded more than once a year. Thereis only an annual standard for NO2 and aquarterly standard for Pb. The PM–10 andozone standards permit the exceedance of aconcentration on an average of not morethan once a year; the convention is to aver-age over a 3-year period.5 86 103 This rep-resents a change from a deterministic to amore statistical form of the standard andpermits some consideration to be given tounusual circumstances. The NAAQS are sub-jected to extensive review and possible revi-sion every 5 years.

d. This section discusses general require-ments for concentration estimates and iden-tifies the relationship to emission limits.The recommendations in section 11.2 applyto: (1) revisions of State ImplementationPlans; (2) the review of new sources and theprevention of significant deterioration(PSD); and (3) analyses of the emissionstrades (‘‘bubbles’’).

11.2 Recommendations

11.2.1 Analysis Requirements

a. Every effort should be made by the Re-gional Office to meet with all parties in-volved in either a SIP revision or a PSD per-mit application prior to the start of anywork on such a project. During this meeting,a protocol should be established between thepreparing and reviewing parties to define theprocedures to be followed, the data to be col-lected, the model to be used, and the anal-ysis of the source and concentration data.An example of requirements for such an ef-fort is contained in the Air Quality AnalysisChecklist included here as appendix C. Thischecklist suggests the level of detail re-quired to assess the air quality resultingfrom the proposed action. Special cases mayrequire additional data collection or analysisand this should be determined and agreedupon at this preapplication meeting. Theprotocol should be written and agreed uponby the parties concerned, although a formallegal document is not intended. Changes insuch a protocol are often required as thedata collection and analysis progresses. How-ever, the protocol establishes a common un-derstanding of the requirements.


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b. An air quality analysis should beginwith a screening model to determine the po-tential of the proposed source or controlstrategy to violate the PSD increment orNAAQS. It is recommended that the screen-ing techniques found in ‘‘Screening Proce-dures for Estimating the Air Quality Impactof Stationary Sources’’ 18 be used for pointsource analyses. Screening procedures forarea source analysis are discussed in ‘‘Apply-ing Atmospheric Simulation Models to AirQuality Maintenance Areas’’. 87 For mobilesource impact assessments the ‘‘Guidelinefor Modeling Carbon Monoxide from Road-way Intersections’’ 34 is available.

c. If the concentration estimates fromscreening techniques indicate that the PSDincrement or NAAQS may be approached orexceeded, then a more refined modeling anal-ysis is appropriate and the model user shouldselect a model according to recommenda-tions in sections 4.0–8.0. In some instances,no refined technique may be specified in thisguide for the situation. The model user isthen encouraged to submit a model devel-oped specifically for the case at hand. If thatis not possible, a screening technique maysupply the needed results.

d. Regional Offices should require permitapplicants to incorporate the pollutant con-tributions of all sources into their analysis.Where necessary this may include emissionsassociated with growth in the area of impactof the new or modified source’s impact. PSDair quality assessments should consider theamount of the allowable air quality incre-ment that has already been granted to anyother sources. Therefore, the most recentsource applicant should model the existingor permitted sources in addition to the onecurrently under consideration. This wouldpermit the use of newly acquired data or im-proved modeling techniques if such have be-come available since the last source was per-mitted. When remodeling, the worst caseused in the previous modeling analysisshould be one set of conditions modeled inthe new analysis. All sources should be mod-eled for each set of meteorological condi-tions selected and for all receptor sites usedin the previous applications as well as newsites specific to the new source.

11.2.2 Use of Measured Data in Lieu of ModelEstimates

a. Modeling is the preferred method for de-termining emission limitations for both newand existing sources. When a preferred modelis available, model results alone (includingbackground) are sufficient. Monitoring willnormally not be accepted as the sole basisfor emission limitation determination in flatterrain areas. In some instances when themodeling technique available is only ascreening technique, the addition of air qual-

ity data to the analysis may lend credence tomodel results.

b. There are circumstances where there isno applicable model, and measured data mayneed to be used. Examples of such situationsare: (1) complex terrain locations; (2) land/water interface areas; and (3) urban locationswith a large fraction of particulate emis-sions from nontraditional sources. However,only in the case of an existing source shouldmonitoring data alone be a basis for emis-sion limits. In addition, the following itemsshould be considered prior to the acceptanceof the measured data:

i. Does a monitoring network exist for thepollutants and averaging times of concern?

ii. Has the monitoring network been de-signed to locate points of maximum con-centration?

iii. Do the monitoring network and thedata reduction and storage procedures meetEPA monitoring and quality assurance re-quirements?

iv. Do the data set and the analysis allowimpact of the most important individualsources to be identified if more than onesource or emission point is involved?

v. Is at least one full year of valid ambientdata available?

vi. Can it be demonstrated through thecomparison of monitored data with model re-sults that available models are not applica-ble?

c. The number of monitors required is afunction of the problem being considered.The source configuration, terrain configura-tion, and meteorological variations all havean impact on number and placement of mon-itors. Decisions can only be made on a case-by-case basis. The Interim Procedures forEvaluating Air Quality Models 15 should beused in establishing criteria for dem-onstrating that a model is not applicable.

d. Sources should obtain approval from theRegional Office or reviewing authority forthe monitoring network prior to the start ofmonitoring. A monitoring protocol agreed toby all concerned parties is highly desirable.The design of the network, the number, typeand location of the monitors, the samplingperiod, averaging time as well as the need formeteorological monitoring or the use of mo-bile sampling or plume tracking techniques,should all be specified in the protocol andagreed upon prior to start-up of the network.

11.2.3 Emission Limits

11.2.3.1 Design Concentrations

a. Emission limits should be based on con-centration estimates for the averaging timethat results in the most stringent control re-quirements. The concentration used in speci-fying emission limits is called the designvalue or design concentration and is a sum ofthe concentration contributed by the sourceand the background concentration.


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b. To determine the averaging time for thedesign value, the most restrictive NationalAmbient Air Quality Standard (NAAQS)should be identified by calculating, for eachaveraging time, the ratio of the applicableNAAQS (S)¥ background (B) to the pre-dicted concentration (P) (i.e., (S¥B)/P). Theaveraging time with the lowest ratio identi-fies the most restrictive standard. If the an-nual average is the most restrictive, thehighest estimated annual average concentra-tion from one or a number of years of data isthe design value. When short term standardsare most restrictive, it may be necessary toconsider a broader range of concentrationsthan the highest value. For example, for pol-lutants such as SO2, the highest, second-highest concentration is the design value.For pollutants with statistically basedNAAQS, the design value is found by deter-mining the more restrictive of: (1) the short-term concentration that is not expected tobe exceeded more than once per year overthe period specified in the standard, or (2)the long-term concentration that is not ex-pected to exceed the long-term NAAQS. De-termination of design values for PM–10 ispresented in more detail in the ‘‘PM–10 SIPDevelopment Guideline’’. 108

c. When the highest, second-highest con-centration is used in assessing potential vio-lations of a short term NAAQS, criteria thatare identified in ‘‘Guideline for Interpreta-tion of Air Quality Standards’’88 should befollowed. This guidance specifies that a vio-lation of a short term standard occurs at asite when the standard is exceeded a secondtime. Thus, emission limits that protectstandards for averaging times of 24 hours orless are appropriately based on the highest,second-highest estimated concentration plusa background concentration which can rea-sonably be assumed to occur with the con-centration.

11.2.3.2 NAAQS Analyses for New or ModifiedSources

a. For new or modified sources predicted tohave a significant ambient impact 63 and tobe located in areas designated attainment orunclassifiable for the SO2, Pb, NO2, or CONAAQS, the demonstration as to whetherthe source will cause or contribute to an airquality violation should be based on: (1) thehighest estimated annual average concentra-tion determined from annual averages of in-dividual years; or (2) the highest, second-highest estimated concentration for aver-aging times of 24-hours or less; and (3) thesignificance of the spatial and temporal con-tribution to any modeled violation. For Pb,the highest estimated concentration basedon an individual calendar quarter averagingperiod should be used. Background con-centrations should be added to the estimatedimpact of the source. The most restrictive

standard should be used in all cases to assessthe threat of an air quality violation. Fornew or modified sources predicted to have asignificant ambient impact 63 in areas des-ignated attainment or unclassifiable for thePM–10 NAAQS, the demonstration of wheth-er or not the source will cause or contributeto an air quality violation should be basedon sufficient data to show whether: (1) theprojected 24-hour average concentrationswill exceed the 24-hour NAAQS more thanonce per year, on average; (2) the expected(i.e., average) annual mean concentrationwill exceed the annual NAAQS; and (3) thesource contributes significantly, in a tem-poral and spatial sense, to any modeled vio-lation.

11.2.3.3 PSD Air Quality Increments andImpacts

a. The allowable PSD increments for cri-teria pollutants are established by regula-tion and cited in § 51.166. These maximum al-lowable increases in pollutant concentra-tions may be exceeded once per year at eachsite, except for the annual increment thatmay not be exceeded. The highest, second-highest increase in estimated concentrationsfor the short term averages as determined bya model should be less than or equal to thepermitted increment. The modeled annualaverages should not exceed the increment.

b. Screening techniques defined in sections4.0 and 5.0 can sometimes be used to estimateshort term incremental concentrations forthe first new source that triggers the base-line in a given area. However, when multipleincrement-consuming sources are involved inthe calculation, the use of a refined modelwith at least 1 year of on-site or 5 years ofoff-site NWS data is normally required. Insuch cases, sequential modeling must dem-onstrate that the allowable increments arenot exceeded temporally and spatially, i.e.,for all receptors for each time periodthroughout the year(s) (time period meansthe appropriate PSD averaging time, e.g., 3-hour, 24-hour, etc.).

c. The PSD regulations require an esti-mation of the SO2, particulate matter, andNO2 impact on any Class I area. Normally,Gaussian models should not be applied atdistances greater than can be accommodatedby the steady state assumptions inherent insuch models. The maximum distance for re-fined Gaussian model application for regu-latory purposes is generally considered to be50km. Beyond the 50km range, screeningtechniques may be used to determine if morerefined modeling is needed. If refined modelsare needed, long range transport modelsshould be considered in accordance with sec-tion 7.2.6. As previously noted in sections 3.0and 7.0, the need to involve the Federal LandManager in decisions on potential air quality


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g Documents not available in the open lit-erature or from the National Technical In-formation Service (NTIS) have been placedin Docket No. A–80–46 or A–88–04. Item Num-bers for documents placed in the Docket areshown at the end of the reference.

h Some EPA references, e.g., model user’sguides, etc., are periodically revised. Usersare referred to the SCRAM BBS19 todownload updates or addenda; see section A.0of this appendix.

i The documents listed here are majorsources of supplemental information on thetheory and application of mathematical airquality models.

impacts, particularly in relation to PSDClass I areas, cannot be overemphasized.

11.2.3.4 Emissions Trading Policy (Bubbles)

a. EPA’s final Emissions Trading Policy,commonly referred to as the ‘‘bubble pol-icy,’’ was published in the FEDERAL REGISTER

in 1986.89 Principles contained in the policyshould be used to evaluate ambient impactsof emission trading activities.

b. Emission increases and decreases withinthe bubble should result in ambient air qual-ity equivalence. Two levels of analysis aredefined for establishing this equivalence. Ina Level I analysis the source configurationand setting must meet certain limitations(defined in the policy) that ensure ambientequivalence; no modeling is required. In aLevel II analysis a modeling demonstrationof ambient equivalence is required but onlythe sources involved in the emissions tradeare modeled. The resulting ambient esti-mates of net increases/decreases are com-pared to a set of significance levels to deter-mine if the bubble can be approved. A LevelII analysis requires the use of a refinedmodel and the most recent readily availablefull year of representative meteorologicaldata. Sequential modeling must demonstratethat the significance levels are met tem-porally and spatially, i.e., for all receptorsfor each time period throughout the year(time period means the appropriate NAAQSaveraging time, e.g., 3-hour, 24-hour, etc.).

c. For those bubbles that cannot meet theLevel I or Level II requirements, the Emis-sions Trading Policy allows for a Level IIIanalysis. A Level III analysis, from a mod-eling standpoint, is generally equivalent tothe requirements for a standard SIP revisionwhere all sources (and background) are con-sidered and the estimates are compared tothe NAAQS as in section 11.2.3.2.

d. The Emissions Trading Policy allowsStates to adopt generic regulations for proc-essing bubbles. The modeling procedures rec-ommended in the Guideline apply to such ge-neric regulations. However, an added re-quirement is that the modeling procedurescontained in any generic regulation must bereplicable such that there is no doubt as tohow each individual bubble will be modeled.In general this means that the models, thedata bases and the procedures for applyingthe model must be defined in the regulation.The consequences of the replicability re-quirement are that bubbles for sources lo-cated in complex terrain and certain indus-trial sources where judgments must be madeon source characterization cannot be han-dled generically.

12.0 REFERENCES g h

1. Code of Federal Regulations (Title 40,part 51): Protection of the Environment; Re-quirements for Preparation, Adoption, andSubmittal of Implementation Plans.

2. Environmental Protection Agency, 1977.Guidelines for the Regional Evaluation ofState and Local New Source Review Pro-gram. EPA Publication No. EPA–450/2–77–027.U.S. Environmental Protection Agency, Re-search Triangle Park, NC. (NTIS No. PB–275053)

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

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

5. Code of Federal Regulations (Title 40,part 50): Protection of the Environment; Na-tional Primary and Secondary Ambient AirQuality Standards.

6. Environmental Protection Agency, 1988.Model Clearinghouse: Operational Plan (Re-vised). Staff Report. U.S. EnvironmentalProtection Agency, Research Triangle Park,NC. (Docket No. A–88–04, II–J–1)

7. Environmental Protection Agency, 1980.Guidelines on Air Quality Models. FEDERALREGISTER, 45: 20157–20158.

8. Londergan, R.J., D.H. Minott, D.J.Wackter, T. Kincaid and D. Bonitata, 1982.Evaluation of Rural Air Quality SimulationModels. EPA Publication No. EPA–450/4–82–020. U.S. Environmental Protection Agency,Research Triangle Park, NC. (NTIS No. PB83–182758)

9. Londergan, R.J., D.H. Minott, D.J.Wackter and R.R. Fizz, 1983. Evaluation ofUrban Air Quality Simulation Models. EPAPublication No. EPA–450/4–83–020. U.S. Envi-ronmental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 84–241173).


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

11. Fox, D.G., 1981. Judging Air QualityModel Performance. Bulletin of the Amer-ican Meteorological Society, 62(5): 599–609.

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114. Environmental Protection Agency,1992. Workbook of Screening Techniques forAssessing Impacts of Toxic Air Pollutants(Revised). EPA Publication No. EPA–454/R–92–024. U.S. Environmental Protection Agen-cy, Research Triangle Park, NC.

115. Environmental Protection Agency,1990. User’s Guide to TSCREEN: A Model forScreening Toxic Air Pollutant Concentra-tions. EPA Publication No. EPA–450/4–90–013.U.S. Environmental Protection Agency, Re-search Triangle Park, NC. (NTIS No. PB 91–141820)

116. Environmental Protection Agency,1989. Hazardous Waste TSDF Fugitive Par-ticulate Matter Air Emissions Guidance Doc-ument. EPA Publication No. EPA–450/3–89–019. U.S. Environmental Protection Agency,Research Triangle Park, NC. (NTIS No. PB90–103250)

117. Environmental Protection Agency,1989. Procedures for Conducting Air PathwayAnalyses for Superfund Applications, Vol-ume I Applications of Air Pathway Analysesfor Superfund Activities and Volume IV Pro-cedures for Dispersion Modeling and AirMonitoring for Superfund Air Pathway Anal-ysis, EPA–450/1–89–001 and 004. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC. (NTIS Nos. PB 89–113374 andPB 89–113382)

118. Environmental Protection Agency,1988. Air Dispersion Modeling as Applied toHazardous Waste Incinerator Evaluations,An Introduction For the Permit Writer. U.S.Environmental Protection Agency, ResearchTriangle Park, NC. (Docket No. A–88–04, II–J–10)

119. Environmental Protection Agency,1989. U.S. EPA Office of Toxic SubstancesGraphical Exposure Modeling System(GEMS) User’s Guide and GAMS Version 3.0User’s Guide (DRAFT). Prepared under Con-tract No. 68–02–0481 for the U.S. Environ-mental Protection Agency, Washington, D.C.(Docket No. A–88–04, II–J–5a and II–J–13)

120. Federal Emergency ManagementAgency, 1989. Handbook of Chemical HazardAnalysis Procedures. Available on request bywriting to: Federal Emergency ManagementAgency, Publications Office, 500 C Street,S.W., Washington, D.C. 20472.

121. Environmental Protection Agency,1987. Technical Guidance for Hazards Anal-ysis: Emergency Planning for ExtremelyHazardous Substances. Available on requestby telephone: 1–800–535–0202.

122. Environmental Protection Agency,1988. Superfund Exposure Assessment Man-ual. EPA–540/1–88–001, OSWER Directive9285.5–1. Office of Remedial Response, Wash-ington, D.C. 20460. (NTIS No. PB 89–135859)

123. Environmental Protection Agency,1989. Incineration of Sewage Sludge; Tech-nical Support Document. Office of WaterRegulations and Standards, Washington,D.C. 20460. (NTIS No. PB 89–136592)

124. Environmental Protection Agency,1989. Sludge Incineration Modeling (SIM)System User’s Guide (Draft). Office of Pes-ticides and Toxic Substances, Exposure Eval-uation Division, Washington, D.C. 20460.(NTIS No. PB 89–138762)

125. Environmental Protection Agency,1989. Risk Assessment Guidance for Super-fund. Volume I: Human Health EvaluationManual Part A. (Interim Final). OSWER Di-rective 9285.7–01a. Office of Solid Waste andEmergency Response, Washington, D.C.20460.

126. Environmental Protection Agency,1986. User’s Manual for the Human ExposureModel (HEM). EPA Publication No. EPA–450/5–86–001. Office of Air Quality Planning andStandards, Research Triangle Park, NC.27711.

127. Environmental Protection Agency,1992. A Tiered Modeling Approach for Assess-ing the Risks Due to Sources of HazardousAir Pollutants. EPA Publication No. EPA–450/4–92–001. Environmental Protection Agen-cy, Research Triangle Park, NC. (NTIS No.PB 92–164748)

128. Environmental Protection Agency,1992. Toxic Modeling System Short-term(TOXST) User’s Guide. EPA Publication No.EPA–450/4–92–002. Environmental ProtectionAgency, Research Triangle Park, NC.

129. Environmental Protection Agency,1992. Toxic Modeling System Long-term(TOXLT) User’s Guide. EPA Publication No.EPA–450/4–92–003. Environmental ProtectionAgency, Research Triangle Park, NC.

130. Environmental Protection Agency,1989. User’s Guide for the DEGADIS 2.1 DenseGas Dispersion Model. EPA Publication No.EPA–450/4–89–019. U.S. Environmental Pro-tection Agency, Research Triangle Park, NC.(NTIS No. PB 90–213893)

131. Environmental Protection Agency,1993. Guidance on the Application of RefinedModels for Air Toxics Releases. EPA Publi-cation No. EPA–450/4–91–007. EnvironmentalProtection Agency, Research Triangle Park,NC. (NTIS No. PB 91–190983)

132. Perry, R.H. and Chilton, C.H., 1973.Chemical Engineers’ Handbook, Fifth Edition,McGraw-Hill Book Company, New York, NY.

133. Environmental Protection Agency,1988. User’s Guide to SDM—A Shoreline Dis-persion Model. EPA Publication No. EPA–450/4–88–017. U.S. Environmental ProtectionAgency, Research Triangle Park, NC. (NTISNo. PB 89–164305)

134. Environmental Protection Agency,1987. Analysis and Evaluation of Statistical


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i The documents listed here are majorsources of supplemental information on thetheory and application of mathematical airquality models.

Coastal Fumigation Models. EPA Publica-tion No. EPA–450/4–87–002. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 87–175519)

135. Environmental Protection Agency,1996. Meteorological Processor for Regu-latory Models (MPRM) User’s Guide. EPAPublication No. EPA–454/B–96–002. U.S. Envi-ronmental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 96–180518)

136. Bowen, B.M., J.M. Dewart and A.I.Chen, 1983. Stability Class Determination: AComparison for One Site. Proceedings, SixthSymposium on Turbulence and Diffusion.American Meteorological Society, Boston,MA; pp. 211–214. (Docket No. A–92–65, II–A–7)

137. Environmental Protection Agency,1993. An Evaluation of a Solar Radiation/Delta-T (SRDT) Method for EstimatingPasquill-Gifford (P–G) Stability Categories.EPA Publication No. EPA–454/R–93–055. U.S.Environmental Protection Agency, ResearchTriangle Park, NC. (NTIS No. PB 94–113958)

138. Environmental Protection Agency,1993. PCRAMMET User’s Guide. EPA Publi-cation No. EPA–454/B–93–009. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC.

139. American Society of Mechanical Engi-neers, 1979. Recommended Guide for the Pre-diction of Airborne Effluents, Third Edition.American Society of Mechanical Engineers,New York, NY.

13.0 BIBLIOGRAPHY i

American Meteorological Society, 1971–1985. Symposia on Turbulence, Diffusion, andAir Pollution (1st–7th), Boston, MA.

American Meteorological Society, 1977–1984. Joint Conferences on Applications ofAir Pollution Meteorology (1st–4th). Spon-sored by the American Meteorological Soci-ety and the Air Pollution Control Associa-tion, Boston, MA.

American Meteorological Society, 1978. Ac-curacy of Dispersion Models. Bulletin of theAmerican Meteorological Society, 59(8): 1025–1026.

American Meteorological Society, 1981. AirQuality Modeling and the Clean Air Act:Recommendations to EPA on DispersionModeling for Regulatory Applications, Bos-ton, MA.

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

Dickerson, W.H. and P.H. Gudiksen, 1980.ASCOT FY 79 Program Report. ReportUCRL—52899, ASCOT 80–1. Lawrence Liver-more National Laboratory, Livermore, CA.

Drake, R.L. and S.M. Barrager, 1979. Math-ematical Models for Atmospheric Pollutants.EPRI EA–1131. Electric Power Research In-stitute, Palo Alto, CA.

Environmental Protection Agency, 1978.Workbook for Comparison of Air QualityModels. EPA Publication No. EPA–450/2–78–028a and b. U.S. Environmental ProtectionAgency, Research Triangle Park, NC.

Fox, D.G., and J.E. Fairobent, 1981. NCAQPanel Examines Uses and Limitations of AirQuality Models. Bulletin of the AmericanMeteorological Society, 62(2): 218–221.

Gifford, F.A., 1976. Turbulent DiffusionTyping Schemes: A Review. Nuclear Safety,17(1): 68–86.

Gudiksen, P.H., and M.H. Dickerson, Eds.,Executive Summary: Atmospheric Studies inComplex Terrain Technical Progress ReportFY–1979 Through FY–1983. Lawrence Liver-more National Laboratory, Livermore, CA.(Docket Reference No. II–I–103).

Hales, J.M., 1976. Tall Stacks and the At-mospheric Environment. EPA PublicationNo. EPA–450/3–76–007. U.S. EnvironmentalProtection Agency, Research Triangle Park,NC.

Hanna, S.R., G.A. Briggs, J. Deardorff, B.A.Egan, G.A. Gifford and F. Pasquill, 1977. AMSWorkshop on Stability ClassificationSchemes And Sigma Curves—Summary ofRecommendations. Bulletin of the AmericanMeteorological Society, 58(12): 1305–1309.

Hanna, S.R., G.A. Briggs and R.P. Hosker,Jr., 1982. Handbook on Atmospheric Diffu-sion. Technical Information Center, U.S. De-partment of Energy, Washington, D.C.

Haugen, D.A., Workshop Coordinator, 1975.Lectures on Air Pollution and Environ-mental Impact Analyses. Sponsored by theAmerican Meteorological Society, Boston,MA.

Hoffnagle, G.F., M.E. Smith, T.V. Crawfordand T.J. Lockhart, 1981. On-site Meteorolog-ical Instrumentation Requirements to Char-acterize Diffusion from Point Sources—AWorkshop, 15–17 January 1980, Raleigh, NC.Bulletin of the American Meteorological So-ciety, 62(2): 255–261.

McMahon, R.A. and P.J. Denison, 1979. Em-pirical Atmospheric Deposition Param-eters—A Survey. Atmospheric Environment,13: 571–585.

McRae, G.J., J.A. Leone and J.H. Seinfeld,1983. Evaluation of Chemical Reaction Mech-anisms for Photochemical Smog. Part I:Mechanism Descriptions and Documenta-tion. EPA Publication No. EPA–600/3/83–086.U.S. Environmental Protection Agency, Re-search Triangle Park, NC.

Pasquill, F. and F.B. Smith, 1983. Atmos-pheric Diffusion, 3rd Edition. Ellis HorwoodLimited, Chichester, West Sussex, England,438 pp.

Randerson, D., Ed., 1984. AtmosphericScience and Power Production. DOE/TIC


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2760l. Office of Scientific and Technical In-formation, U.S. Department of Energy, OakRidge, TN.

Roberts, J.J., Ed., 1977. Report to U.S. EPAof the Specialists’ Conference on the EPAModeling Guideline. U.S. EnvironmentalProtection Agency, Research Triangle Park,NC.

Smith, M.E., Ed., 1973. RecommendedGuide for the Prediction of the Dispersion ofAirborne Effluents. The American Society ofMechanical Engineers, New York, NY.

Stern, A.C., Ed., 1976. Air Pollution, ThirdEdition, Volume I: Air Pollutants, TheirTransformation and Transport. AcademicPress, New York, NY.

Turner, D.B., 1979. Atmospheric DispersionModeling: A Critical Review. Journal of theAir Pollution Control Association, 29(5): 502–519.

Whiteman, C.D. and K.J. Allwine, 1982.Green River Ambient Model Assessment Pro-gram FY–1982 Progress Report. PNL–4520.Pacific Northwest Laboratory, Richland,WA.

14.0 GLOSSARY OF TERMS

Air quality. Ambient pollutant concentra-tions and their temporal and spatial dis-tribution.

Algorithm. A specific mathematical cal-culation procedure. A model may containseveral algorithms.

Background. Ambient pollutant concentra-tions 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 adjust-ment based on least-squares linear regres-sion).

Calm. For purposes of air quality modeling,calm is used to define the situation when thewind is indeterminate with regard to speedor direction.

Complex terrain. Terrain exceeding theheight of the stack being modeled.

Computer code. A set of statements thatcomprise a computer program.

Evaluate. To appraise the performance andaccuracy of a model based on a comparisonof concentration estimates with observed airquality data.

Fluid modeling. Modeling conducted in awind tunnel or water channel to quan-titatively evaluate the influence of buildingsand/or terrain on pollutant concentrations.

Fugitive dust. Dust discharged to the at-mosphere in an unconfined flow stream suchas that from unpaved roads, storage pilesand heavy construction operations.

Model. A quantitative or mathematicalrepresentation or simulation which attemptsto describe the characteristics or relation-ships of physical events.

Preferred model. A refined model that is rec-ommended for a specific type of regulatoryapplication.

Receptor. A location at which ambient airquality is measured or estimated.

Receptor models. Procedures that examinean ambient monitor sample of particulatematter and the conditions of its collection toinfer the types or relative mix of sources im-pacting on it during collection.

Refined model. An analytical techniquethat provides a detailed treatment of phys-ical and chemical atmospheric processes andrequires detailed and precise input data. Spe-cialized estimates are calculated that areuseful for evaluating source impact relativeto air quality standards and allowable incre-ments. The estimates are more accuratethan those obtained from conservativescreening techniques.

Rollback. A simple model that assumesthat if emissions from each source affectinga given receptor are decreased by the samepercentage, ambient air quality concentra-tions decrease proportionately.

Screening technique. A relatively simpleanalysis technique to determine if a givensource is likely to pose a threat to air qual-ity. Concentration estimates from screeningtechniques are conservative.

Simple terrain. An area where terrain fea-tures are all lower in elevation than the topof the stack of the source.

APPENDIX A TO APPENDIX W OF PART51—SUMMARIES OF PREFERRED AIRQUALITY MODELS

Table of Contents

A.0 Introduction and AvailabilityA.1 Buoyant Line and Point Source Disper-

sion Model (BLP)A.2 Caline3A.3 Climatological Dispersion Model (CDM

2.0)A.4 Gaussian-Plume Multiple Source Air

Quality Algorithm (RAM)A.5 Industrial Source Complex Model (ISC3)A.6 Urban Airshed Model (UAM)A.7 Offshore and Coastal Dispersion Model

(OCD)A.8 Emissions and Dispersion Modeling Sys-

tem (EDMS)A.9 Complex Terrain Dispersion Model Plus

Algorithms For Unstable Situations(CTDMPLUS)

A.REF References

A.0 Introduction and Availability

This appendix summarizes key features ofrefined air quality models preferred for spe-cific regulatory applications. For eachmodel, information is provided on avail-ability, approximate cost, regulatory use,


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data input, output format and options, sim-ulation of atmospheric physics, and accu-racy. These models may be used without aformal demonstration of applicability pro-vided they satisfy the recommendations forregulatory use; not all options in the modelsare necessarily recommended for regulatoryuse.

Many of these models have been subjectedto a performance evaluation using compari-sons with observed air quality data. A sum-mary of such comparisons for models con-tained in this appendix is included in Mooreet al. (1982). Where possible, several of themodels contained herein have been subjectedto evaluation exercises, including (1) statis-tical performance tests recommended by theAmerican Meteorological Society and (2)peer scientific reviews. The models in thisappendix have been selected on the basis ofthe results of the model evaluations, experi-ence with previous use, familiarity of themodel to various air quality programs, andthe costs and resource requirements for use.

All models and user’s documentation inthis appendix are available from: ComputerProducts, National Technical InformationService (NTIS), U.S. Department of Com-merce, Springfield, VA 22161, Phone: (703)487–4650. In addition, model codes and se-lected, abridged user’s guides are availablefrom the Support Center for Regulatory AirModels Bulletin Board System 19 (SCRAMBBS), telephone (919) 541–5742. The SCRAMBBS is an electronic bulletin board systemdesigned to be user friendly and accessiblefrom anywhere in the country. Model userswith personal computers are encouraged touse the SCRAM BBS to download currentmodel codes and text files.

A.1 Buoyant Line and Point Source DispersionModel (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 Tech-nology, Inc., Concord, MA. (NTIS No. PB 81–164642)

Availability

The computer code is available on the Sup-port Center for Regulatory Models BulletinBoard System and also on diskette (as PB 90–500281) from the National Technical Informa-tion Service (see section A.0).

Abstract

BLP is a Gaussian plume dispersion modeldesigned to handle unique modeling prob-lems associated with aluminum reductionplants, and other industrial sources whereplume rise and downwash effects from sta-tionary line sources are important.

a. Recommendations for Regulatory Use

The BLP model is appropriate for the fol-lowing applications:

Aluminum reduction plants which containbuoyant, elevated line sources;

Rural areas;Transport distances less than 50 kilo-

meters;Simple terrain; andOne 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 sourceplume rise (LTRANS), vertical potentialtemperature gradient (DTHTA), verticalwind speed power law profile exponents(PEXP), maximum variation in number ofstability classes per hour (IDELS), pollutantdecay (DECFAC), the constant in Briggs’ sta-ble plume rise equation (CONST2), constantin Briggs’ neutral plume rise equation(CONST3), convergence criterion for the linesource calculations (CRIT), and maximumiterations allowed for line source calcula-tions (MAXIT); and

Terrain option (TERAN) set equal to 0.0,0.0, 0.0, 0.0, 0.0, 0.0

For other applications, BLP can be used ifit can be demonstrated to give the same esti-mates as a recommended model for the sameapplication, and will subsequently be exe-cuted in that mode.

BLP can be used on a case-by-case basiswith specific options not available in a rec-ommended model if it can be demonstrated,using the criteria in section 3.2, that themodel is more appropriate for a specific ap-plication.

b. Input Requirements

Source data: point sources require stacklocation, elevation of stack base, physicalstack height, stack inside diameter, stackgas exit velocity, stack gas exit tempera-ture, and pollutant emission rate. Linesources require coordinates of the end pointsof the line, release height, emission rate, av-erage line source width, average buildingwidth, average spacing between buildings,and average line source buoyancy parameter.

Meteorological data: hourly surface weath-er data from punched cards or from thepreprocessor program RAMMET which pro-vides hourly stability class, wind direction,wind speed, temperature, and mixing height.

Receptor data: locations and elevations ofreceptors, or location and size of receptorgrid or request automatically generated re-ceptor grid.

c. Output

Printed output (from a separate post-proc-essor program) includes:


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Total concentration or, optionally, sourcecontribution analysis; monthly and annualfrequency distributions for 1-, 3-, and 24-houraverage concentrations; tables of 1-, 3-, and24-hour average concentrations at each re-ceptor; table of the annual (or length of run)average concentrations at each receptor;

Five highest 1-, 3-, and 24-hour averageconcentrations at each receptor; and

Fifty highest 1-, 3-, and 24-hour concentra-tions over the receptor field.

d. Type of Model

BLP is a gaussian plume model.

e. Pollutant Types

BLP may be used to model primary pollut-ants. This model does not treat settling anddeposition.

f. Source-Receptor Relationship

BLP treats up to 50 point sources, 10 par-allel line sources, and 100 receptors arbi-trarily located.

User-input topographic elevation is appliedfor each stack and each receptor.

g. Plume Behavior

BLP uses plume rise formulas of Schulmanand Scire (1980).

Vertical potential temperature gradientsof 0.02 Kelvin per meter for E stability and0.035 Kelvin per meter are used for stableplume rise calculations. An option for userinput 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 isassumed for an hour.

Straight line plume transport is assumedto all downwind distances.

Wind speeds profile exponents of 0.10, 0.15,0.20, 0.25, 0.30, and 0.30 are used for stabilityclasses A through F, respectively. An optionfor user-defined values and an option to sup-press the use of the wind speed profile fea-ture are included.

i. Vertical Wind Speed

Vertical wind speed is assumed equal tozero.

j. Horizontal Dispersion

Rural dispersion coefficients are fromTurner (1969), with no adjustment made forvariations in surface roughness or averagingtime.

Six stability classes are used.

k. Vertical Dispersion

Rural dispersion coefficients are fromTurner (1969), with no adjustment made forvariations in surface roughness.

Six stability classes are used.Mixing height is accounted for with mul-

tiple reflections until the vertical plumestandard deviation equals 1.6 times the mix-ing height; uniform mixing is assumed be-yond that point.

Perfect reflection at the ground is as-sumed.

l. Chemical Transformation

Chemical transformations are treatedusing linear decay. Decay rate is input bythe user.

m. Physical Removal

Physical removal is not explicitly treated.

n. Evaluation Studies

Schulman, L.L. and J.S. Scire, 1980. Buoy-ant Line and Point Source (BLP) DispersionModel User’s Guide, P–7304B. EnvironmentalResearch and Technology, Inc., Concord, MA.

Scire, J.S. and L.L. Schulman, 1981. Eval-uation of the BLP and ISC Models with SF6

Tracer Data and SO2 Measurements at Alu-minum Reduction Plants. APCA SpecialtyConference on Dispersion Modeling for Com-plex Sources, St. Louis, MO.

A.2 CALINE3

Reference

Benson, Paul E., 1979. CALINE3—AVersatile Dispersion Model for PredictingAir Pollutant Levels Near Highways and Ar-terial Streets. Interim Report, Report Num-ber FHWA/CA/TL–79/23. Federal Highway Ad-ministration, Washington, D.C. (NTIS No.PB 80–220841)

Availability

The CALINE3 model is available on disk-ette (as PB 95–502712) from NTIS. The sourcecode and user’s guide are also available onthe Support Center for Regulatory ModelsBulletin Board System (see section A.0).

Abstract

CALINE3 can be used to estimate the con-centrations of nonreactive pollutants fromhighway traffic. This steady-state Gaussianmodel can be applied to determine air pollu-tion concentrations at receptor locationsdownwind of ‘‘at-grade,’’ ‘‘fill,’’ ‘‘bridge,’’and ‘‘cut section’’ highways located in rel-atively uncomplicated terrain. The model isapplicable for any wind direction, highwayorientation, and receptor location. Themodel has adjustments for averaging timeand surface roughness, and can handle up to20 links and 20 receptors. It also contains an


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algorithm for deposition and settling veloc-ity so that particulate concentrations can bepredicted.


CALINE–3 is appropriate for the followingapplications:

Highway (line) sources;Urban or rural areas;Simple terrain;Transport distances less than 50 kilo-

meters; andOne-hour to 24-hour averaging times.


Source data: up to 20 highway links classedas ‘‘at-grade,’’ ‘‘fill’’ ‘‘bridge,’’ or ‘‘de-pressed’’; coordinates of link end points;traffic volume; emission factor; sourceheight; and mixing zone width.

Meteorological data: wind speed, windangle (measured in degrees clockwise fromthe Y axis), stability class, mixing height,ambient (background to the highway) con-centration of pollutant.

Receptor data: coordinates and heightabove ground for each receptor. c.

c. Output

Printed output includes concentration ateach receptor for the specified meteorolog-ical condition.

d. Type of Model

CALINE–3 is a Gaussian plume model.

e. Pollutant Types

CALINE–3 may be used to model primarypollutants.


Up to 20 highway links are treated.CALINE–3 applies user input location and

emission rate for each link. User-input re-ceptor locations are applied.

g. Plume Behavior

Plume rise is not treated.

h. Horizontal Winds

User-input hourly wind speed and directionare applied.

Constant, uniform (steady-state) wind isassumed for an hour.




Six stability classes are used.Rural dispersion coefficients from Turner

(1969) are used, with adjustment for rough-ness length and averaging time.

Initial traffic-induced dispersion is handledimplicitly by plume size parameters.


Six stability classes are used.Empirical dispersion coefficients from Ben-

son (1979) are used including an adjustmentfor roughness length.

Initial traffic-induced dispersion is handledimplicitly by plume size parameters.

Adjustment for averaging time is included.


Not treated.

m. Physical Removal

Optional deposition calculations are in-cluded.


Bemis, G.R. et al., 1977. Air Pollution andRoadway Location, Design, and Operation—Project Overview. FHWA–CA–TL–7080–77–25,Federal Highway Administration, Wash-ington, D.C.

Cadle, S.H. et al., 1976. Results of the Gen-eral Motors Sulfate Dispersion Experiment,GMR–2107. General Motors Research Labora-tories, Warren, MI.

Dabberdt, W.F., 1975. Studies of Air Qual-ity on and Near Highways, Project 2761.Stanford Research Institute, Menlo Park,CA.

A.3 Climatological Dispersion Model (CDM 2.0)

Reference

Irwin, J.S., T. Chico and J. Catalano, 1985.CDM 2.0—Climatological Dispersion Model—User’s Guide. U.S. Environmental ProtectionAgency, Research Triangle Park, NC. (NTISNo. PB 86–136546)

Availability

The source code and user’s guide is avail-able on the Support Center for RegulatoryModels Bulletin Board System. The com-puter code is also available on diskette (asPB 90–500406) from the National TechnicalInformation Service (see section A.0).

Abstract

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


CDM is appropriate for the following appli-cations:

Point and area sources;Urban areas;Flat terrain;


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Transport distances less than 50 kilo-meters;

Long term averages over one month to oneyear or longer.

The following option should be selected forregulatory applications:

Set the regulatory ‘‘default option’’(NDEF=1) which automatically selects stacktip downwash, final plume rise, buoyancy-in-duced dispersion (BID), and the appropriatewind profile exponents.

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


Source data: location, average emissionsrates and heights of emissions for point andarea sources. Point source data requirementsalso include stack gas temperature, stackgas exit velocity, and stack inside diameterfor plume rise calculations for point sources.

Meteorological data: stability wind rose(STAR deck day/night version), average mix-ing height and wind speed in each stabilitycategory, and average air temperature.

Receptor data: cartesian coordinates ofeach receptor.

c. Output

Printed output includes:Average concentrations for the period of

the stability wind rose data (arithmeticmean only) at each receptor, and

Optional point and area concentration rosefor each receptor.

d. Type of Model

CDM is a climatological Gaussian plumemodel.

e. Pollutant Types

CDM may be used to model primary pollut-ants. Settling and deposition are not treated.


CDM applies user-specified locations for allpoint sources and receptors.

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

User specified release heights are appliedfor individual point sources and the areasource grid.

Actual separation between each source-re-ceptor pair is used.

The user may select a single height at orabove ground level that applies to all recep-tors.

No terrain differences between source andreceptor are treated.

g. Plume Behavior

CDM uses Briggs (1969, 1971, 1975) plumerise equations. Optionally a plume rise-windspeed product may be input for each pointsource.

Stack tip downwash equation from Briggs(1974) is preferred for regulatory use. TheBjorklund and Bowers (1982) equation is alsoincluded.

No plume rise is calculated for areasources.

Does not treat fumigation or buildingdownwash.

h. Horizontal Winds

Wind data are input as a stability windrose (joint frequency distribution of 16 winddirections, 6 wind classes, and 5 stabilityclasses).

Wind speed profile exponents for the urbancase (Irwin, 1979; EPA, 1980) are used, assum-ing the anemometer height is at 10.0 meters.




Pollutants are assumed evenly distributedacross a 22.5 or 10.0 degree sector.


There are seven vertical dispersion param-eter schemes, but the following is rec-ommended for regulatory applications:

• Briggs-urban (Gifford, 1976).Mixing height has no effect until disper-

sion coefficient equals 0.8 times the mixingheight; uniform vertical mixing is assumedbeyond that point.

Buoyancy-induced dispersion (Pasquill,1976) is included as an option. Perfect reflec-tion is assumed at the ground.


Chemical transformations are treatedusing exponential decay. Half-life is input bythe user.

m. Physical Removal



Busse, A.D. and J.R. Zimmerman, 1973.User’s Guide for the Climatological Disper-sion Model—Appendix E. EPA PublicationNo. EPA/R4–73–024. Office of Research andDevelopment, Research Triangle Park, NC.

Irwin, J.S. and T.M. Brown, 1985. A Sensi-tivity Analysis of the Treatment of AreaSources by the Climatological DispersionModel. Journal of Air Pollution Control As-sociation, 35: 359–364.


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Londergan, R., D. Minott, D. Wachter andR. Fizz, 1983. Evaluation of Urban Air Qual-ity Simulation Models, EPA Publication No.EPA–450/4–83–020. U.S. Environmental Pro-tection Agency, Research Triangle Park, NC.

Zimmerman, J.R., 1971. Some PreliminaryResults of Modeling from the Air PollutionStudy of Ankara, Turkey, Proceedings of theSecond Meeting of the Expert Panel on AirPollution Modeling, NATO Committee onthe Challenges of Modern Society, Paris,France.

Zimmerman, J.R., 1972. The NATO/CCMSAir Pollution Study of St. Louis, Missouri.Presented at the Third Meeting of the ExpertPanel on Air Pollution Modeling, NATOCommittee on the Challenges of Modern So-ciety, Paris, France.

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

Reference

Turner, D.B. and J.H. Novak, 1978. User’sGuide for RAM. Publication No. EPA–600/8–78–016, Vol. a and b. U.S. Environmental Pro-tection Agency, Research Triangle Park, NC.(NTIS Nos. PB 294791 and PB 294792)

Catalano, J.A., D.B. Turner and H. Novak,1987. User’s Guide for RAM—Second Edition.U.S. Environmental Protection Agency, Re-search Triangle Park, NC.

Availability

The source code and user’s guide is avail-able on the Support Center for RegulatoryModels Bulletin Board System. The com-puter code is also available on diskette (asPB 90–500315) from the National TechnicalInformation Service (see section A.0).

Abstract

RAM is a steady-state Gaussian plumemodel for estimating concentrations of rel-atively stable pollutants, for averagingtimes from an hour to a day, from point andarea sources in a rural or urban setting.Level terrain is assumed. Calculations areperformed for each hour.


RAM is appropriate for the following appli-cations:

Point and area sources;Urban areas;Flat terrain;Transport distances less than 50 kilo-

meters; andOne 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 disper-sion (BID), the new treatment for calms, the

appropriate wind profile exponents, and theappropriate value for pollutant half-life.


Source data: point sources require loca-tion, emission rate, physical stack height,stack gas exit velocity, stack inside diame-ter and stack gas temperature. Area sourcesrequire location, size, emission rate, andheight of emissions.

Meteorological data: hourly surface weath-er data from the preprocessor programRAMMET which provides hourly stabilityclass, wind direction, wind speed, tempera-ture, and mixing height. Actual anemometerheight (a single value) is also required.

Receptor data: coordinates of each recep-tor. Options for automatic placement of re-ceptors near expected concentration maxi-ma, and a gridded receptor array are in-cluded.

c. Output

Printed output optionally includes:One to 24-hour and annual average con-

centrations at each receptor,Limited individual source contribution

list, andHighest through fifth highest concentra-

tions at each receptor for period, with thehighest 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 pollut-ants. Settling and deposition are not treated.


RAM applies user-specified locations forall point sources and receptors. Area sourcesare input as multiples of a user-defined unitarea source grid size.

User specified stack heights are applied forindividual point sources.

Up to 3 effective release heights may bespecified for the area sources. Area sourcerelease heights are assumed to be appro-priate for a 5 meter per second wind and tobe inversely proportional to wind speed.


All receptors are assumed to be at thesame height at or above ground level.

No terrain differences between source andreceptor are accounted for.

g. Plume Behavior

RAM uses Briggs (1969, 1971, 1975) plumerise equations for final rise.

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

A user supplied fraction of the area sourceheight is treated as the physical height. The


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remainder is assumed to be plume rise for a5 meter per second wind speed, and to be in-versely proportional to wind speed.

Fumigation and building downwash are nottreated.

h. Horizontal Winds

Constant, uniform (steady state) wind isassumed for an hour.


Separate wind speed profile exponents(Irwin, 1979; EPA, 1980) for urban cases areused.




Urban dispersion coefficients from Briggs(Gifford, 1976) are used.

Buoyancy-induced dispersion (Pasquill,1976) is included.




Buoyancy-induced dispersion (Pasquill,1976) is included.


tiple reflections until the vertical plumestandard deviation equals 1.6 times the mix-ing height; uniform vertical mixing is as-sumed beyond that point.

Perfect reflection is assumed at theground.



m. Physical Removal



Ellis, H., P. Lou, and G. Dalzell, 1980. Com-parison Study of Measured and PredictedConcentrations with the RAM Model at TwoPower Plants Along Lake Erie. Second JointConference on Applications of Air PollutionMeteorology, New Orleans, LA.

Environmental Research and Technology,1980. SO2 Monitoring and RAM (Urban) ModelComparison Study in Summit County, Ohio.Document P–3618–152, Environmental Re-search & Technology, Inc., Concord, MA.

Guldberg, P.H. and C.W. Kern, 1978. A Com-parison Validation of the RAM and PTMTPModels for Short-Term Concentrations inTwo Urban Areas. Journal of Air PollutionControl Association, 28: 907–910.

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

Kennedy, K.H., R.D. Siegel and M.P. Stein-berg, 1981. Case-Specific Evaluation of theRAM Atmospheric Dispersion Model in anUrban Area. 74th Annual Meeting of theAmerican Institute of Chemical Engineers,New Orleans, LA.

Kummier, R.H., B. Cho, G. Roginski, R.Sinha and A. Greenburg, 1979. A ComparativeValidation of the RAM and Modified SAIModels for Short Term SO2 Concentrationsin Detroit. Journal of Air Pollution ControlAssociation, 29: 720–723.

Londergan, R.J., N.E. Bowne, D.R. Murray,H. Borenstein and J. Mangano, 1980. An Eval-uation of Short-Term Air Quality ModelsUsing Tracer Study Data. Report No. 4333,American Petroleum Institute, Washington,D.C.

Londergan, R., D. Minott, D. Wackter andR. Fizz, 1983. Evaluation of Urban Air Qual-ity Simulation Models. EPA Publication No.EPA–450/4–83–020. U.S. Environmental Pro-tection Agency, Research Triangle Park, NC.

Morgenstern, P., M.J. Geraghty, and A.McKnight, 1979. A Comparative Study of theRAM (Urban) and RAMR (Rural) Models forShort-term SO2 Concentrations in Metropoli-tan Indianapolis. 72nd Annual Meeting of theAir Pollution Control Association, Cin-cinnati, OH.

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

A.5 Industrial Source Complex Model (ISC3)

Reference

Environmental Protection Agency, 1995.User’s Guide for the Industrial Source Com-plex (ISC3) Dispersion Models, Volumes 1 and2. EPA Publication Nos. EPA–454/B–95–003a &b. Environmental Protection Agency, Re-search Triangle Park, NC. (NTIS Nos. PB 95–222741 and PB 95–222758, respectively)

Availability

The model code is available on the SupportCenter for Regulatory Air Models BulletinBoard System. ISCST3 (as PB 96–502000) andISCLT3 (PB 96–502018) are also available ondiskette from the National Technical Infor-mation Service (see section A.0).

Abstract

The ISC3 model is a steady-state Gaussianplume model which can be used to assess pol-lutant concentrations from a wide variety ofsources associated with an industrial sourcecomplex. This model can account for the fol-lowing: settling and dry deposition of par-ticles; downwash; area, line and volume


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sources; plume rise as a function of down-wind distance; separation of point sources;and limited terrain adjustment. ISC3 oper-ates in both long-term and short-termmodes.


ISC3 is appropriate for the following appli-cations:

• Industrial source complexes;• Rural or urban areas;• Flat or rolling terrain;• Transport distances less than 50 kilo-

meters;• 1-hour to annual averaging times; and• Continuous toxic air emissions.The following options should be selected

for regulatory applications: For short termor long term modeling, set the regulatory‘‘default option’’; i.e., use the keywordDFAULT, which automatically selects stacktip downwash, final plume rise, buoyancy in-duced dispersion (BID), the vertical potentialtemperature gradient, a treatment for calms,the appropriate wind profile exponents, theappropriate value for pollutant half-life, anda revised building wake effects algorithm;set the ‘‘rural option’’ (use the keywordRURAL) or ‘‘urban option’’ (use the keywordURBAN); and set the ‘‘concentration option’’(use the keyword CONC).


Source data: location, emission rate, phys-ical stack height, stack gas exit velocity,stack inside diameter, and stack gas tem-perature. Optional inputs include source ele-vation, building dimensions, particle sizedistribution with corresponding settling ve-locities, and surface reflection coefficients.

Meteorological data: ISCST3 requireshourly surface weather data from thepreprocessor program RAMMET, which pro-vides hourly stability class, wind direction,wind speed, temperature, and mixing height.For ISCLT3, input includes stability windrose (STAR deck), average afternoon mixingheight, average morning mixing height, andaverage air temperature.

Receptor data: coordinates and optionalground 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 forany desired source combinations;

• Concentration or deposition values cal-culated for any desired source combinationsat all receptors for any specified day or timeperiod within the day;

• Tables of highest and second highest con-centration or deposition values calculated ateach receptor for each specified time periodduring a(n) ‘‘N’’-day period for any desiredsource combinations, and tables of the max-imum 50 concentration or deposition valuescalculated for any desired source combina-tions for each specified time period.

d. Type of Model

ISC3 is a Gaussian plume model. It hasbeen revised to perform a double integrationof the Gaussian plume kernel for areasources.

e. Pollutant Types

ISC3 may be used to model primary pollut-ants and continuous releases of toxic andhazardous waste pollutants. Settling anddeposition are treated.

f. Source-Receptor Relationships

ISC3 applies user-specified locations forpoint, line, area and volume sources, anduser-specified receptor locations or receptorrings.

User input topographic evaluation for eachreceptor is used. Elevations above stack topare reduced to the stack top elevation, i.e.,‘‘terrain chopping’’.

User input height above ground level maybe used when necessary to simulate impactat elevated or ‘‘flag pole’’ receptors, e.g., onbuildings.


g. Plume Behavior

ISC3 uses Briggs (1969, 1971, 1975) plume riseequations for final rise.

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

Revised building wake effects algorithm isused. For stacks higher than building heightplus one-half the lesser of the buildingheight or building width, the building wakealgorithm of Huber and Snyder (1976) is used.For lower stacks, the building wake algo-rithm of Schulman and Scire (Schulman andHanna, 1986) is used, but stack tip downwashand BID are not used.

For rolling terrain (terrain not abovestack height), plume centerline is horizontalat height of final rise above source.

Fumigation is not treated.

h. Horizontal Winds

Constant, uniform (steady-state) wind isassumed for each hour.


Separate wind speed profile exponents(Irwin, 1979; EPA, 1980) for both rural andurban cases are used.

An optional treatment for calm winds isincluded for short term modeling.


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Rural dispersion coefficients from Turner(1969) are used, with no adjustments for sur-face roughness or averaging time.


Buoyancy induced dispersion (Pasquill,1976) is included.



Rural dispersion coefficients from Turner(1969) are used, with no adjustments for sur-face roughness.


Buoyancy induced dispersion (Pasquill,1976) is included.


tiple reflections until the vertical plumestandard deviation equals 1.6 times the mix-ing height; uniform vertical mixing is as-sumed beyond that point.

Perfect reflection is assumed at theground.


Chemical transformations are treatedusing exponential decay. Time constant isinput by the user.

m. Physical Removal

Dry deposition effects for particles aretreated using a resistance formulation inwhich the deposition velocity is the sum ofthe resistances to pollutant transfer withinthe surface layer of the atmosphere, plus agravitational settling term (EPA, 1994),based on the modified surface depletionscheme of Horst (1983).


Bowers, J.F. and A.J. Anderson, 1981. AnEvaluation Study for the Industrial SourceComplex (ISC) Dispersion Model, EPA Publi-cation No. EPA–450/4–81–002. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC.

Bowers, J.F., A.J. Anderson and W.R.Hargraves, 1982. Tests of the IndustrialSource Complex (ISC) Dispersion Model atthe Armco Middletown, Ohio Steel Mill. EPAPublication No. EPA–450/4–82–006. U.S. Envi-ronmental Protection Agency, Research Tri-angle Park, NC.

Environmental Protection Agency, 1992.Comparison of a Revised Area Source Algo-rithm for the Industrial Source ComplexShort Term Model and Wind Tunnel Data.EPA Publication No. EPA–454/R–92–014. U.S.

Environmental Protection Agency, ResearchTriangle Park, NC. (NTIS No. PB 93–226751)

Environmental Protection Agency, 1992.Sensitivity Analysis of a Revised AreaSource Algorithm for the Industrial SourceComplex Short Term Model. EPA Publica-tion No. EPA–454/R–92–015. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 93–226769)

Environmental Protection Agency, 1992.Development and Evaluation of a RevisedArea Source Algorithm for the Industrialsource complex Long Term Model. EPA Pub-lication No. EPA–454/R–92–016. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 93–226777)

Environmental Protection Agency, 1994.Development and Testing of a Dry Deposi-tion Algorithm (Revised). EPA PublicationNo. EPA–454/R–94–015. U.S. EnvironmentalProtection Agency, Research Triangle Park,NC. (NTIS No. PB 94–183100)

Scire, J.S. and L.L. Schulman, 1981. Eval-uation of the BLP and ISC Models with SF6

Tracer Data and SO2 Measurements at Alu-minum Reduction Plants. Air Pollution Con-trol Association Specialty Conference onDispersion Modeling for Complex Sources,St. Louis, MO.

Schulman, L.L. and S.R. Hanna, 1986. Eval-uation of Downwash Modification to the In-dustrial Source Complex Model. Journal ofthe Air Pollution Control Association, 36:258–264.

A.6 Urban Airshed Model (UAM)

Reference

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

Availability

The model code is available on the SupportCenter for Regulatory Air Models BulletinBoard System (see section A.0).

Abstract

UAM is an urban scale, three dimensional,grid type numerical simulation model. Themodel incorporates a condensed photo-chemical kinetics mechanism for urbanatmospheres. The UAM is designed for com-puting ozone (O3) concentrations undershort-term, episodic conditions lasting oneor two days resulting from emissions of ox-ides of nitrogen (NOX), volatile organic com-pounds (VOC), and carbon monoxide (CO).


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The model treats urban VOC emissions astheir carbon-bond surrogates.


UAM is appropriate for the following appli-cations: urban areas having significant ozoneattainment problems and one hour averagingtimes.

UAM has many options but no specific rec-ommendations can be made at this time onall options. The reviewing agency should beconsulted on selection of options to be usedin regulatory applications.


Source data: gridded, hourly emissions ofPAR, OLE, ETH, XYL, TOL, ALD2, FORM,ISOR, ETOTH, MEOH, CO, NO, and NO2 forlow-level sources. For major elevated pointsources, hourly emissions, stack height,stack diameter, exit velocity, and exit tem-perature.

Meteorological data: hourly, gridded, di-vergence free, u and v wind components foreach vertical level; hourly gridded mixingheights and surface temperatures; hourly ex-posure class; hourly vertical potential tem-perature gradient above and below the mix-ing height; hourly surface atmospheric pres-sure; hourly water mixing ratio; and griddedsurface roughness lengths.

Air quality data: concentration of all car-bon bond 4 species at the beginning of thesimulation for each grid cell; and hourly con-centrations of each pollutant at each levelalong the inflow boundaries and top bound-ary of the modeling region.

Other data requirements are: hourly mixedlayer average, NO2 photolysis rates; andozone surface uptake resistance along withassociated gridded vegetation (scaling) fac-tors.

c. Output

Printed output includes:• Gridded instantaneous concentration

fields at user-specified time intervals foruser-specified pollutants and grid levels;

• Gridded time-average concentrationfields for user-specified time intervals, pol-lutants, 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) for-mation from oxides of nitrogen (NOX) andvolatile organic compound (VOC) emissions.


Low-level area and point source emissionsare specified within each surface grid cell.Emissions from major point sources are

placed within cells aloft in accordance withcalculated effective plume heights.

Hourly average concentrations of each pol-lutant are calculated for all grid cells ateach vertical level.

g. Plume Behavior

Plume rise is calculated for major pointsources using relationships recommended byBriggs (1971).

h. Horizontal Winds

See Input Requirements.


Calculated at each vertical grid cell inter-face from the mass continuity relationshipusing the input gridded horizontal wind field.


Horizontal eddy diffusivity is set to a userspecified constant value (nominally 50 m2/s).


Vertical eddy diffusivities for unstable andneutral conditions calculated using relation-ships of Lamb et al. (1977); for stable condi-tions, the relationship of Businger and Arya(1974) is employed. Stability class, frictionvelocity, and Monin-Obukhov length deter-mined using procedure of Liu et al. (1976).


UAM employs a simplified version of theCarbon-Bond IV Mechanism (CBM–IV) devel-oped by Gery et al. (1988) employing varioussteady state approximations. The CBM–IVmechanism incorporated in UAM utilizes anupdated simulation of PAN chemistry thatincludes a peroxy-peroxy radical terminationreaction, significant when the atmosphere isNOX-limited (Gery et al., 1989). The currentCBM–IV mechanism accommodates 34 spe-cies and 82 reactions.

m. Physical Removal

Dry deposition of ozone and other pollut-ant species are calculated. Vegetation (scal-ing) factors are applied to the reference sur-face uptake resistance of each species de-pending on land use type.


Builtjes, P.J.H., K.D. van der Hurt and S.D.Reynolds, 1982. Evaluation of the Perform-ance of a Photochemical Dispersion Model inPractical Applications. 13th InternationalTechnical Meeting on Air Pollution Mod-eling and Its Application, Ile des Embiez,France.

Cole, H.S., D.E. Layland, G.K. Moss andC.F. Newberry, 1983. The St. Louis OzoneModeling Project. EPA Publication No.EPA–450/4–83–019. U.S. Environmental Pro-tection Agency, Research Triangle Park, NC.


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Dennis, R.L., M.W. Downton and R.S. Keil,1983. Evaluation of Performance Measuresfor an Urban Photochemical Model. EPAPublication No. EPA–450/4–83–021. U.S. Envi-ronmental Protection Agency, Research Tri-angle Park, NC.

Haney, J.L. and T.N. Braverman, 1985.Evaluation and Application of the UrbanAirshed Model in the Philadelphia Air Qual-ity Control Region. EPA Publication No.EPA–450/4–85–003. U.S. Environmental Pro-tection Agency, Research Triangle Park, NC.

Layland, D.E. and H.S. Cole, 1983. A Reviewof Recent Applications of the SAI UrbanAirshed Model. EPA Publication No. EPA–450/4–84–004. U.S. Environmental ProtectionAgency, Research Triangle Park, NC.

Layland, D.E., S.D. Reynolds, H. Hogo andW.R. Oliver, 1983. Demonstration of Photo-chemical Grid Model Usage for Ozone Con-trol Assessment. 76th Annual Meeting of theAir Pollution Control Association, Atlanta,GA.

Morris, R.E. et al., 1990. Urban AirshedModel Study of Five Cities. EPA PublicationNo. EPA–450/4–90–006a-g. U.S. EnvironmentalProtection Agency, Research Triangle Park,NC.

Reynolds, S.D., H. Hogo, W.R. Oliver andL.E. Reid, 1982. Application of the SAIAirshed Model to the Tulsa MetropolitanArea, SAI No. 82004. Systems Applications,Inc., San Rafael, CA.

Schere, K.L. and J.H. Shreffler, 1982. FinalEvaluation of Urban-Scale PhotochemicalAir Quality Simulation Models. EPA Publi-cation No. EPA–600/3–82–094. U.S. Environ-mental Protection Agency, Research Tri-angle 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 PhotochemicalModel Estimates to Detail In Input Informa-tion, Appendix A—A Compilation of Simula-tion Results. EPA Publication No. EPA–450/4–81–031b. U.S. Environmental ProtectionAgency, Research Triangle Park, NC.

South Coast Air Quality Management Dis-trict, 1989. Air Quality Management Plan—Appendix V–R (Urban Airshed Model Per-formance Evaluation). El Monte, CA.

Stern, R. and B. Scherer, 1982. Simulationof a Photochemical Smog Episode in theRhine-Ruhr Area with a Three DimensionalGrid Model. 13th International TechnicalMeeting on Air Pollution Modeling and ItsApplication, 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 PhotochemicalModel Estimates to Detail in Input Informa-tion. EPA Publication No. EPA–450/4–81–031a.U.S. Environmental Protection Agency, Re-search 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 Re-

quirements in the South Coast Air Basin—Appendix A. Performance Evaluation of theSystems Applications Airshed Model for the26–27 June 1974 O3 Episode in the South CoastAir Basin, SYSAPP 83/037. Systems Applica-tions, 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 Re-quirements in the South Coast Air Basin—Appendix B. Performance Evaluation of theSystems Applications Airshed Model for the7–8 November 1978 NO2 Episode in the SouthCoast Air Basin, SYSAPP 83/038. SystemsApplications, Inc., San Rafael, CA.

Tesche, T.W., 1988. Accuracy of Ozone AirQuality Models. Journal of EnvironmentalEngineering, 114(4): 739–752.

A.7 Offshore and Coastal Dispersion Model(OCD)

Reference

DiCristofaro, D.C. and S.R. Hanna, 1989.OCD: The Offshore and Coastal DispersionModel, Version 4. Volume I: User’s Guide,and Volume II: Appendices. Sigma ResearchCorporation, Westford, MA. (NTIS Nos. PB93–144384 and PB 93–144392)

Availability

This model code is available on the Sup-port Center for Regulatory Air Models Bul-letin Board System and also on diskette (asPB 91–505230) from the National TechnicalInformation Service (see section A.0).

Technical Contact

Minerals Management Service, Attn: Mr.Dirk Herkhof, Parkway Atrium Building, 381Elden Street, Herndon, VA 22070–4817, Phone:(703) 787–1735.

Abstract

OCD is a straight-line Gaussian model de-veloped to determine the impact of offshoreemissions from point, area or line sources onthe air quality of coastal regions. OCD incor-porates overwater plume transport and dis-persion as well as changes that occur as theplume crosses the shoreline. Hourly meteoro-logical data are needed from both offshoreand onshore locations. These include watersurface temperature, overwater air tempera-ture, mixing height, and relative humidity.

Some of the key features include platformbuilding downwash, partial plume penetra-tion into elevated inversions, direct use ofturbulence intensities for plume dispersion,interaction with the overland internalboundary layer, and continuous shoreline fu-migation.


OCD has been recommended for use by theMinerals Management Service for emissions


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located on the Outer Continental Shelf (50FR 12248; 28 March 1985). OCD is applicablefor overwater sources where onshore recep-tors are below the lowest source height.Where onshore receptors are above the low-est source height, offshore plume transportand dispersion may be modeled on a case-by-case basis in consultation with the EPA Re-gional Office.


Source data: point, area or line source lo-cation, pollutant emission rate, buildingheight, stack height, stack gas temperature,stack inside diameter, stack gas exit veloc-ity, stack angle from vertical, elevation ofstack base above water surface and griddedspecification of the land/water surfaces. Asan option, emission rate, stack gas exit ve-locity and temperature can be varied hourly.

Meteorological data (over water): wind di-rection, wind speed, mixing height, relativehumidity, air temperature, water surfacetemperature, vertical wind direction shear(optional), vertical temperature gradient(optional), turbulence intensities (optional).

Meteorological data (over land): wind di-rection, wind speed, temperature, stabilityclass, mixing height.

Receptor data: location, height above localground-level, ground-level elevation abovethe water surface.

c. Output

All input options, specification of sources,receptors and land/Water map including lo-cations of sources and receptors.

Summary tables of five highest concentra-tions at each receptor for each averaging pe-riod, and average concentration for entirerun period at each receptor.

Optional case study printout with hourlyplume and receptor characteristics. Optionaltable of annual impact assessment from non-permanent activities.

Concentration files written to disk or tapecan be used by ANALYSIS postprocessor toproduce the highest concentrations for eachreceptor, the cumulative frequency distribu-tions for each receptor, the tabulation of allconcentrations exceeding a given threshold,and the manipulation of hourly concentra-tion files.

d. Type of Model

OCD is a Gaussian plume model con-structed on the framework of the MPTERmodel.

e. Pollutant Types

OCD may be used to model primary pollut-ants. Settling and deposition are not treated.


Up to 250 point sources, 5 area sources, or1 line source and 180 receptors may be used.

Receptors and sources are allowed at anylocation.

The coastal configuration is determined bya grid of up to 3600 rectangles. Each elementof the grid is designated as either land orwater to identify the coastline.

g. Plume Behavior

As in MPTER, the basic plume rise algo-rithms are based on Briggs’ recommenda-tions.

Momentum rise includes consideration ofthe stack angle from the vertical.

The effect of drilling platforms, ships, orany overwater obstructions near the sourceare used to decrease plume rise using a re-vised platform downwash algorithm based onlaboratory experiments.

Partial plume penetration of elevated in-versions is included using the suggestions ofBriggs (1975) and Weil and Brower (1984).

Continuous shoreline fumigation isparametrized using the Turner method wherecomplete vertical mixing through the ther-mal internal boundary layer (TIBL) occursas soon as the plume intercepts the TIBL.

h. Horizontal Winds

Constant, uniform wind is assumed foreach hour.

Overwater wind speed can be estimatedfrom overland wind speed using relationshipof Hsu (1981).

Wind speed profiles are estimated usingsimilarity theory (Businger, 1973). Surfacelayer fluxes for these formulas are cal-culated from bulk aerodynamic methods.




Lateral turbulence intensity is rec-ommended as a direct estimate of horizontaldispersion. If lateral turbulence intensity isnot available, it is estimated from boundarylayer theory. For wind speeds less than 8 m/s, lateral turbulence intensity is assumed in-versely proportional to wind speed.

Horizontal dispersion may be enhanced be-cause of obstructions near the source. A vir-tual source technique is used to simulate theinitial plume dilution due to downwash.

Formulas recommended by Pasquill (1976)are used to calculate buoyant plume en-hancement and wind direction shear en-hancement.

At the water/land interface, the change tooverland dispersion rates is modeled using avirtual source. The overland dispersion ratescan be calculated from either lateral turbu-lence intensity or Pasquill-Gifford curves.The change is implemented where the plumeintercepts the rising internal boundarylayer.


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Observed vertical turbulence intensity isnot recommended as a direct estimate ofvertical dispersion. Turbulence intensityshould be estimated from boundary layertheory as default in the model. For very sta-ble conditions, vertical dispersion is also afunction of lapse rate.

Vertical dispersion may be enhanced be-cause of obstructions near the source. A vir-tual source technique is used to simulate theinitial plume dilution due to downwash.

Formulas recommended by Pasquill (1976)are used to calculate buoyant plume en-hancement.

At the water/land interface, the change tooverland dispersion rates is modeled using avirtual source. The overland dispersion ratescan be calculated from either vertical turbu-lence intensity or the Pasquill-Gifford coeffi-cients. The change is implemented where theplume intercepts the rising internal bound-ary layer.


Chemical transformations are treatedusing exponential decay. Different rates canbe specified by month and by day or night.

m. Physical Removal

Physical removal is also treated using ex-ponential decay.


DiCristofaro, D.C. and S.R. Hanna, 1989.OCD: The Offshore and Coastal DispersionModel. Volume I: User’s Guide. Sigma Re-search Corporation, Westford, MA.

Hanna, S.R., L.L. Schulman, R.J. Paineand J.E. Pleim, 1984. The Offshore and Coast-al Dispersion (OCD) Model User’s Guide, Re-vised. OCS Study, MMS 84–0069. Environ-mental Research & Technology, Inc., Con-cord, MA. (NTIS No. PB 86–159803)

Hanna, S.R., L.L. Schulman, R.J. Paine,J.E. Pleim and M. Baer, 1985. Developmentand Evaluation of the Offshore and CoastalDispersion (OCD) Model. Journal of the AirPollution Control Association, 35: 1039–1047.

Hanna, S.R. and D.C. DiCristofaro, 1988.Development and Evaluation of the OCD/APIModel. Final Report, API Pub. 4461, Amer-ican Petroleum Institute, Washington, D.C.

A.8 Emissions and Dispersion Modeling System(EDMS)

Reference

Segal, H.M., 1991. ‘‘EDMS—MicrocomputerPollution Model for Civilian Airports and AirForce Bases: User’s Guide.’’ FAA Report No.FAA–EE–91–3; USAF Report No. ESL–TR–91–31, Federal Aviation Administration, 800Independence Avenue, S.W., Washington,D.C. 20591. (NTIS No. ADA 240528)

Segal, H.M. and Hamilton, P.L., 1988. ‘‘AMicrocomputer Pollution Model for CivilianAirports and Air Force Bases—Model De-scription.’’ FAA Report No. FAA–EE–88–4;USAF Report No. ESL–TR–88–53, FederalAviation Administration, 800 IndependenceAvenue, S.W., Washington, D.C. 20591. (NTISNo. ADA 199003)

Segal, H.M., 1988. ‘‘A Microcomputer Pollu-tion Model for Civilian Airports and AirForce Bases—Model Application and Back-ground.’’ FAA Report No. FAA–EE–88–5;USAF Report No. ESL–TR–88–55, FederalAviation Administration, 800 IndependenceAvenue, S.W., Washington, D.C. 20591. (NTISNo. ADA 199794)

Availability

EDMS is available for $40 from: FederalAviation Administration, Attn: Ms. DianaLiang, AEE–120, 800 Independence Avenue,S.W., Washington, D.C. 20591, Phone: (202)267–3494.

Abstract

EDMS is a combined emissions/dispersionmodel for assessing pollution at civilian air-ports and military air bases. This model,which was jointly developed by the FederalAviation Administration (FAA) and theUnited States Air Force (USAF), produces anemission inventory of all airport sources andcalculates concentrations produced by thesesources at specified receptors. The systemstores emission factors for fixed sources suchas fuel storage tanks and incinerators andalso for mobile sources such as automobilesor aircraft. EDMS incorporates an emissionsmodel to calculate an emission inventory foreach airport source and a dispersion model,the Graphical Input Microcomputer Model(GIMM) (Segal, 1983) to calculate pollutantconcentrations produced by these sources atspecified receptors. The GIMM, which proc-esses point, area, and line sources, also in-corporates a special meteorologicalpreprocessor for processing up to one year ofNational Climatic Data Center (NCDC) hour-ly data. The model operates in both a screen-ing and refined mode, accepting up to 170sources and 10 receptors.


EDMS is appropriate for the following ap-plications:

• Cumulative effect of changes in aircraftoperations, point source and mobile sourceemissions at airports or air bases;

• Simple terrain;• Transport distances less than 50 kilo-

meters; and• 1-hour to annual averaging times.


All data are entered through a ‘‘runtime’’version of the Condor data base which is an


445


integral part of EDMS. Typical entry itemsare source and receptor coordinates, percentcold starts, vehicles per hour, etc. Somepoint sources, such as heating plants, requirestack height, stack diameter, and effluenttemperature inputs.

Wind speed, wind direction, hourly tem-perature, and Pasquill-Gifford stability cat-egory (P–G) are the meteorological inputs.They can be entered manually through theEDMS data entry screens or automaticallythrough the processing of previously loadedNCDC hourly data.

c. Output

Printed outputs consist of:• A monthly and yearly emission inven-

tory report for each source entered; and• A concentration summing report for up

to 8760 hours (one year) of data.

d. Type of Model

For its emissions inventory calculations,EDMS uses algorithms consistent with theEPA Compilation of Air Pollutant EmissionFactors, AP–42. For its dispersion calcula-tions, EDMS uses the GIMM model which isdescribed in reports FAA–EE–88–4 and FAA–EE–88–5, referenced above. GIMM uses aGaussian plume algorithm.

e. Pollutant Types

EDMS inventories and calculates the dis-persion of carbon monoxide, nitrogen oxides,sulphur oxides, hydrocarbons, and suspendedparticles.


Up to 170 sources and 10 receptors can betreated simultaneously. Area sources aretreated as a series of lines that are posi-tioned perpendicular to the wind.

Line sources (roadways, runways) are mod-eled as a series of points. Terrain elevationdifferences between sources and receptorsare neglected.

Receptors are assumed to be at groundlevel.

g. Plume Behavior

Plume rise is calculated for all pointsources (heating plants, incinerators, etc.)using Briggs plume rise equations (Catalano,1986; Briggs, 1969; Briggs, 1971; Briggs, 1972).

Building and stack tip downwash effectsare not treated.

Roadway dispersion employs a modifica-tion to the Gaussian plume algorithms assuggested by Rao and Keenan (1980) to ac-count for close-in vehicle-induced turbu-lence.

h. Horizontal Winds

Steady state winds are assumed for eachhour. Winds are assumed to be constant withaltitude.

Winds are entered manually by the user orautomatically by reading previously loadedNCC annual data files.


Vertical wind speed is assumed to be zero.


Four stability classes are used (P–G classesB through E).

Horizontal dispersion coefficients are com-puted using a table look-up and linear inter-polation scheme. Coefficients are based onPasquill (1976) as adapted by Petersen (1980).

A modified coefficient table is used to ac-count for traffic-enhanced turbulence nearroadways. Coefficients are based upon dataincluded in Rao and Keenan (1980).


Four stability classes are used (P–G classesB through E).

Vertical dispersion coefficients are com-puted using a table look-up and linear inter-polation scheme. Coefficients are based onPasquill (1976) as adapted by Petersen (1980).

A modified coefficient table is used to ac-count for traffic-enhanced turbulence nearroadways. Coefficients are based upon datafrom Roa and Keenan (1980).


Chemical transformations are not ac-counted for.

m. Physical Removal

Deposition is not treated.


Segal, H.M. and P.L. Hamilton, 1988. AMicrocomputer Pollution Model for CivilianAirports and Air Force Bases—Model De-scription. FAA Report No. FAA–EE–88–4;USAF Report No. ESL–TR–88–53, FederalAviation Administration, 800 IndependenceAvenue, S.W., Washington, D.C. 20591.

Segal, H.M., 1988. A Microcomputer Pollu-tion Model for Civilian Airports and AirForce Bases—Model Application and Back-ground. FAA Report No. FAA–EE–88–5;USAF Report No. ESL–TR–88–55, FederalAviation Administration, 800 IndependenceAvenue, S.W., Washington, D.C. 20591.

A.9 Complex Terrain Dispersion Model Plus Al-gorithms for Unstable Situations(CTDMPLUS)

Reference

Perry, S.G., D.J. Burns, L.H. Adams, R.J.Paine, M.G. Dennis, M.T. Mills, D.G.


446


Strimaitis, R.J. Yamartino and E.M. Insley,1989. User’s Guide to the Complex TerrainDispersion Model Plus Algorithms for Unsta-ble Situations (CTDMPLUS). Volume 1:Model Descriptions and User Instructions.EPA Publication No. EPA–600/8–89–041. Envi-ronmental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 89–181–424)

Paine, R.J., D.G. Strimaitis, M.G. Dennis,R.J. Yamartino, M.T. Mills and E.M. Insley,1987. User’s Guide to the Complex TerrainDispersion Model, Volume 1. EPA Publica-tion No. EPA–600/8–87–058a. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 88–162169)

Availability

This model code is available on the Sup-port Center for Regulatory Air Models Bul-letin Board System and also on diskette (asPB 90–504119) from the National TechnicalInformation Service (see section A.0).

Abstract

CTDMPLUS is a refined point sourceGaussian air quality model for use in all sta-bility conditions for complex terrain applica-tions. The model contains, in its entirety,the technology of CTDM for stable and neu-tral conditions. However, CTDMPLUS canalso simulate daytime, unstable conditions,and has a number of additional capabilitiesfor improved user friendliness. Its use of me-teorological data and terrain information isdifferent from other EPA models; consider-able detail for both types of input data is re-quired and is supplied by preprocessors spe-cifically designed for CTDMPLUS.CTDMPLUS requires the parameterization ofindividual hill shapes using the terrainpreprocessor and the association of eachmodel receptor with a particular hill.

a. Recommendation for Regulatory Use

CTDMPLUS is appropriate for the fol-lowing applications:

• Elevated point sources;• Terrain elevations above stack top;• Rural or urban areas;• Transport distances less than 50 kilo-

meters; and• One hour to annual averaging times

when used with a post-processor programsuch as CHAVG.


Source data: For each source, user suppliessource location, height, stack diameter,stack exit velocity, stack exit temperature,and emission rate; if variable emissions areappropriate, the user supplies hourly valuesfor emission rate, stack exit velocity, andstack exit temperature.

Meteorological data: the user must supplyhourly averaged values of wind, temperatureand turbulence data for creation of the basic

meteorological data file (‘‘PROFILE’’). Me-teorological preprocessors then create aSURFACE data file (hourly values of mixedlayer heights, surface friction velocity,Monin-Obukhov length and surface rough-ness length) and a RAWINsonde data file(upper air measurements of pressure, tem-perature, wind direction, and wind speed).

Receptor data: receptor names (up to 400)and coordinates, and hill number (each re-ceptor must have a hill number assigned).

Terrain data: user inputs digitized contourinformation to the terrain preprocessorwhich creates the TERRAIN data file (for upto 25 hills).

c. Output

When CTDMPLUS is run, it produces aconcentration file, in either binary or textformat (user’s choice), and a list file con-taining a verification of model inputs, i.e.,

• Input meteorological data from ‘‘SUR-FACE’’ and ‘‘PROFILE’’

• Stack data for each source• Terrain information• Receptor information• Source-receptor location (line printer

map).In addition, if the case-study option is se-

lected, the listing includes:• Meteorological variables at plume height• Geometrical relationships between the

source and the hill• Plume characteristics at each receptor,

i.e.,¥> distance in along-flow and cross flow

direction¥> effective plume-receptor height dif-

ference¥> effective σy & σz values, both flat ter-

rain and hill induced (the difference showsthe effect of the hill)

¥> concentration components due toWRAP, LIFT and FLAT.

If the user selects the TOPN option, a sum-mary table of the top 4 concentrations ateach receptor is given. If the ISOR option isselected, a source contribution table forevery hour will be printed.

A separate disk file of predicted (1-houronly) concentrations (‘‘CONC’’) is written ifthe user chooses this option. Three forms ofoutput are possible:

(1) A binary file of concentrations, onevalue for each receptor in the hourly se-quence as run;

(2) A text file of concentrations, one valuefor each receptor in the hourly sequence asrun; or

(3) A text file as described above, but witha listing of receptor information (names, po-sitions, hill number) at the beginning of thefile.


447


Hourly information provided to these filesbesides the concentrations themselves in-cludes the year, month, day, and hour infor-mation as well as the receptor number withthe highest concentration.

d. Type of Model

CTDMPLUS is a refined steady-state, pointsource plume model for use in all stabilityconditions for complex terrain applications.

e. Pollutant Types

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


Up to 40 point sources, 400 receptors and 25hills may be used. Receptors and sources areallowed at any location. Hill slopes are as-sumed not to exceed 15°, so that the linear-ized equation of motion for Boussinesq floware applicable. Receptors upwind of the im-pingement point, or those associated withany of the hills in the modeling domain, re-quire separate treatment.

g. Plume Behavior

As in CTDM, the basic plume rise algo-rithms are based on Briggs’ (1975) rec-ommendations.

A central feature of CTDMPLUS for neu-tral/stable conditions is its use of a criticaldividing-streamline height (Hc) to separatethe flow in the vicinity of a hill into two sep-arate layers. The plume component in theupper layer has sufficient kinetic energy topass over the top of the hill while stream-lines in the lower portion are constrained toflow in a horizontal plane around the hill.Two separate components of CTDMPLUScompute ground-level concentrations result-ing from plume material in each of theseflows.

The model calculates on an hourly (or ap-propriate steady averaging period) basis howthe plume trajectory (and, in stable/neutralconditions, the shape) is deformed by eachhill. Hourly profiles of wind and temperaturemeasurements are used by CTDMPLUS tocompute plume rise, plume penetration (aformulation is included to handle penetra-tion into elevated stable layers, based onBriggs (1984)), convective scaling parameters,the value of Hc, and the Froude number aboveHc.

h. Horizontal Winds

CTDMPLUS does not simulate calm mete-orological conditions. Both scalar and vectorwind speed observations can be read by themodel. If vector wind speed is unavailable, itis calculated from the scalar wind speed. Theassignment of wind speed (either vector orscalar) at plume height is done by either:

• Interpolating between observationsabove and below the plume height, or

• Extrapolating (within the surface layer)from the nearest measurement height to theplume height.


Vertical flow is treated for the plume com-ponent above the critical dividing streamlineheight (Hc); see ‘‘Plume Behavior’’.


Horizontal dispersion for stable/neutralconditions is related to the turbulence veloc-ity scale for lateral fluctuations, σv, forwhich a minimum value of 0.2 m/s is used.Convective scaling formulations are used toestimate horizontal dispersion for unstableconditions.


Direct estimates of vertical dispersion forstable/neutral conditions are based on ob-served vertical turbulence intensity, e.g., σw

(standard deviation of the vertical velocityfluctuation). In simulating unstable (convec-tive) conditions, CTDMPLUS relies on askewed, bi-Gaussian probability densityfunction (PDF) description of the verticalvelocities to estimate the vertical distribu-tion of pollutant concentration.


Chemical transformation is not treated byCTDMPLUS.

m. Physical Removal

Physical removal is not treated byCTDMPLUS (complete reflection at theground/hill surface is assumed).


Burns, D.J., L.H. Adams and S.G. Perry,1990. Testing and Evaluation of theCTDMPLUS Dispersion Model: Daytime Con-vective Conditions. Environmental Protec-tion Agency, Research Triangle Park, NC.

Paumier, J.O., S.G. Perry and D.J. Burns,1990. An Analysis of CTDMPLUS Model Pre-dictions with the Lovett Power Plant DataBase. Environmental Protection Agency, Re-search Triangle Park, NC.

Paumier, J.O., S.G. Perry and D.J. Burns,1992. CTDMPLUS: A Dispersion Model forSources near Complex Topography. Part II:Performance Characteristics. Journal of Ap-plied Meteorology, 31(7): 646–660.

A. REF References

Benson, P.E., 1979. CALINE3—A VersatileDispersion Model for Predicting Air Pollu-tion Levels Near Highways and ArterialStreets. Interim Report, Report NumberFHWA/CA/TL–79/23. Federal Highway Admin-istration, Washington, D.C.

Briggs, G.A., 1969. Plume Rise. U.S. AtomicEnergy Commission Critical Review Series,


448


Oak Ridge National Laboratory, Oak Ridge,TN. (NTIS No. TID–25075)

Briggs, G.A., 1971. Some Recent Analysesof Plume Rise Observations. Proceedings ofthe Second International Clean Air Congress,edited by H.M. Englund and W.T. Berry. Aca-demic Press, New York, NY.

Briggs, G.A., 1974. Diffusion Estimation forSmall 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 Environ-mental Impact Analyses. American Meteoro-logical Society, Boston, MA, pp. 59–111.

Bjorklund, J.R. and J.F. Bowers, 1982.User’s Instructions for the SHORTZ andLONGZ Computer Programs. EPA Publica-tion No. EPA–903/9–82–004a and b. U.S. Envi-ronmental Protection Agency, Region III,Philadelphia, PA.

Businger, J.A., 1973. Turbulence Transferin the Atmospheric Surface Layer. Workshopin Micrometeorology. American Meteorolog-ical Society, Boston, MA, pp. 67–100.

Businger, J.A. and S.P. Arya, 1974. Heightof the Mixed Layer in the Stably StratifiedPlanetary Boundary Layer. Advances in Geo-physics, Vol. 18A, F.N. Frankiel and R.E.Munn (Eds.), Academic Press, New York, NY.

Catalano, J.A., 1986. Addendum to theUser’s Manual for the Single Source(CRSTER) Model. EPA Publication No. EPA–600/8–86–041. U.S. Environmental ProtectionAgency, Research Triangle Park, NC. (NTISNo. PB 87–145843)

Environmental Protection Agency, 1980.Recommendations on Modeling (October 1980Meetings). Appendix G to: Summary of Com-ments and Responses on the October 1980Proposed Revisions to the Guideline on AirQuality Models. Meteorology and Assess-ment Division, Office of Research and Devel-opment, Research Triangle Park, NC.

Gery, M.W., G.Z. Whitten and J.P. Killus,1988. Development and Testing of CBM–IV forUrban and Regional Modeling. EPA Publica-tion No. EPA–600/3–88–012. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 88–180039)

Gery, M.W., G.Z. Whitten, J.P. Killus andM.C. Dodge, 1989. A Photochemical KineticsMechanism for Urban and Regional ScaleComputer Modeling. Journal of GeophysicalResearch, 94: 12,925–12,956.

Gifford, F.A., Jr. 1976. Turbulent DiffusionTyping Schemes—A Review. Nuclear Safety,17: 68–86.

Horst, T.W., 1983. A Correction to theGaussian Source-depletion Model. In Precipi-tation Scavenging, Dry Deposition and Re-suspension. H. R. Pruppacher, R.G. Semoninand W.G.N. Slinn, eds., Elsevier, NY.

Hsu, S.A., 1981. Models for Estimating Off-shore Winds from Onshore MeteorologicalMeasurements. Boundary Layer Meteor-ology, 20: 341–352.

Huber, A.H. and W.H. Snyder, 1976. Build-ing Wake Effects on Short Stack Effluents.Third Symposium on Atmospheric Turbu-lence, Diffusion and Air Quality, AmericanMeteorological Society, Boston, MA.

Irwin, J.S., 1979. A Theoretical Variationof the Wind Profile Power-Law Exponent asa Function of Surface Roughness and Sta-bility. Atmospheric Environment, 13: 191–194.

Lamb, R.G. et al., 1977. Continued Researchin Mesoscale Air Pollution Simulation Mod-eling—Vol. VI: Further Studies in the Mod-eling of Microscale Phenomena, ReportNumber EF77–143. Systems Applications,Inc., San Rafael, CA.

Liu, M.K. et al., 1976. The Chemistry, Dis-persion, and Transport of Air PollutantsEmitted from Fossil Fuel Power Plants inCalifornia: Data Analysis and Emission Im-pact Model. Systems Applications, Inc., SanRafael, CA.

Moore, G.E., T.E. Stoeckenius and D.A.Stewart, 1982. A Survey of Statistical Meas-ures of Model Performance and Accuracy forSeveral Air Quality Model. EPA PublicationNo. EPA–450/4–83–001. U.S. EnvironmentalProtection Agency, Research Triangle Park,NC.

Pasquill, F., 1976. Atmospheric DispersionParameters in Gaussian Plume ModelingPart II. Possible Requirements for Change inthe Turner Workbook Values. EPA Publica-tion No. EPA–600/4–76–030b. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC.

Petersen, W.B., 1980. User’s Guide forHIWAY–2 A Highway Air Pollution Model.EPA Publication No. EPA–600/8–80–018. U.S.Environmental Protection Agency, ResearchTriangle Park, NC. (NTIS PB 80–227556)

Rao, T.R. and M.T. Keenan, 1980. Sugges-tions for Improvement of the EPA–HIWAYModel. Journal of the Air Pollution ControlAssociation, 30: 247–256 (and reprinted as Ap-pendix C in Petersen, 1980).

Segal, H.M., 1983. Microcomputer Graphicsin Atmospheric Dispersion Modeling. Jour-nal of the Air Pollution Control Association,23: 598–600.

Turner, D.B., 1969. Workbook of Atmos-pheric Dispersion Estimates. PHS Publica-tion No. 999–26. U.S. Environmental Protec-tion Agency, Research Triangle, Park, NC.

Weil, J.C. and R.P. Brower, 1984. An Up-dated Gaussian Plume Model for Tall Stacks.Journal of the Air Pollution Control Asso-ciation, 34: 818–827.

APPENDIX B TO APPENDIX W OF PART51—SUMMARIES OF ALTERNATIVE AIRQUALITY MODELS

Table of Contents

B.0 Introduction and AvailabilityB.1 AVACTA II ModelB.2 Dense Gas Dispersion Model (DEGADIS)


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B.3 ERT Visibility ModelB.4 HGSYSTEMB.5 HOTMAC/RAPTADB.6 LONGZB.7 Maryland Power Plant Siting Program

(PPSP) ModelB.8 Mesoscale Puff Model (MESOPUFF II)B.9 Mesoscale Transport Diffusion and Depo-

sition Model For Industrial Sources(MTDDIS)

B.10 Multi-Source (SCSTER) ModelB.11 PANACHEB.12 PLUME Visibility Model (PLUVUE II)B.13 Point, Area, Line Source Algorithm

(PAL–DS)B.14 Reactive Plume Model (RPM–IV)B.15 Shoreline Dispersion Model (SDM)B.16 SHORTZB.17 Simple Line-Source ModelB.18 SLABB.19 WYNDvalley ModelB.REF References

B.0 Introduction and Availability

This appendix summarizes key features ofrefined air quality models that may be con-sidered on a case-by-case basis for individualregulatory applications. For each model, in-formation is provided on availability, ap-proximate cost, regulatory use, data input,output format and options, simulation of at-mospheric physics and accuracy. The modelsare listed by name in alphabetical order.

There are three separate conditions underwhich these models will normally be ap-proved for use:

1. A demonstration can be made that themodel produces concentration estimatesequivalent to the estimates obtained using apreferred model (e.g., the maximum or high,second-high concentration is within 2% ofthe estimate using the comparable preferredmodel);

2. A statistical performance evaluation hasbeen conducted using measured air qualitydata and the results of that evaluation indi-cate the model in appendix B performs betterfor the application than a comparable modelin appendix A; and

3. There is no preferred model for the spe-cific application but a refined model is need-ed to satisfy regulatory requirements.

Any one of these three separate conditionsmay warrant use of these models. See sec-tion 3.2, Use of Alternative Models, for addi-tional details.

Many of these models have been subject toa performance evaluation by comparisonwith observed air quality data. A summaryof such comparisons for models contained inthis appendix is included in Moore et al.(1982). Where possible, several of the modelscontained herein have been subjected to rig-orous evaluation exercises, including (1) sta-tistical performance measures recommendedby the American Meteorological Society and(2) peer scientific reviews.

A source for some of these models anduser’s documentation is: Computer Products,National Technical Information Service(NTIS), U.S. Department of Commerce,Springfield, VA 22161, Phone: (703) 487–4650. Anumber of the model codes and selected,abridged user’s guides are also availablefrom the Support Center for Regulatory AirModels Bulletin Board System19 (SCRAMBBS), Telephone (919) 541–5742. The SCRAMBBS is an electronic bulletin board systemdesigned to be user friendly and accessiblefrom anywhere in the country. Model userswith personal computers are encouraged touse the SCRAM BBS to download currentmodel codes and text files.

B.1 AVACTA II Model

Reference

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

Availability

A 31⁄2’’ diskette of the FORTRAN codingand the user’s guide are available at a cost of$3,500 (non-profit organization) or $5,000(other organizations) from: AeroVironment,Inc., 222 Huntington Drive, Monrovia, CA91016, Phone: (818) 357–9983.

Abstract

The AVACTA II model is a Gaussian modelin which atmospheric dispersion phenomenaare described by the evolution of plume ele-ments, either segments or puffs. The modelcan be applied for short time (e.g., one day)simulations in both transport and calm con-ditions.

The user is given flexibility in defining thecomputational domain, the three-dimen-sional meteorological and emission input,the receptor locations, the plume rise for-mulas, the sigma formulas, etc. Without ex-plicit user’s specifications, standard defaultvalues are assumed.

AVACTA II provides both concentrationfields on the user specified receptor points,and dry/wet deposition patterns throughoutthe domain. The model is particularly ori-ented to the simulation of the dynamics andtransformation of sulfur species (SO2 andSO4

=), but can handle virtually any pair ofprimary-secondary pollutants.


AVACTA II can be used if it can be dem-onstrated to estimate concentrations equiva-lent to those provided by the preferred modelfor a given application. AVACTA II must beexecuted in the equivalent mode.

AVACTA II can be used on a case-by-casebasis in lieu of a preferred model if it can bedemonstrated, using the criteria in section


450


3.2, that AVACTA II is more appropriate forthe specific application. In this case themodel options/modes which are most appro-priate for the application should be used.


A time-varying input is required at eachcomputational step. Only those data whichhave changed need to be input by the user.

Source data requirements are: Coordinates,emission rates of primary and secondary pol-lutants, initial plume sigmas (for non-pointsources), exit temperature, exit velocity,stack inside diameter.

Meteorological data requirements are: sur-face wind measurements, wind profiles (ifavailable), atmospheric stability profiles,mixing heights.

Receptor data requirements are: receptorcoordinates.

Other data requirements: coordinates ofthe computational domain, grid cell speci-fication, terrain elevations, user’s computa-tional and printing options.

c. Output

The model’s output is provided accordingto user’s printing flags. Hourly, 3-hour and24-hour concentration averages are com-puted, together with highest and highest-sec-ond-highest concentration values. Both par-tial and total concentrations are provided.

d. Type of Model

AVACTA II is Gaussian segment/puffmodel.

e. Pollutant Types

AVACTA II can handle any couple of pri-mary-secondary pollutants (e.g., SO2 andSO4

=).

f. Source Receptor Relationship

The AVACTA II approach maintains thebasic Gaussian formulation, but allows a nu-merical simulation of both nonstationaryand nonhomogeneous meteorological condi-tions. The emitted pollutant material is di-vided into a sequence of ‘‘elements,’’ eithersegments or puffs, which are connected to-gether but whose dynamics are a function ofthe local meteorological conditions. Sincethe meteorological parameters vary withtime and space, each element evolves accord-ing to the different meteorological condi-tions encountered along its trajectory.

AVACTA II calculates the partial con-tribution of each source in each receptorduring each interval. The partial concentra-tion is the sum of the contribution of all ex-isting puffs, plus that of the closest segment.

g. Plume Behavior

The user can select the following plumerise formulas:

Briggs (1969, 1971, 1972)CONCAWE (Briggs, 1975)Lucas-Moore (Briggs, 1975)User’s function, i.e., a subroutine supplied

by the userWith cold plumes, the program uses a spe-

cial routine for the computation of the jetplume rise. The user can also select severalcomputational options that control plumebehavior in complex terrain and its total/partial reflections.

h. Horizontal Winds

A 3D mass-consistent wind field is option-ally generated.


A 3D mass-consistent wind field is option-ally generated.


During each step, the sigmas of each ele-ment are increased. The user can select thefollowing sigma functions:

Pasquill-Gifford-Turner (in the functionalform specified by Green et al., 1980)

Brookhaven (Gifford, 1975)Briggs, open country (Gifford, 1975)Briggs, urban, i.e., McElroy-Pooler (Gif-

ford, 1975)Irwin (1979a)LO–LOCAT (MacCready et al., 1974)User-specified function, by pointsUser-specified function, with a user’s sub-

routineThe virtual distance/age concept is used for

incrementing the sigmas at each time step.


During each step, the sigmas of each ele-ment are increased. The user can select thefollowing sigma functions:

Pasquill-Gifford-Turner (in the functionalform specified by Green et al., 1980)

Brookhaven (Gifford, 1975)Briggs, open country (Gifford, 1975)Briggs, urban, i.e., McElroy-Pooler (Gif-

ford, 1975)LO–LOCAT (MacCready et al., 1974)User-specified function, with a user’s sub-

routineThe virtual distance/age concept is used for

incrementing the sigmas at each time step.


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

m. Physical Removal

First order dry and wet deposition schemes


Zannetti P., G. Carboni and A. Ceriani,1985. AVACTA II Model Simulations ofWorst-Case Air Pollution Scenarios in


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Northern Italy. 15th International TechnicalMeeting on Air Pollution Modeling and ItsApplication, St. Louis, Missouri, April 15–19.

B.2 Dense Gas Dispersion Model (DEGADIS)

Reference

Environmental Protection Agency, 1989.User’s Guide for the DEGADIS 2.1—DenseGas Dispersion Model. EPA Publication No.EPA–450/4–89–019. U.S. Environmental Pro-tection Agency, Research Triangle Park, NC27711. (NTIS No. PB 90–213893)

Availability

The model code is only available on theSupport Center for Regulatory Air ModelsBulletin Board System (see section B.0).

Abstract

DEGADIS 2.1 is a mathematical dispersionmodel that can be used to model the trans-port of toxic chemical releases into the at-mosphere. Its range of applicability includescontinuous, instantaneous, finite duration,and time-variant releases; negatively-buoy-ant and neutrally-buoyant releases; ground-level, low-momentum area releases; ground-level or elevated upwardly-directed stack re-leases of gases or aerosols. The model simu-lates only one set of meteorological condi-tions, and therefore should not be consideredapplicable over time periods much longerthan 1 or 2 hours. The simulations are car-ried out over flat, level, unobstructed terrainfor which the characteristic surface rough-ness is not a significant fraction of the depthof the dispersion layer. The model does notcharacterize the density of aerosol-type re-leases; rather, the user must assess thatindependently prior to the simulation.


DEGADIS can be used as a refined mod-eling approach to estimate short-term ambi-ent concentrations (1-hour or less averagingtimes) and the expected area of exposure toconcentrations above specified threshold val-ues for toxic chemical releases. The model isespecially useful in situations where densityeffects are suspected to be important andwhere screening estimates of ambient con-centrations are above levels of concern.


Data may be input directly from an exter-nal input file or via keyboard using an inter-active program module. The model is not setup to accept real-time meteorological dataor convert units of input values. Chemicalproperty data must be input by the user.Such data for a few selected species areavailable within the model. Additional datamay be added to this data base by the user.

Source data requirements are: emissionrate and release duration; emission chemical

and physical properties (molecular weight,density vs. concentration profile in the caseof aerosol releases, and contaminant heat ca-pacity in the case of a nonisothermal gas re-lease; stack parameters (i.e., diameter, ele-vation above ground level, temperature atrelease point).

Meteorological data requirements are:wind speed at designated height aboveground, ambient temperature and pressure,surface roughness, relative humidity, andground surface temperature (which in mostcases can be adequately approximated by theambient temperature).

Receptor data requirements are: averagingtime of interest, above-ground height of re-ceptors, and maximum distance between re-ceptors (since the model computes downwindreceptor distances to optimize model per-formance, this parameter is used only fornominal control of the output listing, and isof secondary importance). No indoor con-centrations are calculated by the model.

c. Output

Printed output includes in tabular form:• Listing of model input data;• Plume centerline elevation, mole frac-

tion, concentration, density, and tempera-ture at each downwind distance;

• σy and σz values at each downwind dis-tance;

• Off-centerline distances to 2 specifiedconcentration values at a specified receptorheight at each downwind distance (these val-ues can be used to draw concentrationisopleths after model execution);

• Concentration vs. time histories for fi-nite-duration releases (if specified by user).

The output print file is automaticallysaved and must be sent to the appropriateprinter by the user after program execution.

No graphical output is generated by thecurrent version of this program.

d. Type of Model

DEGADIS estimates plume rise and disper-sion for vertically-upward jet releases usingmass and momentum balances with air en-trainment based on laboratory and field-scale data. These balances assume Gaussiansimilarity profiles for velocity, density, andconcentration within the jet. Ground-leveldenser-than-air phenomena is treated using apower law concentration distribution profilein the vertical and a hybrid top hat-Gaussianconcentration distribution profile in the hor-izontal. A power law specification is used forthe vertical wind profile. Ground-level cloudslumping phenomena and air entrainmentare based on laboratory measurements andfield-scale observations.


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e. Pollutant Types

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


Only one source can be modeled at a time.There is no limitation to the number of re-

ceptors; the downwind receptor distances areinternally-calculated by the model. TheDEGADIS calculation is carried out untilthe plume centerline concentration is 50%below the lowest concentration level speci-fied by the user.

The model contains no modules for sourcecalculations or release characterization.

g. Plume Behavior

Jet/plume trajectory is estimated frommass and momentum balance equations. Sur-rounding terrain is assumed to be flat, andstack tip downwash, building wake effects,and fumigation are not treated.

h. Horizontal Winds

Constant logarithmic velocity profilewhich accounts for stability and surfaceroughness is used.

The wind speed profile exponent is deter-mined from a least squares fit of the loga-rithmic profile from ground level to the windspeed reference height. Calm winds can besimulated for ground-level low-momentumreleases.

Along-wind dispersion of transient releasesis treated using the methods of Colenbrander(1980) and Beals (1971).


Not treated.


When the plume centerline is above groundlevel, horizontal dispersion coefficients arebased upon Turner (1969) and Slade (1968)with adjustments made for averaging timeand plume density.

When the plume centerline is at groundlevel, horizontal dispersion also accounts forentrainment due to gravity currents asparameterized from laboratory experiments.


When the plume centerline is above groundlevel, vertical dispersion coefficients arebased upon Turner (1969) and Slade (1968).Perfect ground reflection is applied.

In the ground-level dense-gas regime,vertical dispersion is also based upon resultsfrom laboratory experiments in density-stratified fluids.


Not specifically treated.

m. Physical Removal

Not treated.


Spicer, T.O. and J.A. Havens, 1986. Devel-opment of Vapor Dispersion Models for Non-neutrally Buoyant Gas Mixtures—Analysisof USAF/N2O4 Test Data. USAF Engineeringand Services Laboratory, Final Report ESL–TR–86–24.

Spicer, T.O. and J.A. Havens, 1988. Devel-opment of Vapor Dispersion Models for Non-neutrally Buoyant Gas Mixtures—Analysisof TFI/NH3 Test Data. USAF Engineeringand Services Laboratory, Final Report.

o. Operating Information

The model requires either a VAX computeror an IBM—compatible PC for its execution.The model currently does not require sup-porting software. A FORTRAN compiler isrequired to generate program executables inthe VAX computing environment. PCexecutables are provided within the sourcecode; however, a PC FORTRAN compilermay be used to tailor a PC executable to theuser’s PC environment.

B.3 ERT Visibility Model

Reference

ENSR Consulting and Engineering, 1990.ERT Visibility Model: Version 4; TechnicalDescription and User’s Guide. DocumentM2020–003. ENSR Consulting and Engineer-ing, 35 Nagog Park, Acton, MA 01720.

Availability

The user’s guide and model code on disk-ette are available as a package (as PB 96–501978) from the National Technical Informa-tion Service (see section B.0).

Abstract

The ERT Visibility Model is a Gaussiandispersion model designed to estimate visi-bility impairment for arbitrary lines of sightdue to isolated point source emissions bysimulating gas-to-particle conversion, drydeposition, NO to NO2 conversion and linearradiative transfer.


There is no specific recommendation at thepresent time. The ERT Visibility Model maybe used on a case-by-case basis.


Source data requirements are: stackheight, stack temperature, emissions of SO2,

NOX, TSP, fraction of NOX as NO2, fraction ofTSP which is carbonaceous, exit velocity,and exit radius.

Meteorological data requirements are:hourly ambient temperature, mixing depth,


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wind speed at stack height, stability class,potential temperature gradient, and wind di-rection.

Receptor data requirements are: observercoordinates with respect to source, latitude,longitude, time zone, date, time of day, ele-vation, relative humidity, background visualrange, line-of-sight azimuth and elevationangle, inclination angle of the observed ob-ject, distance from observer to object, objectand surface reflectivity, number and spacingof integral receptor points along line ofsight.

Other data requirements are: ambient con-centrations of O3 and NOX, deposition veloc-ity of TSP, sulfate, nitrate, SO2 and NOX,

first-order transformation rate for sulfateand nitrate.

c. Output

Printed output includes both summary anddetailed results as follows: Summary output:Page 1—site, observer and object parameters;Page 2—optical pollutants and associated ex-tinction coefficients; Page 3—plume modelinput parameters; Page 4—total calculatedvisual range reduction, and each pollutant’scontribution; Page 5—calculated plume con-trast, object contrast and object contrastdegradation at the 550nm wavelength; Page6—calculated blue/red ratio and ΛE (U*V*W*)values for both sky and object discoloration.

Detailed output: phase functions for eachpollutant in four wavelengths (400, 450, 550,650nm), concentrations for each pollutantalong sight path, solar geometry contrastparameters at all wavelengths, intensities,tristimulus values and chromaticity coordi-nates for views of the object, sun, back-ground sky and plume.

d. Type of Model

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

e. Pollutant Types

Optical activity of sulfate, nitrate (derivedfrom SO2 and NOX emissions), primary TSPand NO2 is simulated.


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

g. Plume Behavior

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

h. Horizontal Wind Field

A single wind speed and direction is speci-fied for each case study. The wind is assumedto be spatially uniform.




Rural dispersion coefficients from Turner(1969) are used.


Rural dispersion coefficients from Turner(1969) are used. Mixing height is accountedfor with multiple reflection handled by sum-mation of series near the source, and Fourierrepresentation farther downwind.


First order transformations of sulfates andnitrates are used.

m. Physical Removal

Dry deposition is treated by the source de-pletion method.


Seigneur, C., R.W. Bergstrom and A.B.Hudischewskyj, 1982. Evaluation of the EPAPLUVUE Model and the ERT VisibilityModel Based on the 1979 VISTTA Data Base.EPA Publication No. EPA–450/4–82–008. U.S.Environmental Protection Agency, ResearchTriangle Park, NC.

White, W.H., C. Seigneur, D.W. Heinold,M.W. Eltgroth, L.W. Richards, P.T. Roberts,P.S. Bhardwaja, W.D. Conner and W.E. Wil-son, Jr., 1985. Predicting the Visibility ofChimney Plumes: An Inter-comparison ofFour Models with Observations at a Well-Controlled Power Plant. Atmospheric Envi-ronment, 19: 515–528.

B.4 HGSYSTEM

(Dispersion Models for Ideal Gases and Hy-drogen Fluoride)

Reference

Post, L. (ed.), 1994. HGSYSTEM 3.0 Tech-nical Reference Manual. Shell Research Lim-ited, Thornton Research Centre, Chester,United Kingdom. (TNER 94.059)

Post, L., 1994. HGSYSTEM 3.0 User’s Man-ual. Shell Research Limited, Thornton Re-search Centre, Chester, United Kingdom.(TNER 94.059)

Availability

The PC–DOS version of the HGSYSTEMsoftware (HGSYSTEM: Version 3.0, Programsfor modeling the dispersion of ideal gas andhydrogen fluoride releases, executable pro-grams and source code can be installed fromdiskettes. These diskettes and all docu-mentation are available as a package fromAPI [(202) 682–8340] or from NTIS as PB 96–501960 (see section B.0).


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Technical Contacts

Doug N. Blewitt, AMOCO Corporation, 1670Broadway/MC 2018, Denver, CO, 80201, (303)830–5312.

Howard J. Feldman, American PetroleumInstitute, 1220 L Street Northwest, Wash-ington, DC 20005, (202) 682–8340.

Abstract

HGSYSTEM is a PC-based software pack-age consisting of mathematical models forestimating of one or more consecutivephases between spillage and near-field andfar-field dispersion of a pollutant. The pol-lutant can be either a two-phase, multi-com-pound mixture of non-reactive compounds orhydrogen fluoride (HF) with chemical reac-tions. The individual models are:

Database program:DATAPROP Generates physical properties

used in other HGSYSTEM modelsSource term models:SPILL Transient liquid release from a

pressurized vesselHFSPILL SPILL version specifically for

HFLPOOL Evaporating multi-compound liq-

uid pool modelNear-field dispersion models:AEROPLUME High-momentum jet disper-

sion modelHFPLUME AEROPLUME version specifi-

cally for HFHEGABOX Dispersion of instantaneous

heavy gas releasesFar-field dispersion models:HEGADAS(S,T) Heavy gas dispersion

(steady-state and transient version)PGPLUME Passive Gaussian dispersionUtility programs:HFFLASH Flashing of HF from pressurized

vesselPOSTHS/POSTHT Post-processing of

HEGADAS(S,T) resultsPROFILE Post-processor for concentration

contours of airborne plumesGET2COL Utility for data retrievalThe models assume flat, unobstructed ter-

rain. HGSYSTEM can be used to modelsteady-state, finite-duration, instantaneousand time dependent releases, depending onthe individual model used. The models canbe run consecutively, with relevant databeing passed on from one model to the nextusing link files. The models can be run inbatch mode or using an iterative utility pro-gram.


HGSYSTEM can be used as a refined modelto estimate short-term ambient concentra-tions. For toxic chemical releases (non-reac-tive chemicals or hydrogen fluoride; 1-houror less averaging times) the expected area ofexposure to concentrations above specifiedthreshold values can be determined. For

flammable non-reactive gases it can be usedto determine the area in which the cloudmay ignite.


HFSPILL input data: reservoir data (tem-perature, pressure, volume, HF mass, mass-fraction water), pipe-exit diameter and ambi-ent pressure.

EVAP input data: spill rate, liquid prop-erties, and evaporation rate (boiling pool) orambient data (non-boiling pool).

HFPLUME and PLUME input data: res-ervoir characteristics, pollutant parameters,pipe/release data, ambient conditions, sur-face roughness and stability class.

HEGADAS input data: ambient conditions,pollutant parameters, pool data or data attransition point, surface roughness, stabilityclass and averaging time.

PGPLUME input data: link data providedby HFPLUME and the averaging time.

c. Output

The HGSYSTEM models contain threepost-processor programs which can be usedto extract modeling results for graphical dis-play by external software packages.GET2COL can be used to extract data fromthe model output files. HSPOST can be usedto develop isopleths, extract any 2 param-eters for plotting and correct for finite re-lease duration. HTPOST can be used toproduce time history plots.

HFSPILL output data: reservoir mass,spill rate, and other reservoir variables as afunction of time. For HF liquid, HFSPILLgenerates link data to HFPLUME for the ini-tial phase of choked liquid flow (flashingjet), and link data to EVAP for the subse-quent phase of unchoked liquid flow(evaporating liquid pool).

EVAP output data: pool dimensions, poolevaporation rate, pool mass and other poolvariables for steady state conditions or as afunction of time. EVAP generates link datato the dispersion model HEGADAS (pool di-mensions and pool evaporation rate).

HFPLUME and PLUME output data:plume variables (concentration, width, cen-troid height, temperature, velocity, etc.) as afunction of downwind distance.

HEGADAS output data: concentrationvariables and temperature as a function ofdownwind distance and (for transient case)time.

PGPLUME output data: concentration as afunction of downwind distance, cross-winddistance and height.

d. Type of Model

HGSYSTEM is made up of four types ofdispersion models. HFPLUME and PLUMEsimulate the near-field dispersion andPGPLUME simulates the passive-gas disper-sion downwind of a transition point.


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HEGADAS simulates the ground-level heavy-gas dispersion.

e. Pollutant Types

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


HGSYSTEM estimates the expected area ofexposure to concentrations above user-speci-fied threshold values. By imposing conserva-tion of mass, momentum and energy the con-centration, density, speed and temperatureare evaluated as a function of downwind dis-tance.

g. Plume Behavior

HFPLUME and PLUME: (1) are steady-state models assuming a top-hat profile withcross-section averaged plume variables; and(2) the momentum equation is taken into ac-count for horizontal ambient shear, gravity,ground collision, gravity-slumping pressureforces and ground-surface drag.

HEGADAS: assumes the heavy cloud tomove with the ambient wind speed, andadopts a power-law fit of the ambient windspeed for the velocity profile.

PGPLUME: simulates the passive-gas dis-persion downwind of a transition point fromHFPLUME or PLUME for steady-state andfinite duration releases.

h. Horizontal Winds

A power law fit of the ambient wind speedis used.


Not treated.


HFPLUME and PLUME: Plume dilution iscaused by air entrainment resulting fromhigh plume speeds, trailing vortices in wakeof falling plume (before touchdown), ambientturbulence and density stratification. Plumedispersion is assumed to be steady and mo-mentum-dominated, and effects of downwinddiffusion and wind meander (averaging time)are not taken into account.

HEGADAS: This model adopts a concentra-tion similarity profile expressed in terms ofan unknown center-line ground-level con-centration and unknown vertical/cross-winddispersion parameters. These quantities aredetermined from a number of basic equationsdescribing gas-mass conservation, air en-trainment (empirical law describing verticaltop-entrainment in terms of global Richard-son number), cross-wind gravity spreading(initial gravity spreading followed by grav-ity-current collapse) and cross-wind diffu-sion (Briggs formula).

PGPLUME: This model assumes aGaussian concentration profile in which the

cross-wind and vertical dispersion coeffi-cients are determined by empirical expres-sions. All unknown parameters in this pro-file are determined by imposing appropriatematching criteria at the transition point.


See description above.


Not treated.

m. Physical Removal

Not treated.


PLUME has been validated against fielddata for releases of liquified propane, andwind tunnel data for buoyant and vertically-released dense plumes. HFPLUME andPLUME have been validated against fielddata for releases of HF (Goldfish experi-ments) and propane releases. In addition, theplume rise algorithms have been testedagainst Hoot, Meroney, and Peterka, Oomsand Petersen databases. HEGADAS has beenvalidated against steady and transient re-leases of liquid propane and LNG over water(Maplin Sands field data), steady and finite-duration pressurized releases of HF (Goldfishexperiments; linked with HFPLUME), in-stantaneous release of Freon (Thorney Islandfield data; linked with the box modelHEGABOX) and wind tunnel data for steady,isothermal dispersion.

Validation studies are contained in the fol-lowing references.

McFarlane, K., Prothero, A., Puttock, J.S.,Roberts, P.T. and H.W.M. Witlox, 1990. Devel-opment and validation of atmospheric dis-persion models for ideal gases and hydrogenfluoride, Part I: Technical Reference Man-ual. Report TNER.90.015. Thornton ResearchCentre, Shell Research, Chester, England.[EGG 1067–1151] (NTIS No. DE 93–000953)

Witlox, H.W.M., McFarlane, K., Rees, F.J.and J.S. Puttock, 1990. Development and val-idation of atmospheric dispersion models forideal gases and hydrogen fluoride, Part II:HGSYSTEM Program User’s Manual. ReportTNER.90.016. Thornton Research Centre,Shell Research, Chester, England. [EGG 1067–1152] (NTIS No. DE 93–000954)

B.5 HOTMAC/RAPTAD

Reference

Mellor, G.L. and T. Yamada, 1974. A Hier-archy of Turbulence Closure Models for Plan-etary Boundary Layers. Journal of Atmos-pheric Sciences, 31: 1791–1806.

Mellor, G.L. and T. Yamada, 1982. Develop-ment of a Turbulence Closure Model for Geo-physical Fluid Problems. Rev. Geophys.Space Phys., 20: 851–875.


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Yamada, T. and S. Bunker, 1988. Develop-ment of a Nested Grid, Second Moment Tur-bulence Closure Model and Application tothe 1982 ASCOT Brush Creek Data Simula-tion. Journal of Applied Meteorology, 27: 562–578.

Availability

For a cost to be negotiated with the modeldeveloper, a 1⁄4–inch data cartridge or a 4mmDAT tape containing the HOTMAC/RAPTADcomputer codes including pre- and post-proc-essors and hard copies of user manuals(User’s Manual, Maintenance Manual, Oper-ations Manual, Maintenance Interface Man-ual, Topo Manual, and 3–Dimensional PlumeManual) are available from YSA Corpora-tion, Rt. 4 Box 81–A, Santa Fe, NM 87501;Phone: (505) 989–7351; Fax: (505) 989–7965; e-mail: [email protected]

Abstract

YSA Corporation offers a comprehensivemodeling system for environmental studies.The system includes a mesoscale meteoro-logical code, a transport and diffusion code,and extensive Graphical User Interfaces(GUIs). This system is unique because thediffusion code uses time dependent, three-di-mensional winds and turbulence distribu-tions that are forecasted by a mesoscaleweather prediction model. Consequently thepredicted concentration distributions aremore accurate than those predicted by tradi-tional models when surface conditions areheterogeneous. In general, the modeled con-centration distributions are not Gaussian be-cause winds and turbulence distributionsThe models were originally developed byusing super computers. However, recent ad-vancement of computer hardware has madeit possible to run complex three-dimensionalmeteorological models on desktopworkstations. The present versions of theprograms are running on super computersand workstations. GUIs are available on SunMicrosystems and Silicon Graphicsworkstations. The modeling system can alsorun on a laptop workstation which makes itpossible to run the programs in the field oraway from the office. As technology con-tinues to advance, a version of HOTMAC/RAPTAD suitable for PC-based platformswill be considered for release by YSA.

HOTMAC, Higher Order Turbulence Modelfor Atmospheric Circulation, is a mesoscaleweather prediction model that forecastswind, temperature, humidity, and atmos-pheric turbulence distributions over complexsurface conditions. HOTMAC has options toinclude non-hydrostatic pressure computa-tion, nested grids, land-use distributions,cloud, fog, and precipitation physics.HOTMAC can interface with tower, rawin-sonde, and large-scale weather data using afour-dimensional data assimilation method.

RAPTAD, Random Puff Transport and Diffu-sion, is a Lagrangian random puff model thatis used to forecast transport and diffusion ofairborne materials over complex terrain.Concentrations are computed by summingthe concentration of each puff at the recep-tor location. The random puff method isequivalent to the random particle methodwith a Gaussian kernel for particle distribu-tion. The advantage of the puff method is theaccuracy and speed of computation. The par-ticle method requires the release of a largenumber of particles which could becomputationally expensive. The puff methodrequires the release of a much less number ofpuffs, typically 1⁄10 to 1⁄100 of the number ofparticles required by the particle method.

The averaging time for concentration esti-mates is variable from 5 minutes to 15 min-utes for each receptor. In addition to theconcentration computation at the receptorsites, RAPTAD computes and graphicallydisplays hourly concentration contours atthe ground level. RAPTAD is applicable topoint and area sources.

The meteorological data produced fromHOTMAC are used as input to RAPTAD.RAPTAD can forecast concentration dis-tributions for neutrally buoyant gas, buoy-ant gas and denser-than-air gas. The modelsare significantly advanced in both theirmodel physics and in their operational pro-cedures. GUIs are provided to help the userprepare input files, run programs, and dis-play the modeled results graphically in threedimensions.

a. Recommendation for Regulatory Use

There are no specific recommendations atthe present time. The HOTMAC/RAPTADmodeling system may be used on a case-by-case basis.


Meteorological Data: The modeling systemis significantly different from the majorityof regulatory models in terms of how mete-orological data are provided and used in con-centration simulations. Regulatory modelsuse the wind data which are obtained di-rectly from measurements or analyzed byusing a simple constraint such as a massconservation equation. Thus, the accuracy ofthe computation will depend significantly onthe quantity and quality of the wind data.This approach is acceptable as long as thestudy area is flat and the simulation periodis short. As the regulations become morestringent and more realistic surface condi-tions are required, a significantly large vol-ume of meteorological data is required whichcould become very expensive.

An alternative approach is to augment themeasurements with predicted values from amesoscale meteorological model. This is theapproach we have taken here. This approach


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has several advantages over the conventionalmethod. First, concentration computationsuse the model forecast wind while the con-ventional method extrapolates the observedwinds. Extrapolation of wind data over com-plex terrain and for an extended period oftime quickly loses its accuracy. Secondly,the number of stations for upper air sound-ings is typically limited from none to atmost a few stations in the study area. Thecorresponding number in a mesoscale modelis the number of grid points in the horizontalplane which is typically 50 X 50. Con-sequently, concentration distributions usingmodel forecasted winds would be much moreaccurate than those obtained by using windswhich were extrapolated from the limitednumber of measurements.

HOTMAC requires meteorological data forinitialization and to provide boundary condi-tions if the boundary conditions change sig-nificantly with time. The minimum amountof data required to run HOTMAC is wind andpotential temperature profiles at a singlestation. HOTMAC forecasts wind and turbu-lence distributions in the boundary layerthrough a set of model equations for solarradiation, heat energy balance at theground, conservation of momentum, con-servation of internal energy, and conserva-tion of mass.

Terrain Data: HOTMAC and RAPTAD usethe digitized terrain data from the U.S. Geo-logical Survey and the Defense MappingAgency. Extraction of terrain data is greatlysimplified by using YSA’s GUI softwarecalled Topo. The user specifies the latitudesand longitudes of the southwest and north-east corner points of the study area. Then,Topo extracts the digitized elevation datawithin the area specified and converts fromthe latitudes and longitudes to the UTM(Universal Transverse Mercator) coordinatesfor up to three nested grids.

Emission Data: Emission data require-ments are emission rate, stack height, stackdiameter, stack location, stack gas exit ve-locity, and stack buoyancy.

Receptor Data: Receptor data require-ments are names, location coordinates, anddesired averaging time for concentration es-timates, which is variable from 5 to 15 min-utes.

c. Output

HOTMAC outputs include hourly winds,temperatures, and turbulence variables atevery grid point. Ancillary codes graphicallydisplay vertical profiles of wind, tempera-ture, and turbulence variables at selected lo-cations and wind vector distributions atspecified heights above the ground. Thesecodes also produce graphic files of wind di-rection projected on vertical cross sections.

RAPTAD outputs include hourly values ofsurface concentration, time variations ofmean and standard deviation of concentra-

tions at selected locations, and coordinatesof puff center locations. Ancillary codesproduce color contour plots of surface con-centration, time variations of mean con-centrations and ratios of standard deviationto mean value at selected locations, and con-centration distributions in the vertical crosssections. The averaging time of concentra-tion at a receptor location is variable from 5to 15 minutes. Color contour plots of surfaceconcentration can be animated on the mon-itor to review time variations of high con-centration areas.

d. Type of Model

HOTMAC is a 3-dimensional Eulerianmodel for weather forecasting, and RAPTADis a 3-dimensional Lagrangian random puffmodel for pollutant transport and diffusion.

e. Pollutant types

RAPTAD may be used to model any inertpollutants, including dense and buoyantgases.


Up to six point or area sources are speci-fied and up to 50 sampling locations are se-lected. Source and receptor heights are spec-ified by the user.

g. Plume Behavior

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

h. Horizontal Winds

RAPTAD uses wind speed, wind direction,and turbulence on a gridded array that issupplied hourly by HOTMAC. Stability effectand mixed layer height are incorporatedthrough the intensity of turbulence which isa function of stability. HOTMAC predictsturbulence intensity by solving a turbulencekinetic energy equation and a length scaleequation. RAPTAD interpolates winds andturbulence at puff center locations every 10seconds from the values on a gridded array.RAPTAD can also use the winds observed attowers and by rawinsondes.


RAPTAD uses vertical winds on a griddedarray that are supplied hourly by HOTMAC.HOTMAC computes vertical wind either bysolving an equation of motion for thevertical wind or a mass conservation equa-tion. RAPTAD interpolates vertical winds atpuff center locations every 10 seconds fromthe values on a gridded array.


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Horizontal dispersion is based on thestandard deviations of horizontal winds thatare computed by HOTMAC.


Vertical dispersion is based on the stand-ard deviations of vertical wind that are com-puted by HOTMAC.


HOTMAC can provide meteorological in-puts to other models that handle chemicalreactions, e.g., UAM.

m. Physical Removal

Not treated.


Yamada, T., S. Bunker and M. Moss, 1992.A Numerical Simulation of AtmosphericTransport and Diffusion over Coastal Com-plex Terrain. Journal of Applied Meteor-ology, 31: 565–578.

Yamada, T. and T. Henmi, 1994. HOTMAC:Model Performance Evaluation by UsingProject WIND Phase I and II Data. MesoscaleModeling of the Atmosphere, American Me-teorological Society, Monograph 47, pp. 123–135.

B.6 LONGZ

Reference

Bjorklund, J.R. and J.F. Bowers, 1982.User’s Instructions for the SHORTZ andLONGZ Computer Programs, Volumes I andII, EPA Publication No. EPA–903/9–82–004.U.S. Environmental Protection Agency, Re-gion III, Philadelphia, PA.

Availability

The computer code is available on the Sup-port Center for Regulatory Air Models Bul-letin Board System and on diskette (as PB96–501994) from the National Technical Infor-mation Service (see section B.0).

Abstract

LONGZ utilizes the steady-state univariateGaussian plume formulation for both urbanand rural areas in flat or complex terrain tocalculate long-term (seasonal and/or annual)ground-level ambient air concentrations at-tributable to emissions from up to 14,000 ar-bitrarily placed sources (stacks, buildingsand area sources). The output consists of thetotal concentration at each receptor due toemissions from each user-specified source orgroup of sources, including all sources. Anoption which considers losses due to deposi-tion (see the description of SHORTZ) isdeemed inappropriate by the authors forcomplex terrain, and is not discussed here.


LONGZ can be used if it can be dem-onstrated to estimate concentrations equiva-lent to those provided by the preferred modelfor a given application. LONGZ must be exe-cuted in the equivalent mode.

LONGZ can be used on a case-by-case basisin lieu of a preferred model if it can be dem-onstrated, using the criteria in section 3.2 ofappendix W, that LONGZ is more appropriatefor the specific application. In this case themodel options/modes which are most appro-priate for the application should be used.


Source data requirements are: for point,building or area sources, location, elevation,total emission rate (optionally classified bygravitational settling velocity) and decaycoefficient; for stack sources, stack height,effluent temperature, effluent exit velocity,stack radius (inner), emission rate, andground elevation (optional); for buildingsources, height, length and width, and ori-entation; for area sources, characteristicvertical dimension, and length, width andorientation.

Meteorological data requirements are:wind speed and measurement height, windprofile exponents, wind direction standarddeviations (turbulent intensities), mixingheight, air temperature, vertical potentialtemperature gradient.

Receptor data requirements are: coordi-nates, ground elevation.

c. Output

Printed output includes total concentra-tion due to emissions from user-specifiedsource groups, including the combined emis-sions from all sources (with optional allow-ance for depletion by deposition).

d. Type of Model

LONGZ is a climatological Gaussian plumemodel.

e. Pollutant Types

LONGZ may be used to model primary pol-lutants. Settling and deposition are treated.


LONGZ applies user specified locations forsources and receptors. Receptors are as-sumed to be at ground level.

g. Plume Behavior

Plume rise equations of Bjorklund andBowers (1982) are used.

Stack tip downwash (Bjorklund and Bow-ers, 1982) is included.

All plumes move horizontally and willfully intercept elevated terrain.

Plumes above mixing height are ignored.


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Perfect reflection at mixing height is as-sumed for plumes below the mixing height.

Plume rise is limited when the mean windat stack height approaches or exceeds stackexit velocity.

Perfect reflection at ground is assumed forpollutants with no settling velocity.

Zero reflection at ground is assumed forpollutants with finite settling velocity.

LONGZ does not simulate fumigation.Tilted plume is used for pollutants with

settling velocity specified.Buoyancy-induced dispersion is treated

(Briggs, 1972).

h. Horizontal Winds

Wind field is homogeneous and steady-state.

Wind speed profile exponents are functionsof both stability class and wind speed. De-fault values are specified in Bjorklund andBowers (1982).




Pollutants are initially uniformly distrib-uted within each wind direction sector. Asmoothing function is then used to removediscontinuities at sector boundaries.


Vertical dispersion is derived from inputvertical turbulent intensities using adjust-ments to plume height and rate of plumegrowth with downwind distance specified inBjorklund and Bowers (1982).



m. Physical Removal

Gravitational settling and dry depositionof particulates are treated.


Bjorklund, J.R. and J.F. Bowers, 1982.User’s Instructions for the SHORTZ andLONGZ Computer Programs, Volume I andII. EPA Publication No. EPA–903/9–82–004.U.S. Environmental Protection Agency, Re-gion III, Philadelphia, PA.

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

Reference

Brower, R., 1982. The Maryland PowerPlant Siting Program (PPSP) Air QualityModel User’s Guide. Ref. No. PPSP–MP–38.Prepared for Maryland Department of Nat-

ural Resources by Environmental Center,Martin Marietta Corporation, Baltimore,MD. (NTIS No. PB 82–238387)

Weil, J.C. and R.P. Brower, 1982. The Mary-land PPSP Dispersion Model for Tall Stacks.Ref. No. PPSP–MP–36. Prepared for Mary-land Department of Natural Resources byEnvironmental Center, Martin Marietta Cor-poration, Baltimore, MD. (NTIS No. PB 82–219155)

Availability

The model code and test data are availableon diskette for a nominal cost to defray ship-ping and handling charges from: Mr. RogerBrower, Versar, Inc., 9200 Rumsey Road, Co-lumbia, MD 21045; Phone: (410) 964–9299.

Abstract

PPSP is a Gaussian dispersion model appli-cable to tall stacks in either rural or urbanareas, but in terrain that is essentially flat(on a scale large compared to the groundroughness elements). The PPSP model fol-lows the same general formulation and com-puter coding as CRSTER, also a Gaussianmodel, but it differs in four major ways. Thedifferences are in the scientific formulationof specific ingredients or ‘‘sub-models’’ tothe Gaussian model, and are based on recenttheoretical improvements as well as sup-porting experimental data. The differencesare: (1) stability during daytime is based onconvective scaling instead of the Turner cri-teria; (2) Briggs’ dispersion curves for ele-vated sources are used; (3) Briggs plume riseformulas for convective conditions are in-cluded; and (4) plume penetration of elevatedstable layers is given by Briggs’ (1984) model.


PPSP can be used if it can be dem-onstrated to estimate concentrations equiva-lent to those provided by the preferred modelfor a given application. PPSP must be exe-cuted in the equivalent mode.

PPSP can be used on a case-by-case basisin lieu of a preferred model if it can be dem-onstrated, using the criteria in section 3.2 ofappendix W, that PPSP is more appropriatefor the specific application. In this case themodel options/modes which are most appro-priate for the application should be used.


Source data requirements are: emissionrate (monthly rates optional), physical stackheight, stack gas exit velocity, stack insidediameter, stack gas temperature.

Meteorological data requirements are:hourly surface weather data from the EPAmeteorological preprocessor program.


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Preprocessor output includes hourly sta-bility class, wind direction, wind speed, tem-perature, and mixing height. Actual ane-mometer height (a single value) is also re-quired. Wind speed profile exponents (one foreach stability class) are required if on-sitedata are input.

Receptor data requirements are: distanceof each of the five receptor rings.

c. Output

Printed output includes:Highest and second highest concentrations

for the year at each receptor for averagingtimes of 1, 3, and 24-hours, plus a user-se-lected averaging time which may be 2, 4, 6, 8,or 12 hours;

Annual arithmetic average at each recep-tor; and

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

d. Type of Model

PPSP is a Gaussian plume model.

e. Pollutant Types

PPSP may be used to model primary pol-lutants. Settling and deposition are nottreated.


Up to 19 point sources are treated.All point sources are assumed at the same

location.Unique stack height and stack exit condi-

tions are applied for each source.Receptor locations are restricted to 36 azi-

muths (every 10 degrees) and five user-speci-fied radial distances.

g. Plume Behavior

Briggs (1975) final rise formulas for buoy-ant plumes are used. Momentum rise is notconsidered.

Transitional or distance-dependent plumerise is not modeled.

Penetration (complete, partial, or zero) ofelevated inversions is treated with Briggs(1984) model; ground-level concentrations aredependent on degree of plume penetration.

h. Horizontal Winds

Wind speeds are corrected for releaseheight based on power law variation, withdifferent exponents for different stabilityclasses and variable reference height (7 me-ters is default). Wind speed power law expo-nents are 0.10, 0.15, 0.20, 0.25, 0.30, and 0.30 forstability classes A through F, respectively.

Constant, uniform (steady-state) wind as-sumed within each hour.




Rural dispersion parameters are Briggs(Gifford, 1975), with stability class defined byu/w* during daytime, and by the method ofTurner (1964) at night.

Urban dispersion is treated by changing allstable cases to stability class D.

Buoyancy-induced dispersion (Pasquill,1976) is included (using ∆Η/3.5).


Rural dispersion parameters are Briggs(Gifford, 1975), with stability class defined byu/w* during daytime, and by the method ofTurner (1964).

Urban dispersion is treated by changing allstable cases to stability class D.

Buoyancy-induced dispersion (Pasquill,1976) is included (using ∆Η/3.5).


Not treated.

m. Physical Removal

Not treated.


Londergan, R., D. Minott, D. Wackter, T.Kincaid and D. Bonitata, 1983. Evaluation ofRural Air Quality Simulation Models, Ap-pendix G: Statistical Tables for PPSP. EPAPublication No. EPA–450/4–83–003. Environ-mental Protection Agency, Research Tri-angle Park, NC.

Weil, J.C. and R.P. Brower, 1982. The Mary-land PPSP dispersion model for tall stacks.Ref. No. PPSP MP–36. Prepared for MarylandDepartment of Natural Resources. Preparedby Environmental Center, Martin MariettaCorporation, Baltimore, Maryland. (NTISNo. PB 82–219155)

B.8 Mesoscale Puff Model (MESOPUFF II)

Reference

Scire, J.S., F.W. Lurmann, A. Bass andS.R. Hanna, 1984. User’s Guide to theMesopuff II Model and Related ProcessorPrograms. EPA Publication No. EPA–600/8–84–013. U.S. Environmental Protection Agen-cy, Research Triangle Park, NC. (NTIS No.PB 84–181775)

A Modeling Protocol for ApplyingMESOPUFF II to Long Range TransportProblems, 1992. EPA Publication No. EPA–454/R–92–021. U.S. Environmental ProtectionAgency, Research Triangle Park, NC.

Availability

This model code is available on the Sup-port Center for Regulatory Air Models Bul-letin Board System and also on diskette (asPB 93–500247) from the National TechnicalInformation Service (see section B.0).


461


Abstract

MESOPUFF II is a short term, regionalscale puff model designed to calculate con-centrations of up to 5 pollutant species (SO2,

SO4, NOX, HNO3, NO3). Transport, puff growth,chemical transformation, and wet and drydeposition are accounted for in the model.


There is no specific recommendation at thepresent time. The model may be used on acase-by-case basis.


Required input data include four types: (1)input control parameters and selected tech-nical options, (2) hourly surface meteorolog-ical data and twice daily upper air measure-ments, hourly precipitation data are op-tional, (3) surface land use classification in-formation, (4) source and emissions data.

Data from up to 25 surface National Weath-er Service stations and up to 10 upper airstations may be considered. Spatially vari-able fields at hour intervals of winds, mixingheight, stability class, and relevant turbu-lence parameters are derived by MESOPACII, the meteorological preprocessor programdescribed in the User Guide.

Source and emission data for up to 25 pointsources and/or up to 5 area sources can be in-cluded. Required information are: location ingrid coordinates, stack height, exit velocityand temperature, and emission rates for thepollutant to be modeled.

Receptor data requirements: up to a 40×40grid may be used and non-gridded receptorlocations may be considered.

c. Output

Line printer output includes: all input pa-rameters, optionally selected arrays ofground-level concentrations of pollutant spe-cies at specified time intervals.

Line printer contour plots output fromMESOFILE II post-processor program. Com-puter readable output of concentration arrayto disk/tape for each hour.

d. Type of Model

MESOPUFF II is a Gaussian puff super-position model.

e. Pollutant Types

Up to five pollutant species may be mod-eled simultaneously and include: SO2, SO4,

NOX, HNO3, NO3.


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

g. Plume Behavior

Briggs (1975) plume rise equations are used,including plume penetration with buoyancyflux computed in the model.

Fumigation of puffs is considered and mayproduce immediate mixing or multiple re-flection calculations at user option.

h. Horizontal Winds

Gridded wind fields are computed for 2 lay-ers; boundary layer and above the mixedlayer. Upper air rawinsonde data and hourlysurface winds are used to obtain spatiallyvariable u,v component fields at hourly in-tervals. The gridded fields are computed byinterpolation between stations in theMESOPAC II preprocessor.


Vertical winds are assumed to be zero.


Incremental puff growth is computed overdiscrete time steps with horizontal growthparameters determined from power law equa-tions fit to sigma y curves of Turner out to100km. At distances greater than 100km, puffgrowth is determined by the rate given byHeffter (1965).

Puff growth is a function of stability classand changes in stability are treated. Option-ally, user input plume growth coefficientsmay be considered.


For puffs emitted at an effective stackheight which is less than the mixing height,uniform mixing of the pollutant within themixed layer is performed. For puffs centeredabove the mixing height, no effect at theground occurs.


Hourly chemical rate constants are com-puted from empirical expressions derivedfrom photochemical model simulations.

m. Physical Removal

Dry deposition is treated with a resistancemethod.

Wet removal may be considered if hourlyprecipitation data are input.


Results of tests for some model parametersare discussed in:

Scire, J.S., F.W. Lurmann, A. Bass andS.R. Hanna, 1984. Development of theMESOPUFF II Dispersion Model. EPA Publi-cation No. EPA–600/3–84–057. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC.


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B.9 Mesoscale Transport Diffusion and Deposi-tion Model for Industrial Sources (MTDDIS)

Reference

Wang, I.T. and T.L. Waldron, 1980. User’sGuide for MTDDIS Mesoscale Transport, Dif-fusion, and Deposition Model for IndustrialSources. EMSC6062.1UR(R2). Combustion En-gineering, Newbury Park, CA.

Availability

A diskette copy of the FORTRAN codingand the user’s guide are available for a costof $100 from: Dr. I. T. Wang, EnvironmentalModeling & Analysis, 2219 E. Thousand OaksBlvd., Suite 435, Thousand Oaks, CA 91362.

Abstract

MTDDIS is a variable-trajectory Gaussianpuff model applicable to long-range trans-port of point source emissions over level orrolling terrain. The model can be used to de-termine 3-hour maximum and 24-hour aver-age concentrations of relatively nonreactivepollutants from up to 10 separate stacks.


There is no specific recommendation at thepresent time. The MTDDIS Model may beused on a case-by-case basis.


Source data requirements are: emissionrate, physical stack height, stack gas exitvelocity, stack inside diameter, stack gastemperature, and location.

Meteorological data requirements are:hourly surface weather data, from up to 10stations, including cloud ceiling, wind direc-tion, wind speed, temperature, opaque cloudcover and precipitation. For long-range ap-plications, user-analyzed daily mixingheights are recommended. If these are notavailable, the NWS daily mixing heights willbe used by the program. A single upper airsounding station for the region is assumed.For each model run, air trajectories are gen-erated for a 48-hour period, and therefore,the afternoon mixing height of the day be-fore and the mixing heights of the day afterare also required by the model as input, inorder to generate hourly mixing heights forthe modeled period.

Receptor data requirements are: up tothree user-specified rectangular grids.

c. Output

Printed output includes:Tabulations of hourly meteorological pa-

rameters include both input surface observa-tions and calculated hourly stability classesand mixing heights for each station;

Printed air trajectories for the two con-secutive 24-hour periods for air parcels gen-

erated 4 hours apart starting at 0000 LST;and

3-hour maximum and 24-hour average gridconcentrations over user-specified rectan-gular grids are output for the second 24-hourperiod.

d. Type of Model

MTDDIS is a Gaussian puff model.

e. Pollutant Types

MTDDIS can be used to model primary pol-lutants. Dry deposition is treated. Expo-nential decay can account for some reac-tions.


MTDDIS treats up to 10 point sources.Up to three rectangular receptor grids may

be specified by the user.

g. Plume Behavior

Briggs (1971, 1972) plume rise formulas areused.

If plume height exceeds mixing height,ground level concentration is assumed zero.

Fumigation and downwash are not treated.

h. Horizontal Winds

Wind speeds and wind directions at eachstation are first corrected for release height.Speed conversions are based on power lawvariation and direction conversions arebased on linear height dependence as rec-ommended by Irwin (1979b).

Converted wind speeds and wind directionsare then weighted according to the algo-rithms of Heffter (1980) to calculate the ef-fective transport wind speed and direction.

i. Vertical Wind Field



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


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



m. Physical Removal

Dry deposition is treated. User input depo-sition velocity is required.

Wet deposition is treated. User input hour-ly precipitation rate and precipitation layerdepth or cloud ceiling height are required.


463



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

B.10 Multi-Source (SCSTER) Model

Reference

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

Availability

The SCSTER model and user’s manual areavailable at no charge on a limited basisthrough Southern Company Services. Thecomputer code may be provided on a disk-ette. Requests should be directed to: Mr.Stanley S. Vasa, Senior Environmental Spe-cialist, Southern Company Services, P.O.Box 2625, Birmingham, AL 35202.

Abstract

SCSTER is a modified version of the EPACRSTER model. The primary distinctions ofSCSTER are its capability to consider mul-tiple sources that are not necessarily collo-cated, its enhanced receptor specifications,its variable plume height terrain adjustmentprocedures and plume distortion from direc-tional wind shear.


SCSTER can be used if it can be dem-onstrated to estimate concentrations equiva-lent to those provided by the preferred modelfor a given application. SCSTER must be ex-ecuted in the equivalent mode.

SCSTER can be used on a case-by-casebasis in lieu of a preferred model if it can bedemonstrated, using the criteria in section3.2 of appendix W, that SCSTER is more ap-propriate for the specific application. In thiscase the model options/modes which aremost appropriate for the application shouldbe used.


Source data requirements are: emissionrate, stack gas exit velocity, stack gas tem-perature, stack exit diameter, physical stackheight, elevation of stack base, and coordi-nates of stack location. The variable emis-sion data can be monthly or annual aver-ages.

Meteorological data requirements are:hourly surface weather data from the EPAmeteorological preprocessor program.Preprocessor output includes hourly sta-bility class wind direction, wind speed, tem-

perature, and mixing height. Actual ane-mometer height (a single value) is optional.Wind speed profile exponents (one for eachstability class) are optional.

Receptor data requirements are: cartesiancoordinates and elevations of individual re-ceptors; distances of receptor rings, with ele-vation of each receptor; receptor grid net-works, with elevation of each receptor.

Any combination of the three receptorinput types may be used to consider up to 600receptor locations.

c. Output

Printed output includes:Highest and second highest concentrations

for the year at each receptor for averagingtimes of 1-, 3-, and 24-hours, a user-selectedaveraging time which may be 2–12 hours, anda 50 high table for 1-, 3-, and 24-hours;

Annual arithmetic average at each recep-tor; and the highest 1-hour and 24-hour con-centrations over the receptor field for eachday considered.

Optional tables of source contributions ofindividual point sources at up to 20 receptorlocations for each averaging period;

Optional magnetic tape output in either bi-nary or fixed block format includes:

All 1-hour concentrations.Optional card/disk output includes for each

receptor:Receptor coordinates; receptor elevation;

highest and highest, second-highest, 1-, 3-,and 24-hour concentrations; and annual aver-age concentration.

d. Type of Model

SCSTER is a Gaussian plume model.

e. Pollutant Types

SCSTER may be used to model primarypollutants. Settling and deposition are nottreated.


SCSTER can handle up to 60 separatestacks at varying locations and up to 600 re-ceptors, including up to 15 receptor rings.

User input topographic elevation for eachreceptor is used.

g. Plume Behavior

SCSTER uses Briggs (1969, 1971, 1972) finalplume rise formulas.

Transitional plume rise is optional.SCSTER contains options to incorporate

wind directional shear with a plume distor-tion method described in appendix A of theUser’s Guide.

SCSTER provides four terrain adjustmentsincluding the CRSTER full terrain height ad-justment and a user-input, stability-depend-ent plume path coefficient adjustment for re-ceptors above stack height.


464


h. Horizontal Winds

Wind speeds are corrected for releaseheight based on power law exponents fromDeMarrais (1959), different exponents for dif-ferent stability classes; default referenceheight of 7m. Default exponents are 0.10, 0.15,0.20, 0.25, 0.30, and 0.30 for stability classes Athrough F, respectively.

Steady-state wind is assumed within agiven hour.

Optional consideration of plume distortiondue to user-input, stability-dependent wind-direction shear gradients.








Six stability classes are used.An optional test for plume height above

mixing height before terrain adjustment isincluded.



m. Physical Removal

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


Londergan, R., D. Minott, D. Wackter, T.Kincaid and D. Bonitata, 1983. Evaluation ofRural Air Quality Simulation Models. EPAPublication No. EPA–450/4–83–003. U.S. Envi-ronmental Protection Agency, Research Tri-angle Park, NC.

B.11 PANACHE

Reference

Transoft Group, 1994. User’s Guide ofFluidyn-PANACHE, a Three-DimensionalDeterministic Simulation of Pollutants Dis-persion Model for Complex Terrain; Cary,North Carolina.

Availability

For a cost to be negotiated with the modeldeveloper, the computer code is availablefrom: Transoft US, Inc., 818 Reedy CreekRoad, Cary, NC 27513–3307; Phone: (919) 380–7500, Fax: (919) 380–7592.

Abstract

PANACHE is an Eulerian (and Lagrangianfor particulate matter), 3-dimensional finitevolume fluid mechanics code designed tosimulate continuous and short-term pollu-tion dispersion in the atmosphere, in simpleor complex terrain. For single or multiplesources, pollutant emissions from stack,point, area, volume, general sources and dis-tant sources are treated. The model auto-matically treats obstacles, effects of vegeta-tion and water bodies, the effects of verticaltemperature stratification on the wind anddiffusion fields, and turbulent shear flowscaused by atmospheric boundary layer orterrain effects. The code solves NavierStokes equations in a curvilinear mesh es-pousing the terrain and obstacles. A 2ndorder resolution helps keep the number ofcells limited in case of shearing flow. An ini-tial wind field is computed by using aLagrangian multiplier to interpolate winddata collected on site. The mesh generator,the solver and the numerical schemes havebeen adopted for atmospheric flows with orwithout chemical reactions. The model codeoperates on any workstation or IBM—com-patible PC (486 or higher). Gaussian and puffmodes are available in PANACHE for fast,preliminary simulation.


On a case-by-case basis, PANACHE may beappropriate for the following types of situa-tions: industrial or urban zone on a flat orcomplex terrain, transport distance from afew meters to 50km, continuous releaseswith hourly, monthly or annual averagingtimes, chemically reactive or non-reactivegases or particulate emissions for stationaryor roadway sources.


Data may be input directly from an exter-nal source (e.g., GIS file) or interactively.The model provides the option to use defaultvalues when input parameters are unavail-able.

PANACHE user environment integratesthe pre- and post-processor with the solver.The calculations can be done interactivelyor in batch mode. An inverse scheme is pro-vided to estimate missing data from a fewmeasured values of the wind.

Terrain data requirements:• Location, surface roughness estimates,

and altitude contours.• Location and dimensions of obstacles,

forests, fields, and water bodies.Source data requirements:For all types of sources, the exit tempera-

ture and plume mass flow rates and con-centration of each of the pollutants are re-quired. External sources require mass flowrate. For roadways, estimated traffic volumeand vehicular emissions are required.


465


Meteorological data requirements:Hourly stability class, wind direction, wind

speed, temperature, cloud cover, humidity,and mixing height data with lapse rate belowand above it.

Primary meteorological variables avail-able from the National Weather Service canbe processed using PCRAMMET (see section9.3.3.2 of appendix W) to an input file.

Data required at the domain boundary:Wind profile (uniform, log or power law),

depending on the terrain conditions (e.g.,residential area, forest, sea, etc.).

Chemical source data requirements:A database of selected species with specific

heats and molecular weights can be extendedby the user. For heavy gases the database in-cludes a compressibility coefficients table.

Solar reflection:For natural convection simulation with

low wind on a sunny day, approximate valuesof temperature for fields, forests, water bod-ies, shadows and their variations with thetime of the day are determined automati-cally.

c. Output

Printed output option: pollutant con-centration at receptor points, and listing ofinput data (terrain, chemical, weather, andsource data) with turbulence and precisioncontrol data.

Graphical output includes: In 3-dimen-sional perspective or in any crosswind, down-wind or horizontal plane: wind velocity, pol-lutant concentration, 3-dimensionalisosurface. The profile of concentration canbe obtained along any line on the terrain.The concentration contours can be either in-stantaneous or time integrated for the emis-sion from a source or a source combination.A special utility is included to help preparea report or a video animation. The user canselect images, put in annotations, or do ani-mation.

d. Type of Model

The model uses an Eulerian (andLagrangian for particulate matter) 3-dimen-sional finite volume model solving fullNavier-Stokes equations. The numerical dif-fusion is low with appropriate turbulencemodels for building wakes. A second orderresolution may be sought to limit the diffu-sion. Gaussian and puff modes are available.The numerical scheme is self adaptive forthe following situations:

• A curvilinear mesh or a chopped Carte-sian mesh is generated automatically ormanually;

• Thermal and gravity effects are simu-lated by full gravity (heavy gases), no grav-ity (well mixed light gases at ambient tem-

perature), and Boussinesq approximationmethods;

• K-diff, K-e or a boundary layer turbu-lence models are used for turbulence calcula-tions. The flow behind obstacles such asbuildings, is calculated by using a modifiedK-e.

• For heavy gases, a 3-dimensional heatconduction from the ground and a stratifica-tion model for heat exchange from the at-mosphere are used (with anisotropic turbu-lence).

• If local wind data are available, an ini-tial wind field with terrain effects can becomputed using a Lagrangian multiplier,which substantially reduces computationtime.

e. Pollutant Types

• Scavenging, Acid Rain: A module forwater droplets traveling through a plumeconsiders the absorption and de-absorptioneffects of the pollutants by the droplet.Evaporation and chemical reactions withgases are also taken into account.

• Visibility: Predicts plume visibility andsurface deposition of aerosol.

• Particulate matter: Calculates settlingand dry deposition of particles based on aProbability Density Function (PDF) of theirdiameters. The exchange of mass, momen-tum and heat between particles and gas istreated with implicit coupling procedures.

• Ozone formation and dispersion: The pho-tochemical model computes ozone formationand dispersion at street level in the presenceof sunlight.

• Roadway Pollutants: Accounts for heatand turbulence due to vehicular movement.Emissions are based on traffic volume andemission factors.

• Odor Dispersion: Identifies odor sourcesfor waste water plants.

• Radon Dispersion: Simulates naturalradon accumulation in valleys and mine en-vironments.

PANACHE may also be used in emergencyplanning and management for episodic emis-sions, and fire and soot spread in forestedand urban areas or from combustible pools.


Simultaneous use of multiple kinds ofsources at user defined locations. Any num-ber of user defined receptors can identifypollutants from each source individually.

g. Plume Behavior

The options influencing the behavior arefull gravity, Boussinesq approximation or nogravity.


466


h. Horizontal Winds

Horizontal wind speed approximations aremade only at the boundaries based on Na-tional Weather Service data. Inside the do-main of interest, full Navier-Stokes resolu-tion with natural viscosity is used for 3-di-mensional terrain and temperature depend-ent wind field calculation.


Vertical wind speed approximations aremade only at the boundaries based on Na-tional Weather Service data. The domain ofinterest is treated as for horizontal winds.


Diffusion is calculated using appropriateturbulence models. A 2nd order solution forshearing flow can be sought when the num-ber of meshes is limited between obstacles.


Dispersion by full gravity unlessBoussinesq approximation or no gravity re-quested. Vertical dispersion is treated asabove for horizontal dispersion.


PANCHEM, an atmospheric chemistrymodule for chemical reactions, is available.Photochemical reactions are used for tropo-spheric ozone calculations.

m. Physical Removal

Physical removal is treated using dry dep-osition coefficients


Goldwire, H.C. Jr, T.G. McRae, G.W. John-son, D.L. Hipple, R.P. Koopman, J.W.McClure, L.K. Morris and R.T. Cederhall,1985. Desert Tortoise Series Data Report: 1983Pressurized Ammonia Spills. UCID 20562,Lawrence Livermore National Laboratory;Livermore, California.

Green, S.R., 1992. Modeling Turbulent AirFlow in a Stand of Widely Spaced Trees, ThePHOENICS Journal of Computational FluidDynamics and Its Applications, 5: 294–312.

Gryning, S.E. and E. Lyck, 1984. Atmos-pheric Dispersion from Elevated Sources inan Urban Area: Comparison Between TracerExperiments and Model Calculations. Jour-nal of Climate and Applied Meteorology, 23:651–660.

Havens, J., T. Spicer, H. Walker and T.Williams, 1995. Validation of MathematicalModels Using Wind-Tunnel Data Sets forDense Gas Dispersion in the Presence of Ob-stacles. University of Arkansas, 8th Inter-national Symposium-Loss Prevention andSafety Promotion in the Process Industries;Antwerp, Belgium.

McQuaid, J. (ed), 1985. Heavy Gas Disper-sion Trials at Thorney Island. Proc. of a

Symposium held at the University of Shef-field, Great Britain.

Pavitskiy, N.Y., A.A. Yakuskin and S.V.Zhubrin, 1993. Vehicular Exhaust DispersionAround Group of Buildings. The PHOENICSJournal of Computational Fluid Dynamicsand Its Applications, 6: 270–285.

Tripathi, S., 1994. Evaluation of Fluidyn-PANACHE on Heavy Gas Dispersion TestCase. Seminar on Evaluation of Models ofHeavy Gas Dispersion Organized by Euro-pean Commission; Mol, Belgium.

B.12 Plume Visibility Model (PLUVUE II)

Reference

Environmental Protection Agency, 1992.User’s Manual for the Plume VisibilityModel, PLUVUE II (Revised). EPA Publica-tion No. EPA–454/B–92–008, (NTIS PB93–188233). U.S. Environmental ProtectionAgency, Research Triangle Park, NC.

Availability

This model code is available on the Sup-port Center for Regulatory Air Models Bul-letin Board System and also on diskette (asPB 90–500778) from the National TechnicalInformation Service (see section B.0).

Abstract

The Plume Visibility Model (PLUVUE II)is used for estimating visual range reductionand atmospheric discoloration caused byplumes consisting of primary particles, ni-trogen oxides and sulfur oxides emitted froma single emission source. PLUVUE II usesGaussian formulations to predict transportand dispersion. The model includes chemicalreactions, optical effects and surface deposi-tion. Four types of optics calculations aremade: horizontal and non-horizontal viewsthrough the plume with a sky viewing back-ground; horizontal views through the plumewith white, gray and black viewing back-grounds; and horizontal views along the axisof the plume with a sky viewing background.


The Plume Visibility Model (PLUVUE II)may be used on a case-by-case basis as athird level screening model. When applyingPLUVUE II, the following precautionsshould be taken:

1. Treat the optical effects of NO2 and par-ticles separately as well as together to avoidcancellation of NO2 absorption with particlescattering.

2. Examine the visual impact of the plumein 0.1 (or 0), 0.5, and 1.0 times the expectedlevel of particulate matter in the back-ground air.

3. Examine the visual impact of the plumeover the full range of observer-plume sun an-gles.


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4. The user should consult the appropriateFederal Land Manager when using PLUVUEII to assess visibility impacts in a Class Iarea.


Source data requirements are: location andelevation; emission rates of SO2, NOX, andparticulates; flue gas flow rate, exit velocity,and exit temperature; flue gas oxygen con-tent; properties (including density, mass me-dian and standard geometric deviation of ra-dius) of the emitted aerosols in the accumu-lation (0.1–1.0µm) and coarse (1.0–10.µm) sizemodes; and deposition velocities for SO2,

NOX, coarse mode aerosol, and accumulationsmode aerosol.

Meteorological data requirements are: sta-bility class, wind direction (for an observer-based run), wind speed, lapse rate, air tem-perature, relative humidity, and mixingheight.

Other data requirements are: ambientbackground concentrations of NOX, NO2, O3,

and SO2, and background visual range of sul-fate and nitrate concentrations.

Receptor (observer) data requirements are:location, terrain elevation at points alongplume trajectory, white, gray, and blackviewing backgrounds, the distance from theobserver to the terrain observed behind theplume.

c. Output

Printed output includes plume concentra-tions and visual effects at specified down-wind distances for calculated or specifiedlines of sight.

d. Type of Model

PLUVUE II is a Gaussian plume model.Visibility impairment is quantified once thespectral light intensity has been calculatedfor the specific lines of sight. Visibility im-pairment includes visual range reduction,plume contrast, relative coloration of aplume to its viewing background, and plumeperceptibility due to its contrast and colorwith respect to a viewing background.

e. Pollutant Types

PLUVUE II treats NO, NO2, SO2, H2SO4,

HNO3, O3, primary and secondary particles tocalculate effects on visibility.


For performing the optics calculations atselected points along the plume trajectory,PLUVUE II has two modes: plume based andobserver based calculations. The major dif-ference is the orientation of the viewer tothe source and the plume.

g. Plume Behavior

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

h. Horizontal Winds

User-specified wind speed (and directionfor an observer-based run) are assumed con-stant for the calculation.




Constant, uniform (steady-state) wind isassumed for each hour. Straight line plumetransport is assumed to all downwind dis-tances.


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


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

reactions.

m. Physical Removal

Dry deposition of gaseous and particulatepollutants is treated using deposition veloci-ties.


Bergstrom, R.W., C. Seigneur, B.L. Babson,H.Y. Holman and M.A. Wojcik, 1981. Com-parison of the Observed and Predicted VisualEffects Caused by Power Plant Plumes. At-mospheric Environment, 15: 2135–2150.

Bergstrom, R.W., Seigneur, C.D. Johnsonand L.W. Richards, 1984. Measurements andSimulations of the Visual Effects of Particu-late Plumes. Atmospheric Environment,18(10): 2231–2244.

Seigneur, C., R.W. Bergstrom and A.B.Hudischewskyj, 1982. Evaluation of the EPAPLUVUE Model and the ERT VisibilityModel Based on the 1979 VISTTA Data Base.EPA Publication No. EPA–450/4–82–008. U.S.Environmental Protection Agency, ResearchTriangle Park, NC.

White, W.H., C. Seigneur, D.W. Heinold,M.W. Eltgroth, L.W. Richards, P.T. Roberts,P.S. Bhardwaja, W.D. Conner and W.E. Wil-son, Jr, 1985. Predicting the Visibility ofChimney Plumes: An Inter-comparison ofFour Models with Observations at a Well-Controlled Power Plant. Atmospheric Envi-ronment, 19: 515–528.


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

Reference

Petersen, W.B, 1978. User’s Guide for PAL—A Gaussian-Plume Algorithm for Point,Area, and Line Sources. EPA Publication No.EPA–600/4–78–013. Office of Research and De-velopment, Research Triangle Park, NC.(NTIS No. PB 281306)

Rao, K.S. and H.F. Snodgrass, 1982. PAL–DS Model: The PAL Model Including Deposi-tion and Sedimentation. EPA PublicationNo. EPA–600/8–82–023. Office of Research andDevelopment, Research Triangle Park, NC.(NTIS No. PB 83–117739)

Availability

The computer code is available on diskette(as PB 90–500802) from the National Tech-nical Information Service (see section B.0).

Abstract

PAL–DS is an acronym for this point, area,and line source algorithm and is a method ofestimating short-term dispersion usingGaussian-plume steady-state assumptions.The algorithm can be used for estimatingconcentrations of non-reactive pollutants at99 receptors for averaging times of 1 to 24hours, and for a limited number of point,area, and line sources (99 of each type). Thisalgorithm is not intended for application toentire urban areas but is intended, rather, toassess the impact on air quality, on scales oftens to hundreds of meters, of portions ofurban areas such as shopping centers, largeparking areas, and airports. Level terrain isassumed. The Gaussian point source equa-tion estimates concentrations from pointsources after determining the effectiveheight of emission and the upwind and cross-wind distance of the source from the recep-tor. Numerical integration of the Gaussianpoint source equation is used to determineconcentrations from the four types of linesources. Subroutines are included that esti-mate concentrations for multiple lane lineand curved path sources, special line sources(line sources with endpoints at differentheights above ground), and special curvedpath sources. Integration over the areasource, which includes edge effects from thesource region, is done by considering finiteline sources perpendicular to the wind at in-tervals upwind from the receptor. The cross-wind integration is done analytically; inte-gration upwind is done numerically by suc-cessive approximations.

The PAL–DS model utilizes Gaussianplume-type diffusion-deposition algorithmsbased on analytical solutions of a gradient-transfer model. The PAL–DS model can treatdeposition of both gaseous and suspendedparticulate pollutants in the plume sincegravitational settling and dry deposition of

the particles are explicitly accounted for.The analytical diffusion-deposition expres-sions listed in this report in the limit whenpollutant settling and deposition velocitiesare zero, they reduce to the usual Gaussianplume diffusion algorithms in the PALmodel.


PAL–DS can be used if it can be dem-onstrated to estimate concentrations equiva-lent to those provided by the preferred modelfor a given application. PAL–DS must be ex-ecuted in the equivalent mode.

PAL–DS can be used on a case-by-casebasis in lieu of a preferred model if it can bedemonstrated, using the criteria in section3.2, that PAL–DS is more appropriate for thespecific application. In this case the modeloptions/modes which are most appropriatefor the application should be used.


Source data: point-sources—emission rate,physical stack height, stack gas tempera-ture, stack gas velocity, stack diameter,stack gas volume flow, coordinates of stack,initial σy and σz; area sources—sourcestrength, size of area source, coordinates ofS.W. corner, and height of area source; andline sources—source strength, number oflanes, height of source, coordinates of endpoints, initial σy and σz, width of line source,and width of median. Diurnal variations inemissions are permitted. When applicable,the settling velocity and deposition velocityare also permitted.

Meteorological data: wind profile expo-nents, anemometer height, wind directionand speed, stability class, mixing height, airtemperature, and hourly variations in emis-sion rate.

Receptor data: receptor coordinates.

c. Output

Printed output includes:Hourly concentration and deposition flux

for each source type at each receptor; andAverage concentration for up to 24 hours

for each source type at each receptor.

d. Type of Model

PAL–DS is a Gaussian plume model.

e. Pollutant Types

PAL–DS may be used to model non-reac-tive pollutants.


Up to 99 sources of each of 6 source types:point, area, and 4 types of line sources.

Source and receptor coordinates areuniquely defined.

Unique stack height for each source.


469


Coordinates of receptor locations are userdefined.

g. Plume Behavior

Briggs final plume rise equations are used.Fumigation and downwash are not treated.If plume height exceeds mixing height,

concentrations are assumed equal to zero.Surface concentrations are set to zero

when the plume centerline exceeds mixingheight.

h. Horizontal Winds

User-supplied hourly wind data are used.Constant, uniform (steady-state) wind is

assumed within each hour. Wind is assumedto increase with height.

i. Vertical Wind Speeds

Assumed equal to zero.


Rural dispersion coefficients from Turner(1969) are used with no adjustments made forsurface roughness.

Six stability classes are used.Dispersion coefficients (Pasquill-Gifford)

are assumed based on a 3cm roughnessheight.


Six stability classes are used.Rural dispersion coefficients from Turner

(1969) are used; no further adjustments aremade for variation in surface roughness,transport or averaging time.

Multiple reflection is handled by summa-tion of series until the vertical standard de-viation equals 1.6 times mixing height. Uni-form vertical mixing is assumed thereafter.


Not treated.

m. Physical Removal

PAL–DS can treat deposition of both gas-eous and suspended particulates in the plumesince gravitational settling and dry deposi-tion of the particles are explicitly accountedfor.


None Cited.

B.14 Reactive Plume Model (RPM–IV)

Reference

Environmental Protection Agency, 1993.Reactive Plume Model IV (RPM–IV) User’sGuide. EPA Publication No. EPA–454/B–93–012. U.S. Environmental Protection Agency(ESRL), Research Triangle Park, NC. (NTISNo. PB 93–217412)

Availability

The above report and model computer codeare available on the Support Center for Reg-ulatory Air Models Bulletin Board System.The model code is also available on diskette(as PB 96–502026) from the National Tech-nical Information Service (see section B.0).

Abstract

The Reactive Plume Model, RPM–IV, is acomputerized model used for estimatingshort-term concentrations of primary andsecondary reactive pollutants resulting fromsingle or, in some special cases, multiplesources if they are aligned with the meanwind direction. The model is capable of sim-ulating the complex interaction of plumedispersion and non-linear photochemistry. IfCarbon Mechanism IV (CBM–IV) is used,emissions must be disaggregated into carbonbond classes prior to model application. Themodel can be run on a mainframe computer,workstation, or IBM-compatible PC with atleast 2 megabytes of memory. A major fea-ture of RPM–IV is its ability to interfacewith input and output files from EPA’s Re-gional Oxidant Model (ROM) and UrbanAirshed Model (UAM) to provide an inter-nally consistent set of modeled ambient con-centrations for various pollutant species.


There is no specific recommendation at thepresent time. RPM–IV may be used on acase-by-case basis.


Source data requirements are: emissionrates, name, and molecular weight of eachspecies of pollutant emitted; ambient pres-sure, ambient temperature, stack height,stack diameter, stack exit velocity, stackgas temperature, and location.

Meteorological data requirements are:wind speeds, plume widths or stability class-es, photolytic rate constants, and plumedepths or stability classes.

Receptor data requirements are: downwinddistances or travel times at which calcula-tions are to be made.

Initial concentration of all species is re-quired, and the specification of downwindambient concentrations to be entrained bythe plume is optional.

c. Output

Short-term concentrations of primary andsecondary pollutants at either user specifiedtime increments, or user specified downwinddistances.

d. Type of Model

Reactive Gaussian plume model.


470


e. Pollutant Types

Currently, using the Carbon Bond Mecha-nism (CBM–IV), 34 species are simulated (82reactions), including NO, NO2, O3, SO2, SO4,

five categories of reactive hydrocarbons, sec-ondary nitrogen compounds, organicaerosols, and radical species.


Single point source.Single area or volume source.Multiple sources can be simulated if they

are lined up along the wind trajectory.Predicted concentrations are obtained at a

user specified time increment, or at userspecified downwind distances.

g. Plume Behavior

Briggs (1971) plume rise equations are used.

h. Horizontal Winds

User specifies wind speeds as a function oftime.


Not treated.


User specified plume widths, or user mayspecify stability and widths will be computedusing Turner (1969).


User specified plume depths, or user mayspecify stability in which case depths will becalculated using Turner (1969). Note thatvertical uniformity in plume concentrationis assumed.


RPM–IV has the flexibility of using anyuser input chemical kinetic mechanism. Cur-rently it is run using the chemistry of theCarbon Bond Mechanism, CBM–IV (Gery etal., 1988). The CBM–IV mechanism, as incor-porated in RPM–IV, utilizes an updated sim-ulation of PAN chemistry that includes aperoxy-peroxy radical termination reaction,significant when the atmosphere is NOX-lim-ited (Gery et al., 1989). As stated above, thecurrent CBM–IV mechanism accommodates34 species and 82 reactions focusing primarilyon hydrocarbon/nitrogen oxides and ozonephotochemistry.

m. Physical Removal

Not treated.


Stewart, D.A. and M–K Liu, 1981. Develop-ment and Application of a Reactive PlumeModel. Atmospheric Environment, 15: 2377–2393.

B.15 Shoreline Dispersion Model (SDM)

Reference

PEI Associates, 1988. User’s Guide to SDM–A Shoreline Dispersion Model. EPA Publica-tion No. EPA–450/4–88–017. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 89–164305)

Availability

The model code is available on the SupportCenter for Regulatory Air Models BulletinBoard System (see section B.0).

Abstract

SDM is a hybrid multi-point Gaussian dis-persion model that calculates source impactfor those hours during the year when fumiga-tion events are expected using a special fu-migation algorithm and the MPTER regu-latory model for the remaining hours (seeappendix A).


SDM may be used on a case-by-case basisfor the following applications:

• Tall stationary point sources located at ashoreline of any large body of water;

• Rural or urban areas;• Flat terrain;• Transport distances less than 50 km;• 1-hour to 1-year averaging times.


Source data: location, emission rate, phys-ical stack height, stack gas exit velocity,stack inside diameter, stack gas tempera-ture and shoreline coordinates.

Meteorological data: hourly values ofmean wind speed within the Thermal Inter-nal Boundary Layer (TIBL) and at stackheight; mean potential temperature overland and over water; over water lapse rate;and surface sensible heat flux. In addition tothese meteorological data, SDM accessstandard NWS surface and upper air mete-orological data through the RAMMETpreprocessor.

Receptor data: coordinates for each recep-tor.

c. Output

Printed output includes the MPTER modeloutput as well as: special shoreline fumiga-tion applicability report for each day andsource; high-five tables on the standard out-put with ‘‘F’’ designation next to the con-centration if that averaging period includesa fumigation event.

d. Type of Model

SDM is hybrid Gaussian model.


471


e. Pollutant Types

SDM may be used to model primary pollut-ants. Settling and deposition are not treated.


SDM applies user-specified locations ofstationary point sources and receptors. Userinput stack height, shoreline orientation andsource characteristics for each source. Notopographic elevation is input; flat terrain isassumed.

g. Plume Behavior

SDM uses Briggs (1975) plume rise for finalrise. SDM does not treat stack tip or build-ing downwash.

h. Horizontal Winds

Constant, uniform (steady-state) wind isassumed for an hour. Straight line plumetransport is assumed to all downwind dis-tances. Separate wind speed profile expo-nents (EPA, 1980) for both rural and urbancases are assumed.




For the fumigation algorithm coefficientsbased on Misra (1980) and Misra and McMil-lan (1980) are used for plume transport in sta-ble air above TIBL and based on Lamb (1978)for transport in the unstable air below theTIBL. An effective horizontal dispersion co-efficient based on Misra and Onlock (1982) isused. For nonfumigation periods, algorithmscontained in the MPTER model are used (seeappendix A).


For the fumigation algorithm, coefficientsbased on Misra (1980) and Misra and McMil-lan (1980) are used.


Chemical transformation is not included inthe fumigation algorithm.

m. Physical Removal



Environmental Protection Agency, 1987.Analysis and Evaluation of StatisticalCoastal Fumigation Models. EPA Publica-tion No. EPA–450/4–87–002. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC. (NTIS PB 87–175519)

B.16 SHORTZ

Reference

Bjorklund, J.R. and J.F. Bowers, 1982.User’s Instructions for the SHORTZ andLONGZ Computer Programs, Volumes I andII. EPA Publication No. EPA–903/9–82–004aand b. U.S. Environmental Protection Agen-cy, Region III, Philadelphia, PA.

Availability

The computer code is available on the Sup-port Center for Regulatory Air Models Bul-letin Board System and on diskette (as PB96–501986) from the National Technical Infor-mation Service (see section B.0).

Abstract

SHORTZ utilizes the steady state bivariateGaussian plume formulation for both urbanand rural areas in flat or complex terrain tocalculate ground-level ambient air con-centrations. The model can calculate 1-hour,2-hour, 3-hour etc. average concentrationsdue to emissions from stacks, buildings andarea sources for up to 300 arbitrarily placedsources. The output consists of total con-centration at each receptor due to emissionsfrom each user-specified source or group ofsources, including all sources. If the optionfor gravitational settling is invoked, anal-ysis cannot be accomplished in complex ter-rain without violating mass continuity.


SHORTZ can be used if it can be dem-onstrated to estimate concentrations equiva-lent to those provided by the preferred modelfor a given application. SHORTZ must be ex-ecuted in the equivalent mode.

SHORTZ can be used on a case-by-casebasis in lieu of a preferred model if it can bedemonstrated, using the criteria in section3.2, that SHORTZ is more appropriate for thespecific application. In this case the modeloptions/modes which are most appropriatefor the application should be used.


Source data requirements are: for point,building or area sources, location, elevation,total emission rate (optionally classified bygravitational settling velocity) and decaycoefficient; for stack sources, stack height,effluent temperature, effluent exit velocity,stack radius (inner), actual volumetric flowrate, and ground elevation (optional); forbuilding sources, height, length and width,and orientation; for area sources, char-acteristic vertical dimension, and length,width and orientation.

Meteorological data requirements are:wind speed and measurement height, windprofile exponents, wind direction, standarddeviations of vertical and horizontal wind di-rections, (i.e., vertical and lateral turbulent


472


intensities), mixing height, air temperature,and vertical potential temperature gradient.

Receptor data requirements are: coordi-nates, ground elevation.

c. Output

Printed output includes total concentra-tion due to emissions from user-specifiedsource groups, including the combined emis-sions from all sources (with optional allow-ance for depletion by deposition).

d. Type of Model

SHORTZ is a Gaussian plume model.

e. Pollutant Types

SHORTZ may be used to model primarypollutants. Settling and deposition of partic-ulates are treated.


User specified locations for sources and re-ceptors are used.

Receptors are assumed to be at groundlevel.

g. Plume Behavior

Plume rise equations of Bjorklund andBowers (1982) are used.

Stack tip downwash (Bjorklund and Bow-ers, 1982) is included.

All plumes move horizontally and willfully intercept elevated terrain.

Plumes above mixing height are ignored.Perfect reflection at mixing height is as-

sumed for plumes below the mixing height.Plume rise is limited when the mean wind

at stack height approaches or exceeds stackexit velocity.

Perfect reflection at ground is assumed forpollutants with no settling velocity.

Zero reflection at ground is assumed forpollutants with finite settling velocity.

Tilted plume is used for pollutants withsettling velocity specified. Buoyancy-in-duced dispersion (Briggs, 1972) is included.

h. Horizontal Winds

Winds are assumed homogeneous andsteady-state.

Wind speed profile exponents are functionsof both stability class and wind speed. De-fault values are specified in Bjorklund andBowers (1982).


Vertical winds are assumed equal to zero.


Horizontal plume size is derived from inputlateral turbulent intensities using adjust-ments to plume height, and rate of plumegrowth with downwind distance specified inBjorklund and Bowers (1982).


Vertical plume size is derived from inputvertical turbulent intensities using adjust-ments to plume height and rate of plumegrowth with downwind distance specified inBjorklund and Bowers (1982).



m. Physical Removal

Settling and deposition of particulates aretreated.


Bjorklund, J.R. and J.F. Bowers, 1982.User’s Instructions for the SHORTZ andLONGZ Computer Programs. EPA Publica-tion No. EPA–903/9–82–004. EPA Environ-mental Protection Agency, Region III, Phila-delphia, PA.

Wackter, D. and R. Londergan, 1984. Eval-uation of Complex Terrain Air Quality Sim-ulation Models. EPA Publication No. EPA–450/4–84–017. U.S. Environmental ProtectionAgency, Research Triangle Park, NC.

B.17 Simple Line-Source Model

Reference

Chock, D.P., 1980. User’s Guide for the Sim-ple Line-Source Model for Vehicle ExhaustDispersion Near a Road. Ford Research Lab-oratory, Dearborn, MI.

Availability

Copies of the above reference are availablewithout charge from: Dr. D.P. Chock, FordResearch Laboratory, P.O. Box 2053; MD–3083, Dearborn, MI 48121–2053. The shortmodel algorithm is contained in the User’sGuide.

Abstract

The Simple Line-Source Model is a simplesteady-state Gaussian plume model whichcan be used to determine hourly (or half-hourly) averages of exhaust concentrationswithin 100m from a roadway on a relativelyflat terrain. The model allows for plume risedue to the heated exhaust, which can be im-portant when the crossroad wind is very low.The model also utilizes a new set of verticaldispersion parameters which reflects the in-fluence of traffic-induced turbulence.


The Simple Line-Source Model can be usedif it can be demonstrated to estimate con-centrations equivalent to those provided bythe preferred model for a given application.The model must be executed in the equiva-lent mode.


473


The Simple Line-Source Model can be usedon a case-by-case basis in lieu of a preferredmodel if it can be demonstrated, using cri-teria in section 3.2, that it is more appro-priate for the specific application. In thiscase the model options/modes which aremost appropriate for the application shouldbe used.


Source data requirements are: emissionrate per unit length per lane, the number oflanes on each road, distances from lane cen-ters to the receptor, source and receptorheights.

Meteorological data requirements are:buoyancy flux, ambient stability condition,ambient wind and its direction relative tothe road.

Receptor data requirements are: distanceand height above ground.

c. Output

Printed output includes hourly or (half-hourly) concentrations at the receptor dueto exhaust emission from a road (or a systemof roads by summing the results from re-peated model applications).

d. Type of Model

The Simple Line-Source Model is aGaussian plume model.

e. Pollutant Types

The Simple Line-Source Model can be usedto model primary pollutants. Settling anddeposition are not treated.


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

g. Plume Behavior

Plume-rise formula adequate for a heatedline source is used.

h. Horizontal Winds

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



j. Dispersion Parameters

Horizontal dispersion parameter is notused.


A vertical dispersion parameter is usedwhich is a function of stability and wind-road angle. Three stability classes are used:

unstable, neutral and stable. The parameterstake into account the effect of traffic-gen-erated turbulence (Chock, 1980).


Not treated.

m. Physical Removal

Not treated.


Chock, D.P., 1978. A Simple Line-SourceModel for Dispersion Near Roadways. Atmos-pheric Environment, 12: 823–829.

Sistla, G., P. Samson, M. Keenan and S.T.Rao, 1979. A Study of Pollutant DispersionNear Highways. Atmospheric Environment,13: 669–685.

B.18 SLAB

Reference:

Ermak, D.L., 1990. User’s Manual forSLAB: An Atmospheric Dispersion Model forDenser-than-Air Releases (UCRL–MA–105607),Lawrence Livermore National Laboratory.

Availability

The computer code can be obtained from:Energy Science and Technology Center, P.O.Box 1020, Oak Ridge, TN 37830, Phone (615)576–2606.

The User’s Manual (as DE 91–008443) can beobtained from the National Technical Infor-mation Service. The computer code is alsoavailable on the Support Center for Regu-latory Air Models Bulletin Board System(Public Upload/ Download Area; see sectionB.0.)

Abstract

The SLAB model is a computer model, PC-based, that simulates the atmospheric dis-persion of denser-than-air releases. Thetypes of releases treated by the model in-clude a ground-level evaporating pool, anelevated horizontal jet, a stack or elevatedvertical jet and an instantaneous volumesource. All sources except the evaporatingpool may be characterized as aerosols. Onlyone type of release can be processed in anyindividual simulation. Also, the model simu-lates only one set of meteorological condi-tions; therefore direct application of themodel over time periods longer than one ortwo hours is not recommended.

a. Recommendations for use

The SLAB model should be used as a re-fined model to estimate spatial and temporaldistribution of short-term ambient con-centration (e.g., 1-hour or less averagingtimes) and the expected area of exposure to


474


concentrations above specified threshold val-ues for toxic chemical releases where the re-lease is suspected to be denser than the am-bient air.


The SLAB model is executed in the batchmode. Data are input directly from an exter-nal input file. There are 29 input parametersrequired to run each simulation. These pa-rameters are divided into 5 categories by theuser’s guide: source type, source properties,spill properties, field properties, and mete-orological parameters. The model is not de-signed to accept real-time meteorologicaldata or convert units of input values. Chem-ical property data are not available withinthe model and must be input by the user.Some chemical and physical property dataare available in the user’s guide.

Source type is chosen as one of the fol-lowing: evaporating pool release, horizontaljet release, vertical jet or stack release, orinstantaneous or short duration evaporatingpool release.

Source property data requirements arephysical and chemical properties (molecularweight, vapor heat capacity at constantpressure; boiling point; latent heat of vapor-ization; liquid heat capacity; liquid density;saturation pressure constants), and initialliquid mass fraction in the release.

Spill properties include: source tempera-ture, emission rate, source dimensions, in-stantaneous source mass, release duration,and elevation above ground level.

Required field properties are: desired con-centration averaging time, maximum down-wind distance (to stop the calculation), andfour separate heights at which the con-centration calculations are to be made.

Meteorological parameter requirementsare: ambient measurement height, ambientwind speed at designated ambient measure-ment height, ambient temperature, surfaceroughness, relative humidity, atmosphericstability class, and inverse Monin-Obukhovlength (optional, only used as an input pa-rameter when stability class is unknown).

c. Output

No graphical output is generated by thecurrent version of this program. The outputprint file is automatically saved and must besent to the appropriate printer by the userafter program execution. Printed output in-cludes in tabular form:

Listing of model input data;Instantaneous spatially-averaged cloud pa-

rameters—time, downwind distance, mag-nitude of peak concentration, cloud dimen-sions (including length for puff-type simula-tions), volume (or mole) and mass fractions,downwind velocity, vapor mass fraction, den-sity, temperature, cloud velocity, vapor frac-

tion, water content, gravity flow velocities,and entrainment velocities;

Time-averaged cloud parameters—param-eters which may be used externally to cal-culate time-averaged concentrations at anylocation within the simulation domain (tab-ulated as functions of downwind distance);

Time-averaged concentration values atplume centerline and at five off-centerlinedistances (off-centerline distances are mul-tiples of the effective cloud half-width,which varies as a function of downwind dis-tance) at four user-specified heights and atthe height of the plume centerline.

d. Type of Model

As described by Ermak (1989), transportand dispersion are calculated by solving theconservation equations for mass, species, en-ergy, and momentum, with the cloud beingmodeled as either a steady-state plume, atransient puff, or a combination of both, de-pending on the duration of the release. In thesteady-state plume mode, the crosswind-averaged conservation equations are solvedand all variables depend only on the down-wind distance. In the transient puff mode,the volume-averaged conservation equationsare solved, and all variables depend only onthe downwind travel time of the puff centerof mass. Time is related to downwind dis-tance by the height-averaged ambient windspeed. The basic conservation equations aresolved via a numerical integration scheme inspace and time.

e. Pollutant Types

Pollutants are assumed to be non-reactiveand non-depositing dense gases or liquid-vapor mixtures (aerosols). Surface heattransfer and water vapor flux are also in-cluded in the model.


Only one source can be modeled at a time.There is no limitation to the number of re-

ceptors; the downwind receptor distances areinternally-calculated by the model. TheSLAB calculation is carried out up to theuser-specified maximum downwind distance.

The model contains submodels for thesource characterization of evaporating pools,elevated vertical or horizontal jets, and in-stantaneous volume sources.

g. Plume Behavior

Plume trajectory and dispersion is basedon crosswind-averaged mass, species, energy,and momentum balance equations. Sur-rounding terrain is assumed to be flat and ofuniform surface roughness. No obstacle orbuilding effects are taken into account.


475


h. Horizontal Winds

A power law approximation of the loga-rithmic velocity profile which accounts forstability and surface roughness is used.


Not treated.

j. Vertical Dispersion

The crosswind dispersion parameters arecalculated from formulas reported by Mor-gan et al. (1983), which are based on experi-mental data from several sources. The for-mulas account for entrainment due to at-mospheric turbulence, surface friction, ther-mal convection due to ground heating, dif-ferential motion between the air and thecloud, and damping due to stable densitystratification within the cloud.

k. Horizontal Dispersion

The horizontal dispersion parameters arecalculated from formulas similar to thosedescribed for vertical dispersion, also fromthe work of Morgan et al. (1983).


The thermodynamics of the mixing of thedense gas or aerosol with ambient air (in-cluding water vapor) are treated. The rela-tionship between the vapor and liquid frac-tions within the cloud is treated using thelocal thermodynamic equilibrium approxi-mation. Reactions of released chemicalswith water or ambient air are not treated.

m. Physical Removal

Not treated.


Blewitt, D.N., J.F. Yohn and D.L. Ermak,1987. An Evaluation of SLAB and DEGADISHeavy Gas Dispersion Models Using the HFSpill Test Data. Proceedings, AIChE Inter-national Conference on Vapor Cloud Mod-eling, Boston, MA, November, pp. 56–80.

Ermak, D.L., S.T. Chan, D.L. Morgan andL.K. Morris, 1982. A Comparison of Dense GasDispersion Model Simulations with BurroSeries LNG Spill Test Results. J. Haz.Matls., 6: 129–160.

Zapert, J.G., R.J. Londergan and H. This-tle, 1991. Evaluation of Dense Gas Simula-tion Models. EPA Publication No. EPA–450/4–90–018. U.S. Environmental Protection Agen-cy, Research Triangle Park, NC.

B.19 WYNDvalley Model

Reference

Harrison, Halstead, 1992. ‘‘A User’s Guideto WYNDvalley 3.11, an Eulerian-Grid Air-Quality Dispersion Model with VersatileBoundaries, Sources, and Winds,’’ WYNDsoftInc., Mercer Island, WA.

Availability

Copies of the user’s guide and the execut-able model computer codes are available at acost of $295.00 from: WYNDsoft, Incor-porated, 6333 77th Avenue, Mercer Island, WA98040, Phone: (206) 232–1819.

Abstract

WYNDvalley 3.11 is a multi-layer (up tofive vertical layers) Eulerian grid dispersionmodel that permits users flexibility in defin-ing borders around the areas to be modeled,the boundary conditions at these borders,the intensities and locations of emissionssources, and the winds and diffusivities thataffect the dispersion of atmospheric pollut-ants. The model’s output includes griddedcontour plots of pollutant concentrations forthe highest brief episodes (during any singletime step), the highest and second-highest24-hour averages, averaged dry and wet depo-sition fluxes, and a colored ‘‘movie’’ showingevolving dispersal of pollutant concentra-tions, together with temporal plots of theconcentrations at specified receptor sitesand statistical inference of the probabilitiesthat standards will be exceeded at thosesites. WYNDvalley is implemented on IBMcompatible microcomputers, with inter-active data input and color graphics display.


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

Variable Recommended value

Horizontal cell dimension ...... 250 to 500 meters.Vertical layers ........................ 3 to 5.Layer depth ............................ 50 to 100 meters.Background (internal to

model).Zero (background should be

added externally to modelestimates).

Lateral meander velocity ....... Default.Diffusivities ............................. Default.Ventilation parameter (upper

boundary condition).Default.

Dry deposition velocity .......... Zero (site-specific).Washout ratio ........................ Zero (site-specific).


Input data, including model options, mod-eling domain boundaries, boundary condi-tions, receptor locations, source locations,and emission rates, may be entered inter-actively, or through existing template filesfrom a previous run. Meteorological data, in-cluding wind speeds, wind directions, rainrates (optionally, for wet deposition calcula-tions), and time of day and year, may be ofarbitrary time increment (usually an hour)and are entered into the model through anexternal meteorological data file. Option-ally, users may specify diffusivities and


476


upper boundary conditions for each time in-crement. Source emission rates may be con-stant or modulated on a daily, weekly, and/or seasonal basis.

c. Output

Output from WYNDvalley includes griddedcontour maps of the highest pollutant con-centrations at each time step and the high-est and second-highest 24-hour average con-centrations. Output also includes the deposi-tion patterns for wet, dry, and total fluxes ofthe pollutants to the surface, integrated overthe simulation period. A running ‘‘movie’’ ofthe concentration patterns is displayed onthe screen (with optional printout) as theyevolve during the simulation. Output filesinclude tables of daily-averaged pollutantconcentrations at every modeled grid cell,and of hourly concentrations at up to eightspecified receptors. Statistical analyses areperformed on the hourly and daily data toestimate the probabilities that specified lev-els will be exceeded more than once duringan arbitrary number of days with similarweather.

d. Type of Model

WYNDvalley is a three dimensionalEulerian grid model.

e. Pollutant Types

WYNDvalley may be used to model anyinert pollutant.


Source and receptors may be located any-where within the user-defined modeling do-main. All point and area sources, or portionsof an area source, within a given grid cell aresummed to define a representative emissionrate for that cell. Concentrations are cal-culated for each and every grid cell in themodeling domain. Up to eight grid cells maybe selected as receptors, for which time his-tories of concentration and deposition fluxesare determined, and probabilities of exceed-ance are calculated.

g. Plume Behavior

Emissions for buoyant point sources areplaced by the user in a grid cell which bestreflects the expected effective plume heightduring stagnation conditions. Five verticallayers are available to the user.

h. Horizontal Winds

During each time step in the model, thewinds are assumed to be uniform throughoutthe modeling domain. Numerical diffusion isminimized in the advection algorithm. Toaccount for terrain effects on winds and dis-persion, an ad hoc algorithm is employed inthe model to distribute concentrations nearboundaries.


Winds are assumed to be constant withheight.


Horizontal eddy diffusion coefficients maybe entered explicitly by the user at everytime step. Alternatively, a default algorithmmay be invoked to estimate these coeffi-cients from the wind velocities and theirvariances.


Vertical eddy diffusion coefficients and atop-of-model boundary condition may be en-tered explicitly by the user at every timestep. Alternatively, a default algorithm maybe invoked to estimate these coefficientsfrom the horizontal wind velocities and theirvariances, and from an empirical time-of-daycorrection derived from temperature gra-dient measurements and Monin-Obukhovsimilarities.


Chemical transformation is not explicitlytreated by WYNDvalley.

m. Physical Removal

WYNDvalley optionally simulates both wetand dry deposition. Dry deposition is propor-tional to concentration in the lowest layer,while wet deposition is proportional to rainrate and concentration in each layer. Appro-priate coefficients (deposition velocities andwashout ratios) are input by the user.


Harrison, H., G. Pade, C. Bowman and R.Wilson, 1990. Air Quality During Stagna-tions: A Comparison of RAM andWYNDvalley with PM–10 Measurements atFive Sites. Journal of the Air & Waste Man-agement Association, 40: 47–52.

Maykut, N. et al., 1990. Evaluation of theAtmospheric Deposition of Toxic Contami-nants to Puget Sound. State of Washington,Puget Sound Water Quality Authority, Se-attle, WA.

Yoshida, C., 1990. A Comparison ofWYNDvalley Versions 2.12 and 3.0 with PM–10 Measurements in Six Cities in the PacificNorthwest. Lane Regional Air Pollution Au-thority, Springfield, OR.

B. REF References

Beals, G.A., 1971. A Guide to Local Disper-sion of Air Pollutants. Air Weather ServiceTechnical Report ⊥214 (April 1971).

Bjorklund, J.R. and J.F. Bowers, 1982.User’s Instructions for the SHORTZ andLONGZ Computer Programs. EPA Publica-tion No. EPA–903/9–82–004a and b. U.S. Envi-ronmental Protection Agency, Region III,Philadelphia, PA.


477


Briggs, G.A., 1969. Plume Rise. U.S. AtomicEnergy Commission Critical Review Series,Oak Ridge National Laboratory, Oak Ridge,TN. (NTIS No. TID–25075)

Briggs, G.A., 1971. Some Recent Analysesof Plume Rise Observations. Proceedings ofthe Second International Clean Air Congress,edited by H.M. Englund and W.T. Berry. Aca-demic Press, New York, NY.

Briggs, G.A., 1972. Discussion on ChimneyPlumes in Neutral and Stable Surroundings.Atmospheric Environment, 6: 507–510.

Briggs, G.A., 1974. Diffusion Estimation forSmall 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 Environ-mental Impact Analyses. American Meteoro-logical Society, Boston, MA, pp. 59–111.

Briggs, G.A., 1984. Plume Rise and Buoy-ancy Effects. Atmospheric Science andPower Production, Darryl Randerson (Ed.).DOE Report DOE/TIC–27601, Technical Infor-mation Center, Oak Ridge, TN. (NTIS No.DE84005177)

Carpenter, S.B., T.L. Montgomery, J.M.Leavitt, W.C. Colbaugh and F.W. Thomas,1971. Principal Plume Dispersion Models:TVA Power Plants. Journal of Air PollutionControl Association, 21: 491–495.

Chock, D.P., 1980. User’s Guide for the Sim-ple Line-Source Model for Vehicle ExhaustDispersion Near a Road. EnvironmentalScience Department, General Motors Re-search Laboratories, Warren, MI.

Colenbrander, G.W., 1980. A MathematicalModel for the Transient Behavior of DenseVapor Clouds, 3rd International Symposiumon Loss Prevention and Safety Promotion inthe Process Industries, Basel, Switzerland.

DeMarrais, G.A., 1959. Wind Speed Profilesat Brookhaven National Laboratory. Journalof Applied Meteorology, 16: 181–189.

Ermak, D.L., 1989. A Description of theSLAB Model, presented at JANNAF Safetyand Environmental Protection Sub-committee Meeting, San Antonio, TX, April,1989.

Gery, M.W., G.Z. Whitten and J.P. Killus,1988. Development and Testing of CBM-IV forUrban and Regional Modeling. EPA Publica-tion No. EPA–600/3–88–012. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC. (NTIS No. PB 88–180039)

Gery, M.W., G.Z. Whitten, J.P. Killus andM.C. Dodge, 1989. A Photochemical KineticsMechanism for Urban and Regional ScaleComputer Modeling. Journal of GeophysicalResearch, 94: 12,925–12,956.

Gifford, F.A. and S.R. Hanna, 1970. UrbanAir Pollution Modeling. Proceedings of theSecond International Clean Air Congress,Academic Press, Washington, D.C.; pp. 140–1151.

Gifford, F.A., 1975. Atmospheric DispersionModels for Environmental Pollution Applica-

tions. Lectures on Air Pollution and Envi-ronmental Impact Analyses. American Mete-orological Society, Boston, MA.

Green, A.E., Singhal R.P. and R.Venkateswar, 1980. Analytical Extensions ofthe Gaussian Plume Model. Journal of theAir Pollution Control Association, 30: 773–776.

Heffter, J.L., 1965. The Variations of Hori-zontal Diffusion Parameters with Time forTravel Periods of One Hour or Longer. Jour-nal of Applied Meteorology, 4: 153–156.

Heffter, J.L., 1980. Air Resources Labora-tories Atmospheric Transport and DispersionModel (ARL-ATAD). NOAA Technical Memo-randum ERL ARL–81. Air Resources Labora-tories, Silver Spring, MD.

Irwin, J.S., 1979a. Estimating Plume Dis-persion—A Recommended GeneralizedScheme. Fourth Symposium on Turbulence,Diffusion and Air Pollution, Reno, Nevada.

Irwin, J.S., 1979b. A Theoretical Variationof the Wind Profile Power-Law Exponent asa Function of Surface Roughness and Sta-bility. Atmospheric Environment, 13: 191–194.

MacCready, P.B., Baboolal, L.B. and P.B.Lissaman, 1974. Diffusion and TurbulenceAloft Over Complex Terrain. Preprint Vol-ume, AMS Symposium on Atmospheric Dif-fusion and Air Pollution, Santa Barbara, CA.American Meteorological Society, Boston,MA.

Moore, G.E., T.E. Stoeckenius and D.A.Stewart, 1982. A Survey of Statistical Meas-ures of Model Performance and Accuracy forSeveral Air Quality Models. EPA Publica-tion No. EPA–450/4–83–001. U.S. Environ-mental Protection Agency, Research Tri-angle Park, NC.

Morgan, D.L., Jr., L.K. Morris and D.L.Ermak, 1983. SLAB: A Time-Dependent Com-puter Model for the Dispersion of Heavy GasReleased in the Atmosphere, UCRL–53383,Lawrence Livermore National Laboratory,Livermore, CA.

Pasquill, F., 1976. Atmospheric DispersionParameters in Gaussian Plume Modeling,Part II. EPA Publication No. EPA–600/4–76–030b. U.S. Environmental Protection Agency,Research Triangle Park, NC.

Slade, D.H., 1968. Meteorology and AtomicEnergy, U.S. Atomic Energy Commission, 445pp. (NTIS No. TID–24190)

Turner, D.B., 1964. A Diffusion Model of AnUrban Area. Journal of Applied Meteorology,3: 83–91.

Turner, D.B., 1969. Workbook of Atmos-pheric Dispersion Estimates. PHS Publica-tion No. 999–AP–26. U.S. Environmental Pro-tection Agency, Research Triangle Park, NC.

Van Dop, H., 1992. Buoyant Plume Rise ina Lagrangian Frame Work. Atmospheric En-vironment, 26A: 1335–1346.


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1 The ‘‘Screening Procedures for Esti-mating the Air Quality Impact of StationarySources, Revised’’, October 1992 (EPA–450/R–92–019), should be used as a screening tool todetermine whether modeling analyses are re-quired. Screening procedures should be re-fined by the user to be site/problem specific.

2 Within 50km or distance to which sourcehas a significant impact, whichever is less.

3 Particulate emissions should be specifiedas a function of particulate diameter anddensity ranges.

4 See footnote 2 of this appendix C.

APPENDIX C TO APPENDIX W OF PART51—EXAMPLE AIR QUALITY ANAL-YSIS CHECKLIST

C.0 Introduction

This checklist recommends a standardizedset of data and a standard basic level of anal-ysis needed for PSD applications and SIP re-visions. The checklist implies a level of de-tail required to assess both PSD incrementsand the NAAQS. Individual cases may re-quire more or less information and the Re-gional Meteorologist should be consulted atan early stage in the development of a database for a modeling analysis.

At pre-application meetings betweensource owner and reviewing authority, thischecklist should prove useful in developing aconsensus on the data base, modeling tech-niques and overall technical approach priorto the actual analyses. Such agreement willhelp avoid misunderstandings concerning thefinal results and may reduce the later needfor additional analyses.

EXAMPLE AIR QUALITY ANALYSISCHECKLIST 1

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

• Urban areas 2

• PSD Class I areas• Nonattainment areas 2

• Topographic features (terrain, lakes,river valleys, etc.) 2

• Other major existing sources 2

• Other major sources subject to PSD re-quirements

• NWS meteorological observations (sur-face and upper air)

• On-site/local meteorological observations(surface and upper air)

• State/local/on-site air quality monitoringlocations 2

• Plant layout on a topographic map cov-ering a 1km radius of the source with infor-mation sufficient to determine GEP stackheights

2. Information on urban/rural characteris-tics:

• Land use within 3km of source classifiedaccording to Auer (1978): Correlation of landuse and cover with meteorological anoma-lies. Journal of Applied Meteorology, 17: 636–643.

• Population¥> total

¥> density• Based on current guidance determination

of whether the area should be addressedusing urban or rural modeling methodology

3. Emission inventory and operating/designparameters for major sources within regionof significant impact of proposed site (sameas required for applicant)

• Actual and allowable annual emissionrates (g/s) and operating rates 3

• Maximum design load short-term emis-sion rate (g/s) 3

• Associated emissions/stack characteris-tics as a function of load for maximum, aver-age, and nominal operating conditions ifstack height is less than GEP or located incomplex terrain. Screening analyses asfootnoted above or detailed analyses, if nec-essary, must be employed to determine theconstraining load condition (e.g., 50%, 75%,or 100% load) to be relied upon in the short-term modeling analysis.

—location (UTM’s)—height of stack (m) and grade level above

MSL—stack exit diameter (m)—exit velocity (m/s)—exit temperature (°K)• Area source emissions (rates, size of area,

height of area source)3• Location and dimensions of buildings

(plant layout drawing)—to determine GEP stack height—to determine potential building

downwash considerations for stack heightsless than GEP

• Associated parameters—boiler size (megawatts, pounds/hr. steam,

fuel consumption, etc.)—boiler parameters (% excess air, boiler

type, type of firing, etc.)—operating conditions (pollutant content

in fuel, hours of operation, capacity factor,% load for winter, summer, etc.)

—pollutant control equipment parameters(design efficiency, operation record, e.g., canit be bypassed?, etc.)

• Anticipated growth changes4. Air quality monitoring data:• Summary of existing observations for

latest five years (including any additionalquality assured measured data which can beobtained from any State or local agency orcompany) 4

• Comparison with standards• Discussion of background due to

uninventoried sources and contributionsfrom outside the inventoried area and de-scription of the method used for determina-tion of background (should be consistentwith the Guideline)


479


5. Meteorological data:• Five consecutive years of the most re-

cent representative sequential hourly Na-tional Weather Service (NWS) data, or one ormore years of hourly sequential on-site data

• Discussion of meteorological conditionsobserved (as applied or modified for the site-specific area, i.e., identify possible vari-ations due to difference between the moni-toring site and the specific site of the source)

• Discussion of topographic/land use influ-ences

6. Air quality modeling analyses:• Model each individual year for which

data are available with a recommendedmodel or model demonstrated to be accept-able on a case-by-case basis

—urban dispersion coefficients for urbanareas

—rural dispersion coefficients for ruralareas

• Evaluate downwash if stack height is lessthan GEP

• Define worst case meteorology• Determine background and document

method—long-term—short-term• Provide topographic map(s) of receptor

network with respect to location of allsources

• Follow current guidance on selection ofreceptor sites for refined analyses

• Include receptor terrain heights (if appli-cable) used in analyses

• Compare model estimates with measure-ments considering the upper ends of the fre-quency distribution

• Determine extent of significant impact;provide maps

• Define areas of maximum and highest,second-highest impacts due to applicantsource (refer to format suggested in AirQuality Summary Tables)

¥> long-term¥> short-term7. Comparison with acceptable air quality

levels:• NAAQS• PSD increments• Emission offset impacts if nonattain-

ment8. Documentation and guidelines for mod-

eling methodology:• Follow guidance documents¥> appendix W to 40 CFR part 51¥> ‘‘Screening Procedures for Estimating

the Air Quality Impact of StationarySources, Revised’’ (EPA–450/R–92–019), 1992

¥> ‘‘Guideline for Determination of GoodEngineering Practice Stack Height (Tech-nical Support Document for the StackHeight Regulations)’’ (EPA–450/4–80–023R),1985

¥> ‘‘Ambient Monitoring Guidelines forPSD’’ (EPA–450/4–87–007), 1987

¥> Applicable sections of 40 CFR parts 51and 52.

AIR QUALITY SUMMARY—FOR NEW SOURCE ALONEPollutant: lllllllll1 lllllllll2 lllllllll2

Highest Highest2d high Highest Highest

2d high Annual

Concentration Due to Modeled Source (µg/m3) ........Background Concentration (µg/m3) ...........................Total Concentration (µg/m3) ......................................Receptor Distance (km) (or UTM easting) ................Receptor Direction (°) (or UTM northing) ..................Receptor Elevation (m) ..............................................Wind Speed (m/s) ......................................................Wind Direction (°) ......................................................Mixing Depth (m) .......................................................Temperature (°K) .......................................................Stability ......................................................................Day/Month/Year of Occurrence .................................

Surface Air Data From llllllllll Surface Station Elevation (m) llllllllllAnemometer Height Above Local Ground Level (m) llllllllllUpper Air Data From llllllllllllllllllllllllPeriod of Record Analyzed lllllllllllllllllllllModel Used llllllllllllllllllllllllllllRecommended Model lllllllllllllllllllllll

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

AIR QUALITY SUMMARY—FOR ALL NEW SOURCESPollutant: lllllllll1 lllllllll2 lllllllll2

Highest Highest 2ndhigh Highest Highest 2nd

high Annual

Concentration Due to Modeled Source (µg/m3) ........


480


AIR QUALITY SUMMARY—FOR ALL NEW SOURCES—ContinuedPollutant: lllllllll1 lllllllll2 lllllllll2


high Annual

Background Concentration (µg/m3) ...........................Total Concentration (µg/m3) ......................................Receptor Distance (km) (or UTM easting) ................Receptor Direction (°) (or UTM northing) ..................Receptor Elevation (m) ..............................................Wind Speed (m/s) ......................................................Wind Direction (°) ......................................................Mixing Depth (m) .......................................................Temperature (°K) .......................................................Stability ......................................................................Day/Month/Year of Occurrence .................................


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

AIR QUALITY SUMMARY—FOR ALL SOURCESPollutant: lllllllll1 lllllllll2 lllllllll2


high Annual

Concentration Due to Modeled Source (µg/m3) ........Background Concentration (µg/m3) ...........................Total Concentration (µg/m3) ......................................Receptor Distance (km) (or UTM easting) ................Receptor Direction (°) (or UTM northing) ..................Receptor Elevation (m) ..............................................Wind Speed (m/s) ......................................................Wind Direction (°) ......................................................Mixing Depth (m) .......................................................Temperature (°K) .......................................................Stability ......................................................................Day/Month/Year of Occurrence .................................


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

STACK PARAMETERS FOR ANNUAL MODELING

StackNo. Serving

Emis-sion

rate foreachpollut-

ant(g/s)

Stackexit di-ameter

(m)

Stackexit ve-locity(m/s)

Stackexittem-pera-ture(°K)

Phys-ical

height

Stack(m)

GEPstack

ht. (m)

Stackbaseele-

vation(m)

Building dimensions (m)

Height Width Length


481


STACK PARAMETERS FOR SHORT-TERM MODELING 1

StackNo. Serving

Emis-sion

rate foreachpollut-

ant(g/s)

Stackexit di-ameter

(m)

Stackexit ve-locity(m/s)

Stackexittem-pera-ture(°K)

Phys-ical

height

Stack(m)

GEPstack

ht. (m)

Stackbaseele-

vation(m)

Building dimensions (m)

Height Width Length

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

[61 FR 41840, Aug. 12, 1996]

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`
