UnitedStates Environmental Protection Agency Air OfficeofAirQuality PlanningandStandards ResearchTriangle ParkNC27711 EPA-450/2-86-001 June 1987 

# &EPA PM10SIP Development Guideline

### EPA-450/2-86-001



# PM10 SIPDevelopmentGuideline

U. S. ENVIRONMENTALPROTECTIONAGENCY OfficeofAirandRadiation OfficeofAirQualityPlanningandStandards ResearchTrianglePark, NorthCarolina 27711 June 1987Thisguidelineis issued bythe Environmental ProtectionAgencytoassistStateandlocal airpollutioncontrol agencies in developing State implementation plans (SIP's)forthe national ambient airqualitystandards for particulate matter. The standardsforparticulate matterhave been revised toaddressparticles nominally 10 micrometers and smaller indiameter(PMlO). Copiesareavailable· in limitedquantities

-from the Library ServicesOffice(MD-35),U.S.EnvironmentalProtectionAgency,ResearchTrianglePark,NorthCarolina 27711; or.forafee,fromtheNationalTechnicalInformationService,5285PortRoyalRoad,Springfield.Virginia 22161.

This report hasbeen reviewedbytheOffice OfAirQualityPlanningAndStandards, U.S. Environmental ProtectionAgency, andapprovedforpublication.Anymentionoftradenamesorcommercialproducts is not intendedtoconstituteendorsementorrecommendationforuse.

Publication No. EPA-45012-86-001

### TABLE OF CONTENTS Page 1.0 INTRODUCTION
a • • • • • • •• 1-1 Overy;ew .., ft ••••••••• ' •••••••••••••••••••••••••••••••• Guideline Contents ••.•..•.•....•••••••••••••••.••••••

General SIP Approach •••••••.••••••••••.•••.•••.•••••• 

### 1.1 1.2

1.3 1.2.1 1.2.2 1.2.3 1.2.4 1.2.5 1.2.6 1.2.7 1.2.8 1.2.9 Determining the air quality status of areas•••• Ambient monitoring .••..••••••••••••••••.•••••• Mode1;n9 ••••••••••••.••••••••••••••••••••••••• Emission inventories ••.••••••••••••••••••••••• 

### Development ofcontrol strategies ••••••••••••• SIP requirements and data reporting ••••••••••• Types of receptor models ....................•. 

### Preliminary estimate ofPMI0 design concentration using TSP data •••••••••••••••••• Source test;ng . 

### 1-1 1-2 1-3 1-3 1-3 1-3 1-4 1-4 1-4 1-4 1-5 1-5 2.0 DETERMINING AIR QUALITY STATUS OF AREAS ••••••••••••••••••• 2-1 2.1 Policy Overview •...........................•.....•... 2-1 2.2 Demonstrating Attainment ......•...•...•...•...••..•.. 2-2 

### 2.3 Estimating Probability ofNonattainment to ••••••••••• 2-4 Determine Initial SIP Requirements 2.4 Area Categorization ••.••..••..••.•••••••••••.•••••••• 2-7 2.5 Area Boundaries ••••••...•••.•••.•••••.•••.••..••••••• 2-9 

### 3.0 AMBIENT MONITORING AND DATA USAGE ••••••••••••••••••••••••• 3-1 3.1 Ambient PMIO Monitoring ..•••••••••••••••..••••••••••• 3-1 3.2 Part 58 Requirements •.•..••••••••••••.•...•...••••••. 3-1 3.2.1 Quality assurance •••••••••••.•••••.••••••••••• 3-1 

### 3.2.2 Ambient monitoring methodology •••••••••••••••• 3-2 3.2.3 Network establishment ••••••••••••••••••••••••• 3-3 3.2.4 Network design •••.•.•••••••••••••.••.•.••••.•• 3-3 3.2.5 Sampling interval ••••••••••••••.•••••••••••.•. 3-4 3.2.6 Sampler inlet siting ••••••.••••.••••••.••••••. 3-5 3.2.7 Ambient data reporting •••••••••••••••.•••••••• 3-6 3.2.8 Air pollution i_ndex •.••.•••••..•.•.•...•••.••• 3-7

i

### Page 3.3 Ambient Samplers and Ambient Data Usage •••••••••••••• 3-7 3.3.1 Ambient samplers in use ••••••••••••••••••••••• 3-7 

### 3.3.2 Interimuse ofnonreference or equivalent method samplers ••••••••••••••••.•••••••••••••• 3-7 3.3.3 Episode monitoring •••••••••.•••••••••••••••••• 3-8 3.4 SIP Revisions •••••••••.•••••••••••••••••••••••••••••• 3-8 

### 4.0 AIR QUALITY MODELING FOR PM10 ••••••••••••••••••••••••••••• 4-1 4.1 Introduction ••••••••••••••••••••••••••••••••.•••••••• 4-1 

### 4.2 Considerations in ReceptorModel Selection .••.•...•..••...•....•...•..••.•.•.••.•...•. 4-2 4.2.1 Particle size •••••.••••••••••••••••••••••••••• 4-2 

### 4.2.2 Prior knowledge of sources and emissions •••••• 4-4 4.2.3 Chemical similarity •••••••••••••••••••..•••••• 4-4 

### 4.2.4 Sources versus source categories •••••••••••••• 4-5 4.2.5 Particle size and time scale ••••••,••••••••••• 4-5 4.3 Screening Techniques and Refined Dispersion Models for PMIO Concentrations •••••••••••••••••••••••••••••• 4-7 

### 4.3.1 Selection ofappropriate dispersion models ••••••••••••••••••••••••••••• 4-8 

### 4.3.2 Special considerations for PM10 dispersion modeling ••••••••••••••••••••••••••• 4-9 

### 4.4 ReceptorModels forEstimating PM10 Concentrations ••• 4-11 4.4.1 Control strategy analyses using receptor models ••••••••• •••••••••••••••••••••••••••••• 4-11 

### 4.4.2 Preliminary analyses using receptormodels •••• 4-12 4.5 Use ofReceptor and Dispersion Models in Combination. 4-13 5.0 DEVELOPMENT OF PM10EMISSION INVENTORIES •••••••••••••••••• 5-1 5.1 Overview •••••••••••••••••••••••••.••••••••••••••••••• 5-1 

### 5.2 PMIO Emission Factors and Fractional Multipliers ••••• 5-3 5.3 PM10 Source Data for Dispersion Models ••••••••••••••• 5-20 5.4 Inventory Data Needed in ReceptorModels ••••••••••••• 5-29 5.5 Condensable**Parti~ulate** •••••••••••••••••••••••••••••• 5-32 5.6 Secondary Particulate •••••••••••••••••••••••••••••••• 5-32 

### 5.7 Use ofExisting Emission Inventory ••••••••••••••••••• 5-35 5.8 Data Handling •••••••••••••••••••••••••••.•••••••••••• 5-38



### iip'a.9.e 

### 6.0 DEVELOPMENT OF CONTROL STRATEGIES •.••••••••••••••••••••••. 6-1 6.1 Overview

-

_ -..•...•.•.•..... 6-1 6.2 Data Base Requirements •..•••..••.••••..••••..••••.•.• 6-2 

### 6.3 Methodologies for Determining Design Concentrations .• 6-2 6.3.1 Table look-up ...................•............. 6-3 

### 6.3.2 Fitting one statistical distribution to several years ofdata ••.•••••••.•.•.•••••••••. 6-6 

### 6.3.3 Using the empirical frequency distribution of several years ofdata (graphical estimation) .• 6-7 6.3.4 Conditional probability approach •••.•..••.•... 6-8 6.4 Determining Emission Limits ••.,.,.•.........•........ 6-8 

### 6.4.1 General ••.•••..•.....••...-.••.••••...••••.... 6-8 6.4.2 Example for annual averages ••••••••••.•••••••• 6-9 6.4.3 Considerations for 24-hour averages .•••••.•••. 6-11 7.0 SIP REQUIREMENTS AND DATA REPORTING ••••••••••••••••••••••• 7-1 7.1 Introduction

- '" 7-1 7.2 Clean Air Act Requirements ........•....•........•.• 7-1 

### 7.2.1 Time limits

_SIPrequirements 7-1 7.2.2 Regulations in 40CFR Part 51 •••••••••••••.•••• 7-2 7.3 PMI0 SIP Development Policy 7-2 7.3.1 Group I PMI0 SIP requirements

- 7-2 7.3.2 Group II PMI0 SIP requirements 7-3 7.3.3 Group III PMI0 SIP requirements 7-5 7.4 SIP Content

- 7-5 7.5 Control Strategy Transition ••••••••••••••••••••••• 7-6 7.6 Current Emission Regulations 7-7 7.7 Surrogate Emission Regulations •••••••••••••••••••• 7-8 7.7.1 Retaining existing emission limits ••••.•••.•••• 7-8 7.7.2 Setting new emission limits 7-10 7.8 PMI0-Specific Emission Regulations 7-10 7.9 Reporting Emissions Data 7-11 7.10 Emissions Trading (Bubble) Policy ••••••••••••••••• 7-13 7.11 Fugitive Dust Policy 7-13

iii

### APPENDIXA - TYPES OF RECEPTOR MODELS ••••••••••••••••••••• A-I 1.0 Chemical Mass. Balance •••.•.•.•..........•.•••.••••..• A-I 2.0 Factor Analysis '".•..'"..'".••..•.•.....• A-2 

### 3.0 Optical Microscopy

• '" •• '" '" • '" a '" •• '" • '" '" '" 

### A-2 4.0 Automated Scanning Electron Microscopy ••••••••••••••• A-2 5.0 OtherReceptorModels A-3 5.1 X-Ray Diffraction (XRD) ••••••.•••••••••••••••••• A-3 5.2 Trajectory Analysis ,"' A-3 5.3 Microinventory ......•.•.•.....•..•.•..••.•.•.... A-3 

### APPENDIX B - PRELIMINARY ESTIMATES OF PMlO DESIGN CONCENTRATIONS USING TSP DATA••••'"•••••••••.•••.•••••••.••'".• 8-1 1.0 Annual NAAQS •... A •••••••••••••••••••••••••••••••••••• 8-1 1.1 Recommendations .........••...••..'".•••.......... 8-1 1.2 Examp1e '" '"
.'"
..'" '" '"'" '" B-2 2.0 24-Hour NAAQS •••'"•••••••...•.••••..•••••••'"••••.•.••. B-2 2.1 Background .................................•.... B-2 2.2 Recommendations .......................••.•.....• 8-4 2.3 Example**~.~ ~.~**  8-6 

### APPENDIX C - GUIDELINES FOR SOURCE TESTING FOR SIZE SPECIFIC PARTICULATE EMISSIONS C-l 1.0 Introduction .......••................................ C-l 

### 2.0 AvailableMethods for SizeSpecific Source Measurements •............•••••......... 'O ••••••••••••• C-2 

### 3.0 Selection ofSamplingTraversePoints C-3 4.0 Sampler Selection and Operation •••••••••••••••••••••• C-7 5.0 Sampling Trains .•.................•.... C-9 6.0 Collection Media .......•.••.............•............ C-IO 

### 7.0 Data Reduction andAnalysis C-13

iv

### APPENDIX D - DETERMINATION OF PMIO BACKGROUND 1.0 Introduction ....•.•.....•.....•.•....•....•.......•.•.• 0-1 

### 2.0 Determining Background Estimates from Measured PMIO or Surrogate Data •••••••••••.••••••••••••••••••••••••••••• 0-2

, 

### 3.0 Special Considerations forReceptorModeling ••••••••••• 

### D-5 4.0 Summary ofAlternatives for Estimating PMIO Background 0-6 5.0 Example 1 - Compositing Data from Several Perimeter Sites •....•........•.••...•.............•..•..•.•.•...• 0-6 

### 6.0 Example 2 - Use ofSurrogate Data •••••••••••••••••••••• D-8

v

### TABLE4-1 LIST OFTABLES AND FIGURES

SELECTING FEASIBLE SOURCEAPPORTIONMENTMETHODS BASEDON DATAAVAILABILITY AND SOURCE CHARACTERISTICS •.•..•..••..••.•...••.••..• r •••• ff. 4-3 

### TABLE4-2 RECOMMENDED APPROACHES FORPM10 SOURCE APPORTIONMENT ••""""""""""""""""""""""""""""""""""" 4-6 

### TABLE4-3 DISPERSIONMODELS APPLICABLE TO PM10**~NALYSES** 

### ..••• 4-9 TABLE 5-1 PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS """"""""""""""""""""""""""""""""""",,,,",,,,",," 5-5 

### TABLE 5-2 CUMULATIVE EMISSION FACTORS AND PARTICAL SIZE DISTRIBUTIONFOR AHYPOTHETICAL LIME PROCESS •••••• 5-18 TABLE 5-3 INDUSTRIAL SOURCE COMPLEXMODEL (ISC) 5-24 TABLE 5-4 CLIMATOLOGICAL DISPERSIONMODEL (CDM 2.0)•••••••••• 5-25 TABLE 5-5 RAM""" """"""""""" """""""""""""""""""""""""""". """" 5-26 

### TABLE 5-6 SINGLE SOURCE (CRSTER) MODEL •.••••.••••••.•••••••• 5-27 TABLE 5-7 MULTIPLE POINTSOURCEMODELWITH TERRAIN ADJUSTMENT (MPTER) ...............•..•...•......... 5-28 

### TABLE 5-8 EXAMPLE OFDATA IN EPA'S PARTICULATE SOURCE

.LIBRARy""""""""""""""""""""""""""""""""""""""""""" 5-31 

### TABLE 5-9 POTENTIAL SOURCES OF CONDENSABLE PARTICULATE .••••• 5-33 TABLE 6-1 TABULAR ESTIMATIONOF PM10 DESIGN CONCENTRATIONS •• 6-5 FIGURE 5-1 EXAMPLE GRAPHICAL DISPLAY OF CUMULATIVE EMISSION FACTORS VS. 

### PARTICLE SIZE FOR AHYPOTHETICAL PROCESS """""""",,",,""",, t " , " " " " " " " • " " " " " • " " , f " " " " " "" 5-19 

### FIGURE B-1 DISTRIBUTIONOF PM10/TSP RATIOS FOR INDIVIDUAL 24-HOUR OBSERVATIONS .••..••••.••••••••••••.•..•••• B-5

vi

### FIGURE C-l RECOMMENDED SAMPLING POINTS FOR CIRCULARAND SQUARE OR RECTANGULAR DUCTS ••••••••••••••••••••••• C-7 FIGURE C-2 PMIO PARTICULATE SAMPLINGTRAIN FOR NONCONDENSABLE PARTICULATE (MODIFIED EPA METHOD 5TRAIN) ••••••••• C-12 FIGURE C-3 PMIO PARTICULATE SAMPLINGTRAIN FOR CONDENSABLE AND NONCONDENSABLE PARTICULATE (MODIFIED EPA



### .METHOD 5TRAIN) ••••••••••••••••••••••••••••.••.•.. C-13

vii1.0 INTRODUCTION 1.1 Overview The 1977 amendments to the Clean Air Act (Act) Pub. L. 95-95) require the Environmental Protection Agency (EPA) to review periodically and, if appropriate. revise the criteria on which each national ambient air quality standard (NAAQS) is based along with the NAAQS themselves.1•2 In response to these requirements. EPA revised the primary and secondary NAAQS to apply to particulate matter in a size range defined by the collection characteristics ofanew ambient reference method that has a 50 percent collection efficiency (050) at 10micrometers.3•4 The material collected by the reference method is nominally below 10 micrometers and is referred to as "PM10." The purpose ofthis document is to provide guidance on how States

can develop PM10 control programs for attaining and maintaining ambient PM10 stdndards. Existing State control strategies to attain and maintain the former NAAQS for total suspended particulates (TSP) provided for controlling particulate matter sources; however. they must be evaluated and revised as necessary to attain or maintain the PM10 NAAQS. The guidance herein addresses the transition that States must make fromTSP control programs to PM10 control programs and the necessary revisions to State implementation plans (SIP's) to account for PM10. This document is intended as a starting point for anyone seeking information, policy, and guidance on developing PM10 SIP's. Most aspects ofdeveloping aPM10program from problem determination to final SIP submittal are covered. Certain details of program development. however.

1-1require explanation not included herein. Reference is therefore made to other documents for those additional details. Guidance for revising existing SIP's to address the review ofPM10 under State and local preconstruction review programs is not included in this document. It should be noted, however. that EPA's decision to implement the revised particulate matter NAAQS via section 110 ofthe Act
means that EPA will not impose Part Dnew source review (NSR) requirements as
ameans of revising the SIP's. Instead, the preconstruction review ofmajor
new and modified stationary sources which emit PMI0 will be carried out largely under regulations for the prevention of significant deterioration (PSD). The reader is referred to section IV.D in the preamble ofthe FEDERAL REGISTER notice promulgating PMI0 amendments to the PSD regulations for adescription ofthe changes that EPA has made to address PMI0. Additional guidance will be forthcoming in the form ofworkshop and policy and guidance memoranda to address revisions needed for State and local preconstruction review procedures. 1.2 Guideline Contents Since the size range ofparticles covered by the PM10 NAAQS is different from that included in TSP. the actions that States will have to take in preparing PMI0 control programs are more complicated than they would be if only the levels or averaging method ofthe NAAQS had been changed. As a result, States must look at the spatial distribution of ambient PMI0 concentrations and at source emissions from aparticle size distribution standpoint. Themajor sections ofthis document are intended to provide guidance on the different aspects of PMI0 control strategy

1-2development and SIP submittal to EPA in light ofthis new focus on the PMI0 size range. 1.2.1 Determining the Air Quality Status ofAreas The Act requires control strategies to be submitted to EPAwithin 9months ofthe revision ofNAAQS. Section 2.0 describes EPA's policy for determining when the air quality of an area is ormay be violating the PMI0 NAAQS and the extent ofthe area violation. The EPA's policy for categorizing areas into three groups based on the need to revise their SIP is also discussed. 1.2.2 Ambient monitoring Each State must establish an ambient monitoring network utilizing PMI0 samplers. Section 3.0 discusses the changes made to the ambient monitoring requirements of40CFR Part 58. These changes address PMIO ambient network design, various aspects of network implementation and operation, ambient data reporting, and the air pollution subindex for PMI0. Other facets of ambient monitoring are also discussed, such as episode monitoring and the use ofdata obtained by non-reference or equivalent method samplers. 1.2.3 Modeling Section 4.0 discusses modeling PMI0 with dispersion models or with receptor models and describes how the two techniques can be interfaced. The section specifies models or combinations that States should use depending on various considerations such as source types, modeling objectives, and available ambient data. 1.2.4 Emission inventories Where States use dispersion models to assess the impact of a source
or sources on ambient PMI0 levels, the model inputs must be in terms 1-3

### ofPMI0 emissions. Section 5.0 discusses how PMI0 emissions can be calculated and explains in detail the type ofinformation needed for various dispersion models now available as well as models now under development. Data needs for receptormodels are also included. 1.2.5 Development ofcontrol strategies Section 6.0 identifies general approaches to the use of ambient measurements and model estimates in setting control strategy emission limits. It explains how design concentrations may be determined and how control strategy demonstrations may be conducted using models. 1.2.6 SIP requirements and data reporting Section 7.0 discusses EPA's policies for requiring PMI0 SIP's based
on area categorization and for transferring from acontrol strategy for TSP to one for PMI0' Also discussed are PMI0 emission data reporting requirements in 40CFR 51.322, the fugitive dust policy, and the emission trading policy. 1.2.7 Types of receptormodels Appendix Alists various receptormodel techniques available and discusses each. 1.2.8 Preliminary Estimate ofPM10 Design Concentration Using TSP Data Appendix Bprovides amethod for making apreliminary estimate of the PMI0 design concentration when only TSP data are available; however, this estimate alone cannot be used for developing'control strategies. In addition to providing apreliminary estimate, it can also provide useful information for evaluating design concentrations estimated by dispersion modeling.
1-41.2.9 Source testing One ofthe methods for determining a source's PM10 emissions is to conduct atest which measures the amount ofemissions plus particle size distribution. Appendix Cdiscusses the state-of-the-art for such

source testing and provides information upon which States can base an emissions measurement or compliance test method for PMI0' 1.3 General SIP Approach States have manyyears ofexperience in developing and implementing plans for abating particulate matter air pollution. In general, the activities States will engage in to attain PMI0 NAAQS will not differ radically from past activities to attain TSP NAAQS. That is, the basic approach will still be to examine air quality across the State, delineate

areas where air quality needs improvement, determine the degree of improvement necessary, inventory the sources contributing to the problem, develop a strategy to reduce emissions from contributing sources enough to bring about attainment of the NAAQS, implement the strategy, and take the steps necessary to ensure that NAAQS are not violated in the future. The change in the particle size range to which the standards apply from TSP to PMI0, together with the emergence of receptor models as effective tools in strategy development, necessitates changes in the specifics ofparticulate matter control strategy development. The major difference in past TSP**strategi~s** and new PM10 strategies will be the need to inventory the sources ofthe PMI0 size fraction and to focus on control of PMI0 emissions to reduce ambient PMI0. This could include applying PMI0 emission factors to sources, in some cases testing sources for PMI0 emissions, and utilizing receptor models, where feasible to refine

1-5

### emission inventories and augment dispersion models. The contribution of'
sources to ambient PM10 and the degree ofemission reductions necessary
can then be determined.

1-6References
1. Air Quality Criteria for Particulate Matter. AP-49, U.S. Department ofHealth. Education. and Welfare, Washington, D.C•• January 1969. 2. Air Quality Criteria for Particulate Matter and Sulfur Oxides, EPA-600/8-82-029a-c. Environmental Criteria and Assessment Office, Environmental Protection Agency, Research Triangle Park, N.C., March 1983. 3. High VolumeMethod for Suspended Particulate Matter, Part 50 of Chapter I ofTitle 40 ofthe Code ofFederal Regulations, Appendix B, 4. Reference Method for the Determination of PM10 in the Atmosphere, Part 50 ofChapter I of Title 40 of the Code of Federal Regulations, Appendix J.

1-72.0 DETERMINING AIR QUALITY STATUS OF AREAS
2.1 Policy Overview TheAct requires that States develop SIP's that provide for timely attainment and maintenance ofNAAQS. The method ofdetermining whether an
area is in attainment ofthe PM10 NAAQS using ambient air quality data is described in Appendix Kof40 CFR 50. Generally, 3years of PM10 data are required to make the determination; however, data collected over shorter periods oftime may be adequate as explained in Appendix 

### Kand in the Guideline on Exceptions to Data Reguirements for Determining Attainment of Particulate Matter Standards.1 In areas where too little PM10data are available to demonstrate attainment ofthe NAAQS, EPA's policy is to use whatever PM10 data are available and supplement it with TSP data to determine the probability that the area will violate the PM10 NAAQS. The EPA will

use the report, Procedures for Estimating Probability ofNonattainment of a PM1Q NAAQS Using Total Suspended Particulate or PM1Q Data,2 hereafter referred to as the "probability guideline," for calculating an area's probability of nonattainment. Areas will be grouped into three categories based on their probability ofnonattainment and other factors influencing the degree to which the existing SIP may need revisions. Generally, Group I areas are shown to be or have high probability of nonattainment, Group II areas are those areas where nonattainment is uncertain, and Group III areas are shown to be or have ahigh probability ofattaining the NAAQS. After the initial grouping of areas and promulgation ofthe PM10 NAAQS, only PM10 data may be used to make definitive attainment/ nonattainment decisions in accordance with Appendix 

### Kor reference 1, above. The probability guideline may 2-1continue to be used for planning purposes such as to assess new areas where TSP data indicate a need for more extensive PMI0 monitoring. 2.2 DemonstratingAttainment Appendix Kspecifies the procedures which States can use to demonstrate attainment ofthe 24-hour and annual PMI0 standards at aparticular location using ambient air quality data. To determine PMI0 attainment ofthe 24-hour standard, EPA generally requires averaging the number ofestimated 24-hour exceedances over 3 consecutiveyears of representative data. If the average number of 24-hour NAAQS exceedances is greater than 1.0peryear, then a nonattainment problemwill be evident and States should proceed to examine the adequacy ofthe existing SIP. Apotential problem ofnonattainment of the 24-hour PM10 standard can be projected on the basis of fewer than 3years ofdata, since the criteria for nonattainment can be equivalently expressed in terms ofthe total number ofestimated exceedances within a3-year period. In the simplest case, iffour exceedances are observed in a single year, the average number ofexceedances over a 3-year period will exceed 1.0. Ifthe total number of estimated exceedances in 3years is equal to
or greaterthan 3.2, then the number ofexceedances averaged over 3years would also exceed 1.0. Thus, 3.2/3 

### =1.07, which rounds to 1.1 3.1/3

## =1.03, which rounds to 1.0 As soon as 3.2 estimated exceedances are accumulated anonattainment problem will be evident. This may occur in even less than a singleyear. As specified in Appendix 

### K, EPA will not require adjustment ofthe first observed exceedance in orderto account for incomplete sampling if (1) only one exceedance occurred in the calendarquarter, (2) ifeveryday
2-2sampling is initiated as expeditiously as possible as required by 40CFR 58.13, and (3) 75 percent data capture is achieved (see section 3.2.5 ofthis document). As specified in 40CFR 58.13 for the first year ofmonitoring, everyday sampling must commence within 90 days ofthe end ofthe calendar quarter in which the first exceedance is observed and continue for 4consecutive calendarquarters. In addition, ifa site is already monitoring every day and observes its first exceedance, no adjustment for missing data will be made to this first exceedance if75 percent data capture is achieved. By not adjusting the first observed exceedance, EPA is assuming that this first exceedance may bethe actual exceedance during the entire year. If, however, one ormore subsequent exceedances are observed during the transition period following the first exceedance, but priorto the actual initiation ofevery day monitoring, the additional exceedances would be adjusted to account for incomplete sampling using formula 

### [IJ ofAppendix 

### K, according to the sampling frequency and data capture at that time. Days on which exceedances ofthe NAAQS were recorded may be flagged as exceptional events in accordance with the Guideline on the Identification and Use ofAir Quality DataAffected by Exceptional Events, EPA-450/4-86-007.3 State or local control agency flagged data values may be excluded from initial NAAQS calculations and not be considered by EPA in determining an area's apparent nonattainment status. The flagged data will be excluded from final consideration ofSIP adequacy ifthe responsible control agency determines in conjunction with apublic review that the flagged data are inappropriate for use. When the minimum 3year data requirements specified in Appendix 

### Kare not satisfied, attainment can still be demonstrated, but such demonstrations 2-3

### must be approved by the appropriateRegional Administrator, in accordance with the Guideline on Exceptions to DataRequirements for Determining Attainment ofParticulateMatter Standards.1 In particular, ifonly 1or .2

### years ofPMI0 data are available, then supplementary information provided by modeling or other surrogatemeasures must be used to demonstrate the adequacy ofthe SIPto attain and maintain the standards. Finally, in the event that there has been atrend in emissions during the attainment test period, attainment with the standards can be demonstrated according to Appendix K, even though the most recent 3years ofdata do not**indicat~** 

### attainment. Either the most current representativeyear(s) could be used or statistical techniques or models could be used in conjunction with previous years ofdata to adjust for trends. Such analyses would be performed in accordance with existing guidance and must be approved by the appropriate Regional Administrator.4 2.3 Estimating Probability ofNonattainment to Determine Initial SIP Requirements The procedures EPAwill use to categorize areas into Group I, II, or III are discussed in section 2.4. The schedules for submitting SIP revisions and control strategy demonstrations for each group are discussed in section 7.3. If available PMI0 data for an area are adequate to demonstrate attainment
as discussed in section 2.2, then the area will be put into Group III. On the other hand, ifthe PMI0 data alone are adequate to demonstrate that the
area is nonattainment, it will be put into Group I. If available PMI0 data alone are not adequate to demonstrate attainment or nonattainment, EPA's policy is to supplement available PMIO data with inhalable particulate matter data (particles with an aerodynamic diameter less than 15 micrometers) and TSP

2-4data. These air quality data will be used in a statistical methodology as described in the probability guideline to estimate the probability that the PM10 NAAQS will be violated in the area. Initially, areas will be grouped based on probabilities of nonattainment of95 percent or greater (Group I), from 20to 95 percent (Group II) and less than 20 percent (Group III). Other factors that may influence the degree to which the existing SIP may need revisions will also be considered in the area grouping process. Also, EPA has found that some uncertainty exists in the PM10 data collected priorto 1987 with available PM10 instruments. Astudy performed by EPA in Phoenix has shown that in extreme situations data collected by Sierra Anderson SA-321A size selective PM10 instruments can be influenced by coarse particles to the extent that concentrations may be biased high by up to 20 percent. In addition, data collected with Wedding or GMW-9000 instrument may be biased low by up to 20 percent due to soiling problems.5,6 In order to account for the uncertainty associatedwith such PM10concentrations in calculating the probability of nonattainment, azone ofuncertainty or "gray zone" of 20 percent is being placed around the standard (0.8 NAAQS to NAAQS, lower gray zone; NAAQS to 1.2 NAAQS, upper gray zone). By design, the zone ofuncertainty will only cause areas to move from Group I to Group II,
or from Group III to Group II. In particular, for SA-321A instruments, 24-hour PMIO observations and annual PMIO means (using all data) within the upper gray zone are not to be counted as exceedances ofthe respective standards in the probability calculations. Similarly, for the GMW-9000 instrument, 24-hour PM10 values and annual PMIO means (using all data) that
are within the lower gray zone are to be counted as potential exceedances ofthe respective standards in the probability calculations.
2-5

### If an area's nonattainment probability using TSP data and PMIO data drops below 0.95 or rises above 0.20, as a result ofPMIO data in the "gray lone," itwill be classified Group II in orderto resolve the possible uncertainty associatedwith the PMIO data and to ensure that adetermination is made as to whether the existing SIP provides for attainment and maintenance ofthe PMIO standards. Also, PMIO data in the gray lone, by itself, will not be allowed tomove an area into Groups I or III from any other Group. Areas classified Group II as aresult ofPMlO data in the upper "gray lone" are still required to operate airquality monitors on an everyday sampling schedule for the first year. This is consistent with the requirements for Group Iareas as explained in section 3.2.5. The following table presents the actual values associatedwith the "gray zones." TABLE 2-1 PM10Gray Zone Limits (ug/m3)

Upper Lower Gray ZoneLimits Averaging Time Annual 24-Hours PM10 

### NAAQS

50 150 60* 180* 40** 120**

*Applies to readings made with aSA-321A **Applies to readings made with aGMW-9000

Sincethe conditions underwhich these potential instrument biases were observed are not considered typical, data collectedwith all instruments will be taken at face value when demonstrating attainment or nonattainment 2-6

### with the standards. Fordata produced by the SA-321A (with the potential positive bias), however, an appropriate downward adjustment will be permitted for attainment demonstrations ifinfluence by coarse particles
can be demonstrated. Fordata produced by the GMW-9000 (with the potential negative bias), no adjustments to observed data are necessary for demonstration ofnonattainment. Such areas with potential exceedances ofthe PMIO NAAQS (i.e. with data in the lower gray zone) will be required to samplemore frequently for the firstyear·(according to the Group II monitoring frequency ofevery-other-day), so that true nonattainment situations will soon become apparent. 2.4 Area**Categorizatio~** 

### SIP development requirements are based on an area's categorization into Group I, II, or III ofEPA's policy for PMIO SIP development. If ambient PM10 data adequate to determine attainment or nonattaiment ofPM10 standards are available, they are to be used in categorizing areas. Areas for which adequate ambient PM10 data are not available are initially categorized into these groups using nonattainment probability (section 2.3) cut points of95 percent to distinguish between Group I and Group II and 20 percent to distinguish betweenGroup II and Group III. TheEPA Regional Offices, in conjunction with State and local agencies may then make adjustments to the grouping based on the adequacy ofthe existing particulatematter SIP's for achieving and maintaining PM10 standards. The general criteria for group categorization are:

Group I - Areas for which the existing particulatematterSIPmay need substantial revision to be adequate for attaining and maintaining PM10 standards. 2-7

### Group II

- Areas forwhich the existing particulatematterSIP may be adequate or need only minor adjustment. Group III

- Areas forwhich the existing particulatematterSIP's are believed adequateto attain andmaintainthe PMIO standards. Examples of information otherthan air quality data thatmay warrant moving an area fromGroup III to Group II, orfromGroup II to Group I,
are:

° facts showing that the current air quality is attributableto an economic slowdown or some othertemporary phenomenon ratherthan the stringency ofthe area's TSP SIP requirements,

° facts suggesting that there are few enforceablemeasures in existing SIP's yet to be implemented thatwould reduce emissions that significantly affect air quality, and

° evidencethat the area has an unusually high proportion of sources in categories whose emissions typically have ahigher ratio ofPMIOto total particulatematter. Examples ofinformation otherthan airquality data that may warrant moving an area fromGroup Ito Group II, orfrom Group II to Group III, are:

° factors suggesting that sources are notyet in compliancewith SIP
measures that, ifenforced would reduce emissions that significantly affect the area's airquality; °evidencethat the area has an unusually high proportion of sources in categories whose emissions typically have alow ratio ofPMIO to total particulatematter; and

2-8o evidence that the area is rural in nature and is aclearly impacted by fugitive dust (i.e. qualifies as a rural fugitive dust area under the fugitive dust policy described in section 7.11). Examples of information that may affect EPA's classification ofan
area not preliminarily classified using anonattainment probability are:

o the amount and density of industrial activity that would likely result in significant ambient PMlO concentrations in the area;

o the number and density of roadways in the area that are near activities likely to generate significant particulate matter emissions and that are subject to moderate and heavy vehicle traffic; and

o the degree to which the existing TSP SIPwill likely limit PMlO emissions from these traditional and nontraditional sources. 2.5 Area Boundaries Section 6.0 ofthe probability guideline describes several approaches for determining the boundaries ofan air quality level represented by one
ormore monitoring stations. In some instances the spatial extent of areas ofuniform PMIO air quality status may differ from the areas previously defined for TSP; therefore. States may wish to use the guidance in these instances to more specifically define the spatial extent of PMIO air quality problems. The approaches that have been used and that are recommended for defining area boundaries are:

-- a qualitative analysis ofthe area of representativeness ofthe monitoring station, together with consideration ofterrain, meteorology, and sources of emissions;
2-9

### -- spatial interpolation of airmonitoring data; and

-- airquality simulation by dispersion modeling. These techniques can be used singly or in combination depending onthe complexity ofthe area where amonitoring station(s) is located. More detailed discussions ofthe techniques are in the probability guideline.

2-10

### References
1. Guideline on Exceptions toDataRequirements for Determining Attainment ofParticulateMatterStandards, U.S. EPA, RTP, N.C. EPA-450/4-87-005, April 1987. 2. Pace, T. G" N. H. Frank, E. L. Meyer, and S. F. Sleva, Procedures for Estimating Probability ofNonattainment ofaPM]O NAAQS Using Total Suspended ParticulateorPMjO Data, U.S. EPA, RTP, N.C. EPA-450!4-86-017, December 1986. 3. Guideline on Identification and Use ofAir QualityDataAffected by Exceptional Events, EPA-450/4-86-007, U.S. EPA, RTP, N.C. July 1986. 4. Guidance on Accounting for Trends in ParticulateMatterEmission and AirQuality Oata, memorandum fromR.G. Rhoads to Regional AirDirectors, U.S. EPA, RTP, NC. May 11, 1987. 5. L. Purdueet. al., Intercomparison ofHigh-VolumePMI0 Samplers at a Sitewith High ParticulateConcentrations, JAPCA, Vol. 36, No.8, Aug. 1986, pp. 917-920. 6. Uncertainty ofPMI0 DataCollectedwith High-Volume Size Selective Inlets, Memorandumfrom L.J. Purdueto Richard Rhoads, U.S. EPA, RTP, N.C. September 15, 1986.

2-113.0 AMBIENT MONITORING AND DATA USAGE
3.1 Ambient PM10 Monitoring States must establish ambient monitoring networks to measure PM10just

as they measure ambient levels of other pollutants for which NAAQS have been set. Requirements for aPM10 network are included in 40 CFR 58 as
are the ambient monitoring requirements for otherpollutants. Existing ambient monitoring networks measure total suspended particulate with high volume samplers (hi-vo1s.)* Data from hi-vols, as discussed previously in section 2.0, will not allow sufficiently accurate estimates ofPM10 levels to determine PM10 attainment or nonattainment with certainty•. Ambient samplers meeting the requirements of40CFR 50 and 53 will be necessary to meet the ambient monitoring requirements ofPart 58 except as specified in Appendix C of Part 58. The requirements for PM10 reference or equivalent methods with anominal D50 of 10 micrometers are contained in 40 CFR 50, Appendix J, and in 40CFR 53. TheEPAwill designate reference and equivalent method samplers as
soon afterpromulgation ofthese requirements as possible. Some provisions

are made, however, as discussed in subsection 3.3, for using other ambient data until data from ambient PM10 networks are available. 3.2 Part 58 Requirements 3.2.1 Quality Assurance The quality assurance (Q/A) requirements for PM10 samplers are essentially the same as for hi-vo1s. Only one minor change was made tothe quality

*Described in 40 CFR 50Appendix BofPart 50 3-1

### assurance procedures in Appendix AofPart 58. Section 4.2.1(a) ofAppendix A requires only that the percent differences for paired measurements from collocated samplers above certain levels (20 ug/m3 for PM10) be calculated and reported. Previously. measurements below these levels were reported. Otherwise. Q/A procedures for PM10 monitoring in both Appendices Aand Bare identical to those forTSPmonitoring. 3.2.2 Ambient monitoring methodology The requirements in Appendix CofPart 58 which require reference or equivalent method samplers be used in State and Local AirMonitoring Stations (SLAMS) apply to PM10 with one exception. Section 2.2 ofAppendix 

### Callows the hi-vol method to be used in aPM10 station as a substitute for aPM10 reference or equivalent method sampler as long as the ambient concentrations measured by the hi-vol are below the levels ofthe PM10 NAAQS. In such an instance. compliance with PM10 NAAQS is assured ifthe hi-vol levels are consistently below those NAAQS; thus. there is no real need from acompliance standpoint to install aPM10 sampler. IfTSP levels rise above PM10 NAAQS. however. the State must install PM10 samplers in orderto measure actual PM10 levels to be certain PM10 NAAQS are maintained. Also. section 2.2 ofAppendix C

### requires that at TSP National Air Monitoring Stations (NAMS) the hi-vol be continued in operation for at least ayear after the PM10 monitoring begins. This will allow asite-specific relationship between TSP and PM10 data to be developed and provide assistance in checking validity ofPMIO data. Historical trends for ambient levels ofparticulate matter can thus be estimated. Section 4.0 ofAppendix 

### C, which previously applied to episode monitoring forTSP. has been revised to apply to PM10. 3-23.2.3 Network establishment Title 40 CFR 58.20 requires adescription ofthe SLAMS network be available to the general public and be submitted to EPA approximately 6months after PMlO NAAQS are promulgated. The network description need not be included as part ofthe SIP; it need only be kept updated and made available for public inspection. Two dates, 1year and 2years after promulgation,
are included in section 58.23 for completion ofthe PMlO SLAMS network. By 1year after promulgation ofthe NAAQS, each area within the approved SLAMS network for which aprobability ofPMIO NAAQS nonattainment is greater than or equal to 20 percent, must have at least one PMlO sampler in operation which is (1) located in the area ofexpected maximum concentration (2) sited in accordance with Appendix E, (3) located as described on the station's Storage and Retrievel ofAerometric Data (SAROAD) identification form, and (4) meeting all quality assurance requirements in Appendix Apertinent to PM10. The remaining PMlO samplers, including those in areas with nonattainment probabilities below 20 percent, have until 2years after promulgation of the NAAQS to be fully operational and to meet the siting and quality
assurance requirements. The same submittal date (6 months after promulgation) also applies for the description ofthe NAMS portion ofthe SLAMS network as required by section 58.30. The date by which all stations in the NAMS portion of the SLAMS network are to be in operation and meeting Part 58 requirements is Iyear after PMlO NAAQS are promulgated (section 58.34). 3.2.4 Network design Information on designing SLAMS and NAMS networks is contained in Appendix Dof Part 58. The material is largely self-explantory and, as 3-3indicated in Appendix D, should be usedjointly by States and EPA Regional Offices for designing ambient monitoring networks. To account for PM10 network design, Appendix Dhas been amended to include criteria for determining the number ofPM10 stations and areas in which to locate them. In addition to Appendix D, adocument entitled Network Design and Optimum Site Exposure Criteria for Particulate Matterl is available and provides further information on designing PM10 networks and siting ambient samplers. 3.2.5 Sampling interval The sampling interval for PM10 data collection will not necessarily be once in 6days as it has been for TSP. Short-term and long-term strategies have been added to section 58.13. During the first full year of sampling the short-term strategy requires daily sampling at at least one site (the site ofexpected maximum concentration) in areas with a PM10 nonattainment probability of95 percent or greater. Sampling is required every other day at least one site in areas with nonattainment probabilities between 20 percent and 95 percent. Sampling in areas with aprobability below 20 percentmay be once every sixth day. The use of the term "area"

as it app1ies to the required samp1ing frequencies ofthe "area" is as follows: (1) any urbanized area as defined by the U.S. 8ureau ofCensus, (2) any incorporated place such as a city or town as defined by the U.S. Bureau ofCensus or group of cities or towns, and (3) any "area" designated by the responsible air pollution control agency. In designating these latter "areas," the control agency should considertechnical factors such

as the types of emissions, their spatial distribution, meterology, and topography and how these factors contribute to the uniqueness ofthe "area" thereby distinguishing it from other designated "areas." The
3-4first year of PMIO data collection is to start no later than the appropriate dates in section 58.23 and section 58.34 for completion ofthe SLAMS and NAMS networks, respectively. Theyear can start, however, as soon as the station is put into operation. The long-term strategy applies after the short-term strategy ends (in most cases after the first full year of sampling ends). The sampling interval will then vary according to the relationship of air quality levels to the NAAQS. The closer the airquality levels to the NAAQS, the more frequently sampling must be carried out. Atable depicting the requirements appears in section 58.13. As soon as the first exceedance of the 24-hour PMIO NAAQS occurs in
an area which historically has had a less than everyday sampling interval, the requirements change to everyday sampling for at least one site (the site ofexpected maximum concentration). The State will have up to90days after the end ofthe quarter in which the exceedance occurred to implement everyday sampling at the site of expected maximum concentration, and it must be maintained for at least 4calendarquarters, but preferrably 1 full calenderyear. Ifthe day on which the first exceedance occurred
was flagged as an exceptional event, the State must still implement everyday sampling, unless exempted by the Regional Administrator. 3.2.6 Sampler inlet siting The criteria in Appendix

### EofPart 58 for siting ambient PM10 samplers is very similarto the previous siting criteria for hi-vol samplers. The
new .criteria for the PMIO SLAMS include microscale stations which were not previously included for the TSP SLAMS network. An inlet height of 2to 7meters is specified for PM10microscale stations. The inlet height for all 3-5

### other scales for PM10 stations is 2to 15 meters as it was previously for all TSP SLAMS. The spacing requirement from roadways is also different. Formicroscale roadway stations, the PM10 sampler must be between 5to 15 meters from major roadways. Formiddle, neighborhood, and urban scale PM10 stations a range of separation distances are specified for each as a function oftraffic volume and the scale of representativeness. 3.2.7 Ambient data reporting The reporting requirements in Part 58 for ambient data apply for PM10
as they do for the other pollutants; i.e., all SLAMS data must be reported annually in summary form and all NAMS data must be reported quarterely. Since the regulations allow for the use ofTSPmonitors as a surrogate for PMIO monitors under certain conditions, "the reporting requirements for TSP data remain in Appendix FofPart 58. TheTSP data summary format in section 2.2 ofAppendix F, however, has been changed to correspond somewhat with the format added for PM10 data in section 2.7 ofAppendix F. The ranges for the 24-hour summary report have been in 50 ug/m3 increments. The format added for PM10 is in 25 ug/m3 increments. Asignificant change made in the reporting requirements is the provision that annual TSP data must be reported as arithmetic means, 24-hourTSP data values must be reported ifthey exceed the level ofthe 24-hour PM10 NAAQS, and the sampling interval must be reported. OtherwiseTSP reporting requirements remain unchanged. Section 2.7 has been added toAppendix Fto cover reporting ofPM10 data. All exceedances ofthe 24-hour PM10 NAAQS, the sampling interval, and the number of24-hour values within 30ug/m3 ranges must be reported.

3-63.2.8 Air pollution index The air pollution subindex for TSP has been replaced by a subindex for PM10. The breakpoints for the subindex reflect the levels ofthe PM10 short-term standard. Anew table of breakpoints and a graphic display ofthe breakpoints is included inAppendix Gof Part 58. 3.3 Ambient Samplers and Ambient Data Usage 3.3.1 Ambient samplers in use Considerable ambient data have been collected using hi-vols, hi-vols with size-selective inlets with aD50 of 15 micrometers (SSI15), and dichotomous samplers with inlets designed for D50 of 15 micrometers. Some data have been collected using samplers with inlets designed for aD50 of 10 micrometers. The PM10 samplers cannot be designated as PM10 reference or equivalent method samplers, however, until Part 50 and Part 53 requirements are promulgated, and subsequent manufacturer test data are examined and accepted by EPA. Thus, the PM10 data now available have not been collected by reference or equi.valent methods. Therefore, nonreference or equivalent method PM10 data, PM15 data, orTSP data may be used for initial SIP development purposes, subject to certain constraints. 3.3.2 Interim use of non-reference or equivalent method samplers Until PM10 reference or equivalent method samplers can be designated, manufactured, purchased, and installed in PM10 SLAMS, States should continue to operate approved ambient samplers with inlets designed for anominal D50 of 10 micrometers. Approval of ambient PM10 samplers for SIP purposes will be made by the Environmental Monitoring Systems Laboratory, MD-77, Environmental Protection Agency, Research Triangle Park, North Carolina 27711.
3-7Data from these approved samplers will be acceptable for use in modeling and in determining an area's attainment status. Use ofthese data for initial SIP classifications are discussed in section 2.4. 3.3.3 Episode monitoring The criteria for determining which areas are required to have air pollution episode contingency plans are in Subpart HofPart 51. The sampling procedures that should be used for determining air quality levels during air pollution episodes are similarto those used priorto promulgation of the PM10 NAAQS. These procedures are identified in section 4.0 of Appendix CofPart 58 and are further described in the document, Guideline for PM10 Episode MonitoringMethods.3 Briefly, two methods based on the filtration principle are recommended; staggered PM10 sampling and short-term interval sampling. The staggered sampling procedure uses a24-hour sampling procedure followed by a2-hour post-sampling filter equilibration period. The short-term interval sampling method requires a4-hour sampling period followed by a2-hour filter equilibration period. In addition to these two procedures, othermethods may be used provided the user demonstrates a site-specific correlation of the alternative method with the reference method. 3.4 SIP Revisions That portion ofthe SIP providing for ambient monitoring should be revised, if necessary, to include provisions for a PM10 ambient network. If the ambient monitoring portion ofthe SIP provides for monitoring pollutants "forwhich NAAQS have been set," then no revision is necessary. Ifthe SIP lists pollutants, PM10 would need to be added to the list. The PM10 SLAMS network design need not be included in the SIP and aSIP revision is not necessary as a result of any network modification. Any
3-8

### SLAMS network modifications, however, must be approved by the appropriate EPA Regional Office and any NAMS modifications by EPA's Office ofAir Quality Planning and Standards.

3-9

### Reference 1.

2.

3. 

### Koch, R.C. and H.E. Rector, Network Desi¥n and Optimum Site Exposure Criteria for Particulate Matter, GEOMETechnologies, Inc., Rockville, 

## MD. Prepared for U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA Contract No. 68-02-3584. March 1983. Pace, T.G. and N.H. Frank, Procedures for Estimating Probability of Nonattainment ofaPM10 NAAQS using Total Suspended Particulate or PM10Data, U.S. Environmental protection Agency, ResearchTriangle Park, NC. EPA450/4-86-017, December 1986. Pelton, D.J., Guideline for PM10 Episode Monitoring Methods, GEOMET Technologies, Inc. Rockville, MD. Prepared for U.S. Enviromental Protection Agency, Research Triangle Park, NC. EPA Contract No. 68-02-3584. February 1983.

3-10

### 4.0 AIR QUALITYMODELING FOR PMI0
4.1 Introduction Section 51.12 of40 CFR requires that the adequacy of acontrol strategy for attainment and maintenance ofNAAQS be demonstrated by means ofadispersion model or other procedure which is shown to be adequate and appropriate for this purpose. The Guideline on Air QualityModels**(Rev1se~)1** 

### provides guidance on dispersion modeling for particulate matterand lists preferred dispersion models for this purpose. Procedures collectively known as receptor models are also available that examine an ambient monitor sample ofparticulate matter and the conditions ofits collection to inferthe types or relative mix of sources impacting on it during collection. Receptormodels
are described briefly in'AppendixAand the references to this section.2,3 The most widely used and accepted quantitative receptormodel is the chemical
mass balance (CMB).4 The proper use ofthe CMB is described in two protocols referenced in this section.5,6 Three options are presented herein for estimating the airquality impact ofemissions ofPMI0 using dispersion and receptormodels: (1) use ofreceptor and dispersion models in combination (preferred): (2) use ofdispersion models alone; and (3) use oftwo receptormodels, with control strategydeveloped using aproportional model (discussed in Section 6.0). This latter approach is only encouraged ifno applicable dispersion model is available. Several considerations relevant to model selection are presented in Section 4.2 (ReceptorModels) and 4.3 (Dispersion Models). The PMI0 is generally referred to in the following discussion. Where there is insufficient technical data to support an analysis for PMI0 with these models, the sameprocedures may be applied to TSP, except as specifically noted, as asurrogate for PMI0.
4-1

### 4.2 Considerations in Receptor Model**Selectio~** 

### Several considerations related to the nature of sources emitting PMIO influence selection ofthe receptormodel(s) for SIP purposes. These are the availability ofparticle size data and the size range ofthe emissions from predominant sources, prior knowledge ofthe sources, chemical similarity ofthe sources, the need to identify individual sources vis-a-vis

source categories, and the time scale of interest. Different factors affecting choice ofreceptor models are summarized in Table 4-1 and discussed below. One key overriding selection consideration is that CMB is considered the most advanced ofthe receptormethods listed in Table 4-1. The other methods include factor analysis (FA), automatic scanning electron microscopy (ASEM), and microscopy (OM). TheFA, ASEM, and OM are not generally considered quantitative and FA requires at least 40 samples to complete an analysis. The reader is referred to the ReceptorModel Technical Series (references 2, 4, 5, 6, 7, 12, 13, 16) for technical, cost, and applicability information. 4.2.1 Particle size Many researchers have discussed the bimodal distribution ofparticulate matterwhich is related to the tendency of some source categories to emit predominantly fine «2-3 um) or coarse (>2-3 um) aerodynamic diameter particles. Sources which emit predominantly fine particles include those involving the combustion offuels (motor vehicles, boilers, field burning, woodstoves. etc.) and industrial processes involving combustion, chemical reaction, or condensation of vapors. Contributors to coarse particles in the atmosphere include windblown dust from storage piles, agricultural fields, etc•• vehicle resuspended road dust, pollens, and fugitive emissions from industrial process sources. Various receptor models such as species

mass balance and factor analysis are well suited to analyze sources of 4-2

### TABLE 4-1 SELECTING FEASIBLE SOURCE APPORTIONMENT METHODS BASED ON DATA AVAILABILITY AND SOURCE CHARACTERISTICS

Chemi

- Fingerca1 prints Sources Sources Simi- DisFine Coarse**Know::.:n'----'U:..:.n:.:.:k~no::::w~n'____..:..:l a::.:r--,i~tY,,--_--,s:.;i.:;:mc:.i.:..:1** 

### a::.:r Iso1ated Single Source Air Shed (SourceCategories) Air Shed (Specific Sources Within 

### Category)

ISS 

### 1ance (CMB) Y !ctor ,a1ysis (FA)

. X 

### Y

X 

### Y



### x 

## x a,b,Y b,X 

### Y



### x Y

X 

### Y

X 

### b,Y



### --- 

### -------------- Itomated .anning ectron croscopy .SEM) croscopy 1M) 

### spersion .de1 (OM) c,X

c,X



### YY 

## x

X



### YY 

### x

X



### YY X

X b,X b,X



### YY 

## x

X



### YY X

X



### YY X

X



### YY b,X b,X



### YY

- Initial use offactor analysis may be helpful.

- Useful if a source can be isolated from other similar sources by wind direction. Method usually cannot otherwise distinguish between sources in same category.

- Useful for fine particles largerthan 1.0 um

- Appropriate to use in conjunction with CMB orOM.

- Appropriate to use with OMorFA, ASEMorOM.

- Appropriate to use.

4-3either fine or coarse particles. Optical and scanning electron microscopy

are suitable for coarse and fine particles down to about 1 micrometer in size but work better on coarse particles. Most receptor models generally work best when the sample is segregated by size range (e.g., fine and coarse) because these two size ranges are associated with different types of sources. 4.2.2 Prior knowledge of sources and emissions In many instances, the sources suspected of contributing to ambient PMIO concentrations at a particular site are apparent. However, the relative contribution ofeach source is needed. Any ofthe methods discussed in Appendix Acould be used to give source apportionment information ifthe

sources are identifiable, provided the other requirements for using the method are also met. However the mass balance requires knowledge of sources and their emission characteristics. If some sources are unknown, FA, ASEM,

or OM might prove useful ifdone prior to aCMB analysis. 4.2.3 Chemical similarity The availability of "fingerprints" for the sources of interest will often determine the optimum receptor model to use. Afingerprint is the characteristic chemical or morphological pattern ofthe emissions from a
source that is used to distinguish it from other sources.17 Some sources have fairly distinct fingerprints, while others do not. Since combustion

source emissions are predominantly composed of carbon, there is very little information upon which to differentiate among the different types ofcombustion sources. Some help might be gained by examining the optical properties, by using carbon dating (CI4/CI2 ratios) which can distinguish between modern

or fossil carbon (e.g•• wood smoke versus fuel oil), or by using minor tracer constituents (e.g., 

### Kin wood). Other common fingerprint problems include the difficulty in distinguishing among sources whose emissions are 4-4

### comprised ofvarious soil components or between flyash and soil. Optical properties can be useful for some situations where fingerprints are**simila~.** 

### Also. X-ray diffraction (XRD) is 

### very useful in identifying various minerals by examining their crystalline structure. 4.2.4 Sources 

### versus source categories Any ofthe receptormodels listed in Table 4-1 orAppendix Acan be useful in identifying the impact of an isolated specific source unless its fingerprint is similarto that ofbackground. Likewise. the techniques can be used to identify many ofthe source categories within an airshed consistent with the limitations identified in the preceding discussion. However. the impact ofspecific individual sources within an airshed containing MUltiple
sources ofthe same type may not be reliably identifiable except on wind directional samples or by adispersion model. 4.2.5 Particle size andtime"scale In some cases ambient PM10 or PM15 data may be available for SIP apportionment. In other cases. only TSP data are available. The availability ofPM10 data, or PM15. orTSP will determine to some extent the type ofsource apportionment method used for PM10 since the PMIO signature of a
source may be significantly different from the PM15 orTSP signature. Another important factor is the time scale ofthe nonattainment situation (annual
or 24-hour). Table 4-2 contains recommended approaches for source apportionment based on the time scale (annual or 24-hour) ofthe nonattainment problem and the data base available. The choices listed in each block of the table are in order ofpreference. with the preferred approach listed first. These approaches are discussed in more detail in the following sections.

4-5

### TABLE 4-2



### RECOMMENDED APPROACHES FOR PMIOSOURCE APPORTIONMENT

AMBIENT DATA BASE AVAILABLE
PM10 TSP* Applicable dispersion Applicable dispersion and receptormodel model corroborated by ASEM oroptical microscopy** Applicable dispersion model Receptor methods Applicable dispersion (at least 2) CMB 

### w. model corroborating method

*TSPmay be used as asurrogate, where PMIOdata bases are inadequate.
** Other receptor models such as Mass Balancemay be used-iffine particle data (generally less than 2-3 

### micrometers) are collected in addition to TSP.

4-6

### 4.3 Screening Techniques and Refined Dispersion Models for PM10 Concentrations It may sometimes be appropriate to conduct apreliminary screening study to determine likely causes ofnonattainment priorto amajor control strategy development effort. This generally is confined to use ofexisting ambient samples and emissions data and usually gives only aqualitative or "first approximation" of sources. Such ascreening study has two advantages. First, the qualitative prioritization of sources can be used to design the
more definitive study, which is usually required for control strategy development. This may enable substantial cost savings, since the screening study can be focused on specific areas ofconcern. Second, in some cases, the source contribution may be very clear and the screening study may be all that is required priorto control strategy development. This is further discussed in the references to this section.7 4.3.1 Selection ofappropriate dispersion models Several publications are available that contain hand calculation methods for arriving at preliminary (screening) estimates ofPM10 concentrations.

## a,9,IO In general, these methods are based on the assumption that all particulate matter behaves as a gas in the atmosphere, i.e., neglecting settling and deposition. Although subjective values for "half-life" or pollutant decay have been used occasionally as asurrogate for particle removal in screening methods and also some models, such procedures are not generally recommended for PMIO analysis. For screening analyses, the conservative assumption ofnegligible removal is warranted, considering the size ofthe particles.

4-7

### Dispersion models that can be used for estimating PMI0 concentrations
are listed in Table 4-3. All models are available on UNAMAP Version 6from National Technical Information Service (NTIS) as PB 86-222361. Emission inputs for those models not considered screening techniques are discussed in Section 5.0. It should be noted that only the ISC model explicitly treats settling and deposition ofparticles and can accept particle size data, in as many as 11 size fractions. No model recommended for regulatory use at this time handles secondary particle formation or othertransformations in a
manner suitable for SIP control strategy demonstrations. However, EPA has completed work on two models, PEM-2 (an urban model) and MESOPUFF-II (a medium scale transport model), that c.an provide supporting analyses. These models include provision to input a settling velocity appropriate to particle size, adeposition velocity characteristic ofthe pollutant-surface interaction, and arate (percent per hour) to describe the transformation ofprimary gas pollutant to secondary particle pollutant (e.g., sulfur dioxide to sulfate), Since their accuracy. suitability, and
resource requirements for regulatory applications are not well tested, no specific recommendation on the use ofthese models is provided here, Nevertheless, in selected applications, they may be useful in assessing the significance of background concentrations and of secondary sulfate particles in the effectiveness ofcontrol strategies. Application ofthese models should follow recommendation in the modeling guideline concerning the use of alternative models. 4.3.2 Special considerations for PM10 dispersion modeling The Modeling Guidelinel contains guidance that should be followed on (1) selection of appropriate source and meteorological data for use with dispersion models, (2) location of receptor sites. (3) selection ofmodel 4-8

### TABLE 4-3
DISPERSION MODELS ApPLICABLE TO PM10ANALYSES*

1to 24-HourAverage Annual Average Screening Techniques**
CRSTER CRSTER PTPLU-2 MPTER MPTER COMPLEX I RAM RAM VALLEY ISCST ISCLT CDM 2.0

*Formore information concerning the applicability ofthese models, consult

the Guideline onAir QualityModels (Revised). As noted in this document, these models may also be used forTSP modeling analyses in conjunction with asuitableTSP emission inventory, as asurrogate, where PMIOdata bases are inadequate. **These models are consideredto be screening techniques_for use priorto a more refined analysis as outlined in the**Gu~deline** 

### onAir QualityModels (Revised)•

4-9

### options, (4) determination ofurban/rural classification, and (5) determination ofbackground airquality. With regard to background airquality values, there is now limited data that might be used to support specific values for PMI0background concentrations. General guidance on the use ofavailable data is provided in Appendix D. Rollback and roll forward are appropriate only for preliminary analyses. Proportional models may be used in conjunction with receptormodeling if the airquality problem is clearly associated with afew specific sources. Such procedures are discussed in Section 4.4 and 6.4. For urban-wide refined analyses, COM 2.0 orRAMmay be used. For source-specific analyses ofcOMplicated sources, the ISCmodel is preferredto CRSTER/MPTER because ISC is the only.model that is capable oftreating deposition, area and volume sources, building downwash, etc. Forthose cases where no recommended technique is available or applicable, nonguideline modeling approaches for use in each specific situation must be approved by the appropriate Regional Office. Dispersion models are more reliable for estimating longertime-averaged concentrations (e.g., annual average) than for estimating short-termconcentrations (e.g., 24-hour) at specific 10cations.11 Point source models are reasonably reliable in estimating the magnitude ofthe highest concentrations occurring sometime, somewhere within an area. Errors in highest estimated concentrations of 10to40percent are found to be typical for sources that can be adequately characterized. Themultiple source urban model RAM showed no significant bias in estimating l-hour ground level concentrations for the l3-station RAPS network in St. Louis. The average network cumulative frequency distributions ofhourly estimated and observed concentrations differed by no more than 30percent overthe entire concentration range. 4-10

### However, estimates ofconcentrations that occur at aspecific time and site tend to be poorly correlatedwith actually observed concentrations and are much less reliable, shouldthis performance attribute be important in a regulatory application. 4.4 Receptor Models for Estimating PM10 Concentrations* 4.4.1 Control strategy analyses using receptormodels Receptor models tend to be well suited for source apportionment of 24-hour PM10 samples. However, care must be taken to ensure that the samples analyzed are representative ofthe conditions causing NAAQS exceedances. The CMB is recommended as the primary method to be used in regulatory applications of receptor models to PM10 data.4,12,13 There is uncertainty in any source apportionment approach. Therefore, ifCMB is used for source apportionment (without combiningwith aOM), it is required that at least
one other receptormodeling approach be used as a corroborating analysis. This may be FA, OM, ASEM, microinventory, trajectory analysis, XRD, or other corroborating approach as selected from those discussed in Volume I ofthe ReceptorModel Technical Series2orthe Digest ofAmbient Particulate Analysis and Assessment Methods.14 It is strongly urged that either optical microscopy orASEM be used to corroborate CMB, along with intensive chemical analySis (sulfate, carbon and other elements) ofthe samples. The OMor ASEM should be used instead ofCMB ifonly TSP data are available. It is also strongly urged that the CMB be performed on size fractionated PM10 (into fine and coarse fractions, below and above 2.5 urn). This greatly

*The terms "model" and "method" are used interchangeably, even though

analysis methods such as scanning electron or optical microscopy are methods, not models. 4-11

### increases the resolution ofthe techniques. The results of receptor model
source apportionment may be used with the method discussed in Section 6.4 to estimate the degree ofcontrol required to demonstrate attainment at a given monitoring site. Forexceedances ofthe annual NAAQS, the selection of days on which to perform receptormodel analyses should be governed by the representativeness ofthe seasons where exceedances occur. For exceedances of" the 24-hour NAAQS consideration, emphasis should be on those days where exceedances occurred. This selection process is discussed further in reference.6 4.4.2 Preliminary analyses using receptormodels

Preliminary analyses can also be done using receptor models. Themost
common methods employed are optical or automatic scanning e1ecron microscopy. These techniques are relatively inexpensive (less than $500 per sample) and
can give a variety ofinformation about likely sources. They are especially useful when only TSP data are available because they can discriminate between particles less than and greater than 10 um. Othermethods which may be helpful include microinventories, chemical emission inventories, and mass ba1ance.4,12,13 In those cases where only TSP data on glass fiber filters

are available, the CMB approach is limited because some key tracers (silicon and aluminum) cannot be used. However, the CMB may be auseful preliminary procedure in areas where no steel mills or coal fired power plants are likely contributors. In such instances, certain chemical elements associated with emissions from steel mills and coal fired power plants (such as iron, Fe) may serve as useful replacements for ordinary tracers ofcrustal material (e.g., Si, All which cannot be reliably measured on aglass fiber filter. In
areas where steel mills and power plants are likely sources, the microscopic
4-12

### methods (OM and ASEM) would usually be preferred for preliminary analyses. As suggested in Table 4-2, these are the recommended, corroborative Methods for use with dispersion modeling when only TSP data are available. 4.5 Use ofReceptor and DispersionModels in Combination Several demonstrations have been made where receptormodels were used to help evaluate the results ofdispersion modeling.15 This is the recommended approach for source apportionment. It is especially useful when the emission inventory used in adispersion model is determined to be marginally adequate. The results ofthe receptormodel can be used to carefully scrutinize the inventory assumed in the dispersion model to deduce whether emissions from certain source categories appearto have been adequately characterized. The
use of a receptormodel, such as CMB, in conjunction with dispersion modeling, is highly recommended in such situations. Procedures for using theCMB and dispersion model in concert are specified in reference6to this section. Guidance in Section 5.0 describes the kinds ofdata necessary in aPMIO emissions inventory used as input to adispersion model for aPMIO analysis. For determining compliancewith the annual PMIONAAQS, dispersion models based on an annual PMIOemission inventory and sequential or frequency distributions ofobserved meteorological conditions can be used. Also. receptor models and methods such as CMB, FA, OM andASEM, perhaps corroborated by microinventories, trajectory analysis, and XRD can be used on samples which have been carefully selected to represent the annual average. Thus, the
source contributions would reflect those which cause the annual average to exceed the NAAQS. It may be difficult to devise aPMIO short-termemission inventory for
use in adispersion model to characterize 24-hour episodes ofhigh ambient 4-13particulate matter. Therefore, analysis ofthe observed monitoring data using a receptormodel is likely to be particularly useful, in concert with dispersion model estimates. Ofthe receptor models discussed, CMB, OM, orASEM, perhaps corroborated by XRD ortrajectory analysis are appropriate for use with 24-hour observations, with CMB the preferred method. Factor analysis is limited to long-term data sets and is more useful in conjunction with the annual NAAQS.16 It is recommended that TSP data not be used with receptor models for either 24-hour or annual PMIO analyses because the particles largerthan 10 ummay bias the results. An exception to this is the use of ASEM or optical microscopy in association with dispersion model estimates. This exception is permitted because ASEM and optical microscopy involve the analysis of discrete particles where their size (relative to PMIOl can be estimated. Once specific 24-hour or annual average source contribution factors

are obtained, the proportioning method discussed in Section 6.4 may be used to estimate control requirements.

4-141.

2. 

### References
Guideline OnAir Quality Models (Revised), EPA-450/2-78-027R. U.S. tnvironmental Protection Agency, Research Triangle Park, NC 27711 July 1986. Receetor Model Technical Series, Volume I: OverviewOfRecettor Model Appllcation To Particulate Source Apportionment, EPA-450/4-8 -016a, PB82-139429. U.S. Environmental Protection Agency, Research Triangle Park, NC, July 1981. 3. Gordon, G. E., ReceptorModelS. Environmental Science and Technology, 1980, 14. 792-800. 4. 

### Rece§torModel Technical Series. Volume II: Chemical Mass Balance. EPA- 50/81-016b. PB82-187345. U.S. Environmental ProtectionAgency. Research Triangle Park. NC. July 1981. 5. 

### Protocol ForApplying And Validating The CMB. Protection Agency. Research Triangle Park. 

## Nc. U.S. Environmental In Preparation. 6. 

### Protocol For Reconciling Differences Among ReceptorAnd**Dis~ersion** 

### Models. U.S. Environmental Protection Agency. Research Triangle Par. 

## Nc. In Preparation. 7. ReceptorModel Technical Series. Volume V: SourceApportionment Techniques And Considerations In Combining TheirUse. EPA45074-84-020. PB85-111524. U.S. Environmental Protection Agency. Research Triangle Park. NC. July 1984. 8. Turner, D. B.,**~orkbook** 

### ofAtmospheric Dispersion Estimates. PHS Publication No. 999-Ap-26. U.S. Environmental Protection Agency. Research Triangle Park. NC, 1970. 9. Budney. L. E., Procedures ForEvaluating Air Quality Impact OfNew Stationary Sources. Guidelines ForAir QualityMaintenancePlanning And Analysis Volume lOR, EPA450/4-77-001. U.S. Environmental Protection Agency, Research Triangle Park, NC. October 1977. 10. Hanna, S. R., G. A. Briggs andR. P. Hosker, Jr•• Handbook on Atmospheric Diffusion. DOE/TIC-11223. Technical Information Center, Department of Energy, Washington, DC, 1982. 11. 

### Rhoads, R. G., "Accuracy ofAir QualityModels," MemorandumtoAir and Hazardous Division Directors. U.S. Environmental Protection Agency, Research Triangle Park, NC, July 22, 1981. 12.**Rece~tor** 

### Model Technical Series, Volume III (Revised): User's Manual For pecies Mass Balance Computer Program. U.S. Environmental Protection Agency, Research Triangle Park, NC. In Preparation.

4-15

### 13. Receptor Model Technical Series, Volume IV: Technical Considerations In Source**A~aort10nment** 

### SfParticle Identification, EPA

### 450/4-83-018, PB84-103 

## o. U.S. Env ronmenta1 Protection Agency, Research Triangle Park, NC, June 1983. 14.

15. Core, J. E., J. A. Cooper, P. L. Hanrahan, andW. M. Cox, Particle Dispersion Model Evaluation: ANewApproach Usin9 ReceptorModels. Journal oftheAirPollution Control Association, 1982, 32, 1142-1147. 16. ReceptorModel Technical Series, Volume VI: AGuideTo The Use of FactorAnalysis And**MUlti~'e** 

### Regression (FA/MR) Techniques in Source Apportionment, EPA 450/4- 5-007, PB86-107638. U.S. Environmental Protection Agency, Research Triangle Park, NC, July 1985. 17. ReceptorModel Source Composition Library, EPA 450/4-85-002, PB85-228823. U.S. Environmental Protection Agency, Research Triangle Park, NC, 1985

4-165.0 DEVELOPMENT OF PMlO EMISSION INVENTORIES
5.1 Over..vi**e~.** As part ofthe SIP development process following promulgation ofPMlO NAAQS, emission inventories may have to be developed for PMlO. This section discusses various technical considerations involving the preparation ofPMlO emission inventories. Though inventories are useful for various purposes, the primary focus ofthis section is on developing or reviewing PMlO emission inventories to be used as input to applicable air quality dispersion models. Such models are expected to be utilized as part ofSIP control strategy demonstrations. Some discussion is also included on the emission inventory requirements of receptor models, which differ considerably from dispersion models in the approach used to identify source/receptor relationships as well

as in the inventory data needed as input. This section does not repeat basic guidance on the fundamentals of compiling emission inventories. It is assumed that most users ofthis document will have had direct experience compiling particulate matter or other inventories and will find the same general procedures applicable to PMlO inventories. The primary thrust ofthis section is to alert users to circumstances where additional materials and different methods are needed to compile PMlO inventories. The two major components ofPMlO inventories, or inventories ofany pollutant to be used in dispersion modeling, are (l) a set of emission factors, and (2) source data to which the emission factors are applied to estimate emissions. Emission factors are generally compiled from source test data and represent emissions, before and/or after controls, that may be expected from
atypical facility within aparticular source category. The most commonly
5-1used source ofemission factors is AP-42, Compilation ofAir Pollutant Emission Factors,!which is revised periodically to include particle size data and size specific emission factors. Emission factors in AP-42 are contained in two separate volumes for stationary point and areas sources (Volume I) and mobile sources (Volume II), The PMIO emission factors for reentrained dust from roadways are included in Volume I. This section**de~cribes** the format ofthese PMIO emission factors for stationary sources and how they can be applied to the appropriate source data to develop PMIO inventories. Emission inventory procedures for mobile sources are presented in a separate EPA publication.11 The second component ofthe PMIO inventory, source data, consists of stack and exhaust flow parameters, production levels, throughputs, control device efficiencies, etc., as well as information on source locations and plant layouts. The specific kinds of source data that are needed in the PMIO inventory depend on the model used for PMIO simulation, with the major differences involving (1) the levels oftemporal and spatial resolution, (2) the description and configuration ofeach source, and (3) the need for chemical composition and particle size distributions. Thus, this section also identifies models currently available for PMIO, as well as those anticipated to be available in the next several years, and delineates the specific differences in emission inventory requirements ofeach of these models. Finally, anumber of specific considerations need to be made when dealing with air quality models and PMIO inventories. First, condensable and secondarily formed particles may become more important since both are primarily within the PMIO fraction. Second, since PMIO is a subset oftotal 5-2particulate matter, the question arises whether an existing particulate matter inventory, compiled for TSP analyses, can simply be modified for use in aPMI0 application. Third, as in any modeling application requiring large amounts of input, data handling becomes an important consideration. These topics are addressed in the latter part of this section. 5.2**~Emission** Factors and Fractional Multipliers Any emission factor is basically amultiplier which is applied to appropriate

source activity levels, such as throughput or production rates, to estimate emissions. An emission factor can be aconstant, implying a linear relationship between source activity and emissions, or can be variable, with parameters other than just activity levels influencing emissions. In either case, emissions are generally computed by the following relationship: Emissions factor xActivity level xControl device penetration = Emissions
In the above equation, the control device penetration factor is calculated
as (1 minus the control efficiency), which becomes unity ifuncontrolled emissions are being calculated. This simple equation is employed regardless ifemissions are being computed for total particulate matter, PMI0, or any other pollutant. If a set ofPMI0 emission factors is available, the preceding equation becomes: Emission factorlpM xActivity level xControl device 10 penetrationlPM 

## =EmissionslpM 10 10
Ofcourse, emission factors may be utilized that already reflect the effect ofcertain controls, in which cases the control device penetration factor drops out ofthe above equation.
5-3Alternatively, PM10 emissions may be computed by applying fractional multipliers to particulate matter emission estimates in an existing inventory as follows: (PM10 fraction) xEmissionslparticulate

## =EmissionslpM10 An advantage ofthe latter approach is that PM10 fractions may be available in certain cases whereas PM10 emission factors may not. This approach may also be easier from adata handling standpoint when converting an existing total particulate matter inventory to a PM10 inventory. Thus, in compiling aPM10 inventory for use in dispersion modeling,

one must first obtain requisite PM10 emission factor or size fraction information. Such information is becoming available for a large number of
sources. Table 5.1 lists the stationary source categories for which EPA has developed PM10 emission factor and other particle size information. Amajority ofthese factors have been distributed to State/local agencies. Additional PM10 information will be published in supplements to AP-42. The PM10 fractions and emission factors are presented in tabular form such as are shown in Table 5-2. In the hypothetical example shown in Table 5-2, cumulative mass percents and emission factors are shown at various particle size cut points for acontrolled process. In this case, PM10 emissions after controls could be calculated either by applying afactor of0.06 lb/ton to the process production rate or by multiplying the existing total particulate matter emissions for the facility by 60 percent. Agraphical display ofthe information in Table**5~2** is included.in Figure 5-1. Some agencies may elect to obtain their own particle size data for certain sources. Source testing is generally encouraged, especially for

5-4

## '" I 

## '" Table 5.1 PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

1.1 Combustion-Bituminous and Subbituminous Coal Dry Bottom Boiler x x x x x Wet Bottom Boiler x x x Cyclone Furnace x x x Spreader Stoker1 x x x Spreader Stoker2 x x Overfeed Stoker x Underfeed Stoker x x 1.2 Combustion-Anthracite Coal Dry Bottom Boiler x x x Traveling Grate Boiler x 1.3 Combustion-Residual Oil Utility Boiler x x x Industrial Boiler x x Commercial Boiler x Combustion-Distillate Oil Industrial Boiler x Commercial Boiler x 1.4 Combustion-Natural Gas All Boilers x 1.6 Combustion-Bark Wood Waste Boiler x x xl,2

lWithout flyaah reinjection. 2With flyash reinjection.U'I I en Table 5.1 (continued) PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOl/RCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE, AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

1.7 Combustion-Lignite Coal Boilers x x 1.8 Combustion-Bagasse Vibrating Grate Stoker x 2.1 Refuse Incineration Municipal Incinerator x 4.2 Surface Coating Spray Booth Water x Based Water Curtain 5.3 Carbon Black Oil Furnace x 5.15 Detergent Spray Dryer x 5.16 Sodium Carbonate Manufacturing Bleacher Dryer x x x Calciner x x x3 x3 Dryer x Rotary Predryer x x3 x3

3eombined controls.(J'I 

## ,

..... Table 5.1 (continued) PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE 

# I AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

5.17 Sulfuric Acid Absorber x Absorber 20% x Absorber 32% x Secondary Absorber x 5. Boric Acid Dryer x x 5. Potassium Chloride Dryer x x3 x3 6.1 Alfalfa Dehydrating Drum Dryer x 6.3 Cotton Ginning Battery Condenser x x Lint Cleaner x3 x3 Roller Gin Bale Press x Gin Stand x Saw Gin x Bale Press x Gin Stand x

3Combined controls.Table 5.1 (continued) \ , '! ,,

# .. '.': '. 

# ... 

## \ ,

### ,. .,'
" i I'. ";.1 .•1 '!,.

# • 

# .(

,

# .

U1 I CO PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES. PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber CyClone Other

6.4 Feed and Grain Carob Kibble Roaster x Cereal Dryer x Unloading and Conveying x Rice Dryer x 6.18 Ammonium Sulfate Rotary Dryer x 7.1 Primary Aluminum Production Bauxite Ore Unloading x Storage x Prebake Roof Monitor x HSS Cell x 7.2 Coke Production Coal Preheat x x Coal Charging x Coke Pushing x x Mobile Scrubber Car

- Travel Mode xU1 I 

## '" Table 5.1 (continued) PMIO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES. PROCESSES AND CONTROL SYSTEMS

.. CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other
7.2 CokeProduction (cont.) Mobile Scrubber Car Push Mode ·x Quenching with Dirty Water x Baffle Quenching with Clean Water x Baffle Combustion Stack x 7.3 Primary Copper Multiple Hearth Roaster and Reverberatory Smelter x x Reverberatory Smelter x x Converter x x Matte Tapp.ing x Slag Tapping x Converter Slag and Blow Operations xU'1 I


## .... o Table 5.1 (continued) PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other
7.4 .Ferroalloy OpenFurnace 50% FeSi x x 80% FeNn x x Production Si Metal x x FeCr (HC) x x SiMn x x 7.5 Iron and Steel Production Sintering Windbox x x x x Sintering Discharge Breaker x Blast Furnace Casthouse x Blast Furnace with Local Evacuation x Hot Metal Desulfurization x x BOF Top Blown Furnace Melting and Refining x Q-BOP Melting and Refining x BOF Charging x x BOF Tapping x x EAF Melting and Refining xTable 5.1 (continued) PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

7.5 Iron and Steel Production (cont.) EAF Melting, Refining, and Charging Direct Shell Evacuation x Open Hearth Furnace Melting and Refining x x 

## ,
, 7.6 Primary Lead Smelting Blast Furnace Flue x , Blast Furnace x Ore Storage x SinterMachine x Dross Kettle x Reverberatory Furnace x 7.8 Secondary Aluminum Reverberatory Furnace x Chlorination Station x en I 

# ... ...U1 I


## .... N Table 5.1 (continued) PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

7.10 Gray Iron Foundries Cupola Furnace x x x EAF x Pouring and Cooling x Shakeout x 7.11 Secondary Lead Smelting Blast Furance Flue x Blast Furnace Ventilation x x 7.13 Steel Foundries Casting Shakeout x Open Hearth x x 7.15 Storage Battery Production Grid Casting x Grid Casting and Paste MiXing x Lead Oxide Mill x Paste Mixing x x Three Process x 7. Tinner Batch Tinner x 8.1 Asphaltic Concrete Conventional Plant x x x Spray Tower Drum Mix Plant x x<n I



## .... W Table 5.1 (continued) PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

8.3 Brick and Related Clay Products Coal Fired Tunnel Kiln x Sawdust Fired Kiln x Raw Material Screening and Grinding x 8.6 Portland Cement Wet Kiln x x Dry Kiln x x x Clinker Cooler x Gravity Filter 8.9 Coal Cleaning Dry Process x Thermal Dryer x x Thermal Incinerator x 8.13 Glass Manufacturing Furnace Exhaust x xU'l I


## .... -I'> Table 5.1 (continued) PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

8.15 Lime Rotary Kiln x x xa x product Loading Into Open Bed Trucks x Into Tank Truck x Glass Line Into Tank Truck x 8.18 Phosphate Rock Processing Ball Mill x Calciner x3 x3 Rotary Dryer x3 x Rotary Dryer and Fluidized Bed Dryer x3 x3 x3 Roller Mill and Ball Mill x3 x 8.22 Taconite Ore Processing Main Waste Gas Stream x3 x 8. Feldspar Ball Mill x 8. Fluorspar Rotary Drum Dryer x 8. Lighweight Aggregate (Clay) Coal Fired Rotary Kiln x S. C.b Dryer x Reciprocating Grate Clinker Cooler x x SCyclone With Baghouse. bS. C. 

## =Settling Chamber.U1 I


## .... U1 Table 5.1 (continued) PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

8. Lightweight Aggregate (Shale) Reciprocating Grate Clinker Cooler S.C.a 8. Lightweight Aggregate Coal Fired Rotary (Slate) Kiln x x Redprocating Grate Clinker Cooler S.C.a 8. Talc Pebble Mill x 10.1 Kraft Pulp Recovery Boiler with DCE x x Recovery Boiler without DCE x x Lime Kiln x x x Smelt Dissolving Tank Vent x x Packed Tower

as.C•• Settling Chamber.U1 I Table 5.1 (continued) PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontroned ESP Filter Scrubber Cyclone Other

10.4 Wood Working Waste Belt Sander x 11.2.1 Unpaved Roads Rural Gravel x Dirt x Crushed Lime Stone x Industrial Copper Smelting x Iron &Steel Production x Sand &Gravel Processing x Stone Quarrying &Processing x Taconite Mining &Processing x Western Surface Coal Mining x 11.2.2 Agricultural Tilling x 11.2.3 Aggregate Handling Batch Drop x Continuous Drop xU1 I


## ....
..... Table 5.1 (continued) PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS 

## I CONTROL DEVICE AP-42 Fabric Multiple Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

11.2.5 Paved Urban Roads Local Streets x Collector Streets x Major Streets/ Highways xFreeways/Expressways x 11.2.6 Industrial Paved Roads Copper Smelters x Iron and Steel x Asphalt Batching x Concrete Batching x Sand and Gravel x

-TABLE 5-2. CUMULATIVE EMISSION FACTORS AND PARTICLE SIZE DISTRIBUTION FOR AHYPOTHETICAL LIME PROCESS

Particle Size Cumulative Mass Percent Less Cumulative Emission

(micrometer) than Stated Size Factors (Pound/Ton of Product)
Uncontrolled Contro11eda Uncontro11ed Controlleda Total Catch 100 100 85 0.100 15.0 70 82 59 0.082 r.n I

...... 10.0 51 60 43 0.060 co 7.5 42 56 36 0.056 5.0 33 50 28 0.050 2.5 13 31 11 0.032 1.0 2.8 11 2.5 0.010 0.5 0.9 4 0.8 0.004
aControl Device: Baghouse..'fll!;l'I]';:'I'jll-li!I·I!II'lIll·III'tllt' II!'(I III;;.!! 1 I 1 '1 1

### 1111It!:q!"II~i' 0.001 0.5 1 2.5 5 7.5 10 15 

### AERODYNAMICPARTICLEDIAMETER, MICROMETeRS



### FIGURE5-1 EXAMPLEGRAPHICALDISPLAYOFCUMULATIVEEMISSION FACTORSVS. PARTICLESIZE FORAHYPOTHETICAL PROCESS

### large contributors, since facility or point-specific emission estimates are considered more accurate than average estimates calculated fromAP-42 factors. Any testing should be consistent with the methods described in Appendix Cof this document. 5.3 PM10 SourceData for Dispersion Models The secondmajor component necessary for inventory compilation, along with emission factors or fractional multipliers, is source data describing the nature and level of activity at each facility or operation. Source data include all information on the nature and location ofeach source, operating rates, stack and exhaust gas parameters, and control devices employed. The kinds ofsource data that must be obtained depend on whether aparticular
source can best be described as apoint, area, or line source. The degree of detail required in aPMIO emission inventory is dictated by the dispersion model employed for simulating airquality impacts. As described in more detail in section 4.0, anumber ofmodels are currently available for assessing the airquality impact ofPMIO, differing in many respects. Generically, most suchmodels can be categorized along the following lines: dispersion vs. receptor individual source vs. areawide (grid) short-term vs. long-term Dispersion models calculate ambient air concentrations primarily as functions ofsource configurations, emission strengths, terrain features, and meteorological conditions. Receptor models (discussed in section 5.4) infer the relative impact ofvarious sources on ambient airquality by
5-20

### reconciling particle size, and shape and chemical composition data ofambient air samples with particle size, shape and chemical composition data for various emission sources in the vicinity ofthe ambient air measurement site. Individual source models evaluatethe impact ofa single source or source complex whereas areawide (grid) models evaluatethe impact ofnumerous sources, including area and line sources, over a larger area. Short termmodels estimate air quality levels for time periods from 1 hourto 24 hours whereas long-termmodels generally predict monthly, seasonal, or annual average concentrations. Themajor impacts ofmodel choice on requisite source data in the PMI0 emission inventories are itemized below:

-- Area coveredby the inventory

- Individual source dispersion models require source data to be collected for only asingle stack, facility,
orcomplex. Areawide models require source data for all sources within the defined grid system.

-- Source configuration

- Some (especially point source) dispersion models require information on individual stack heights and building sizes and locations. Many dispersion models require area sources to be assigned to grid squares and some distinguish line sources from
area sources.

-- Temporal resolution

- Typically, the emission rates input to most applicable dispersion models are expressed in terms ofgrams per second for point sources, grams per second per square meter for area sources, and grams per second permeter for line sources. Actually, the emission rates input to dispersion models need not be as resolved
as might be inferred fromthese units. Ideally, the emission rates
5-21

### should be as resolved as themodel output. For example, ifthemodel predicts a24-hour average ambient air concentration, emissions should be input that reflect conditions over that 24-hour period for best results. In cases wheremaximumconcentrations needto be predicted, maximum emission rates may need to be considered instead of atime-averaged rate.

-- Spatial resolution

- Most dispersion models require point source coordinates for each stack. Area and line source emissions are typically assigned to grid squares and line segments, respectively, having arbitrary dimensions. The scale ofthe coordinate systems in most models is at the discretion ofthe user. Because ofcomputer constraints and regulatory requirements, tradeoffs usually exist between the need for finer resolution and the overall area that can be modeled. In some cases, the maximumdegree of spatial resolution is limited by the units and number ofsignificant figures built into themodel's software.

-- Size fractions

- Most applicable dispersion models consider the PM10 fraction as asingle lump sumentity requiring no particle size resolution below 10micrometers. Several models havethe capability to treat various fractions differently within the PM10 fraction.
Chemical composition

- Oispersionmodels rarely require chemical composition data, although many receptormodels do.
Tables 5-3 through 5-7 list various dispersion models that may be used for simulating PM10 levels, along with specific source data requirements ofeach
5-22

### model. References 2through 7present adetailed description ofeach of these models. The reader should consult these user's manuals as well as
someone with modeling experience before developing an inventory as input to any ofthese models. The readerwill note fromthe information in Tables 5-3 through 5-7 that dispersion models vary considerably in their source data requirements. As was mentioned earlier, it is not the intent ofthis section to reiterate guidance on the fundamentals ofcompiling inventories. Rather, this**sec~** 

### tlon's primary purpose is to highlight special considerations that may have to be madewhen developing aPM10 emissions data base for modeling. Basic guidance on compiling emission Inventories is given In References 8through 14. Additional guidance Is presented in Reference 15 on obtaining different levels oftemporal and spatial resolution, Including techniques for assigning area
source emissions to grid cells. Information on dispersion models used for PM10 applications is given in section 4.0 ofthis document.

5-23

### TABLE 5-3 INDUSTRIAL SOURCE COMPLEX MODEL (ISC)2

- Dispersion Application

- Estimating local impact around complex industrial sources in rural orurban areas. Stack. area. and volume sources withinthe industrial complex can be accommodated. Area sources can include fugitive sources such as storage piles and slag dumps. Volume sources can include sources such as building roofmonitors and conveyorbelts and may also be used for simulating line sources. Short/long term

- The ISC contains both short and long term versions called ISCST and ISCLT. respectively. The ISCSTmaybe run for each hour ofa year or ISCLTmay be run for ayear using frequency distributions of meteorological data and annual emissions. Inventory area - All sources within the boundaries ofthe industrial'complex must be included in the inventory. Stack and exhaustdata

- Physical stack height. inner diameter and elevation above sea level. gas exittemperature. and velocity. Ifthe stack is adjacent to abuilding and aerodynamic wake effects are to beconsidered. the length. width. and height ofthe building must be known. Nonpoint sourceconfigurations

- The horizontal dimension, elevation above sea level and effective emission heights are required for each area source or volume source. Temporal resolution ofmodel output

- 1-. 

### 2-. 3-. 

### 4-, 6-. 8-. 

### 12- and 24-hour values may be selected for calculation by ISCST and annual averages are calculated by averaging all 24-hour concentrations. Seasonal and/or annual values are calculated by ISCLT. Actual emissions units input tomodel

- grams/second for stack and volume sources. grams/second-meter2for area sources. Spatial resolution ofsource data

- Source elevation abovemean sea level and source locations with respect to auser-specified origin are required for all sources. Universal TransverseMercator coordinates may beused to define source locations. Aplant layout drawn to scale is required to obtain coordinates and building dimensions to the nearest meter. Particle size resolution

- Required if settling and deposition areto be considered. Deposition is generally only important for particles greater than 20micrometers in diameter. The PMI0 fraction couldthus be treated as alump sumwithout any need for particle size data although ISC allows input ofup to 11 size fractions. Chemical composition data

- None required. 5-24

### TABLE 5-4 CLIMATOLOGICAL DISPERSION MODEL (COM 2.0)3,4 

## IlE!

-Dispersion. Application

- Estimating urban scale impact ofmultiple point and area sources distributed over asquare grid system. Line sources are handled as area sources. Short/long term

- Long term (seasonal/annual) Inventory area - All sources within the boundaries ofthe grid systemmust be inventoried. Stack and exhaust data

- Physical stack height and inside diameter, exhaust gas exit velocity, and temperature are neededto calculate plume rise. Nonpoint source configuration

- Area source emissions are allocated to square grid cells. The coordinates ofthe southwest corner andthewidth of each grid are required. Stack height (ifapplicable) is also required. Temporal resolution ofthe model output

- Seasonal or annual average concentrations are predicted by the model. Actual emission units input to model

- grams/second. Also required are ratios ofaverage daytime and nighttime emission rates to the 24-hour average. Spatial resolution of source data

- The scale ofthe coordinate systemfor locating sources is completely arbitrary. Area source grid squares can be defined arbitrarily small; however, since the programwill only handle 2500 grid squares, atradeoffexists between area source resolution and model coverage. SinceCOM**2~0** 

### operates on the implicit assumption that area source emissions are relatively uniform, grid square sizes should not be selected that are too small. Grid squares ofvarying sizes are allowed, but their side length must be an integer multiple ofacommon side length. Particle size resolution

- No particle size resolution below 10microns is required. The PMI0 fraction can betreated as asingle lump sum. Chemical composition data

- None required. Other

- An assumed pollutant half life, may be entered into COM 2.0, but should not be considered in PMIO SIP's.

5-25

### TABLE 5-5 RAMS

- Dispersion Application

- Estimating urban scale impact ofmultiple point and area sources distributed overasquare grid system. Line sources are handled as area sources. Short/long term

- TheRAM is asequential model designed to process hourly inputs. TheRAM calculates concentrations for I, 2, 3, 4, 6, 8, 12and 24 hourly averages. TheRAMmay be run for each hour ofayear or by averaging all 24-hour concentrations. Inventory area - All sources within the boundaries ofthe grid systemmust be inventoried. Stack and exhaust data

- Stack height and inside diameter, stack gas exit temperature, and velocity are needed for plume rise calculations. Nonpoint source configurations

- Area source emissions are allocatedto square grid cells. The coordinates ofthe southwest cornerandthe side length ofeach grid are required. Height ofemissions is also needed, ifapplicable. Temporal resolution ofmodel output

- One hour to 1day (24-hour) average concentrations are calculated by RAM, and annual averages are calculated by averaging all 24-hour concentrations. Actual emission units inputtomodel

- grams/second for both point and area sources. Spatial resolution of source data

- The scale ofthe coordinate system is completely arbitrary, allowing any level of resolution. Particle size resolution

- No particle size resolution below 10microns is required. The PM10 fraction can betreated as asingle lump sum. Chemical composition data· None required. Other

- An assumed pollutanthalf lifemay be entered intoRAM, but should not be considered in PM10SIP's.

5-26

### TABLE 5-6 SINGLE SOURCE (CRSTER) MODEL6

- Dispersion. Application

- Estimating local impact ofasingle stack or up to 19,sources considered to be emitting at asingle point in rural or urban areas where terrain is below physical stack height. Short/long-term

- The basic estimate is for aI-hourperiod; multiples up to 24 hours may be selected and the annual mean concentration is calculated from the average of all hourly concentrations. Inventory area - No area, per se, is included in the inventory, because the single stack or collection of stacks are considered to be emitted at a cOllll1on point. Stack and exhaust data

- Physical stack height and inside diameter, exhaust gas exit velocity, and temperature are needed to calculate plume rise. Nonpoint source configurations

- CRSTER does not handle nonpoint sources. Temporal resolution ofmodel output

- Themodel calculates I-hour, 3-hour, 24-hour, and annual mean concentrations. Optional averaging times of2, 4, 6, 8 

### or 12 hours may be selected. Actual emission units input to model

- grams/second. Spatial resolution of source data

- None; all sources are considered to emit from the same point. Particle size resolution

- No particle size resolution below 10microns is required. The PMI0 fractioncan be treated as asingle lump sum. Chemical composition data

- None required. Other

- An assumed pollutant halflife may be entered intoCRSTER, but should not be considered in PMI0 SIP's.

5-27TABLE 5-7 MULTIPLE POINT SOURCE MODEL WITH TERRAIN ADJUSTMENT (MPTER)7

- Dispersion. Application

- Estimating local impact ofa number ofpoint sources in rural or urban areas where terrain is below physical stack height. Short/long term

- Short-term (1-24 hours) and annual calculations can be made. Inventory area - That area including all ofthe point sources (up to 250) that are being modeled. Stack and exhaust data

- For each stack, physical stack height and inside diameter, stack gas temperature and exit velocity, and stack ground level elevation are needed to calculate plume rise. Nonpoint source configuration

- MPTER does not handle nonpoint sources. Temporal resolution ofmodel output

- One hour or any multiple thereof, including hour-by-hour concentrations for an annual average. Actual emission units input to model

- grams/second for each point source. Spatial resolution of**sourc~** data

- The scale ofthe point source coordinate system is arbitrary, allowing any level of resolution. Particle size resolution

- No particle size resolution below 10microns is required. The PMIO fraction can be treated as a single lump sum. Chemical composition data

- None required. Other

- An assumed pollutant half life may be entered, but should not be considered in PMI0 SIP's.

5-28

### 5.4 Inventory Data Needed in Receptor Models The considerations in sections 5.2 and 5.3 apply primarilyto PMIO inventories developed for use in dispersion models. Different source and emissions data are required in receptor models. As discussed in section4.0, receptormodels use techniques to estimatethe contributions ofemission
sources based on specific characteristics ofparticulatematter measured at various receptor sites. Receptormodels should be used togetherwith rollback procedures
or dispersion models to define source/receptor relationships. Because ofthe nature of receptormodels, emission factors and mass emission estimates are not needed as input asthey are for dispersion models. Nor, generally, are detailed source data required such as activity levels and control techniques.* The only source emission data that are needed for direct use in receptormodels involve similarkinds ofmorphological or chemical characterizations ofemissions that are made on the ambient samples collected at the receptor site(s). Moreover, such emission characterizations
are usually not obtained for each individual source within an area being modeled, but typically are representative ofbroad classes of sources whose particle composittons are similar (e.g., motor vehicles, oil fired boilers, etc.). Themajortype of receptormodel utilizes various chemical methods.16,17 In general, chemical methods identify source contributions by comparing the relative amounts and temporal variability ofvarious chemical species at ambient sampling sites with source chemical patterns (1.e., "fingerprints").

*Notethat while detailed source and emissions data on individual facilities are not required to run receptor models, these kinds ofdata are needed in interpreting the results and in defining specific source/receptor relationships.
5-29

### Chemical methods are comprised ofmass balancetechniques, which identify the most probable combinations ofsources to explain the chemical pattern on a single filter, and multi samplemethods (including factor analysis), which identify the most probable linear combination ofsources to explain either thetime or spatial variability in ambient chemical patterns. Microscopy represents another important type ofreceptormethodtypically used to confirmthe results obtained from chemical methods. Optical microscopy relies on identification ofparticles collected at the receptor site bytheir size, shape, color, surfaceproperties and birefringence (an optical property). Scanning electronmicroscopy, commonly automated by computerand supplemented with x-ray fluorescence analysis, relies on aparticle-by-particle analysis of particle size, shape, andelemental composition. The use ofsuch particle identification techniques requires aknowledge ofthe physical characteristices (e.g. shape, size, brightness, color) ofparticles from various source categories
or access to references that catalog particle size characteristics from various
sources.16,18,19 Since nearly all receptormodels require aknowledge ofthe chemistry
ormorphology ofsource emissions, the development ofacollection ofemission chemistry data orparticle reference samples (or both) is often required. Such data maybe compiled fromthe literature (e.g., previous receptormodel applications); however, experience has demonstrated the value ofdeveloping locale-specific emission characterizations, especially for more important
source categories. TheEPA has compiled alibrary ofchemical and elemental composition data that will provide acommon basis for receptormodel sourcecharacterizations.20 An example ofthe types ofdata in this· library is shown in Table 5-8.

5-30TABLE 5- 8 .' EXAt'PLE OF DATA IN EPA'S PARTICULATE SOURCE LIBRARY 

## (29)

s.:lJRCE: S:C: :ONTROLS:**S~EEL** PRODUCTIOH • OPEH HEARTH FURHAHCE 3'03-009-0'

ESP PROFILE:**RAH~ING:** RATlHG : 26302 2'21

., 0



### ....................................................................................................................................................... FINE S>=CJES ••••••••• <2.5 UK ••••••

N

...."': XBY \IT
... • UNC COARSE

......... 2.5·10UM •••••• XBY WT +. UHC S,ECIES**"'U~SER.** 4 5 9 11 12 13 U 15 16 17 19 20 21 22 23 24 25 26 27 26 29 30 31 32 33 35 37 3.5 40 47 t.a 50 5' 55 56 56 SO 62 201 202 203 2Q.1, SL,'M( X) BE B F HA MG AL 51 P S CL CA SC TI V CR NH FE CO HI ZH GA 

### "E AS SE DR RB SR ZR 

### A" c:o SH sa CS BA CE 

### H" PB oc EC 

## = N03 HA NA NA NA HR HR NR

< 13.300

< 5.000 0.550

< < 0.550 2:000 0.550 1'.000

< 0.550 0.550

< < < 0.050

< 0.050 0.050

< < 0.050 0.050 0.050 0.050

< < < < 0.550 NA HA 40.000

0.550 62.200 

# ·

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. HR NR HR HR NR NR NR NR HR HR NR HR HR HR HR HR NR HR HR HR HR HR HR HR HR HR

.HR NR HR NR HR HR HR NR HR HR HR HR NR HR HR HR HR NA HA HA HA NR NR HR

< 3.000

< 5.000 0.550

< < 0.550 0.550 0.550 16.000

< 0.550 0.550

, <

< < 0.050

< 0.050 0.050

< <. 0.050 0.050 0.050 0.050

< < < < 0.550 HA HA 35.000

0.550 60.750 + 

# ·

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. • HR NR NR NR NR NR HR NR HR HR HR NR HR HR HR HR HR HR HR HR HR HR HR HR HR HR HR HR HR HR HR NR NR NR NR NR HR HR HR HR HR HR HR TSP

......... <30 UM ••••••

%BY +.**U~:**

NA .. NA
. NA
. MA + .. NR ... NR + • NR
.....

< + • 13.300 •

< •• 5.000 • 0.550 •

< •• < •• O.!50 ..... 2.000 • 0.550 • 11.000 ••

< • 0.550 •• 0.550 +.

< • < • < • • 0.050 ••

< • • 0.050 +. 0.050 ••

< + < + 0.050 + 0.050 •• 0.050 • 0.050 ••

< • < + • < •• < • 0.550 • NA
.. NA
.. 40.000 •

0.550 + 62.200 HOTES: CC' OR"AHIC CARBOH EC • ELEMEHTAL CARBOH: HA. NOT ANALYZEO: HR' HOT REPORTED < • LESS THAH DETECTIOH LIMIT CTHER HOTES : 20X CARBOH, 22·26X OTHER (51, OZ, AL, M") TSP AHO COARSE. REF, 20



### 5-31

### These data are most useful in the chemical methods such as themass balance model and factor analysis described in Appendix Aofthis guideline. For
more information on this particulatematter source composition library, contact the U.S. Environmental Protection Agency, AirManagement Technology Branch, MD-15, Research Triangle Park, North Carolina, 27711. The appropriate references cited in section 4.0 and Appendix Amay be consulted for more details on the specific characterizations needed for each type of receptormodel. 5.5 CondensableParticulateMatter Condensable particulatematter (or condensed particulatematter, as it is synonymously described) can be broadly defined as material that is not particulatematter at stack conditions butwhich condenses and/or reacts (upon cooling and dilution in the ambient air) to formparticulatematter immediately after discharge fromthe stack. Condensable particulatematter is ofpotential importance because it usually is quite fine and thus falls primarilywithin the PM10 fraction.21 Sources suspected ofemitting significant amounts of condensable material are shown inTable 5-9. Condensable particle factors, as such, are not explicitly included in AP-42 for most source categories. To the extent that EPA's Method 5 (Reference 22) captures aportion ofthe condensable fraction, AP-42particulate matter factors for certain categories that are currently based on Method 5 include
some condensable particles. Somecondensable particulatematterwill also be collected by the testing procedures outlined in Appendix Cofthis guideline. 5.6 Secondary ParticulateMatter Secondary particulate matter can be broadly defined as particles that form through chemical reactions in the ambient airwell afterdilution and condensation have occurred. An example ofthis phenomenon is the formation of
5-32

### TABLE 5-9
POTENTIAL SOURCES OF CONDENSABLE PARTICULATEMATTER21 Stationary Sources Alfalfa Dryers Anode Baking Furnaces Asphalt Plants AsphaIt 

### Roofing felt saturating asphalt blowing Boilers/OtherCombustion bagasse coal 1ignite oil wood/bark Charcoal Kilns Chemical Production boric acid phosphoric acid potassium sti11 zinc sulfate Citrus Peel Dryers Coke Plants Corn Processing wet mi11ing syrup manufacturing Elemental Phosphorus electric arc furnace Expanded Vinyl FerroalloyMills FertilizerPlants ammoniumnitrate diammoniumphosphate

5-33 

### Fiberboard dryer press Glass FiberandMineral Wool curing ovens blow chambers Glass Plants Grain Dryers Iron and Steel Mills sinterplant electric arc furnace basic oxygen furnace open hearth furnace heat treating scrap steel melting Gray Iron Foundries Kraft Pulp and PaperMills recovery boilers lime kiln smelt dissolvetank blowtank/hotwater accumulator Lime Kiln Manure Dryers Mineral Products gypsum clay dryers feldspar dryers clay kilns Municipal Incinerators

### TABLE 5-9 (CONTINUED)

PetroleumRefineries FCC catalytic regenerator heaters petroleum coke Portland Cement Plants kiln finish mill Primary Nonferrous Smelters Cu converter Cu electric furnace Cu fluid bed roaster Cu sinter line Pb zinter line Pb blast furnace Mo roaster Zn ore briquet dryer Zn sweat kiln Zn fume kiln Residential Heating oil wood Secondary Metal Smelters A1 scrap furnace A1 dross furnace brass and bronze furnaces copper furnace Pb furnaces Pb02 mi11s Pb grid casting Pb remelt pot othermetal furnaces Mobile Sources All Mobile Sources

5-34 

### Sewage Sludge Incinerator Silicon CarbideFurnaces Spray Paint Booths Sulfite Pulp Mill recovery boiler blowtanks Rubber Incineration RubberCuring Press Textile nylon polymerization melt polymer spinning tenter frame dye beck heat set texturizing 1atex backin9 Tire Buffing Operations Wood Products veneer plant dryer resawingsulfate particles in aplume from the oxidation of sulfur dioxide by one of several atmospheric transformation mechanisms. Generally, secondary particulate matter can be distinguished from condensable particulate matter by the time and/or distance downwind from the stack required for formation. Condensable particles form in amatter of seconds in the stack exhaust due primarily to immediate cooling and air dilution whereas secondary transformation requires minutes, hours, oreven days. Unlike condensable particulate matter, secondary particulate matter should not be included in the PMlO inventory as if directly emitted as particles. Condensable particulate matter, because it forms so quickly, will likely impact
on any nearby receptor, and thus can be treated as ifit were emitted as particulate matter. Secondary particle formation, conversely, is afunction oftime and distance downwind from the source, as well as chemical composition and reactivity. Since secondary particle formation is an atmospheric phenomenon, it should be simulated by an air quality model if it is considered to be an important component of ambient PMIO concentrations. Precursors of secondary particles especially SOx and NOx, need to be included in the inventory when such models are used. 5.7 Use ofExisting Emission Inventory An important consideration when planning aPMIO inventory is whether an existing particulate matter inventory can be used as afoundation or starting point. Since many areas have compiled annual, countywide inventories ofparticulate matter (e.g., for previous SIPs or for submission into EPA's NEDS or Compliance Data System), an incentive exists for building on such inventories rather than compiling new ones altogether. If an existing inventory is comprehensive,
current, and accurate, much of the source data ne.eded in the PMIO inventory is already available. 5-35The utility ofthe existing inventory depends on the particular air quality model employed. In some instances, aconventional inventory of annual, countywide particulate matter emissions will provide most ofthe needed information. Actually, the terms "annual" and "countywide" do not give a complete picture ofthe resolution commonly inherent in this kind of inventory, at least for point sources. First, while the emissions are typically expressed in units oftons peryear, the available operating pattern information for each point source generally allows the user to extrapolate emissions for a given season, workday, and even for a given hour during a workday. Details on performing this kind oftemporal apportionment are given in Chapter 6ofReference 18. Second, stack data are available for point sources in the annual, countywide inventory that are sufficient for many PM10 modeling applications. Specifically present are stack heights, diameters, flow rates, and plume heights, which are the major stack and exhaust gas parameters commonly needed by most applicable dispersion models. {Note: Some models also require exit gas velocities, but flow rates can be easily converted to exit velocities by dividing by the cross sectional area ofthe stack.} Moreover, the location ofeach stack is commonly specified to the nearest tenth kilometer, providing acceptable spatial resolution for applications where the grid systemwill be comprised of relatively large grid cells. Thus, relating back to the inventory specifications for each model outlined in Tables 5-3 through 5-7, it can be seen that, for point sources at least, conventional inventories of particulate matter may provide sufficient temporal and spatial resolution and stack data for a number of dispersion models such

as COM 2.0, RAM, CRSTER, and MPTER. 5-36The area source data available from conventional (i.e., annual, countywide) particulate matter inventories can also be useful in some PM10 modeling applications; however, additional data manipulation is generally needed to develop the requisite temporal and spatial resolution. First, most existing

area source records do not include operating rate data comparable to those included in the point source record. Hence, to develop emissions estimates for aperiod oftime less than ayear, the user must impose an operating pattern for each source category. Detailed operating patterns for many important area source categories are suggested in Chapter 6ofReference 18 along with recommendations for employing them. Probably the most difficult aspect of using conventional area source data in aPM10 inventory is that for certain applications, the emission totals must be allocated from the county level to the grid cell level. This is generally done by assuming that the distribution of agiven area source activity behaves similarly to some surrogate indicator (e.g., population) whose distribution is known at the subcounty level. Forexample, emissions from residential fuel combustion might logically be apportioned according to the distribution of dwelling units. More specifics on gridding countywide emissions are given in Chapter 6ofReference 18. Referring to the inventory specifications in Tables 5-3 through 5-7, the only models which could use the typical existing area source inventory as primary input areCOM 2.0 and RAM. The othermodels either do not handle area
sources or else require more detailed information. Several models require such specific inventory data on individual sources
or source complexes that an existing inventory may be of little use at all. The ISC, for example, requires input on the dimensions and juxtaposition 5-37of buildings near each stack in orderto considerthe aerodynamic wake effects

on plumes. The ISC also requires finer spatial resolution than is typically available. Receptormodels are probably most unique in terms oftheir input requirements. Generally, the location of sources, stack and exhaust data, and even the rates ofemissions are not required by receptor models. As an example, the only inventory data needed by the Mass Balance (MB) model are the chemical or elemental composition of emissions for each source category
as a function of particle size (i.e., in the <2.5 micrometer and**~2.5** to

### to micrometer size ranges). Particle size, shape, and composition data will not be contained in most inventories that have been developed for dispersion modeling applications. In any case, whenever an existing particulate matter inventory is being used to directly generate the PMIO inventory, total particulate matter emissions have to be converted to PMIO emissions. As was discussed in section 5.2, the easiest way to accomplish this ,is to multiply existing particulate matter estimates by appropriate PMIO fractional multipliers. Formaximum accuracy, PMIO fractions should be applied to individual sources rather than aggregated

source categories and should account for the existence of any emission controls. Alternatively, instead of applying PMIO fractions, PMIO emission totals can be calculated by applying the appropriate PMIO emission factor to the existing activity level for each source. 5.8 Data Handling An important consideration in any inventory compilation effort is the potential need to develop special data handling software. In anumber of models, data handling can be readily accomplished manually because the input

5-38

### requirements aremeager. Forcertainmodels, however, computerized data handling capabilities may be desirable to compile and properly format the large quantity ofinventory data needed to drive the models. As ageneral rule, the amount ofinventory data that must be handled is directly proportional to the number ofsources being modeled. Hence, forthe single source models such as CRSTER, manually preparing the input data in the necessary formats should present no problems. The same will be true formodels like ISC and MPTER ifonly afew sources are considered. The primary applications where computerized data handling will prove essential are 

### 0) when dealing with hundreds ofpoint sources; (2) where area
source emissions must be subcounty allocated to grid cells; (3) when numerous line sources must be considered individually; and (4) where an existing particulate matter inventory is being converted to aPMIO inventory, necessitating
anumber ofpreprocessing computations. Computerizeddata handling is desirable when preparing PMIO inventories for**i~put** 

### toCOM 2.0, RAM andMB, as well as MPTER if large numbers of
sources are considered. The COM 2.0 and RAM are most likely to be applied to large, urban scale applications involving many point and area sources and large grid systems. Moreover, existing particulatematter inventories are most likely to be utilized as input for thesetwo models, necessitating some preprocessing to convert total particulatematterto PMlO. Hence, if 

### either COM 2.0 orRAM is to be employed for PMIO modeling, and ifan existing annual, countywide inventorywill beused to provide the necessary input, sufficient
resources should be reserved for developing and running an auxiliary data handling systemthat will facilitate timely processing ofthe input data. Oata handling is frequently an overlooked aspect of inventory compilation and

5-39

### airquality simulation modeling that can consume agreat deal ofresources. Thus, the inventory specialist, modeler, and programmer should all be involved in the planning ofthis kind ofPMIO inventory effort.

5-40

### References
1. Compilation ofAir Pollutant Emission Factors, Fourth Edition, Volumes I and II, U. S. Environmental Protection Agency, Research Triangle Park, North Carolina, September 1985 and Supplements. (Supplement Awas issued in early 1987). 2. EnvironmentalProtection Agency,**-=-::r;;.r-7.;rr~~~rr~~-+~",""~=:,:,,,=c.:..::.,,­** 

### Model, Second Edition, Volumes I Environmental Protection Agency, June 1986. 3.



### 4.

5.

6.

7.

8.

9.



### 10.

11. A. D. Busse and J. R. Zimmerman, User's Guide for the Cllmatololical Dispersion Model, EPA-R4-73-024, U.S. Environmental Protectiongency, ResearchTriangle Park, North Carolina, December 1973. Irwin, J. S., T. Chico and J. Catalano, COM 2.0

- Climatological Dispersion Model, EPA/600/8-029, U.S. Environmental ProtectionAgency, Research Triangle Park, North Carolina, November 1985. D. B. Turner and J. H. Novak, User's Guide for RAM, Volume I, Algorithm Description and Use, EPA-600/8-78-016a, U.S. Environmental Protection Agency, Research Triangle Park, North Caorolina, November 1978. Users Manual for Single-Source (CRSTER) Model, EPA-450/2-n-013, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, July 19n. 

### T. E. Pierce and D. B. Turner, Users guide forMPTER, AMUltiple Point Gaussian DispersionAlgorithmwith Optional TerrainAdjustment, EPA-600/8-80-016, U.S. Environmental ProtectionAgency, Research Triangle Park, North Carolina, April 1980. Procedures for Emission Inventor Pre aration, Volume I: Emission Inventory Fundamentals, EPA- 5 a, U.S. Envlronmental

- Protection Agency, Research Triangle Park, North Carolina, September 1981. Procedures for Emission Inventory Preparation, Volume II: Point Sources,**EPA~450/4-81-026b,** 

### U.S. Environmental ProtectionAgency, Research Triangle Park, North Carolina, September 1981. Procedures for Emission Inventory Preparation, Volume III: Area Sources EPA-450/4-81-026c, U.S. Environmental ProtectionAgency, Research Triangle Park, North Carolina, September 1981. Procedures for Emission Inventor Pre aration, Volume IV: Mobile Sources, EPA-450 4-80-026d, U.S. Environmental ProtectionAgency, Research Triangle Park, North Carolina, September 1981. 5-41

### 12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22. Procedures for Emission Inventor Pre arationl Volume V:**BibliO~raphY,** 

### P - - - e, •• nVlronmenta Protect1

### on Agency, Researc Triangle Park, North carolina, September 1981. 

### W. H. Lamason andT. F. Lahre, Procedures for the Preparation of Emission Inventories for Volatile OranlC

### Comounds Volume 

### I, Second Eltlon, , U. S. Environmenta Protectl0n Agency, Research Triangle Park, NC, September 1980. Exam Ie Emission Inventor Documentation For 1982 Ozone State

mp ementatlon ans S s, , U. • nVlronmental Protection Agency Research Triangle Park, North Carolina, March 1981. Procedures for the Preparation ofEmission Inventories for Volatile Organic Compounds Volume II: Emission Inventory Reauirements for Photochemical Air Quality simulation Models, EPA-45 /4-79-018, U.S. Environmental Protection Agency, Research Triangle Park, NC, September 1979. J. E. Core, Receptor Model Technical Series, Volume I, Overviewof ReceptorModel Application to Particulate SourceApportionment, EPA-450/4-81-016a, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, July 1981. J. E. Core, Rece

# 7 tor Model Technical Series, Volume II, Chemical Mass Balance, EPA-450 4-81-016b, U.S. Environmental ProtectionAgency, Research Triangle Park, North Carolina, July 1981. Receptor Model Technical Series, Volume IV

- Technical Considerations in Source Aeportionment by Particle Identification, Draft Final Report from Engineerlng-Science to U.S. Environmental ProtectionAgency, Research Triangle Park, North Carolina, September 1982. 

### W. C. McCrone andJ. 

### G. Delley, The ParticleAtlas, Volume I, Principles and Techniques, 2nd Edition, Ann Arbor Science Publishers Inc., Ann Arbor, Michigan, 1973. ReceptorModel Source Composition Librarf' EPA 450/4-85-002, U.S. Environmenta1 

### Protection Agency, Researc TrianglePark, North Carolina, November 1984. 1m ortance ofCondensed Particulate Matterto Inhalable
• U.. nvironmenta Protectlon Agency. Park, N.C. 27711. ApriI 1983. "Method 5 - Determination ofParticuIate Emissions FromStationary Sources," 40 CFR 60, AppendixA.

5-42

### 6.0 OEVELOPMENT OF CONTROL STRATEGIES
6.1 Overview This section identifies general approaches with respect to the use of ambient measurements and model estimates in determining the level ofcontrol needed to demonstrate attainment ofPM10 NAAQS. Conceptually, this involves determining the PM10 design concentration for aparticular site or receptor that must be reduced tothe level ofthe NAAQS, thereby assuring attainment. These design concentrations are used to determine the level ofcontrol needed. The guidance contained in this section focuses on particulatematter in the specific size fraction, PM10, although it may be applied to particulate matter in general as asurrogatewhere PM10data bases are inadequate. In all cases where PM10ambient concentrations estimated by a dispersion model are used, the design concentration is assumed to be the
sum ofconcentrations contributed by the source(s) and an appropriate background concentration. With the PM10 annual and 24-hour NAAQS, two separate design concentrations, one for each standard, are needed per site. Attainment ofthe annual NAAQS requires that the expected annual PM10concentration be less than or equal to the level ofthe NAAQS, Attainment ofthe24-hour NAAQS requires that the expected numberofexceedances ofthe NAAQS be less than or equal to one peryear. The SIP-related emission limits should be based on the NAAQS (annual or 24-hour) which result in the most stringent control requirements. For example, ifthe annual NAAQS requires more stringent control requirements than the 24-hour NAAQS, the annual NAAQS is considered the more restrictive standard and the corresponding emission limit(s) would be adopted. 6-1

### 6.2 Data BaseRequirements The design concentrations for attainment ofthe 24-hour PM10 NAAQS
can be based on ambientmeasurements ofPM10, ormodel estimates ofambient concentrations at individual sites during 1 ormoreyears ofstable emissions conditions. Ideally, (1) 

### modeling estimates using 5years of National Weather Servicemeteorological data (or at least 1yearof on-site data), or (2) 3years of representative airqualitymeasurements should be considered in determining 24-hour design concentrations. If
more years ofdatawith relatively unchanging emissions are available, they also may be considered in calculating design concentrations. The
moreyears ofdata available, themore stable the estimate ofPM10 design concentrations. Thepreferred approach for estimating adesign value is through the
use ofan applicable dispersion model corroborated by receptormodels, any available TSP data (using Appendix B), and any available PM10 data. If there is no applicable dispersion model and at least 1completeyearof PM10 data are available,* the PM10data would be used to estimatethe design value; ifthe PM10data are insufficient,* the design valuewould be based
onAppendix Bofthis guideline and corroborated by the available PM10 data. 6.3 Methodologies forDetermining Design Concentrations The annual design concentration is the expected annual arithmetic
mean determined by the approach discussed inAppendix K, ofPart 50. In the simplest case, the design concentration can be determined by averaging 3years ofmonitored or 5years ofmodeled PM10 concentrations.
*These data shouldmeet first year sampling requirements found inCFR 58,13. 6-2

### There are several acceptable approaches for determining appropriate 24-hour PM10 design concentrations. These approaches which are described in the next sections are based on monitored ormodeled PMIO concentrations. They include: (1) atable look-up procedure; (2) fitting astatistical distribution; (3) graphical estimation; and (4) the use ofconditional probabilities. Each ofthese approaches and corresponding data usage requirements are presented in detail in the ozone guideline.1 The following sections briefly summarize each ofthese approaches and indicate howthe technique may be applied for determining PM10 concentrations. 6.3.1 Table look-up The 24-hour PMIO design concentration is influenced primarily by the few highest measured or estimated concentrations at asite. Availability ofthe highest concentrations makes it possible to construct asimple table look-up procedure to determine the design concentration. All portions oftheyear should be adequately reflected in themeasurements. To use the tabular approach for the 24-hour PM10 standard, it is necessary to know the total number of24-hour PM10 concentrations at the site and then select the design value from among the highest concentrations. The number of available 24-hourconcentrations determines which ofthe highest concentrations is chosen as the design concentration. For example, ifacomprehensive monitoring programprovides 1,095 24-hour concentration measurements (or 3full years ofdata) at a site, then the ranks ofthe lower and upper bounds obtained fromTable 6.1 are 4 and 3respectively. This means that an appropriate design concentration 6-3for that sitewould be between the fourth-highest and third-highest concentrations. In using this table, the lower ofthe two concentrations should be used as the design concentration, i.e•• the fourth-highest concentration. Therefore, in this example, it suffices to know only the four highest values during the time period. With multiple monitoring sites, the highest PM10 concentrations at each site would have to be considered and adesign concentration established for each location. For example, the "controlling" design concentration for an-areawith
seven sites, each having 1,095 values, would be the highest ofthe
seven fourth-highest values. For routine model applications with 5full years of24-hour concentration estimates, the PM10 design concentration ofcritical interest becomes the highest of sixth-highest concentrations for the entire receptor network. The look-up procedure is basically atabular technique for determining what point on the empirical frequency distribution corresponds to afrequency of 1/365. 

### By construction, the table look-up procedure tends to provide adesign concentration slightly lowerthan would be derived using acontinuous curve representing atheoretical frequency distribution for PM10 values. For example, use ofthe table-derived estimate might be modified by interpolation between the



### 6-4

### TABLE 6-1
TABULAR ESTIMATION OF PM10DESIGN CONCENTRATIONS

Number ofDaily Values Rank ofUpper Bound Rank ofLower Bound Data Point Used for Design Concentration

<347 348

- 695 1 696

- 1042 2**I~'l"** 1043

-

# J.G% 3 

## /3j/

-**113~** 

### 4- 

## tl3't--;u3b ]jJB! '2.1-(:)5

6-5 1 

### Highest Value 2- 

### Second Highest Value 3 

### Third Highest Value 4 

### Fourth Highest Value 

## S 

## FIFt,third- and fourth-highest values. However, this adds an additional element of calculation. Nevertheless, if amore precise design concentration should be desirable, the use ofinterpolation formulas (Section 6.3.2)
or more simple graphical procedures (Section 6.3.3) may be necessary. Forthe cases which are limited to less than acompleteyear ofdata, (i.e., 365 observations) the maximum concentration must generally be used

as atentative design value. In this case it should be recognized that the maximum concentration generally represents a lower-bound estimate for the true design concentration. In order to provide an alternative higher estimate for the design concentration, the extrapolated value derived from
afitted distribution (Section 6.3.2) can be used.* With sparse data sets, the tentative design concentrations defined as the maximum concentration orthe extrapolated concentration are quite likely to require further revision as more data become available. In addition, the failure to adequately account for yearly variations in meteorological conditions makes any estimate based on a singleyear ofdata very tentative. 6.3.2 Fitting one statistical distribution to several years ofdata With several years.of fairly complete PM10 24-hour airquality measurements or model estimates, a statistical distribution could be selected that "fits" the data. Information on fitting statistical distributions

can be found on pages 18-20 in the ozone gUidel1ne.1,2,3 Becausewe are interested in peak concentrations, emphasis would be placed on the top

*An extrapolated value must be used, however, instead ofthe maximum observed concentration in orderto evaluate the possibility that the 24-hour standard is controlling, using procedures described in Sections 6.3.2

- 6.3.4. 6-6

### 5to 10 percent ofthe concentrations. This approach, at least conceptually, provides amore stable estimate ofthe design concentration; but it also involves additional computations and interpretation. Criteria for judging reasonable fit are also given in the ozone guideline. The design concentration corresponds to afrequency of 1/365. In some cases the available data will fall in this frequency range. In such cases. the fitted distribution should be consistent with the data in this range. With adequate data, there will be concentration data points on either side ofthe design frequency. data which can be used
as a constraint in fitting the distribution. When less concentration data are available. i.e•• infrequent airqualitymeasurements, it may not be possible to "bound" the design concentration. For example, ifthere are no measured values on the empirical frequency distribution with frequencies less than 1/365. the estimated design concentration will represent an extrapolated concentration. This extrapolated design concentration will be higher than the maximum observed
or estimated (via airqualitymodel) value. When this is the case. caution should be exercised in the use ofthe extrapolated value. distribution ofseveral ears
With sufficient data it may not be necessary to fit a statistical distribution. as discussed in Section 6.3.2. The concentration value corresponding to afrequency of 1/365may be read directly offagraph ofthe empirical distribution and used as the design concentration. The description ofthis approach is given on pages 25 and 34 in the ozone guideline. 6-7

### 6.3.4**Co~ditional** 

### probability approach While the previous methods required grouping concentration data from several years, this approach allows individual years ofdata to be treated separately. This is done by fitting a separate statistical distribution to eachyear ofdata and assuming a given probability that eachyear will reoccur. This approach is of interest when different importance should be placed on the individual years in terms ofmeteorological conditions or sampling completeness. For this reason, this approach may also be ofinterest for the annual standard. The conditional probability approach is somewhat theoretical in nature, but is adequately discussed in the ozone guideline. 6.4 Determining Emission Limits 6.4.1 General Once PM10 design concentrations have been established through the use of air quality measurements or model estimates, aproportioning method can be used at each site to estimate control requirements for SIP development. This proportioning method differs from simple rollback in that the source contributions are determined from receptor ordispersion modeling and not directly from the emissions as in simple rollback. Nevertheless, the method conceptually considers that thetotal reduction (TR) in pollutant concentration should be: TR (ug/m3)

## =PM10 Design Concentration

- PM10 NAAQS If adesign concentration is greaterthan the NAAQS (TR is positive), a reduction in PM10 emissions is required. This might be accomplished by

6-8reducing the contribution ofasingle source or it may require reduction in several individual sources or source categories so that TR (ug/m3)

## =SISRi (ug/m3) where ISRi is theIndividual Source Reduction desired from
a source or source category i. These ISR's are generally selected based on many considerations, including the technical feasibility ofachieving a given emission reduction or additional reduction at that source. The percent reduction in emissions (%RE) for a source or source category is given by ISRj**~)** %REi = ACi (ug/mJ ) whereAC is the ambient concentration due to the individual
source (i) or source category as determined through amodel. The SIP must demonstrate that the control requirements will be adequate to meet the NAAQS under other situations where the relative source contributions may be different from that on the design day. This is discussed further in Section 6.4.3. If receptor models are used to determine the relative source contributions, the operating rates ofthe sources overthe time period studied must be evaluated. Ifthey are inappropriate for the SIP development, the source contributions should be adjusted proportionally. 6.4.2 Example for annual averages Assume for discussion that the annual NAAQS for PM10 is 75 ug/m3 and that the site with the "controlling" annual design concentration for the
area is 100 ug/m3• Then the total required reduction for the area would be: TR

### =100

- 75 

### =25 ug/m3•

6-9

### Source Next, for the site ofthe most restrictive design concentration, consider the following source contributions estimated by areceptor or adispersion model: Source Category Contributions to Ambient Conc. (AC) Steel mill roads Steel mill coke ovens Coal storage pile Urban paved roads Cement plant Background 15 ug/m3 10 ug/m3

5ug/m3 20 ug/m3 10 ug/m3 40 ug/m3 100 ug/m3 The TR would be the sums ofthe individual reductions in ambient concentration. Assume that the following reductions in source concentration
are selected for consideration: Source Steel mi11 

### roads Steel mill coke ovens Cement plant Coal storage pile Individual Source Reduction (ISR) 8ug/m3 5ug/m3 8ug/m3 4

### ug/m3 TR = 

### 25 ug/m3


### Since the TR sums to 25 ug/m3, it is assumed that the individual source reductions, ifimplemented, would reduce the annual 

### PMIO concentration at this "controlling site" to 75 ug/m3. The required emission reductions (%RE) to accomplish this are calculated for each source category. For example: ISRcement 

## = =80%; %REcement = 

### ACcement 10
so that control requirements would be 80 percent for the cement plant. It is assumed that the selected emission reductions will allow the NAAQSto be met at the "controlling site," as well as all othermonitoring or receptor sites. This assumption should be tested by reversing this procedure and determining whetherthe anticipated reduction impacts on other sites above 6-10the standard will result in attainment at those sites. Ifthis is not found to be true, which is more likely in multisource situations than areas affected by asmall number of sources, individual design concentrations at other sites will have to be considered and the emission reductions reassessed. In such cases. the control strategy will be composed ofthe maximum of each ofthe individual source reductions calculated across the network. For the proportioning method discussed here, the background concentration should be estimated as that portion ofthe concentration which is not attributed to the sources being investigated. This estimate should be based on actual observations in nonurban areas near the boundary ofthe

area or on model estimates of the actual impact ofthe sources not under investigation. 6.4.3**Cons~~rati~~_Lor** 24-hourave_ra.9..e_s_ An approach similarto that discussed in the above section can be employed to determine control requirements for the 24-hour standard. The PMIO design concentrations and individual source contributions for each site, as well as background concentrations, must be available. However, for short-term averages (e.g., 24-hour), it is likely that a single high concentration is dominated by relatively few sources with source contributions varying with meteorological conditions. Thus identification of emission limits that assure the short-term 24-hour NAAQS will be met at all sites is likely to require an iterative and perhaps lengthy process. This is especially true ofmultisource areas. An alternative is to develop control strategies with emission limits specified for each source category, perhaps based on a preliminary 6-11application ofthe modified rollback model. The strategies would then be tested with adispersion model to determine ifthe NAAQS are met everywhere. Those strategies that allow the NAAQS to be met would be identified for further consideration in preparing a set of preferred emission limitations. This alternative, too, could involve an iterative procedure. However, it provides ameans by which factors such as the best technological, most cost effective, and most enforceable set of emission controls can be considered. Such aprocedure might also be employed for annual concentrations, especially where many receptor sites are ofconcern.

6-12

### Reference 1. Guideline for the Interpretation ofOzone Air Quality Standards, EPA-450/4-79-003, U.S. Environmental Protection Agency, Research Triangle Park, NC, January 1979. 2. Curran, T.C. andW.M. Cox, "Data analysis Procedures for the Ozone NAAQS Statistical Format," J. Air Pollution Control Association, June 1980. 3. Curran, LC., "DataScreening for Large Air Quality Data Sets," paper no. 84-52.3, presented at the 27th Annual Meeting ofthe Air Pollution Control Association, San Francisco, CA, 1984.

6-13

### 7.0 SIP REQUIREMENTS AND DATA REPORTING
7.1 Introduction This section describes the requirements for SIP revisions in the Act, EPA's PMI0 SIP development policy, the interface between PMI0 and TSP control strategies, and summarizes how PMI0 emissions data should be reported to EPA's data bank. To develop an acceptable control strategy
as required for aPMI0 SIP, aState can utilize the information summarized in section4.0 ofthis document for carrying out appropriate modeling, the information in section 5.0to develop an emissions inventory for model inputs, and the information in section 6.0 for developing control strategies. 7.2 Clean AirAct Requirements 7.2.1 Time limits, SIP requirements Section 110(a) ofthe Act requires every State to submit to EPA a SIP which provides for implementation, maintenance, and enforcement ofeach NAAQS in each air quality control region within the State. The SIP is required to be submitted within 9months ofthe promulgation ofanew NAAQS orthe revision ofexisting NAAQS. TheSIP must provide for attaining
aprimary NAAQS as expeditiously as practicable, but no laterthan 3

### years after SIP approval by EPA. Up to an additional 2years could be provided for attainment ifthe requirements in section 110(e) ofthe Act are met. These section 110 requirements are applicable to PMI0 NAAQS. The PM10 SIP's therefore are due within 9

### months ofpromulgation ofPMI0 NAAQS and are to cover all areas ofthe State as qualified in section 2.4 ofthis document.
7-1

### 7.2.2 Regulations in 40CFR Part 51 The regulations promulgated in Part 51 address the preparation, adoption, and submittal to EPA ofSIP's for implementing the NAAQS. These regulations reflect the requirements set forth in the Act. Part 51 has recently been restructured to contain more subparts and sections to make it more organized and readable. That restructuring effort consisted only ofdeleting obsoletematerial and reorganizing the remaining material and
no substantive changes were made to the regulatory requirements at that time. The Part 51 regulations still apply to PMI0. 7.3 PM10 SIP Development Policy As discussed in section 7.2.1, the entire Statemust be covered by a PMI0 SIP and the SIP is to be submitted within 9

### months ofthe datethe PMI0 NAAQS are promulgated. However, because PMI0 data are not available in all areas, EPA has placed areas into three groups. Section 2.4 ofthis document discusses grouping procedures. The requirements for PM10 SIP development are described in the following sections. 7.3.1 Group I PM10 SIP requirements States are required to submit to EPA a revision to the particulate matter SIP within 9

### months ofpromulgation ofPM10 standards for Group I
areas. The SIP revision must provide for attaining PM10 NAAQS as expeditiously as practicable, but no later than 3years after the SIPis approved by EPA. The 3-year attainment deadline may be extended for up to 2additional years by the Administrator ifthe conditions in section 110(e) oftheAct aremet. The submittal must include amodeled demonstration that provides for attainment and maintenance ofthe PMIO
7-2

### standards. Portions ofthe SIP pertaining to prevention of significant deterioration/new source review section and air monitoring will also require revision. TheSIP must contain control measures sufficient to demonstrate attainment. Provisions for studies or demonstration projects ofnontraditional particulatematter sources and measures necessary for attainment at a later date will not be acceptable as they were in the past forTSP standards. 7.3.2 Group II PMI0 SIP requirements States are also required to submit aSIPwithin 9months ofpromulgation of PMI0 standards for Group II areas. The States may submit aSIPfor these areas as required for Group I areas, ifthey wish. Otherwise, States must submit aSIPwhich contains enforceable commitments to take the following actions. (1) 

### Gather ambient PMI0 data, at least to an extent consistent with minimum EPA requirements and guidance (see subsections 3.2.3 and 3.2.5) (2) Analyze and verify the ambient PMI0 data and report 24-hour PMIO NAAQS exceedances to the appropriate Regional Office within 45 days ofeach exceedance. (3) When an appropriate number ofverifiable 24-hourNAAQS exceedances become available (see section 2.0) orwhen an annual arithmetic
mean above the level ofthe annual PMI0 NAAQS becomes available, acknowledge that anonattainment problemexists and immediately notify the appropriate Regional Office. (4) Within 30 days ofthe notification referred to in (3) above, or by the date 37 months after promulgation, whichever comes first, determine 7-3whether the existing SIP will assure timely attainment and maintenance ofthe PMI0 standards, and immediately notify the appropriate Regional Officeo (5) Within 6months ofthe notification referred to in (4) above (if necessary), adopt and submit to EPA aPMI0 control strategy that

assures timely attainment and mai.ntenance within aperiod of 3years from approval ofthe committal SIP. The following factors should be considered in determining the adequacy ofthe existing SIP in item (4) above: (1) Air quality data. (Time is alloted for up to 3years ofPMI0 data to be collected ifaNAAQS is not violated sooner. At**~he** end ofthat time, the available PMI0 data must be examined to determine if attainment can be demonstrated in accordance with Appendix

## K of40 CFR Part 50 orthe Guideline on Exceptions to Data Requirements for Determining Attainment of Particulate Matter Standards in the absence of adequate PMI0 data.) (2) Emissions data. (The emission inventories must be evaluated to determine ifemissions can increase significantly because actual emissions are far below allowable emissions for the area; determine if sources with operating permits are not operating or operating at reduced capacity and if "banked" emissions impact future air quality.) (3) The present control strategy. (The existing control strategy should be evaluated to determine if it is fully implemented; if it is adequately enforced; start-up, shutdown, and malfunction


### 7-4

### regulations are adequateto prevent circumvention ofemission limits; and it can adequately attain and maintain the PMIO NAAQS if the above conditions are met. The evaluation should include the

use ofdispersion and receptormodeling techniques where appropriate). The committal SIP must include an enforceable schedulewith appropriate milestones or checkpoints. The EPA will review and act on both the committal SIP's and control strategies submitted under step (5). Also, revisions required in the PSD/NSR and airmonitoring portions ofthe SIP must be submitted with the committal SIP. 7.3.3 Group III PM10 SIP requirements ForGroup III areas, EPA will presume that the existing SIP is adequate to demonstrate attainment and maintenance ofthe PMIO standards. States are therefore only required to revise the PSD/NSR and airmonitoring portions oftheir SIP's within 9months. 7.4 SIP Content Amost important first step in developing acontrol strategy will be to inventory particulate matter sources and include all possible contributors. Receptor models applied to TSP samples, or PMIO samples ifavailable, may be useful for this analysis as discussed in section 5ofthis document. The State should include in its emission inventory point sources, fugitive emission points within industrial plants, plus all area sources such as unpaved and paved roads, unpaved parking lots, construction activity, open lots with no vegetative cover, woodstoves, agricultural activity, and similar sources. As previously stated, the option to study nontraditional
sources and commit to implementing controls at a later date will no longer be acceptable. Information at the State level or from EPA-funded

7-5studies1,2,3,4,5,6 should provide enough information to make determinations of which controls may be effective for these sources. In determining which controls to employ, the State should reassess existing control technology requirements for traditional sources, previously approved emission trades, and the effectiveness of startup, shutdown, and malfunction regulations. 7.5 Control Strategy Transition Particulate matter emissions from most point sources and many area
sources have been controlled as a result ofSIP's to implement the former NAAQS for TSP. The regulatory requirements ofan existingTSP SIP must remain in effect in accordance with section lI0(i) ofthe Act, until a PMlO SIP is approved by EPA. Therefore, regulations in the existing SIP cannot be relaxedwithout ademonstration that the revision will not interfere with attainment ormaintenance ofthe PMlO NAAQS. The existing regulations must continueto be enforced by Federal and State agencies during the period oftransition from aTSP SIP to aPMlO SIP. States will no doubt want to minimize any unnecessary disruption caused by going from these control programs to PMlO programs. Therefore, to the extent possible, States should utilize the existing control strategy in aTSP SIP as the basis for aPMlO program sufficient to attain and maintain the PMI0 NAAQS. The EPA expects States to build on the current control strategies to whatever degree necessary to demonstrate attainment and maintenance of PMI0 NAAQS. This may include adopting the current control strategy in full, ifit can be shown to be sufficient for PMI0 purposes, or adopting it in part.

7-6

### 7.6 Current Emission Regulations Particulate matter SIP's contain emission regulations expressed in various terms including the following:

-- Mass ofparticulate matter per heat input into the process. Example: 0.30 pounds permillion British thermal unit.

-- Mass ofparticulate matter per unit ofexhaust gas. Examples: 0.05 grams per dry standard cubic foot. 0.01 grains per actual cubic foot. 0.02 grains per standard cubic foot. 0.04 pounds perton ofexhaust. 0.02 grains per actual cubic foot perminute. Opacity ofexhaust. Example: 20 opacity. Mass ofparticulate matter pertime period.


### , Examples: 144.30pounds per hour. 631.00 tons peryear. 13.44pounds per day. 4.5 grams per day.

-- Mass ofparticulate matter per area exposed to wind erosion. Example: 5.80 pounds per acre.

-- Mass ofparticulate matter permass ofproduct input or output. Examples: 0.02 pounds perton coke. 0.30 pounds per ton kiln feed. 52.00 pounds permetric ton.

7-7-- Mass ofparticulate matter per unit ofpower generated. Example: 0.07 grains per horsepower hour. Percent control ofemissions. Example: 98.00 percent control ofparticulatematter emitted due to overspray ofcoating material. Regulations ofthese types for controlling all emitted particles have been the accepted practice for affecting ambient concentrations ofparticulate matter as measured by the high volume sampler even though emissions as measured by stack test methods may not be identical to the material measured by ahigh volume sampler. Despite this discrepancy, EPA has developed stack sampling trains now described in Methods 5and 17 of Appendix 

### Bto 40 CFR Part 60which represent the present state-of-the-art in techniques for collecting particulate matter. Many emission limits for particulate matter therefore have been established in terms of these measurement methods. In this document, the term "particulate matter emissions" is used to denote material measured by Methods 5 or 17 or by acomparable measurement method approved by EPA in aState implementation plan. 7.7 Surrogate Emission Regulations This subsection discusses setting or retaining emission limits in terms ofparticulate matter emissions for the purpose of controlling PM10 emissions as part ofaPM10 control strategy. 7.7.1 Retaining existing emission limits. If aState finds that its existing particulate matter emission limits in its TSP SIP are sufficient to prevent PM10 NAAQS violations, there is no need to go through a resource intensive process ofmodifying the emission limits to express them in terms

7-8of PMlO.* Modeling can be used in two ways to determine that aparticulate matter emission limit or combination of limits for several sources is acceptable for PMlO control. First, particulate matter emissions can be modeled to determine the impact on ambient TSP levels. Ifthe ambient TSP levels determined through modeling are below the concentration levels ofthe PMIO NAAQS, then the modeling results themselves indicate that PMIO NAAQS would not be violated. The particulate matter emission limits will then be acceptable for controlling PMlO emissions. Second, modeling can be done with particulate matter emission rates converted to PMIO emission rates. If it can be assumed that for any source the PMIO fraction of particulate matter does not vary appreciably during normal operation of the source, then a certain particulate matter emission rate will represent
a certain emission rate of PMIO for that source. The PMIO fraction will, of course, vary by source and could be up to 100 percent ofparticulate matter emissions. For some sources the particulate matter emission rate in the TSP SIP can be converted to the corresponding emission rate for PMIO through an emission factor (see section 5.0 ofthis document). If a State uses this corresponding PMIO emission rate as input for modeling and finds that the modeling results indicate no violation ofthe PMIO NAAQS, then the particulate matter emission rate will be an acceptable emission limit for the PM10 SIP. In either ofthe above cases, States may leave the existing emission limits unchanged and may continue to conduct compliance tests with appropriate

*Regulations may consist oftechniques such as paving acertain amount of

dirt roads or streets, street sweeping schedules, tailings pile spraying, construction activity procedures, street salting restrictions, unpaved road speed limits, etc., rather than an emission limit. 

### 7-9particulate matter emission measurement methods. The PM10 control strategy analysis should include the modeling results and confirmation that the particulate matter emission limits were found to be acceptable surrogate emission limits for PM10. 7.7.2 Setting new emission limits. In the event that aState finds that an emission rate lower than the current particulate matter emission limit in the TSP SIP is necessary for PM10 NAAQS attainment and maintenance, the PM10 rate which is found to be necessary through modeling can be converted (through the use ofan emission factor) to an equivalent particulate matter emission rate and expressed as such in the PM10 SIP. The emission limit expressed in terms of particulate matter emissions will be acceptable provided the modeling results are valid and the ratio ofPM10 to particulate matter emissions is not likely to drastically change. If it is found that substantially all of a source's emissions are PM10, then limits for that source could be expressed as either particulatematter emission limits or PM10 emission limits, and compliance testing could be performed with eitherMethod 5 or amethod that measures only PM10 emissions. For such an instance, the SIP would have to indicate that the source's emissions are essentially all PM10. In any case where an emission regulation is in terms other than PM10, such as particulate matter emissions or opacity, PM10 levels must not increase while the indicator used in the emission limit remains constant. Otherwise, the indicator will not be a valid surrogate and emission limits using the surrogate will not be acceptable. 7.8 PMlo-Specific Emission Regulations Emissions limits developed to meet the NAAQS for TSPtook many forms

as illustrated in section 7.6. Compliancewith these limits was determined 7-10by reference method emission tests or certified visible emission observers. The emission limits and compliance methods had no direct link to TSP as measured by ambient monitors, however, acorrelation was made through the
use ofdispersion and receptor models. Similarly, it would be difficult, and perhaps technically infeasible, to develop amethod for measuring precisely that exhausted material that would contribute to ambient levels ofparticulate matter measured by an ambient PM10 sampler. Particles emitted from stacks are subject to agglomeration and separation after they reach the ambient air, both ofwhich affect their inclusion or exclusion in ambient PM10. Additional complications arise due to the presence in emissions ofcondensables and various precursors of secondarily formed particulate matter. Regulations specifying emission limits as part ofPM10 control strategies, therefore, cannot be directed toward exactly that exhausted material that contributes to ambient PM10. Rather, PM10 emission limits must be directed toward reducing the amount ofPM10 emitted from asource as measured by an approved compliance test method. Suggested procedures for measuring PM10 emitted through stacks

are discussed in Appendix C. Further guidance is being prepared by EPA. 7.9 Reporting Emissions Data Emissions ofpollutants for which NAAQS have been set are reported by States to EPA in an annual report required by 40 CFR 51.322. As aminimum, the sources for which emissions data are to be reported are those whose actual emissions are over 90.7 metric tons (100 tons) peryear ofthe pollutant ofconcern. Since the indicator for both the primary and secondary,standards for particulate matter is being changed from TSPto PM10, reporting ofparticulate matter as required in section 51.322will 7-11simply be replaced by reporting ofPM10 emissions. The present requirement to report particulate matter emissions will end with the reporting of calendaryear 1987 emissions. The reporting requirement for PM10 emissions will begin with the reporting of calendaryear 1988 emissions. TheEPA recognizes that time and resources are needed for States to develop the capability to report PM10 emissions data for EPAto develop the capability to process, store, and retrieve the data. States are required to begin the annual reporting.ofPMlO emissions data with calendaryear 1988 data, which are to be sent to EPA Regional Offices bymid-1989. The EPA plans to provide States with the needed technical and procedural information in time for States to meet that requirement. This information includes PMlO-related changes to the following: AP-42, "NEDS Source Classification Codes and Emission Factor Listing;" the computer program for calcuIating emissions that is provided routinely to those States which use EPA's Emissions Inventory System (EIS); and provided to other States on request; and procedural information such as that contained in Aeros manuals. The EPA has underway aproject to replace the UNIVAC systems, e.g., NEDS, Hazardous and Trace Emissions System, that have been used for many years to process, store, and retrieve the emissions data provided by States. These systems will be replaced by IBM systems under EPA's Aerometric Inventory Retrieval System (AIRS) development program. The current schedule for haVing AIRS operational on the IBM system is March 1989. The EPA will provide the necessary guidance to the States in time for States to respond to the requirement for reporting calendaryear 1988 PMlO emissions.

7-127.10**~!l1_ts_~i_o.n~ "I!~_d** **~.n.9-1BTu_bl>.1_**e1.P_l?l"W'. This section clarifies the effect ofPM10 NAAQS on alternative emission reduction options (bubbles) that have been previously approved for TSP SIP's. In the initial bubble policy, as published on December II, 1979 (44 FR 71780),

sources were warned that EPA was considering revising its particulate matter NAAQS and that if such size-specific standards were promulgated,

some alternative approaches. initially approved by EPA, might no longer be adequate under the revised standards. In a sense, the policy indicated that sources which used a bubble approach to meet SIP emission limitations couldbe treated no differently from sources which didnot. That is, if additional emission reductions are required to avoid violations ofthe ambient air quality standards, the State may have to revise emission limits previously approved under the bubble policy. In general, bubbles cannot interfere with aState's efforts to attain and maintain ambient air quality standards even ifthose standards are revised. The final Emission Trading Policy Statement promulgated December 4, 1986 (51 FR 43814) affirms this position. 7.11 Fugitive Dust.Policy_ The EPA will continue to implement its existing fugitive dust policy

as it was applied in rural areas violating the TSP NAAQS. The EPA issued guidance on SIP development and new source review in areas impacted by fugitive dust on August 16,1977.7 This guidance, known as the Fugitive Dust Policy, states that "urban areas shouldreceive the highest priority for development ofcomprehensive and reasonable programs to control

7-13

### fugitive dust." Control programs in rural areas are to "centeron the control oflarge existing man-made fugitive dust sources (i.e., tailings, piles, mining operations, etc.) which in themselves are presently causing violations ofthe NAAQS or are sources of aknown toxic orhazardous material (e.g., asbestos)." Another aspect ofthe fugitive dust policy is that, "new sources that wish to construct in rural fugitive dust areas should be allowed to do so without the need ofemission offsets, as long as they comply with the applicable emission regulation and the impact oftheir emissions plus the emissions from other stationary sources in the vicinity ofthe proposed location, along with normal background, is not projected to cause violations ofthe NAAQS." The following criteriawere to be used in defining a rural area under the fugitive dust policy: "(1) the lack ofmajor industrial development or absence of significant industrial particulate emissions and (2) low urbanized population (i.e. eastern states <100,000-200,000 or western states <25,000-50,000)." In the initial process ofcategorizing areas into Groups I, II, and III, 'Group I and II areas that qualify as rural fugitive dust areas wi11 be placed in Group III. TheState will be expectedto provide rationale and justification at the time ofarea categorization for the claimthat a PMI0 NAAQS may be violated primarily because ofthe impact offugitive dust sources that are unreasonable to control. In response to abroad range ofcomments on the proposal to continue this policy, EPA has developed several options for revising the fugitive dust policy for app1ciation to the PMIO NAAQS. Public comment on these

7-14

### options is currently being solicited. If necessary, arevised policy will be issued in the future. At that time, the categorization of areas initially placed in Group III under the existing policy will be reviewed for compliance with the revised policy.

7-15

### References

1.

2.

3. 

### Identification, Assessment

### j and Control**of~itive** 

### Particulate Emissions, EPA-600/8-86-02 

### U.S. Environmental ProtectionAgency, Research Triangle Park, North Carolina, August 1986. Kinsey, J.S., P. Englehart, andA.L. Jirik, Study ofConstruction RelatedDust Control, Midwest Research Institute and ETA Engineering. Prepared for Minnesota Pollution Control Agency, Roseville, Minnesota. December 1982. Portland Road Dust Demonstration proiect, Final Report, Seton, Johnson and Odell, Inc., for City ofPortlan , Oregon, Department ofPublic Works, July 1983. 4. 

### DenverDemonstration Study, PEDCo Environmental, Inc., Kansas City, MO. Prepared for Colorado Division ofAir Pollution Control, Denver, Colorado. October 1981. 5. User's Guide: FugitiveDust Control DemonstrationStudies, EPA-600/8-84-032, U.S. Environmental ProtectionAgency, Research Triangle Park, NC, January 1985. 6. Field Evaluation ofWind Screens as aFugitive Dust Control Measure for Material Storage Piles, EPA 600/7-86-027 U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. July 1986. 7. Memorandum from·Edward F. Tuerk, ActingAssistantAdministrator forAir and WasteManagement to Regional Administrators, August 16, 1977.

7-16APPENDIX A
TYPES OF RECEPTOR MODELS
There are several major categories of receptor models which are potentially valuable for better understanding an area's ambient particulate problem, both PMIO and TSP, and in formulating strategies for use in State implementation plans (SIP"s). These include chemical mass balance (CMS), factor analysis (FA), optical microscopy (OM), and automated scanning electron microscopy (ASEM). While there are other techniques in addition to these, information and availability ofthe models or analytical methods are limited. The major receptor models are described briefly below and references are provided for more detailed information. 1,2,3,4,5.6,7,8


### 1.0 Chemical Mass Balance This method compares the chemica1 "fingerprints" or profiles of emissions from several source categories to the chemical composition ofthe sample. It is referred to in the literature by various names. including chemical mass balance. chemical element ba"lance. species mass balance, and mass balance. Weighted least squares or some other statistical routine is used to find the relative proportions or mix ofthese sources which best "explains" or accounts for the composition ofthe sample. Species data (chemical compounds or elements) are required for both source and sample data. The method is most effective when the ambient samples are collected in at least two size fractions (larger than and smaller than 2-3 micrometers). This method must be validated in accordance with the protocol referenced at the end ofthis Appendix.9

A-I2.0**Fa~or !\_':!..'!1~is**  Factor analysis and othermultivariate statistical methods, such as multiple linear regression, target transformation factor analysis (TTFA), and cluster analysis, are all variations ofthe least squares routine used in

mass balance. However, these methods (except TTFA) require no prior assumption ofthe impacting sources and can be useful where the types of sources are uncertain, Like the mass balance method, this method is most effectivewhen used on size segregated samples. Factor analysis usually requires at least 40 observations to complete the analysis. 3.0 Optical Microscopy Particle identification by optical microscopy is one ofthe first and most widely used methods of source apportionment ofcoarse particles. The technique relies on identification ofparticles by their size, shape, color, surface properties, and birefringence (i,e., an optical property). It is generally used on particles larger than 1 micrometer, Several analysts have been asked to provide quality assurance oftheir analyses and the results have been mixed. Optical microscopy appears to be somewhat analyst dependent and semiquantitative. However, it is an excellent confirmatory or supplementary techniques. 4.0 Automated Scanning Electron Microscopy Acomputer-driven scanning electron microscope, equipped with x-ray fluorescence capability to identify the elemental composition, provides a particle-by-particle analysis ofparticle size, shape and elemental cOl11'os1tion. This elemental composition and cOl11'uter assisted sizing allows large numbers

A-2

### ofparticles to be analyzed and. thus. identified by source category in a relatively inexpensive manner. 5.0 OtherReceptorMethods The following methods are suggested to corroborate or refine MB, FA, OM,
or ASEM analyses. 5.1 X-Ray Diffraction (XRD)

- This method provides direct information
on the crystalline nature ofparticles. This is particularly useful for distinguishing among minerals. It requires aheavy loading ofcoarse particulate matter on the filter and cannot be used on ammorphous (i.e., non-crystalline) particles. 5.2 Trajectory Analysis

- This technique is used for tracing the history of an airmass to determine the possible area of origin of sources of ambient particulate. The simplest form is the pollution rose, where only averagewind direction is considered. More sophisticated approaches account for the changing wind direction with time to trace air mass in time and space for as long
as several days. 5.3 Microinventory

- This technique is an orderly compilation of sources
near a receptor. This is particularly useful as afinely gridded input to dispersion models, and in qualitatively estimating the potential sources of soil and otherminerals to help interpret an observed mix ofparticulate matter
on an ambient filter.



### ,**~3**

### References 1.**~eciPtor** 

### Model Technical Series. Volume I: OverviewOf**Rec8~tor** 

### Model**~ication** 

### To Particulate Source**~.ortl()nment. EP)f-lP.)O~** **-OT6~­** 

### U.S. Environmental Protection Agency. Research Triangle Park, NC. July 1981. 2.

3.

4.

5.

6.**Rece~tor** 

### Model Technical Series. Volume II: Chemical Mass Balance. EPA- 50/81-016b. U. S. Environmental Protectfon Agency, ResearchTriangle Park, NC. July 1981. Receptor Model Technical Series. Volume III: User's Manual For Species Mass Balance ComputerProgram. EPA 450/4-83-014. U.S. Environmental Protection Agency. Research Triangle Park, NC. July 1983. Receptor Model Technical Series. Volume IV: Technical Considerations In Source Apportionment By Particle Identification. EPA 450/4-83-018. U.S. Environmental Protection Agency. Research Triangle Park. NC, June 1983. Digest ofAmbient Particulate Analysis and Assessment Methods. EPA-450/ 3-78-113. 

## u. S. Environmental ProtectionAgency. Research Triangle Park, NC. September 1978. G. E. Gordon, ReceptorModels. Environmental Science and Technology. 1980, 14. 792-800. 7.

8. 

### Receptor Model Technical Series. Volume V: SourceAP&Ortionment Techniques And Considerations In CombiningTheirUse. EPA 450/4- 4-020. U.S. Environmeotal Protection Agency. Research Triangle Park. NC, July 1984. Receptor Model Technical Series. Volume VI: AGuide To The Use of FactorAnalysis And Multiple Regression (FA/MR) Techniques in Source Apportionment. EPA 450/4-85-007. U.S. Environmental ProtectionAgency. Research Triangle Park, NC, July 1985. 9. Pace, T. G •• et a1•• Protocol ForApplying And Validating TheCMS. U.S. Environmental Protection Agency. Research Triangle Park, NC. Draft.**~4**APPENDIX B
PRELIMINARY ESTIMATES OF PM10 DESIGN CONCENTRATIONS USING TSP DATA

This appendix provides amethodology for making a preliminary estimate ofparticulatematter (PM10) design concentrations where actual PM10measurements are not available. These estimates may be derived based on relationships between PM10 and TSP, as described in the probability gUideline. The definitions ofdesign concentrations used in this appendix are consistent with those presented in Section 6.0. Preliminary design concentrations developed by this Appendix method shall not be used for developing control strategies. They may be used for preliminary planning and to help evaluate design concentrations estimated by dispersion models. Procedures for making preliminary estimates ofannual design concentrations and 24-hour design concentrations.are included.
1.0 Annual NAAQS 1.1 Recommendations (1) Use the most recent years for Which avalid annual arithmetic mean* exists to determine annual arithmetic mean TSP concentration, and (2) Multiply the annual arithmetic mean TSP concentration determined in (1) times a factor of0.47.**

*A valid arithmetic mean requires at least 12 observations per quarter. In

the simplest case, to compute the design concentration one should use the average mean from the 3most recent years ofdata. However, if only 1-2 years of valid means are available, these may also be used. **The 0.47 factor is the 50th percentile ratio in Table 2of Procedures For Estimating Probability Of Nonattainment OfAPM,o NAAQS Using Total Suspended Particulate Or Inha1ab1e Particulate Data.
B-1(3) The resulting product of (1) and**(~)** is the "design concentration" for the annual arithmetic mean PMIO value. 1.2 Example (1) Given: Valid average**annua~** arithmetic mean TSP

## =140**~/m3** PMIO NAAQS

## =50**~/m** annual arithmetic mean. Sampling is performed according to a systematic

sampling schedule. (2) Solution: d = (0.47) (140) d

## =66**~g/m3** 2.0 24-hour NAAQS 2.1 Background Unlike the annual design concentration, adesign concentration for implementing the 24-hour NAAOS requires some explanation before recommendations concerning the derivation can be given. The 24-hour NAAQS is met when the expected exceedance rate (EER) is less than or equal to 1.0 peryear. When TSP measurements are the onlymeasurements ofparticulate matter available, the calculated EER is a function of: (1) the relationships between PMIO and TSP as described in the probability guideline, (2) the observed distribution ofTSP values, and (3) the number ofsamples. In deriving the design concentration, one should remember that the design concentration, d, is that value of PMIO which, when reduced to the level ofthe NAAOS, t, would result in an expected exceedance rate (EER) of 1.0 peryear. Hence,
t = (m)(d) (1) where m

## =(I-[required reduction in dl) Two assumptions are implicit in the definitions above: (1) all TSPi values in the existing distribution ofTSP concentrations are reduced by (I-m), and (2) all corresponding (PMIO)i values are also reduced by (I-m).
B-2The expected exceedance rate (EER) is calculated using equation (2)•

n N EEl{ =-S ( E P Ri i=1 where > R. ) (TSP)i (2)

N 

## =number ofdays peryear (e.g., 365) S 

## =number ofTSP samples n 

## =number ofTSP samples with values greater than R. , the level ofthe NAAQS Ri 

## =ratio of (PM10)i/(TSP)i 

## =level ofthe NAAQS (e.g., 150**~g/m3** and P(Ri> the probability that agiven (TSP)i value corresponds to a (PMIO)i concentration which is greater than the level ofthe NAAQS. Noting that the design concentration is defined as that value which must be reduced to the level ofthe NAAQS such that EER

## =1.0, and that for this to occur each (TSP)i value must be reduced to m(TSP)i, equation (2) becomes

n 1.0 =365 E S i=l P(Ri > R. ) m(TSP)i (3)
One further substitution can be made by substituting the relationship in equation (1) into equation (3).

n 1.0 = 365 E P(Ri > S i=l d ) (TSP); (4)
This allows the design concentration "d" to be expressed explicitly in equation (4). To calculate the average exceedance rate over aperiod ofkyears, equation (4) should be applied separately for eachyear to estimate each annual expected exceedance rate (EER)j' The (EER)j are then averaged as shown in equation (5).

B-31.0 = 365-kwhere k 1:j=1 (5)

k 

## =number ofyears considered Sj 

## =number ofobservations during year j Equation (5) is solved iteratively for "d" until the right hand side (rhs) ofthe equation becomes equal to 1.0. 2.2 Recommendations Because the recommended procedure for estimating the 24-hour design concentration for PMIO fromTSP data is an iterative one, it is recommended that the estimate be made using a computer. Software for doing this is available and is described in User's Guide for PM10 Probability Guideline Software.* For those wishing to performthe calculations by hand, a suitable procedure is described below. (1) Assume an initial value of "d"**tw~ce** as high as the level ofthe PM10 NAAQS (i.e., 300 pg/m for aNAAQS having a level of 150 pg/m3); (2) Using Figure B.l,** for each (TSP)ij value greater than the 1evel of lid, II calculate the prooability that the corresponding (PMIO)ij value would be greater than this 1evel, i.e., d P(Rij > (TSP)

.

### .l lJ Note thatd (a) if (ill)ij d (b) if (TSP)ij d <0.14, assume P(Rij > (TSPlij) = 0.99 d >0.95 but 1.00, assume P(Rij > (TSP)ij) 

## =0.01

*w. P. Freas, User's Guide for PM18 Probability Guideline Software, U.S.

Environmental Protection AgencY,ffice ofAir Quality Planning and Standards, Monitoring and Data Analysis Division, Research Triangle Park, North Carolina 27711. In Preparation. **Figure B.l has been reproduced fromTable 2ofthe probability guideline.
B-4'I 1]1' I1111 

### K·E I"'I'fUt:lAtllLlI Y ,1\ UIVl510NS KEUFFEL &: ESSER CO. MADI I" U5.• 'lUll 468000 'IIII" '1111"

0.01 I 0.5 0.2 0.1 0.05 2 5 10 20 70 60 50 40 30 80 90 95 99 98 99.9 99.8

- - -

-



### FIGURE S.l Example Distribution of**PM~Q!TSP** 

### Ratios for In lvidual 24-hour Observations.

f-
.

.

.. - .. . l-

.

.

.

- -
. ..

### .- -. . - 1-:::

-... .. - - .. -. _ ..
. - 0.9

0.8

0.7

0.5

0.3

0.1 0.4

0.2 0.6 

### "" I 0"1

0.0: 0.05 0.1 0.2 0.5 I 2 5 10 20 30 40 50 60.7080 90 95 98 99 

### Cumulative Frequency of PMlO!TSP Ratios Less Than aGiven Value 99.899.9 99.99or d d (c) if (TS"P)ij 1.00, assume P(Rij > (TSP)ij) =0 (3) Use the information in step (2) to determine the value of the rhs ofequation (5). (4) (a) if0.99 <rhs <1.01, then the design concentration is equal to the value of "d,"

- - (b) if rhs >1.01, increase the value of lidII and repeat steps (2) and (3); (c) if rhs <0.99, decrease the value of IIdu and repeat steps (2) and (3). (5) Ifthere are fewer than 48 samples in any year, disregard the data from that year in performing the preceding calculations. 2.3 Example Given: (1) Threeyears ofTSP sampling data in which 61 days peryear are sampled, (2) Five days in which (TSP)i was observed to be greater than 150 pg/m3• These values are 500 and 240 pg/m3 inyear I, 400 and 350 pg/m3 inyear 2, and 300 pg/m3 in year 3. Find: The design concentration ofthe 24-hour NAAQS for PM10. d Solution: (1) Assume d

## =300 pg/m3
, compute each P(Rij >TS"P)ij) and tabulate the information as shown below 

### ..i.. i (TSP)j 300/(TSP)j P(R> 300/(TSP)j) 1 1 500 0.60· 0.20 1 2 240 >1.00 0 2 3 400 0.75 0.05 2 4 300 1.00 O. 3 5 350 0.86 0.02 (2) Use the information in the last column in the preceding table to test equation (5) 1.00 365/3 [ 1 [0.20 +0] + 1 [0.05 +0] + 1 [0.02]] (5) 

### I 6T 6T 

### iiI 1.00;'0.54

B-6(3) Since 0.54 <0.99, we need to lower the trial design concentration, d. For a second trial value of "d," halve the difference between the level ofthe NAAQS and trial 1.. Therefore, the value for**"d~** in trial 2is 225**~g/m3.** 

### 1. (TSP)i 225/(TSP)i P(R> 225/(TSP)j) 1 1 500 0.45 0.56 1 2 240 0.94 0.01 2 3 400 0.56 0.26 2 4 300 0.75 0.05 3 5 350 0.64 0.14 and 1.00

## =365/3 [ 1 [.56 + .01] + 1 [.26 + .05] + 1 [.14]] 

# or 6T 

### Of 1.00

### t- 2.03

Since 2.03 > 1.01, we need to raise the design concentration "dO for trial 3. This is done by taking halfthe difference between trials 1 and 2 and adding it to the design concentration in trial 2. Hence, the (300-225)

## =263**~g/m3** design concentration for trial 3 becomes 225 + 2 

### --.L i (TSP)j 263/(TSP)j P(R> 263/(TSP)j

1 1 500 0.53 0.34 1 2 240 >1.00 0 2 3 400 0.66 0.12 2 4 300 0.88 0.02 3 5 350 0.75 0.05 365 1 1 1 1.00 ¥--3--**~(0.32** +0)) +

# or- (0.07 +0.01) +

# or- (0.02)] 1.00

### t- 1.06

Since 1.06 >1.01, we must raise the design concentration for trial 4by adding one-halfthe difference between the design concentrations used in trials 2and 3 to the design concentration in trial 3. Hence, the design concentration for trial 4becomes 263 + 1/2 (263-225) 

## =282**~g/m3.** This binary search procedure is continued to derive subsequent trial values for "d" until finally we reach a trial value of268**~g/m3.**

8-7

### i.. i (TSP

### li 268/(TSP)j P(R> 268/(TSP)1 1 1 500 0.54 0.32 1 2 240 >1.00 0 2 3 400 0.67 0.11 2 4 300 0.89 0.02 3 5 350 0.77 0.05 365 1 1 1 1.00 ¥

# -:r- [bf (0.32 + 0) +

# or (0.11 + 0.02) +

# or (0.05)] 1.00 = 1.00 Therefore, use d

## =268**~g/m3** as the design concentration for PM10 at this monitoring site. Note that ifthe sample size in year 2had been less than 48, we would ignore the data fromyear 2, and equation (5) would have been applied as shown below, 

# L n d 1.00

### =365/ 1 E P(Rij > (TSPj ) 2 

## Si i=l 11

8-8APPENDIX C GUIDELINES FOR SOURCE TESTING FOR SIZE SPECIFIC PARTICULATE EMISSIONS 1.0 Introduction The introduction of a size-specific, ambient particulate standard may necessitate source measurements on a similiar basis. The EPA is providing this guideline not as amandated requirement, but as guidance for those States that want to develop emission factors beyond those which we will provide in Section 5. This guidance may also be useful to States which want to develop compliance methods for future PMlO SIP emissions limits. The material presented here is intended for the use ofknowledgeable stack testers in modifying existing techniques for gathering PMlO data. TheEPA is currently developing PMlO measurement methods and will provide more detailed guidance following promulgation. The net contribution of a source to PMlO in the ambient air arises from both primary and secondary emissions. Secondary emissions are defined here as those which result from chemical or physical reactions in the ambient air. Primary particulate emissions are defined here to include particles which were present in the process gas stream prior to discharge to the atmosphere as well as modifications to those particles and new particles formed by condensation phenomena as the process gases are cooled upon discharge. Testing methods which include the contributions from "condensation" are currently under development but are not yet generally available (Smith et al, 1982). The data currently available indicatethat the "condensibles" from many sources with good particulate controls may constitute amajor fraction oftheir present contributions to ambient air particulate concentrations (Williamson et al•• 1982). The EPA Method 5 (40 CFR
C-l60). with a filter temperature of 120°C. includes some condensibles in the material collected (EPA 450/3-81-005a). However, further temperature reduction in many cases results in substantial increases in measured emission rates (Williamson et al.: op cit). The methods to be described below focus primarily on the measurement of size fractionated particulate matter as it exists at stack conditions. However, the inclusion ofthose portions of the condensibles that are currently collected as part ofthe EPA Method 5 catch can be readily achieved by the removal ofthe final filter from the instack samplers described below and instead using aMethod 5filtration system. The problem ofcomplete measurements of primary emissions including proper handling ofcondensibles measurement will be addressed at a future date. 2.0 Available Methods for Size Specific Source Measurements Because the PMIO standard is one based on the aerodynamic behavior of particles, inertial classification is the appropriate technique to use. The likelihood of irreversible agglomeration or coalescence occuring upon contact ofparticles with one another precludes the use of laboratory particle sizing methods for the determination of PMIO emissions from industrial

sources. Rather, particle classification on an aerodynamic size basis must be accomplished while the sample is being taken. This can be done using either oftwo types of inertial classifiers--cascade impactors or cyclones. IfPMIO emissions only (not detailed size distributions) are to be measured,
a single stage classifier would be the most desirable device to use. Size fractionating source samplers have been produced with as few as
one and as many as 25 stages with each stage ofthe multistage devices having a different cutoffdiameter. The samplers having the greater number of stages provide more detailed information on the aerosol size distribution, C-2

### and generally have higher capital and operating costs. Impaction type size classifiers are much more prone to errors due to overload and particle bounce than are cyclones; consequently. multistage operation is virtually mandatory with them in orderto insure data reliability (Harris, 1977). A sound theoretical basis exists for predictin9 the performance ofcascade impactors (Marple, 1970). Cyclone samplers performwell as single stage collectors; however, no adequate theory exists at present to predict their behavior over awide range ofoperating conditions. This limits the use of cyclones to those which have been demonstrated to produce the necessary particle size cutoffby empirical calibration procedures (Smith et al, 1982). Cyclones with acceptable sharpness ofcut have been developed and
are available from commerical vendors. Evaluation ofthese devices have demonstrated nominal 10 um cuts over alimited range ofoperating conditions, and further characterization ofthe devices is in progress. Cyclone samplers
are preferred for sampling to quantify size specific mass emissions; however, the protocol described here is generally applicable to both impactor and cyclone samplingwith details that are pertinent to only one ofthe methods noted as they arise.

3.0 Selection ofSampling Traverse Points In orderto obtain arepresentative measurement, one must obtain samples at representative points across the duct (stack) at isokinetic rates. In the case ofconventional particulate testing (e.g. EPA Methods 5and 17), this is accomplished by dividing the duct into a large number ofequal area segments and traversing this sampling plane to obtaining an isokinetic sample at the center ofeach ofthese areas. Isokinetic sampling is achieved by selecting a nozzlewhich is appropriate for the combination ofthe nominal**~3**flow rate at which the sampler is intended to operate and the average duct gas velocity. Compensation for variations in the duct gas velocity is then achieved by modulating the sampling rate. This procedure cannot be used with inertial particle size classifiers because changes in the sampling rate also changes the diameter(s) at which size fractionation takes place. However, to require fixed sampling rates adds immensely to the complexity ofthe testing and subsequent data reduction and interpretation. Amethod is currently being investigated which permits complete traverses ofthe sampling plane using variable sampling rates to be made with cyclones and impactors in a fashion similarto EPA Method 5. It is called the emission gas recycle (EGR) approach. The method uses a recirculation loop to augment the sample flow through the sizing device. This permits the operator to maintain a fixed flow through the sampler while varying the sampling rate fromthe duct by means of compensating changes in the recirculation flow. Upon satisfactory development ofthe EGR method, the protocol for traversing the sampling plane for size specific sampling will be virtually identical to that ofMethod 5. An alternative procedure for obtaining a representative multipoint traverse in a single run with afixed flow rate sampler involves changing the sampling nozzle as necessary at each sampling point to match the sampling velocity to the duct velocity within an allowable 20 percent tolerance. The volume ofgas sampled at each point is then made proportional to the volumetric flow through the area represented by the sample point by making the sampling duration at each point proportional to the gas velocity at the point. This technique will work with any velocity distribution but may be cumbersome to implement.
C-4

### Another method ofobtaining an isokinetic sample using afixed flow rate sampler is to synthesize acomplete sample from amultiplicity of single point samples. Obviously the time and expense involved in such an approach would be excessive ifthe number of samples taken equaled the number oftraverse points normally required in Method 5compliance testing. However, amethod which utilizes this approach. called simulated Method 5 (SIM-5) is being developed. In the SIM-5 method. one sample is obtained from all traverse points for which the duct gas velocity falls within a specified range (usually 20 percent). Then acomplete sample can be composed from the partial samples obtained fromtraverse points with similar gas velocities. In the interim. while awaiting the development ofthe SIM-5 method. a simple four point sampling grid may be used (Smith et a1., 1982). With a particle size sampler located at each grid location. one sample at each location is needed to obtain one complete measurement ofthe net source emission rate (the net source emission rate being the sum ofthe emission rates from each ofthe four quadrants). Three completemeasurements should be made to characterize the source emission rate; thus. atotal of 12 particle size samples (3 samples at each of4grid locations) will usually be required to obtain one complete measurement. However. ifthe velocities at the four sampling points are within 20 percent ofone another. one sampler operating for equal time at each ofthe four grid points can obtain one sample. This will reduce the number of samples required from 12to 3. The locations of the four sampling points are shown in Figure C-l.

C-5I 

# •

II' R I



## -

# ..**---~~~-**

# -.-- I I I I 

# •

I I



## • I I I I a/4 I 

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o I b/4 I

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### +---------



# •

i 

# •

FIGUREC·T. RECOMMENDEDSAMPLINGPOINTSFORCIRCULARANDSQUARE ORRECTANGULARDUCTS.

C-6-

### Implementation of all ofthe traverse methods described above require obtaining velocity traverses and gas composition using EPA Methods 1, 2, 3, and 4 followed by precalculation of sampling flow rate, nozzle selection, etc. before sampling commences. The total gas flow to be used in calculating emission rates using any ofthe reduced point traverses must be obtained using EPA Method 2. 4.0 Sampler Selection and Operation Cyclone samplers for PMIO measurements are available on the**co~ercial** 

### market. Any ofa number ofcascade impactors are available, also. High flow rate (10 to 30 alpm) samplers are desirable for sampling lowconcentration streams such as are anticipated herein. If ahigh concentration (>500mg/m3) stream is to be sampled an impactorwith adesign flow rate ofabout 3lpm would be more useful. The need to use different samplers for different particle concentrations arises from afundamental operating limit of impactors. Under most circumstances, no more than about 10mg can be collected on anyone impactor stage; beyond this limit particle reentrainment
can invalidate the data. As arulethis means that only about 50mg can be collected in total throughout the impactor. High flow rate impactors reach this load limit too rapidly for practical use in high concentration situations (Harris, 1977). Cyclone samplers have sufficient capacity that asingle high flow rate device will suffice for all applications. Sampling nozzle selection is more complexwhen sampling to obtain particle size related information than when sampling for total particulate loading. Two major factors a,re added regarding nozzle selection. Because of size selective deposition in bends, the sampling nozzles must be straight for use in particle sizing (Knapp, 1979; Felix and McCain,
C-71981). Thus one cannot use "gooseneck" or other nozzles designed to turn the sampled gas flow 90 degrees (as is done in Method 5). The natural right angle orientation ofthe inlet of acyclone sampler with respect to the sampler body and exit makes the use of straight nozzles normal with them. This is not the case with impactors. One approach to the use of straight nozzles with impactors is to align the impactor axis with the direction of gas flow in the duct. However, in many sampling locations this is impractical if not virtually impossible. The use of ahigh capacity precollector having
an inlet at 90 degrees to the body eliminates this difficulty and, at the

same time, eliminates another operational problemwith impactors. If a significant proportion ofthe particles being sampled are larger than about 10to 15 urn, the first collection stage tends to reach its loading limit before the succeeding stages collect enough material for reliable weighing. The use of ahigh capacity precollectorwhich has acut larger than that of the first impactor stage helps alleviatethe overload problem. Several such precol1ectors. some of cyclonic design and others operating on impaction principles, are comercia1ly available. Ofthe impaction type precol1ectors, only those which have inlets and exits at 90 degrees to one another are acceptable for this application. The particle size ofthe samplers, both cyclones and impactors, are dependent on anumber of factors with the sampling flow rate being the only variable which can be adjusted by the user. However, the sample flow rate required to obtain the PMIO cut will be dictated by the sampler used (given the gas composition and temperature ofthe process stream being measured). This means that one does not have the latitude in selecting the sampler flow to be used that one has in simple total particulate measurements. The
C-8

### matching ofthe sample inlet velocity with the gas stream velocity for isokinetic sampling must be accomplished entirely through the cross-sectional

area ofthe sampling nozzle. This means that amuch larger array ofnozzles must be available than those used in Method 5sampling. Ifthe isokinetic

error is no largerthan 20 percent, the maximum error in the measured emission rate of 10 umparticles will be about 15 percent and the errors for smaller particles will be lower. Thus, the actual error in the total concentration ofparticles smaller than 10 umwill. be substantially lower than 15 percent. Thus deviations of 20 percent from isokentic can probably be tolerated. If sampling is to be done within 20 percent ofisokinetic,
an array ofnozzles must be available that step by 20 percent in diameter from one to the next. Ifa single stage collector is used, the geometry ofthe collector and the flue gas conditions will completely dictate the sampling flow rate as only one flow rate will produce the required size cut. Ifamultistage device is used to measure the complete size distribution, some latitude is available in setting the flow because interpolation can be used to determine the concentration ofparticles in the designated size range. 5.0 Sampling Trains The sampling train requirements for the particle size specific method(s)
are similar to those ofMethods 5and 17 and trains designed for the latter applications can be used with some impactors and cyclones. However, Method 5 systems are not adaptable to the intermediate and low flow rate «20 alpm) devices without modification. The orifice meters used in the Method 5/17 systems are too large to permit accurate flow measurement and control at these reduced flow rates. Asubstitute orifice meter, properly sized for C-9the specific sampler, can be used in place ofthe standard orifice to**~ake** aMethod 5/17 system compatible with the sampler. Alternative trains and probes are described by Harris (Harris, 1977). As the sampling procedure described here is intended to measure only particulate material as it exists at stack conditions, the sampler must be operated instack. That is, the size fractionating device and backup filter must be mounted on the outboard end ofthe probe (replacing the usual sampling nozzle). Atypical setup is illustrated in Figure C-2. The particle size fractionating portion ofthe system cannot be located at the exhaust end ofthe probe because ofsize selective particle losses resulting from deposition in the probe. AMethod 5 filter systemmay be.used at the exhaust end ofthe probe, with the instack filter removed as illustrated in Figure C-3, to obtain an estimate ofthe condensible emissions and to provide amethod for determining the fraction ofMethod 5concentrations that fall within the defined particle size range. Detailed procedures for the use of cascade impactors for industrial

source sampling are described by Harris (Harris, 1977). Procedures for the

use ofcyclone samplers are described by Smith et al., (Smith et al, 1982). Some ofthe procedures described in the latter document are specific to a set ofcyclones designed to provide a 15 um size cut, but with the exception ofthe actual flow rates to be used, the same protocol is to be followed for the sampling described here. 6.0 Collection Media In sampling operations in which the particles are not further size fractionated beyond the PM10 cyclone, the filter specifications are the same

C-10

## • IMPINGERTRAINOPTIONAL: MAYBEREPLACEOBYAN EQUIVALENTCONDENSER

HEATED AREA..-

__-. THERMOMETER HEATEDPROBE

...- - - -- - --- - - -,

IP

# 9~*~;t! I,' SAMPLER

FILTER PITOT 

# I

HOLDER 

# t TUBE 

## i
IMPINGERSINICEBATH CHECK VALVE

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.L. 

### , B VACUUM I(y GAGE

### --e;:~--r,~~~-d<l-~

MANOMETER DRYTESTMETER AIRTIGHTPUMP**4111~4IA**

FIGUREC-2.PM10PARTICULATESAMPLING TRAINFORNONCONDENSIBLE

. PARTICULATE(MODIFIEDEPAMETHOD5 TRAIN)

C-ll

## • IMPINGERTRAINOPTIONAL: MAYBE REPLACEDBYAN EQUIVALENTCONDENSER

HEATED AREA FILTER HOLDER THERMOMETER

SAMPLER I CHECK VALVE 

# t PITOT TUBE 

# !

IMPINGERSIN ICE BATH

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FIGUREC-3.PM10PARTICULATESAMPLINGTRAINFORCONDENSIBLEAND NONCONDENSIBLEPARTICULATE(MODIFIEDEPAMETHOD5 TRAIN).



### C-12as those for EPA Methods 5and 17. However, if acascade impactor is used, extra requirements are imposed. Asurface coating is required on the collection plates ofthe impactor in order to insure adequate retention of impacted particles. This coating can be in the form ofaglass fiber mat
or a grease or polymeric coating on alightweight metal foil (Harris, 1977). Chemical reactions involving gas phase constituents ofthe process stream, especially S02, can occur with these coatings and significantly alterthe apparent weight collected by each stage (Smith et al., 1975). All glass fiber materials used in impactor sampling must be treated to minimize S02 uptake. The procedure for the treatment is given by Cushing and Smith (Cushing and Smith, 1979). Because the interferences are not predictable, control runs must be made togetherwith the actual sample runs as the

.testing takes place (Harris, 1977). All collection media must be dessicated priorto the initial weighing and dessicated for aminimum of 12 hours prior to the final weighing after sampling. 7.0 Data Reduction andAnalysis Ultimately, the information to be determined from a set ofmeasurements is the particulate emission rate in the PM10 size fraction together with a
measure ofthe uncertainty in the calculated rate. Ifthe traverse ofthe stack required multiple single point samples, emission rates must be separately calculated for each point and summed to obtain the total rate. Emission rates should be calculated for each of the three traverses ofthe duct and averaged. The average represents the final value to be reported. Isokinetic

errors would be calculated for each sample in the manner prescribed by EPA
C-13Method 5. The average isokinetic error for each traverse must be less than 20 percent for the individual traverse to be accepted. The relaxation from the 10 percent requirements ofMethod 5is possible because the

errors associated with an isokinetic sampling of small particles are not as great as those for larger particles, and Method 5must be able to deal with all sizes. Ifthe samp1er(s) are operated so as to provide a size fractionation point at the required 10 Un! diameter, the particle concentration to be used in the emission rate calculation is that based on the quantity ofmaterial which passed the appropriate stage ofthe sampler. Ifa single stage sampler is used, the actual size fractionation diameter for each sample must be calculated based on the calibration ofthe sampler, the gas volume sampled, and the composition and physical conditions ofthe flue gas. For
a sample to be accepted, the size fractionation diameter calculated for the sampler from actual run conditions must be within 10 percent ofthe required diameter. Ifthe complete size distribution is measured, the particle concentration to be used in calculating the emission rate can be found by interpolation (or, within limits, extrapolation) ofthe cumulative concentration versus particle size curve generated from the data for each run. Methods for accomplishing this are described by Smith (Smith et a1., 1982). Cascade impactor data can be reduced on a single run basis using a sophisticated data reduction program, PADRE, which was developed under EPA sponsorship. This program can be accessed through a local phone link in most U.S. cities and there are no charges for the use ofthe service. Details on the data reducion systemcan be found in "PADRE User's Guide" (EPA-600/58-84-012).

C-14References Anom. "Code of Federal Regulations" Title 40, Chapter 1. Sub Chapter C. Part 60. Anom. "Control for Particulate Emissions from Stationary Sources" Vol. 1 EPA 450/3-81-005a, September 1982. Cushing, K.M. and Smith, W.B. "Particulate Sampling and Support: Final Report" EPA-600/2-79-114, June 1979. Felix, L.G. and McCain, J.D. "Errors in Recovered Particle Size Distributions Caused by Sampling with Bent Nozzles" Paper 81-7.4 presented at the 1981 Annual Meeting ofthe Air Pollution Control Association, Philadelphia, PA, 1981. Harris, D.B. "Procedures for Cascade Impactor Calibration and Operation in Process Streams" EPA-600/2-77-004, January 1977. Smith, W.B, Cushing, K.M., Johnson, J.W" Parsons, C.T., Williamson, A,D., and Wilson, R.R., Jr. "Sampling and Data Handling Methods for Inhalable Particulate" EPA-600/7-82-036, May 1982. Knapp, K;T. "The Effects of Nozzle Losses on Impactor Sampling." In: Proceedings, Advances in Particulate Sampling and Measurement (Daytona Beach, Fl, October, 1979), W.B. Smith, Ed. U.S. EPA Report EPA-600/9-80- 004, Southern Research Institute, Birmingham, Alabama, January, 1980. pp 101-107. Marple, V.A. AFundamental Study of Inertial Impactors. Ph.D. Thesis, University of Minnesota, Mechanical Engineering Department, Mineapolis, Minnesota, Particle Technology Laboratory Publication 144, 1970. 243 pp. Smith, W.B., Cushing, K.M., Lacey, G.E., and McCain, J.D. "Particulate Sizing Techniques for Control Device Evaluation" EPA-650/2-74-102-a, August 1975. Williamson, A.D., McCain, J.D., and Parsons, S.C. "IP Sampling for Condensible Emissions" Paper 82-61M.3 presented at the 1982 Annual Meeting of the Air Pollution Control Association, New Orleans, LA, June 1982.

C-15

### APPENDIX D**-----~"** .. 

### DETERMINATION OF PM10BACKGROUND
1.0 Introduction Development ofState implementation plans (SIPs) for PM10 will require
an estimate of PM10 background concentrations for usewith eitherdispersion
or receptormodel based control strategy development. The term "background" has anumber ofdifferent interpretations. For the purpose ofSIPdevelopment, background is defined as that portion of (ambient) concentrations due to natural sources, nearby sources other than the one(s) currently under consideration, and unidentified sources. General guidance on background concentrations for all pollutants is provided in Guidelines on Air**Quali~** 

### Models.1 The purpose ofthis Appendix is to outline in greater detail the considerations and procedures which should beused to determine background concentrations ofPM10. As will be seen, selection ofan appropriate background concentration depends on the time period ofconcern (e.g., annual or 24-hour), meteorological conditions ofinterest and the availability ofdata. In adispersion modeling study, background should account for the impact ofsource emissions not considered in themodel. Such emissions may be eitherofmanmade or natural origin and may be either primary (directly emitted as particles or condensable vapors) or secondary (emitted as gases and transformed into particles during transport). Ideally, all sources should bemodeled, but this is not always possible or practical for particulate matter. In some cases, the sources may be within the agency's jurisdiction, but their emissions are not included explicitly in the model (perhaps becausethey are too small ortoo numerous to inventory). In other cases,

D-1

### the sources may be outside the region ofjurisdiction and not identifiable. However. all sources must be accounted for either explicitly by theModel or in the background value. Receptor modeling studies may also be used to develop control strategy emission limits. as outlined in Section 6.0 ofthis document. Since a receptor analysis may apportion the entire sample (including background). the analysis may reflect contributions from some sources which are or should be ascribedto background. Section 3.0 ofthis Appendix discusses this in more detail. Fordispersion model studies. the background concentration is usually needed for the set ofmeteorological conditions·and time periods (e.g., annual, seasonal or 24-hour) associatedwith the exceedances ofthe NAAQS. Receptormodels usually requirethe estimate for the specific time period represented by the receptor analysis. However, receptormodel studies may also be developed that represent aparticular set ofmeteorological conditions. This need for background to represent aparticular set ofmeteorological conditions is discussed in Sections 3.0 and 4.0 ofthis Appendix.
2.0 Determining**Back~ound** 

### Estimates FromMeasured PM10 Or Surrogate Data The PM10 background values are most appropriately taken from measurements at anearby site, representative ofbackground for the area being studied. Such asite would ideallymeasure PM10and not be influenced by (1) sources being studied and explicitly included in adispersion or receptormodel, or (2) sources not impacting the study area. This usually means that the site be located in the vicinity ofthe study area. For asite that meets these prerequisites, the mean annual background for use in the SIP analysis would be the average ofthe annual concentrations at that monitoroverthe time
D-2period being evaluated (usually 3years). Ideally, 12 valid 24-hour samples for each quarter constitute sufficient data to calculate an annual average. However, for the purpose of estimating background. less data may be used ifit can be shown that an estimate by any alternate method is

more reliable. Dispersion modelers identify specific sets of 24-hourmeteorological conditions that may lead to or be associated with high 24-hour concentrations of particulate matter. Also, receptor modelers often group the samples by meteorological "regime" and consider each group separately in receptor analyses. These specific 24-hour sets ofmeteorological conditions are matched with meteorological conditions on days when measured particulate matter data

are available. The measured data collected on days whose meteorological conditions best match the specific conditions are then averaged to obtain
a representative background concentration. As discussed above, care must be taken to ensure that sources being explicitly considered are not upwind ofthe "background" monitor on the days selected. In the absence ofa single monitoring site which is clearly not influenced by any ofthe sources considered explicitly in adispersion or receptormodel, a number ofjudgments and adjustments may be necessary to obtain a background estimate. Three alternatives are available. The first alternate method is to exclude the impact ofthese sources on the monitored background value by using awind direction analysis. This consists of calculating the average background concentration for those days when the wind direction is such that the monitor is not downwind ofa significant
source. Monitoring sites outside of a900 sector downwind ofa source may be used to determine background concentrations. The mean annual or seasonal

0-3average concentration is the 3-year mean of the annual or seasonal concentrations which are calculated in this manner. Ifthe location ofthe

sources or the frequency of sampling is such that there are an insufficient number of days at a single monitor to calculate abackground value which is not influenced by those sources, background may be determined by "compositing" the air quality concentrations from several monitoring sites located in the vicinity ofthe study area. This composite average concentration may be developed for the time period being studied by selecting the "upwind" site
on each monitoring day and averaging these "upwind" values (see Section 5.0 ofthis Appendix). The mean annual concentration is then the average of the annual concentrations which are calculated by the composite method. This procedure is greatly simplified for 24-hour cases. The background for a given day or set ofmeteorological conditions is simply the average of the concentrations at a site or sites that are upwind on the day or days which are selected to represent those conditions. As a second alternative for using measured data, background concentrations may be derived using site measurements for a different day oryear than the time period being studied. Conceptually, using data from the same site but during adifferent day oryear is similar to the first alternative above but additional care must be exercised in showing that the data used

are representative ofthe background sources and climatological conditions during the study period. As athird alternate, ifPM15 orTSP data are available, they may be used as surrogates to estimate PMIO concentrations. Use of eitherTSP or PM15 data involves the use of ratios, ormultipliers, to adjust measured background PM15 orTSP to PMIO. Then the estimated PMIO data would

D-4

### be treated in the same manner as aPMIO measurement in the preceding paragraphs. Surrogate data for PMIO should only be used ifthere are insufficient PMIO data for use in applying the procedures above. In cases where PMIO data are available for part of ayear and the PMIO monitor is collocatedwith TSP or PM15 monitors, the ratio approach may be used for the remainder oftheyear when no PMIO data are available. The ratio approach is discussed in Section 6.0ofthis Appendix.
3.0 Special Considerations ForReceptor Modeling In some cases, receptormodels are used for source apportionment at urban or source oriented sampling sites. In such cases, it may be necessary to performa source receptor analysis on a nearby background receptor so that the background for specific source components can be determined and discounted prior to using the apportionment (as outlined in Section 6.0of this Appendix). This receptor analysis at abackground site may be necessary, for example, to distinguish between locally generated soil dust and that transported into the area. Source apportionment analysis ofthe background samples should use the same procedures as used for the source analysis. Then the receptor analyses fromthe background and studymonitors are compared. The sources that were identified on both monitors should be noted and the concentations ofthese sources at the study monitor should be reduced by the amount oftheir impact at the background site. In this adjustment process, one must carefully select the background days for receptor analysis to make surethey are "upwind" and are not impacted by the sources ofconcern.



### D-5

### 4.0**~ummary** 

### OfAlternatives For Estimating PM10**Bac~ound** 

### The preferred method for background determination is to use an upwind PMIO site in the vicinity ofthe study area (air shed, city, or source) under review, which reported data during thetime period in question but which is not influenced by the sources in question or by other sources which do not impact the study area. Since such ideal measurements are not always available, particularly for an annual average determination, the alternate methods discussed previously are summarized below. in order ofpreference.
First A1ternative

Second Alternative Third Alternative Use perimeter site(s) in the vicinity ofthe study area. The effect ofnearby sources that are in the modeling analysis must be eliminated through the use ofwind direction analysis (annual or 24-hour). A"composite" background value based on several nearby sites andwind direction analyses may be developed. Use PMIOdata from adifferent days or adifferent year to represent the day oryear being studied. Use surrogate data (TSP or PMIS), adjusted to PMIO.

5.0 EXAMPLE 1 - 

### Compositing Data From Several Perimeter Sites In the absence ofa single appropriate PMIO background site with a sufficiently large data base, data from two or more sites in the vicinity of the study area may be combinedto provide acomposite estimate ofbackground concentration. Forexample, assume that there are two sites located on the area perimeter (one to the north and one to the south). The observations from both sites should be sorted by wind direction into three groups: Group I - days when the Northern site is upwind (use Northern site data for NI days).

0-6Group II

- days when the Southern site is upwind (use Southern site data for N2 days). Group III

- days with calms, substantial shifts in wind direction, during the day or with winds predominantly from the east or west (and both sites are upwind). (Use average of data from both sites for N3 days.)
The Group I, Group II, and Group III days are used to compile an annual average:

annual average

## = Nl E Group I + E N3 Group II + E. Group III

where Nl and N2 are the number of days where only the northern or southern sites, respectively are upwind. N3 is the number of days where both sites are upwind. This calculation can, of course, accommodate more than two monitoring sites. Also, a single site may be used if it is generally upwind ofthe study area, so long as there are sufficient data to represent average upwind concentrations after the days when the source is upwind are removed from the data base.



### D-7

### 6.0 EXAMPLE 2 - Use Of**Sur~ogate** 

### Data Most regional monitoring networks contain one ormore designated background hi-vol sites and afew have PM15 background sites. However, there
are relati'vely few size-fractionated particulate samplers measuring PMIO compared with thosemeasuringTSP; and almost all ofthe former are in urban locations. Therefore, States may not have direct measurements ofPMIO background availablewhen they prepare their SIP's. The following is the recommended procedure for using PM15 and TSP surrogates to estimate PMIO, based on an analysis ofregional scale PM10. PM15 and TSP sites. The PM15 measurements may be used as a surrogate for PMIO. The PM15 measurement (either annual average or 24-hour) is multiplied by aconstant ratio to give an estimate ofPM10.2 The following adjustment applies. PMIO

## =0.85 xPM15 (annual or 24-hour) Reference 3suggests PM15 to TSP ratios of .61 (annual) and .75 (high 24-hour). Using the above 0.85 ratio for PMIO to PM15, the following ratios apply for estimating PMIO background using aTSP surrogate:
PMIO

## =0.52 xTSP (annual) PMIO 

## =0.64 xTSP (high 24-hour)
The concentrations to be adjusted are obtained using the procedures in Section 2.0 ofthis Appendix. The "adjustment" is simply the last step in the process, wherein aTSP orPM15 background estimate is adjusted to represent an estimate ofthe PMIObackground.

D-8

### References -----,-- 

### 1.**~uid~Li~~_~_Ai~_~~aJJLt~~odels** 

### (Revised) EPA-450/2-78-027 R. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, July 1986. 2. A. K. Pollock, A. B. Hudeschewskyj andA. O. Thrall, An Examination Of 1982-83 ParticulateMatterRatios And Their Use In The Estimation0(-- ?M10 NAAQS Attainmynt Status, EPA450/4-85-010. U.S. Environmental-- Protection Agency, OAQPS, Research Triangle Park, NC 27711, August, 1985. 3. PEOCo Environmental, Inc.,**Estimatin9-P~n~j=JP** 

### __**Bac~Jlro~nd** 

### Concentrations FromTSP And OtherMeasurements, EPA450/4-84-021. U.S. Environmental ProtectionAgency, OAQPS, Research Triangle Park, NC 27711, July 1984.

0-9

### 1 

### 1 1 1 1 1 

### 1 1 1 1 1 

### 1 1 1 1 1 

### 1 1 1 1 1 

### 1 1 1 1 1 

### 1 1 1 1 1 

### 1 1 1 1 1 

### 1 1 1 1 1 

### 1 1. _ . ,t"

- =- 

### - ':'".**~:.!.-"'_** ••- TECHNICALREPORTDATA rPitcu,,.D,1J"r1?'l..lC'tiomG'/Irht**rrl'~f"St'** bt/ortCOMl'Jtr1n,J 

### ,**'"-E:~O~·'i** ""C I' 13, RECIPIENT'S ACCESSION NO. ; 

### EPA-450/2-86-00l

-- 

### ----- ,.: --**_~_ .',:~_:."'7-:';.,~**  is. R:.FDCct":" :-.t.T=: ! 

### : June 1987 

## I PM10 SIP Development**~uideline** 15.PERFO'UJlING**O~GA.I\;IZATtON** CODE 

### i



## - ':'\

..:.....:-Cl!S· 8.**PERFOR~lI\'G OI:l.G"'~:ZATlO'"**  REPOFIT NO. 

### , I I ;S.**PEP.cC~MJ/** ...:? OR:GANIZ":';IOf\ J\;':'ME**~NO** "-OORESS 10. PROGR.:.M Ei..EMEN' NO. 

### Control Programs DevelopmentDivision (MD-15) , 

### Office ofAir Quality Planning and Standards \"_**CO~TRAC-'** -GRANT NO_ 

### Research Triangle Park, NC 27711 ; I ':'.**S?O"'lSOFlI~G** AGEr'.CV !\.AME**.41\,~** l..:J,;)Fi.ESS 

### 113. TYPE OF AEPOAT AND PEAIOD COVEPlEO I 14, SPONSORING AGENCV CODE !

# I ! 5. S\JP::

...**E.~.~E'" ":"~;;**  I( NC7::S 

# I

--**::.-;~:-**

- 

### t This guideline document was prepared to briefly describe the actions that must betaken by State and local air pollution control agencies to I 

### develop SIP's that demonstrate attainment and maintenance ofthe PMI0 NAAQS.



### , The guideline describes howto: (1) demonstrate attainment ofthe PMlO NAAQS, 

## , ' (2) 

### determine the size ofan area exceeding the NAAQS. (3) 

### select areceptor 

### , I 

### ordispersion model. (4) 

### prepare an emission inventoryand. (5) 

### determine a I I 

### control strategy design concentration. The guideline also discusses ambient 

## i- PMI0 monitoring requirements and data usage and EPA's policies for making I 

### the transition from aSIP designed to protect TSP standards to one designed 

# I to protect PMIO standards. References to supporting documents are given at the end ofeach section. I 

### I This guidelinedoes not discuss implementation ofPSD orpreconstruction 

## I review programs for PMI0. Guidance for implementation ofthese programs


### , will be issued separately. I

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### , Particulate regulations , 

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### - .- 

### - - = --

### - ':'.:; . ..- -
- 

## -

-