# &EPA UnitedStates Environmental Protection Agency**--~.** Air OfficeofAirQuality PlanningandStandards ResearchTriangleParkNC27711 

# PM10SIP Development Guideline EPA-450/2-S6-001 June 1987

### EPA·450/2·86-001



# PM10 

# SIPDevelopmentGuideline

U. S. ENVIRONMENTALPROTECTIONAGENCY OfficeofAirandRadiation OfficeofAirQualitvPlanningandStandards ResearchTrianglePark, NorthCarolina 27111 June1987Thisguidelineis issuedbythe EnvironmentalProtectionAgencytoassistStateandlocalairpollutioncontrol agencies indevelopingStateimplementationplans(SIP's)forthe nationalambientairqualitystandardsfor particulatematter.Thestandardsfor particulatemalterhave been revisedtoaddresspanicles nominally 10 micrometersand smaller indiameter(PMlO). Copies areavailable· in limitedquantities·fromtheLibrary ServicesOffice(MD·35).U.S.EnvironmentalProtectionAgency,ResearchTrianglePark,NorthCarolina 27711; or,loralee,IromtheNationalTechnicalInformationService,5285PortRoyalRoad,Springfield,Virginia22161.

This reporthasbeenreviewedbythe OfficeOfAirQualityPlanningAndStandards, U.S. Environmental ProtectionAgency,andapprovedlorpublication.Anymentionoftradenamesorcommercialproductsis notintendedtoconstituteendorsementorrecommendationforuse.

PublicationNo. EPA·45012·86·001TABLE OF CONTENTS

1.0 INTRODUCTION


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

### ............................................... Page 1-1 1.1 Overview .••**~'**••••••••**,_.~~**............................. 1-1 1.2 Guideline Contents ••••••••••••••••••••••••••••••••••• 1-2 1.2.1 Determining the air quality status of areas•••• 1-3 1.2.2 Ambient monitoring •••••••••••••••••••••••••••• 1-3 1.2.3 Modeling

....................................... 1-3 1.2.4 Emission inventories •••••••••••••••••••••••••• 1-3 1.2.5 Development of control strategies ••••••••••••• 1-4 1.2.6 SIP requirements and data reporting ••••••••••• 1-4 1.2.7 Types of receptor models •••••••••••••••••••••• 1-4 1.2.8 Preliminary estimate of PMI0 design concentration using TSP data •••••••••••••••••• 1-4 1.2.9 Source testing •••••••••••••••••••••••••••••••• 1-5 1.3 General SIP Approach ••••••••••••••••••••••••••••••••• 1-5 2.0 DETERMINING AIR QUALITY STATUS OF AREAS

................... 2-1 2.1 Policy Overview

....................................... 2-1 2.2 Demonstrating Attainment 

## .5.••..••..•••.•••.•••••••.•• 2-2 2.3 Estimating Probability of Nonattainment to ••••••••••• 2-4 Determine Initial SIP Requirements 2.4 Area Categorization ••••••••••••• 5 •••••••••••••••••••• 2-7 2.5 Area Boundaries •••••••••••••••••••••••••••••••••••••• 2-9 3.0 AMBIENT MONITORING AND DATA USAGE ••••••••••••••••••••••••• 3-1 3.1 Ambient PMIO Monitor1ng •••••••••••••••••••••••••••••• 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 s1ting •••••••••••••••••••••••••• 3-5 3.2.7 Ambient data reporting •••••••••••••••••••••••• 3-6 3.2.8 Air pollution index •••••••••••••••••••,••••,•• 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 of nonreference or equivalent method samplers ••••••••••••••••••••••••••••••• 3-1 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 Receptor Model Selection •••••••••••••••••••••••••••••••••••••••••••• 4-2 4.2.1 Particle size ••••••••••••••••••••••••••••••••• 4-2 4.2.2 Priorknowledge 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 sizeand time scale ••••••,•••••••••••4-5 4.3 Screening Techniques and Refined Dispersion Models for PMIO Concentrations ••••••••••••••••••••••••••••••4-7 4.3.1 Selection of appropriate dispersion models ••••••••••••••••••••••••••••• 4-8 4.3.2 Special considerations for PM10 dispersion modeling ••••••••••••••••••••••••••• 4·9 4.4 Receptor Models for Estimating PM10 Concentrations ••• 4-11 4.4.1 Control strategy analyses using receptor models •••••••••**~**•••••••••••••••••••••••••••••• 4-11 4.4.2 Preliminary analyses using receptor models •••• 4-12 4.5 Use ofReceptor and Dispersion Models in Combination. 4-13 5.0 DEVELOPMENT OF PM10 EMISSION INVENTORIES •••••••••••••••••• 5-1 5.1 Overview ............................................... 5-1 5.2 PM10 Emission Factors and Fractional Multipliers ••••• 5-3 5.3 PM10 Source Data for Dispersion Models ••••••••••••••• 5-20 5.4 Inventory Data Needed in Receptor Models ••••••••••••• 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 Oata Handling •••••••••••••••••••••••••••••••••••••••• 5-38

11**~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 forDetermining Design Concentrations •• 6-2 6.3.1 Table look-up •..•.••••,••••••••••.•••...••••.• 6-3 6.3.2 Fitting one statistical distributionto several years of data ••.••.••••••.•.•••••••••• 6-6 6.3.3 Using the empirical frequency distribution of several years ofdata (graph1cal 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, SIP requirements

.................. 7-1 7.2.2Regulations in 40CFR Part 51 •••••••••••••.•••• 7-2 7.3 PMlO SIP Development Policy

..................,..... 7-2 7.3.1 Group I PM10 SIP requirements

......'

........... 7-2 7.3.2 Group II PMlo SIP requirements

................. 7-3 7.3.3 Group III PMlO SIP requirements

................ 7-5 7.4 SIP Content

..............**~~**.....'"**.·.~4**..........,.......**~**.... 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 PM10-Specific Emission Regulations

................ 7-10 7.9 Reporting Emissions Data

.......................... 7-11 7.10 Emissions Trading (Bubble) Policy ••••••••••••••••• 7-13 7.11 FugitiveDust Policy ••••..•.••....••••....•••••••• 7-13

iii

### APPENDIX A - TYPES OF RECEPTOR MODELS ••••••••••••••••••••• A-I 1.0 Chemical Mass. Balance ,............................... A-I 2.0 Factor Analysis .•..........•....•...•...•......•....• A-2 

### 3.0 Optical Microscopy



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

### .................................. A-2 4.0 Automated Scanning Electron Microscopy ••••••••••••••• A-2 5.0 Other Receptor Models ...............................**~**...."
................... A-3 5.1 X-Ray Diffraction (XRD) •••.•..••••••••.••••••.•• A-3 5.2 Trajectory Analysis ...................."........................... u A-3 5.3 Microinventory .••••.••.......••.••••••••.•••...• A-3 

### APPENDIX B - PRELIMINARY ESTIMATES OF PMlO DESIGN CONCENTRATIONS USING TSP DATA••••.•..••...•.•••••••••••..•..• B-1 1.0 Annual NMQS

....**~**..................................."••. B-1 1.1 Recommendations ••..••••••••.••••••.••••..•....t. 8-1 1.2 Example ...................." ........................ .o .............. B-2 2.0 24-Hour NAAQS •••.•••.••.••••••••••••••••••.••••.•.••. B-2 2.1 Background ..............'"............**~**.••'"••••••..• B-2 2.2 Recommendations**~.~**•.••••.•...••••••••..••••••••. 8-4 

### 2.3 Example

......................................... B-6 APPENDIX C

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

### 2.0 Available Methods for SizeSpecificSource Measurements ••••.•••••.••••••••••••••....••.••••••••.

## c-2 

### 3.0 Selection ofSamplingTraverse Points

................ C-3 4.0 Sampler Selection and Operation •••••••••••••••••••••• C-7 5.0 Sampling Trains .•........••..••.......•..•..•,•.•.•.. C-9 6.0 Collection Media ........................................**C~10**



# . 7.0 Data Reduction and Analysis ••••••.•,••••••••••••••••• C-13

ivAPPENDIX D

- DETERMINATION OF PMIO BACKGROUND 1.0 Introduction ••••••••••••••••••••••••••••••••••••••••••• 0-1 2.0 Determining Background Estimates from Measured PMIO or Surrogate Data ••••••••••••••••••••••••••••••••••••••••• D-2 3.0 Special Considerations for ReceptorModeling ••••••••••• 0-5 4.0 Summary ofAlternatives for Estimating PMIO Background. 0-6 5.0 Example 1 - Compositing Data from Several Perimeter Sites ••••.•••••••••••••••••••••••••••••••••••••••.••••• D-6 6.0 Example 2

- Use of Surrogate Data •••••••••••••••••••••• 0-8

v

### TABLE 4-1 LIST OF TABLES AND FIGURES

SELECTING FEASIBLE SOURCE APPORTIONMENTMETHODS BASED ON DATAAVAILABILITY AND SOURCE CHARACTERISTICS ...•..•••....•,..•.••.••..•**~**••..•.. 4-3 

### TABLE 4-2 RECOMMENDED APPROACHES FOR PM10 SOURCE APPORTIONMENT •.••..,.••...........•••••..••••.•••• 4-6 

### TABLE4-3 DISPERSION MODELS 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 DISTRIBUTION FOR AHYPOTHETICAL LIME PROCESS •••••• 5-18 TABLE 5-3 INDUSTRIAL SOURCE COMPLEXMODEL (ISC)

............. 5-24 TABLE 5-4 CLIMATOLOGICAL DISPERSION MODEL (COM 2.0)•••••••••• 5-25 TABLE 5
...5 RAM
........."
................................................................."5
...26 

### TABLE 5-6 SINGLE SOURCE (CRSTER) MODEL •.•••••••••••••••••••• 5-27 TABLE 5-7 MULTIPLE POINT SOURCE MODELWITH TERRAIN ADJUSTMENT (MPTER) ..•...,.•......•..•...•.••.••..•**5~28** 

### TABLE 5-8 EXAMPLE OF DATA IN EPA'S PARTICULATE SOURCE LIBRARY
............................................................."
..... 5-31 

### TABLE 5-9 POTENTIAL SOURCES OF CONDENSABLE PARTICULATE .••••• 5-33 TABLE 6-1 TABULAR ESTIMATION OF PM10 DESIGN CONCENTRATIONS •• 6-5 FIGURE 5-1 EXAMPLE GRAPHICAL DISPLAY OF CUMULATIVE EMISSION FACTORS VS. PARTICLE SIZE FOR AHYPOTHETICAL PROCESS

..........................."

.........."

..... I

... " t

... " • " 5-19 

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

vi

### FIGURE C-l 

### RECOMMENDED SAMPLING POINTS FOR CIRCULAR AND SQUARE OR RECTANGULAR DUCTS ••••••••••••••••••••••• C-l FIGURE C-2 PMIO PARTICULATE SAMPLING TRAIN FOR NONCONDENSABLE PARTICULATE (MODIFIED EPA METHOD 5TRAIN) ••••••••• C-12 FIGURE C-3 PMIO PARTICULATE SAMPLING TRAIN 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 airquality standard (NAAQS) is based along with the NAAQS themselves.1•

### 2 In response tothese requirements, EPA revised the primary and secondary NAAQS to apply toparticulatematter in a size range defined by the collection characteristics of a new ambient reference method that has a 50 percent collection efficiency (050) at 10 micrometers.3,4 The material collected by the reference method is nominally below 10 micrometers and is referred to as "PM10." The purpose of this document is toprovide guidance on how States
can develop PM10 control programs for attaining and maintaining ambient PMIO stdndards. Existing State control strategies toattain and maintain the former NAAQS for total suspended particulates (TSP) provided for controlling particulatematter 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 toPMIO control programs and the necessary revisions to State implementation plans (SIP's) to account for PMIO' This document is intended as a startingpoint for anyone seeking information, policy, and guidance on developing PM10 SIP's. Most aspects of developing a PM10 program from problem determination tofinal SIP submittal are covered. Certain details of program development, however,
1-1require explanation not included herein. Reference is thereforemade 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 particulatematter NAAQS via section 110 oftheAct
means that EPA will not impose Part 0new source review (NSR) requirements as
ameans of revising theSIP's. Instead, the preconstruction review ofmajor
new and modified stationary sources which emit PM10 will be carried out largely under regulations for the prevention of significant deterioration (PSD). The reader is referred to section IV.D inthe preamble ofthe FEDERAL REGISTER notice promulgating PM10 amendments tothe PSD regulations for adescription ofthe changes that EPA has made to address PM10. 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 of particles covered by the PMIO NAAQS is different from that included in TSP, the actions that States will have to take in preparing PMIO control programs are more complicated than they would be if only the levels or averaging method of the NAAQS had been changed. As a result, Statesmust look at the spatial distribution of ambient PM10 concentrations and at source emissions from aparticle size distribution standpoint. The major sections of this document are intended to provide guidance on the different aspects of PM10 control strategy

1-2development and SIP submittal to EPA in light ofthis new focus on the PM10 size range. 1.2.1 Determining the Air Quality Status ofAreas The Act requires control strategies to be submitted to EPA within 9months ofthe revision of NAAQS. Section 2.0 describes EPA's policy for determining when the air quality of an area is ormay be violating the PM10 NAAQS and the extent ofthe area violation. The EPA's policy for categorizing areas intothree groups based on the need to revise their SIP is also discussed. 1.2.2 Ambient monitoring Each Statemust establish an ambient monitoring network utilizing PM10 samplers. Section 3.0 discusses the changes made tothe ambient monitoring requirements of 40 CFR 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 PM10. 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 PM10 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 PM10 levels, the model inputs must be in terms 1-3ofPMI0 emissions. Section 5.0 discusses how PMIO emissions can be calculated and explains in detail the type of information needed for various dispersion models now available as well as models now under development. Data needs for receptor models are also included. 1.2.5 Development ofcontrol strategies Section 6.0 identifies general approaches tothe use of ambient measurements and model estimates in settingcontrol strategy emission limits. It explains how design concentrations may be determined and how control strategydemonstrations may be conducted using models. 1.2.6 SIP requirements and data reporting Section 7.0 discusses EPA's policies for requiring PMIO SIP's based
on area categorization and for transferring from a control strategy for TSP to one for PMI0. Also discussed are PMI0 emission data reporting requirements in 40 CrR 51.322, the fugitive dust policy, and the emission trading policy. 1.2.7 Types of receptormodels Appendix Alists various receptor model techniques availableand discusses each. 1.2.8 Preliminary Estimate ofPM]O Design Concentration Using TSP Data Appendix Bprovides amethod for making apreliminary estimate of the PMIO design concentration when only TSP data are available; however, this estimate alone cannot be used for developing'control strategies. In addition to providing a preliminary estimate, it can also provide useful information for evaluating design concentrations estimated by dispersion modeling.
1-41.2.9 Source testing One of the methods for determining a source's PM10 emissions is to conduct atest which measures the amount of emissions 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 PMIO. 1.3 General SIP Approach States have manyyears of experience in developing and implementing plans for abattng particulatematter air pollution. In general, the activities States will engage in to attain PM10 NAAQS will not differ radically from past activities to attainTSP NAAQS. That is, the basic approach will still be to examine airquality across the State, delineate
areas where airquality 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 thatNAAQS are not violated in the future. The change in the particle size range to which the standards apply from TSP to PM10, together with the emergence of receptor models as effective tools in strategy development, necessitates changes in the specifics of particulatematter control strategydevelopment. The major difference in past TSP**strategi~s** and new PMIO strategies will be the need to inventory the sources of the PM10 size fraction and tofocus on control of PMIO emissions to reduce ambient PM10. This could include applying PMIO emission factors to sources, in some cases testing sources for PMIO emissions, and utilizing receptor models, where feasible to refine

1-5emission inventories and augment dispersion models. The contribution of'
sources to ambient PH10 and the degree of emission reductions necessary
can then be determined.

1-6References
1. Air Quality Criteria for ParticulateMatter. AP-49. U.S. Department of Health. Education, and Welfare. Washington. D.C., January 1969. 2. Air Quality Criteria for ParticulateMatter and Sulfur Oxides, EPA-600/8-82-029a-c, Environmental Criteria and Assessment Office, Environmental ProtectionAgency, Research Triangle Park. N.C., March 1983. 3. High Volume Method for Suspended ParticulateMatter, Part 50 of Chapter I of Title40 of the Code of Federal Regulations, Appendix B. 4. Reference Method for the Determination of PM10 in the Atmosphere, Part 50 ofChapter I of Title 40 of theCode of Federal Regulations, Appendix J.

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

### Kand in the Guideline on EKceptions toData Requirements for Determining Attainment of ParticulateMatter Standards.l In areas where too little PMlO data are available todemonstrate attainment ofthe NAAQS, EPA's policy is touse whatever PM10 data are available and supplement it with TSP data to determine the probability that the area will violatethe PM10 NAAQS. The EPA will
use the report, Procedures for Estimating Probability ofNonattainment of a PM10 NAAQS Using Total Suspended Particulateor PM10 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 of nonattainment 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 ofnonattainment. Group 11 areas are those areas where nonattainment is uncertain, and Group III areas are shown tobe or have a high probability of attainingthe NAAQS. After the initial grouping of areas and promulgation ofthe PMlO NAAQS, only PM10 data may be used tomake definitive attainment/ nonattainment decisions in accordance with Appendix Kor reference 1, above. The probability guidelinemay 2-1continue to be used for planning purposes such as to assess new areas where TSP data indicate a need for more extensive PMIO monitoring. 2.2 Demonstrating Attainment Appendix Kspecifies the procedures which States can use todemonstrate attainment ofthe 24-hour and annual PM10 standards at aparticular location using ambient airquality data. To determine PMIO attainment ofthe24-hour standard, EPA generally requires averaging the number of estimated 24-hour exceedances over 3consecutiveyears of representative data. If the average number of 24-hour NAAQS exceedances 1s greater than 1.0 peryear, then a nonattainment problemwill oe evident and States should proceed to examine the adequacy ofthe existingSIP. Apotential problem ofnonattainment of the 24-hour PMlO standard can oe projected on the basis of fewer than 3years ofdata, since the criteria for nonattainment can be equivalently expressed in terms of thetotal number of estimated exceedances within a3-year period. In the simplest case, iffour exceedances are ooserved in aSingle year, the average numoer ofexceedances over a 3-year period will exceed 1.0. If the total numoer ofestimated exceedances in 3years is equal to
or greater than 3.2. then the numoer of exceedances 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 3.2estimated exceedances are accumulated anonattainment proolemwill be evident. This may occur in even less than a singleyear. As specified inAppendix K, EPA will not require adjustment ofthe first observed exceedance in order to account for incomplete sampling if (1) only one exceedance occurred in the calendar quarter, (2) ifeveryday
2-2sampling is initiated as expeditiously as possible as required by 40

### eFR 58.13, and (3) 75 percent data capture is achieved (see section 3.2.5 ofthis document). As specified in 40

### eFR 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 thefirst observed exceedance, EPA is assuming that this first exceedance may be the**~**actual exceedance during the entire year. If, however, one or more subsequent exceedances are observed during the transition period following the first exceedance, but prior to the actual initiation ofevery day monitoring, the additional exceedances would be adjusted to account for incomplete sampling using formula [1] 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 Data Affected 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 of SIP adequacy ifthe responsible control agency determines in conjunction with apublic review thatthe flagged data are inappropriate for use. When theminimum 3year data requirements specified in Appendix Kare not satisfied, attainment can still be demonstrated, but such demonstrations 2-3must be approved by the appropriateRegional Admin1strator, in accordance w1th theGuideline on Exceptions toData Requirements for Determining Attainment ofParticulateMatter Standards.1 In particular, ifonly 1or .2years of PMIO data are available, then supplementary information provided by modeling or other surrogate measures must be used todemonstrate the adequacy of theSIPto attain and maintain the standards. Finally, in theevent that there has been atrend in emissions during the attainment test period, attainment with the standards can be demonstrated according toAppendix K, even though the most recent 3years ofdata do not indicate. attainment. Either the most current representativeyear(s) could be used or statistical techniques or models could be used in conjunction with previousyears 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 toDetermine Initial SIP Requirements The procedures EPA will use tocategorize 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 PMIO data for an area are adequate to demonstrate attainment
as discussed in section 2.2, then the area will be put intoGroup III. On the other hand, ifthe PMIO data alone are adequate todemonstrate that the
area is nonattainment, it will be put into Group I. If available PMI0 data alone are not adequate todemonstrate attainment or nonattainment. EPA's policy isto supplement available PMlO data with inhalable particulatematter data (particleswith an aerodynamic diameter less than 15 micrometers) and TSP

2-4data. These airquality data will be used in a statistical methodology as described in the probability guideline to estimate the probability that the PMIO NAAQS will be violated in the area. Initially, areas will be grouped based on probabilities of nonattainment of95 percent or greater (Group I), from 20 to95 percent (Group II) and less than 20 percent (Group III). Other factors thatmay influence the degree towhich the existingSIP 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 PMIO instruments. Astudy performed by EPA in Phoenix has shown that in extreme situations data collected by SierraAnderson SA-321A size selectivePM10 instruments can be influenced by coarse particles to the extent that concentrations may be biased high by up to 20 percent. In addition, data collectedwith Wedding or GMW-9000 instrument may be biased low by up to20 percent due to soiling problems.5,6 In order to account for the uncertainty associated with such PMIO concentrations in calculatingthe probability ofnonattainment, a zone 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 ofuncertaintywill only cause areas tomove from Group I toGroup II,
or from Group IIIto Group II. In particular, forSA-32IA instruments, 24-hour PMIO observations and annual PM10 means (using all data) within the upper gray zone are not to be counted as exceedances of the respective standards in the probability calculations. Similarly, for the GMW-9000 instrument, 24-hour PMIO values and annual PM10 means (using all data) that
are within the lower gray zone are to be counted as potential exceedances of the respective standards in theprobability calculations.
2-5If an area's nonattainment probability using TSP data and PMIO data drops below 0.95 or rises above 0.20, as a result of PMIO data in the "gray zone," itwill be classified Group II in order to resolve the possible uncertainty associated with the PMIO data and to ensure that adetermination ismade as to whether the existingSIP provides for attainment and maintenance ofthe PMIO standards. Also, PMIO data 1n the gray zone, by itself, will not be allowed tomove an area into Groups I or III from any other Group. Areas classified Group II as a result of PMIO data in the upper "gray zone" are still required to operate airqualitymonitors on an everyday sampling schedule for the firstyear. This is consistent with the requirements for Group I areas as explained in section 3.2.5. The following table presents the actual values associated with the "gray
zones. II

Averaging Time Annual PMIO 

### NAAQS

50 150 TABLE 2-1 PM10 Gray Zone Limits (ug/m3)

Gra.y Zone L illlits Upper Lower

60* 180* 40** 120**

*App1ies to readings made with aSA-321A **App1ies to readings made with a GMW-9000

Since the conditions underwhich these potential instrument biases were observed are not considered typical, data collected with all instruments will be taken at face value when demonstrating attainment or nonattainment 2-6with the standards. Fordata produced by the SA-321A (with the potential positivebias), however, an appropriate downward adjustment will be permitted for attainment demonstrations ifinfluence by coarse particles
can be demonstrated. For data 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 PM10 NAAQS (i.e. with data in the lower gray zone) will be required tosample more frequently for thefirstyear,(according to the Group I Imonitoring frequency ofevery-other-day), so that true nonattainment situationswill soon become apparent. 2.4 Area Categorization SIP development requirements are based on an area's categorization into Group I, II, or III ofEPA's policy for PM10 SIP development. If ambient PM10 data adequate todetermine 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 initiallycategorized intothese groups using nonattainment probability (section 2.3) cut points of95 percent to distinguish between Group I and Group II and 20 percent to distinguish between Group II and Group III. The EPA 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 criteriafor group categorization are:

Group I

- Areas for which the existing particulatematter SIP may need substantial revisionto be adequate for attaining and maintaining PM10 standards. 2-7Group II

- Areas forwhich the existing particulatematter SIP may be adequate or need onlyminor adjustment. Group III

- Areas forwhich the existing particulatematter SIP's are believed adequate to attain and maintain thePMIO standards. Examples of information otherthan air qualitydata thatmay warrant moving an area from Group III toGroup II. or from Group II toGroup 1.
are:

o facts showing thatthe current airquality is attributableto an economic slowdown or some other temporary phenomenon rather than the stringency ofthe area'sTSP SIP requirements,

o facts suggesting that there are few enforceable measures in existing SIP'syetto be implemented thatwould reduce emissions that Significantly affect air quality, and

o evidence that the area has an unusually high proportion of sources in categories whose emissions typically have ahigher ratioof PMIO to total particulatematter. Examples ofinformation otherthan airquality data thatmay warrant moving an area from Group ItoGroup II, orfrom Group II to Group III, are:

o factors suggesting that sources are notyet in compliance with SIP
measures that. ifenforcedwould reduce emissions that significantly affect the area's airquality; °evidence that the area has an unusually high proportion of sources in categories whose emissions typically have a low ratioof PM10 to total particulatematter; and

2-8o evidence that the area 1s rural in nature and 1s 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 thatmay affect EPA's classificationof an
area not preliminarily classified using a nonattainment probability are:

o the amount and density of industrial activitythat would likely result in significant ambient PM10 concentrations in the area;

o the number and density of roadways in the area that are near activities likelyto generate significant particulatematter emissions and that are subject to moderate and heavy vehicletraffic; and

o the degree towhich the existingTSP SIP will likely limit PM10 emissions from these traditional and nontraditional sources. 2.5 Area Boundaries Section 6.0 ofthe probability guideline describes several approaches for determining the boundaries of an air quality level represented by one
or more monitoring stations. In some instances the spatial extent of areas ofuniform PM10 airquality status may differ from the areas previously defined for TSP; therefore. Statesmay wish touse the guidance in these instances tomore specificallydefine the spatial extent of PM10 airquality problems. The approaches that have been used and that are recommended for defining area boundaries are:

-- aqualitative analysis of the area of representativeness ofthe monitoring station. together with consideration of terrain. 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 on the complexity of the areawhere amonitoring station(s) is located. More detailed discussions ofthetechniques are in the probability guideline.

2-10

### References
1. Guideline on Exceptions toData Requirements for Determining Attainment ofParticulateMatter Standards, 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 ofa PMIO NAAQS Using Total Suspended ParticulateorPM]O Data, U.S. EPA, RTP, N.C.**EPA-450/4-86~17,** 

### December 1986. 3. Guideline on Identification and Use ofAir Quality Data Affected by Exceptional Events, EPA-450/4-86-007, U.S. EPA, RTP, N.C. July 1986. 4. Guidance on Accounting for Trends inParticulateMatter Emission and Air Quality Data, memorandum fromR.G. Rhoads toRegional Air Directors, U.S. EPA, RTP, NC. May 11, 1987. 5. L. Purdue et. a1., Intercomparison ofHigh-Volume PMIO Samplers at a Sitewith High ParticulateConcentrations, JAPCA, Vol. 36, No.8, Aug. 1986, pp. 917-920. 6. Uncertainty ofPMI0 Data Collected with High-Volume Size Selective Inlets,Memorandum from L.J. Purdue toRichard Rhoads, U.S. EPA, RTP, N.C. September 15, 1986.

2-113.0 AMBIENT MONITORING AND DATA USAGE
3.1 Ambient PMIO Monitoring States must establish ambient monitoring networks tomeasure PM10 just
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 other pollutants. Existing ambient monitoring networks measure total suspended particulatewith high volume samplers (hi-vols.)* Data from hi-vols, as discussed previously in section 2.0, will not allow sufficiently accurate estimates of PM10 levels to determine PM10 attainment or nonattainment with certainty. Ambient samplers meeting the requirements of 40 CFR 50 and 53 will be necessary tomeet the ambient monitoring requirements of Part 58 except as specified inAppendix C of Part 58. The requirements for PMIO reference or equivalent methods with a nominal 050 of 10 micrometers are contained in 40 CFR 50, Appendix J, and in 40 CFR 53. The EPA will designate reference and equivalent method samplers as
soon afterpromulgation of these requirements as possible. Some provisions
are made, however, as discussed in subsection 3.3, for using other ambient data until data from ambient ?M10 networks are available. 3.2 Part 58 Requirements 3.2.1 Quality Assurance The quality assurance (Q/A) requirements for PMIO samplers are essentially the same as for hi-vols. Only one minor change was made tothequality

*Oescribed in 40 CFR 50 Appendix BofPart 50 3-1assurance 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/m3for PM10) be calculated and reported. Previously, measurements below these levels were reported. Otherwise, Q/A procedures for PMlO monitoring in both Appendices Aand Bare identical tothose forTSPmonitoring. 3.2.2 Ambient monitoring methodology The requirements in Appendix Cof Part 58 which require reference or equivalent method samplers be used in State and Local AirMonitoring Stations (SLAMS) apply to PMlO with one exception. Section 2.2 ofAppendix Callows thehi-VOl method tobe 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 of the PMlO 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 a compliance standpoint toinstall a PMlO sampler. If,TSP levels rise above PMlO NAAQS, however, the Statemust install PM10 samplers in ordertomeasure actual PMlO levels to be certain PMlO NAAQS are maintained. Also, section 2.2 of Appendix Crequires that at TSP National Air Monitoring Stations (NAMS) the hi-vol be continued in operation for at least ayear afterthe PMlO monitoring begins. This will allow a site-specific relationship between TSP and PM10 data to be developed and provide assistance in checking validity of PMIO data. Historical trends for ambient levels of particulatematter can thus be estimated. Section 4.0 ofAppendix 

### C, which previously applied toepisode monitoring forTSP, has been revised to apply to PMlO. 3-2**3.2.~** Network establishment Title40 CFR 58.20 requires a description ofthe SLAMS network be available to the general public and be submitted toEPA approximately 6months after PM10 NAAQS arepromulgated. The network description need not be included as part of the 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 PM10 SLAMS network. By 1year after promulgation of the NAAQS, each area within the approved SLAMS network for which a probability of PM10 NAAQS nonattainment is greater than or equal to 20 percent, must have at least one PMIO sampler in operation which is (1) located in the area of expected maximum concentration (2) sited 1n accordance with Appendix 

### E, (3) located as described on the station's Storage and Retrievel ofAerometric Oata (SAROAO) identification form, and (4) meeting all quality assurance requirements in Appendix Apertinent to PMIO' The remaining PM10 samplers, including those in areas with nonattainment probabilities below 20 percent, have until 2years after promulgation of the NAAQS to be fully operational and tomeet the siting and quality
assurance requirements. The same submittal date (6 months after promulgation) also applies for the description ofthe NAMS portion oftheSLAMS network as required by section 58.30. The date by which all stations in theNAMS portion of the SLAMS network are to be in operation and meeting Part 58 requirements is 1year after PM10 NAAQS are promulgated (section 58.34). 3.2.4Network deSign Information on designing SLAMS and NAMS networks is contained in Appendix 0of Part 58. The material is largely self-explantory and, as 3-3indicated in Appendix D, should be used jointly by States and EPA Regional Offices for designing ambient monitoring networks. To account for PMIO network design, Appendix Dhas been amended to include criteria for determining the number of PMlO stations and areas in which to locate them. In addition toAppendix D, a document entitled Network Design and Optimum Site Exposure Criteria for Particulate Matterl is available and provides further information on designing PMIO 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 ithas 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 of expected 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 a probability below 20 percentmay be once every sixth day. The use of the term "area"
as it applies tothe required sampling frequencies ofthe "area" is as follows; (I) any urbanized area as defined by the U.S. Bureau 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 consider technical factors such
as the types of emissions, their spatial distribution, meterology, and topography and how these factors contributetothe uniqueness of the "area" thereby distinguishing it from other designated "areas," The
3-4first year ofPMIO 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. The year can start, however, as soon as the station is put into operation. The long-term strategy applies afterthe short-term strategy ends (in most cases afterthe first full year of sampling ends). The sampling interval will then vary according tothe relationship of airquality levels tothe NAAQS. The closerthe airquality levels totheNAAQS, themore frequently sampling must be carried out. Atable depicting the requirements appears in section 58.13. As soon as the first exceedance ofthe 24-hour PM10 NAAQS occurs in
an area which historically has had a less than everyday sampling interval, the requirements change toeveryday sampling for at least one site (the site ofexpected maximum concentration). The State will have up to90 days afterthe end of the 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 4calendar quarters, but preferrably 1 full calenderyear. Ifthe day on which the first exceedance occurred
was flagged as an exceptional event, the Statemust 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 PMIO samplers is very similartothe previous siting criteria for hi-vol samplers. The
new**~rjteria** 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 PM10 microscale stations. The inlet height for all 3-5other scales for PM10 stations is 2to 15 meters as itwas previously for all TSP SLAMS. The spacing requirement from roadways is also different. For microscale roadway stations, the PM10 sampler must be between 5to 15 meters from major roadways. For middle, neighborhood, and urban scale PM10 stations a range of separation distances are specified for each as a function of traffic volume and the scale of representativeness. 3.2.7 Ambient data reporting The rep,orting 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 PMIO data in section 2.7 ofAppendix F. The ranges for the 24-hour summary report have been in 50 ug/m3increments. The format added for PMIO is in 25 ug/m3increments. Asignificant change made in the reporting requirements is the,provision that annual TSP data must be reported as arithmetic means, 24-hour TSP data values must be reported ifthey exceed the level ofthe 24-hour PMIO NAAQS, and the sampling interval must be reported. OtherwiseTSP reporting requirements remain unchanged. Section 2.7 has been added toAppendix Ftocover reporting of PM10 data. All exceedances ofthe 24-hour PM10 NAAQS, the sampling interval, and the number of 24-hour values within 30 ug/m3 ranges must be reported.

3-63.2.8Air pollution index The air pollution subindex forTSP has been replaced by a subindex for PM10. The breakpoints for the subindex reflect the levels of the 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 a050 of 15 micrometers (SSI15), and dichotomous samplers with inlets designed for 050 of 15 micrometers. Some data have been collected USing samplers with inlets designed for a050 of 10 micrometers. The PMIO samplers cannot be designated as PMIO 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 equiva1 ent methods. Therefore, nonreference or equiva1 ent method PMlO data, PMl5 data, orTSP data may be used for initial SIP development purposes, subject tocertain 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 a nominal 050 of 10 micrometers. Approval of ambient PM10 samplers for SIP purposes wi11 be made by the Environmental Monitoring Systems Laboratory, MO-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 of these data for initial SIP classifications are discussed in section 2.4. 3.3.3 Episode monitoring The criteria for determining which areas are required tohave air pollution episode contingency plans are in Subpart Hof Part 51. The sampling procedures that should be used for determining airquality levels during air pollution episodes are similartothose used priorto promulgation of the PMIO NAAQS. These procedures are identified in section 4.0 of Appendix Cof Part 58 and are further described in the document, Guideline for PM10 Episode Monitoring Methods.3 Briefly, two methods based on the filtration principle are recommended; staggered PMIO sampling and short-term interval sampling. The staggered sampling procedure uses a24-hour sampling procedure followed by a 2-hour post-sampling filter equilibration period. The short-term interval sampling method requires a 4-hour sampling period followed by a2-hour filter equilibration period. In addition to these two procedures, other methods may be used provided the user demonstrates a site-specific correlation of the alternativemethod with the reference method. 3.4 SIP Revisions That portion ofthe SIP providing for ambient monitoring should be revised, if necessary, toinclude provisions for a PMIO ambient network. Ifthe ambient monitoring portion ofthe SIP provides for monitoring pollutants "forwhich NAAQS have been set," then no revision is necessary. If the SIP lists pollutants, PMIO would need tobe added tothe list. The PMIO SLAMS network design need not be included in the SIP and aSIP revision is not necessary as a result of any network modification. Any
3-8SLAMS 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.

3-9Reference 1. Koch, R.C. and H.E. Rector, Network Desifn and Optimum SiteExposure 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. 2. Pace, T.G. and N.H. Frank. Procedures for Estimatin Probabi1it of Nonattainment of a PM NAA Us nota Sus en e artlcu ate or

MIO ata. U •• nVlronmenta Protec 10n gency, Research Triangle Park, NC. EPA 450/4-86-017, December 1986. 3. Pelton. O.J., Guideline for PM10 Episode Monitoring Methods, GEOMET Technologies. Inc. Rockville. MD. Prepared for U.S. Enviromenta1 Protection Agency, Research Triangle Park. NC. EPA Contract No. 68-02-3584. February 1983.

3-104.0 AIR QUALITY MODELING FOR PMIO
4.1 Introduction Section 51.12 of 40 CFR requires that the adequacy of acontrol strategy for attainment and maintenance ofNAAQS be demonstrated by means of adispersion model or other procedure which 1s shown to be adequate and appropriate for this purpose. The Guideline on Air QualityModels**(Revlse~)l** provides guidance on dispersion modeling for particulatematter and 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 of its collection to inferthetypes or relativemix of sources impacting on it during collection. Receptor models
are described briefly in'Appendix Aand the references tothis section.2,3 The most widely used and accepted quantitative receptormodel is the chemical
mass balance (CHB).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 of emissions of PMIO using dispersion and receptor models: (1) use of receptor and dispersion models 1n combination (preferred); (2) use ofdispersion models alone; and (3) use oftwo receptor models, with control strategy developed using a proportional model (discussed in Section 6.0). This latter approach is only encouraged if no applicable dispersion model is available. Several considerations relevant tomodel selection are presented in Section 4.2 (Receptor Models) and 4.3 (Dispersion Models). The PM10 is generally referred to in the following discussion. Where there is insuffiCient technical data to support an analysis for PMIO with these models, the same procedures may be applied toTSP, except as specifically noted, as a surrogate for PM10.

4-14.2 Constderations in Receptor Mode1Selection. Several considerations related tothe nature of sources emitting PMIO influence selection ofthe receptor model(s) for SIP purposes. These are the availability of particle size data and the size range of the 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 of receptor models are summarized in Table 4-1 and discussed below. One key overriding selection consideration is that CMS is considered the most advanced ofthe receptor methods 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 least40 samples to complete an analysis. The reader is referred tothe Receptor Model 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 matter which is related to the tendency of some source categories to emit predominantly fine (<2-3 urn) or coarse (>2-3 um) aerodynamic diameter particles. Sources which emit predominantly fine particles includethose involving the combustion of fuels (motor vehicles, boilers, field burning, woodstoves, etc.) and industrial processes involving combustion, chemical reaction, orcondensation of vapors. Contributors to coarseparticles 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 arewell suitedto analyze sources Of 4-2TABLE 4-1 SELECTING FEASIBLE SOURCE APPORTIONMENT METHODS BASED ON DATA AVAILABILITY AND SOURCE CHARACTERISTICS

Chemi- Finger- Iso- Air Shed cal prints lated (Source Sources Sources Simi- Ois- Single CateFine Coarse Known Unknown 1 arity simllar Source .90r1es)
ISS .1ance (eMB) Y y Y a,b,Y Y Y Y ,ctor lalysis (FA) .X X X X b,X X X X ltomated .anning ectron croscopy .SEM) c,X X X X b,X X X X croscopy 1M) c,X X X X b,X X X X spersion ,del (OM) YY YY YY YY YY YY yy**.-------~-**

. Initial use of factor 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 eMB orOM.

- Appropriate to use with OM orFA, ASEM or OM.

- Appropriate to use.

4-3 Air Shed {Specific Sources Within Category) b,Y

b,X b,X

YYeither fine or coarse particles. Optical and scanning electron microscopy
are suitable for coarse and fine particles down to about 1micrometer 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 thesetwo size ranges are associated with different types of sources. 4.2.2 Prior knowledge of sources and emissions In many instances, the sources suspected ofcontributing to ambient PMIO concentrations at aparticular 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 alsomet. However themass balance requires knowledge of sources and theiremission characteristics. If some sources are unknown, FA, ASEM,
orOM might prove useful ifdone prior to aeMS analysis. 4.2.3 Chemical similarity The availability of "fingerprints" for the sources of interestwill often determine the optimum receptor model touse. 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 arepredominantly composed of carbon, there is very little information upon which to differentiate among the different types of combustion 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 includethedifficulty in distinguishing among sources whose emissions are 4-4comprised ofvarious soil components or between f1yash and soil. Optical properties can be useful for some situations where fingerprints are similar. Also, X-ray diffraction (XRO) is very useful in identifying various minerals by examining theircrystalline structure. 4.2.4 Sources versus source categories Any ofthe receptor models listed in Table 4-1 or Appendix Acan be useful in identifying the impact of an isolated specific source unless its fingerprint is similartothat of background. Likewise, thetechniques can be used to identifymany ofthe source categories within an airshed consistent with the limitations identified in the preceding discussion. However, the impact of specific individual sources within an airshed containing MUltiple
sources ofthe same type may not be reliably identifiableexcept on wind directional samples or by a disperSion model. 4.2.5 Particlesizeand time'scale In some cases ambient PHlO or PMl5 data may be available for SIP apportionment. In other cases, only TSP data are available. The availability of PM10 data, or PH15, orTSP will determine to some extent thetype of source apportionment method used for PMIO since the PMIO signatureof a
source may be significantlydifferent from the PHl5 orTSP signature. Another important factor is the time scale of the nonattainment situation (annual
or Z4-hour). Table 4-2 contains recommended approaches for source apportionment based on the time scale (annual or 24-hour) of the nonattainment problem and the data base available. The choices listed in each block of thetable 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 PMIO SOURCE 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

*TSP may be used as a surrogate, where PMIO data bases are inadequate. ** Other receptor models such as Mass Balance may be used if fine particledata (generally less than 2-3 micrometers) are collected in addition toTSP.

4-64.3 Screening Techniques and Refined Dispersion Models for PM10 ConcentratIons It may sometimes be appropriate toconduct a preliminary screening study to determine likely causes of nonattainment 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 a screening study has two advantages. First, the qualitative prioritization of sources can be used todesign the
more definitive study, which is usually required for control strategy development. This may enable substantial cost savings, sincethe screening study can be focused on specific areas of concern. Second, in some cases, the source contribution may be very clear and the screening study may be all that is required priortocontrol strategy development. This is further discussed in the references tothis section.7 4.3.1 Selection of appropriate 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 particulatematter 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 a surrogate 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 of negligible removal iswarranted, conSidering the size of the particles.

4-7Dispersion 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 settlingand deposition ofparticles and can accept particle sizedata, 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 scaletransport model), that can provide supporting analyses. These models include provision to input a settling velocity appropriate to particle size, a deposition velocity characteristic of the pollutant-surface interaction, and a rate (percent per hour) to describe the transformation of primary gas pollutant to secondary particlepollutant (e.g., sulfurdioxide tosulfate). Since their accuracy, suitability, and
resource requirements for regulatory applications are not well tested, no specific recommendation on the use ofthesemodels is provided here. Nevertheless. in selected applications, they may be useful in asseSSing the significance of background concentrations and of secondary sulfateparticles in the effectiveness of control strategies. Application of thesemodels should follow recommendation in themodeling guideline concerning the use of alternativemodels. 4.3.2 SpeCial considerations for 

### PM1C dispersion modeling The Modeling Guideline1contains 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 4-81 TABLE 4-3
DISPERSION MODELS ApPLICABLE TO PM10 ANALYSES*

to 24-Hour Average Annual Average Screening Techniques**
CRSTER CRSTER PTPLU-2 MPTER MPTER COMPLEX I RAM 

### RAM VALLEY IS.CST ISCLT COM 2.0

*Formore information concerning the applicability ofthese models, consult

theGuideline on Air Quallt Models Revised. As noted in this document, these models maya so e use forTS mo e ng analyses in conjunction with a suitableTSP emission inventory. as a surrogate. where PMIOdata bases are inadequate. **These models are considered to be screening techniques.forusepr10r.toa more refined analysis as outlined in the Guideline on Air QualityModels (Revised). _.-

4-9opt10ns, (4) determ1nation ofurban/rural class1ficat10n, and (5) determ1nat10n ofbackground airquality. With regard to background airquality values, there is now limited data thatmight be used tosupport specific values for PMI0 background concentrations. General guidance on theuse of available data is provided inAppendix 

### D. Rollback and roll forward are appropriate only for preliminary analyses. Proport1onal models may be used 1n conjunction with receptor modeling 1f the airquality problem is clearly associated with a few specific sources. Such procedures are discussed in Section 4.4and 6.4. For**urban~wide** refined analyses, COM 2.0 or RAM may be used. For**source~specific** analyses ofcOMp11- cated sources, the ISC model is preferred toCRSTER/ MPTER because ISC isthe only,model that is capable oftreatingdeposition, area and volume sources, build1ng downwash, etc. For those cases where no recommended technique is available or applicable, nonguideline modeling approaches for use in each specific situation must be approved by the appropriateRegional Office. Dispersion models are more reliable for estimating longer time-averaged concentr'ations (e.g., annual average) than for estimating**short~term** concentrations (e.g., 24-hour) at specific 10cations.11 Point source models are reasonably reliable in estimating the magnitude ofthehighest concentrations occurring some time, somewhere within an area. Errors in highest estimated concentrations of! lOto40 percent are found to be typical for sources that can be adequately characterized. The multiple source urban model RAM showed no Significant bias in estimating l-hour ground level concentrations for the 13-stationRAPS network in St. Louis. The average network cumulative frequency distributions ofhourly estimated and observed concentrations differedby no more than 30 percent over the entire concentration range. 4-10However, estimates of concentrations that occur at a specifictime and site tend to be poorly correlatedwith actually observed concentrations and are much less reliable, should this performance attribute be important in a regulatory application. 4.4 Receptor Models for Estimating PM10 Concentrations* 4.4.1 Control strategy analyses using receptor models Receptor models tend to be well suited for source apportionment of 24-hour PM10 samples. However, care must be taken toensurethat the samples analyzed are representative ofthe conditions causing NAAQS exceedances. The CMB is recommended as the primarymethod to be used in regulatory applications of receptor models toPM10 data.4,12.13 There is uncertainty in any source apportionment approach. Therefore, ifCMB is used for source apportionment (without combiningwith a OM). it is required that at least
one other receptor modeling 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 oftheReceptor Model Technical Series2orthe Digest ofAmbient Particulate Analysis and Assessment Methods.14 It is strongly urged that eitheroptical microscopy orASEM be used tocorroborate CMS, along with intensive chemical analysis (sulfate, carbon and other elements) ofthe samples. The OM or ASEM should be used instead ofCMB if only TSP data are available. It Is also strongly urged that theCMB be performed on size fractionated PMIO (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. 4-11increases the resolution of the techniques. The results of receptor model
source apportionment may be used with the method discussed in Section 6.4 to estimate the degree of control required to demonstrate attainment at a given monitoring site. For exceedances of the annual NAAQS, the selection of days on which to perform receptor model analyses should be governed by the representativeness of the 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 Preliminarx analyses using receptor models

Preliminary analyses can also be done using receptor models. The most
common methods employed are optical or automatic scanning elecron microscopy. These techniques are relatively inexpensive {less than $500 per sample} and
can give a varietyof information about likely sources. They are especially useful when only T5P data are available because they can discriminate between particles less than and greater than 10 um. Other methods which may be helpful include microinventories, chemical emission inventories, and mass balance.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, theCMB may be a useful 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., 51, AI) which cannot be reliably measured on a glass fiber filter. In
areas where steel mills and power plants are likely sources, the microscopic
4-12methods (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 of Receptor and Dispersion Models in Combination Several demonstrations have been made where receptor models 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 a dispersion model is determined to be marginally adequate. The results ofthe receptor model can be used to carefully scrutinizethe inventory assumed In the dispersion model to deduce whether emissions from certain source categories appearto have been adequately characterized. The
use of a receptor model, such as CMS, in conjunction with dispersion modeling, is highly recommended in such situations. Procedures for USing the CMS and dispersion model in concert are specified in reference6tothis section. Guidance in Section 5.0 describes the kinds ofdata necessary in a PMI0 emissions inventory used as input to adispersion model for a PMI0 analysis. For determining compliance with the annual PMIO NAAQS, dispersion models based on an annual PMI0 emission inventory and sequential or frequency distributions ofobserved meteorological conditions can be used. Also, receptor models and methods such as eMS, FA, OM and ASEM, perhaps corroborated by microinventories, trajectory analysiS, and XRO 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 theNAAQS. It may be difficult to devise a PM10 short-termemission inventory for
use in adispersion model to characterize24-hour episodes of high ambient 4-13particulatematter. Therefore, analysis ofthe observed monitoring data using a receptor model is likely to be particularly useful, 1n concert with dispersion model estimates. Of the receptor models discussed. CMB. OM, or ASEM, perhaps corroborated by XRO or trajectory 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 either24-hour or annual PMIO analyses because the particles largerthan 10 ummay bias the results. An exception tothis is the use of ASEM or optical microscopy in association with dispersionmodel estimates. This exception is permitted because ASEM and optical microscopy involve the analYSis of discreteparticleswhere their size (relativetoPMIOl 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 toestimate control requirements.

4-141.

2. References
Guideline On Air Quality Models (Revised), EPA-450/2-7B-027R. U.S.**~nvironmental** Protection Agency, Research Triangle Park, NC 27711 July 1986. Receftor Model Technical Series, Volume I: Overview Of**Rece~tor** Model Appl cation To ParticulateSource Apportionment, EPA-450!4-8 -016a, PB82-139429. U.S, Environmental Protection Agency, Research Triangle Park, NC, July 1981. 3. Gordon, G. E., Receptor Models. Environmental Science and Technology, 1980, 14, 792-800. 4.

5.

6.

7.

8.

9.

10.

11.

12. Rece§tor Model Technical Series, Volume II: Chemical Mass Balance, EPA- 50/81-016b, PB82-187345. U.S. Environmental ProtectionAgency, Research Triangle Park, NC, July 1981. Protocol ForApplying And Validating The CMB. Protection Agency, Research Triangle Park, 

### Nc. U.S. Environmental In Preparation. Protocol For Reconciling Differences Among Recettor And**Dis~ersion** Models. U.S. Environmental Protection Agency, Research riangle Par,

## Nc. In Preparation. Receptor Model Technical Series, Volume V: SourceApportionment Technirues And Considerations In Combining Their 

### Use, EPA 450/4-84-020, PBS5-1 1524. U.S. Environmental Protection Agency, Research Triangle Park, NC, July 1984. Turner, D. B •• Workbook ofAtmospheric Dispersion Estimates, PHS Publication No. 999-Ap-26. O.S. Environmental Protection Agency, Research Triangle Park, NC, 1970. Budney, 

### L. E., Procedures For Evaluatin Air uality Impact OfNew Stationary Sources, ulde lnes For r Qua l'y"Maintenance Planning And AnalYSis Volume lOR, 

### EPA 450/4-77-001. U.S. Environmental Protection Agency, Research Triangle Park, NC, October 1977. Hanna, S. R., G. A. Briggs and

### R. P. Hosker, Jr., Handbook on Atmospheric Diffusion, DOE/TIC-11223. Technical Information Center, Department of Energy, Washington, DC, 1982. Rhoads, R. G., "Accuracy ofAir Quality Models," Memorandum toAir and Hazardous Div;sion Directors. U.S. Environmental Protection Agency, Research Triangle Park, NC, July 22, 1981.

4-15

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

## Bf Particle Identification. EPA 450/4-83-018, PB84-103 O. U.S. Env ronmental Protection Agency, Research Triangle Park, NC, June 1983. 14. Digest 

### OfAmbient ParticulateAnalysis And Assessment Methods, EPA-450/ 

### 3-78-113. U.S.**Environ~ental** 

### ProtectionAgency, Research Triangle Park, NC, September 1978. 15.

16. Receptor Model Technical Series, Volume VI:· AGuideTo The Use of Factor

### Anal sisAnd Multi le Re ression R Tec ni ues nSource Apport onment, EPA 4501

- 

### -00, PB86-10 6 • U.S. Environmental Protection Agency, Research Triangle Park, NC, July 1985. •



### 17. ReceptorModel Source Comrosition Library, EPA 450/4-85-002, PB85-228823. 

### U.S. Environmental Protec ion Agency. Research Triangle Park, NC, 1985

4-165.0 DEVELOPMENT OF PMIO EMISSION INVENTORIES
5.1**Overvi~** As part oftheSIP development process following promulgation of PMIO NAAQS, emission inventories may have to be developed for PMI0. This section discusses various technical considerations involving the preparation of PMI0 emission inventories. Though inventories are useful for various purposes, the primary focus ofthis section is on developing or reviewing PMI0 emission inventories tobe used as input to applicable air quality dispersion models. Such models are expected tobe utilized as part of SIP 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 of this document will have had direct experience compiling particulatematter or other inventories and will find the same general procedures applicableto PMI0 inventories. The primary thrust of this section is to alert users to circumstances where additional materials and different methods are needed to compile PMIQ inventories. The two major components ofPMIO inventories, or inventories of any pollutant tobe used in dispersion modeling, are (l) a set of emission factors, and (2) source datatowhich 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
a typical facility within a particular source category. The most commonly
5-1used source of emission factors 1s 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 11). The PMIO emission factors for reentrained dust from roadways are included in Volume I. This section**de~cribes** the format of these PM10 emission factors for stationary sources and how they can be applied tothe appropriate source data to develop PMIO inventories. Emission inventory procedures for mobile sources arepresented in a separateEPA publication.11 The second component ofthe PM10 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 PM10 inventory depend on the model used for PMIO simulation, with themajor differences involving (l) the levels oftemporal and spatial resolution, (2) the description and configuration of each source, and (3) the need for chemical composition and particle sizedistributions. Thus, this section also identifiesmodels currently available for PM10, as well as those anticipatedto be available in the next several years, and delineates the specific differences in emission inventory requirements of each ofthese models. Finally, a number of specific considerations need to be made when dealingwith airquality models and PMIO inventories. First, condensable and secondarily formed particlesmay become more important Since both are primarily within the PMIO fraction. Second, since PM10 is a subset oftotal 5-2particulatematter, the question arises whether an existing particulatematter inventory, compiled forTSP analyses, can simply be modified for use in a PM10 application. Third, as in any modeling application requiring large amounts of input, data handling becomes an important consideration. These topics are addressed in the latterpart of this section. 5.2**~Emission** Factors and Fractional Multipliers Any emission factor is basically amultiplierwhich is applied to appropriate
source activity levelS, such as throughput or production rates. toestimate 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 eithercase, 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 if uncontrolled emissions are being calculated. This simple equation is employed regardless if emissions are being computed for total particulatematter, PM10. or any other pollutant. If a set ofPM10 emission factors is available. the preceding equation becomes: Emission factorlpM 

### xActivity level 

### xControl device 10 penetrationlpM =EmissionslpM 10 10
Of course. emission factors may be utilizedthat already reflect the effect of certain controls, in which cases the control device penetration factor drops out ofthe above equation.
5-3Alternatively. PMIO emissions may be computed by applying fractional multipliers to particulatematter emission estimates in an existinginventory as follows: (PMIO fraction) xEmissionslparticulate**~** EmissionslpM10 An advantage ofthe latter approach is that PMIO fractions may be available in certain cases whereas PMIO emission factors may not. This approach may also be easier from adata handling standpoint when converting an existing total particulatematter inventory to a PMIO inventory. Thus, in compiling aPMIO inventory for use in dispersion modeling.
one must first obtain requisitePM10 emission factor or size fraction information. Such information is becoming available for a large number of
sources. Table 5.1 liststhe stationary source categories forwhich EPAhas developed PMIO emission factor and other particle size information. Amajority ofthese factors have been distributed toState/local agencies. Additional PM10 information will be published in supplements toAP-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, PMIO emissions after controls could be calculated either by applying a factor of0.06 lb/ton tothe process production rate or by multiplying the existingtotal particulate matter emissions for the facility by 60 percent. Agraphical display ofthe information in Table 5-2 is included, 1n 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(J'1 

## , (J'1 Table 5.1 PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

AP-42 Section Source Category

1.1 Combustion-Bituminous and Subbituminoua Coal

1.2 Combustion-Anthracite Coal

1.3 Combustion-Residual Oil

Combustion-Distillate Oil

1.4 Combustion-Natural Gaa 1.6 Combustion-Bark

lWithout flyaeh reinjection. 2With flyash reinjection. Fabric Process Uncontrolled ESP Filter

Dry Bottom Boiler x x 

# " Wet Bottom Boiler 

## " x Cyclone Furnace x x Spreader Stoker1 

# " x Spreader Stoker2 x Overfeed Stoker x Underfeed Stoker 

## " Dry Bottom Boiler x x Traveling Grate Boiler x Utility Boiler x x Industrial Boiler 

# " Commercial Boiler x Industrial Boiler x Commercial Boiler x All Boilers x Wood Waste Boiler 

## " CONTROL DEVICE Multiple Scrubber Cyclone Other

i

x x x x 

## " x

x

x

x 

## "

x ,,1,2V'I I 0\ AP-42 Section

1.1 1.8
2.1
4.2

5.3 5.15 5.16

- Table 5.1 (continued) PMI0 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

Fabric Source Category Process Uncontrolled ESP Filter Combustion-Lignite Coal BoUers x Combustion-Bagasse Vibrating Grate Stoker Refuse Incineration Municipal Incinerator x Surface Coating Spray Booth Water Based
Carbon Black 011 Furnace x Detergent Spray Dryer Sodium Carbonate Manufacturing Bleacher Dryer x x Calc1ner x x Dryer x Rotary Predryer x

lCombined controls. CONTROL DEVICEMultiple Scrubber Cyclone Other

x

x

x Water Curtain

x

x x3 x3

x3 x3

!<J't I 

## '" AP-42 Table 5.1 (continued) PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

Fabric Section Source Category Process Uncontrolled ESP Filter
5.17 Sulfuric Add Absorber 

## " Absorber 20% 

# " Absorber 32% 

# " Secondary Absorber 

# " 5. Boric Acid Dryer 

## " " 5. Potassium Chloride Dryer 

# " 6.1 AlfalfaDehydrating Drum Dryer 

## " 6.3 CottonGinning Battery Condenser 

# " Lint Cleaner Roller Gin Bale Press 

## " Gin Stand 

# " Saw Gin 

## " BalePress 

## " Gin Stand It

- - _.- ....

### _ ...._- 3Combined controls. CONTROL DEVICE Multiple Scrubber Cyclone Other

,,3 ,,3


## " ,,3 ,,3

-- -- ....-<.n 

### • CO 

# ,

AP-42 Section

6.4

6.18 7.1

7.2 .,',.1 , , . '.': '



### .. 

# ... \**"~f.** .'" I ", ;.1**~** " .' .\ • ,.t> .'

### (

,

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

Fabric Source Category Process Uncontrolled ESP Filter
Feed and Grain Carob Kibble Roaster x Cereal Dryer x Unloading and Conveying x Rice Dryer x Ammonium Sulfate Rotary Dryer x Primary Aluminum Production Bauxite Ore Unloading Storage x PrebakeRoof Monitor x HSS Cell x Coke Production Coal Preheat x Coal Charging x Coke Pushing x Mobile Scrubber Car

- Travel Mode j.

CONTROL DEVICE Multiple Serubber CyClone

x

x

x



### x

- OtherU1 I <0 AP-42 Section
1.2

1.3 Source Categ0l'Y Table 5.1 (continued) PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

Fabric Process Uncontrolled ESP Filter

Coke Production (cont.) Mobile Scrubber Car Push Mode Quenching with Dirty Water x Quenching with CleanWater x Combustion Stack x Primal'Y Copper Multiple Hearth Roaster and Reverberatol'Y Smelter x x Reverberatol'Y Smelter x x Converter x x Matte Tapp.ing x Slag Tapping x Converter Slag aod Blow Operlltions x**~** - --- _. CONTROL DEVICE Multiple Scrubber Cyclone Other

x Baffle Baffle

## '" I 

### :::; AP-42 Section

7.4

7.5

--

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

Fabric Source Category Process Uncontrolled ESP Filter
,Ferroalloy Open Furnace 50% FeSi x :It 80% FeMn x x Production Si Metal x :It FeCr (HC) :It x SiMn x Iron and Steel Production Sintering Windbox 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 Q-BOP Melting and Refining BOF Charging x x BOF Tapping x x RAP Melting and Refining x

- CONTROL DEVICE Multiple Scrubber Cyclone Other

x

x x

x

x

-- ---_

..._-U> 

## ,

.... 

# .... AP-42 Section

7.5

7.6

7.8

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

Fabric Source Category Process Uncontrolled ESP Filter

Iron and Steel Production (cont.) HAP Melting, Refining, and Charging Direct Shell Evacuation x Open Hearth FurnaceMelting and Refining x x Primary Lead Smelting Blast Furnace Flue x Blast Furnace x Ore Storage x SinterMachine x Dross Kettle x Reverberatory Furnace x Secondary Aluminum Reverberatory Furnace x Chlorination Station x CONTROL DEVICB Multiple Scrubber Cyclone Other

- -

.......

### __....-U'I t**~** 

## "" AF-42 Section

7.10

7.11

7.13

7.15

7. 8.1 Table 5.1 (continued) PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, FROCESSES AND CONTROL SYSTEMS

Fabric Source Category Frocess Uncontrolled ESF Filter Gray IronFoundries Cupola Furnace x x RAP x Fouring and Cooling x Shakeout x secondaryLead Smelting Blast Furance Flue x Blast Furnace Ventilation x x Steel Foundries Casting Shakeout x Open Hearth x x Storage Battery Froduction Grid Casting x Grid Caating and FasteMixing x Lead Oxide Mill x FasteMixing x x Three Frocess x Tinner Batch Tinner x Asphaltic Concrete Conventional Flant x x DrUIII Mix Flant x x

--- 

## - ---- - -- ---- ---- --- ---- CONTROL DEVICE Multiple Scrubber Cyclone Other

x

x Spray Tover<.n I



### ..... W AP-42 Section
8.3

8.6

8.9

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

Fabric Source Category Process Uncontrolled ESP Filter

Brick snd Related Clay Products Coal Fired Tunnel Kiln x Sawdust Fired Kiln x Raw Material Screening and Grinding x Portland Cement Wet Kiln x x Dry Kiln x x Clinker Cooler x Coal Cleaning Dry Process x Thermal Dryer x Thermal Incinerator x Glass Manufacturing Furnace Exhaust x x CONTROL DEVICE Multiple Scrubber Cyclone Other

x Gravity Filter

xU'I 

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

AP-42 Section Source Category

8.15 Lime

8.18 PhosphateRock Proceasing

8.22 Taconite Ore Processing

8. Feldspar 8. Fluorspar 8. Lighweight Aggregate (Clay)

&Cyclone With Baghouse. bS. C.**~** Settling Chamber. Fabric Process Uncontrolled ESP Filter Rotary Kiln x x xa Product Loading Into Open Bed Trucks x Into Tank Truck x Glass Line Into Tank Truck x Ball Kill Calciner Rotary Dryer Rotary Dryer and Fluidized Bed Dryer x3 Roller Kill and Ball Kill x3 Main Waste Gas Stream x3 Ball Mill x Rotary Drum Dryer x Coal Fired Rotary Kiln Dryer x Reciprocating Grate Clinker Cooler x CONTROL DEVICE Multiple Scrubber Cyclone Other

x

x x3 x3 x3 x

x3 x3

x

x

x S. C.b

x

-.." I



## .... .." AP-42 Section

8.

8.

8. 10.1

--

....**-.~.** Table 5.1 (continued) PMIO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES. PROCESSES AND CONTROL SYSTEMS

Fabric Source Category Process Uncontrolled ESP Filter
Lightweight Aggregate (Shale) Reciprocatil18 Grate Clinker Cooler Lightweight Aggregate COal F1red Rotary (Slate) Kiln x Redprocati118 Grate Clinker Cooler Talc PebbleMill x Kraft Pulp Recovery Boiler with DeE x x Recovery Boiler without DCE x x Lime Kiln x x Smelt Disso1vil18 Tank Vent x**~** .......-- -- .. as.C•• Settlil18 Chamber. CONTROL DEVICE ,
I Multiple Scrubber Cyclone Other

S.C.8

x
S.C.a

x

x Packed TowerU'I 

### , S; AP-42 Section

10.4 11.2.1

11.2.2 11.2.3 Table 5.1 (continued) PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE CATEGORIES, PROCESSES AND CONTROL SYSTEMS

Fabric Source Category Process Uncontrolled ESP Filter

Wood Working Waste Belt Sander 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 Agricultural Tilling x Aggregate Handling Batch Drop x Continuous Drop x CONTROL DEVICE Multiple Scrubber Cyclone Other

x(Jl r


## ....

...... AP-42 Section

11.2.5

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

### I Fabric Source Category Process Uncontrolled ESP Filter
Paved Urban Roads Local Streets x Collector Streets x Major Streets/ Highways xFreeways/Expressways x Industrial Paved Roads Copper Smelten x Iron and Steel x Asphalt Batching x ConcreteBatching x Sand and Gravel :It

- CONTROL DEVICE

, Multiple Scrubber Cyclone Other

!Particle Size (micrometer)

Total Catch 15.0 <TI 

## ,

.... 10.0 0::> 7.5 5.0 2.5 1.0 0.5
aControl Device: Baghouse. TABLE 5-2. CUMULATIVE EMISSION FACTORS AND PARTICLE SIZE DISTRIBUTION FOR AHYPOTHETICAL LIME PROCESS

Cumulative Mass Percent Less Cumulative Emission than Stated Size Factors (Pound/Ton of Product)
Uncontrolled Controlleda Uncontrolled Controlleda 100 100 85 0.100

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## ~large contributors, since facility or point-specific emission estimates are considered more accurate than average estimates calculated from AP-42 factors. Any testing should be consistent with the methods described in Appendix Cof this document. 5.3 PMIC Source Data for DisperSion Models The second major 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 of each source, operating rates, stack and exhaust gas parameters, and control devices employed. The kinds of source data thatmust be obtained depend on whether aparticular
source can best be described as a point, area, or line source. The degree of detail required in a PMIO emission inventory is dictated by the dispersion model employed for Simulating airquality impacts. As described in more detail in section 4.0, a number ofmodels are currently available for assessing the airquality impact of PMIO, differing in many respects. Generically, most such models can be categorized along the following lines: dispersion vs. receptor individual source vs. areawide (grid) short-term vs. long-term Dispersion models calculate ambient airconcentrations primarily as functions of source configurations, emission strengths, terrain features, and meteorological conditions. Receptor models (discussed in section 5.4) inferthe relative impact of various sources on ambient airquality by
5-20reconciling 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 airmeasurement site. Individual source models evaluatethe impact of a single source or source complex whereas areawide (grid) models evaluate the impact of numerous sources, including area and line sources, over a larger area. Short term models estimate air quality levels fortime periods from 1hourto 24 hours whereas long-term mOdels generally predict monthly, seasonal, or annual average concentrations. The major impacts ofmodel choice on requisite source data in the 

### PMIO emission inventories are itemized below:

-- Area covered by the inventory

- Individual source dispersion models require source data to be collected for only a single stack, facility,
orcomplex. Areawide models require source data for all sources within thedefined 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 tomost applicable dispersion models are expressed in terms of grams per second for point sources, grams per second per square meter for area sources, and grams per second per meter for line sources. Actually. the emission rates input todispersion models need not be as resolved
as might be inferred from these units. Ideally, the emission rates
5-21should be as resolved as themodel output. For example, ifthemodel predicts a24-hour average ambient airconcentration, emissions should be input that reflect conditions over that 24-hour period for best results. In cases where maximum concentrations need tobe 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 togrid squares and line segments, respectively, having arbitrarydimensions. The scale ofthecoordinate 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 maximum degree of spatial resolution is limited by the units and number of significant figures built intothemodel's software.

-- Size fractions

- Most applicable dispersion models consider thePM10 fraction as a single lump sum entity requiring no particle size resolution below 10micrometers. Several models have thecapability totreat various fractions differentlywithin the PMIO fraction.

-- Chemical composition

- Oispersion models rarely require chemical composition data, although many receptor models do.
Tables 5-3 through 5-7 list various dispersion models thatmay be used for simulating

### PMIO levels, along with specific source data requirements ofeach
5-22model. References 2through 7present adetailed description of each of thesemodels. The reader should consult these user's manuals as well as
someone with modeling experience before developing an inventory as input to any of these models. The reader will note from the information inTables 5-3 through 5-7 that dispersion models vary considerably in their source data requirements. As was mentioned earlier, it is not the intent of this section to reiterate guidance on the fundamentals of compiling inventories. Rather, this**sec~** tion's primary purpose is tohighlight special considerations that may have to be made when developing aPMIO 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 obtainingdifferent levels oftemporal and spatial resolution, including techniques for assigning area
source emissions to grid cells. Information on dispersion models used for PMIO applications is given in section 4.0of this document.

5-23TABLE 5-3 INDUSTRIAL SOURCE COMPLEX MODEL (ISC)2**~** - Dispersion Application

- Estimating local impact around complex industrial sources in rural or urban areas. Stack, area, and volume sources within the 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 conveyor belts 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 ISCST maybe run for each hour of a 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 exhaust data

- Physical stack height, inner diameter and elevation above sea level. gas exit temperature, and velocity. Ifthe stack is adjacent to abuilding and aerodynamiC wake effects are tobe considered, the length, width, and height ofthe building must be known. Nonpoint source**confi~urations** - The horizontal dimension, elevation above sea level and efective 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 avera'ging 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 of source data

- Source elevation above mean sea level and source locations with respect to auser-specified origin are required for all sources. Universal Transverse Mercator coordinates may be used todefine source locations. Aplant layout drawn to scale is required to obtain coordinates and building dimensions tothe nearest meter. Particle size resolution

- Required if settling and deposition areto be considered. Deposition is generally only important for particles greaterthan 20 micrometers in diameter. The PMIO fraction could thus be treated as a lump sumwithout any need forparticle sizedata although ISC allows input ofup to 11 size fractions. Chemical composition data

- None required. 5-24TABLE 5-4 CLIMATOLOGICAL DISPERSION MODEL (CDM 2.0)3,4**~** - Dispersion. Application

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

- Long term (seasonal/annual) Inventory area - All sources within theboundaries ofthe grid system must be inventoried. Stack and exhaust data

- PhySical stack he1ght and insidediameter, exhaust gas exit velocity, and temperature are needed to calculateplume rise. Nonpoint source configuration

- Area source emissions are allocated to square grid cells. The coordinates ofthe southwest corner and thewidth 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 of average daytime and nighttime emission rates to the24-hour average. Spatial resolution of source data

- The scale ofthe coordinate system for locating sources is completely arbitrary. Area source grid squares can be defined arbitrarily small; however, since the programwill only handle 2500 grid squares, atradeoff exists between area source resolution and model coverage. Since COM 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 of varying sizes are allowed, but their side length must be an integer multiple ofa common side length. 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 11fe, may be entered into COM 2.0, but should not be conSidered in PMIO SIP's.

5-25**~** - Dispersion TABLE 5-5 RAMS
Application

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

- The RAM is a sequential model designed to process hourly inputs. The RAM calculates concentrations for I, 2, 3, 4, 6, 8, 12 and 24 hourly averages. The RAM may be run for each hour of ayear 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 allocated to square grid cells. The coordinates ofthe southwest corner and the side length of each grid are required. Height of emissions is also needed, if applicable. 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 input tomodel

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

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

- No particle size resolution below 10microns is required. The PMI0 fraction can be treated as a single lump sum. Ch@mical composition data" None required. Other

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

5-26TABLE 5-6 SINGLE SOURCE (CRSTER) MODEL6**~** - Dispersion. Application

- Estimating local impact of a single stack or up to 19,sources

, considered to be emitting at a single point in rural or urban areas where terrain is below physical stack height.**Short/lon~-term** - The basic estimate is for a I-hour period; multiples up to 2 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 tobe emitted at a common point. Stack and exhaust data

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

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

- The model 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 tomodel

- grams/second. Spatial resolution of source data

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

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

- None required. Other

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

5-27**~** - Dispersion. TABLE 5-7 MULTIPLE POINT SOURCE MODEL 

### WITHTERRAIN ADJUSTMENT (MPTER)7

Application

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

- Short-term (1-24 hours) and annual calculations can be made. Inventor! area - That area including all ofthe pOint sources (up to250) tha 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 tocalculate plume rise.**N~npoint** source configuration

- MPTER does not handle nonpo;nt sources. Temporal resolution ofmodel output

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

- 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 PMI0 fraction can be treated as a single lump sum. Chemical composition data

- None required. Other

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

5-285.4 Inventory Data Needed in Receptor Models The considerations in sections 5.2 and 5.3 apply primarilytoPM}O inventories developed for use in dispersion models. Different source and emissions data are required in receptor models. As discussed in section 4.0, receptor models use techniques to estimatethe contributions of emission
sources based on specific characteristics of particulatematter measured at various receptor sites. Receptor models should be used together with rollback procedures
or dispersion models todefine source/receptor relationships. Because ofthe nature of receptor models, emission factors and mass emissicn estimates are not needed as input as they are for dispersion models. Nor, generally, aredetailed source data required such as activity levels and control techniques.* The only source emission data that are needed for direct use in receptor models involve similarkinds ofmorphological or chemical characterizations of emissions 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 compositions are similar (e.g., motor vehicles, oil fired boilers, etc.). The major type of receptor model utilizes various chemical methods.16,17 In general, chemical methods identify source contributions by comparing the relative amounts and temporal variabilityof various chemical species at ambient sampling siteswith source chemical patterns (i.e., "fingerprints").

*Note that while detailed source and emissions data on individual facilities are not required to run receptor models, these kinds ofdata are needed in interpretingthe results and in defining specific source/receptor relationships.
5-29Chemical methods are comprised ofmass balancetechniques, which Identify the most probable combinations of sources to explain the chemical pattern on a single filter, and multi sample methods (including factor analysis), which Identify the most probable linearcombination of sources toexplain either thetime or spatial variability in ambient chemical patterns. Microscopy represents another Important type of receptor method typically used toconfirm the results obtained from chemical methods. Optical microscopy relies on ldentlfication ofparticles collected at the receptor siteby their size, shape, color, surface properties and birefringence (an optical property). Scanning electronmicroscopy, commonly automated by computer and supplemented with x-ray fluorescence analysis, relies on apartlcle-by-particleanalysis of particle size, shape, and elemental composition. The use of such particle identificationtechniques requires aknowledge ofthephysical characteristices (e.g. shape, size, brightness, color) ofparticles from various source categories
or access to references that catalog particle sizecharacteristics from various
sources.16,18,19 Since nearly all receptormodels require aknowledge of the chemistry
ormorphology of source emissions, the development ofa collection of emission chemistry data or particle reference samples (or both) is often required. Such data may be compiled from the literature (e.g., previous receptor model applications); however, experience has demonstrated the value ofdeveloping locale-specific emission characterizations, especially for more important
source categories. The EPA has compiled a libraryofchemical and elemental composition data that will provide a common basis for receptor model source characterizations.20 An example ofthetypes ofdata in this library is shown In Table 5-8.
5-30TABLE 5- 8 .' EXAt'PLE OF DATA 

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5-3}These data are most useful in the chemical methods such as themass balance model and factor analysis described in Appendix Aof this guideline. For
more information on this particulatematter source composition library, contact the U.S. Environmental Protection Agency, Air Management Technology Branch, ND-15, Research TrianglePark, 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 receptor model. 5.5 Condensable ParticulateMatter Condensable particulatematter (or condensed particulatematter, as it is synonymously described) can be broadly defined as material that is not particulatematter at stacK conditions but which condenses and/or reacts (upon cooling and dilution in the ambient air) to form particulatematter immediately after discharge from the stacK. Condensable particulatematter is of potential importance because it usually is quite fine and thus falls primarily within thePMI0 fraction.21 Sources suspected of emitting 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-42 particulate matter factors for certain categories that are currently based on Method 5 include
some condensable particles. Some condensable particulatematterwill alsobe collected by the testing procedures outlined in Appendix 

### Cofthis guideline. 5.6 Secondary ParticulateMatter Secondary particulatematter can be broadly defined as particles that form through chemical reactions in the ambient afrwell after dilution and condensation have occurred. An example ofthis phenomenon is the formation of
5-32TABLE 5-9
POTENTIAL SOURCES OF CONDENSABLE PARTICULATE**MATTER~l** Stationary Sources AlfaHa Dryers Anode Baking Furnaces Asphalt Plants Asphalt Roofing felt saturating asphalt blowing Boilers/OtherCombustion bagasse coal lignite oi1 wood/bark Charcoa1Kflns Chemical Production boric acid phosphoric acid potassium sti11 zinc sulfate Citrus Peel Dryers COKe Plants Corn Processing wet milling syrup manufacturing Elemental Phosphorus electric arc furnace Expanded Vinyl FerroalloyMills FertilizerPlants ammonium nitrate diammonium phosphate

5-33 Fiberboard dryer press Glass Fiber and Mineral Wool curing ovens blow chambers Glass Plants Grain Dryers Iron and Steel Mills sinter plant 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 blow tank/hot water accumulator Lime Kiln Manure Dryers Mineral Products gypsum clay dryers feldspar dryers clay k1lns Municipal IncineratorsTABLE 5-9 (CONTINUED)

Petroleum Refineries FCC catalytic regenerator heaters petro1eum coke Portland Cement Plants kiln finish mill Primary Nonferrous Smelters Cu converter Cu electric furnace Cu fluid bed roaster Cu sinter line Pb sinter line Pb blast furnace Mo roaster Zn ore briquet dryer Zn sweat k.11n Zn fume k.11n Residential Heating oil wood Secondary Metal Smelters Al scrap furnace Al dross furnace brass and bronze furnaces copper furnace Pb furnaces Pb02 mi11s Pb gridcasting Pb remelt pot othermetal furnaces Mobile Sources All Mobile Sources

5-34 Sewage Sludge Incinerator Silicon Carbide Furnaces Spray Paint Booths Sulfite Pulp Mill recovery boiler blow tanks Rubber Incineration Rubber Curing Press Textile nylon polymerization melt polymer spinning tenter frame dye beck heat set texturizing latex backing Tire Buffing Operations Wood PrOducts veneer plant dryer resawingsulfateparticles in aplume from the oxidation of sulfurdioxide by one of several atmospheric transformation mechanisms. Generally, secondary particulate matter can be distinguished from condensable particulatematter 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 primarilyto immediate cooling and air dilution whereas secondary transformation requires minutes, hours, or even days. Unlike condensable particulatematter, secondary particulatematter should not be included in the PMIO inventory as ifdirectly emitted as particles. Condensable particulatematter, because it forms so quickly, will likely impact
on any nearby receptor, and thus can be treated as if itwere emitted as particulate matter. Secondary particle formation, conversely, is a function of time 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 airquality 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 existingparticulatematter inventory can be used as afoundation or starting point. Since many areas have compiled annual, countywide inventories of particulate matter (e.g., for previous SIPs or for submission intoEPA'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 needed in the PMIO inventory is already available.The utility ofthe existing inventory depends on the particular air quality model employed. In some instances, aconventional inventory of annual, countywide particulatematter emissions will provide most ofthe needed information. Actually, theterms "annual" and "countywide" do not give a complete picture of the 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 arethe major stacK and exhaust gas parameters commonly needed by most applicabledispersion models. (Note: Some models also require exit gas velocities, but flow rates can be easily converted toexit velocities by dividing by the cross sectional area of the stack.) Moreover, the location of each stack is commonly specifiedtothe 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 particulatematter 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) particulatematter inventories can also be useful in some PM10 modeling applications; however. additional data manipulation is generally needed to develop the requisitetemporal and spatial resolution. First, most existing
area source records do not include operating rate data comparable tothose included in the point source record. Hence, to develop emissions estimates for a period of time 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 inChapter 6of Reference 18 along with recommendations for employing them. Probably the most difficult aspect of using conventional area source data in a PM10 inventory is that for certain applications, the emission totals must be allocated from the county level tothe grid cell level. This is generally done by assuming that the distribution of a given area source activity behaves similarly to some surrogate indicator (e.g., population) whose distribution is known at the subcounty level. For example, 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 6of Reference 18. Referring tothe 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 littleuse at all. The 

## rsc, for example, requires input on the dimensions and juxtaposition 5-37of buildings near each stack in ordertoconsiderthe aerodynamic wake effects
on plumes. The ISC also requires finer spatial resolution than is typically available. Receptor models are probably most unique in terms of their input requirements. Generally, the location of sources, stack and exhaust data, and even the rates of emissions are not required by receptor models. As an example, the only inventory data needed by the Mass Balance {MBl 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 10 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 particulatematter emissions have to be converted toPMIO emissions. As was discussed in section 5.2, the easiest way to accomplish this ,is tomultiply existing particulatematter estimates by appropriate PMIO fractional multipliers. for maximum accuracy, PMIO fractions should be applied to individual sources rather than aggregated
source categories and should account for theexistence of any emission controls. Alternatively, instead of applying PMIO fractions, PMIO emission totals can be calculated by applying the appropriate PMIO emission factor tothe existing activity level for each source. 5.8 Data Handling An important consideration in any inventory compilation effort isthe potential need to develop special data handling software. In anumber of models, data handling can be readily accomplished manually because the input

5-38requirements are meager. For certain models, however, computerized data handling capabilitiesmay be desirable to compile and properly format the large quantity of inventory data needed to drive themodels. As a general rule, the amount of inventory data that must be handled is directly proportional tothe number of sourceS being modeled. Hence, for the 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 if only a few sources are considered. The primary applications where computerized data handling will prove essential are (-I) when dealingwith 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 existingpar· ticulatematter inventory is being converted to a PMIO inventory, necessitating
a number of preprocessing computations. Computerized data handling is desirable when preparing PMIO inventories for input toCDM 2.0, RAM and MB, as well as MPTER if large numbers of
sources are considered. The CDM 2.0 and RAM aremost likely to be applied to large, urban scale applications involving many point and area sources and large grid systems. Moreover, existing particulatematterinventories are most likely to be utilized as input for these two models, necessitating some preprocessingto convert total particulatematter to PMIO. Hence, if-either CDM 2.0 orRAM is to be employed for PMI0 modeling, and if an existing annual, countywide inventory will be used to provide the necessary input, sufficient

resources should be reserved for developing and running an auxiliary data handling system that will facilitate timely processing ofthe input data. Data handling is frequently an overlooked aspect of inventory compilation and
5·39airquality simulation modeling that can consume a great deal of resources. Thus, the inventory specialist, modeler, and programmer should all be involved in theplanning ofthis kind ofPMIO inventory effort.

5-40References

1.

2.

3. '

4.

5.

6.

7. 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). Environmental Protection Agency.**=:;;:.:;;.r..:..;;r;~mm...;:crlm;;::-tc::.::.;;.t-,,::..:-::..r:..::.:...::.=** Model, Second Edition, Volumes 1 Environmental Protection Agency, June 1986. A. D. Busse and J. R. Zimmerman, User's Guide for the climatololical Dispersion Model, EPA-R4-73-024, U.S. Environmental Protectiongency, Research Triangle Park, North Carolina, December 1973. Irwin, J. S., T. Chico and J. Catalano, COM 2.0

- Climatological Dispersion Model, EPA/600/S-029, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, November 1985. D. B. Turner and J. H. Novak, User's Guide for RAM, Volume I Al orithm Description and Use, EPA-600/8-

- a, •• nVlronmenta ro ect on Agency, Research Triangle Park, North Caorolina, November 1978. Users Manual for Sin le-Source {CRSTER Model, EPA-450/2-77-013, U.S. Envlronmental Protection Agency, esearc Tr angle Park, North Carolina, July 1977. T. E. Pierce and D. B. Turner, Users guide for MPTER, AMultiple Point §aussian Dispersion Algorithmwith Optional TerrainAdjustment, EPA-600/8-80-016, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, April 1980. S. Procedures for Emission**Invento~** pregaration, Volume I: Emission Inventor Fundamentals, EPA-450/ -81- 268, U.S. Environmental

-

9.

10.

11. Protect on Agency, Research Triangle Park, North Carolina, September 1981. Procedures for Emission Inventory Preparatlon

## l Volume II: Point Sources, EPA-450/4-81-026b, U.S. Environmental Protect on Agency, Research TrianglePark, North Carolina, September 1981. Procedures for Emission Inventory Preparation, Volume III: Area Sources EPA-450/4-81-026c, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, September 1981. Procedures for Emission Inventor Pre aration, Volume IV: Mobile Sources, EPA- 4- 0-0 6d, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, September 1981. 5-41Procedures for Emission Inventor Pre aration, Volume V:**BibliO~raphY.** P

- - - e, •• nVlronmenta Protectlon Agency, Researc 12. Triangle Park, North carolina, September 1981. 13. W. H. Lamason and T. F. Lahre, Procedures for the**pre~aration** of Emission Inventories for VolatileOr anic

### Com oundsolume 

### I, Second

14.

15.

16.

17.

18.

19.

20.



## n. ltlon, ,U. • Envlronmenta Protectlon Agency, Research Triangle Park, NC, September 1980.

Procedures for the Preparation ofEmission Inventories for Volatile Organic Compounds Volume 11: Emission Inventory Reguirements for Photochemical Air ualit Simulation Models, EPA-45 /4-19-018, 

## u.s. Environmenta Protect on Agency, Research riangle Park, NC, September 1979. J. E. Core, Receptor Model Technical Series, Volume 1, Overview of Recegtor Model Application toParticulate Source Apportionment, EPA- 50/4-81-016a, 

## u.s. Environmental Protection Agency, Research Triangle Park, North carolina, July 1981. J. E. Core, Rece

# 7 torModel Technical Series, Volume lIt Chemical Mass Balance, EPA-450 4-81-016b, U.S. Environmental ProtectlonAgency, 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 toU.S. Environmental Protection Agency. Research Triangle Park, North Carolina, September 1982. 

### W. C. McCrone and J. 

### G. Delley. The ParticleAtlas, Volume 

### I. Principles and Techniques, 2nd Edition, Ann Arbor science publishers Inc•• Ann Arbor, Michigan, 1973. Receptor Model Source**com~osition Librar~.**  EPA 450/4-85-002, U.S. Environmental Protectiongency, Researc Triangle Park, North Carolina, November 1984.

22. "Method 5

- Determination ofParticulateEmissions FromStationary Sources," 40CFR 60. AppendixA.

5·426.0 DEVELOPMENT 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 ofPMI0 NAAQS. Conceptually, this involves determining the PMI0 design concentration for a particular siteor 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, PMIO, although itmay be applied to particulate matter in general as a surrogatewhere PMIO data bases are inadequate. In all cases where PMIO ambient 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 thePMIO 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 PMIOconcentration be less than or equal to the level ofthe NAAQS. Attainment ofthe24-hour NAAQS requires thattheexpected number ofexceedances 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 themost 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. 6-16.2 Data Base Requirements The design concentrations for attainment ofthe 24-hour PMIO NAAQS
can be based on ambient measurements of PMIO, or model estimates of ambient concentrations at individual sites during 1ormoreyears of stable emissions conditions. Ideally. (1) modeling estimates using 5years of National Weather Servicemeteorological data (or at least 1year of on-site data), or (2) 3years of representative airqualitymeasurements should be considered in determining 24-hour design concentrations. If
more years ofdata with relatively unchanging emissions are available, they also may be considered in calculatingdesign concentrations. The
moreyears ofdata available, themore stable the estimate of PMIO design concentrations. The preferred approach for estimating adesign value is through the
use of an applicabledispersion model corroborated by receptor models, any available TSP data (using Appendix B). and any available PMIO data. If there is no applicable dispersion model and at least 1completeyearof PM10 data. are available,*the PM10 data would be used to estimate the design value; ifthe PM10 data are insufficient,*the design value would be based
on Appendix Bofthis guideline and corroborated by the available PMIO data. 6.3 Methodologies for Determining 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 PMIO concentrations.
*These data should meet firstyear sampling requirements found inCFR**58~13.** 6-2There 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 or modeled PM10 concentrations.**Th~** include: (1) atable look-up procedure; (2) fitting a statistical 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 how the 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 a site. Availability of the highest concentrations makes it possible to construct a simple table look-up procedure to determine the design concentration. All portions of theyear should be adequately reflected in the measurements. To use the tabular approach for the 24-hour PM10 standard, it is necessary toknow the total number of 24-hour PMIO concentrations at the site and then select the design value from among the highest concentrations. The number of available 24-hour concentrations determines which ofthe highest concentrations is chosen as the design concentration. For example, if a comprehensive monitoring program provides 1,095 24-hour concentration measurements (or 3full years of data) at a site, then the ranks of the lower and upper bounds obtained from Table 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-h1ghest concentration. Therefore, in this example, it suffices toknow only the four h1ghest values during the time period. With multiple monitoring Sites, the highest PM10 concentrations at each sitewould have tobe considered and adesign concentration established for each location. For exalllPle, the "controlling" design concentration for an-area with
seven Sites, each having 1,095 values, would be the highest ofthe
seven fourth-highest values. For routinemodel applications with 5full years of 24-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 atabulartechnique for determining what point on the empirical frequency distributioncorresponds to a frequency of 1/365. By construction, the table look-up procedure tends toprovide a design concentration slightly lowerthan would be derived using a continuous curve representing atheoretical frequency distribution for PM10 values. For example, use ofthe table-derived estimatemight be modified by interpolation between the

6-4

### TABLE 6-1
TABULAR ESTIMATION OF 

### PMIO DESIGN CONCENTRATIONS

Number ofDaily Values

<347 348

- 695 696

- 1042 I,'j'" 1043

- 

### J.99f{ 

### 13jl**-113~** 

## 1l3<t-**;~b** 

## 1»)37**~L**

### \.f:'.>5 Rank 0f 

### Upper 

### Bound

1 2 3 

### 4-

6-5

### Rank ofLower Bound Data Point Used for Design Concentration
1 Highest Value 2 Second Highest Va1 ue 3 Third Highest Value 4 Fourth Highest Value 

## Sr,Ft..**--~**third- and fourth-highest values. However, this adds an additional element of calculat10n. Nevertheless, if amore precise des1gn concentration should be desirable, the use of interpolation formulas (Section 6.3.2)
or more simple graphical procedures (Section 6.3.3) may be necessary. For the cases which are limited to lessthan acompleteyear ofdata, (1.e., 365 observations) the maximum concentration must generally be used
as atentativedesign value. In this case it should be recognized that the maximum concentration generally represents a lower-bound est1mate 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 tentativedesign 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 Fittingone statistical distribution to several years ofdata With several years.of fa1rly complete PM10 24-hour airquality measurements or model estimates, a statistical distribution could be selected that "fits" the data. Information on fitting statistica1dlstrlbutions
can be found on pages 18-20 in the ozone guideline.1,2,3 Becausewe are interested in peak concentrations, emphasis would be placed on the top

*An extrapolated value must be used, however, instead ofthemaximum observed concentration in ordertoevaluate the possibilitythat the24-hour standard is controlling, using procedures described 1n Sections 6.3.2

- 6.3.4. 6-65to 10 percent ofthe concentrations. This approach, at least conceptually, provides amore stableestimate of the 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 a frequency 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, therewill be concentration dati 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 airquality measurements, itmay 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 themaximum observed
or estimated (via airqualitymodel) value. When this is the case, caution should be exercised in the use ofthe extrapolated value. 6.3.3 Using the emgirlcal frequency distribution of several years ofdata (graphical estimation) With sufficient data itmay not be necessary to fit a statistical distribution, as discussed in Section 6.3.2. The concentration value corresponding to a frequency of 1/365may be read directly off agraph ofthe empirical distribution and used as the design concentration. The description ofthis approach is given on pages 25 and 34 in the ozone guideline. 6-76.3.4 Conditional probability approach While the prev10us methods required group1ng concentrat10n data from several years, this approach allows Ind1v1dua1 years ofdata tobe treated separately. This 1s done by fltt1nga separate stat1st1cal distribution to each year ofdata and assuming a given probabi11ty that eachyear w1ll reoccur. This approach is of interest when different 1mportance should be placed on the indiv1dual years in terms ofmeteorological conditions or sampling completeness. For this reason, this approach may also be of interest for the annual standard. The conditional probability approach is somewhat theoretical in nature, but is adequately d1scussed in the ozone guideline. 6.4 Determining Emission Limits 6.4.1 General Once PMIO design concentrations have been established through the use of airqualitymeasurements or model estimates, a proportioning method can be used at each siteto estimate control requirements for SIP development. This proportioning method differs from simple rollback in that the source contributions are determined from receptor or dispersion 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 a design concentration is greaterthan theNAAQS (TR is positive), a reduction in PMI0 emissions is required. This might be accomplished by

6-8reducing the contribution of a single source or itmay require reduction in several individual sources or source categories so that TR (u9/m3) : SISRj (ug/m3) where ISRi is the Individual Source Reduction desired from
a source or source category i. These ISR's are generally selected based on many considerations, including the technical feasibility of achieving 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 ) where AC is the ambient concentration due tothe individual
source (1) or source category as determined through 

### amodel. The SIP must demonstrate that the control requirements will be adequate to meet the NAAQS under other situationswhere 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 over the time period studied must be evaluated. Ifthey are inappropriate for theSIP 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 sitewith the "controlling" annual design concentration for the
area is 100 ug/m3• Then thetotal required reduction forthe area would be: TR

## =100

- 75

## =25 ug/m3•

6-9Next, for the site ofthemost restrictive design concentration, consider the following source contributions estimated by a receptor or a dispersion model:
Source Stee1mi11 roads Steel mill coke ovens Coal storage pile Urban paved roads Cement plant Background Source Category Contributions to Ambient Conc. (AC) 15 ug/m3 10 ug/m3

5ug/m3 20 ug/m3 10 u9/m3 40 ug/m3 100 u9/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 mill roads Steel mill coke ovens Cement plant Coal storage pile Individual Source Reduction (ISR) 8 ug/m3 5ug/m3
TR ; 8 ug/m3 

### 4ug/m

### 3 25 u9/ m3
Since the TR sums to 25 ug/m3, it is assumed that the individual source reductions, ifimplemented, would reduce the annual PM10 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: ISRcemen:t 

### =!! =80%; %REcement 

### = ACcement 10
so that control requirements would be 80 percent for the cement plant. It assumed thatthe selected emission reductions will allow the NAAQS to 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 whether the anticipated reduction impacts on other sites above 6-10 isthe standard will result in attainment at those sites. Ifthis is not found to be true, which ismore likely in multisource situations than areas affected by a small 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 of the 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 attributedto the sources being investigated, This estimate should be based on actual observations in nonurban areas near the boundary of the
area or on model estimates of the actual impact ofthe sources not under investigation, 6.4.3**Cons!.~ratio..!l~_Ior 24-hourA.~.e_ra~e_s_**  An approach similar to 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-termaverages (e.g., 24-hour), it is likelythat 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-term24-hour NAAQS will be met at all sites is likelyto require an iterative and perhaps lengthy process. This is especiallytrue ofmultisource areas. An alternative is to develop control strategies with emission limits specified for each source category. perhaps based on a preliminary 6-11application of the modified rollback model. The strategies would then be tested with a dispersion model to determine if the 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 a means by which factors such as the best technological, most cost effective, and most enforceable set of emission controls can be considered. Such a procedure might also be employed for annual concentrations, especially where many receptor sites are of concern.

6-12Reference 1. Guideline for the Interpretation of Ozone Air Quality Standards, EPA-450/4-79-003, U.S. Environmental Protection Agency, Research Triangle Park, NC, January 1979. 2. Curran, T.C. and W.M. Cox, "Data analysis Procedures for the Ozone NMQS Statistical Format," J. Air Pollution Control Association, June 1980. 3. Curran, T.C., "Data Screening for Large Air Quality Data Sets," paper no. 84-52.3, presented at the 27th Annual Meeting of the Air Pollution Control Association, San Francisco, CA, 1984.

6-137.0 SIP REQUIREMENTS AND DATA REPORTING
7.1 Introduction This section describes the requirements for SIP revisions in the Act, EPA's ?MI0 SIP development policy, the interface between PMI0 and TSP control strategies, and summarizes how ?MI0 emissions data should be reported toEPA's data bank. To develop an acceptable control strategy
as required for a PMI0 SIP, aStatecan utilizethe information summarized in section 4.0ofthis document for carrying out appropriate modeling, the information in section 5.0 todevelop an emissions inventory for model inputs, and the information in section 6.0 for developing control strategies. 7.2 Clean Air Act Requirements 7.2.1 Time limits, SIP reguirements Section 110(a) oftheAct requires every Stateto submit toEPA a SIP which provides for implementation, maintenance, and enforcement ofeach NAAQS in each airquality control region within the State. The SIP is required to be submitted within 9months ofthe promulgation of a new NAAQS or the revision ofexistingNAAQS. The SIP must provide for attaining
a primary NAAQS as expeditiously as practicable, but no laterthan 3years after SIP approval by EPA. Up to an additional 2years could be provided for attainment ifthe requirements in section 110(e) of theAct are met. These section 110 requirements are applicabletoPMIO NAAQS. The PMI0 SIP's therefore are due within 9months of promulgation of PM10 NAAQS and are tocover all areas ofthe State as qualified in section 2.4 ofthis document.
7-17.2.2 Regulations in 40 CFR Part 51 The regulations promulgated in Part 51 address the preparation, adoption, and submittal to EPA of SIP's for implementing the NAAQS. These regulations reflect the requirements set forth in theAct. Part 51 has recently been restructured to contain more subparts and sections to make it more organized and readable. That restructuring effort consisted only of deleting obsolete material and reorganizing the remaining material and
no substantive changes were made tothe regulatory requirements at that time. The Part 51 regulations still apply to PMI0. 7.3 PM)O 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 9months of the date the 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 PM]O SIP requirements States are required to submit toEPA a revision to the particulate matter SIP within 9months of promulgation of PM10 standards for Group I

areas. The SIP revision must provide for attaining PM10 NAAQS as expeditiously as practicable, but no later than 3years afterthe SIP is 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) ofthe Act are met. The submittal must include amodeled demonstration that provides for attainment and maintenance ofthe PMI0
7-2standards. Portions ofthe SIP pertaining toprevention of significant deterioration/new source review section and air monitoring will also require revision. The SIP must contain control measures sufficient todemonstrate attainment. Provisions for studies or demonstration projects ofnontraditional particulatematter sources and measures necessary for attainment at a laterdate will not be acceptable as they were in the past for TSP standards. 7.3.2 Sroup II PM]O SIP requirements States are also required to submit aSIP within gmonths of promulgation of PMI0 standards for Group II areas. The Statesmay submit aSIP for these areas as required for Group I areas, ifthey wish. Otherwise, States must submit aSIPwhich contains enforceable commitments totake 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 tothe appropriate Regional Office within 45 days ofeach exceedance. (3) When an appropriate number ofverifiable 24-hour NAAQS exceedances become available (see section 

### 2.0) orwhen an annual arithmetic
mean above the level ofthe annual PHIO NAAQS becomes available, acknowledge that anonattainment problem exists and immediately notify the appropriateRegional Office. (4) Within 30 days ofthe notification referred to in (3) above, or by the date 37 months afterpromulgation, whichever comes first, determine 7-3whether the existing SIP will assure timely attainment and maintenance ofthe PMI0 standards, and immediately notify the appropriate Regional Office. (5) Within 6months ofthe notification referred to in (4) above (if necessary), adopt and submit toEPA a PMIO 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 ofPMIO data to be collected if aNAAQS is not violated sooner. At**~he** end of that time, the available PMIO data must be examined to determine if attainment can be demonstrated in accordance with Appendix

## K of40 CFR Part 50 or the Guideline on Exceptions to Data Requirements for Determining Attainment ofParticulate Matter Standards inthe absence of adequate PMIO 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
7-4regulations are adequate to 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 receptor modeling 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 PM]O SIP requirements For Group 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 of theirSIP's within 9months. 7.4 SIP Content Amost**impo~ant** first step in developing a control strategy will be to inventory particulatematter sources and include all possiblecontributors. Receptormodels applied toTSP samples, or PMIO samples if available. 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 datewill 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 toemploy, 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 StrategyTransition Particulatematter emissions from most point sources and many area
sources have been controlled as a result of SIP's to implement the former NAAQS for TSP. The regulatory requirements of an existingTSP SIP must remain in effect in accordance with section 110(i) of the Act, until a PMIO SIP is approved by EPA. Therefore, regulations in the existing SIP cannot be relaxed without ademonstration that the revision will not interferewith attainment ormaintenance of the PMIO NAAQS. The existing regulations must continue to be enforced by Federal and State agencies during the period oftransition from aTSP SIP to aPMI0 SIP. States will no doubt want tominimize any unnecessary disruption caused by going from these control programs to PMI0 programs. Therefore, tothe extent pOSSible, States should utilizethe existing control strategy in aTSP SIP as the basis for a PMI0 program sufficient to attain and maintain the PMIO NAAQS. The EPA expects States to build on the current control strategies towhateverdegree necessary to demonstrate attainment and maintenance of PMIO NAAQS. This may include adopting the current control strategy in full, if it can be shown to be sufficient for PMIO purposes, or adopting it in part.

7-67.6 Current Emission Regulations Particulatematter SIP's contain emission regulations expressed 1n various terms including the following:

-- Mass of particulatematter per heat input intothe process. Example: 0.30 pounds per million British thermal unit.

-- Mass ofparticulatematter 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. 0.04pounds per ton of exhaust. 0.02 grains per actual cubic foot per minute.

-- Opacity of exhaust. Example: 20 opacity.

-- Mass of particulatematter per time period.


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

-- Mass ofparticulatematter per area exposed towind erosion. Example: 5.80 pounds per acre.

-- Mass of particulatematter per mass of product input or output. Examples: 0.02 pounds per ton coke. 0.30 pounds per ton kiln feed. 52.00 pounds per metric ton.

7-7-- Mass of particulatematter per unit of power generated. Example: 0.07 grains per horsepower hour. Percent control ofemissions. Example: 98.00 percent control ofparticulatematter emitted due to overspray ofcoating material. Regulations ofthesetypes 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 testmethods may not be identical tothe material measured by ahigh volume sampler. Despite this discrepancy, EPA has developed stacK sampling trains now described in Methods 5and 17 of Appendix Bto40 CFR Part 60 which represent the present state-of-the-art in techniques for collectingparticulatematter. Many emission limits for particulatematter therefore have been established in terms ofthese measurement methods. In this document, the term "particulatematter emissions" is used to denote material measured by Methods 5or 17 or by acomparable measurement method approved by EPA in aState implementation plan. 7.7 Surrogate Emission Regulations This subsection discusses settingor retaining emission limits in terms ofparticulatematter emissions forthe purpose of controlling

### PM10 emissions as part of a PM10 control strategy. 7.7.1 Retaining existing emission limits. If aState finds that its existingparticulatematter emission limits in its TSP SIP are sufficient toprevent PM10 NAAQS violations, there is no need to go through aresource intensive process ofmodifying the emission limits toexpress them in terms

7-8of PMIO.* Modeling can be used in two ways to determine that aparticulate matter emission limit or combination of limits for several sources is acceptable for PMIO control. First, particulatematter emissions can be modeled to determine the impact on ambient TSP levels. If the 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 PMIO emissions. Second, modeling can be done with particulatematter emission rates converted to PMIO emission rates. If it can be assumed that for any source the PMIO fraction of particulatematter does not vary appreciably during normal operation of the source, then a certain particulate matter emission rate will represent
a certain emission rateof 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 theTSP SIP can be converted tothe corresponding emission rate for PMIO through an emission factor (see section 5.0 of this document). If a State uses this corresponding PMIO emission rate as input for modeling and finds that the modeling results indicate no violation ofthePMIO NAAQS, then the particulatematter emission rate will be an acceptable emission limit for the PMIO SIP. In eitherofthe above cases, Statesmay 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., ratherthan an emission limit. 7-9particulatematter emission measurement methods. The PMI0 control strategy analysis should includethe modeling results and confirmation that the particulatematter emission limits were found tobe acceptable surrogate emission limits for PMIO. 7.7.2 Setting new emission limits. In the event that aState finds that an emission rate lower than the current particulatematter emission limit in theTSP SIP is necessary for PMIO NAAQS attainment and maintenance, the PMI0 rateWhich is found tobe necessary through modeling can be converted (through the use of an emission factor) toan equivalent particulate matter emission rate and expressed as such in the PMIO SIP. The emission limit expressed in terms of particulatematter emissions will be acceptable provided the modeling results are valid and the ratio ofPMIO toparticulate matter emissions is not likelyto drastically change. If it is found that substantially all of a source's emissions arePMIO. then limits for that source could be expressed as either particulatematter emission limits or PMIO emission limits, and compliance testingcould be performed with eitherMethod 5or amethod thatmeasures only PMI0 emiSSions. For such an instance, the SIP would have to indicatethatthe source's emissions are essentially all PMIO. In any case where an emission regulation is in terms other than PMIO, such as particulatematter emissions or opacity. PMIO levels must not increasewhilethe 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.S PMJo-Specific Emission Regulations Emissions limits developed tomeet the NAAQS for TSPtook many forms
as illustrated in section 7.6. Compliance with these limitswas determined 7-10 

### ,by reference method emission tests or certified visibleemission observers. The emission limits and compliance methods had no direct link toTSP as measured by ambient monitors, however, acorrelation was made through the
use of dispersion and receptor models. Similarly, itwould be difficult, and perhaps technically infeasible. to develop amethod for measuring precisely that exhausted material that would contributeto ambient levels of particulate matter measured by an ambient PM10 sampler. Particles emitted from stacks are subjectto agglomeration and separation after they reach the ambient air, both ofwhich affect their inclusion or exclusion 1n ambient PM10. Additional complications arise due tothe presence in emissions of condensables and various precursors of secondarily formed particulatematter. Regulations specifying emission limits as part of PM10 control strategies, therefore, cannot be directed toward exactly that exhausted material that contributes to ambient PMI0. Rather, PMI0 emission limits must be directed toward reducing the amount of PMI0 emitted from a source as measured by an approved compliance test method. Suggested procedures for measuring PMI0 emitted through stacks
are discussed inAppendix 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 toEPA 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 indicatorfor both the primary and secondary,standards for particulatematter is being changed from TSP to PMIO. reporting ofparticulatematter as required in section 51.322 will 7-11simply be replaced by reporting of PMIO emissions. The present requirement to report particulatematter emissions will end with the reporting of calendaryear 1987 emissions. The reporting requirement for PMIO emissions will begin with the reporting of calendaryear 1988 emissions. TheEPA recognizes that time and resources are needed for Statestodevelop the capability to report PM10 emissions data for EPA to develop the capability toprocess, store, and retrievethe data. States are required to begin the annual reporting.of PHlO emissions data with calendaryear 1988 data, which are to be sent to EPA Regional Offices by mid-1989. The EPA plans to provide States with the needed technical and procedural information in time for States tomeet that requirement. This information includes PHlO-related changes tothe following: AP-42, "NEDS Source Classification Codes and Emission Factor Listing;" the computer program for calculating emissions that is provided routinely to those States which use EPA's Emissions Inventory System (EIS); and provided to otherStates 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 formany years toprocess, store, and retrievethe emissions data provided by States. These systems will be replaced by IBH systems under EPA's Aerometr1c Inventory Retrieval System (AIRS) development program. The current schedule for having AIRS operational on the IBH system is Harch 1989. The EPA will provide the necessary guidance tothe States in time for States to respond to the requirement for reporting calendaryear 1988 PMI0 emissions.

7-127.10**~.Ill.ts_~i_o.n!.**D'il_dl!1.ltlB,u_bP.1_e1?_oltg This section clarifiesthe effect ofPMI0 NAAQS on alternativeemission reduction options (bubbles) that have been previously approved for TSP SIP's. In the initial bubble policy, as published on December 11, 1979 (44 FR 71780),
sources were warned that EPA was considering revising itsparticulate 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, thepolicy indicated that sources which used a bubble approach tomeet SIP emission limitations could be treated no differently from sources which did not. That is, if additional emission reductions are required to avoid violations ofthe ambient air quality standards, the Statemay have to revise emission limits previously approved under the bubble policy. In general, bubbles cannot interferewith aState's efforts to attain and maintain ambient airquality 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!.DustPolicX The EPA will continue to implement its existing fugitive dust policy
as it was applied in rural areas violating theTSP 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 Oust Policy, statesthat "urban areas should receive the highest priority for development ofcomprehensive and reasonable programs to control

7-13fugitive dust." Control programs in rural areas areto "center on the control of large existing man-made fugitive dust sources (i.e., tailings, piles, mining operations, etc.) which in themselves are presently causing violations oftheNAAQS or are sources of a known toxic or hazardous material (e.g., asbestos)." Another aspect ofthe fugitive dust policy is that, "new sources thatwish toconstruct 1n 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 of their emissions plus the emissions from other stationary sources in the vicinityofthe 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 intoGroups I, II, and III,Group I and II areas that qualify as rural fugitive dust areas will be placed in Group III. The Statewill be expected toprovide rationale and justification at the time of area categorization for the claim that a PMIO NAAQS may be violated primarily because ofthe impact of fugitive dust sources that are unreasonable to control. In response to abroad range of comments on the proposal tocontinue this policy, EPA has developed several options for revising the fugitive dust policy for applciation tothe PMIO NAAQS. Public comment on these

7-14options is currently being solicited. If necessary, a rev1sed po11cy will be issued in the future. At that time, the categor1zation of areas initiallyplaced in Group III under the existing policy w1ll be reviewed for compliance with the revised policy.

7-15

### References

1.

2.

3.

4. 

### Identification, Assessment and Control of Fugitive Particulate Emissions, EPA-600/8-86-023

## u.s. Environmental PrOtection Agency, Research Triangle Park, North Carolina, August 1986. 

### Kinsey, J.S•• P. Englehart, and A.L. Jirik, Study of Construction Related Dust Control, Midwest Research Institute and ETA Engineering. Prepared for Minnesota Pollution Control Agency, Roseville, Minnesota. December 1982. 

### Portland Road Dust Demonstration**pro~ect,** 

### Final Report, Seton, Johnson and Odell, Inc., for City of Portlan , Oregon. Department ofPublic Works. July 1983. 

### Denver Demonstration Study, PEDCo Environmental, Inc•• Kansas City. MO. Prepared for ColoradO Division of Air Pollution Control, Denver, Colorado. October 1981. 

### 5. User's Guide: FugitiveDust Control Demonstration Studies. EPA-600/8-84-032, U.S. Environmental Protection Agency, Research Triangle Park, NC, January 1985. 

### 6. Field Evaluation ofWind Screens as aFugitive Oust 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, Acting AssistantAdministrator for Air and Waste Management toRegional Administrators, August 16. 1977.

7-16TYPES 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 PMlO 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 tothese, 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 chemical "fingerprints" or profiles of emissions from several source categories to the'chemical composition ofthe sample. It is referred to inthe literature by various names. including chemical mass balance, chemical element ba1ance. 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 ismost effective when the ambient samples are collected in at least two size fractions (largerthan and smaller than 2-3 micrometers). This method must be validated in accordance with the protocol referenced at the end ofthis Appendix.9

A-l2.0 Factor**~!!.~!~is** Factor analys1s and other multivariate statistical methods, such as multiple linear regression, target transformation factor analysis (TTFA), and cluster analysis, are all variations of the least squares routine used in
mass balance. However, these methods (except TTFA) require no priorassumption ofthe impacting sources and can be useful where thetypes of sources are uncertain. Like the mass balance method, this method ismost 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 of coarse particles. The technique relies on identification of particles by their size, shape, color, surface properties, and birefringence (i.e., an optical property). It 1s generally used on particles larger than 1micrometer. Several analysts have been asked to provide quality assurance oftheiranalyses 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, equippedwith x-ray fluorescence capability to identify the elemental composition, provides a partic1e-by-particle analysis ofparticle size, shape and elemental composition. This elemental composition and computer assisted sizing allows large numbers



### A-2of particles tobe analyzed and. thus. identified by source category in a relatively inexpensive manner. 5.0 Other Receptor Methods The following methods are suggested to corroborate or refineMB, FA, OM,
orASEM analyses. 5.1 X-Ray Diffraction (XRD)

- This method provides direct information
on the crystalline nature ofparticles. This is particularlyuseful for distinguishing among minerals. It requires aheavy loading of coarse particulatematter 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 isthe pollution rose, where only average wind direction is considered. More sophisticated approaches account for the changing wind directionwith time to trace airmass 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 a finely grldded input to dispersion models, and in qualitatively estimating the potential sources of soil and otherminerals to help interpret an observed mix of particulatematter
on an ambient filter•



### •

A-3

### References 1.**~ecfPtor** 

### Model Technical Series, Volume I: Overview Of**Rec~tor** 

### Model**~icat10n** 

### To Part1culate Source**~~rtlCinment. EPA-lP.)~T~** **-OTO~­** 

### U.S. Environmental Protection Agency. Research Triangle Park. NC. July 1981. 2.**Rece~tor** 

### Model Technical Series, Volume II: 

### Chem1cal Mass Balance. EPA· 50/81-016b, U.**S.~nvironmental** 

### ProtectfonAgency, Research Triangle Park. NC, July 1981. 3. Receptor Model Technical Ser1es. Volume III: User's Manual For Species Mass Balance Computer Program. EPA 450/4-83-014. U.S. Environmental Protect1on Agency. Research Triangle Park, NC, July 1983. 4. Receptor Model Technical Series, Volume IV: Technical Considerat1ons In Source Apportionment By Particle Identification. EPA 450/4-83-018. O,S. Environmental Protection Agency. Research Triangle Park, NC. June 1983. 5. Digest ofAmbient Particulate Analysis and Assessment Methods,**EPA-450~3-78-ll3,** U. 

### S. Environmental'l'rotection Agency, Research Triangle Park, NC, September 1978. 6. 

### G. E. Gordon, Receptor Models, Environmental Science and Technology. 1980, 14. 792-800. 7. 

### Receptor Model Technical Series. Volume V: Source APGortionment Techniques And Considerations I"

### Combining Their Use, EPA 450/4- 4-020. U,S. Environmeotal Protection Agency. Research Triangle Park, NC. July 1984. 8. 

### Receptor Model Technical Series, Volume VI: AGuide To The Use of factorAnalysis And**MUlti~'e** 

### Regression (FA/MR) Techniques 1n Source Apportionment. EPA'450/4- 5-007. 

### U.S. Environmental ProtectionAgency. Research Triangle Park, NC, 

### July 1985. 9. 

### Pace, T. G •• et a1., 

### Protocol For Applying And Validating The CMB. U.S. Environmental Protection Agency. Research Triangle Park.

## Ne.**~** 

### Oraft.

A-4APPENDIX B
PRELIMINARY ESTIMATES OF PMIO DESIGN CONCENTRATIONS USING TSP DATA

This appendix provides amethodology for making a preliminary estimate ofparticulate matter (PMIO) design concentrations where actual PMIO**measure~** ments are not available. These estimates may be derived based on relationships between PMI0 and TSP, as described in the probability guideline. The definitions of design 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 of annual design concentrations and 24-hour design concentrations are included.
1.0 Annual NAAQS 1.1 Recommendations (I) Use the most recent years for Which a valid annual arithmetic luean* exists to determine annual arithmetic mean TSP concentration, and (2) Multiply the annual arithmetic mean TSP concentration determined in (1) times a factor of 0.47.**

*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 of data. However, ifonly 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 Of APM10 NAAQS USing Total Suspended Particulate Or Inhalable Particulate Data.
B-1(3) The resulting product of (1) and (2) is the "design concentration" for the annual arithmeticmean PMIO value. 1.2 Example (1) Given: Valid average**annua~** arithmeticmean TSP =140 p/m3 PM10 NAAQS

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

sampling schedule. (2) Solution: d

### =(0.47) (140) d

## =66 1l9/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 per year. When TSP measurements arethe only measurements of particulate 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 of TSP values, and (3) the number of samples. In deriving the design concentration, one should remember that the design concentration, d, is that value of PMIO which, when reduced tothe level of the NAAOS, t, would result in an expected exceedance rate (EER) of 1.0 peryear. Hence,
t

.. (m)(d) (1) where m.. (1-[required reduction in dJ) Two assumptions are implicit 1n the definitions above: (1) all TSPi values in the existing disfribution of TSP concentrations are reduced by (1-m). and (2) all corresponding (PMI0); values are also reduced by (1-m)•
B-2(2)• The expected exceedance rate 

### (EER) is calculated using equation

and N 

### EEl{ =

### -S
where n 1: P(R1 i=1 > R. ) (fSp

### li (2)

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

## =number ofTSP samples n =number ofTSP samples with values greaterthan t , the level ofthe NAAQS Ri = ratio of (PMlO)1/(TSPli 

## =level ofthe NAAQS (e.g., 150**~g/m3**

=the probability that a given (TSP)i value corresponds to a (PMI0)j concentration which 1s greater than the level of the NAAQS. Not1ng that the design concentration is defined as that value which must be reduced to the level of the NAAQS such that 

### EER 

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

n 1 0 =365 E'

, i=1 P(Ri > t ) m

### (TSPJi (3)
One further substitution can be made by substitutingthe relationship in equation (1) into equation (3),

n 1 0 =365 1: P(R; >

• S ;=1 d ) (TSPh (4)
This allows the design concentration "d" to be expressed explicitly 1n equation (4). To calculatethe average exceedance rate over a period of kyears, equation (4) should be applied separately for each year 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 "number ofyears considered Sj " number ofobservations during year j (5)

Equation (5) is solved iteratively for "d" until the right hand side (rhs) of the equation becomes equal to 1.0. 2.2 Recommendations Because the recommended procedure for estimating the 24-hour design concentration for PM10 fromTSP data is an iterative one, it is recommended thatthe estimate be made using a computer. Software for dOing this is available and is described in User's Guide for PMto Probability Guideline Software.* For those wishing to perform the ca culations by hand, a suitable procedure is described below. (1) Assume an initial value of lid"**tw~ce** as high as the level ofthe PM10 NAAQS (i.e•• 300 pg/m for a NAAQS having a level of 150 pg/m3); (2) Using Figure B.1,** for each (TSP)ij value greater than the level of lid," calculatethe prooabllity that the corresponding (PM10)ij value would be greater than this level. i.e•• d peRij > (TSP)

..) lJ Note thatd d (a) if (fSi5)ij <0.14, assume peRij > (TsPJij) " 0.99 d d (b) if (iSP)ij ) 0.95 but**~** 1.00, assume peRij > (TSP)ij) " 0.01

*W. P. freas, User's Guide for PM1B Probability Guideline Software, U.S.

.

Environmental Protection Agency,fficeofAir Quality Planning and Standards, Monitoring and Data Analysis Division, Research Triangle Park. North Carolina 27711. In Preparation. **Figure B.1 has been reproduced fromTable 2ofthe probability guideline.
B-4co I <.n 99.99 1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1 0.0: 'II]I' IIIll 

### K·E t"NUtiAdILIIY .It W UIVISIONS Kili:UFFELa. ESSER CO. 1oU.O:t '" ItU ItU.i' 468000 '1111" ,IIII'

99.9 99.8 99 98 95 90 80 70 60 50 40 30 20 10 5 2 1 0.5 0.2 0.1 0.05 0.01

-

FIGURE B.l Example Distribution of**PM~Q/TSP** Ratios for In lvidual 24-hour Observations.

f-

-



## - ..- - .-t-I-

-

-



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

## -

-

### .. .. - .- ...-

### - .-::1--:-; 0.05 0.1 0.2 05 1 2 5 10 20 30 40 50 60.70 80 90 95 98 99 99.899.9 99.99 Cumulative Frequency of PMIO/TSP Ratios Less Than a Given Valueor d d (c) if (TSPlij**~** 1.00, assume P(Rij > (ffl)ij) ,. 0 (3) Use the information in step (2) todetermine 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 "d" and repeat steps (2) and (3); (c) if rhs <0.99, decrease the value of**Old"~** and repeat steps (2) and (3)• (5) If there are fewer than 48 samples in any year, disregard the data from thatyear in performing the preceding calculations. 2.3 Example Given: 

### (I)Three years 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 in year 

### I, 400 and 350 pg/m3 in year 

### 2, and 300 pg/m3 inyear 3. Find: The design concentration ofthe 24-hour NAAQS for PM10. d Solution: (1) Assume d

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

### 1. 1 1 2 2 3 i 1 2 3 4 5 (TSP)j 500 240 400 300 350 3001(TSP)j P{R> 300/{TSP)j) 0.60· 0.20 >1.00 0 0.75 0.05 1.00 O. 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) 

## Inn 61 1.00;'0.54

8-6(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 of the NAAQS and trial 1.·Therefore, the value for**"d~** in trial 2 is 225 pg/m3• 

### i i (TSP

### li 225/(TSP); PIR> 225lITSP)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 or or 1.00 'f 2.03

Since 2.03 > 1.01, we need to raise the design concentration lid" for trial 3. This is done by taking half the difference between trials 1 and 2 and adding it to the design concentration in trial 2. Hence, the design concentration for trial 3 becomes 225 + 1300-225) 

### 2 

## =263 Ilg/m3 i (TSPli 253/ITSP)j P(R> 263/(TSP)j 1 1 500 U.53 0.34 1 2 240 >1.00 0 2 3 400 0.65 0.12 2 4 300 0.88 0.02 3 5 350 0.75 0.05 365 1 1 1 1.00

### ¥-X- ror-(O.32 +0» +

## or- (0.07 + 0.01) +

## or- (0.02)] 1.00 'f 1.05

Since 1.06 > 1.01, we must raisethe design concentration for trial 4by adding one-half the difference between the design concentrations used in trials 2and 3tothe design concentration in trial 3. Hence, the design concentration for trial 4 becomes 263 +1/2 (263-225) =282 pg/m3•
This binary search procedure is continued to derive subsequent trial values for "d" until finally we reach a trial value of 268 Ilg/m3.



### B-7

### -L i (TSPb 268/(TSP}i P (R) 268/(TSP

## 1 j 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- [Of (0.32 + 0) +

## or (0.11 + 0.02) +

## or (0.05)] 1.00 '" 1.00 Therefore. use d '" 268**~9/m3** as the design concentration for PMI0 at this monitoring site. Note that ifthe sample size in year 2had been less than 48. we would ignore thedata from year 2. and equation (5) would have been applied as shown below.

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 simil;arbasis. The EPA is providing this guideline not as amandated requirement, but as guidance for those States that want to develop emission factors beyond thosewhich we will provide in Section 5. This guidance may also be useful to States which want to develop compliance methods for future PMIO SIP emissions limits. The material presented here is intended for the use ofknowledgeable stack testers in modifying existingtechniques for gathering PMIO data. TheEPA Is currently developing PMI0 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 particleswhich were present in the process gas stream prior to discharge tothe atmosphere as well as modifications tothose 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 "condensib1es" from many sources with good particulatecontrols 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 filtertemperature 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 themeasurement of size fractionated particulatematter 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 of complete measurements ofprimary emissions including proper handling of condensibles 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 appropriatetechnique to use. The likelihood of irreversible agglomeration or coalescence occuring upon contact of particles 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 of two types of inertial classifiers--cascade impactors or cyclones. If PMIO emissions only (not detailed size distributions) are to be measured,
a single stage classifierwould 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 of the multistage devices having adifferent cutoffdiameter. The samplers having the greater number of stages provide more detailed information on the aerosol sizedistribution, C-2and 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 order to insure data reliability (Harris, 1977). A sound theoretical basis exists for predicting the performance of cascade impactors (Marple, 1970). Cyclone samplers perform well as single stage collectors; however, no adequate theory exists at present to predicttheir behavior over awide range ofoperating conditions. This limits the use of cyclones tothose which have been demonstrated to produce the necessary particle size cutoff by empirical calibration procedures (Smith et aI, 1982). Cyclones with acceptable sharpness of cut have been developed and
are available from commerical vendors. Evaluation ofthese devices have demonstrated nominal 10 urn cuts over a limited range of operating conditions. and further characterization ofthe devices is in progress. Cyclone samplers
are preferred for sampling toquantify size specific mass emissions; however, the protocol described here is generally applicableto both impactor and cyclone sampling with details that are pertinent to only one ofthe methods noted as they arise.

3.0 Selection ofSampling Traverse Points In order toobtain a representativemeasurement, one must obtain samples at representative points across the duct (staCK) at iSOKinetic rates. In the case of conventional particulatetesting (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 of each ofthese areas. IsoKinetic sampling is achieved by selecting a nozzle which is appropriate for the combination ofthe nominalflow rate at which the sampler is intended to operate and the average duct gas velocity. Compensation for var1ations in the duct gas ve1oc1ty is then achieved by modulating the samp11ng rate. This procedure cannot be used with inertial particle sizeclassifiers 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 of the testing and subsequent data reduction and interpretation. Amethod is currently being investigated which**per~its** complete traverses of the sampling planeusing variable sampling rates to be made with cyclones and impactors In a fashion similartoEPA 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 from the duct by means of compensating changes ; nthe recirculation flow. Upon satisfactorydevelopment of the EGR method, the protocol for traversing the sampling plane for size specific sampling will be virtually identical tothat ofMethod 5. An alternative procedure for obtaining a representative multipoint traverse in a single run with a fixed flow rate sampler involves changing the sampling nozzle as necessary at each sampling point tomatch the sampling velocitytothe duct velocitywithin an allowable 20 percent tolerance. The volume ofgas sampled at each point is then made proportional tothe volumetric flow through the area represented by the sample point by making the sampling duration at each point proportional to the gas velocity atthe point. This technique will work with any velocitydistribution but may be cumbersome to implement. C-4Another method of obtaining an isokinetic sample using a fixed flow rate sampler is to synthesize a complete 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 oftraversepoints 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 complete measurements should be made tocharacterizethe source emission rate; thus, atotal of 12 particle size samples (3 samples at each of 4grid locations) will usually be required to obtain one complete measurement. However, ifthe velocities atthe four sampling points arewithin!20 percent ofone another, one sampler operating for equal time at each of the four grid points can obtain one sample. This will reduce the number of samples required from 12 to 3. The locations of the four sampling points are shown in Figure C-l.

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C-6- •Implementation of all ofthe traverse methods described above require obtaining velocitytraverses 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 of the 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~rcial** market. Any of anumber 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 a high concentration (>500 mg/m3) stream is to be sampled an impactor with adesign flow rate ofabout 3lpm would be more useful. The need to use different samplers for different particle concentrations arises from a fundamental operating limit of impactors. Under most circumstances. no more than about 10 mg can be collected on anyone impactor stage; beyond this limit particle reentrainment
can invalidatethe data. As a rulethismeans 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 1s more complex when 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 selectivedeposition in bends, the sampling nozzles must be straight for use in particle sizing (Knapp, 1979; Felix and McCain,
C-l1981}. Thus one cannot use "gooseneck" or other nozzles designed toturn the sampled gas flow 90 degrees (as is done in Method 5). The natural right angle orientation ofthe inlet of acyclone sampler with respect tothe sampler body and exitmakes theuse of straight nozzles normal with them. This is not the case with impactors. One approach tothe use of straight nozzles with impactors isto align the impactor axis with the direction of gas flow in the duct. However. 1n many sampling locations this is impractical if not virtually impossible. The use of ahigh capacity precollector having
an inlet at 90 degrees tothebody eliminates this difficulty and, at the
same time, eliminates another operational problem with impactors. If a significant proportion ofthe particles being sampled are largerthan about 10to 15 um, the first collection stage tends to reach its loading limit before the succeeding stages collect enough material for reliableweighing. The use of a high capacity precollectorwhich has a cut larger than that of the first impactor stage helps alleviatethe overload problem. Several such precollectors, some of cyclonic design and others operating on impaction principles, are comercially available. Ofthe impaction type precollectors, 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 a number offactors with the sampling flow rate being the only variable which can be adjusted by the user. However, the sample flow rate required to obtain thePMIO 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 selectingthe sampler flow tobe used that one has in simple total particulate measurements. The

C-8matching of the sample inlet velocity with the gas stream velocity for isokinetfc sampling must be accomplished entirelythrough the cross-sectional
area ofthe sampling nozzle. This means that amuch larger array ofnozzles must be availablethan those used inMethod 5sampling. Ifthe isoklnetic
error is no largerthan 20 percent, themaximum error in the measured emission rate of 10 umparticles will be about 15 percent and the errors for smaller particleswill be lower. Thus, the actual error in the total concentration ofparticles smaller than 10 umWill. be substantiallylower than 15 percent. Thus deviations of**~**20 percent from isoKentic can probably be tolerated. If sampling is tobe done within**~**20 percent of isokinetic,
an array of nozzles must be availablethat step by 20 percent in diameter from one to the next. If a single stage collector is used, the geometry ofthe collector and the flue gas conditions will completely dictatethe sampling flow rate as only one flow ratewill produce the required size cut. If amultistage device is used tomeasure the complete size distribution, some latitude is available in settingthe 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 specificmethod(s)
are similartothose ofMethods 5and 17 and trainsdesigned for the latter applications can be used with some impactors and cyclones. However, Method 5 systems are not adaptable tothe intermediate and low flow rate «20alpm) devices without modification. The orificemeters used in the Method 5/17 systems aretoo large to permit accurate flow measurement and control at these reduced flow rates. Asubstitute orificemeter, properly sized for 

### C-9the specific sampler, can be used in place of the standard orificeto**~ake** a Method 5/17 system compatiblewith the sampler. Alternativetrains and probes are described by Harris (Harris, 1977). As the sampling procedure described here is intended tomeasure only particulatematerial 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 (replacingthe usual sampling nozzle). Atypical setup is illustrated in FigureC-2. The particle sizefractionating portion of"the system cannot be located at the exhaust end ofthe probe because ofsize selective particle losses resulting from deposition in the probe. AMethod 5 filter system may beused at the exhaust end ofthe probe, with the instack filter removed as illustrated in FigureC-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 theuse of cascade impactors for industrial
source sampling are described by Harris (Harris, 1977). Procedures for the
use ofcyclone samplers are described by Smith et a1., (Smith et a1, 1982). Some ofthe procedures described in the latterdocument are specificto a set ofcyclones designed to provide 

### a 15 urn sizecut, but with the exception ofthe actual flow rates to be used, the same protocol isto 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 PMI0 cyclone, the filter specifications are the same

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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 of aglass 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 treatedto minimize S02 uptake. The procedure for thetreatment is given by Cushing and Smith (Cushing and Smith, 1979). Because the interferences are not predictable, control runs must be made together with the actual sample runs as the

.testing takes place (Harris, 1977). All collectionmedia must be dessicated priortothe initial weighing and dessicated for aminimum of 12 hours priorto the final weighing after sampling. 7.0 Data Reduction and Analysis Ultimately. the information to be determined from a set ofmeasurements Is the particulate emission rate in the PMIO size fraction togetherwith a
measure ofthe uncertainty in thecalculated rate. Ifthetraverseof the 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 of the duct and averaged. The average represents the final value to be reported. Isokinetic
errors would be calculated for each sample In themanner prescribed by EPA

C-13Method 5. The average isokinetic error for each traverse must be less than
+20 percent for the individual traverseto be accepted. The relaxation from the + 10 percent requirements ofMethod 5is possible because the
errors associatedwith 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 sampler(s) are operated so as to provide a size fractionation pOint at the required 10 urn diameter, the particle concentration to be used in the emission rate calculation is that based on thequantity ofmaterial which passed the appropriate stage ofthe sampler. If a single stage sampler is used, the actual size fractionation diameter for each sample must be calculated based on the calibration of the 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 of the

. 

## - required diameter. If the complete sizedistribution is measured, the particle concentration to be used in calculatingthe emission rate can be found by interpolation (or, within limits, extrapolation) of the 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 of the service. Details on the data reducion system can be found in "PADRE User's Guide" (EPA-600/58-84-012).

C-14References Anom. "Code of Federal Regulations" Title40, Chapter 1, Sub ChapterC, Part 60. Anom. "Control for Particulate Emissions from Stationary Sources" Vol. 1EPA 450/3-81-005a, September 1982. Cushing, K.M. and Smith, W.B. "ParticulateSampling and Support: Final Report" EPA-600/2-79-1l4, 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 ParticulateSampling 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, ParticleTechnology Laboratory Publication 144, 1970. 243 pp. Smith, W.B•• Cushing, K.M., Lacey, G.E., and McCain, J.D. "ParticulateSizing 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 1982Annual Meeting oftheAir Pollution Control Association, New Orleans, 

### LA, June 1982.

C-15APPENDIX D**-----~.""** DETERMINATION OF PM10BACKGROUND
1.0 Introduction Development of State implementation plans (SIPs) for PMIO will require
an estimate of PMIO background concentrations for use with eitherdisperSion
or receptor model based control strategy development. The term "background" has a number 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.'"_u1delines on Air**Qual1~** Models.1 The purpose ofthisAppendix isto outline in greater detail the considerations and procedures which should be used todetermine background concentrations of PMIO. As will be seen, selection ofan appropriate background concentration depends on the time period ofconcern (e.g., annual or 24-hour), meteorological conditions of interest and the availabilityofdata. In a dispersion modeling study, background should account for the impact of source emissions not considered in themodel. Such emissions may be eitherofmanmade or natural origin and may be eitherprimary (directly emitted as particles or condensable vapors) or secondary (emitted as gases and transformed intoparticles during transport). Ideally, all sources should be modeled, but this is not always possible or practical for particulatematter. In some cases, the sources may be within the agency's jurisdiction, but theiremissions are not included expliCitly in the model (perhaps because they are too small ortoo numerous to inventory). In other cases,

D-1the sources may be outside the region ofjurisdiction and not identifiable. However, all sources must be accounted for eitherexplicitly by theModel or in the bacKground value. Receptor modeling studiesmay also be used todevelop control strategy emission limits, as outlined in Section 6.0 ofthis document. Since a receptor analysismay apportion the entire sample (including bacKground), the analysismay reflect contributions from some sources which are or should be ascribed tobacKground. Section 3.0ofthisAppendix discusses thisin more detail. For dispersion model studies, the bacKground concentration is usually needed for the set ofmeteorological conditions·and time periods (e.g., annual, seasonal or 24-hour) associatedwith theexceedances ofthe NAAQS. Receptor models usually requirethe estimate for the specific time period represented by the receptor analysis. However, receptormodel studies may also be developed that represent a particular set ofmeteorological conditions. This need for background to represent a particular set ofmeteorological conditions is discussed in Sections 3.0and 4.0 ofthis Appendix.
2.0 Determining BacKground Estimates·FromMeasured PM}o Or Surrogate Data The PMIO background values are most appropriately taken from**~asurements** at a nearby site, representative ofbaCKground for the area being studied. Such a sitewould ideallymeasure PMIO and not be influenced by (1) sources being studied and explicitly included in a dispersion or receptor model, or (2) sources not impacting the study area. This usually means that the site be located in the vicinity ofthe study area. For a sitethat meets these prerequisites, themean annual baCKground for use in the SIP analysis would be the average of the annual concentrations at that monitor over the 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 if it can be shown that an estimate by any alternate method is

more reliable. Dispersion modelers identify specific sets of 24-hour meteorological 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 of meteorological 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, caremust be taken to ensure that sources being explicitly considered are not upwind of the "background" monitor on the days selected. In the absence of a single monitoring site which is clearly not influenced by any of the sources considered explicitly in a dispersion or receptor model, 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 of these sources on the monitored background value by using a wind 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 of a significant

source. Monitoring sites outside of a 900 sector downwind of a 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 singlemonitor to calculate abackground value which is not influenced by those sources, background may be determined by "compositing" the airquality 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 selectingthe "upwind" site
on each monitoring day and averaging these "upwind" values (see Section 5.0 of this Appendix). The mean annual concentration is then the average of the annual concentrations which are calculatedby 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 selectedto represent those conditions. As a second alternative for using measured data, background concentrations may be derived using sitemeasurements for a different day oryear than the time period being studied. Conceptually, using data from the same site but during adifferent day or year is similar tothe first alternative above but additional care must be exercised in showing that the data used
are representative ofthebackground sources and climatological conditions during the study period. As athird alternate, ifPM15 or TSP data are available, they may be used as surrogates to estimate PMIO concentrations. Use of either TSP or PM15 data involves the use of ratios, ormultipliers, to adjust measured background PM15 or TSP to PMIO. Then the estimated PMIO data would

0-4be treated in the same manner as aPMIO measurement in the preceding paragraphs. Surrogate data for PMIO should only be used ifthere are insufficient PM10 data for use in applying the procedures above. In cases where PMIO data are available for part of ayear and thePM10 monitor is collocated with TSP or PH15 monitors, the ratio approach may be used for the remainder oftheyear when no PMIO data are available. The ratio approach is discussed in Section 6.0ofthisAppendix.
3.0 Special Considerations For Receptor 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 perform a source receptor analysis on a nearby background receptor so that the background for specific source components can be determined and discounted priorto using the apportionment (as outlined in Section 6.0 of thisAppendix). This receptor analysis at a background sitemay be necessary, for example, to distinguish between locally generated soil dust and that transported intothe 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 study monitors 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 of their impact at the background site. In this adjustment process, one must carefully selectthe background days for receptor analysiS tomake sure they are ·upwind" and are not impacted by the sources ofconcern.

0-54.0 ?ummary OfAlternatives For Estimating PMIO**Bac~ound** The preferred method for background determination isto use an upwind PMI0 sitein 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 alternatemethods discussed previously are summarized below, in order of preference.
First Alternative

Second Alternative Third Alternative Use perimeter site(s) in the vicinityofthe study area. The effect of nearby sources that are in themodeling analysis must be eliminatedthrough the use ofwind direction analysis (annual or 24-hour). A"composite" background value based on several nearby sites and wind direction analyses may be developed. Use PMIOdata from a different days or a different year to represent theday oryear being studied. Use surrogate data (TSP orPMIS), adjusted to PMI0'

5.0 EXAMPLE 1

- Compositing Data From Several Perimeter Sites In the absence of a Single appropriate PMI0 background sitewith a sufficiently large data base, data from two or more sites in the vicinity of the study area may be combined toprovide a composite estimate of bacKground 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 intothree groups: Group I

- days when the Northern site ;s upwind (use Northern site data for HI days).Group II

- days when the Southern site 1s upwind (use Southern site data for**~2** days). Group III

- days with calms, substantial shifts in wind direction, during the day or with winds predominantly from the east orwest (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**~ ~** **~** 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 of the 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-16.0 EXAMPLE 2

- Use Of Surrogate Data Most regional monitoring networks contain one or more designated background hi-vol sites and a few have PH15 background sites. However, there
are relati'vely few size-fractionatedparticulate samplers measuring PHlO compared with those measuring TSP; and almost all ofthe former are in urban locations. Therefore, Statesmay not have direct measurements of PMIO background availablewhen they prepare their SIP's. The following is the recommended procedure for using PHlS and TSP surrogates to estimate PMIO, based on an analysis of regional scalePHIO. PM15 and TSP sites. The PHIS measurements may be used as a surrogate for PHI0. The PMlS measurement (either annual average or 24-hour) is multiplied by a constant ratio to give an estimate of PM10.2 The following adjustment applies. PHI0 = 0.85 xPHIS (annual or24-hour) Reference 3suggests PMlS toTSP ratios of .61 (annual) and .75 (high 24-hour). Using the above 0.85 ratio for PMI0 to PM15, the following ratios apply for estimating PMI0 background using aTSP surrogate:
PMIO 

### =0.S2 xTSP (annual) PMIO 

### =0.64 xTSP (high 24-hour)
The concentrations to be adjusted are obtained using the procedures in Section 2.0 of this Appendix. The "adjustment" is simply the last step in the process, wherein aTSP orPMl5 background estimate is adjusted to represent an estimate of the PMIObackground.



### D-8References -----,-- 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 and A. O. Thrall, An Examination Of 1982-83 Particulate Matter Ratios And Their Use In The Estimation 0(-- PM10 NAAQS**Attainm~nt** Status, EPA 450/4-85-010. U.S. Environmental-- Protection Agency, OAQPS, Research Triangle Park, NC 27711, August, 1985. 3. PEOCo Environmental, Inc.,**Estimatin~~An1j=JP** __**Bac~Jlround** Concentrations From TSP And Other Measurements, EPA 450/4-84-021. U.S. Environmental Protection Agency, OAQPS, Research Triangle Park, NC 27711, July 1984.

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### This guideline document was prepared tobrieflydescribe the actions that must be taken by State and local air pollution control agencies to develop SIP's that demonstrate attainment and maintenance ofthe PMIO NAAQS. The guideline describes how to: (1) demonstrate attainment ofthe PMIO NAAQS.

.(2) determine the sizeofan area exceeding the NAAQS. (3) select a receptor

or dispersion model, (4) prepare an emission inventory and, (5) determine a control strategydesign concentration. The guideline also discusses ambient PMIO monitoring requirements and data usage and EPA's policies for making the transition from aSIP designed to protectTSP standards toone designed toprotect PMIO standards. References to supporting documents are given at the end ofeach section. This guidelinedoes not discuss implementation ofPSD orpreconstruction review programs for PMIO. Guidance for implementation of these programs will be issued separately•

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