&EPA 

United States 

Environmental Protection 

Agency 

--~. 

Air 

Office of Air Quality 

Planning and Standards 

Research Triangle Park NC 27711 

PM10 SIP 

Development 

Guideline 

EPA-450/2-S6-001 

June 1987 
EPA·450/2·86-001 

PM10 

SIP Development Guideline 

U. S. ENVIRONMENTAL PROTECTION AGENCY 

Office of Air and Radiation 

Office of Air Qualitv Planning and Standards 

Research Triangle Park, North Carolina 27111 

June 1987 
This guideline is issued by the Environmental Protection Agency to assist State and local air pollution control 

agencies in developing State implementation plans (SIP's) for the national ambient air quality standards for 

particulate matter. The standards for particulate malter have been revised to address panicles nominally 10 

micrometers and smaller in diameter ( PM lO ). Copies are available· in limited quantities· from the Library 

Services Office (MD·35). U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711; 

or, lor a lee, Irom the National Technical Information Service, 5285 Port Royal Road, Springfield, Virginia 22161. 

This report has been reviewed by the Office Of Air Quality Planning And Standards, U.S. Environmental 

Protection Agency, and approved lor publication. Any mention of trade names or commercial products is 

not intended to constitute endorsement or recommendation for use. 

Publication No. EPA·45012·86·001 
TABLE 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 Interim use 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 Prior knowledge of sources and emissions •••••• 4-4 

4.2.3 Chemical similarity ••••••••••••••••••••••••••• 4-4 

4.2.4 Sources versus source categories •••••••••••••• 4-5 

4.2.5 Particle size and time scale •••••• , ••••••••••• 4-5 

4.3 Screening Techniques and Refined Dispersion Models 

for PMIO Concentrations •••••••••••••••••••••••••••••• 4-7 

4.3.1 Selection 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 of Receptor 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 of Existing 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 for Determining Design Concentrations •• 6-2 

6.3.1 Table look-up •..•.•••• , ••••••••••.•••...••••.• 6-3 

6.3.2 Fitting one statistical distribution to 

several years of data ••.••.••••••.•.•••••••••• 6-6 

6.3.3 Using the empirical frequency distribution of 

several years of data (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 In t roduct i on .... " ............ ~ .......... '" ................... £ .. .. .. .. .. • • .... 7-1 

7.2 Clean Air Act Requirements •.•••••.•.•••• ~ •••••••••• 7-1 

7.2.1 Time limits, SIP requirements .................. 7-1 

7.2.2 Regulations in 40 CFR 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 Fugitive Dust 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 Model s ............................... ~ .... " ................... 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 Size Specific Source 

Measurements ••••.•••••.••••••••••••••....••.••••••••. c- 2 

3.0 Selection of Sampling Traverse 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 

iv 
APPENDIX 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 Receptor Modeling ••••••••••• 0-5 

4.0 Summary of Alternatives 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 APPORTIONMENT METHODS 

BASED ON DATA AVAILABILITY AND SOURCE 

CHARACTERISTICS ...•..•••....• , ..•.••.••..• ~ ••..•.. 4-3 

TABLE 4-2 RECOMMENDED APPROACHES FOR PM10 SOURCE 

APPORTIONMENT •.••.. , .••...........•••••..••••.•••• 4-6 

TABLE 4-3 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 A HYPOTHETICAL LIME PROCESS •••••• 5-18 

TABLE 5-3 INDUSTRIAL SOURCE COMPLEX MODEL (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 MODEL WITH 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 A HYPOTHETICAL 

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 5 TRAIN) ••••••••• C-12 

FIGURE C-3 PMIO PARTICULATE SAMPLING TRAIN FOR CONDENSABLE 

AND NONCONDENSABLE PARTICULATE (MODIFIED EPA . METHOD 5 TRAIN) •••••••••••••••••••••••••••••••••.• C-13 

vii 
1.0 INTRODUCTION 

1.1 Overvi ew 

The 1977 amendments to the Clean Air Act (Act) Pub. L. 95-95) 

require the Environmental Protection Agency (EPA) to review periodically 

and, if appropriate, revise the criteria on which each national ambient 

air quality standard (NAAQS) is based along with the NAAQS themselves. 1•2 

In response to these requirements, EPA revised the primary and secondary 

NAAQS to apply to particulate matter in a size range defined by the 

collection characteristics 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 to provide guidance on how States 

can develop PM10 control programs for attaining and maintaining ambient 

PMIO stdndards. Existing State control strategies to attain and maintain 

the former NAAQS for total suspended particulates (TSP) provided for 

controlling particulate matter sources; however, they must be evaluated 

and revised as necessary to attain or maintain the PM10 NAAQS. The 

guidance herein addresses the tranSition that States must make from TSP 

control programs to PMIO control programs and the necessary revisions to 

State implementation plans (SIP's) to account for PMIO' 

This document is intended as a starting point for anyone seeking 

information, policy, and guidance on developing PM10 SIP's. Most aspects 

of developing a PM10 program from problem determination to final SIP 

submittal are covered. Certain details of program development, however, 

1-1 
require explanation not included herein. Reference is therefore made to 

other documents for those additional details. 

Guidance for revising existing SIP's to address the review of PM10 

under State and local preconstruction review programs is not included in 

this document. It should be noted, however, that EPA's decision to 

implement the revised particulate matter NAAQS via section 110 of the Act 

means that EPA will not impose Part 0 new source review (NSR) requirements as 

a means of revising the SIP's. Instead, the preconstruction review of major 

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 in the preamble of the FEDERAL REGISTER 

notice promulgating PM10 amendments to the PSD regulations for a description 

of the changes that EPA has made to address PM10. Additional guidance 

will be forthcoming in the form of workshop 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, States must look at the spatial distribution of 

ambient PM10 concentrations and at source emissions from a particle size 

distribution standpoint. The major sections of this document are intended 

to provide guidance on the different aspects of PM10 control strategy 

1-2 
development and SIP submittal to EPA in light of this new focus on the 

PM10 size range. 

1.2.1 Determining the Air Quality Status of Areas 

The Act requires control strategies to be submitted to EPA within 

9 months of the revision of NAAQS. Section 2.0 describes EPA's policy 

for determining when the air quality of an area is or may be violating 

the PM10 NAAQS and the extent of the area violation. The EPA's policy for 

categorizing areas into three groups based on the need to revise their 

SIP is also discussed. 

1.2.2 Ambient monitoring 

Each State must establish an ambient monitoring network utilizing 

PM10 samplers. Section 3.0 discusses the changes made to the 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 of data 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 

1-3 
of PMI0 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 of control strategies 

Section 6.0 identifies general approaches to the use of ambient 

measurements and model estimates in setting control strategy emission 

limits. It explains how design concentrations may be determined and how 

control strategy demonstrations may be conducted using models. 

1.2.6 SIP requirements and data reporting 

Section 7.0 discusses EPA's policies for requiring 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 receptor models 

Appendix A lists various receptor model techniques available and 

discusses each. 

1.2.8 Preliminary Estimate of PM]O Design Concentration Using TSP 

Data 

Appendix B provides a method for making a preliminary 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. 

1-4 
1.2.9 Source testing 

One of the methods for determining a source's PM10 emissions is 

to conduct a test which measures the amount of emissions plus particle 

size distribution. Appendix C discusses 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 many years of experience in developing and implementing 

plans for abattng particulate matter air pollution. In general, the 

activities States will engage in to attain PM10 NAAQS will not differ 

radically from past activities to attain TSP NAAQS. That is, the basic 

approach will still be to examine air quality across the State, delineate 

areas where air quality needs improvement, determine the degree of 

improvement necessary. inventory the sources contributing to the problem, 

develop a strategy to reduce emissions from contributing sources enough 

to bring about attainment of the NAAQS, implement the strategy, and take 

the steps necessary to ensure that NAAQS are not violated in the future. 

The change in the particle size range to which the standards apply 

from TSP to PM10, together with the emergence of receptor models as 

effective tools in strategy development, necessitates changes in the 

specifics of particulate matter control strategy development. 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 to focus 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 

1-5 
emission inventories and augment dispersion models. The contribution of' 

sources to ambient PH10 and the degree of emission reductions necessary 

can then be determined. 

1-6 
References 

1. Air Quality Criteria for Particulate Matter. AP-49. U.S. Department 

of Health. Education, and Welfare. Washington. D.C., January 1969. 

2. Air Quality Criteria for Particulate Matter and Sulfur Oxides, 

EPA-600/8-82-029a-c, Environmental Criteria and Assessment Office, 

Environmental Protection Agency, Research Triangle Park. N.C., 

March 1983. 

3. High Volume Method for Suspended Particulate Matter, Part 50 of 

Chapter I of Title 40 of the Code of Federal Regulations, Appendix B. 

4. Reference Method for the Determination of PM10 in the Atmosphere, 

Part 50 of Chapter I of Title 40 of the Code of Federal Regulations, Appendix J. 

1-7 

2.0 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 of NAAQS. The method of determining whether an 

area is in attainment of the PMlO NAAQS using ambient air quality data is 

described in Appendix K of 40 CFR 50. Generally. 3 years of PMlO data are 

required to make the determination; however, data collected over shorter 

periods of time may be adequate as explained in Appendix K and in the 

Guideline on EKceptions to Data Requirements for Determining Attainment of 

Particulate Matter Standards. l In areas where too little PM lO data are 

available to demonstrate attainment of the NAAQS, EPA's policy is to use 

whatever PM10 data are available and supplement it with TSP data to determine 

the probability that the area will violate the PM10 NAAQS. The EPA will 

use the report, Procedures for Estimating Probability of Nonattainment of a 

PM10 NAAQS Using Total Suspended Particulate or 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 of nonattainment. Group 11 areas are 

those areas where nonattainment is uncertain, and Group III areas are shown 

to be or have a high probability of attaining the NAAQS. After the initial 

grouping of areas and promulgation of the PMlO NAAQS, only PM10 data may be 

used to make definitive attainment/ nonattainment decisions in accordance 

with Appendix K or reference 1, above. The probability guideline may 

2-1 
continue 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 K specifies the procedures which States can use to demonstrate 

attainment of the 24-hour and annual PM10 standards at a particular location 

using ambient air quality data. To determine PMIO attainment of the 24-hour 

standard, EPA generally requires averaging the number of estimated 24-hour 

exceedances over 3 consecutive years of representative data. If the average 

number of 24-hour NAAQS exceedances 1s greater than 1.0 per year, then a 

nonattainment problem will oe evident and States should proceed to examine 

the adequacy of the existing SIP. A potential problem of nonattainment of 

the 24-hour PMlO standard can oe projected on the basis of fewer than 3 years 

of data, since the criteria for nonattainment can be equivalently expressed 

in terms of the total number of estimated exceedances within a 3-year 

period. In the simplest case, if four exceedances are ooserved in a Single 

year, the average numoer of exceedances over a 3-year period will exceed 

1.0. If the total numoer of estimated exceedances in 3 years is equal to 

or greater than 3.2. then the numoer of exceedances averaged over 3 years 

would also exceed 1.0. Thus, 

3.2/3 = 1.07, which rounds to 1.1 

3.1/3 ~ 1.03, which rounds to 1.0 

As soon as 3.2 estimated exceedances are accumulated a nonattainment 

proolem will be evident. This may occur in even less than a single year. 

As specified in Appendix K, EPA will not require adjustment of the 

first observed exceedance in order to account for incomplete sampling if 

(1) only one exceedance occurred in the calendar quarter, (2) if everyday 

2-2 
sampling 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 of this 

document). As specified in 40 eFR 58.13 for the first year of monitoring, 

everyday sampling must commence within 90 days of the end of the calendar 

quarter in which the first exceedance is observed and continue for 4 consecutive 

calendar quarters. In addition, if a site is already monitoring every day 

and observes its first exceedance, no adjustment for missing data will be 

made to this first exceedance if 75 percent data capture is achieved. 

By not adjusting the first 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 of every day monitoring, the additional exceedances would 

be adjusted to account for incomplete sampling using formula [1] of Appendix K, 

according to the sampling frequency and data capture at that time. 

Days on which exceedances of the NAAQS were recorded may be flagged as 

exceptional events in accordance with the Guideline on the Identification 

and Use of Air 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 if the responsible control agency 

determines in conjunction with a public review that the flagged data are 

inappropriate for use. 

When the minimum 3 year data requirements specified in Appendix K are 

not satisfied, attainment can still be demonstrated, but such demonstrations 

2-3 
must be approved by the appropriate Regional Admin1strator, in accordance 

w1th the Guideline on Exceptions to Data Requirements for Determining 

Attainment of Particulate Matter Standards. 1 In particular, if only 1 or 

.2 years of PMIO data are available, then supplementary information provided 

by modeling or other surrogate measures must be used to demonstrate the 

adequacy of the SIP to attain and maintain the standards. 

Finally, in the event that there has been a trend in emissions during 

the attainment test period, attainment with the standards can be demonstrated 

according to Appendix K, even though the most recent 3 years of data do not 

indicate. attainment. Either the most current representative year(s) could be 

used or statistical techniques or models could be used in conjunction with 

previous years of data 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 of Nonattainment to Determine Initial SIP 

Requirements 

The procedures EPA will use to categorize areas into Group I, II, or 

III are discussed in section 2.4. The schedules for submitting SIP revisions 

and control strategy demonstrations for each group are discussed in section 

7.3. If available PMIO data for an area are adequate to demonstrate attainment 

as discussed in section 2.2, then the area will be put into Group III. On 

the other hand, if the PMIO data alone are adequate to demonstrate that the 

area is nonattainment, it will be put into Group I. If available PMI0 data 

alone are not adequate to demonstrate attainment or nonattainment. EPA's 

policy is to supplement available PMlO data with inhalable particulate matter 

data (particles with an aerodynamic diameter less than 15 micrometers) and TSP 

2-4 
data. These air quality 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 of 95 percent or greater (Group I), 

from 20 to 95 percent (Group II) and less than 20 percent (Group III). 

Other factors that may influence the degree to which the existing SIP may 

need revisions will also be considered in the area grouping process. 

Also, EPA has found that some uncertainty exists in the PM10 data 

collected prior to 1987 with available PMIO instruments. A study performed 

by EPA in Phoenix has shown that in extreme situations data collected by 

Sierra Anderson SA-321A size selective PM10 instruments can be influenced 

by coarse particles to the extent that concentrations may be biased high by 

up to 20 percent. In addition, data collected with Wedding or GMW-9000 

instrument may be biased low by up to 20 percent due to soiling problems. 5,6 

In order to account for the uncertainty associated with such PMIO concentrations 

in calculating the probability of nonattainment, a zone of uncertainty 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 of uncertainty will only cause areas to move from Group I to Group II, 

or from Group III to Group II. In particular, for SA-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 the probability calculations. 

2-5 
If an area's nonattainment probability using TSP data and PMIO data drops 

below 0.95 or rises above 0.20, as a result of PMIO data in the "gray zone," 

it will be classified Group II in order to resolve the possible uncertainty 

associated with the PMIO data and to ensure that a determination is made as to 

whether the existing SIP provides for attainment and maintenance of the PMIO 

standards. Also, PMIO data 1n the gray zone, by itself, will not be allowed 

to move 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 requi red 

to operate air quality monitors on an everyday sampling schedule for the 

first year. 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 a SA-321A 

**App1ies to readings made with a GMW-9000 

Since the conditions under which 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 

2-6 
with the standards. For data produced by the SA-321A (with the potential 

positive bias), however, an appropriate downward adjustment will be 

permitted for attainment demonstrations if influence 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 

of nonattainment. Such areas with potential exceedances of the PM10 NAAQS 

(i.e. with data in the lower gray zone) will be required to sample more 

frequently for the first year, (according to the Group I I monitoring frequency 

of every-other-day), so that true nonattainment situations will soon become 

apparent. 

2.4 Area Categorization 

SIP development requirements are based on an area's categorization 

into Group I, II, or III of EPA's policy for PM10 SIP development. If 

ambient PM10 data adequate to determine attainment or nonattaiment of PM10 

standards are available, they are to be used in categorizing areas. Areas 

for which adequate ambient PM10 data are not available are initially categorized 

into these groups using nonattainment probability (section 2.3) cut points 

of 95 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 of the existing particulate matter 

SIP's for achieving and maintaining PM10 standards. 

The general criteria for group categorization are: 

Group I - Areas for which the existing particulate matter SIP may 

need substantial revision to be adequate for attaining 

and maintaining PM10 standards. 

2-7 
Group II - Areas for which the existing particulate matter SIP 

may be adequate or need only minor adjustment. 

Group III - Areas for which the existing particulate matter SIP's are 

believed adequate to attain and maintain the PMIO standards. 

Examples of information other than air quality data that may warrant 

moving an area from Group III to Group II. or from Group II to Group 1. 

are: 

o facts showing that the current air quality is attributable to an 

economic slowdown or some other temporary phenomenon rather than the 

stringency of the area's TSP SIP requirements, 

o facts suggesting that there are few enforceable measures in existing 

SIP's yet to be implemented that would 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 a higher ratio of PMIO to 

total particulate matter. 

Examples of information other than air quality data that may warrant 

moving an area from Group I to Group II, or from Group II to Group III, are: 

o factors suggesting that sources are not yet in compliance with SIP 

measures that. if enforced would reduce emissions that significantly 

affect the area's air quality; 

°evidence that the area has an unusually high proportion of sources 

in categories whose emissions typically have a low ratio of PM10 to total 

particulate matter; and 

2-8 
o evidence that the area 1s rural in nature and 1s a clearly impacted 

by fugitive dust (i.e. qualifies as a rural fugitive dust area under 

the fugitive dust policy described in section 7.11). 

Examples of information that may affect EPA's classification of an 

area not preliminarily classified using a nonattainment probability are: 

o the amount and density of industrial activity that 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 

likely to generate significant particulate matter emissions and that are subject 

to moderate and heavy vehicle traffic; and 

o the degree to which the existing TSP SIP will likely limit PM10 emissions 

from these traditional and nontraditional sources. 

2.5 Area Boundaries 

Section 6.0 of the 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 

of uniform PM10 air quality status may differ from the areas previously 

defined for TSP; therefore. States may wish to use the guidance in these 

instances to more specifically define the spatial extent of PM10 air quality 

problems. 

The approaches that have been used and that are recommended for 

defining area boundaries are: 

-- a qualitative analysis of the area of representativeness of the 

monitoring station. together with consideration of terrain. 

meteorology. and sources of emissions; 

2-9 
-- spatial interpolation of air monitoring data; and 

-- air quality simulation by dispersion modeling. 

These techniques can be used singly or in combination depending on the 

complexity of the area where a monitoring station(s) is located. More 

detailed discussions of the techniques are in the probability guideline. 

2-10 
References 

1. Guideline on Exceptions to Data Requirements for Determining Attainment 

of Particulate Matter 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 of Nonattainment of a PMIO NAAQS Using Total 

Suspended Particulate or PM]O Data, U.S. EPA, RTP, N.C. EPA-450/4-86~17, December 1986. 

3. Guideline on Identification and Use of Air 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 in Particulate Matter Emission and 

Air Quality Data, memorandum from R.G. Rhoads to Regional Air Directors, 

U.S. EPA, RTP, NC. May 11, 1987. 

5. L. Purdue et. a1., Intercomparison of High-Volume PMIO Samplers at a 

Site with High Particulate Concentrations, JAPCA, Vol. 36, No.8, Aug. 1986, 

pp. 917-920. 

6. Uncertainty of PMI0 Data Collected with High-Volume Size Selective 

Inlets, Memorandum from L.J. Purdue to Richard Rhoads, U.S. EPA, 

RTP, N.C. September 15, 1986. 

2-11 

3.0 AMBIENT MONITORING AND DATA USAGE 

3.1 Ambient PMIO Monitoring 

States must establish ambient monitoring networks to measure PM10 just 

as they measure ambient levels of other pollutants for which NAAQS have 

been set. Requirements for a PM10 network are included in 40 CFR 58 as 

are the ambient monitoring requirements for other pollutants. Existing 

ambient monitoring networks measure total suspended particulate with 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 to meet the 

ambient monitoring requirements of Part 58 except as specified in Appendix 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 after promulgation 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 to the quality 

*Oescribed in 40 CFR 50 Appendix B of Part 50 

3-1 
assurance procedures in Appendix A of Part 58. Section 4.2.1(a) of Appendix A 

requires only that the percent differences for paired measurements from 

collocated samplers above certain levels (20 ug/m3 for PM10) be calculated 

and reported. Previously, measurements below these levels were reported. 

Otherwise, Q/A procedures for PMlO monitoring in both Appendices A and Bare 

identical to those for TSP monitoring. 

3.2.2 Ambient monitoring methodology 

The requirements in Appendix C of Part 58 which require reference or 

equivalent method samplers be used in State and Local Air Monitoring Stations 

(SLAMS) apply to PMlO with one exception. Section 2.2 of Appendix C allows 

the hi-VOl method to be used in a PM10 station as a substitute for a PM10 

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 if the hi-vol levels are 

consistently below those NAAQS; thus, there is no real need from a compliance 

standpoint to install a PMlO sampler. If, TSP levels rise above PMlO NAAQS, 

however, the State must install PM10 samplers in order to measure actual 

PMlO levels to be certain PMlO NAAQS are maintained. 

Also, section 2.2 of Appendix C requires that at TSP National Air 

Monitoring Stations (NAMS) the hi-vol be continued in operation for at 

least a year after the 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 particulate matter can thus be estimated. 

Section 4.0 of Appendix C, which previously applied to episode 

monitoring for TSP, has been revised to apply to PMlO. 

3-2 
3.2.~ Network establishment 

Title 40 CFR 58.20 requires a description of the SLAMS network be 

available to the general public and be submitted to EPA approximately 6 months 

after PM10 NAAQS are promulgated. 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, 1 year and 2 years after promulgation, 

are included in section 58.23 for completion of the PM10 SLAMS network. By 

1 year 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 of Aerometric Oata (SAROAO) identification form, and 

(4) meeting all quality assurance requirements in Appendix A pertinent to 

PMIO' The remaining PM10 samplers, including those in areas with nonattainment 

probabilities below 20 percent, have until 2 years after promulgation of 

the NAAQS to be fully operational and to meet the siting and quality 

assurance requirements. 

The same submittal date (6 months after promulgation) also applies 

for the description of the NAMS portion of the SLAMS network as required 

by section 58.30. The date by which all stations in the NAMS portion of 

the SLAMS network are to be in operation and meeting Part 58 requirements 

is 1 year after PM10 NAAQS are promulgated (section 58.34). 

3.2.4 Network deSign 

Information on designing SLAMS and NAMS networks is contained in 

Appendix 0 of Part 58. The material is largely self-explantory and, as 

3-3 
indicated 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 D has been amended to include criteria for determining the number of PMlO stations and areas in which to locate them. In 

addition to Appendix 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 6 days as it has been for TSP. Short-term and long-term 

strategies have been added to section 58.13. During the first full year 

of sampling the short-term strategy requires daily sampling at at least one 

site (the site of expected maximum concentration) in areas with a PM10 

nonattainment probability of 95 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 percent may be once every sixth day. The use of the term "area" 

as it applies to the required sampling frequencies of the "area" is as 

follows; (I) any urbanized area as defined by the U.S. Bureau of Census, 

{2} any incorporated place such as a city or town as defined by the U.S. 

Bureau of Census 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 contribute to the uniqueness of the 

"area" thereby distinguishing it from other designated "areas," The 

3-4 
first year of PMIO data collection is to start no later than the appropriate 

dates in section 58.23 and section 58.34 for completion of the SLAMS and 

NAMS networks, respectively. The year can start, however, as soon as the 

station is put into operation. 

The long-term strategy applies after the short-term strategy ends 

(in most cases after the first full year of sampling ends). The sampling 

interval will then vary according to the relationship of air quality levels 

to the NAAQS. The closer the air quality levels to the NAAQS, the more 

frequently sampling must be carried out. A table depicting the requirements 

appears in section 58.13. 

As soon as the first exceedance of the 24-hour PM10 NAAQS occurs in 

an area which historically has had a less than everyday sampling interval, 

the requirements change to everyday sampling for at least one site (the 

site of expected maximum concentration). The State will have up to 90 days 

after the 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 4 calendar quarters, but preferrably 1 

full calender year. If the day on which the first exceedance occurred 

was flagged as an exceptional event, the State must still implement everyday 

sampling. unless exempted by the Regional Administrator. 

3.2.6 Sampler inlet siting 

The criteria in Appendix E of Part 58 for siting ambient PMIO samplers 

is very similar to the 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 2 to 

7 meters is specified for PM10 microscale stations. The inlet height for all 

3-5 
other scales for PM10 stations is 2 to 15 meters as it was previously for 

all TSP SLAMS. The spacing requirement from roadways is also different. 

For microscale roadway stations, the PM10 sampler must be between 5 to 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 of TSP monitors as a surrogate 

for PMIO monitors under certain conditions, 'the reporting requirements for 

TSP data remain in Appendix F of Part 58. The TSP data summary format in 

section 2.2 of Appendix F, however, has been changed to correspond somewhat 

with the format added for PMIO data in section 2.7 of Appendix F. The 

ranges for the 24-hour summary report have been in 50 ug/m3 increments. 

The format added for PM IO is in 25 ug/m3 increments. A significant 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 if they exceed the level of the 24-hour PMIO NAAQS, and the 

sampling interval must be reported. Otherwise TSP reporting requirements 

remain unchanged. 

Section 2.7 has been added to Appendix F to cover reporting of PM10 

data. All exceedances of the 24-hour PM10 NAAQS, the sampling interval, 

and the number of 24-hour values within 30 ug/m3 ranges must be reported. 

3-6 
3.2.8 Air pollution index 

The air pollution subindex for TSP has been replaced by a subindex 

for PM10. The breakpoints for the subindex reflect the levels of the 

PM10 short-term standard. A new table of breakpoints and a graphic display 

of the breakpoints is included in Appendix G of 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 a 050 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 a 050 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 

equiva 1 ent methods. Therefore, nonreference or equi va 1 ent method PMlO 

data, PMl5 data, or TSP data may be used for initial SIP development 

purposes, subject to certain constraints. 

3.3.2 Interim use of non-reference or equivalent method samplers 

Until PM10 reference or equivalent method samplers can be designated, 

manufactured, purchased, and installed in PM10 SLAMS, States should continue 

to operate approved ambient samplers with inlets designed for a nominal 

050 of 10 micrometers. Approval of ambient PM10 samplers for SIP purposes 

wi 11 be made by the Envi ronmental Monitori ng Systems La boratory, MO-77 , 

Environmental Protection Agency, Research Triangle Park, North Carolina 27711. 

3-7 
Data 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 to have air 

pollution episode contingency plans are in Subpart H of Part 51. The 

sampling procedures that should be used for determining air quality levels 

during air pollution episodes are similar to those used prior to promulgation 

of the PMIO NAAQS. These procedures are identified in section 4.0 of 

Appendix C of 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 a 24-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 a 2-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 alternative method with the reference method. 

3.4 SIP Revisions 

That portion of the SIP providing for ambient monitoring should be 

revised, if necessary, to include provisions for a PMIO ambient network. 

If the ambient monitoring portion of the SIP provides for monitoring 

pollutants "for which NAAQS have been set," then no revision is necessary. 

If the SIP lists pollutants, PMIO would need to be added to the list. 

The PMIO SLAMS network design need not be included in the SIP and a SIP 

revision is not necessary as a result of any network modification. Any 

3-8 
SLAMS network modifications, however, must be approved by the appropriate 

EPA Regional Office and any NAMS modifications by EPA's Office of Air 

Quality Planning and Standards. 

3-9 
Reference 

1. Koch, R.C. and H.E. Rector, Network Desifn and Optimum Site Exposure Criteria for Particulate Matter, GEOMETechnologies, Inc., Rockville, 

MD. Prepared for U.S. Environmental Protection Agency, Research 

Triangle Park, NC. EPA Contract No. 68-02-3584. March 1983. 

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. 

3-10 
4.0 AIR QUALITY MODELING FOR PMIO 

4.1 Introduction 

Section 51.12 of 40 CFR requires that the adequacy of a control strategy 

for attainment and maintenance of NAAQS be demonstrated by means of a dispersion model or other procedure which 1s shown to be adequate and appropriate 

for this purpose. The Guideline on Air Quality Models (Revlse~)l provides 

guidance on dispersion modeling for particulate matter and lists preferred 

dispersion models for this purpose. Procedures collectively known as receptor 

models are also available that examine an ambient monitor sample of particulate matter and the conditions of its collection to infer the types or 

relative mix of sources impacting on it during collection. Receptor models 

are described briefly in 'Appendix A and the references to this section.2,3 

The most widely used and accepted quantitative receptor model is the chemical 

mass balance (CHB).4 The proper use of the CMB is described in two protocols 

referenced in this section. 5,6 

Three options are presented herein for estimating the air quality impact 

of emissions of PMIO using dispersion and receptor models: (1) use of receptor 

and dispersion models 1n combination (preferred); (2) use of dispersion models 

alone; and (3) use of two 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 to model 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 to TSP, except as specifically noted, as a surrogate for PM10. 

4-1 
4.2 Cons tde rat ions in Receptor Mode 1 Select ion. 

Several considerations related to the nature of sources emitting PMIO 

influence selection of the 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 of the sources, chemical similarity of the 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 of the receptor methods listed in Table 4-1. The other 

methods include factor analysis (FA), automatic scanning electron microscopy 

(ASEM), and microscopy (OM). The FA, ASEM, and OM are not generally considered 

quantitative and FA requires at least 40 samples to complete an analysis. The 

reader is referred to the 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 of particulate 

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 include those 

involving the combustion of fuels (motor vehicles, boilers, field burning, 

woodstoves, etc.) and industrial processes involving combustion, chemical 

reaction, or condensation of vapors. Contributors to coarse particles in 

the atmosphere include windblown dust from storage piles, agricultural 

fields, etc •• vehicle resuspended road dust, pollens, and fugitive emissions 

from industrial process sources. Various receptor models such as species 

mass balance and factor analysis are well suited to analyze sources Of 

4-2 
TABLE 4-1 

SELECTING FEASIBLE SOURCE APPORTIONMENT METHODS BASED ON 

DATA AVAILABILITY AND SOURCE CHARACTERISTICS 

Chemi- Fi nger- Iso- Ai r Shed 

cal prints lated (Source 

Sources Sources Simi- Ois- Single CateFine Coarse Known Unknown 1 arity simllar Source .90r1es) 

ISS 

.1 ance (eMB) Y y Y a,b,Y Y Y Y 

,ctor 

lalysis (FA) .X X X X b,X X X X 

ltomated 

.anni ng 

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 larger than 1.0 um 

- Appropriate to use in conjunction with eMB or OM. 

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

- Appropriate to use. 

4-3 

Ai r Shed 

{Specifi c 

Sources 

Withi n 

Category) 

b,Y 

b,X 

b,X 

YY 
either fine or coarse particles. Optical and scanning electron microscopy 

are suitable for coarse and fine particles down to about 1 micrometer in 

size but work better on coarse particles. Most receptor models generally 

work best when the sample is segregated by size range (e.g., fine and coarse) 

because these two size ranges are associated with different types of sources. 

4.2.2 Prior knowledge of sources and emissions 

In many instances, the sources suspected of contributing to ambient 

PMIO concentrations at a particular site are apparent. However, the relative 

contribution of each source is needed. Any of the methods discussed in 

Appendix A could be used to give source apportionment information if the 

sources are identifiable, provided the other requirements for using the 

method are also met. However the mass balance requires knowledge of sources 

and their emission characteristics. If some sources are unknown, FA, ASEM, 

or OM might prove useful if done prior to a eMS analysis. 

4.2.3 Chemical similarity 

The availability of "fingerprints" for the sources of interest will 

often determine the optimum receptor model to use. A fingerprint is the 

characteristic chemical or morphological pattern of the emissions from a 

source that is used to distinguish it from other sources. 17 Some sources 

have fairly distinct fingerprints, while others do not. Since combustion 

source emissions are predominantly composed of carbon, there is very little 

information upon which to differentiate among the different types 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., K in wood). Other common fingerprint problems 

include the difficulty in distinguishing among sources whose emissions are 

4-4 
comprised of various 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 their crystalline structure. 

4.2.4 Sources versus source categories 

Any of the receptor models listed in Table 4-1 or Appendix A can be 

useful in identifying the impact of an isolated specific source unless its 

fingerprint is similar to that of background. Likewise, the techniques can 

be used to identify many of the 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 of the same type may not be reliably identifiable except on wind 

directional samples or by a disperSion model. 

4.2.5 Particle size and 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, or TSP will determine to some extent the type 

of source apportionment method used for PMIO since the PMIO signature of a 

source may be significantly different from the PHl5 or TSP 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 

the table are in order of preference, 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 receptor model model corroborated by ASEM or optical 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 

particle data (generally less than 2-3 micrometers) are collected 

in addition to TSP. 

4-6 
4.3 Screening Techniques and Refined Dispersion Models for PM10 

Concent rat Ions 

It may sometimes be appropriate to conduct a preliminary screening 

study to determine likely causes of nonattainment prior to a major control 

strategy development effort. This generally is confined to use of existing 

ambient samples and emissions data and usually gives only a qualitative or 

"fi rst approximation" of sources. Such a screening study has two advantages. 

First, the qualitative prioritization of sources can be used to design the 

more definitive study, which is usually required for control strategy 

development. This may enable substantial cost savings, since the screening 

study can be focused on specific areas of concern. Second, in some cases, 

the source contribution may be very clear and the screening study may be 

all that is required prior to control strategy development. This is further 

discussed in the references to this 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 of PM10 concentrations.a,9,IO In general, these methods are based on the assumption that 

all particulate matter behaves as a gas in the atmosphere, i.e •• neglecting 

settling and deposition. Although subjective values for "half-life" or 

pollutant decay have been used occasionally as 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 is warranted, conSidering the 

size of the particles. 

4-7 
Dispersion models that can be used for estimating PMI0 concentrations 

are listed in Table 4-3. All models are available on UNAMAP Version 6 from 

National Technical Information Service (NTIS) as PB 86-222361. Emission 

inputs for those models not considered screening techniques are discussed 

in Section 5.0. It should be noted that only the ISC model explicitly treats 

settling and deposition of particles and can accept particle size data, in as 

many as 11 size fractions. No model recommended for regulatory use at this 

time handles secondary particle formation or other transformations in a 

manner suitable for SIP control strategy demonstrations. 

However. EPA has completed work on two models, PEM-2 (an urban model) 

and MESOPUFF-II (a medium scale transport model), that 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 particle pollutant 

(e.g., sulfur dioxide to sulfate). Since their accuracy, suitability, and 

resource requirements for regulatory applications are not well tested, no 

specific recommendation on the use of these models is provided here. 

Nevertheless. in selected applications, they may be useful in asseSSing the 

significance of background concentrations and of secondary sulfate particles 

in the effectiveness of control strategies. Application of these models 

should follow recommendation in the modeling guideline concerning the use 

of alternative models. 

4.3.2 SpeCial considerations for PM1C dispersion modeling 

The Modeling Guideline1 contains guidance that should be followed on 

(1) selection of appropriate source and meteorological data for use with 

dispersion models, {2} location of receptor sites, (3) selection of model 

4-8 
1 

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 

*For more information concerning the applicability of these models, consult 

the Guideline on Air Quallt Models Revised. As noted in this document, 

these models maya so e use for TS mo e ng analyses in conjunction with 

a suitable TSP emission inventory. as a surrogate. where PMIO data bases 

are inadequate. 

**These models are considered to be screening techniques.forusepr10r.to a 

more refined analysis as outlined in the Guideline on Air Quality Models 

(Revised). _.-

4-9 
opt10ns, (4) determ1nation of urban/rural class1ficat10n, and (5) determ1nat10n 

of background air quality. With regard to background air quality values, 

there is now limited data that might be used to support specific values for 

PMI0 background concentrations. General guidance on the use of available data 

is provided in Appendix D. 

Rollback and roll forward are appropriate only for preliminary analyses. 

Proport1onal models may be used 1n conjunction with receptor modeling 1f 

the air quality problem is clearly associated with a few specific sources. 

Such procedures are discussed in Section 4.4 and 6.4. For urban~wide refined 

analyses, COM 2.0 or RAM may be used. For source~specific analyses of cOMp11-

cated sources, the ISC model is preferred to CRSTER/ MPTER because ISC is the 

only,model that is capable of treating deposition, 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 appropriate Regional 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 of the highest concentrations 

occurring some time, somewhere within an area. Errors in highest estimated 

concentrations of ! lOto 40 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-station RAPS network in St. Louis. The average network cumulative 

frequency distributions of hourly estimated and observed concentrations 

differed by no more than 30 percent over the entire concentration range. 

4-10 
However, estimates of concentrations that occur at a specific time and site 

tend to be poorly correlated with 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 to ensure that the samples 

analyzed are representative of the conditions causing NAAQS exceedances. 

The CMB is recommended as the primary method to be used in regulatory 

applications of receptor models to PM10 data. 4,12.13 There is uncertainty 

in any source apportionment approach. Therefore, if CMB is used for source 

apportionment (without combining with 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 

of the Receptor Model Technical Series2 or the Digest of Ambient Particulate 

Analysis and Assessment Methods. 14 It is strongly urged that either optical 

microscopy or ASEM be used to corroborate CMS, along with intensive chemical 

analysis (sulfate, carbon and other elements) of the samples. The OM or 

ASEM should be used instead of CMB if only TSP data are available. It Is 

also strongly urged that the CMB 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. 

4-11 
increases 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 variety of 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, the CMB 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 of crustal 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 

4-12 
methods (OM and ASEM) would usually be preferred for preliminary analyses. 

As suggested in Table 4-2, these are the recommended, corroborative methods 

for use with dispersion modeling when only TSP data are available. 

4.5 Use of Receptor and Dispersion Models in Combination 

Several demonstrations have been made where receptor models were used 

to help evaluate the results of dispersion 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 of the receptor model can be used to carefully scrutinize the 

inventory assumed In the dispersion model to deduce whether emissions from 

certain source categories appear to 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 reference6 to this section. 

Guidance in Section 5.0 describes the kinds of data necessary in a PMI0 

emissions inventory used as input to a dispersion 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 of observed 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 the NAAQS. 

It may be difficult to devise a PM10 short-term emission inventory for 

use in a dispersion model to characterize 24-hour episodes of high ambient 

4-13 
particulate matter. Therefore, analysis of the 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 either 24-hour or annual PMIO analyses because the particles 

larger than 10 um may bias the results. An exception to this is the use of 

ASEM or optical microscopy in association with dispersion model estimates. 

This exception is permitted because ASEM and optical microscopy involve the 

analYSis of discrete particles where their size (relative to PMIOl can be 

estimated. 

Once specific 24-hour or annual average source contribution factors 

are obtained, the proportioning method discussed in Section 6.4 may be used 

to estimate control requirements. 

4-14 
1. 

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 Particulate Source Apportionment, EPA-450!4-8 -016a, 

PB82-139429. U.S, Environmental Protection Agency, Research Triangle Park, NC, July 1981. 

3. Gordon, G. E., 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 Protection Agency, Research Triangle Park, NC, July 1981. 

Protocol For Applying 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 

Preparat ion. 

Receptor Model Technical Series, Volume V: Source Apportionment 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 of Atmospheric 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 Of New 

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, Washi ngton, DC, 1982. 

Rhoads, R. G., "Accuracy of Air Quality Models," Memorandum to Air and 

Hazardous Div; sion Di rectors. U.S. Envi ronmental Protect ion 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 Of Ambient Particulate Analysis And Assessment Methods, 

EPA-450/ 3-78-113. U.S. Environ~ental Protection Agency, Research 

Triangle Park, NC, September 1978. 

15. 

16. Receptor Model Technical Series, Volume VI:· A Guide To The Use of 

Factor Anal sis And Multi le Re ression R Tec ni ues n Source 

Apport onment, EPA 4501 - -00, PB86-10 6 • U.S. Environmental 

Protection Agency, Research Triangle Park, NC, July 1985. 

• 

17. Receptor Model Source Comrosition Library, EPA 450/4-85-002, PB85-228823. 

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

4-16 
5.0 DEVELOPMENT OF PMIO EMISSION INVENTORIES 

5.1 Overvi~ 

As part of the SIP 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 of this section is on developing or reviewing PMI0 emission 

inventories to be used as input to applicable air quality dispersion models. 

Such models are expected to be 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 particulate matter or other 

inventories and will find the same general procedures applicable to 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 of PMIO inventories, or inventories of any 

pollutant to be used in dispersion modeling, are (l) a set of emission factors, 

and (2) source data to which the emission factors are applied to estimate 

emissions. Emission factors are generally compiled from source test data and 

represent emissions, before and/or after controls, that may be expected from 

a typical facility within a particular source category. The most commonly 

5-1 
used source of emission factors 1s AP-42, Compilation of Air 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 to the appropriate source data to develop PMIO 

inventories. Emission inventory procedures for mobile sources are presented 

in a separate EPA publication. 11 

The second component of the 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 the major 

differences involving (l) the levels of temporal and spatial resolution, 

(2) the description and configuration of each source, and (3) the need for 

chemical composition and particle size distributions. Thus, this section 

also identifies models currently available for PM10, as well as those 

anticipated to be available in the next several years, and delineates the 

specific differences in emission inventory requirements of each of these 

models. 

Finally, a number of specific considerations need to be made when 

dealing with air quality models and PMIO inventories. First, condensable 

and secondarily formed particles may become more important Since both are 

primarily within the PMIO fraction. Second, since PM10 is a subset of total 

5-2 
particulate matter, the question arises whether an existing particulate matter 

inventory, compiled for TSP 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 latter part of this section. 

5.2 ~Emission Factors and Fractional Multipliers 

Any emission factor is basically a multiplier which is applied to appropriate 

source activity levelS, such as throughput or production rates. to estimate 

emissions. An emission factor can be a constant, implying a linear relationship 

between source activity and emissions, or can be variable, with parameters 

other than just activity levels influencing emissions. In either case, 

emissions are generally computed by the follOWing relationship: 

Emissions factor x Activity level x Control 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 particulate matter, PM10. or any 

other pollutant. If a set of PM10 emission factors is available. the preceding 

equation becomes: 

Emission factorlpM x Activity level x Control device 10 

penetrationlpM = EmissionslpM 10 10 

Of course. emission factors may be utilized that already reflect the effect 

of certain controls, in which cases the control device penetration factor 

drops out of the above equation. 

5-3 
Alternatively. PMIO emissions may be computed by applying fractional 

multipliers to particulate matter emission estimates in an existing inventory as 

follows: 

(PMIO fraction) x Emissionslparticulate ~ EmissionslpM 10 

An advantage of the 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 a data handling standpoint when converting an existing 

total particulate matter inventory to a PMIO inventory. 

Thus, in compiling a PMIO inventory for use in dispersion modeling. 

one must first obtain requisite PM10 emission factor or size fraction 

information. Such information is becoming available for a large number of 

sources. Table 5.1 lists the stationary source categories for which EPA has 

developed PMIO emission factor and other particle size information. A majority 

of these factors have been distributed to State/local agencies. Additional 

PM10 information will be published in supplements to AP-42. 

The PM10 fractions and emission factors are presented in tabular form 

such as are shown in Table 5-2. In the hypothetical example shown in 

Table 5-2, cumulative mass percents and emission factors are shown at various 

particle size cut points for a controlled process. In this case, PMIO emissions 

after controls could be calculated either by applying a factor of 0.06 lb/ton 

to the process production rate or by multiplying the existing total particulate 

matter emissions for the facility by 60 percent. A graphical display of the 

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 reinj ection. 

2Wi th flyash reinj ection. 

Fabric 

Process Uncont roll ed 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 ,,1,2 
V'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 

Manufacturi ng 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 Alfalfa Dehydrating Drum Dryer " 

6.3 Cotton Ginning Battery Condenser 

Lint Cleaner " 

Roller Gin 

Bale Press 

Gin Stand " 

Saw Gin " 

Bale Press " 

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 

Prebake Roof 

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 

-

Other 
U1 

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 

Clean Water 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 

Cas thouse x 

Blast Furnace wi th 

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 Melti ng 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 Uncont roll ed ESP Filter 

Iron and Steel Production 

(cont.) HAP Melting, 

Refining, and 

Charging 

Direct Shell 

Evacuation x 

Open Hearth 

Furnace Melting 

and Refining x x 

Primary Lead Smelting Blast Furnace Flue x 

Blast Furnace x 

Ore Storage x 

Sinter Machine 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 Iron Foundries Cupola Furnace x x 

RAP x 

Fouring and 

Cooling x 

Shakeout x 

secondary Lead 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 

Faste Mixing x 

Lead Oxide Mill x 

Faste Mixing 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 Cl eani ng Dry Process x 

Thermal Dryer x 

Thermal 

Incinerator x 

Glass Manufacturing Furnace Exhaust x x 

CONTROL DEVICE 

Multiple 

Scrubber Cyclone Other 

x 

Gravity 

Filter 

x 
U'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 Phosphate Rock Proceasing 

8.22 Taconite Ore Processing 

8. Feldspar 

8. Fluorspar 

8. Lighweight Aggregate (Clay) 

&Cyclone With Baghouse. 

bS. C. ~ Settling Chamber. 

Fabric 

Process Uncont roll ed 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) 

PM IO 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 

Red procati 118 

Grate Clinker 

Cooler 

Talc Pebble Mill 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 

Tower 
U'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 x 

Freeways/Expressways x 

Industrial Paved Roads Copper Smel ten x 

Iron and Steel x 

Asphalt Batching x 

Concrete Batching x 

Sand and Gravel :It 

-

CONTROL DEVICE , 

Multiple 

Scrubber Cyclone Other 

! 
Particle Size 

(micrometer) 

Total Catch 

15.0 

<TI 

, 

.... 

0::> 10.0 

7.5 

5.0 

2.5 

1.0 

0.5 

aControl Device: Baghouse. 

TABLE 5-2. CUMULATIVE EMISSION FACTORS AND PARTICLE SIZE 

DISTRIBUTION FOR A HYPOTHETICAL 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 

70 82 59 0.082 

51 60 43 0.060 

42 56 36 0.056 

33 50 28 0.050 

13 31 11 0.032 

2.8 11 2.5 0.010 

0.9 4 0.8 0.004 
~ c 

51 

ill n 

'" ~ cl S 

!i!; "" -

~ 

" !:i! ~ -

en c t11 q -'" J il ..... 

I.CI ." <= :J> 

~ n 

cl 

=i '" '" <:> 

§ 

50 I! i III ill 

20 

10 

5 

1 

.5 

1 : 

0.10 

0.0.5 

0.02 

0.01 

" I 0.00.5 

001 

0.5 1 2.5 5 7.5 10 15 

AERODYNAMIC PARTICLE DIAMETER, MICROMETeRS 

FIGURE 5-1 EXAMPLE GRAPHICAL DISPLAY OF CUMULATIVE EMISSION 

FACTORS VS. PARTICLE SIZE FOR A HYPOTHETICAL PROCESS 

" <:> 

= es 

en n 

ill <= ii! -t '" 

<:> S :z <: <:> ,.., ." !:i! " 

~ -

en 

cn <= 

q il -

~ 

8 

=i ~ 

es '" 

~ 
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 C of 

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 that must be obtained depend on whether a particular 

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 air quality impacts. 

As described in more detail in section 4.0, a number of models are 

currently available for assessing the air quality 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 air concentrations primarily as 

functions of source configurations, emission strengths, terrain features, 

and meteorological conditions. Receptor models (discussed in section 5.4) 

infer the relative impact of various sources on ambient air quality by 

5-20 
reconciling particle size, and shape and chemical composition data of ambient 

air samples with particle size, shape and chemical composition data for 

various emission sources in the vicinity of the ambient air measurement site. 

Individual source models evaluate the 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 for time periods from 1 hour to 24 hours whereas 

long-term mOdels generally predict monthly, seasonal, or annual average concentrations. The major impacts of model 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, 

or complex. Areawide models require source data for all sources 

within the defined grid system. 

-- Source configuration - Some (especially point source) dispersion 

models require information on individual stack heights and building 

sizes and locations. Many dispersion models require area sources to 

be assigned to grid squares and some distinguish line sources from 

area sources. 

-- Temporal resolution - Typically. the emission rates input to most 

applicable dispersion models are expressed in terms 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 to dispersion models need not be as resolved 

as might be inferred from these units. Ideally, the emission rates 

5-21 
should be as resolved as the model output. For example, if the model 

predicts a 24-hour average ambient air concentration, emissions 

should be input that reflect conditions over that 24-hour period for 

best results. In cases where maximum concentrations need to be 

predicted, maximum emission rates may need to be considered instead 

of a time-averaged rate. 

-- Spatial resolution - Most dispersion models require point source 

coordinates for each stack. Area and line source emissions are 

typically assigned to grid squares and line segments, respectively, 

having arbitrary dimensions. The scale of the coordinate systems in 

most models is at the discretion of the user. Because of computer 

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 into the model's 

software. 

-- Size fractions - Most applicable dispersion models consider the PM10 

fraction as a single lump sum entity requiring no particle size 

resolution below 10 micrometers. Several models have the capability 

to treat various fractions differently within 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 that may be used for 

simulating PMIO levels, along with specific source data requirements of each 

5-22 
model. References 2 through 7 present a detailed description of each of 

these models. The reader should consult these user's manuals as well as 

someone with modeling experience before developing an inventory as input to 

any of these models. 

The reader will note from the information in Tables 5-3 through 5-7 

that dispersion models vary considerably in their source data requirements. 

As was mentioned earlier, it is not the intent of this section to reiterate 

guidance on the fundamentals of compiling inventories. Rather, this sec~ 

tion's primary purpose is to highlight special considerations that may have 

to be made when developing a PMIO emissions data base for modeling. Basic 

guidance on compiling emission inventories is given in References 8 through 14. 

Additional guidance is presented in Reference 15 on obtaining different levels 

of temporal 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 4.0 of this document. 

5-23 
TABLE 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 roof monitors 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 ISCLT may be run for a year using frequency distributions of 

meteorological data and annual emissions. 

Inventory area - All sources within the boundaries of the 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. If the stack is 

adjacent to a building and aerodynamiC wake effects are to be considered, 

the length, width, and height of the building must be known. 

Nonpoint source confi~urations - The horizontal dimension, elevation above 

sea level and ef ective emission heights are required for each area 

source or volume source. 

Temporal resolution of model 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 to model - grams/second for stack and volume 

sources. 

grams/second-meter2 for area sources. 

Spatial resolution of source data - Source elevation above mean sea level 

and source locations with respect to a user-specified origin are required for all sources. Universal Transverse Mercator coordinates may be used 

to define source locations. A plant layout drawn to scale is required 

to obtain coordinates and building dimensions to the nearest meter. 

Particle size resolution - Required if settling and deposition are to be 

considered. Deposition is generally only important for particles 

greater than 20 micrometers in diameter. The PMIO fraction could thus 

be treated as a lump sum without any need for particle size data 

although ISC allows input of up to 11 size fractions. 

Chemical composition data - None required. 

5-24 
TABLE 5-4 

CLIMATOLOGICAL DISPERSION MODEL (CDM 2.0)3,4 

~ - Dispersion. 

Application - Estimating urban scale impact of multiple 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 the boundaries of the grid system must be 

inventoried. 

Stack and exhaust data - PhySical stack he1ght and inside diameter, exhaust 

gas exit velocity, and temperature are needed to calculate plume rise. 

Nonpoint source configuration - Area source emissions are allocated to square grid cells. The coordinates of the southwest corner and the width of 

each grid are required. Stack height (if applicable) is also required. 

Temporal resolution of the 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 the 24-hour 

average. 

Spatial resolution of source data - The scale of the coordinate system for 

locating sources is completely arbitrary. Area source grid squares can 

be defined arbitrarily small; however, since the program will only handle 

2500 grid squares, a tradeoff 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 of a common 

side length. 

Particle size resolution - No particle size resolution below 10 microns 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 of multiple 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 a year or by averaging all 24-hour concentrations. 

Inventory area - All sources within the boundaries of the grid system must 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 of the southwest corner and the 

side length of each grid are required. Height of emissions is also 

needed, if applicable. 

Temporal resolution of model output - One hour to 1 day (24-hour) average concentrations are calculated by RAM, and annual averages are calculated 

by averaging all 24-hour concentrations. 

Actual emission units input to model - 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 10 microns is 

required. The PMI0 fraction can be treated as a single lump sum. 

Ch@mical composition data" None required. 

Other - An assumed pollutant half life may be entered into RAM, but should 

not be considered in PMI0 SIP's. 

5-26 
TABLE 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 to be emitted at a 

common point. 

Stack and exhaust data - Physical stack height and inside diameter, exhaust 

gas exit velocity, and temperature are needed to calculate plume rise. 

Nonpoint source configurations - CRSTER does not handle nonpoint sources. 

Temporal resolution of model output - The model calculates I-hour, 3-hour, . 24-hour, and annual mean concentrations. Optional averaging times of 2, 4, 

6, 8 or 12 hours may be selected. 

Actual emission units input to model - grams/second. 

Spatial resolution of source data - None; all sources are considered to emit 

from the same point. 

Particle size resolution - No particle size resolution below 10 microns is 

required. The PMlO fraction can be treated as a single lump sum. 

Chemical composition data - None required. 

Other - An assumed pollutant half life may be entered into CRSTER, but should 

not be considered in PMI0 SIP's. 

5-27 
~ - Dispersion. 

TABLE 5-7 

MULTIPLE POINT SOURCE MODEL WITH TERRAIN 

ADJUSTMENT (MPTER)7 

Application - Estimating local impact of a number of point sources in rural 

or urban areas where terrain is below phySical stack height. 

Short/long term - Short-term (1-24 hours) and annual calculations can be made. 

Inventor! area - That area including all of the pOint sources (up to 250) 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 to calculate plume rise. 

N~npoint source configuration - MPTER does not handle nonpo;nt sources. 

Temporal resolution of model output - One hour or any multiple thereof, 

including hour-by-hour concentrations for an annual average. 

Actual emission units input to model - grams/second for each point source. 

Spatial resolution of sourc~ data - The scale of the point source coordinate 

system is arbitrary, allowing any level of resolution. 

Particle size resolution - No particle size resolution below 10 microns is 

required. The PMI0 fraction can be treated as a single lump sum. 

Chemical composition data - None required. 

Other - An assumed pollutant half life may be entered, but should not be 

considered in PMI0 SIP's. 

5-28 
5.4 Inventory Data Needed in Receptor Models 

The considerations in sections 5.2 and 5.3 apply primarily to PM}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 estimate the contributions of emission 

sources based on specific characteristics of particulate matter measured at various 

receptor sites. Receptor models should be used together with rollback procedures 

or dispersion models to define source/receptor relationships. 

Because of the nature of receptor models, emission factors and mass 

emissicn estimates are not needed as input as they are for dispersion models. 

Nor, generally, are detailed source data required such as activity levels and 

control techniques.* The only source emission data that are needed for 

direct use in receptor models involve similar kinds of morphological 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 of broad 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 variability of various chemical species at 

ambient sampling sites with 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 of data are needed in 

interpreting the results and in defining specific source/receptor relationships. 

5-29 
Chemical methods are comprised of mass balance techniques, 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 linear combination of sources to explain either 

the time or spatial variability in ambient chemical patterns. 

Microscopy represents another Important type of receptor method typically 

used to confirm the results obtained from chemical methods. Optical microscopy 

relies on ldentlfication of particles collected at the receptor site by their 

size, shape, color, surface properties and birefringence (an optical property). 

Scanning electron microscopy, commonly automated by computer and supplemented 

with x-ray fluorescence analysis, relies on a partlcle-by-particle analysis of 

particle size, shape, and elemental composition. The use of such particle 

identification techniques requires a knowledge of the physical characteristices 

(e.g. shape, size, brightness, color) of particles from various source categories 

or access to references that catalog particle size characteristics from various 

sources. 16,18,19 

Since nearly all receptor models require a knowledge of the chemistry 

or morphology of source emissions, the development of a 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 of developing 

locale-specific emission characterizations, especially for more important 

source categories. 

The EPA has compiled a library of chemical and elemental composition data 

that will provide a common basis for receptor model source characterizations. 20 

An example of the types of data in this library is shown In Table 5-8. 

5-30 
TABLE 5- 8 

.' 

EXAt'PLE OF DATA IN EPA'S PARTICl/LATE SOURCE LIBRARY (29) 

s.::llRCE: $~EEL PRaoUCTIOH . OPEH HEART~ FUR"ANCE PROFILE: 28302 

S:C: 3-03-009-01 RA"~lNe: 2,121 =OHTROLS: ESP RATlHG : D ......... ~- ... ~.- .... -.. -... ~ ... ~ ....................•• ~.-- •..•.......•............•• ~ ......••.... ~ ......... .... FtNE COARSE TSP S~EC!!S S~WE$ ............ '11'. <2.5 UH •••••• . ................ 2.S·101..1K •••••• 'It ......... <30 UH ......... tt!')!';Sii( " .... "':- 1 BY \IT + - VNe % BY lIT . - UHe % BY \IT •• u.:e . " .......... ... ~ ... -.. ............ .......... , BE HA + • NR HA • • NR HA · . ~~ 

S B HA + - HR HA + - HR HA · . ~~ 

9 F HA + • NR HA • • HR HA · . ~i! 

11 HA NA + • HR HA · . HR HA · . Iii! 

12 HG NR .. . NR WR .. . HR Hi! · . ~it 

13 AL HR .. . HR HR + • Hi! Hi! + • lilt 

14 st ~. · . HR Hil · - HR Hil .. . q 15 p < • • Hi! < · . Hi! < • • .... ~ 

16 S 13.300 · - HR 3.000 • • Ni! 13.300 + • ,. 17 .L < .. . HR < · . HR < .. . ~i! 

a ( 5.000 HR 5.000 _. NR 5.000 • • ,i! 20 CA 0.550 · - Hil 0.550 ... Ni! 0.550 • • 2T SC < " .. . Hi! < · . HR < .. . 22 11 < · " - HR < • • Hil < • • Ii. 

23 v 0.550 .. . HR 0.550 + • HR O.SSO .. . ;.. 

24 •• 2:000 + - HR 0.550 • • HR 2.000 ,it 25 ~H 0.550 + . NR 0.550 • • NR 0.550 • • ~, 

26 FE 11.000 ... NR 16.000 .. . Hi! 11.000 .. . NR 

21 CO < .. . NR < ... Hi! < · . IiR 28 "t 0.550 ... NR 0.550 · . Hil 0.550 .. . 2<;- ~. ." 0.550 • Hi! 0.550 .. Hi! 0.550 .. - " -" 30 2N < .. . NR . < .. - Nil < ... ,. 31 CA < .. - HR < .. - NR < .. . .. 

32 eE < ... NR < .. - HR < + • Iii! 

33 AS 0.050 NR 0.050 · - HR 0.050 .. . M 

>L Sf < .. - HR < · . Hi! < .. . ", 35 Gil 0.050 · - ·NR 0.050 .. . HR 0.050 .. . ,,, 

37 IS 0.050 .. HR 0.050 · . HR 0.050 • • ~, 

3.5 SR < · . NR < .. . HR < · . ,. '0 lR < + • HR <. .. . H. c • • H .<G 0.050 .. . HR •• 0.050 • • NR 0.050 .. . ~. 

La I:D 0.050 + • HR 0.050 .. . Hi! 0.050 · - ~i 50 SH 0.050 '. HR 0.050 .. . Hi! 0.050 · - '1 51 S. 0.050 • • NR 0.050 · . HR 0.050 · . .~ 55 CS c • . HR < • • HR c · . ~R 56 SA < · . HR < .. . HR c · , ~. 5$ CE < .. - HR < + • HR c · . ,I 8:) HG < ... HR < · - NR < .. . ,. $2 PB 0.550 • • Hi! 0.550 · . HR 0.550 .. . r.:~ 201 oc HA .. . Hil HA · - Hi! HA · . ,. 2e2 fC HA · - NR NA · . NR HA • • h. 

203 S04 40.000 .. - HR 35.000 .. - HR 40.000 • • ,. 21).1. H03 0.550 · . HR 0.550 · - HR 0.550 .. . ,. S~"'( % ) 62.200 60.150 62.200 

HOTES: DC • ORGANIC CARBON EC • ELEMENTAL CARSON , HA • NOT ANALYZED, HR • HOT REPORTED < • LESS THAN OETECTION ,IHIT 

CTHER HOTES : 2C~ .ARBOH, 22'26% OTHER (SI, OZ, AL, Me) TSP AND COARSE. REf, 20 

5-3} 
These data are most useful in the chemical methods such as the mass balance 

model and factor analysis described in Appendix A of this guideline. For 

more information on this particulate matter source composition library, contact the 

U.S. Environmental Protection Agency, Air Management Technology Branch, ND-15, 

Research Triangle Park, North Carolina, 27711. The appropriate references 

cited in section 4.0 and Appendix A may be consulted for more details on the 

specific characterizations needed for each type of receptor model. 

5.5 Condensable Particulate Matter 

Condensable particulate matter (or condensed particulate matter, as it 

is synonymously described) can be broadly defined as material that is not 

particulate matter at stacK conditions but which condenses and/or reacts (upon 

cooling and dilution in the ambient air) to form particulate matter immediately 

after discharge from the stacK. Condensable particulate matter is of potential 

importance because it usually is quite fine and thus falls primarily within 

the PM I0 fraction. 21 Sources suspected of emitting significant amounts of 

condensable material are shown in Table 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 a portion of the condensable fraction, AP-42 particulate 

matter factors for certain categories that are currently based on Method 5 include 

some condensable particles. Some condensable particulate matter will also be 

collected by the testing procedures outlined in Appendix C of this guideline. 

5.6 Secondary Particulate Matter 

Secondary particulate matter can be broadly defined as particles that form 

through chemical reactions in the ambient afr well after dilution and condensation have occurred. An example of this phenomenon is the formation of 

5-32 
TABLE 5-9 

POTENTIAL SOURCES OF CONDENSABLE PARTICULATE MATTER~l 

Stationary Sources 

Al faHa Dryers 

Anode Baking Furnaces 

Asphalt Plants 

Asphalt Roofing felt saturating asphalt blowing 

Boilers/Other Combustion 

bagasse 

coal 

lignite 

oi 1 

wood/bark 

Charcoa 1 Kfl ns 

Chemical Production 

boric acid 

phosphoric acid 

potassi um sti 11 

zinc sulfate 

Citrus Peel Dryers 

COKe Plants 

Corn Processing 

wet milling 

syrup manufacturing 

Elemental Phosphorus electric arc furnace 

Expanded Vinyl 

Ferroalloy Mills 

Fertilizer Plants 

ammonium nitrate 

diammonium phosphate 

5-33 

Fi berboard 

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 Paper Mills 

recovery boil ers 

lime kiln 

smelt dissolve tank 

blow tank/hot water 

accumulator 

Lime Kiln 

Manure Dryers 

Mineral Products 

gypsum 

clay dryers feldspar dryers clay k1lns 

Municipal Incinerators 
TABLE 5-9 (CONTINUED) 

Petroleum Refineries 

FCC 

catalytic regenerator heaters 

pet ro 1 eum 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. 11 n 

Zn fume k. 11 n 

Residential Heating oil 

wood 

Secondary Metal Smelters 

Al scrap furnace 

Al dross furnace 

brass and bronze furnaces 

copper furnace 

Pb furnaces 

Pb02 mi 11s 

Pb gri d cast i ng Pb remelt pot other metal furnaces 

Mobile Sources 

All Mobile Sources 

5-34 

Sewage Sludge Incinerator 

Silicon Carbide Furnaces 

Spray Paint Booths 

Sulfite Pulp Mill 

recovery boil er 

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 resawing 
sulfate particles in a plume from the oxidation of sulfur dioxide by one of 

several atmospheric transformation mechanisms. Generally, secondary particulate 

matter can be distinguished from condensable particulate matter by the time 

and/or distance downwind from the stack required for formation. Condensable 

particles form in a matter of seconds in the stack exhaust due primarily to 

immediate cooling and air dilution whereas secondary transformation requires 

minutes, hours, or even days. 

Unlike condensable particulate matter, secondary particulate matter should 

not be included in the PMIO inventory as if directly emitted as particles. 

Condensable particulate matter, because it forms so quickly, will likely impact 

on any nearby receptor, and thus can be treated as if it were 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 air quality model if it is considered to be an 

important component of ambient PMIO concentrations. Precursors of secondary 

particles especially SOx and NOx, need to be included in the inventory when such 

models are used. 

5.7 Use of Existing Emission Inventory 

An important consideration when planning a PMIO inventory is whether an 

existing particulate matter inventory can be used as a foundation or starting point. 

Since many areas have compiled annual, countywide inventories of particulate 

matter (e.g., for previous SIPs or for submission into EPA's NEDS or Compliance 

Data System), an incentive exists for building on such inventories rather than 

compiling new ones altogether. If an existing inventory is comprehensive, 

current, and accurate, much of the source data needed in the PMIO inventory is 

already available. 
The utility of the existing inventory depends on the particular air 

quality model employed. In some instances, a conventional inventory of 

annual, countywide particulate matter emissions will provide most of the needed 

information. Actually, the terms "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 of tons per year, 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 of temporal apportionment are given 

in Chapter 6 of Reference 18. 

Second, stack data are available for pOint sources in the annual, 

countywide inventory that are sufficient for many PM10 modeling applications. 

Specifically present are stack heights, diameters, flow rates, and.plume 

heights, which are the major stacK and exhaust gas parameters commonly needed 

by most applicable dispersion models. (Note: Some models also require exit 

gas velocities, but flow rates can be easily converted to exit velocities by 

dividing by the cross sectional area of the stack.) Moreover, the location 

of each stack is commonly specified to the nearest tenth kilometer, providing 

acceptable spatial resolution for applications where the grid system will be 

comprised of relatively large grid cells. 

Thus, relating back to the inventory specifications for each model 

outlined in Tables 5-3 through 5-7, it can be seen that, for point sources at 

least, conventional inventories of particulate matter may provide sufficient temporal 

and spatial resolution and stack data for a number of dispersion models such 

as COM 2.0, RAM, CRSTER, and MPTER. 

5-36 
The area source data available from conventional (i.e., annual, 

countywide) particulate matter inventories can also be useful in some PM10 modeling 

applications; however. additional data manipulation is generally needed to 

develop the requisite temporal and spatial resolution. First, most existing 

area source records do not include operating rate data comparable to those 

included in the point source record. Hence, to develop emissions estimates 

for a period of time less than a year, the user must impose an operating 

pattern for each source category. Detailed operating patterns for many 

important area source categories are suggested in Chapter 6 of 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 to the 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 6 of Reference 18. 

Referring to the inventory specifications in Tables 5-3 through 5-7, 

the only models which could use the typical existing area source inventory as 

primary input are COM 2.0 and RAM. The other models either do not handle area 

sources or else require more detailed information. 

Several models require such specific inventory data on individual sources 

or source complexes that an existing inventory may be of little use at all. 

The rsc, for example, requires input on the dimensions and juxtaposition 

5-37 
of buildings near each stack in order to consider the 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 particulate matter emissions have 

to be converted to PMIO emissions. As was discussed in section 5.2, the easiest 

way to accomplish this ,is to multiply existing particulate matter estimates 

by appropriate PMIO fractional multipliers. for maximum accuracy, PMIO 

fractions should be applied to individual sources rather than aggregated 

source categories and should account for the existence of any emission controls. 

Alternatively, instead of applying PMIO fractions, PMIO emission totals can 

be calculated by applying the appropriate PMIO emission factor to the existing 

activity level for each source. 

5.8 Data Handling 

An important consideration in any inventory compilation effort is the 

potential need to develop special data handling software. In a number of 

models, data handling can be readily accomplished manually because the input 

5-38 
requirements are meager. For certain models, however, computerized data 

handling capabilities may be desirable to compile and properly format the 

large quantity of inventory data needed to drive the models. 

As a general rule, the amount of inventory data that must be handled is 

directly proportional to the 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 for models like ISC and MPTER if only a few sources are considered. 

The primary applications where computerized data handling will prove 

essential are (-I) when dealing with hundreds of point sources; (2) where area 

source emissions must be subcounty allocated to grid cells; (3) when numerous 

line sources must be considered individually; and (4) where an existing par· 

ticulate matter 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 to CDM 2.0, RAM and MB, as well as MPTER if large numbers of 

sources are considered. The CDM 2.0 and RAM are most likely to be applied to 

large, urban scale applications involving many point and area sources and 

large grid systems. Moreover, existing particulate matter inventories are 

most likely to be utilized as input for these two models, necessitating some 

preprocessing to convert total particulate matter to PMIO. Hence, if -either 

CDM 2.0 or RAM 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 of the input data. 

Data handling is frequently an overlooked aspect of inventory compilation and 

5·39 
air quality simulation modeling that can consume a great deal of resources. 

Thus, the inventory specialist, modeler, and programmer should all be involved 

in the planning of this kind of PMIO inventory effort. 

5-40 
References 

1. 

2. 

3. ' 

4. 

5. 

6. 

7. 

Compilation of Air Pollutant Emission Factors, Fourth Edition, 

Volumes I and II, U. S. Environmental Protection Agency, Research 

Triangle Park, North Carolina, September 1985 and Supplements. (Supplement A was issued in early 1987). 

Envi ronmental Protecti on 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, A Multiple Point 

§aussian Dispersion Algorithm with Optional Terrain Adjustment, 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 Preparatlonl Volume II: Point Sources, 

EPA-450/4-81-026b, U.S. Environmental Protect on Agency, Research 

Triangle Park, 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. 

5-41 
Procedures 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 Volatile Or 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 of Emission 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 to Particulate Source Apportionment, EPA- 50/4-81-016a, u.s. Environmental Protection Agency, Research 

Triangle Park, North carolina, July 1981. 

J. E. Core, Rece7tor Model Technical Series, Volume lIt Chemical Mass 

Balance, EPA-450 4-81-016b, U.S. Environmental Protectlon Agency, Research Triangle Park, North Carolina, July 1981. 

Receptor Model Technical Series. Volume IV - Technical Considerations in 

Source Aeportionment by Particle Identification, Draft Final Report from 

Engineerlng-Science to U.S. Environmental Protection Agency. Research 

Triangle Park, North Carolina, September 1982. 

W. C. McCrone and J. G. Delley. The Particle Atlas, 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 of Particulate Emissions From Stationary Sources," 40 CFR 60. Appendi x A. 

5·42 
6.0 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 of control 

needed to demonstrate attainment of PMI0 NAAQS. Conceptually, this involves 

determining the PMI0 design concentration for a particular site or receptor 

that must be reduced to the level of the NAAQS, thereby assuring attainment. 

These design concentrations are used to determine the level of control 

needed. The guidance contained in this section focuses on particulate Matter 

in the specific size fraction, PMIO, although it may be applied to particulate 

matter in general as a surrogate where 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 of concentrations contributed by the source(s) and an appropriate background concentration. With the PMIO annual and 24-hour NAAQS, two separate 

design concentrations, one for each standard, are needed per site. Attainment of the annual NAAQS requires that the expected annual PMIO concentration 

be less than or equal to the level of the NAAQS. Attainment of the 24-hour 

NAAQS requires that the expected number of exceedances of the NAAQS be less 

than or equal to one per year. 

The SIP-related emission limits should be based on the NAAQS (annual or 

24-hour) which result in the most stringent control requirements. For 

example, if the annual NAAQS requires more stringent control requirements 

than the 24-hour NAAQS. the annual NAAQS is considered the more restrictive 

standard and the corresponding emission limit(s) would be adopted. 

6-1 
6.2 Data Base Requirements 

The design concentrations for attainment of the 24-hour PMIO NAAQS 

can be based on ambient measurements of PMIO, or model estimates of ambient 

concentrations at individual sites during 1 or more years of stable 

emissions conditions. Ideally. (1) modeling estimates using 5 years of 

National Weather Service meteorological data (or at least 1 year of 

on-site data), or (2) 3 years of representative air quality measurements 

should be considered in determining 24-hour design concentrations. If 

more years of data with relatively unchanging emissions are available, 

they also may be considered in calculating design concentrations. The 

more years of data available, the more stable the estimate of PMIO 

design concentrations. 

The preferred approach for estimating a design value is through the 

use of an applicable dispersion 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 1 complete year of 

PM10 data. are available,* the PM10 data would be used to estimate the design 

value; if the PM10 data are insufficient,* the design value would be based 

on Appendix B of this 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 in Appendix K, of Part 50. In 

the simplest case, the design concentration can be determined by averaging 

3 years of monitored or 5 years of modeled PMIO concentrations. 

*These data should meet first year sampling requirements found in CFR 58~13. 

6-2 
There are several acceptable approaches for determining appropriate 

24-hour PM10 design concentrations. These approaches which are described 

in the next sections are based on monitored or modeled PM10 concentrations. 

Th~ include: (1) a table look-up procedure; (2) fitting a statistical 

distribution; (3) graphical estimation; and (4) the use of conditional 

probabilities. Each of these approaches and corresponding data usage 

requirements are presented in detail in the ozone guideline. 1 The 

following sections briefly summarize each of these 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 the year should be adequately reflected in the measurements. 

To use the tabular approach for the 24-hour PM10 standard, it is 

necessary to know 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 of the 

highest concentrations is chosen as the design concentration. For 

example, if a comprehensive monitoring program provides 1,095 24-hour 

concentration measurements (or 3 full years of data) at a site, then 

the ranks of the lower and upper bounds obtained from Table 6.1 are 4 

and 3 respectively. This means that an appropriate design concentration 

6-3 
for that site would be between the fourth-highest and third-highest 

concentrations. In using this table, the lower of the two concentrations 

should be used as the design concentration, i.e., the fourth-h1ghest 

concentration. Therefore, in this example, it suffices to know only 

the four h1ghest values during the time period. With multiple monitoring 

Sites, the highest PM10 concentrations at each site would have to be 

considered and a design 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 of the 

seven fourth-highest values. 

For routine model applications with 5 full years of 24-hour 

concentration estimates, the PM10 design concentration of critical 

interest becomes the highest of sixth-highest concentrations for the 

entire receptor network. 

The look-up procedure Is basically a tabular technique for 

determining what point on the empirical frequency distribution corresponds to a frequency of 1/365. By construction, the table look-up 

procedure tends to provide a design concentration slightly lower than 

would be derived using a continuous curve representing a theoretical 

frequency distribution for PM10 values. For example, use of the 

table-derived estimate might be modified by interpolation between the 

6-4 
TABLE 6-1 

TABULAR ESTIMATION OF PMIO DESIGN CONCENTRATIONS 

Number of Daily Values 

< 347 

348 - 695 

696 - 1042 

I,'j'" 

1043 - J.99f{ 

13jl -113~ 

1l3<t- ;~b 

1»)3 7 

~L \.f:'.>5 

Rank 0 f Uppe r 

Bound 

1 

2 

3 

4-

6-5 

Rank of Lower 

Bound 

Data Point Used for 

Design Concentration 

1 Highest Value 

2 Second Highest Va 1 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 a more 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 less than a complete year of data, 

(1.e., 365 observations) the maximum concentration must generally be used 

as a tentative design 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 

a fitted distribution (Section 6.3.2) can be used.* With sparse data 

sets, the tentative design concentrations defined as the maximum concentration or the 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 single year of data very tentative. 

6.3.2 Fitting one statistical distribution to several years of data 

With several years. of fa1rly complete PM10 24-hour air quality 

measurements or model estimates, a statistical distribution could be selected 

that "fits" the data. Informati on on fitti ng stat i sti ca 1 dl strl butions 

can be found on pages 18-20 in the ozone guideline. 1,2,3 Because we are 

interested in peak concentrations, emphasis would be placed on the top 

*An extrapolated value must be used, however, instead of the maximum 

observed concentration in order to evaluate the possibility that the 24-hour 

standard is controlling, using procedures described 1n Sections 6.3.2 - 6.3.4. 

6-6 
5 to 10 percent of the concentrations. This approach, at least conceptually, provides a more stable estimate 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, there will be concentration dati 

points on either side of the design frequency, data which can be used 

as a constraint in fitting the distribution. 

When less concentration data are available, i.e., infrequent 

air quality measurements, it may not be possible to "bound" the design concentration. For example, if there are no measured values on the empirical 

frequency distribution with frequencies less than 1/365, the estimated 

design concentration will represent an extrapolated concentration. This 

extrapolated design concentration will be higher than the maximum observed 

or estimated (via air quality model) value. When this is the case, caution 

should be exercised in the use of the extrapolated value. 

6.3.3 Using the emgirlcal frequency distribution of several years of data (graphical estimation) 

With sufficient data it may not be necessary to fit a statistical 

distribution, as discussed in Section 6.3.2. The concentration value 

corresponding to a frequency of 1/365 may be read directly off a graph 

of the empirical distribution and used as the design concentration. The 

description of this approach is given on pages 25 and 34 in the ozone 

guideline. 

6-7 
6.3.4 Conditional probability approach 

While the prev10us methods required group1ng concentrat10n data 

from several years, this approach allows Ind1v1dua1 years of data to be 

treated separately. This 1s done by fltt1nga separate stat1st1cal distribution to each year of data and assuming a given probabi11ty that each year 

w1ll reoccur. This approach is of interest when different 1mportance 

should be placed on the indiv1dual years in terms of meteorological 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 air quality measurements or model estimates, a proportioning 

method can be used at each site to estimate control requirements for SIP 

development. This proportioning method differs from simple rollback in 

that the source contributions are determined from receptor or dispersion 

modeling and not directly from the emissions as In simple rollback. 

Nevertheless, the method conceptually considers that the total reduction 

(TR) in pollutant concentration should be: 

TR (ug/m3) ~ PM10 Design Concentration - PM10 NAAQS 

If a design concentration is greater than the NAAQS (TR is positive), a 

reduction in PMI0 emissions is required. This might be accomplished by 

6-8 
reducing the contribution of a single source or it may 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 

ISR j ~) %REi : ACi (ug/mJ ) 

where AC is the ambient concentration due to the individual 

source (1) or source category as determined through a model. 

The SIP must demonstrate that the control requirements will be adequate to 

meet the NAAQS under other situations where the relative source contributions 

may be different from that on the design day. This is discussed further in 

Section 6.4.3. If receptor models are used to determine the relative source 

contributions, the operating rates of the sources over the time period 

studied must be evaluated. If they are inappropriate for the SIP development, 

the source contributions should be adjusted proportionally. 

6.4.2 Example for annual averages 

Assume for discussion that the annual NAAQS for PM10 is 75 ug/m3 

and that the site with the "controlling" annual design concentration for the 

area is 100 ug/m3• Then the total required reduction for the area would be: 

TR = 100 - 75 = 25 ug/m3• 

6-9 
Next, for the site of the most restrictive design concentration, consider 

the following source contributions estimated by a receptor or a dispersion model: 

Source 

St ee 1 mi 11 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 

5 ug/m3 

20 ug/m3 

10 u9/m3 

40 ug/m3 

100 u9/m3 

The TR would be the sums of the 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 

5 ug/m3 

TR ; 

8 ug/m3 

4 ug/m3 

25 u9/m3 

Since the TR sums to 25 ug/m3, it is assumed that the individual source 

reductions, if implemented, 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 that the selected emission reductions will allow the NAAQS to be 

met at the "controll ing site," as well as all other monitoring 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 

is 
the standard will result in attainment at those sites. If this is not found 

to be true, which is more 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 of the individual source reductions calculated across 

the network. 

For the proportioning method discussed here, the background 

concentration should be estimated as that portion of the concentration which 

is not attributed to 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 of the sources not under 

invest i gat ion, 

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-term averages (e.g., 24-hour), it is likely that a single high 

concentration is dominated by relatively few sources with source contributions varying with meteorological conditions. Thus identification of 

emission limits that assure the short-term 24-hour NAAQS will be met at 

all sites is likely to require an iterative and perhaps lengthy process. 

This is especially true of multisource areas. 

An alternative is to develop control strategies with emission 

limits specified for each source category. perhaps based on a preliminary 

6-11 
application 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. 

6-12 
Reference 

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. 

6-13 

7.0 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 to EPA's data bank. To develop an acceptable control strategy 

as required for a PMI0 SIP, a State can utilize the information summarized 

in section 4.0 of this document for carrying out appropriate modeling, 

the information in section 5.0 to develop an emissions inventory for 

model inputs, and the information in section 6.0 for developing control 

strategi es. 

7.2 Clean Air Act Requirements 

7.2.1 Time limits, SIP reguirements 

Section 110(a) of the Act requires every State to submit to EPA a 

SIP which provides for implementation, maintenance, and enforcement of each 

NAAQS in each air quality control region within the State. The SIP is 

required to be submitted within 9 months of the promulgation of a new 

NAAQS or the revision of existing NAAQS. The SIP must provide for attaining 

a primary NAAQS as expeditiously as practicable, but no later than 3 years 

after SIP approval by EPA. Up to an additional 2 years could be provided 

for attainment if the requirements in section 110(e) of the Act are met. 

These section 110 requirements are applicable to PMIO NAAQS. The PMI0 

SIP's therefore are due within 9 months of promulgation of PM10 NAAQS and are 

to cover all areas of the State as qualified in section 2.4 of this document. 

7-1 
7.2.2 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 the Act. Part 51 has 

recently been restructured to contain more subparts and sections to make 

it more organized and readable. That restructuring effort consisted only 

of deleting obsolete material and reorganizing the remaining material and 

no substantive changes were made to the regulatory requirements at that 

time. The Part 51 regulations still apply to PMI0. 

7.3 PM)O SIP Development Policy 

As discussed in section 7.2.1, the entire State must be covered by a 

PMI0 SIP and the SIP is to be submitted within 9 months 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 of this 

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 to EPA a revision to the particulate 

matter SIP within 9 months 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 3 years after the SIP is 

approved by EPA. The 3-year attainment deadline may be extended for up 

to 2 additional years by the Administrator if the conditions in 

section 110(e) of the Act are met. The submittal must include a modeled 

demonstration that provides for attainment and maintenance of the PMI0 

7-2 
standards. Portions of the SIP pertaining to prevention of significant 

deterioration/new source review section and air monitoring will also 

require revision. 

The SIP must contain control measures sufficient to demonstrate 

attainment. Provisions for studies or demonstration projects of nontraditional particulate matter sources and measures necessary for attainment 

at a later date 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 a SIP within g months of promulgation 

of PMI0 standards for Group II areas. The States may submit a SIP for 

these areas as required for Group I areas, if they wish. Otherwise, States must 

submit a SIP which contains enforceable commitments to take the following actions. 

(1) Gather ambient PMI0 data, at least to an extent consistent with minimum 

EPA requirements and guidance (see subsections 3.2.3 and 3.2.5) 

(2) Analyze and verify the ambient PMI0 data and report 24-hour PMIO NAAQS 

exceedances to the appropriate Regional Office within 45 days of each 

exceedance. 

(3) When an appropriate number of verifiable 24-hour NAAQS exceedances 

become available (see section 2.0) or when an annual arithmetic 

mean above the level of the annual PHIO NAAQS becomes available, 

acknowledge that a nonattainment problem exists and immediately notify 

the appropriate Regional Office. 

(4) Within 30 days of the notification referred to in (3) above, or by 

the date 37 months after promulgation, whichever comes first, determine 

7-3 
whether the existing SIP will assure timely attainment and maintenance 

of the PMI0 standards, and immediately notify the appropriate Regional 

Office. 

(5) Within 6 months of the notification referred to in (4) above (if 

necessary), adopt and submit to EPA a PMIO control strategy that 

assures timely attainment and mai.ntenance within a period of 3 years 

from approval of the committal SIP. 

The following factors should be considered in determining the adequacy 

of the existing SIP in item (4) above: 

(1) Air quality data. (Time is alloted for up to 3 years of PMIO 

data to be collected if a NAAQS 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 of 40 CFR Part 50 or the Guideline on Exceptions to 

Data Requirements for Determining Attainment of Particulate 

Matter Standards in the absence of adequate PMIO data.) 

(2) Emissions data. (The emission inventories must be evaluated to 

determine if emissions can increase significantly because actual 

emissions are far below allowable emissions for the area; determine 

if sources with operating permits are not operating or operating 

at reduced capacity and if "banked" emissions impact future air 

quality. ) 

(3) The present control strategy. (The existing control strategy 

should be evaluated to determine if it is fully implemented; if 

it is adequately enforced; start-up, shutdown, and malfunction 

7-4 
regulations are adequate to prevent circumvention of emission 

limits; and it can adequately attain and maintain the PMIO NAAQS if 

the above conditions are met. The evaluation should include the 

use of dispersion and receptor modeling techniques where appropriate). 

The committal SIP must include an enforceable schedule with 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 air monitoring portions of the 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 of the PMIO standards. 

States are therefore only required to revise the PSD/NSR and air monitoring 

portions of their SIP's within 9 months. 

7.4 SIP Content 

A most impo~ant first step in developing a control strategy will be to 

inventory particulate matter sources and include all possible contributors. 

Receptor models applied to TSP samples, or PMIO samples if available. may 

be useful for this analysis as discussed in section 5 of this document. 

The State should include in its emission inventory pOint sources, fugitive 

emission points within industrial plants, plus all area sources such as 

unpaved and paved roads, unpaved parking lots, construction activity. 

open lots with no vegetative cover, woodstoves, agricultural activity, 

and similar sources. As previously stated, the option to study nontraditional 

sources and commit to implementing controls at a later date will no 

longer be acceptable. Information at the State level or from EPA-funded 

7-5 
studies1,2,3,4,5,6 should provide enough information to make determinations of 

which controls may be effective for these sources. In determining which 

controls to employ, the State should reassess existing control technology 

requirements for traditional sources, previously approved emission trades, 

and the effectiveness of startup, shutdown, and malfunction regulations. 

7.5 Control Strategy Transition 

Particulate matter emissions from most point sources and many area 

sources have been controlled as a result of SIP's to implement the former 

NAAQS for TSP. The regulatory requirements of an existing TSP 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 a demonstration that the revision will not 

interfere with attainment or maintenance of the PMIO NAAQS. The existing 

regulations must continue to be enforced by Federal and State agencies 

during the period of transition from a TSP SIP to a PMI0 SIP. 

States will no doubt want to minimize any unnecessary disruption 

caused by going from these control programs to PMI0 programs. Therefore, 

to the extent pOSSible, States should utilize the existing control strategy 

in a TSP 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 to whatever degree 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. 

7-6 
7.6 Current Emission Regulations 

Particulate matter SIP's contain emission regulations expressed 1n 

various terms including the following: 

-- Mass of particulate matter per heat input into the process. 

Example: 0.30 pounds per million British thermal unit. 

-- Mass of particulate matter per unit of exhaust gas. 

Examples: 0.05 grams per dry standard cubic foot. 

0.01 grains per actual cubic foot. 

0.02 grains per standard cubic foot. 

0.04 pounds per ton of exhaust. 

0.02 grains per actual cubic foot per minute. 

-- Opacity of exhaust. 

Example: 20 opacity. 

-- Mass of particulate matter per time period. , 

Examples: 144.30 pounds per hour. 

631.00 tons per year. 

13.44 pounds per day. 

4.5 grams per day. 

-- Mass of particulate matter per area exposed to wind erosion. 

Example: 5.80 pounds per acre. 

-- Mass of particulate matter 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 particulate matter per unit of power generated. 

Example: 0.07 grains per horsepower hour. 

Percent control of emissions. 

Example: 98.00 percent control of particulate matter emitted due 

to overspray of coating material. 

Regulations of these types for controlling all emitted particles have 

been the accepted practice for affecting ambient concentrations of particulate 

matter as measured by the high volume sampler even though emissions as 

measured by stacK test methods may not be identical to the material 

measured by a high volume sampler. Despite this discrepancy, EPA has 

developed stacK sampling trains now described in Methods 5 and 17 of 

Appendix B to 40 CFR Part 60 which represent the present state-of-the-art 

in techniques for collecting particulate matter. Many emission limits for 

particulate matter therefore have been established in terms of these 

measurement methods. In this document, the term "particulate matter emissions" 

is used to denote material measured by Methods 5 or 17 or by a comparable 

measurement method approved by EPA in a State implementation plan. 

7.7 Surrogate Emission Regulations 

This subsection discusses setting or retaining emission limits in 

terms of particulate matter emissions for the purpose of controlling PM10 

emissions as part of a PM10 control strategy. 

7.7.1 Retaining existing emission limits. If a State finds that its 

existing particulate matter emission limits in its TSP SIP are sufficient 

to prevent PM10 NAAQS violations, there is no need to go through a resource 

intensive process of modifying the emission limits to express them in terms 

7-8 
of PMIO.* Modeling can be used in two ways to determine that a particulate 

matter emission limit or combination of limits for several sources is 

acceptable for PMIO control. First, particulate matter 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 

of the 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 particulate matter emission rates converted to PMIO emission 

rates. If it can be assumed that for any source the PMIO fraction of 

particulate matter does not vary appreciably during normal operation of 

the source, then a certain particulate matter emission rate will represent 

a certain emission rate of PMIO for that source. The PMIO fraction will, 

of course, vary by source and could be up to 100 percent of particulate 

matter emissions. For some sources the particulate matter emission rate 

in the TSP SIP can be converted to the corresponding emission rate for 

PMIO through an emission factor (see section 5.0 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 of the PMIO NAAQS, 

then the particulate matter emission rate will be an acceptable emission 

limit for the PMIO SIP. 

In either of the above cases, States may leave the existing emission 

limits unchanged and may continue to conduct compliance tests with appropriate 

*Regulations may consist of techniques such as paving a certain amount of 

dirt roads or streets, street sweeping schedules, tailings pile spraying, construction activity procedures, street salting restrictions, unpaved 

road speed limits, etc., rather than an emission limit. 

7-9 
particulate matter emission measurement methods. The PMI0 control strategy 

analysis should include the modeling results and confirmation that the 

particulate matter emission limits were found to be acceptable surrogate 

emission limits for PMIO. 

7.7.2 Setting new emission limits. In the event that a State finds 

that an emission rate lower than the current particulate matter emission 

limit in the TSP SIP is necessary for PMIO NAAQS attainment and maintenance, 

the PMI0 rate Which is found to be necessary through modeling can be converted (through the use of an emission factor) to an equivalent particulate 

matter emission rate and expressed as such in the PMIO SIP. The emission 

limit expressed in terms of particulate matter emissions will be acceptable 

provided the modeling results are valid and the ratio of PMIO to particulate 

matter emissions is not likely to drastically change. 

If it is found that substantially all of a source's emissions are PMIO. 

then limits for that source could be expressed as either particulate matter 

emission limits or PMIO emission limits, and compliance testing could be 

performed with either Method 5 or a method that measures only PMI0 emiSSions. 

For such an instance, the SIP would have to indicate that the source's 

emissions are essentially all PMIO. In any case where an emission regulation 

is in terms other than PMIO, such as particulate matter emissions or opacity. 

PMIO levels must not increase while the indicator used in the emission 

limit remains constant. Otherwise. the indicator will not be a valid 

surrogate and emission limits USing the surrogate will not be acceptable. 

7.S PMJo-Specific Emission Regulations 

Emissions limits developed to meet the NAAQS for TSP took many forms 

as illustrated in section 7.6. Compliance with these limits was determined 

7-10 , 
by reference method emission tests or certified visible emission observers. 

The emission limits and compliance methods had no direct link to TSP as 

measured by ambient monitors, however, a correlation was made through the 

use of dispersion and receptor models. 

Similarly, it would be difficult, and perhaps technically infeasible. to 

develop a method for measuring precisely that exhausted material that would 

contribute to ambient levels of particulate matter measured by an ambient 

PM10 sampler. Particles emitted from stacks are subject to agglomeration 

and separation after they reach the ambient air, both of which affect 

their inclusion or exclusion 1n ambient PM10. Additional complications 

arise due to the presence in emissions of condensables and various precursors 

of secondarily formed particulate matter. 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 in Appendix C. Further guidance is being prepared by EPA. 

7.9 Reporting Emissions Data 

Emissions of pollutants for which NAAQS have been set are reported by 

States to EPA in an annual report required by 40 CFR 51.322. As a minimum, 

the sources for which emissions data are to be reported are those whose 

actual emissions are over 90.7 metric tons (100 tons) per year of the 

pollutant of concern. Since the indicator for both the primary and 

secondary, standards for particulate matter is being changed from TSP to 

PMIO. reporting of particulate matter as required in section 51.322 will 

7-11 
simply be replaced by reporting of PMIO emissions. The present requirement 

to report particulate matter emissions will end with the reporting of 

calendar year 1987 emissions. The reporting requirement for PMIO emissions 

will begin with the reporting of calendar year 1988 emissions. The EPA 

recognizes that time and resources are needed for States to develop the 

capability to report PM10 emissions data for EPA to develop the capability 

to process, store, and retrieve the data. States are required to begin 

the annual reporting.of PHlO emissions data with calendar year 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 to meet that requirement. This information includes 

PHlO-related changes to the 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 other States on request; 

and procedural information such as that contained in Aeros manuals. 

The EPA has underway a project to replace the UNIVAC systems, e.g., 

NEDS, Hazardous and Trace EmiSSions System, that have been used for many 

years to process, store, and retrieve the emissions data provided by 

States. These systems will be replaced by 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 to the States in time for 

States to respond to the requirement for reporting calendar year 1988 

PMI0 emi ssi ons. 

7-12 
7.1 0 ~.Ill.ts_~i_o.n!. D'il_dl!1.ltlB,u_bP.1_e1 ?_oltg 

This section clarifies the effect of PMI0 NAAQS on alternative emission 

reduction options (bubbles) that have been previously approved for TSP SIP's. 

In the initial bubble policy, as published on December 11, 1979 (44 FR 71780), 

sources were warned that EPA was considering revising its particulate 

matter NAAQS and that if such size-specific standards were promulgated, 

some alternative approaches. initially approved by EPA, might no longer 

be adequate under the revised standards. In a sense, the policy indicated 

that sources which used a bubble approach to meet SIP emission limitations 

could be treated no differently from sources which did not. That is, if 

additional emission reductions are required to avoid violations of the 

ambient air quality standards, the State may have to revise emission limits 

previously approved under the bubble policy. In general, bubbles 

cannot interfere with a State's efforts to attain and maintain ambient 

air quality standards even if those 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 the TSP NAAQS. The EPA issued 

guidance on SIP development and new source review in areas impacted by 

fugitive dust on August 16,1977. 7 This guidance. known as the Fugitive 

Oust Policy, states that "urban areas should receive the highest priority 

for development of comprehensive and reasonable programs to control 

7-13 
fugitive dust." Control programs in rural areas are to "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 of the NAAQS or are sources of a known toxic or hazardous 

material (e.g., asbestos)." 

Another aspect of the fugitive dust policy is that, "new sources 

that wish to construct 1n rural fugitive dust areas should be allowed to 

do so without the need of emission 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 vicinity of the proposed 

location, along with normal background, is not projected to cause violations 

of the NAAQS." The following criteria were to be used in defining a 

rural area under the fugitive dust policy: "(1) the lack of major 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 of categorizing areas into Groups I, II, and 

II I ,Group I and II areas that qual ify as rural fugiti ve dust areas will 

be placed in Group III. The State will be expected to provide rationale 

and justification at the time of area categorization for the claim that a 

PMIO NAAQS may be violated primarily because of the impact of fugitive 

dust sources that are unreasonable to control. 

In response to a broad range of comments on the proposal to continue 

this policy, EPA has developed several options for revising the fugitive 

dust policy for applciation to the PMIO NAAQS. Public comment on these 

7-14 
options is currently being solicited. If necessary, a rev1sed po11cy 

will be issued in the future. At that time, the categor1zation of areas 

initially placed 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 of Public 

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: Fugitive Dust Control Demonstration Studies. 

EPA-600/8-84-032, U.S. Environmental Protection Agency, Research 

Triangle Park, NC, January 1985. 

6. Field Evaluation of Wind Screens as a Fugitive 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 Assistant Administrator for Air 

and Waste Management to Regional Administrators, August 16. 1977. 

7-16 
TYPES OF RECEPTOR MODELS 

There are several major categories of receptor models which are 

potentially valuable for better understanding an area's ambient particulate 

problem, both 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 to these, 

information and availability of the 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 of the sample. It 

is referred to in the 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 of these sources which best "explains" or accounts 

for the composition of the sample. Species data (chemical compounds or 

elements) are required for both source and sample data. The method is most 

effective when the ambient samples are collected in at least two size fractions 

(larger than and smaller than 2-3 micrometers). This method must be validated 

in accordance with the protocol referenced at the end of this Appendix. 9 

A-l 
2.0 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 prior assumption 

of the impacting sources and can be useful where the types of sources are 

uncertain. Like the mass balance method, this method is most effective when 

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 of the 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 1 micrometer. Several analysts 

have been asked to provide quality assurance of their analyses and the results 

have been mixed. Optical microscopy appears to be somewhat analyst dependent 

and semiquantitative. However, it is an excellent confirmatory or supplementary 

techni ques. 

4,0 Automated Scanning Electron Microscopy 

A computer-driven scanning electron microscope, equipped with x-ray 

fluorescence capability to identify the elemental composition, provides a 

partic1e-by-particle analysis of particle size, shape and elemental composition. 

This elemental composition and computer assisted sizing allows large numbers 

A-2 
of particles to be 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 refine MB, FA, OM, 

or ASEM analyses. 

5.1 X-Ray Diffraction (XRD) - This method provides direct information 

on the crystalline nature of particles. This is particularly useful for 

distinguishing among minerals. It requires a heavy loading of coarse 

particulate matter on the filter and cannot be used on ammorphous (i.e., 

non-crystalline) particles. 

5.2 Trajectory Analysis - This technique is used for tracing the history 

of an air mass to determine the possible area of origin of sources of ambient 

particulate. The simplest form is the pollution rose, where only average wind 

direction is considered. More sophisticated approaches account for the changing wind direction with time to trace air mass in time and space for as long 

as several days. 

5.3 Microinventory - This technique is an orderly compilation of sources 

near a receptor. This is particularly useful as a finely grldded input to 

dispersion models, and in qualitatively estimating the potential sources of 

soil and other minerals to help interpret an observed mix of particulate matter 

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 Protectfon Agency, 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 of Ambient 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: A Guide To The Use of 

factor Analysis And MUlti~'e Regression (FA/MR) Techniques 1n Source 

Apportionment. EPA'450/4- 5-007. U.S. Environmental Protection Agency. 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-4 
APPENDIX B 

PRELIMINARY ESTIMATES OF PMIO DESIGN CONCENTRATIONS USING TSP DATA 

This appendix provides a methodology for making a preliminary estimate 

of particulate 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 3 most recent years of data. However, if only 1-2 

years of valid means are available, these may also be used. 

**The 0.47 factor is the 50th percentile ratio in Table 2 of Procedures For 

Estimating Probability Of Nonattainment Of A PM10 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 arithmetic mean PMIO value. 

1.2 Example 

(1) Given: Valid average annua~ arithmetic mean 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, a design 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 are the 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 to the 

level of the NAAOS, t, would result in an expected exceedance rate (EER) 

of 1.0 per year. 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 l i 

(2) 

N = number of days per year (e.g., 365) 

S = number of TSP samples 

n = number of TSP samples with values greater than t , 

the level of the NAAQS 

Ri = ratio of (PMlO)1/(TSPli 

= level of the 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 to m (TSP)i. equation 

(2) becomes 

n 

1 0 = 365 E' , i =1 P(Ri > t ) 

m (TSPJ i 

(3) 

One further substitution can be made by substituting the 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 

eq uat i on (4). 

To calculate the average exceedance rate over a period of k years, 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-3 
1.0" 365 -kwhere 

k "number of years considered 

Sj " number of observations 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 from TSP data is an iterative one, it is 

recommended that the estimate be made using a computer. Software for dOing this is available and is described in User's Guide for 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 

of the 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," calculate the prooabllity that the 

corresponding (PM10)ij value would be greater than this 

level. i.e •• 

d 

peRij > (TSP) .. ) lJ 

Note that 

d 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,ffice of Air Quality Planning and Standards, 

Monitoring and Data Analysis Division, Research Triangle Park. North 

Carolina 27711. In Preparation. 

**Figure B.1 has been reproduced from Table 2 of the probability guideline. 

B-4 
co 

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' I IIll K·E t"NUtiAdILII Y .It W UIVISIONS 

Kili:UFFEL a. ESSER CO. 1oU.O:t '" It U 

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 Value 
or 

d d 

(c) if (TSPlij ~ 1.00, assume P(Rij > (ffl)ij) ,. 0 

(3) Use the information in step (2) to determine the value of 

the rhs of equation (5). 

(4) (a) if 0.99 < 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 that year in performing the preceding calculations. 

2.3 Example 

Given: (I) Three years of TSP sampling data in which 61 days 

per year 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 

in year 3. 

Find: The design concentration of the 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 raise the design concentration for 

trial 4 by adding one-half the difference between the design concentrations 

used in trials 2 and 3 to the 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 if the sample size in year 2 had 

been less than 48. we would ignore the data from year 2. and equation (5) 

would have been applied as shown below. 

8-8 
APPENDIX 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;ar basis. The EPA is providing 

this guideline not as a mandated requirement, but as guidance for those 

States that want to develop emission factors beyond those which we will 

provide in Section 5. This guidance may also be useful to States which 

want to develop compliance methods for future PMIO SIP emissions limits. 

The material presented here is intended for the use of knowledgeable stack 

testers in modifying existing techniques for gathering PMIO data. The EPA 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 ai r arises 

from both primary and secondary emissions. Secondary emissions are defined 

here as those which result from chemical or physical reactions in the 

ambient air. Primary particulate emissions are defined here to include 

particles which were present in the process gas stream prior to discharge 

to the atmosphere as well as modifications to those particles and new 

particles formed by condensation phenomena as the process gases are cooled 

upon discharge. Testing methods which include the contributions from 

"condensation" are currently under development but are not yet generally 

available (Smith et al, 1982). The data currently available indicate that 

the "condensib1es" from many sources with good particulate controls may 

constitute a major fraction of their present contributions to ambient air 

particulate concentrations (Williamson et al •• 1982), The EPA Method 5 (40 CFR 

C-l 
60). with a filter temperature of 120°C. includes some condensibles in the 

material collected (EPA 450/3-81-005a). However, further temperature 

reduction in many cases results in substantial increases in measured emission 

rates (Williamson et al.: op cit). The methods to be described below focus 

primarily on the measurement of size fractionated particulate matter as it 

exists at stack conditions. However, the inclusion of those portions of 

the condensibles that are currently collected as part of the EPA Method 5 

catch can be readily achieved by the removal of the final filter from 

the instack samplers described below and instead using a Method 5 filtration 

system. The problem of complete measurements of primary 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 appropriate technique 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 classifier would be the most desirable device to use, 

Size fractionating source samplers have been produced with as few as 

one and as many as 25 stages with each stage of the multistage devices 

having a different cutoff diameter. The samplers having the greater number 

of stages provide more detailed information on the aerosol size distribution, 

C-2 
and generally have higher capital and operating costs. Impaction type size 

classifiers are much more prone to errors due to overload and particle 

bounce than are cyclones; consequently, multistage operation is virtually 

mandatory with them in 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 predict their 

behavior over a wide range of operating conditions. This limits the use of 

cyclones to those 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 of these devices have 

demonstrated nominal 10 urn cuts over a limited range of operating conditions. 

and further characterization of the devices is in progress. Cyclone samplers 

are preferred for sampling to quantify size specific mass emissions; however, 

the protocol described here is generally applicable to both impactor and 

cyclone sampling with details that are pertinent to only one of the methods 

noted as they arise. 

3.0 Selection of Sampling Traverse Points 

In order to obtain a representative measurement, one must obtain samples 

at representative points across the duct (staCK) at iSOKinetic rates. In 

the case of conventional particulate testing (e.g. EPA Methods 5 and 17). 

this is accomplished by dividing the duct into a large number of equal area 

segments and traversing this sampling plane to obtaining an isoKinetic 

sample at the center of each of these areas. IsoKinetic sampling is achieved 

by selecting a nozzle which is appropriate for the combination of the nominal 
flow 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 size classifiers because changes in the sampling 

rate also changes the diameter(s) at which size fractionation takes place. 

However, to require fixed sampling rates adds immensely to the complexity 

of the testing and subsequent data reduction and interpretation. 

A method is currently being investigated which per~its complete traverses 

of the sampling plane using variable sampling rates to be made with cyclones 

and impactors In a fashion similar to EPA Method 5. It is called the emission 

gas recycle (EGR) approach. The method uses a recirculation loop to augment 

the sample flow through the sizing device. This permits the operator to 

maintain a fixed flow through the sampler while varying the sampling rate 

from the duct by means of compensat i ng changes ; n the reci rculat i on flow. 

Upon satisfactory development of the EGR method, the protocol for traversing 

the sampling plane for size specific sampling will be virtually identical 

to that of Method 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 to match the sampling 

velocity to the duct velocity within an allowable 20 percent tolerance. 

The volume of gas sampled at each point is then made proportional to the 

volumetric flow through the area represented by the sample point by making 

the sampling duration at each point proportional to the gas velocity at the 

point. This technique will work with any velocity distribution but may be 

cumbersome to implement. 

C-4 
Another method of obtaining an isokinetic sample using a fixed flow 

rate sampler is to synthesize a complete sample from a multiplicity of 

single point samples. Obviously the time and expense involved in such an 

approach would be excessive if the number of samples taken equaled the 

number of traverse points normally required in Method 5 compliance testing. 

However, a method 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 a complete sample can be 

composed from the partial samples obtained from traverse points with similar 

gas velocities. 

In the interim, while awaiting the development of the 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 of the net source 

emission rate (the net source emission rate being the sum of the emission 

rates from each of the four quadrants). Three complete measurements should 

be made to characterize the source emission rate; thus, a total of 12 particle 

size samples (3 samples at each of 4 grid locations) will usually be required 

to obtain one complete measurement. However, if the velocities at the four 

sampling points are within! 20 percent of one 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. 
• 8 

: 

I 

I 

eJ4 I 

... --..\ . 

• I 

o I 

b/4 I 

! " I 

b :. ____ 1 ___ ~--------- I 

I 

I 

I 

I 

• I • 

I 

I 

I 

I 

I 

! 

, '", 

FIGURE Cot. RECOMMENDED SAMPLING POINTS FOR CIRCULAR AND SQUARE 

OR RECTANGULAR DUCTS. 

C-6-

• 
Implementation of all of the traverse methods described above require 

obtaining velocity traverses and gas composition using EPA Methods 1. 2. 3, 

and 4 followed by precalculation of sampling flow rate, nozzle selection, 

etc. before sampling commences. The total gas flow to be used in calculating 

emission rates using any 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 a number of cascade impactors are available, also. High 

flow rate (10 to 30 alpm) samplers are desirable for sampling low concentration 

streams such as are anticipated herein. If a high concentration (>500 mg/m3) 

stream is to be sampled an impactor with a design flow rate of about 3 lpm 

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 invalidate the data. As a rule this means that only about 50 mg 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 a Single 

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 selective deposition in bends, the sampling nozzles 

must be straight for use in particle sizing (Knapp, 1979; Felix and McCain, 

C-l 
1981}. Thus one cannot use "gooseneck" or other nozzles designed to turn 

the sampled gas flow 90 degrees (as is done in Method 5). The natural 

right angle orientation of the inlet of a cyclone sampler with respect to the 

sampler body and exit makes the use of straight nozzles normal with them. 

This is not the case with impactors. One approach to the use of straight 

nozzles with impactors is to align the impactor axis with the direction of 

gas flow in the duct. However. 1n many sampling locations this is impractical 

if not virtually impossible. The use of a high capacity precollector having 

an inlet at 90 degrees to the body eliminates this difficulty and, at the 

same time, eliminates another operational problem with impactors. If a 

significant proportion of the particles being sampled are larger than about 

10 to 15 um, the first collection stage tends to reach its loading limit 

before the succeeding stages collect enough material for reliable weighing. 

The use of a high capacity precollector which has a cut larger than that of 

the first impactor stage helps alleviate the overload problem. Several 

such precollectors, some of cyclonic design and others operating on impaction 

principles, are comercially available. Of the impaction type precollectors, 

only those which have inlets and exits at 90 degrees to one another are 

acceptable for this application. 

The particle size of the samplers, both cyclones and impactors, are 

dependent on a number of factors with the sampling flow rate being the only 

variable which can be adjusted by the user. However, the sample flow rate 

required to obtain the PMIO cut will be dictated by the sampler used (given 

the gas composition and temperature of the process stream being measured). 

This means that one does not have the latitude in selecting the sampler flow 

to be used that one has in simple total particulate measurements. The 

C-8 
matching of the sample inlet velocity with the gas stream velocity for 

isokinetfc sampling must be accomplished entirely through the cross-sectional 

area of the sampling nozzle. This means that a much larger array of nozzles 

must be available than those used in Method 5 sampling. If the isoklnetic 

error is no larger than 20 percent, the maximum error in the measured 

emission rate of 10 um particles will be about 15 percent and the errors 

for smaller particles will be lower. Thus, the actual error in the total 

concentration of particles smaller than 10 um Will. be substantially lower 

than 15 percent. Thus deviations of ~ 20 percent from isoKentic can probably 

be tolerated. If sampling is to be done within ~ 20 percent of isokinetic, 

an array of nozzles must be available that step by 20 percent in diameter 

from one to the next. 

If a single stage collector is used, the geometry of the collector and 

the flue gas conditions will completely dictate the sampling flow rate as 

only one flow rate will produce the required size cut. If a multistage 

device is used to measure the complete size distribution, some latitude is 

available in setting the flow because interpolation can be used to determine 

the concentration of particles in the designated size range. 

5.0 Sampling Trains 

The sampling train requirements for the particle size specific method(s) 

are similar to those of Methods 5 and 17 and trains designed for the latter 

applications can be used with some impactors and cyclones. However, Method 5 

systems are not adaptable to the intermediate and low flow rate «20 alpm) 

devices without modification. The orifice meters used in the Method 5/17 

systems are too large to permit accurate flow measurement and control at 

these reduced flow rates. A substitute orifice meter, properly sized for 

C-9 
the specific sampler, can be used in place of the standard orifice to ~ake 

a Method 5/17 system compatible with the sampler. Alternative trains and 

probes are described by Harris (Harris, 1977). As the sampling procedure 

described here is intended to measure only particulate material as it exists 

at stack conditions. the sampler must be operated instack. That is, the 

size fractionating device and backup filter must be mounted on the outboard 

end of the probe (replacing the usual sampling nozzle). A typical setup is 

illustrated in Figure C-2. The particle size fractionating portion of "the 

system cannot be located at the exhaust end of the probe because of size 

selective particle losses resulting from deposition in the probe. A Method 5 

filter system may be used at the exhaust end of the probe, with the 

instack filter removed as illustrated in Figure C-3, to obtain an estimate 

of the condensible emissions and to provide a method for determining the 

fraction of Method 5 concentrations that fall within the defined particle 

size range. 

Detailed procedures for the use of cascade impactors for industrial 

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

use of cyclone samplers are described by Smith et a1., (Smith et a1, 1982). 

Some of the procedures described in the latter document are specific to a 

set of cyclones designed to provide a 15 urn size cut, but with the exception 

of the actual flow rates to be used, the same protocol is to be followed 

for the sampling described here. 

6.0 Collection Media 

In sampling operations in which the particles are not further size 

fractionated beyond the PMI0 cyclone, the filter specifications are the same 

C-IO 
SAMPLER , 

HOLDER 

FILTER~t TUBE 

ORIFICE 

MANOMETER 

HEATED 

AREA r---""" 

THERMOMETEF!S 

IMPINGER TRAIN OPTIONAL: 

MAY BE REPLACEO BY AN '. 

EQUIVALENT CONDENSER 

THERMOMETER 

IMPINGERS IN ICE BATH 

BY·PASS 

VALVE B VACUUM \!JI GAGE 

VACUUM LINE 

DRY TEST METER AIR TIGHT PlIMP HIt·UIA 

FIGURE C-2. PM10 PARTICULATE SAMPLING TRAIN FOR NONCONDENSIBLE ' 

PARTICULATE (MODIFIED EPA METHOD 5 TRAIN) 
IMPINGER TRAIN OPTIONAL: 

MA Y BE REPLACED BY AN 

EQUIVALENT CONDENSER 

HEATED 

AREA FIL TER HOLDER 

THERMOMETER 

r------------ , '" "" .. ,'" ~ IP ~~~~;;~;;~~;;~~:=~~ SAMPLER 

t PITOT ~ 

I 

I 

I 

TUBE l 

THERMOMETERS 

ORIFICE 

IMPINGERS IN ICE BATH 

. B VACUUM 

\{JI GAGE 

VACUUM LINE 

MANOMETER DRY TEST METER AIR TIGHT PUMP .IIHUS 

FIGURE C·3. PM 10 PARTICULA TE SAMPLING TRAIN FOR CONDENSIBLE AND 

NONCONDENSIBLE PARTICULATE (MODIFIED EPA METHOD 5 TRAIN). 

C-12 
as those for EPA Methods 5 and 17. However, if a cascade impactor is used, 

extra requirements are imposed. A surface coating is required on the 

collection plates of the impactor in order to insure adequate retention of 

impacted particles. This coating can be in the form of a glass fiber mat 

or a grease or polymeric coating on a lightweight metal foil (Harris, 

1977). Chemical reactions involving gas phase constituents of the process 

stream, especially S02, can occur with these coatings and significantly 

alter the apparent weight collected by each stage (Smith et al., 1975). All 

glass fiber materials used in impactor sampling must be treated to minimize 

S02 uptake. The procedure for the treatment is given by Cushing and Smith 

(Cushing and Smith, 1979). Because the interferences are not predictable, 

control runs must be made together with the actual sample runs as the 

. testing takes place (Harris, 1977). 

All collection media must be dessicated prior to the initial weighing 

and dessicated for a minimum of 12 hours prior to the final weighing after 

sampling. 

7.0 Data Reduction and Analysis 

Ultimately. the information to be determined from a set of measurements 

Is the particulate emission rate in the PMIO size fraction together with a 

measure of the uncertainty in the calculated rate. If the traverse of 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 the manner prescribed by EPA 

C-13 
Method 5. The average isokinetic error for each traverse must be less than 

+ 20 percent for the individual traverse to be accepted. The relaxation 

from the + 10 percent requirements of Method 5 is possible because the 

errors associated with an isokinetic sampling of small particles are not as 

great as those for larger particles, and Method 5 must be able to deal with 

all Sizes. 

If the 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 the quantity of material 

which passed the appropriate stage of the 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 of the 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 size distribution is measured, the 

particle concentration to be used in calculating the 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-14 
References 

An om. "Code of Federal Regulations" Title 40, Chapter 1, 

Sub Chapter C, Part 60. 

Anom. "Control for Particulate Emissions from Stationary Sources" 

Vol. 1 EPA 450/3-81-005a, September 1982. 

Cushing, K.M. and Smith, W.B. "Particulate Sampling and Support: Final Report" EPA-600/2-79-1l4, 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 of the Air Pollution Control Association, Philadelphia, PA, 1981. 

Harris, D.B. "Procedures for Cascade Impactor Calibration and Operation in 

Process Streams" EPA-600/2-77-004, January 1977. 

Smith, W.B. Cushing, K.M., Johnson, J.W., Parsons. C.T., Williamson, A.D., 

and Wilson, R.R., Jr. "Sampling and Data Handling Methods for Inhalable 

Particulate" EPA-600/7-82-036, May 1982. 

Knapp. K;T. "The Effects of Nozzle Losses on Impactor Sampling." In: 

Proceedings, Advances in Particulate Sampling and Measurement (Daytona 

Beach, Fl. October, 1979), W.B. Smith, Ed. U.S. EPA Report EPA-600/9-80-

004, Southern Research Institute, Birmingham, Alabama, January, 1980. 

pp 101-107. 

Marple, V.A. A Fundamental Study of Inertial Impactors. Ph.D. Thesis, 

University of Minnesota, Mechanical Engineering Department, Mineapolis, Minnesota, Particle Technology Laboratory Publication 144, 1970. 243 pp. 

Smith, W.B •• Cushing, K.M., Lacey, G.E., and McCain, J.D. "Particulate Sizing Techniques for Control Device Evaluation" EPA-650/2-74-102-a, August 1975. 

Williamson, A.D., McCain, J.D., and Parsons, S.C. "IP Sampling for Condensible 

Emissions" Paper 82-61M.3 presented at the 1982 Annual Meeting of the Air 

Pollution Control Association, New Orleans, LA, June 1982. 

C-15 

APPENDIX D -----~."" 

DETERMINATION OF PM10 BACKGROUND 

1.0 Introduction 

Development of State implementation plans (SIPs) for PMIO will require 

an estimate of PMIO background concentrations for use with either disperSion 

or receptor model based control strategy development. The term "background" 

has a number of different interpretations. For the purpose of SIP development, 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 of this Appendix is to outline in greater detail the 

considerations and procedures which should be used to determine background 

concentrations of PMIO. As will be seen, selection of an appropriate background concentration depends on the time period of concern (e.g., annual or 

24-hour), meteorological conditions of interest and the availability of data. 

In a dispersion modeling study, background should account for the 

impact of source emissions not considered in the model. Such emissions may 

be either of manmade or natural origin and may be either primary (directly 

emitted as particles or condensable vapors) or secondary (emitted as gases 

and transformed into particles during transport). Ideally, all sources 

should be modeled, but this is not always possible or practical for particulate matter. In some cases, the sources may be within the agency's jurisdiction, 

but their emissions are not included expliCitly in the model (perhaps 

because they are too small or too numerous to inventory). In other cases, 

D-1 
the sources may be outside the region of jurisdiction and not identifiable. 

However, all sources must be accounted for either explicitly by the Model or 

in the bacKground value. 

Receptor modeling studies may also be used to develop control strategy 

emission limits, as outlined in Section 6.0 of this document. Since a 

receptor analysis may apportion the entire sample (including bacKground), 

the analysis may reflect contributions from some sources which are or 

should be ascribed to bacKground. Section 3.0 of this Appendix discusses 

thi sin more detail. 

For dispersion model studies, the bacKground concentration is usually 

needed for the set of meteorological conditions ·and time periods (e.g., 

annual, seasonal or 24-hour) associated with the exceedances of the NAAQS. 

Receptor models usually require the estimate for the specific time period 

represented by the receptor analysis. However, receptor model studies may 

also be developed that represent a particular set of meteorological conditions. This need for background to represent a particular set of meteorological conditions is discussed in Sections 3.0 and 4.0 of this Appendix. 

2.0 Determining BacKground Estimates·From Measured PM}o Or Surrogate Data 

The PMIO background values are most appropriately taken from ~asurements 

at a nearby site, representative of baCKground for the area being studied. 

Such a site would ideally measure 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 of the study area. For a site that meets these 

prerequisites, the mean annual baCKground for use in the SIP analysis would 

be the average of the annual concentrations at that monitor over the time 

D-2 
period being evaluated (usually 3 years). 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 rel i abl e. 

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, care must 

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 of judgments 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 

0-3 
average concentration is the 3-year mean of the annual or seasonal 

concentrations which are calculated in this manner. If the location of the 

sources or the frequency of sampling is such that there are an insufficient 

number of days at a single monitor to calculate a background value which is 

not i nfl uenced by those sources, background may be determi ned by "composit ing" 

the air quality concentrations from several monitoring sites located in the 

vicinity of the study area. This composite average concentration may be 

developed for the time period being studied by selecting the "upwind" site 

on each monitoring day and averaging these "upwind" values (see Section 5.0 

of this Appendix). The mean annual concentration is then the average of 

the annual concentrations which are calculated by the composite method. 

This procedure is greatly simplified for 24-hour cases. The background 

for a given day or set of meteorological conditions is simply the average of 

the concentrations at a site or sites that are upwind on the day or days 

which are selected to represent those conditions. 

As a second alternative for using measured data, background concentrations may be derived using site measurements for a different day or year 

than the time period being studied. Conceptually, using data from the same 

site but during a different day or year is similar to the first alternative 

above but additional care must be exercised in showing that the data used 

are representative of the background sources and climatological conditions 

during the study period. 

As a third alternate, if PM15 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, or multipliers, to adjust measured 

background PM15 or TSP to PMIO. Then the estimated PMIO data would 

0-4 
be treated in the same manner as a PMIO measurement in the preceding 

paragraphs. Surrogate data for PMIO should only be used if there are 

insufficient PM10 data for use in applying the procedures above. In cases 

where PMIO data are available for part of a year and the PM10 monitor is 

collocated with TSP or PH15 monitors, the ratio approach may be used for 

the remainder of the year when no PMIO data are available. The ratio 

approach is discussed in Section 6.0 of this Appendix. 

3.0 Special Considerations For Receptor Modeling 

In some cases, receptor models 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 prior to using the apportionment (as outlined in Section 6.0 of 

this Appendix). This receptor analysis at a background site may be necessary, 

for example, to distinguish between locally generated soil dust and that 

transported into the area. Source apportionment analysis of the background 

samples should use the same procedures as used for the source analysis. 

Then the receptor analyses from the background and study monitors are 

compared. The sources that were identified on both monitors should be 

noted and the concentations of these 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 select the background days for 

receptor analysiS to make sure they are ·upwind" and are not impacted by 

the sources of concern. 

0-5 
4.0 ?ummary Of Alternatives For Estimating PMIO Bac~ound 

The preferred method for background determination is to use an upwind 

PMI0 site in the vicinity of the study area (air shed, city. or source) under 

review, which reported data during the time period in question but which is 

not influenced by the sources in question or by other sources which do not 

impact the study area. Since such ideal measurements are not always available. particularly for an annual average determination, the alternate methods 

discussed previously are summarized below, in order of preference. 

First 

Alternative 

Second 

Alternative 

Third 

Alternative 

Use perimeter site(s) in the vicinity of the study area. 

The effect of nearby sources that are in the modeling analysis must be eliminated through the use of wind 

direction analysis (annual or 24-hour). A "composite" background value based on several nearby sites and wind 

direction analyses may be developed. 

Use PMIO data from a different days or a different year 

to represent the day or year being studied. 

Use surrogate data (TSP or PMIS), adjusted to PMI0' 

5.0 EXAMPLE 1 - Compositing Data From Several Perimeter Sites 

In the absence of a Single appropriate PMI0 background site with a 

sufficiently large data base, data from two or more sites in the vicinity of 

the study area may be combined to provide a composite estimate of bacKground 

concentration. 

For example, assume that there are two sites located on the area perimeter 

(one to the north and one to the south). The observations from both sites 

should be sorted by wind direction into three groups: 

Group I - days when the Northern site ;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 or west (and both sites are upwind). (Use average 

of data from both sites for N3 days.) 

The Group I, Group II, and Group III days are used to compile an annual 

average: 

annual average ~ 

~ ~ 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-1 
6.0 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-fractionated particulate samplers measuring PHlO 

compared with those measuring TSP; and almost all of the former are in urban 

locations. Therefore, States may not have direct measurements of PMIO background 

available when they prepare thei r SIP's. The following is the recommended 

procedure for using PHlS and TSP surrogates to estimate PMIO, based on an 

analysis of regional scale PHIO. 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 x PHIS (annual or 24-hour) 

Reference 3 suggests PMlS to TSP 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 a TSP surrogate: 

PMIO = 0.S2 x TSP (annual) 

PMIO = 0.64 x TSP (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 a TSP or PMl5 background estimate is adjusted to 

represent an estimate of the PMIO background. 

D-8 
References 

-----,--

1. ~uid~Li~~_~_Ai~_~~aJJLt~~odels (Revised) EPA-450/2-78-027 R. 

U.S. Environmental Protection Agency, Research Triangle Park, NC 

27711, July 1986. 

2. A. K. Pollock, A. B. Hudeschewskyj 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. 

0-9 

TECHNICAL REPORT DATA 

fPltlJJt "IJ~ Imt'1'Utfionr 'm ,ht 1(\'ti"U btlort ('omflJtt1n,J 

, IZOQI-Oj!:,';' floC !' ,3. ReCIPIENT'S ACCESSION NO. 

i __ ~A-450/2-86-001 

t,: •• _~. ,',: ~ .. E~ ":'~e -------.---------::-;;==-::7:;:~-----_I is. p:t.l::OO~T =-"T:= 

; June 1987 

I PM10 SIP Development quideline 

I 

!e. P£RFOFU .. ~ING OFtGA/'I;l2ATlON CODE 

I 

I 

Control Programs Development Division (MD-15) 

Office of Air Quality Planning and Standards 

Research Triangle Park, NC 27711 

10, PAOGnAM Ei..EMEN"l' NO, 

I". ~v:"TRACl ·,,,'ANT NO. 

J 

113, Type OF REPOAT AND PEAIOD COVERED , 

,A. SPONSORING AGENCV CODE 

! ~ 5, suP;: ... 1:.~.t..E' '":' t.,;;.., ~C"l"=S 

I 

I 

, 

i 

I 

I 

I 

II 

This guideline document was prepared to briefly describe the actions 

that must be taken by State and local air pollution control agencies to 

develop SIP's that demonstrate attainment and maintenance of the PMIO NAAQS. 

The guideline describes how to: (1) demonstrate attainment of the PMIO NAAQS. . (2) determine the size of an area exceeding the NAAQS. (3) select a receptor 

or dispersion model, (4) prepare an emission inventory and, (5) determine a 

control strategy design concentration. The guideline also discusses ambient 

PMIO monitoring requirements and data usage and EPA's policies for making the transition from a SIP designed to protect TSP standards to one designed 

to protect PMIO standards. References to supporting documents are given at 

the end of each section. 

This guideline does not discuss implementation of PSD or preconstruction review programs for PMIO. Guidance for implementation of these programs will be issued separately • 

. ---.-.--,~-.--------. 

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1·-·~~~:i·i~p~~~:i~~-;~0~p1~n- ~SIP~ -----··T-----~- ---~---~I'.' -. 

Ambient monitoring 1 Receptor models ! 

Dispersion models ! 

Particulate regulations , ! 

i 

--::', r:"~~,t-~ 
United Statos 

Environmental Protection 

Agency 

Official Business 

Penalty for Private Use 

0300 

Office of Air and Radiation 

Office of Air QualitY Planning and Standards 

ResearCh Triangle Park, NOl1h Carolina 27711 

jf your address is incorrect, please d'Ie n90 on the above label:. 

tear off; and taturn to the above address. 

If you do not desire to contin ue receiving this technical report 

series, CHECK HERe C; teat off label; and return it to the 

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