United States

Environmental Protection

Agency

Air

Office of Air Quality

Planning and Standards

Research Triangle Park NC 27711

EPA-450/2-86-001

June 1987

&EPA PM10 SIP

Development

Guideline
EPA-450/2-86-001

PM10

SIP Development Guideline

U. S. ENVIRONMENTAL PROTECTION AGENCY

Office of Air and Radiation

Office of Air Quality Planning and Standards

Research Triangle Park, North Carolina 27711

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 matter have been revised to address particles 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. for a fee, from 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 for 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

Page

1.0 INTRODUCT ION a • • • • • • •• 1-1

Overy; ew .. , ft ••••••••• '••••••••••••••••••••••••••••••••

Guideline Contents ••.•..•.•....•••••••••••••••.••••••

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

1.1

1.2

1.3

1.2.1

1. 2.2

1.2.3

1.2.4

1.2.5

1.2.6

1.2.7

1. 2.8

1.2.9

Determining the air quality status of areas •••• Ambient monitoring .••..••••••••••••••••.••••••

Mode 1; n9 ••••••••••••.•••••••••••••••••••••••••

Emission inventories ••.•••••••••••••••••••••••

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

Preliminary estimate of PMI0 design

concentration using TSP data •••••••••••••••••• Source test; ng .

1-1

1-2

1-3

1-3

1-3

1-3

1-4

1-4

1-4

1-4

1-5

1-5

2.0 DETERMINING AIR QUALITY STATUS OF AREAS ••••••••••••••••••• 2-1

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

2.2 Demonstrating Attainment ......•...•...•...•...••..•.. 2-2

2.3 Estimating Probability of Nonattainment to ••••••••••• 2-4

Determine Initial SIP Requirements

2.4 Area Categorization ••.••..••..••.•••••••••••.•••••••• 2-7

2.5 Area Boundaries ••••••...•••.•••.•••••.•••.••..••••••• 2-9

3.0 AMBIENT MONITORING AND DATA USAGE ••••••••••••••••••••••••• 3-1

3.1 Ambient PMIO Monitoring ..•••••••••••••••..••••••••••• 3-1

3.2 Part 58 Requirements •.•..••••••••••••.•...•...••••••. 3-1

3.2.1 Quality assurance •••••••••••.•••••.••••••••••• 3-1

3.2.2 Ambient monitoring methodology •••••••••••••••• 3-2

3.2.3 Network establishment ••••••••••••••••••••••••• 3-3

3.2.4 Network design •••.•.•••••••••••••.••.•.••••.•• 3-3

3.2.5 Sampling interval ••••••••••••••.•••••••••••.•. 3-4

3.2.6 Sampler inlet siting ••••••.••••.••••••.••••••. 3-5

3.2.7 Ambient data reporting •••••••••••••••.•••••••• 3-6

3.2.8 Air pollution i_ndex •.••.•••••..•.•.•...•••.••• 3-7

i
Page

3.3 Ambient Samplers and Ambient Data Usage •••••••••••••• 3-7

3.3.1 Ambient samplers in use ••••••••••••••••••••••• 3-7

3.3.2 Interim use of nonreference or equivalent

method samplers ••••••••••••••••.•••••••••••••• 3-7

3.3.3 Episode monitoring •••••••••.•••••••••••••••••• 3-8

3.4 SIP Revisions •••••••••.•••••••••••••••••••••••••••••• 3-8

4.0 AIR QUALITY MODELING FOR PM10 ••••••••••••••••••••••••••••• 4-1

4.1 Introduction ••••••••••••••••••••••••••••••••.•••••••• 4-1

4.2 Considerations in Receptor Model

Se lecti on .••.•...•..••...•....•...•..••.•.•.••.•...•. 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

model s ••••••••• •••••••••••••••••••••••••••••• 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 PMIO 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 Data Handling •••••••••••••••••••••••••••.•••••••••••• 5-38

ii
p'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 (graphical estimation) .• 6-7

6.3.4 Conditional probability approach •••.•..••.•... 6-8

6.4 Determining Emission Limits ••. ,., .•.........•........ 6-8

6.4.1 General ••.•••..•.....••... -.••.••••...••••.... 6-8

6.4.2 Example for annual averages ••••••••••.•••••••• 6-9

6.4.3 Considerations for 24-hour averages .•••••.•••. 6-11

7.0 SIP REQUIREMENTS AND DATA REPORTING ••••••••••••••••••••••• 7-1

7.1 Introduction - '" 7-1

7.2 Clean Ai r 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 PMI0 SIP Development Policy 7-2

7.3.1 Group I PMI0 SIP requirements - 7-2

7.3.2 Group II PMI0 SIP requirements 7-3

7.3.3 Group III PMI0 SIP requirements 7-5

7.4 SIP Content - 7-5

7.5 Control Strategy Transition ••••••••••••••••••••••• 7-6

7.6 Current Emi ssi on Regul ati ons 7-7

7.7 Surrogate Emission Regulations •••••••••••••••••••• 7-8

7.7.1 Retaining existing emission limits ••••.•••.•••• 7-8

7.7.2 Setting new emission limits 7-10

7.8 PMI0-Specific Emission Regulations 7-10

7.9 Reporting Emissions Data 7-11

7.10 Emissions Trading (Bubble) Policy ••••••••••••••••• 7-13

7.11 Fugitive Dust Policy 7-13

iii
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 '" •• '" • '" '" '" A-2

4.0 Automated Scanning Electron Microscopy ••••••••••••••• A-2

5.0 Other Receptor Models A-3

5.1 X-Ray Diffraction (XRD) ••••••.•••••••••••••••••• A-3

5.2 Trajectory Analysis ,"' A-3

5.3 Microinventory ......•.•.•.....•..•.•..••.•.•.... A-3

APPENDIX B - PRELIMINARY ESTIMATES OF PMlO DESIGN CONCENTRATIONS

USING TSP DATA •••• '" •••••••••.•••.•••••••.•• '" .• 8-1

1.0 Annual NAAQS •... A •••••••••••••••••••••••••••••••••••• 8-1

1.1 Recommendations .........••...••.. '" .•••.......... 8-1

1.2 Ex amp 1e '" '" . '" .. '" '" '" '" '" B- 2

2.0 24-Hour NAAQS ••• '" •••••••...•.••••..••••••• '" ••••.•.••. B-2

2.1 Background .................................•.... B-2

2.2 Recommendations .......................••.•.....• 8-4

2.3 Example ~.~ ~.~ 8-6

APPENDIX C - GUIDELINES FOR SOURCE TESTING FOR SIZE SPECIFIC

PARTICULATE EMISSIONS C-l

1.0 Introduction .......••................................ C-l

2.0 Available Methods for Size Specific Source

Measurements •............•••••......... 'O ••••••••••••• 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-IO

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 •••••••••••.••••••••••••••••••••••••••••• 0-2 ,

3.0 Special Considerations for Receptor Modeling ••••••••••• D-5

4.0 Summary of Alternatives for Estimating PMIO Background 0-6

5.0 Example 1 - Compositing Data from Several Perimeter

Sites •....•........•.••...•.............•..•..•.•.•...• 0-6

6.0 Example 2 - Use of Surrogate Data •••••••••••••••••••••• D-8

v

TABLE 4-1

LIST OF TABLES AND FIGURES

SELECTING FEASIBLE SOURCE APPORTIONMENT METHODS

BASED ON DATA AVAILABILITY AND SOURCE

CHARACTERISTICS •.•..•..••..••.•...••.••..• r •••• ff. 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 (CDM 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 """""""",,",,""",, t " , " " " " " " " • " " " " " • " " , f " " " " " "" 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-7

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

PM10 stdndards. Existing State control strategies to attain and maintain

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

controlling particulate matter sources; however. they must be evaluated

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

guidance herein addresses the transition that States must make from TSP

control programs to PM10 control programs and the necessary revisions to

State implementation plans (SIP's) to account for PM10.

This document is intended as a starting point for anyone seeking

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

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 D 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 PMI0 will be carried out

largely under regulations for the prevention of significant deterioration (PSD).

The reader is referred to section IV.D in the preamble of the FEDERAL REGISTER

notice promulgating PMI0 amendments to the PSD regulations for a description

of the changes that EPA has made to address PMI0. 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 PM10 NAAQS is

different from that included in TSP. the actions that States will have

to take in preparing PMI0 control programs are more complicated than they

would be if only the levels or averaging method of the NAAQS had been

changed. As a result, States must look at the spatial distribution of

ambient PMI0 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 PMI0 control strategy

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

PMI0 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 PMI0 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

PMI0 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

PMI0. 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 PMI0 with dispersion models or with

receptor models and describes how the two techniques can be interfaced.

The section specifies models or combinations that States should use

depending on various considerations such as source types, modeling

objectives, and available ambient data.

1.2.4 Emission inventories

Where States use dispersion models to assess the impact of a source

or sources on ambient PMI0 levels, the model inputs must be in terms

1-3
of PMI0 emissions. Section 5.0 discusses how PMI0 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 PMI0 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 CFR 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 PM10 Design Concentration Using TSP

Data

Appendix B provides a method for making a preliminary estimate of

the PMI0 design concentration when only TSP data are available; however,

this estimate alone cannot be used for developing' control strategies. In

addition to providing 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 PMI0'

1.3 General SIP Approach

States have many years of experience in developing and implementing

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

activities States will engage in to attain PMI0 NAAQS will not differ

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

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

areas where air quality needs improvement, determine the degree of

improvement necessary, inventory the sources contributing to the problem,

develop a strategy to reduce emissions from contributing sources enough

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

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

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

from TSP to PMI0, together with the emergence of receptor models as

effective tools in strategy development, necessitates changes in the

specifics of particulate matter control strategy development. The major

difference in past TSP strategi~s and new PM10 strategies will be the

need to inventory the sources of the PMI0 size fraction and to focus on

control of PMI0 emissions to reduce ambient PMI0. This could include

applying PMI0 emission factors to sources, in some cases testing sources

for PMI0 emissions, and utilizing receptor models, where feasible to refine

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

sources to ambient PM10 and the degree 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 PM10 NAAQS using ambient air quality data is

described in Appendix K of 40 CFR 50. Generally, 3 years of PM10 data are

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

periods of time may be adequate as explained in Appendix Kand in the

Guideline on Exceptions to Data Reguirements for Determining Attainment of

Particulate Matter Standards. 1 In areas where too little PM 10 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

PM1Q NAAQS Using Total Suspended Particulate or PM 1Q 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 II 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 PM10 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 PMI0 monitoring.

2.2 Demonstrating Attainment

Appendix K specifies the procedures which States can use to demonstrate

attainment of the 24-hour and annual PMI0 standards at a particular location

using ambient air quality data. To determine PMI0 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 is greater than 1.0 per year, then a

nonattainment problem will be evident and States should proceed to examine

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

the 24-hour PM10 standard can be 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 observed in a single

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

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

or greater than 3.2, then the number 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

problem 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 CFR 58.13,

and (3) 75 percent data capture is achieved (see section 3.2.5 of this

document). As specified in 40 CFR 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 [IJ 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 Kare

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

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

with the Guideline on Exceptions to Data Requirements for Determining

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

.2 years of PMI0 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

indicat~ 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 PMI0 data for an area are adequate to demonstrate attainment

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

the other hand, if the PMI0 data alone are adequate to demonstrate that the

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

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

policy is to supplement available PMIO data with inhalable particulate matter

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

2-4
data. These air quality data will be used in a statistical methodology as

described in the probability guideline to estimate the probability that the

PM10 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 PM10 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 PM10 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-321A instruments,

24-hour PMIO observations and annual PMIO means (using all data) within the

upper gray zone are not to be counted as exceedances of the respective

standards in the probability calculations. Similarly, for the GMW-9000

instrument, 24-hour PM10 values and annual PMIO means (using all data) that

are within the lower gray zone are to be counted as potential exceedances

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 lone,"

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 in the gray lone, 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 PMlO data in the upper "gray lone" 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."

TABLE 2-1

PM10 Gray Zone Limits (ug/m3)

Upper Lower

Averaging Gray Zone Limits

Time

Annual

24-Hours

PM 10

NAAQS

50

150

60*

180*

40**

120**

*Applies to readings made with a SA-321A

**Applies 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 PMIO 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 II monitoring frequency

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

apparent.

2.4 Area Categorizatio~

SIP development requirements are based on an area's categorization

into Group I, II, or III of EPA's policy for PMIO 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 I,

are:

° 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,

° 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

° 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:

° 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 PMIO to total

particulate matter; and

2-8
o evidence that the area is rural in nature and is 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 PMlO concentrations in the area;

o the number and density of roadways in the area that are near activities

likely to generate significant particulate matter emissions and that are subject

to moderate and heavy vehicle traffic; and

o the degree to which the existing TSP SIP will likely limit PMlO 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 PMIO air quality status may differ from the areas previously

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

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

problems.

The approaches that have been used and that are recommended for

defining area boundaries are:

-- a qualitative analysis 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 PM]O NAAQS Using Total

Suspended Particulate or PMjO Data, U.S. EPA, RTP, N.C.

EPA-450!4-86-017, 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 Oata, memorandum from R.G. Rhoads to Regional Air Directors,

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

5. L. Purdue et. al., Intercomparison of High-Volume PMI0 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 PM10 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-vo1s.)* 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 PM10 reference or equivalent methods with a nominal

D50 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 PM10 networks are available.

3.2 Part 58 Requirements

3.2.1 Quality Assurance

The quality assurance (Q/A) requirements for PM10 samplers are essentially

the same as for hi-vo1s. Only one minor change was made to the quality

*Described 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 PM 10) be calculated

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

Otherwise. Q/A procedures for PM10 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 PM10 with one exception. Section 2.2 of Appendix Callows

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 PM10 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 PM10 sampler. If TSP levels rise above PM10 NAAQS.

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

PM10 levels to be certain PM10 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 PM10 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 PM10.

3-2
3.2.3 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 PMlO 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 PMlO SLAMS network. By

1 year after promulgation of the NAAQS, each area within the approved

SLAMS network for which a probability of PMIO NAAQS nonattainment is greater

than or equal to 20 percent, must have at least one PMlO sampler in operation

which is (1) located in the area of expected maximum concentration (2) sited

in accordance with Appendix E, (3) located as described on the station's

Storage and Retrievel of Aerometric Data (SAROAD) identification form, and

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

PM10. The remaining PMlO 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 I year after PMlO NAAQS are promulgated (section 58.34).

3.2.4 Network design

Information on designing SLAMS and NAMS networks is contained in

Appendix D 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 PM10

network design, Appendix D has been amended to include criteria for determining the number of PM10 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 PM10 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 app1i es to the requi red samp1i ng frequenci es of the "area" is as

follows: (1) any urbanized area as defined by the U.S. 8ureau 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 PMIO NAAQS occurs in

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

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

site 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 PM10 samplers

is very similar to the previous siting criteria for hi-vol samplers. The

new .criteria for the PMIO SLAMS include microscale stations which were not

previously included for the TSP SLAMS network. An inlet height of 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 reporting requirements in Part 58 for ambient data apply for PM10

as they do for the other pollutants; i.e., all SLAMS data must be reported

annually in summary form and all NAMS data must be reported quarterely.

Since the regulations allow for the use 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 PM10 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 PM10 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 PM10 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 D50 of 15 micrometers (SSI15), and dichotomous

samplers with inlets designed for D50 of 15 micrometers. Some data have

been collected using samplers with inlets designed for a D50 of 10 micrometers.

The PM10 samplers cannot be designated as PM10 reference or equivalent method

samplers, however, until Part 50 and Part 53 requirements are promulgated,

and subsequent manufacturer test data are examined and accepted by EPA.

Thus, the PM10 data now available have not been collected by reference or

equi.valent methods. Therefore, nonreference or equivalent method PM10

data, PM15 data, 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

D50 of 10 micrometers. Approval of ambient PM10 samplers for SIP purposes

will be made by the Environmental Monitoring Systems Laboratory, MD-77,

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

3-7
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 PM10 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 PM10 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 PM10 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, PM10 would need to be added to the list.

The PM10 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.

2.

3.

Koch, R.C. and H.E. Rector, Network Desi¥n and Optimum Site Exposure Criteria for Particulate Matter, GEOMETechnologies, Inc., Rockville, MD. Prepared for U.S. Environmental Protection Agency, Research

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

Pace, T.G. and N.H. Frank, Procedures for Estimating Probability of

Nonattainment of a PM10 NAAQS using Total Suspended Particulate or

PM10 Data, U.S. Environmental protection Agency, Research Triangle Park, NC. EPA 450/4-86-017, December 1986.

Pelton, D.J., Guideline for PM10 Episode Monitoring Methods, GEOMET

Technologies, Inc. Rockville, MD. Prepared for U.S. Enviromental

Protection Agency, Research Triangle Park, NC. EPA Contract

No. 68-02-3584. February 1983.

3-10
4.0 AIR QUALITY MODELING FOR PMI0

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 is shown to be adequate and appropriate

for this purpose. The Guideline on Air Quality Models (Rev1se~)1 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 (CMB).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 PMI0 using dispersion and receptor models: (1) use of receptor

and dispersion models in 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 PMI0 is generally referred to

in the following discussion. Where there is insufficient technical data to

support an analysis for PMI0 with these models, the same procedures may be

applied to TSP, except as specifically noted, as a surrogate for PMI0.

4-1
4.2 Considerations in Receptor Model Selectio~

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 CMB 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 um) 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 ngerca1 pri nts

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

Iso1ated

Single

Source

Ai r Shed

(Source

Categori es)

Ai r Shed

(Specifi c

Sources

Withi n

Category)

ISS

1ance (CMB) Y

!ctor

,a1ysis (FA) . X

Y

X

Y

x x

a,b,Y

b,X

Y

x

Y

X

Y

X

b,Y

--- --------------

Itomated

.anning

ectron

croscopy

.SEM)

croscopy

1M)

spersion

.de1 (OM)

c,X

c, X

YY

x

X

YY

x

X

YY

X

X

b,X

b,X

YY

x

X

YY

X

X

YY

X

X

YY

b,X

b,X

YY

- Initial use 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 CMB or OM.

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

- Appropriate to use.

4-3
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 CMB 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., Kin wood). Other common fingerprint problems

include the difficulty in distinguishing among sources whose emissions are

4-4
comprised of various soil components or between flyash and soil. Optical

properties can be useful for some situations where fingerprints are simila~.

Also. X-ray diffraction (XRD) is very useful in identifying various minerals

by examining their crystalline structure.

4.2.4 Sources versus source categories

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

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

fingerprint is 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 andtime"scale

In some cases ambient PM10 or PM15 data may be available for SIP

apportionment. In other cases. only TSP data are available. The availability of PM10 data, or PM15. or TSP will determine to some extent the type

of source apportionment method used for PM10 since the PMIO signature of a

source may be significantly different from the PM15 or TSP signature. Another

important factor is the time scale of the nonattainment situation (annual

or 24-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

Concentrations

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

"first 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 c.an 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 PM10 dispersion modeling

The Modeling Guideline l 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
TABLE 4-3

DISPERSION MODELS ApPLICABLE TO PM10 ANALYSES*

1 to 24-Hour Average Annual Average Screening Techniques**

CRSTER CRSTER PTPLU-2

MPTER MPTER COMPLEX I

RAM RAM VALLEY

ISCST ISCLT

CDM 2.0

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

the Guideline on Air Quality Models (Revised). As noted in this document,

these models may also be used for TSP modeling 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_for use prior to a

more refined analysis as outlined in the Gu~deline on Air Quality Models

(Revised) •

4-9
options, (4) determination of urban/rural classification, and (5) determination

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.

Proportional models may be used in conjunction with receptor modeling if

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 cOMplicated 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,

building 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

concentrations (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 10to 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 l3-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 Series 2 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 CMB, 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 PM10

(into fine and coarse fractions, below and above 2.5 urn). This greatly

*The terms "model" and "method" are used interchangeably, even though analysis methods such as scanning electron or optical microscopy are

methods, not models.

4-11
increases the resolution 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 Preliminary analyses using receptor models

Preliminary analyses can also be done using receptor models. The most

common methods employed are optical or automatic scanning e1ecron microscopy.

These techniques are relatively inexpensive (less than $500 per sample) and

can give a variety of information about likely sources. They are especially

useful when only TSP data are available because they can discriminate between particles less than and greater than 10 um. Other methods which may

be helpful include microinventories, chemical emission inventories, and mass

ba1ance. 4,12,13 In those cases where only TSP data on glass fiber filters

are available, the CMB approach is limited because some key tracers (silicon

and aluminum) cannot be used. However, the CMB may be 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., Si, All 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 CMB, in conjunction with dispersion modeling,

is highly recommended in such situations. Procedures for using the CMB 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 PMIO

emissions inventory used as input to a dispersion model for a PMIO analysis.

For determining compliance with the annual PMIO NAAQS, dispersion models

based on an annual PMIO emission inventory and sequential or frequency distributions of observed meteorological conditions can be used. Also. receptor

models and methods such as CMB, FA, OM and ASEM, perhaps corroborated by

microinventories, trajectory analysis, and XRD can be used on samples which

have been carefully selected to represent the annual average. Thus, the

source contributions would reflect those which cause the annual average to

exceed the NAAQS.

It may be difficult to devise a PMIO 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, in concert with dispersion

model estimates. Of the receptor models discussed, CMB, OM, or ASEM, perhaps

corroborated by XRD 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-450/2-78-027R. U.S.

tnvironmental Protection Agency, Research Triangle Park, NC 27711

July 1986.

Receetor Model Technical Series, Volume I: Overview Of Recettor Model

Appllcation To Particulate Source Apportionment, EPA-450/4-8 -016a,

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

3. Gordon, G. E., Receptor ModelS. Environmental Science and Technology, 1980, 14. 792-800.

4. 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.

5. Protocol For Applying And Validating The CMB.

Protection Agency. Research Triangle Park. Nc. U.S. EnvironmentalIn Preparation.

6. Protocol For Reconciling Differences Among Receptor And Dis~ersion Models.

U.S. Environmental Protection Agency. Research Triangle Par. Nc. In

Preparation.

7. Receptor Model Technical Series. Volume V: Source Apportionment Techniques And Considerations In Combining Their Use. EPA 45074-84-020.

PB85-111524. U.S. Environmental Protection Agency. Research Triangle Park. NC. July 1984.

8. Turner, D. B., ~orkbook of Atmospheric Dispersion Estimates. PHS

Publication No. 999-Ap-26. U.S. Environmental Protection Agency. Research Triangle Park. NC, 1970.

9. Budney. L. E., Procedures For Evaluating Air Quality Impact Of New

Stationary Sources. Guidelines For Air Quality Maintenance Planning And

Analysis Volume lOR, EPA 450/4-77-001. U.S. Environmental Protection

Agency, Research Triangle Park, NC. October 1977.

10. Hanna, S. R., G. A. Briggs and R. P. Hosker, Jr•• Handbook on Atmospheric Diffusion. DOE/TIC-11223. Technical Information Center, Department of

Energy, Washington, DC, 1982.

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

Hazardous Di vi sion Di rectors. U.S. Envi ronmental Protection Agency, Research Triangle Park, NC, July 22, 1981.

12. Rece~tor Model Technical Series, Volume III (Revised): User's Manual

For pecies Mass Balance Computer Program. U.S. Environmental

Protection Agency, Research Triangle Park, NC. In Preparation.

4-15
13. Receptor Model Technical Series, Volume IV: Technical Considerations In

Source A~aort10nment Sf Particle Identification, EPA 450/4-83-018,

PB84-103 o. U.S. Env ronmenta1 Protection Agency, Research Triangle Park, NC, June 1983.

14.

15. Core, J. E., J. A. Cooper, P. L. Hanrahan, and W. M. Cox, Particle

Dispersion Model Evaluation: A New Approach Usin9 Receptor Models.

Journal of the Air Pollution Control Association, 1982, 32, 1142-1147.

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

Factor Analysis And MUlti~'e Regression (FA/MR) Techniques in Source

Apportionment, EPA 450/4- 5-007, PB86-107638. U.S. Environmental

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

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

U.S. Environmental Protection Agency, Research Triangle Park, NC, 1985

4-16
5.0 DEVELOPMENT OF PMlO EMISSION INVENTORIES

5.1 Over..vi e~.

As part of the SIP development process following promulgation of PMlO

NAAQS, emission inventories may have to be developed for PMlO. This section

discusses various technical considerations involving the preparation of PMlO

emission inventories. Though inventories are useful for various purposes,

the primary focus of this section is on developing or reviewing PMlO emission

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

Such models are expected to be utilized as part 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 PMlO

inventories. The primary thrust of this section is to alert users to

circumstances where additional materials and different methods are needed to

compile PMlO inventories.

The two major components of PMlO 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 is 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 II), The PMIO emission factors for

reentrained dust from roadways are included in Volume I. This section

de~cribes the format of these PMIO emission factors for stationary sources

and how they can be applied to the appropriate source data to develop PMIO

inventories. Emission inventory procedures for mobile sources are presented

in a separate EPA publication. 11

The second component of the PMIO inventory, source data, consists of

stack and exhaust flow parameters, production levels, throughputs, control

device efficiencies, etc., as well as information on source locations and

plant layouts. The specific kinds of source data that are needed in the

PMIO inventory depend on the model used for PMIO simulation, with the major

differences involving (1) the levels 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 PMIO, 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 PMIO 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 PMI0

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, PMI0, or any

other pollutant. If a set of PMI0 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, PM10 emissions may be computed by applying fractional

multipliers to particulate matter emission estimates in an existing inventory as

follows:

(PM10 fraction) x Emissionslparticulate = EmissionslpM10

An advantage of the latter approach is that PM10 fractions may be available

in certain cases whereas PM10 emission factors may not. This approach may

also be easier from a data handling standpoint when converting an existing

total particulate matter inventory to a PM10 inventory.

Thus, in compiling a PM10 inventory for use in dispersion modeling,

one must first obtain requisite PM10 emission factor or size fraction

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

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

developed PM10 emission factor and other particle size information. 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, PM10 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. in Figure 5-1.

Some agencies may elect to obtain their own particle size data for

certain sources. Source testing is generally encouraged, especially for

5-4
'"

I

'"

Table 5.1

PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

1.1 Combustion-Bituminous and

Subbituminous Coal Dry Bottom Boiler x x x x x

Wet Bottom Boiler x x x

Cyclone Furnace x x x

Spreader Stoker1 x x x

Spreader Stoker2 x x

Overfeed Stoker x

Underfeed Stoker x x

1.2 Combustion-Anthracite Coal Dry Bottom Boiler x x x

Traveling Grate

Boiler x

1.3 Combustion-Residual Oil Utility Boiler x x x

Industrial Boiler x x

Commercial Boiler x

Combustion-Distillate Oil Industrial Boiler x

Commercial Boiler x

1.4 Combustion-Natural Gas All Boilers x

1.6 Combustion-Bark Wood Waste Boiler x x xl ,2

lWithout flyaah reinj ection.

2With flyash reinjection.
U'I

I

en

Table 5.1 (continued)

PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOl/RCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE,

AP-42 Fabric Multiple

Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

1.7 Combustion-Lignite Coal Boilers x x

1.8 Combustion-Bagasse Vibrating Grate

Stoker x

2.1 Refuse Incineration Municipal

Incinerator x

4.2 Surface Coating Spray Booth Water x

Based Water

Curtain

5.3 Carbon Bl ack Oil Furnace x

5.15 Detergent Spray Dryer x

5.16 Sodium Carbonate

Manufacturing Bleacher Dryer x x x

Calciner x x x3 x3

Dryer x

Rotary Predryer x x3 x3

3eombined controls.
(J'I

,

.....

Table 5.1 (continued)

PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE I

AP-42 Fabric Multiple

Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

5.17 Sulfuric Acid Absorber x

Absorber 20% x

Absorber 32% x

Secondary

Absorber x

5. Boric Acid Dryer x x

5. Potassium Chloride Dryer x x3 x3

6.1 Alfalfa Dehydrating Drum Dryer x

6.3 Cotton Ginning Battery Condenser x x

Lint Cleaner x3 x3

Roller Gin

Bale Press x

Gin Stand x

Saw Gin x

Bale Press x

Gin Stand x

3Combined controls.
Table 5.1 (continued)

\ ,

'!

,,.. '.': '.

... \ , ,. .,'

" i I'. ";.1

.•1 '!,.

•

.( , .

U1

I

CO

PM 10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES. PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontrolled ESP Filter Scrubber CyClone Other

6.4 Feed and Grain Carob Kibble

Roaster x

Cereal Dryer x

Unloading and

Conveying x

Rice Dryer x

6.18 Ammonium Sulfate Rotary Dryer x

7.1 Primary Aluminum Production Bauxite Ore

Unloading x

Storage x

Prebake Roof

Monitor x

HSS Cell x

7.2 Coke Production Coal Preheat x x

Coal Charging x

Coke Pushing x x

Mobile Scrubber

Car - Travel

Mode x
U1

I

'"

Table 5.1 (continued)

PMIO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES. PROCESSES AND CONTROL SYSTEMS

.. CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontroll ed ESP Filter Scrubber Cyclone Other

7.2 Coke Production (cont.) Mobile Scrubber

Car Push Mode ·x

Quenching with

Dirty Water x Baffle

Quenching with

Clean Water x Baffle

Combustion Stack x

7.3 Primary Copper Multiple Hearth

Roaster and

Reverberatory

Smelter x x

Reverberatory

Smelter x x

Converter x x

Matte Tapp.ing x

Slag Tapping x

Converter Slag

and Blow

Operations x
U'1

I

....

o

Table 5.1 (continued)

PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

7.4 .Ferroalloy Open Furnace 50% FeSi x x

80% FeNn x x

Production Si Metal x x

FeCr (HC) x x

SiMn x x

7.5 Iron and Steel Production Sintering Windbox x x x x

Sintering Discharge Breaker x

Blast Furnace

Casthouse x

Blast Furnace with

Local Evacuation x

Hot Metal

Desulfurization x x

BOF Top Blown

Furnace Melting

and Refining x

Q-BOP Melting and

Refining x

BOF Charging x x

BOF Tapping x x

EAF Melting and

Refining x
Table 5.1 (continued)

PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

7.5 Iron and Steel Production

(cont.) EAF Melting,

Refining, and

Charging

Direct Shell

Evacuation x

Open Hearth

Furnace Melting

and Refining x x ,

,

, 7.6 Primary Lead Smelting Blast Furnace Flue x

Blast Furnace x

Ore Storage x

Sinter Machine x

Dross Kettle x

Reverberatory

Furnace x

7.8 Secondary Aluminum Reverberatory

Furnace x

Chlorination

Station x

en

I

...

...
U1

I

....

N

Table 5.1 (continued)

PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontroll ed ESP Filter Scrubber Cyclone Other

7.10 Gray Iron Foundries Cupola Furnace x x x

EAF x

Pouring and

Cooling x

Shakeout x

7.11 Secondary Lead Smel ting Blast Furance Flue x

Blast Furnace

Ventilation x x

7.13 Steel Foundries Casting Shakeout x

Open Hearth x x

7.15 Storage Battery Production Grid Casting x

Grid Casting and

Paste MiXing x

Lead Oxide Mill x

Paste Mixing x x

Three Process x

7. Tinner Batch Tinner x

8.1 Asphaltic Concrete Conventional Plant x x x Spray

Tower

Drum Mix Plant x x
<n

I

....

W

Table 5.1 (continued)

PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

8.3 Brick and Related Clay

Products Coal Fired Tunnel

Kiln x

Sawdust Fired Kiln x

Raw Material

Screening and

Grinding x

8.6 Portland Cement Wet Kiln x x

Dry Kiln x x x

Clinker Cooler x Gravity

Filter

8.9 Coal Cleaning Dry Process x

Thermal Dryer x x

Thermal

Incinerator x

8.13 Glass Manufacturing Furnace Exhaust x x
U'l

I

....

-I'>

Table 5.1 (continued)

PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

8.15 Lime Rotary Kiln x x xa x

product Loading

Into Open Bed

Trucks x

Into Tank Truck x

Glass Line Into

Tank Truck x

8.18 Phosphate Rock Processing Ball Mill x

Calciner x3 x3

Rotary Dryer x3 x

Rotary Dryer and

Fluidized Bed

Dryer x3 x3 x3

Roller Mill and

Ball Mill x3 x

8.22 Taconite Ore Processing Main Waste Gas

Stream x3 x

8. Feldspar Ball Mill x

8. Fluorspar Rotary Drum Dryer x

8. Lighweight Aggregate (Clay) Coal Fired Rotary

Kiln x S. C.b

Dryer x

Reciprocating

Grate Clinker

Cooler x x

SCyclone With Baghouse.

bS. C. = Settling Chamber.
U1

I

....

U1

Table 5.1 (continued)

PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontroll ed ESP Filter Scrubber Cyclone Other

8. Lightweight Aggregate

(Shale) Reciprocati ng

Grate Clinker

Cooler S.C.a

8. Lightweight Aggregate Coal Fired Rotary

(Slate) Kiln x x

Redprocating

Grate Clinker

Cooler S.C.a

8. Talc Pebble Mill x

10.1 Kraft Pulp Recovery Boiler

with DCE x x

Recovery Boil er

without DCE x x

Lime Kiln x x x

Smelt Dissolving

Tank Vent x x Packed

Tower

as.C•• Settling Chamber.
U1

I

Table 5.1 (continued)

PM10 EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontroned ESP Filter Scrubber Cyclone Other

10.4 Wood Working Waste Belt Sander x

11.2.1 Unpaved Roads Rural

Gravel x

Dirt x

Crushed Lime

Stone x

Industrial

Copper Smelting x

Iron & Steel

Production x

Sand & Gravel

Processing x

Stone Quarrying

& Processing x

Taconite Mining

& Processing x

Western Surface

Coal Mining x

11.2.2 Agricultural Tilling x

11.2.3 Aggregate Handling Batch Drop x

Continuous Drop x
U1

I

....

.....

Table 5.1 (continued)

PMlO EMISSION FACTORS TO BE AVAILABLE FOR INDICATED SOURCE

CATEGORIES, PROCESSES AND CONTROL SYSTEMS

I CONTROL DEVICE

AP-42 Fabric Multiple

Section Source Category Process Uncontrolled ESP Filter Scrubber Cyclone Other

11.2.5 Paved Urban Roads Local St reets x

Collector Streets x

Major Streets/

Highways x

Freeways/Expressways x

11.2.6 Industrial Paved Roads Copper Smel ters x

Iron and Steel x

Asphalt Batching x

Concrete Batching x

Sand and Gravel x

-
TABLE 5-2. CUMULATIVE EMISSION FACTORS AND PARTICLE SIZE

DISTRIBUTION FOR A HYPOTHETICAL LIME PROCESS

Particle Size Cumulative Mass Percent Less Cumulative Emission

(mi crometer) than Stated Size Factors (Pound/Ton of Product)

Uncontrolled Contro11 eda Uncontro11 ed Controlleda

Total Catch 100 100 85 0.100

15.0 70 82 59 0.082 r.n

I

......

co 10.0 51 60 43 0.060

7.5 42 56 36 0.056

5.0 33 50 28 0.050

2.5 13 31 11 0.032

1.0 2.8 11 2.5 0.010

0.5 0.9 4 0.8 0.004

aControl Device: Baghouse.
.'fll!;l'I]';:'I'jll-li!I·I!II'lIll·III'tllt' II!'(I III;;.!! 1 I 1 '1 1 1111It!:q!"II~i' 0.001

0.5 1 2.5 5 7.5 10 15

AERODYNAMIC PARTICLE DIAMETER, MICROMETeRS

FIGURE 5-1 EXAMPLE GRAPHICAL DISPLAY OF CUMULATIVE EMISSION

FACTORS VS. PARTICLE SIZE FOR AHYPOTHETICAL PROCESS
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 PM10 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

PMI0 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 PM10 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 PM10 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~

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

to be made when developing a PM10 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 PM10 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 configurations - The horizontal dimension, elevation above

sea level and effective 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 averaging 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 PMI0 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 (COM 2.0)3,4

IlE! - 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 height 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 PMI0 fraction can be treated as a single lump sum.

Chemical composition data - None required.

Other - An assumed pollutant half life, may be entered into COM 2.0, but should

not be considered in PMIO SIP's.

5-25
TABLE 5-5

RAMS

- Dispersion

Application - Estimating urban scale impact 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 PM10 fraction can be treated as a single lump sum.

Chemical composition data· None required.

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

not be considered in PM10 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/long-term - The basic estimate is for a I-hour period; multiples up

to 24 hours may be selected and the annual mean concentration is

calculated from the average of all hourly concentrations.

Inventory area - No area, per se, is included in the inventory, because the

single stack or collection of stacks are considered to be emitted at a

cOllll1on point.

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

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

Nonpoint source configurations - CRSTER does not handle nonpoint sources.

Temporal resolution 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 PMI0 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
TABLE 5-7

MULTIPLE POINT SOURCE MODEL WITH TERRAIN

ADJUSTMENT (MPTER)7

- Dispersion.

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.

Inventory area - That area including all of the point sources (up to 250)

that are being modeled.

Stack and exhaust data - For each stack, physical stack height and inside

diameter, stack gas temperature and exit velocity, and stack ground level elevation are needed to calculate plume rise.

Nonpoint source configuration - MPTER does not handle nonpoint sources.

Temporal resolution 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 PMIO fraction can be treated as a single lump sum.

Chemical composition data - None required.

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

considered in PMI0 SIP's.

5-28
5.4 Inventory Data Needed in Receptor Models

The considerations in sections 5.2 and 5.3 apply primarily to PMIO

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

emission 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 composittons 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 (1.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 identification 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 particle-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 PARTICULATE SOURCE LIBRARY (29)

s.:lJRCE:

S:C:

:ONTROLS:

S~EEL PRODUCTIOH • OPEH HEARTH FURHAHCE

3'03-009-0'

ESP

PROFILE:

RAH~ING:

RATlHG :

26302

2'21

., 0

....................................................................................................................................................... FINE

S>=CJES ••••••••• <2.5 UK ••••••

N...."': X BY \IT ... • UNC

COARSE ......... 2.5·10UM ••••••

X BY WT +. UHC

S,ECIES

"'U~SER.

4

5

9

11

12

13

U

15

16

17

19

20

21

22

23

24

25

26

27

26

29

30

31

32

33

35

37

3.5

40

47

t.a

50

5'

55

56

56

SO

62

201

202

203

2Q.1,

SL,'M( X )

BE

B

F

HA

MG

AL

51

P

S

CL

CA

SC

TI

V

CR

NH

FE

CO

HI

ZH

GA

"E

AS

SE

DR

RB

SR

ZR

A"

c:o

SH

sa

CS

BA

CE

H"

PB

oc

EC

N03

=

HA

NA

NA

NA

HR

HR

NR

<

13.300 <

5.000

0.550

<

<

0.550

2:000

0.550

1'.000

<

0.550

0.550

<

<

<

0.050

<

0.050

0.050

<

<

0.050

0.050

0.050

0.050

<

<

<

<

0.550

NA

HA

40.000

0.550

62.200

·. •

•

·. + •

·. + •

+ •

·. + •

·. •

+ -

·. ·. •

·. ·. +

•

•

•

·. ·. •

•

•

·. ·. •

·. '.·- •

·.

·. •

·. •

•

•

·.

HR

NR

HR

HR

NR

NR

NR

NR

HR

HR

NR

HR

HR

HR

HR

HR

NR

HR

HR

HR

HR

HR

HR

HR

HR

HR

.HR

NR

HR

NR

HR

HR

HR

NR

HR

HR

HR

HR

NR

HR

HR

HR

HR

NA

HA

HA

HA

NR

NR

HR

<

3.000

<

5.000

0.550

<

<

0.550

0.550

0.550

16.000

<

0.550

0.550

, <

<

<

0.050

<

0.050

0.050

<

<.

0.050

0.050

0.050

0.050

<

<

<

<

0.550

HA

HA

35.000

0.550

60.750

+

·. ·. ·. •

+

·. +

•

+

,+

·. ·. ·. •

+ •

+ •

·. ·. ·. •

•

+

•

•

•

·. ·. •

•

•

+

•

·. + •

·. •

•

·. •

·. ·. •

HR

NR

NR

NR

NR

NR

HR

NR

HR

HR

HR

NR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

HR

NR

NR

NR

NR

NR

HR

HR

HR

HR

HR

HR

HR

TSP ......... <30 UM ••••••

%BY +. U~:

NA .. NA .

NA .

MA + ..

NR ...

NR + •

NR .....

< + •

13.300 • < ••

5.000 • 0.550 • < ••

< ••

O.!50 .....

2.000 • 0.550 • 11.000 •• < •

0.550 •• 0.550 +.

< •

< •

< • •

0.050 •• < • •

0.050 +.

0.050 •• < +

< +

0.050 +

0.050 ••

0.050 • 0.050 •• < •

< + •

< ••

< •

0.550 • NA ..

NA ..

40.000 •

0.550 +

62.200

HOTES: CC' OR"AHIC CARBOH EC • ELEMEHTAL CARBOH: HA. NOT ANALYZEO: HR' HOT REPORTED < • LESS THAH DETECTIOH LIMIT

CTHER HOTES : 20X CARBOH, 22·26X OTHER (51, OZ, AL, M") TSP AHO COARSE. REF, 20

5-31
These data are most useful in the chemical methods such as the mass balance

model and factor analysis described in Appendix Aof this guideline. For

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

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

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

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

specific characterizations needed for each type of 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 10 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 air 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 MATTER21

Stationary Sources

Al falfa Dryers

Anode Baking Furnaces

Asphalt Plants

AsphaIt Roofi ng felt saturating asphalt blowing

Boilers/Other Combustion

bagasse

coal

1ignite

oil

wood/bark

Charcoal Kilns

Chemical Production

boric acid

phosphoric acid

potassi um sti 11

zi nc sulfate

Citrus Peel Dryers

Coke Plants

Corn Processing

wet mi 11ing

syrup manufacturing

Elemental Phosphorus electric arc furnace

Expanded Vinyl

Ferroalloy Mills

Fertil i zer Pl ants

ammonium nitrate

diammonium phosphate

5-33

Fiberboard

dryer

press

Gl ass Fi ber and Mi neral 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 boilers

lime kiln

smelt dissolve tank

blow tank/hot water

accumul ator

Lime Kiln

Manure Dryers

Mineral Products

gypsum

clay dryers feldspar dryers clay kilns

Municipal Incinerators
TABLE 5-9 (CONTINUED)

Petroleum Refineries

FCC

catalytic regenerator

heaters

petroleum coke

Portland Cement Plants

kiln

finish mill

Primary Nonferrous Smelters

Cu converter

Cu electric furnace

Cu fluid bed roaster

Cu sinter line

Pb zinter line

Pb blast furnace

Mo roaster

Zn ore briquet dryer

Zn sweat kil n

Zn fume kiln

Residential Heating oil

wood

Secondary Metal Smelters

A1 scrap furnace

A1 dross furnace

brass and bronze furnaces

copper furnace

Pb furnaces

Pb02 mi 11 s

Pb grid casting Pb remelt pot

other metal furnaces

Mobil e Sou rces

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

1atex backi n9

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 PMlO inventory as if directly emitted as particles.

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

on any nearby receptor, and thus can be treated as 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,

cu rrent, and accurate, much of the source data ne.eded in the PMIO inventory is

already available.

5-35
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

i nformati on. Actually, the terms "annual" and "countywi de" do not gi ve 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 ISC, 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 (MB) model are

the chemical or elemental composition of emissions for each source category

as a function of particle size (i.e., in the <2.5 micrometer and ~2.5 to to

micrometer size ranges). Particle size, shape, and composition data will not

be contained in most inventories that have been developed for dispersion

modeling applications.

In any case, whenever an existing particulate matter inventory is being used

to directly generate the PMIO inventory, total particulate matter emissions have

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

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

by appropriate PMIO fractional multipliers. 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 0) 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 particulate 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 i~put to COM 2.0, RAM and MB, as well as MPTER if large numbers of

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

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

large grid systems. Moreover, existing particulate matter inventories are

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

preprocessing to convert total particulate matter to PMlO. Hence, if either

COM 2.0 or RAM is to be employed for PMIO 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.

Oata 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. 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 Awas issued in early 1987).

2. En vi ronment alP rotect i on Agency, -=-::r;;.r-7.;rr~~~rr~~-+~",""~=:,:,,,=c.:..::.,,­ Model, Second Edition, Volumes I

Environmental Protection Agency, June 1986.

3.

4.

5.

6.

7.

8.

9.

10.

11.

A. D. Busse and J. R. Zimmerman, User's Guide for the Cllmatololical

Dispersion Model, EPA-R4-73-024, U.S. Environmental Protectiongency, Research Triangle Park, North Carolina, December 1973.

Irwin, J. S., T. Chico and J. Catalano, COM 2.0 - Climatological Dispersion Model, EPA/600/8-029, U.S. Environmental Protection Agency, Research

Triangle Park, North Carolina, November 1985.

D. B. Turner and J. H. Novak, User's Guide for RAM, Volume I, Algorithm Description and Use, EPA-600/8-78-016a, U.S. Environmental Protection

Agency, Research Triangle Park, North Caorolina, November 1978.

Users Manual for Single-Source (CRSTER) Model, EPA-450/2-n-013, U.S.

Environmental Protection Agency, Research Triangle Park, North Carolina,

July 19n.

T. E. Pierce and D. B. Turner, Users guide for MPTER, AMUltiple Point

Gaussian Dispersion Algorithm with Optional Terrain Adjustment, EPA-600/8-80-016, U.S. Environmental Protection Agency, Research

Triangle Park, North Carolina, April 1980.

Procedures for Emission Inventor Pre aration, Volume I: Emission

Inventory Fundamentals, EPA- 5 a, U.S. Envlronmental - Protection Agency, Research Triangle Park, North Carolina,

September 1981.

Procedures for Emission Inventory Preparation, Volume II: Point Sources, EPA~450/4-81-026b, U.S. Environmental Protection 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-450 4-80-026d, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, September 1981.

5-41
12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

Procedures for Emission Inventor Pre aration l Volume V: BibliO~raphY,

P - - - e, •• nVl ronmenta Protect 1 on Agency, Researc

Triangle Park, North carolina, September 1981.

W. H. Lamason and T. F. Lahre, Procedures for the Preparation of

Emission Inventories for Volatile Or anlC Com ounds Volume I, Second

E ltlon, , U. S. Environmenta Protectl0n Agency, Research Triangle Park, NC, September 1980.

Exam Ie Emission Inventor Documentation For 1982 Ozone State

mp ementatlon ans S s, , U. • nVlronmental

Protection Agency Research Triangle Park, North Carolina, March 1981.

Procedures for the Preparation of Emission Inventories for Volatile

Organic Compounds Volume II: Emission Inventory Reauirements for

Photochemical Air Quality simulation Models, EPA-45 /4-79-018, U.S.

Environmental Protection Agency, Research Triangle Park, NC,

September 1979.

J. E. Core, Receptor Model Technical Series, Volume I, Overview of

Receptor Model Application to Particulate Source Apportionment, EPA-450/4-81-016a, U.S. Environmental Protection Agency, Research

Triangle Park, North Carolina, July 1981.

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

Balance, EPA-450 4-81-016b, U.S. Environmental Protection 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 Composition Librarf' EPA 450/4-85-002, U.S.

Envi ronmenta 1 Protection Agency, Researc Tri angl e Park, North Carol ina,

November 1984.

1m ortance of Condensed Particulate Matter to Inhalable • U.. nvironmenta Protectlon Agency. Park, N.C. 27711. Apri I 1983.

"Method 5 - Determi nat i on of Pa rti cuI ate Emi ssions From Stationary Sources," 40 CFR 60, Appendi x A.

5-42
6.0 OEVELOPMENT OF CONTROL STRATEGIES

6.1 Overvi ew

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 PM10 NAAQS. Conceptually, this involves

determining the PM10 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, PM10, although it may be applied to particulate

matter in general as a surrogate where PM10 data bases are inadequate.

In all cases where PM10 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 PM10 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 PM10 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 PM10 NAAQS

can be based on ambient measurements of PM10, 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 PM10

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 PM10 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 PM10

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 PM10 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 PMIO concentrations.

They 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 PM10 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-highest

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

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

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

considered and a design concentration established for each location.

For example, 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 PM10 DESIGN CONCENTRATIONS

Number of Daily

Values

Rank of Uppe r

Bound

Rank of Lower

Bound

Data Point Used for

Design Concentration

< 347

348 - 695 1

696 - 1042 2 I~'l"

1043 - J.G% 3

/3j/ - 113~ 4-

tl3't- -;u3b

]jJB!

'2.1-(:)5

6-5

1 Highest Value

2- Second Highest Value

3 Third Highest Value

4 Fourth Highest Value

S FIFt,
third- and fourth-highest values. However, this adds an additional element

of calculation. Nevertheless, if a more precise design 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,

(i.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 estimate 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 fairly complete PM10 24-hour air quality

measurements or model estimates, a statistical distribution could be selected

that "fits" the data. Information on fitting statistical distributions

can be found on pages 18-20 in the ozone gUidel1ne. 1,2,3 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 in 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 data

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.

distribution of several ears

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

distribution. as discussed in Section 6.3.2. The concentration value

corresponding to 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 Co~ditional probability approach

While the previous methods required grouping concentration data

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

treated separately. This is done by fitting a separate statistical distribution to each year of data and assuming a given probability that each year

will reoccur. This approach is of interest when different importance

should be placed on the individual 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 discussed in the ozone

guideline.

6.4 Determining Emission Limits

6.4.1 General

Once PM10 design concentrations have been established through

the use of air quality measurements or model estimates, 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) = PM 10 Design Concentration - PM 10 NAAQS

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

reduction in PM10 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 (ug/m3) = SISRi (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 (i) 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
Source

Next, for the site of the most restrictive design concentration, consider

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

Source Category Contributions

to Ambient Conc. (AC)

Steel mill roads

Steel mill coke ovens

Coal storage pile Urban paved roads

Cement plant

Background

15 ug/m3

10 ug/m3

5 ug/m3

20 ug/m3

10 ug/m3

40 ug/m3

100 ug/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 mi 11 roads

Steel mill coke ovens

Cement plant Coal storage pile

Individual Source Reduction (ISR)

8 ug/m3

5 ug/m3

8 ug/m3

4 ug/m3

TR = 25 ug/m3

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

reductions, if implemented, would reduce the annual PMIO concentration at

this "controlling site" to 75 ug/m3. The required emission reductions

(%RE) to accomplish this are calculated for each source category. For

example:

ISRcement = = 80%;

%REcement = ACcement 10

so that control requirements would be 80 percent for the cement plant. It is

assumed that the selected emission reductions will allow the NAAQS to be

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

to be true, which is more likely in multi source 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

investigation.

6.4.3 Cons~~rati~~_Lor 24-hourave_ra.9..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

NAAQS Statistical Format," J. Air Pollution Control Association,

June 1980.

3. Curran, LC., "Data Screening for Large Ai r 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 PMI0 SIP development policy, the interface between PMI0 and

TSP control strategies, and summarizes how PMI0 emissions data should be

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

as required for 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

strategies.

7.2 Clean Air Act Requirements

7.2.1 Time limits, SIP requirements

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 PMI0 NAAQS. The PM10

SIP's therefore are due within 9 months of promulgation of PMI0 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 PM10 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 PM10 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 PMIO

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 Group II PMI0 SIP requirements

States are also required to submit a SIP within 9 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 PMI0 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

Offi ceo

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

necessary), adopt and submit to EPA a PMI0 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 PMI0

data to be collected if a NAAQS is not violated sooner. At ~he

end of that time, the available PMI0 data must be examined to

determine if attainment can be demonstrated in accordance with

Appendix Kof 40 CFR Part 50 or the Guideline on Exceptions to

Data Requirements for Determining Attainment of Particulate

Matter Standards in the absence of adequate PMI0 data.)

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

determine 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" emi ssi ons impact future ai r

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 PM10 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 important 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 lI0(i) of the Act, until a

PMlO 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 PMlO 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 PMlO SIP.

States will no doubt want to minimize any unnecessary disruption

caused by going from these control programs to PMlO programs. Therefore,

to the extent possible, States should utilize the existing control strategy

in a TSP SIP as the basis for a PMlO program sufficient to attain and

maintain the PMI0 NAAQS. The EPA expects States to build on the current

control strategies to whatever degree necessary to demonstrate attainment

and maintenance of PMI0 NAAQS. This may include adopting the current

control strategy in full, if it can be shown to be sufficient for PMI0

purposes, or adopting it in part.

7-6
7.6 Current Emission Regulations

Particulate matter SIP's contain emission regulations expressed in

various terms including the following:

-- Mass 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 cubi c 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 PMlO.* 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 PMlO 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 PMlO emissions. Second, modeling can be

done with particulate matter emission rates converted to PMIO emission

rates. If it can be assumed that for any source the PMIO fraction of

particulate matter does not vary appreciably during normal operation of

the source, then a certain particulate matter emission rate will represent

a certain emission rate of PMIO for that source. The PMIO fraction will,

of course, vary by source and could be up to 100 percent 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 PM 10 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 PM10 control strategy

analysis should include the modeling results and confirmation that the

particulate matter emission limits were found to be acceptable surrogate

emission limits for PM10.

7.7.2 Setting new emission limits. In the event that a State finds

that an emission rate lower than the current particulate matter emission

limit in the TSP SIP is necessary for PM10 NAAQS attainment and maintenance,

the PM10 rate which is found to be necessary through modeling can be converted (through the use of an emission factor) to an equivalent particulate

matter emission rate and expressed as such in the PM10 SIP. The emission

limit expressed in terms of particulate matter emissions will be acceptable

provided the modeling results are valid and the ratio of PM10 to particulate

matter emissions is not likely to drastically change.

If it is found that substantially all of a source's emissions are PM10,

then limits for that source could be expressed as either particulate matter

emission limits or PM10 emission limits, and compliance testing could be

performed with either Method 5 or a method that measures only PM10 emissions.

For such an instance, the SIP would have to indicate that the source's

emissions are essentially all PM10. In any case where an emission regulation

is in terms other than PM10, such as particulate matter emissions or opacity,

PM10 levels must not increase while the indicator used in the emission

limit remains constant. Otherwise, the indicator will not be a valid

surrogate and emission limits using the surrogate will not be acceptable.

7.8 PMlo-Specific Emission Regulations

Emissions limits developed to meet the NAAQS for 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 in 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 PM10.

Rather, PM10 emission limits must be directed toward reducing the amount

of PM10 emitted from a source as measured by an approved compliance test

method. Suggested procedures for measuring PM10 emitted through stacks

are discussed in Appendix C. Further guidance is being prepared by EPA.

7.9 Reporting Emissions Data

Emissions 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

PM10, reporting of particulate matter as required in section 51.322 will

7-11
simply be replaced by reporting of PM10 emissions. The present requirement

to report particulate matter emissions will end with the reporting of

calendar year 1987 emissions. The reporting requirement for PM10 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 PMlO 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

PMlO-related changes to the following: AP-42, "NEDS Source Classification

Codes and Emi ssi on Factor Li sti ng;" the computer program for cal cuI ati ng

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 IBM systems under EPA's Aerometric

Inventory Retrieval System (AIRS) development program. The current

schedule for haVing AIRS operational on the IBM system is March 1989.

The EPA will provide the necessary guidance to the States in time for

States to respond to the requirement for reporting calendar year 1988

PMlO emissions.

7-12
7. 10 ~!l1_ts_~i_o.n~ "I!~_d~.n.9-1BTu_bl>.1_e1.P_l?l"W'.

This section clarifies the effect of PM10 NAAQS on alternative emission

reduction options (bubbles) that have been previously approved for TSP SIP's.

In the initial bubble policy, as published on December II, 1979 (44 FR 71780),

sources were warned that EPA was considering revising its particulate

matter NAAQS and that if such size-specific standards were promulgated,

some alternative approaches. initially approved by EPA, might no longer

be adequate under the revised standards. In a sense, the policy indicated

that sources which used a bubble approach to meet SIP emission limitations

coul d be treated no different ly from sources whi ch di d 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 Dust.Policy_

The EPA will continue to implement its existing fugitive dust policy

as it was applied in rural areas violating the TSP NAAQS. The EPA issued

guidance on SIP development and new source review in areas impacted by

fugitive dust on August 16,1977. 7 This guidance, known as the Fugitive

Du st Pol i cy, states that "urban areas shoul d recei ve the hi ghest pri ority

for development of comprehensive and reasonable programs to control

7-13
fugiti ve 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 in 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 qualify as rural fugiti ve dust areas wi 11

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

PMI0 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 app1ciation to the PMIO NAAQS. Public comment on these

7-14
options is currently being solicited. If necessary, a revised policy

will be issued in the future. At that time, the categorization of areas

initially placed in Group III under the existing policy will be reviewed

for compliance with the revised policy.

7-15
References

1.

2.

3.

Identification, Assessment j and Control of~itive Particulate

Emissions, EPA-600/8-86-02 U.S. Environmental 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 proiect, Final Report, Seton, Johnson

and Odell, Inc., for City of Portlan , Oregon, Department of Public

Works, July 1983.

4. 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 Dust Control Measure

for Material Storage Piles, EPA 600/7-86-027 U.S. Environmental

Protection Agency, Research Triangle Park, North Carolina. July 1986.

7. Memorandum from·Edward F. Tuerk, Acting Assistant Administrator for Air

and Waste Management to Regional Administrators, August 16, 1977.

7-16
APPENDIX A

TYPES OF RECEPTOR MODELS

There are several major categories of receptor models which are

potentially valuable for better understanding an area's ambient particulate

problem, both PMIO and TSP, and in formulating strategies for use in State

implementation plans (SIP"s). These include chemical mass balance (CMS) ,

factor analysis (FA), optical microscopy (OM), and automated scanning electron

microscopy (ASEM). While there are other techniques in addition to these,

information and availability 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 chemi ca1 "fi ngerpri nts" or profil es of emi ssi ons

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 ba"lance. species mass balance, and mass balance.

Weighted least squares or some other statistical routine is used to find the

relative proportions or mix 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-I
2.0 Fa~or !\_':!..'!1~is

Factor analysis 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 is

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

particle-by-particle analysis of particle size, shape and elemental cOl11'os1tion.

This elemental composition and cOl11'uter 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 gridded 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.

,

~3
References

1. ~eciPtor Model Technical Series. Volume I: Overview Of Rec8~tor Model ~ication To Particulate Source ~.ortl()nment. EP)f-lP.)O~ -OT6~­

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

1981.

2.

3.

4.

5.

6.

Rece~tor Model Technical Series. Volume II: Chemical Mass Balance.

EPA- 50/81-016b. U. S. Environmental Protectfon Agency, Research Triangle Park, NC. July 1981.

Receptor Model Technical Series. Volume III: User's Manual For Species Mass Balance Computer Program. EPA 450/4-83-014. U.S. Environmental

Protection Agency. Research Triangle Park, NC. July 1983.

Receptor Model Technical Series. Volume IV: Technical Considerations In

Source Apportionment By Particle Identification. EPA 450/4-83-018. U.S.

Environmental Protection Agency. Research Triangle Park. NC, June 1983.

Digest of Ambient Particulate Analysis and Assessment Methods.

EPA-450/ 3-78-113. u. S. Environmental Protection Agency. Research Triangle Park, NC. September 1978.

G. E. Gordon, Receptor Models. Environmental Science and Technology. 1980,

14. 792-800.

7.

8.

Receptor Model Technical Series. Volume V: Source AP&Ortionment Techniques And Considerations In Combining Their Use. EPA 450/4- 4-020. U.S.

Environmeotal Protection Agency. Research Triangle Park. NC, July 1984.

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

Factor Analysis And Multiple Regression (FA/MR) Techniques in Source

Apportionment. EPA 450/4-85-007. U.S. Environmental Protection Agency. Research Triangle Park, NC, July 1985.

9. Pace, T. G•• et a1 •• Protocol For Applying And Validating The CMS. U.S.

Environmental Protection Agency. Research Triangle Park, NC.

Draft.

~4
APPENDIX B

PRELIMINARY ESTIMATES OF PM10 DESIGN CONCENTRATIONS USING TSP DATA

This appendix provides a methodology for making a preliminary estimate

of particulate matter (PM10) design concentrations where actual PM10 measurements are not available. These estimates may be derived based on relationships between PM10 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

(1) Use the most recent years for Which a valid annual

arithmetic mean* exists to determine annual arithmetic

mean TSP concentration, and

(2) Multiply the annual arithmetic mean TSP concentration

determined in (1) times a factor 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 PM,o NAAQS Using Total Suspended

Particulate Or Inha1ab1e Particulate Data.

B-1
(3) The resulting product of (1) and (~) is the "design concentration" for the annual arithmetic mean PMIO value.

1.2 Example

(1) Given: Valid average annua~ arithmetic mean TSP =140 ~/m3

PMIO NAAQS = 50 ~/m annual arithmetic mean.

Sampling is performed according to a systematic

sampling schedule.

(2) Solution:

d = (0.47) (140)

d = 66 ~g/m3

2.0 24-hour NAAQS

2.1 Background

Unlike the annual design concentration, 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 = (I-[required reduction in dl)

Two assumptions are implicit in the definitions above:

(1) all TSPi values in the existing distribution of TSP

concentrations are reduced by (I-m), and

(2) all corresponding (PMIO)i values are also reduced by

(I-m).

B-2
The expected exceedance rate (EER) is calculated using equation

(2) •

n

N

EEl{ = -S ( E P Ri i =1

where

> R. )

(TSP) i

(2 )

N = number of days per year (e.g., 365)

S = number of TSP samples

n = number of TSP samples with values greater than R. ,

the level of the NAAQS

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

= level of the NAAQS (e.g., 150 ~g/m3

and

P(Ri > the probability that a given (TSP)i

value corresponds to a (PMIO)i concentration which is greater than the

level of the NAAQS.

Noting 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

S i=l

P(Ri > R. )

m (TSP) i

(3 )

One further substitution can be made by substituting the relationship

in equation (1) into equation (3).

n

1.0 = 365 E P(Ri >

S i=l

d )

(TSP );

(4)

This allows the design concentration "d" to be expressed explicitly in

equation (4).

To calculate the average exceedance rate over 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

1:

j=1

(5)

k = number of years considered

Sj = number of observations during year j

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 PMIO 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 PM10 Probability

Guideline Software.* For those wishing to perform the calculations by hand, a

suitable procedure is described below.

(1) Assume an initial value of "d" 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.l,** for each (TSP)ij value greater than

the 1evel of lid, II cal cul ate the prooabil ity that the

corresponding (PMIO)ij value would be greater than this

1evel, i.e.,

d

P(Rij > (TSP) . .l

lJ

Note that

d

(a) if (ill)ij

d

(b) if (TSP)ij

d

< 0.14, assume P(Rij > (TSPlij) = 0.99

d

> 0.95 but 1.00, assume P(Rij > (TSP)ij) =0.01

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

Environmental Protection AgencY,ffice of Air Quality Planning and Standards,

Monitoring and Data Analysis Division, Research Triangle Park, North

Carolina 27711. In Preparation.

**Figure B.l has been reproduced from Table 2 of the probability guideline.

B-4
'I 1]1' I 1111 K·E I"'I'fUt:lAtllLlI Y ,1\ UIVl510NS

KEUFFEL &: ESSER CO. MADI I" U5.• 'lUll 468000 'IIII" '1111"

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

-

FIGURE S.l Example Distribution of PM~Q!TSP Ratios for

In lvidual 24-hour Observations.

f- .

.

.. - .. . l-

.

.

.

- - -. . - . .. .-

1-::: -... .. - - .. -. _.. . -

0.9

0.8

0.7

0.5

0.3

0.1

0.4

0.2

0.6

""I

0"1

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

Cumulative Frequency of PMlO!TSP Ratios Less Than a Given Value

99.899.9 99.99
or

d d

(c) if (TS"P)ij 1.00, assume P(Rij > (TSP)ij) = 0

(3) Use the information in step (2) to determine the value of

the rhs of equation (5).

(4) (a) if 0.99 < rhs < 1.01, then the design concentration is

equal to the val ue of "d ," - -

(b) if rhs > 1.01, increase the value of lidII and repeat

steps (2) and (3);

(c) if rhs < 0.99, decrease the value of IId u 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: (1) 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 > TS"P)ij)

and tabulate the information as shown below

..i.. i (T SP) j 300/(TSP)j P(R> 300/(TSP)j)

1 1 500 0.60· 0.20

1 2 240 > 1.00 0

2 3 400 0.75 0.05

2 4 300 1.00 O.

3 5 350 0.86 0.02

(2) Use the information in the last column in the

preceding table to test equation (5)

1.00 365/3 [ 1 [0.20 + 0] + 1 [0.05 + 0] + 1 [0.02]] (5) I 6T 6T iiI

1.00;'0.54

B-6
(3) Since 0.54 < 0.99, we need to lower the trial

design concentration, d. For a second trial value of "d," halve the

difference between the level of the NAAQS and trial 1.. Therefore, the

value for "d~ in trial 2 is 225 ~g/m3.

1. (T SP)i 225/(TSP)i P(R> 225/(TSP)j)

1 1 500 0.45 0.56

1 2 240 0.94 0.01

2 3 400 0.56 0.26

2 4 300 0.75 0.05

3 5 350 0.64 0.14

and

1.00 = 365/3 [ 1 [.56 + .01] + 1 [.26 + .05] + 1 [.14]]

or 6T Of

1.00 t- 2.03

Since 2.03 > 1.01, we need to raise the design concentration "dO

for trial 3. This is done by taking half the difference between trials 1

and 2 and adding it to the design concentration in trial 2. Hence, the

(300-225 )

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

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

1 1 500 0.53 0.34

1 2 240 > 1.00 0

2 3 400 0.66 0.12

2 4 300 0.88 0.02

3 5 350 0.75 0.05

365 1 1 1

1.00 ¥ --3-- ~(0.32 + 0)) + or- (0.07 + 0.01) + or- (0.02)]

1.00 t- 1.06

Since 1.06 > 1.01, we must raise the design concentration for

trial 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 ~g/m3.

This binary search procedure is continued to derive subsequent

trial values for "d" until finally we reach a trial value of 268 ~g/m3.

8-7
i.. i (TSP li 268/ (TSP) j P(R> 268/(TSP)1

1 1 500 0.54 0.32

1 2 240 > 1.00 0

2 3 400 0.67 0.11

2 4 300 0.89 0.02

3 5 350 0.77 0.05

365 1 1 1

1.00 ¥ -:r- [bf (0.32 + 0) + or (0.11 + 0.02) + or (0.05)]

1.00 = 1.00

Therefore, use d = 268 ~g/m3 as the design concentration for

PM10 at this monitoring site. Note that 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,

L n d

1.00 = 365/ 1 E P(Rij > (TSPj )

2 Si i=l 11

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 similiar 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 PMlO SIP emissions limits.

The material presented here is intended for the use of knowledgeable stack

testers in modifying existing techniques for gathering PMlO data. The EPA is

currently developing PMlO measurement methods and will provide more detailed

guidance following promulgation.

The net contribution of a source to PMlO in the ambient air arises

from both primary and secondary emissions. Secondary emissions are defined

here as those which result from chemical or physical reactions in the

ambient air. Primary particulate emissions are defined here to include

particles which were present in the process gas stream prior to discharge

to the atmosphere as well as modifications to those particles and new

particles formed by condensation phenomena as the process gases are cooled

upon discharge. Testing methods which include the contributions from

"condensati on" are current ly under development but are not yet generally

available (Smith et al, 1982). The data currently available indicate that

the "condensibles" 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 predictin9 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 al,

1982). Cyclones with acceptable sharpness of cut have been developed and

are available from commerical vendors. Evaluation of these devices have

demonstrated nominal 10 um 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

~3
flow rate at which the sampler is intended to operate and the average duct

gas velocity. Compensation for variations in the duct gas velocity is then

achieved by modulating the sampling rate. This procedure cannot be used

with inertial particle size classifiers because changes in the sampling

rate also changes the diameter(s) at which size fractionation takes place.

However, to require fixed sampling rates adds immensely to the complexity

of the testing and subsequent data reduction and interpretation.

A method is currently being investigated which permits 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 in the reci rcul at 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.

C-5
I

R

•II' I

- ..---~~~- -.--

I

I

I

I

•

I

I

•

I

I

I

I

a/4 I

••--.l • • I

o I

b/4 I

I - I

- , I

b --------- +---------

•

i

•

FIGURE C·T. 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~ercial

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 is 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-7
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, in 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 urn, 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 precol1ectors. some of cyclonic design and others operating on impaction

principles, are comercia1ly available. Of the impaction type precol1ectors,

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

isokinetic 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 isokinetic

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). Atypical 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 al., (Smith et al, 1982).

Some of the procedures described in the latter document are specific to a

set of cyclones designed to provide a 15 um 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 PM10 cyclone, the filter specifications are the same

C-10
•

IMPINGER TRAIN OPTIONAL:

MAY BE REPLACEO BY AN

EQUIVALENT CONDENSER

HEATED

AREA..-__-. THERMOMETER

HEATED PROBE ... - - - --- --- - - -,

IP 9~*~;t! I,'

SAMPLER

HOLDER

FILTER t TUBEPITOT I

i

IMPINGERS IN ICE BATH

CHECK

VALVE

VACUUM LINE

THERMOMETERS

ORIFICE

.L.

,

B VACUUM

I(y GAGE

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

MANOMETER DRY TEST METER AIR TIGHT PUMP 4111~4IA

FIGURE C-2. PM10 PARTICULATE SAMPLING TRAIN FOR NONCONDENSIBLE .

PARTICULATE (MODIFIED EPA METHOD 5 TRAIN)

C-ll
•

IMPINGER TRAIN OPTIONAL:

MAY BE REPLACED BY AN

EQUIVALENT CONDENSER

HEATED

AREA FILTER HOLDER

THERMOMETER

SAMPLER I

CHECK

VALVE

t PITOT

TUBE !

IMPINGERS IN ICE BATH

ORIFICE

THERMOMETERS

MAIN

VALVE

VACUUM LINE

MANOMETER DRY TEST METER AIR TIGHT PUMP 4111·14&8

FIGURE C-3. PM10PARTICULATE 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 PM10 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 samp1er(s) are operated so as to provide a size fractionation

point at the required 10 Un! diameter, the particle concentration to be used

in the emission rate calculation is that based on the quantity 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

Anom. "Code of Federal Regulations" Title 40, Chapter 1.

Sub Chapter C. Part 60.

Anom. "Control for Particulate Emissions from Stationary Sources"

Vol. 1 EPA 450/3-81-005a, September 1982.

Cushing, K.M. and Smith, W.B. "Particulate Sampling and Support:

Final Report" EPA-600/2-79-114, June 1979.

Felix, L.G. and McCain, J.D. "Errors in Recovered Particle Size Distributions

Caused by Sampling with Bent Nozzles" Paper 81-7.4 presented at the 1981

Annual Meeting 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

Part i cul ate" EPA-600/7 -82-036, May 1982.

Knapp, K; T. "The Effects of Nozzl e Losses on Impactor Sampl ing." 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 PM10 will require

an estimate of PM10 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 Guidelines on Air Quali~

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 PM10. 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

this in 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 Back~ound Estimates From Measured PM10 Or Surrogate Data

The PM10 background values are most appropriately taken from measurements

at a nearby site, representative of background for the area being studied.

Such a site would ideally measure PM10 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 reliable.

Dispersion modelers identify specific sets of 24-hour meteorological

conditions that may lead to or be associated with high 24-hour concentrations

of particulate matter. Also, receptor modelers often group the samples by

meteorological "regime" and consider each group separately in receptor analyses. These specific 24-hour sets of meteorological conditions are matched

with meteorological conditions on days when measured particulate matter data

are available. The measured data collected on days whose meteorological

conditions best match the specific conditions are then averaged to obtain

a representative background concentration. As discussed above, 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 influenced by those sources, background may be determined by "compositing"

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

D-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 PMIO data for use in applying the procedures above. In cases

where PMIO data are available for part of a year and the PMIO monitor is

collocated with TSP or PM15 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.

D-5
4.0 ~ummary Of Alternatives For Estimating PM10 Bac~ound

The preferred method for background determination is to use an upwind

PMIO 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

A1ternat i ve

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 PMIO.

5.0 EXAMPLE 1 - Compositing Data From Several Perimeter Sites

In the absence of a single appropriate PMIO background site with a

sufficiently large data base, data from two or more sites in the vicinity of

the study area may be combined to provide a composite estimate of background

concentrati on.

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 is upwind (use Northern

site data for NI days).

0-6
Group II - days when the Southern site is upwind (use Southern

site data for N2 days).

Group III - days with calms, substantial shifts in wind direction,

during the day or with winds predominantly from the

east or west (and both sites are upwind). (Use average

of data from both sites for N3 days.)

The Group I, Group II, and Group III days are used to compile an annual

average:

annual average = Nl

E Group I + E

N3

Group II + E. Group III

where Nl and N2 are the number of days where only the northern or

southern sites, respectively are upwind. N3 is the number of days where both

sites are upwind.

This calculation can, of course, accommodate more than two monitoring

sites. Also, a single site may be used if it is generally upwind 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-7
6.0 EXAMPLE 2 - Use Of Sur~ogate Data

Most regional monitoring networks contain one or more designated

background hi-vol sites and a few have PM15 background sites. However, there

are relati'vely few size-fractionated particulate samplers measuring PMIO

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 their SIP's. The following is the recommended

procedure for using PM15 and TSP surrogates to estimate PMIO, based on an

analysis of regional scale PM10. PM15 and TSP sites.

The PM15 measurements may be used as a surrogate for PMIO. The PM15

measurement (either annual average or 24-hour) is multiplied by a constant

ratio to give an estimate of PM10.2 The following adjustment applies.

PMIO = 0.85 x PM15 (annual or 24-hour)

Reference 3 suggests PM15 to TSP ratios of .61 (annual) and .75 (high

24-hour). Using the above 0.85 ratio for PMIO to PM15, the following ratios

apply for estimating PMIO background using a TSP surrogate:

PMIO = 0.52 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 PM15 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(--

?M10 NAAQS Attainmynt Status, EPA 450/4-85-010. U.S. Environmental--

Protection Agency, OAQPS, Research Triangle Park, NC 27711, August, 1985.

3. PEOCo Environmental, Inc., Estimatin9-P~n~j=JP__Bac~Jlro~nd Concentrations

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
1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1
~:.!.-"'_ •• - - ':'". . _ . ,t" - =-

TECHNICAL REPORT DATA

rPitcu ,,.D,1 J"r1?'l..lC'tiom G'/I rht rrl'~f"St' bt/ort COMl'Jtr1n,J

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

; EPA-450/2-86-00l -- ----- ,.: - - _~_ .',:~_:."'7-:';.,~ is. R:.FDCct":" :-.t.T=:

! : June 1987

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

i

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

I

I

;

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

Control Programs Development Division (MD-15) ,

Office of Air Quality Planning and Standards \"_ CO~TRAC-' -GRANT NO_

Research Triangle Park, NC 27711 ;

I

':'. S?O"'lSOFlI~G AGEr'.CV !\.AME .41\,~ l..:J,;)Fi.ESS 113. TYPE OF AEPOAT AND PEAIOD COVEPlEO

I 14, SPONSORING AGENCV CODE !

I

! 5. S\JP:: ... E.~.~E'" ":"~;; I( NC7::S

I

- - ::.-;~:- -

t This guideline document was prepared to briefly describe the actions

that must be taken by State and local air pollution control agencies to

I

develop SIP's that demonstrate attainment and maintenance of the PMI0 NAAQS. , The guideline describes how to: (1) demonstrate attainment of the PMlO NAAQS,

,

, ' (2) determine the size of an area exceeding the NAAQS. (3) select a receptor I or dispersion model. (4) prepare an emission inventory and. (5 ) determine a I

I control strategy design concentration. The guideline also discusses ambient

i- PMI0 moni tori ng requirements and data usage and EPA's policies for making

I the transition from a SIP designed to protect TSP standards to one designed

I to protect PMIO standards. References to supporting documents are given atthe end of each section.

I

I This guideline does not discuss implementation of PSD or preconstruction

I review programs for PMI0. Guidance for implementation of these programs , will be issued separately.

I

,

- - ',:=:- .',: :.::: ':",:, :.:~ .... !.~::"'T ':'~':'_"'£.S

.--------------------- - -~.~-::'~_.=" -- .. - I:· :~;·,7'= ::;=.;' '::-:;', :",:=: -~:'.,.:: -, ':::::5.:.-:- .. _J (ii.;,.::- .__ .- -- --_._.- ------ . __ .- . __ .. ....- ----- ._._----- , PM10 I ; I , I State Implementation Plan (SIP)

1 I i Emission inventory Ambient monitoring

1 Receptor models I . Dispersion models ,

Particulate regulations , !

;

-- :

_.- -~:_-------. - - - .-

- = - - - ':'.:; . ..- -- - --