&EPA 

United States 

Environmental Protection 

Agency 

Air 

Office of Air Quality 

Planning and Standards 

Research Triangle Park NC 27711 

PM10 SIP 

Development 

Guideline 

EPA-450/2-86-001 

June 1987 
EPA-450/2-86-001 

PM·10 

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 2771 1 

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 10 ). Copies are available - in limited quantities - from the Library 

Services Office (MD-35), U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 2771 1; 

or, for a fee, from the National Technical Information Service. 5285 Port Royal Road, Springfield, Virginia 22161 . 

Th is 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-450/2-86-001 
1.0 

TABLE OF CONTENTS 

INTRODUCTION e a • a I e a a e a e a e e e e a a •• e ~ • • a e • e e a •• e e • e a • e •• I e ••• 

1.1 

1. 2 

1.3 

Overview ~ ..... . ........... .............................. . 

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

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 i n g •••.••..........•..•.••.•• , •••.•.•.••• 

Emission inventories •••••••••••••••••••••••••• 

Development of control strategies •••••.••••••• 

SIP requirements and data reporting ••••••••••• 

Types of receptor mode 1 s •••••••••••••••••••••• 

Preliminary estimate of PM10 design concentration using TSP data •••••••••••••••••• 

Source testing ••••••.••••••••••••••••••••••••• 

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

Page 

1-1 

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 

Determ1ne 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 PM10 Monitoring .............................. 3-1 

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

3.2.l 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 
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 

Selection ••••••••••••••••••••••••••·••••••••••••••••• 4-2 

4.2.1 Particle size ••••••••••••••••••••••••••••••••• 4-2 

4.2.2 Prior knowledge of sources and emissions •••••• 4-4 

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

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

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

4.3 Screening Techniques and Refined Dispersion Models 

for PM10 Concentrations •••••••••••••••••••••••••••••• 4-7 

4.3.1 Selection of appropriate dispersion models ••••••••••••••••••••••••••••• 4-8 

4.3.2 Special considerations for PM10 

dispersion modeling ••••••••••••••••••••••••••• 4-9 

4.4 Receptor Models for Estimating PM10 Concentrations ••• 4-11 

4.4,1 Control strategy analyses using receptor models ••••••••• ~ •••••••••••••••••••••••••••••• 4-11 

4.4.2 Preliminary analyses using receptor models •••• 4-12 

4.5 Use of Receptor and Dispersion Models in Combination • 4-13 

5.0 DEVELOPMENT OF PM10 EMISSION INVENTORIES •••••••••••••••••• 5-1 

5.1 Overview ••••••••••••••••••••••••••••••••••••••••••••• 5-1 

5.2 PM10 Emission Factors and Fractional Multipliers ••••• 5-3 

5.3 PM10 Source Data for Dispersion Models ••••••••••••••• 5-20 

5.4 Inventory Data Needed in Receptor Models ••••••••••••• 5-29 

5,5 Condensable Particulate ••••••••••••••···••••••••••••• 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.g_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 e~irical 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 Air Act Requirements ......................... 7-1 

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

7.2.2 Regulations in 40 CFR Part 51 •.•••••••••.•.•••• 7-2 

7.3 PM10 SIP Development Policy .................. . ..... 7-2 

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

7.3.2 Group II PM10 SIP requirements ...... ........... 7-3 

7.3.3 Group III PM10 SIP requirements ••... .•••••••••. 7-5 

7 .4 SIP Content ..... , ............ .. .................... 7-5 

7.5 Control Strategy Transition ....................... 7-6 

7 .6 Current Emission Regulations ...................... 7-7 

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

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

7.7.2 Setting new emission limits .••••••••••••.•••.•• 7-10 

7.8 PM10-Specific Emission Regulations •••.•.•.••.••... 7-10 

7.9 Reporting Emissions Data .......................... 7-11 

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

7.11 Fugitive Dust Policy •..•. . ..•.. . . .••.... . ..••••..• 7-13 

i i ; 
_e._a_g_e 

APPENDIX A - TYPES OF RECEPTOR MODELS •••••••••••••••.••••• A-1 

1.0 Chemical Mass. Balance ................................ A-1 

2.0 Factor Analysis ••••••••..•.•..•.•...•.••••.••.••••.•• A-2 

3.0 Optical Microscopy A-2 

4.0 Automated Scanning Electron Microscopy·············~· A-2 

5.0 Other Receptor Models •••••••..•..••..••.•••••••.••••• A-3 

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

5.2 Trajectory Analysis ............................. A-3 

5. 3 Mi croi nventory •••••••••••••••••••••••••••••••.•• A-3 

APPENDIX B - PRELIMINARY ESTIMATES OF PM10 DESIGN CONCENTRATIONS 

USING TSP DATA ••••••••••••••••••.••••••••••••• B-1 

1.0 Annual NAAQS ........................................... B-1 

1.1 Recommend at; on s . . . . . . . . . . . . . . • • . . . . • • . . . . . . . . . . . B-1 

1 • 2 Ex amp 1 e • . . . . . . • . • • . . . . . • . . . • • .. . . . • . • • • • • • • • • . . • . B- 2 

2.0 24-Hour NAAQS .••.•••.•••...•.•••.. , ••••.••.••••.•.••. B-2 

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

2.2 Recommendations .....•••...•.•••.•••.•.••.••..•.• B-4 

2.3 Example .......................................... B-6 

APPENDIX C - GUIDELINES FOR SOURCE TESTING FOR SIZE SPECIFIC 

PARTICULATE EMISSIONS •.•••••••••.•••••••••••. C-1 

1.0 Introduction ••••••••••••••••••••••••••••••••••••••••• C-1 

2.0 Available Methods for Size Specific Source 

Measurements ••••••••••.•••••••••••••••••••••••••••.•• C-2 

3.0 Selection of Sampling Traverse Points ................ C-3 

4.0 Sampler Selection and Operation •••••••••••••••••••••• C-7 

5.0 Sampling Trains •••••••••.••..••.•••...•.•••••••.••••• C-9 

6.0 Collection Media ••••••••••••••••••••••••••••••••••••• C-10 

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

iv 
APPENDIX D - DETERMINATION OF PM10 BACKGROUND 

1.0 Introduction •••••••••••••••••••••••••••.••••••••••••••• D-1 

2.0 Determining Background Estimates from Measured PM10 or 

Surrogate Data ••••••••••••••••••••••••••••••••••••••••• D-2 . 3.0 Special Considerations for Receptor Modeling ••••••••••• D-5 

4.0 Summary of Alternatives for Estimating PM10 Background • D-6 

5.0 Example 1 - Compositing Data from Several Perimeter 

Sites ••••.•••••.•••••••••••••••••••••••••••••••••.•••.• D-6 

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

v 
6.2 Data Base Requirements 

The des1gn concentrations for attainment of the 24-hour PM10 NAAQS 

can be based on ambient measurements of PM10, or model estimates of illllb1ent 

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 l complete year of 

PM10 data are available,* the PH10 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 PH10 

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 1n CFR 58~13. 

6-2 
There are several acceptable approaches for determining appropriate 

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

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

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

distribution; (3) graphical estimation; and (4) the use of cond1t1onal 

probabilities. Each of th~se approaches and corresponding data usage 

requirements are presented in detail in the ozone guide11ne.l The 

following sections briefly summarize each of these approaches and 

indicate how the technique may be applied for detenninf ng PM10 

concentrations. 

6.3.1 Table look-up 

The 24-hour PM10 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 determ;nes 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 ~rea 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 

< 347 

348 - 695 

696 - 1042 

I~~" 

1043 - μ% 

13~! -113~ 

! l 39 - {,~ b 

ivB' 

"'L'-f 3S 

Rank of Upper 

Bound 

1 

2 

3 

4-

6-5 

Rank of Lower 

Bound 

1 

2 · 

3 

4 

s 

Data Point Used for 

Design Concentration 

Highest Value 

Second Highest Value 

Third Highest Value 

Fourth Highest Value 

·F,F(A 
third- and fourth-highest values. However, this adds an additional element 

of calculat1on. Nevertheless, if a more precise des1gn concentration 

should be desirable, the use of interpolation formulas (Section 6.3.2} 

or more simple graphical procedures (Section 6.3.3} may be neces~ary. 

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 est1111ate 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 guideli.ne.1,2,3 Because we are 

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

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

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

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

6-6 
5 to 10 percent of the concentrations. Th1s approach. at least conceptually. provides a more stable estimate of the design concentration; but 

it also involves additional computations and 1nterpretat1on. 

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, 1.e •• infrequent 

air quality measurements, it may not be possible to 11 boundw 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. 

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 f n the ozone 

guideline. 

6-7 
6.3.4 Conditional 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 thfs 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 ~ispersion 

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

Nevertheless, the method conceptually considers that the total reductfon 

(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