# &EPA UnitedStates Environmental Protection Agency Air Office ofAir Quality Planningand Standards Research Triangle Park NC27711 

# PM10SIP Development Guideline EPA-450/2-86-001 June 1987

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



# PM· 10 

# SIP DevelopmentGuideline

U

. S. ENVIRONMENTAL PROTECTION AGENCY OfficeofAirand Radiation OfficeofAir QualityPlanningand Standards Research Triangle Park

. NorthCarolina 2771 1 June 1987This guideline is issued by the Environmental ProtectionAgencyto assist State and local air pollutioncontrol 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 ServicesOffice(MD-35), U

.S. EnvironmentalProtectionAgency, ResearchTrianglePark,NorthCarolina 2771 1; or,for afee, fromtheNationalTechnicalInformationService.5285PortRoyal Road,Springfield,Virginia 22161

.

This report has been reviewed bythe Office OfAir Quality PlanningAnd Standards. U

.S. Environmental Protection Agency, andapprovedforpublication

.Anymentionoftradenamesorcommercial products is not intendedto constitute endorsementor recommendationfor use.

Publication No

. EPA-450/2
-86
-0011.0 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 •••••••••••••••••••••••••••, Mode1 in g •••.••..........•..•.••.••,•••.•.•.••• Emission inventories •••••••••••••••••••••••••• Development of control strategies •••••.••••••• SIP requirements and data reporting ••••••••••• Types of receptor mode1s •••••••••••••••••••••• 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

........................... 3-7Page 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
iiP.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 Microinventory •••••••••••••••••••••••••••••••.•• A-3 

### APPENDIX B - PRELIMINARY ESTIMATES OF PM10 DESIGN CONCENTRATIONS USING TSP DATA••••••••••••••••••.••••••••••••• B-1 1.0 Annual NAAQS

........................................... B-1 1.1 Recommendat;ons . . . . . . . . . . . . . .••.. . .••.. . . . . . . . . . B-1 1•2 Examp1 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

ivAPPENDIX 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

v6.2 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 

### Bof 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 3years of monitored or 5 years of modeled PM10 concentrations. *These data should meet first year sampling requirements found 1n CFR**58~13.** 6-2There are several acceptable approaches for determining appropriate 24-hour PM10 design concentrations. These approaches which are described in the next sections are based on monitored or modeled PM10 concentrations. 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 detenninfng 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 6-3for 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

6-4TABLE 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'-f3S Rank of Upper Bound

1 2 3 

### 4-

6-5Rank 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(Athird- 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. 6-65to 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 11boundw 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 fn the ozone guideline. 6-76.3.4 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) = PM10 Design Concentration

- PM10 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