Appendix L 

Attainment Test 
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Table of Contents

List of Figures ................................................................................................................................ iii

List of Tables ................................................................................................................................. iii

1 Attainment Demonstration ........................................................................................................... 1

2 Model Attainment Test ................................................................................................................ 1

2.1 Determine Baseline Design Values ...................................................................................... 1

2.1.1 Mean PM2.5 Baseline Design Values ........................................................................... 2

2.1.2 Speciated Baseline Conditions ....................................................................................... 3

2.2 Relative Response Factor Calculation .................................................................................. 5

2.3. Future Year Quarterly Concentration Calculation ............................................................... 7

2.4. Future Year Annual Average Estimate ................................................................................ 7

3 Supplemental Analysis................................................................................................................. 8

3.1 Additional Air Quality Modeling.......................................................................................... 9

3.1.1 Air Quality Modeling Metrics ....................................................................................... 9

3.1.2 Other Modeling Results ............................................................................................... 11

3.2 Air Quality and Emission Trends Analysis ........................................................................ 12

3.3 Supplemental Analysis Conclusions ................................................................................... 14

4 Unmonitored Area Analysis ...................................................................................................... 14

5 Attachments to Appendix L ....................................................................................................... 16

6 Reference ................................................................................................................................... 16
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List of Figures 

Figure 3-1: Area for which the air quality metrics were applied. ................................................. 10

Figure 3-2: Percentage of Cell in Nonattainment Areas within Concentration Categories for 2002 

and 2009. Table of actual values is presented on the right. ......................................................... 11

Figure 3-3: Annual PM2.5 Average Values for the Monitors in the Hickory and Triad 

Nonattainment Areas. ................................................................................................................... 13

Figure 4-1: PM2.5 Monitors and with Respect to the VISTAS 12km Grid ................................. 15

Figure 4-2: PM2.5 Monitors and 2009 Modeled Attainment Spatial Field .................................. 16

List of Tables

Table 2-1: Quarterly Average PM2.5 Mass for the FRM Monitors in the North Carolina 

Nonattainment Areas ...................................................................................................................... 2

Table 2-2: Observed Quarterly Mean PM2.5 Concentrations for Monitors in the North Carolina 

Nonattainment Areas ...................................................................................................................... 3

Table 2-3: Quarterly Species Fractions from the CAIR SMAT Tool ............................................. 5

Table 2-4: Quarterly PM2.5 Component-Specific Concentrations for Monitors in the North 

Carolina Nonattainment Areas ........................................................................................................ 5

Table 2-5: USEPA’s Recommendation for Defining “Near” Cells ................................................ 6

Table 2-6: Quarterly Component RRFs for Monitors in the North Carolina Nonattainment Areas

......................................................................................................................................................... 7

Table 2-7: Blank Corrected Quarterly Component Future Concentrations Estimates for Monitors 

in the North Carolina Nonattainment Areas ................................................................................... 7

Table 2-8: Quarterly Mean PM2.5 Mass Estimates for 2009 ......................................................... 8

Table 2-9: Estimated Annual Mean 2009 PM2.5 Mass Compared to the DVB ............................. 8

Table 3-1: Number of Cells within Concentration Bins. Increases (decreases) in the number of 

cells within the bins are noted by red (blue) coloration in the last column. ................................. 11

Table 3-2: US EPA CAIR Modeling Results ............................................................................... 12

Table 3-3: Annual Average PM2.5 values for the past 10 years. ................................................. 12

Table 3-4: Three Year Design values for the Monitors in North Carolina’s PM2.5 Nonattainment 

Areas. ............................................................................................................................................ 13

Table 3-5: Projected emission reductions Near the North Carolina PM2.5 Nonattainment Areas.

....................................................................................................................................................... 14
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1 Attainment Demonstration 

This Appendix summarizes the procedures that were used to demonstrate attainment of the 

annual fine particulate matter (PM2.5) National Ambient Air Quality Standard (NAAQS) in this 

State Implementation Plan (SIP) package. As described in the US Environmental Protection 

Agency’s (USEPA’s) Guidance On The Use Of Models And Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze (“Attainment 

Guidance”), an attainment demonstration consists of (a) analyses which estimate whether 

selected emissions reductions will result in ambient concentrations that meet the NAAQS, and 

(b) an identified set of control measures which will result in the required emissions reductions. 

The necessary emission reductions for both of these attainment demonstration components may 

be determined by relying on results obtained with air quality models. 

Section 3.0 of the Attainment Guidance recommends applying both a modeled attainment test 

and a subsequent screening test to the air quality modeling results to determine if the annual 

PM2.5 NAAQS will be met. Additional technical or corroboratory analyses may also be used as 

part of a “supplemental analysis” or a more stringent “weight of evidence” determination to 

supplement the modeled attainment test and to further support a demonstration of attainment of 

the NAAQS. 

The modeled attainment test, additional corroborative analyses and weight of evidence, and 

unmonitored area analysis are described in further detail in the remaining portions of this 

Appendix, detailing how the respective test or analysis was performed and applied to the 

attainment demonstration. 

2 Model Attainment Test 

The purpose of a modeling assessment is to determine if control strategies currently being 

implemented (“on the books”) and proposed control strategies will lead to attainment of the 

NAAQS for PM2.5 by the attainment year of 2009. The modeling is applied in a relative sense, 

similar to the 8-hour ozone attainment test. However, the PM2.5 attainment test is more 

complicated and reflects the fact that PM2.5 has many components. In the test, ambient PM2.5 

is divided into major components, with a separate relative response factor (RRF) and future 

design value (DVF) calculated for each of the PM2.5 components. Since the attainment test is 

calculated on a per species basis, the attainment test for PM2.5 is referred to as the Speciated 

Modeled Attainment Test (SMAT). The following sections outline the process to determine 

2009 projections of PM2.5 will meet the NAAQS from regional modeling, as suggested in the 

US EPA’s Attainment Guidance. 

2.1 Determine Baseline Design Values 

The first step in any attainment test process is to determine the baseline design value (DVB). In 

the Attainment Guidance, the US EPA recommends using a DVB that is the average of the three 

design value periods that straddle the baseline inventory year (e.g., the average of the 2000-2002, 

2001-2003, and 2002-2004 design value periods for a 2002 baseline inventory year). This works 
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out to a 5-year weighted average, with the baseline year having the heaviest weight (e.g. {[2000] 

+ 2*[2001] + 3*[2002] + 2*[2003] + [2004]}/9). 

For the SMAT process, a mean PM2.5 DVB is determined, as well as component specific DVB 

for each quarter. The following section will detail the calculation of baseline design values 

needed for the PM2.5 attainment test. 

2.1.1 Mean PM2.5 Baseline Design Values 

To begin the SMAT process, a mean PM2.5 DVB is calculated on a quarterly basis for each 

Federal Reference Method (FRM) monitor in the PM2.5 nonattainment areas. Concentrations 

are calculated based on calendar quarters (Q1: January - March; Q2: April - June; etc.) as the 

NAAQS is calculated for a calendar year, and the quarters need to fit evenly within a year. Also, 

calculating the attainment test on a quarterly basis allows states to examine the differences in 

PM2.5 composition that occur during the different seasons. 

Table 2-1 contains the quarterly average PM2.5 concentration for the FRM monitors in the 

nonattainment areas. This quarterly data was then averaged, using the weighted scheme 

suggested by the US EPA, to produce a 5-year weighted DVB. In the case of the Guilford 

County FRM site (Mendenhall, 37-081-0013), there is less than 5 years of data available, as the 

site came online late third quarter 2001. Per the Attainment Guidance, when only 3 years of data 

are available at a site, the baseline design value is then based on a single three-year design value. 

This is the case for quarters 1 through 3 at the Mendenhall site. Since there are four years of data 

for fourth quarter, then the baseline design value is based on an average of two design value 

periods (2001-2003 and 2002-2004). Table 2-2 presents the final mean PM2.5 DVBs for the 

nonattainment areas. 

Table 2-1: Quarterly Average PM2.5 Mass for the FRM Monitors in the North Carolina 

Nonattainment Areas 

AIRS ID County Site Name Year Q1 Q2 Q3 Q4 Annual

2000 16.14 16.58 18.9 18.89 17.63

2001 15.3 16.61 18.83 13.16 15.98

2002 13.25 14.32 21.12 12.73 15.36

2003 12.94 16.08 19.34 11.81 15.04

2004 13.1 14.92 19.6 12.39 15.00

2000 17.05 17.8 18.43 18.88 18.04

2001 14.79 18.62 18.84 13.56 16.45

2002 14.94 15 19.28 14.29 15.88

2003 12.56 16.07 19.12 12.91 15.17

2004 13.9 15.69 17.96 13.15 15.18

2000

2001 10.31

2002 11.71 13.14 18.29 11.72 13.72

2003 11.55 13.57 16.47 11.69 13.32

2004 11.76 14.4 16.54 13.19 13.97

37-035-0004 Catawba Hickory

37-057-0002

37-081-0013

Lexington

Guilford Mendenhall

Davidson
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Table 2-2: Observed Quarterly Mean PM2.5 Concentrations for Monitors in the North 

Carolina Nonattainment Areas 

AIRS ID County Site Name 2000-2004 Q1 2000-2004 Q2 2000-2004 Q3 2000-2004 Q4

37-035-0004 Catawba Hickory 13.9 15.5 19.8 13.3

37-057-0002 Davidson Lexington 14.5 16.4 18.9 14.2

37-081-0013 Guilford Mendenhall 11.7 13.4 17.6 11.7

2.1.2 Speciated Baseline Conditions 

The monitored attainment test for PM2.5 utilizes both PM2.5 and individual PM2.5 component 

species. A separate RRF is calculated for each PM2.5 species. In order to perform the 

recommended modeled attainment test, States should divide observed mass concentrations of 

PM2.5 into 7 components (plus passive mass): 

1. Mass associated with sulfates (SO4) 

2. Mass associated with nitrates (NO3) 

3. Mass associated with ammonium (NH4) 

4. Mass associated with organic carbon (OC) 

5. Mass associated with elemental carbon (EC) 

6. Mass associated with particle bound water (PBW) 

7. Mass associated with “other” primary inorganic particulate matter (Crustal) 

8. And passively collected mass or the mass of the blank filter (0.5 μg/m3) 

The second part of the process is to use the quarterly mean PM2.5 DVBs (as calculated in 

Section 2.1.1) with speciated data to calculate the quarterly mean concentrations of these 7 

components at the FRM sites. This need to speciate the FRM data presents two issues: 

1. FRM measurements and speciated PM2.5 measurements do not always measure the same 

mass 

2. Not all FRM monitoring sites have co-located STN speciation monitors. 

The following sections will explain how these issues were overcome to produce the speciated 

values needed for this attainment demonstration. 

2.1.2.1 SANDWICH 

As the Attainment Guidance notes, recent data analyses (Frank, 2006) have noted that the FRM 

monitors do not measure the same components and do not retain all of the PM2.5 that is 

measured by routine speciation samplers and therefore cannot be directly compared to speciation 

measurements from the Speciation Trends Network (STN). By design, the FRM mass 

measurement does not retain all ammonium nitrate and other semi-volatile materials (negative 

sampling artifacts) and includes particle bound water associated with sulfates, nitrates and other 

hygroscopic species (positive sampling artifacts). This results in concentrations (and percent 

contributions to PM2.5 mass), which may be different than the ambient levels of some PM2.5 

chemical constituents. 
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To resolve the differences between FRM and STN total mass, the US EPA has recommended 

using the “sulfate, adjusted nitrate, derived water, inferred carbonaceous material balance 

approach” or SANDWICH approach. With the SANDWHICH approach, nitrate mass is 

adjusted to account for volatilization based on hourly meteorology parameters. Subsequently, 

quarterly average nitrate, sulfate, elemental carbon, and crustal mass can be calculated, as well as 

the Degree of Neutralization (DON) of sulfates. Quarterly average NH4 can then be calculated 

from adjusted the adjusted nitrate mass, sulfate mass, and DON of sulfate. Next the mass of 

particle bound water can be calculated from the previously obtained DON, sulfate, nitrate, and 

ammonium values. Finally, organic carbon is calculated by taking the difference between the 

total PM2.5 mass as measured at the FRM monitor, and the calculated component mass (i.e. OC 

from mass balance ([OCMmb]) = PM2.5FRM - {[EC] + [SO4] + [NO3] + [NH4] + [water] + 

[crustal material] + [passive mass]}). 

2.1.2.2 Speciated Profiles 

While the SANDWICH method reconciles the differences between FRM and STN, a lingering 

issue is not all FRM monitoring sites have co-located STN monitors to provide speciated data. 

The US EPA Attainment Guidance suggests four measures that can be taken to resolve the lack 

of speciated data: 

1. Use of concurrent data from a nearby speciated monitor 

2. Use of representative data (from a different time period) 

3. Use of interpolation techniques to create a spatial field using ambient speciation data 

4. Use of interpolation techniques to create spatial fields, and gridded modeling outputs to 

adjust the species concentrations 

Of the four methodologies, the US EPA recommends using one of the spatial interpolation 

techniques to estimate species concentrations at FRM sites that do not have speciation data 

(numbers 3 and 4 above). To assist in this task, the EPA is developing software tool called 

“Modeled Attainment Test Software” (or MATS) that will perform the spatial analysis of 

described options number 3 and 4. However, the PM2.5 portion of the MATS tool has not been 

released at this time. In trying to pursue the US EPA recommended course, we have used the 

speciated profiles from the Clean Air Interstate Rule (CAIR) SMAT tool, which is the 

predecessor for the MATS program, as an alternative. 

The CAIR SMAT tool uses data from both the Interagency Monitoring of Protected Visual 

Environments (IMPROVE) and the US EPA’s Speciation Network (ESPN) to derive mean 

concentrations for six PM2.5 components. Quarterly average concentrations between Jan 2002 

to December 2002 were retained for sites that had at least 11 monitored values per quarter for 

each of the major PM2.5 species. Major species for ESPN include EC, OC, NH4, SO4, NO3, 

and crustal material (which includes the five trace elements aluminum, calcium, iron, silicon, and 

titanium). The major species for IMPROVE are the same except for NH4, which is not routinely 

measured in the IMPROVE protocol. 

The quarterly averaged species concentrations at the IMPROVE and ESPN monitors were used 

to interpolate concentrations at the PM2.5 FRM monitoring sites using a technique called 
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Voronoi Neighbor Averaging (VNA). Attachment L1 to this appendix contains the document 

“Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by Application of the 

(Revised) Speciated Modeled Attainment Test (SMAT) Updated- 11/8/04”, which describes the 

interpolation process, and the data speciation process in detail. 

As a result of the CAIR SMAT process, quarterly species fraction were generated for the FRM 

site, which are presented in Table 2-3 below. These fractions were then applied to Observed 

Quarterly Mean PM2.5 values calculated in Section 2.1.1 to determine quarterly component 

specific concentrations. Table 2-4 shows the quarterly concentrations for crustal, EC, OC, SO4, 

NO3, NH4 and PBW assuming a constant passive mass of 0.5 μg/m3

. 

Table 2-3: Quarterly Species Fractions from the CAIR SMAT Tool 

AIRS ID County Site Name Blank 

Mass qtr DON Frac. Crustal Frac. EC Frac. OC Frac. SO4 Frac. NO3 Frac. NH4 Frac. H2O

1 0.279900 0.024317 0.059617 0.468778 0.249448 0.047850 0.083697 0.066293

2 0.295620 0.041253 0.036910 0.367295 0.348840 0.000724 0.103334 0.101645

3 0.287220 0.028618 0.025223 0.330820 0.392896 0.000485 0.112988 0.108969

4 0.315780 0.020443 0.048246 0.486327 0.241229 0.041704 0.088270 0.073781

1 0.295120 0.021555 0.050295 0.503129 0.242853 0.032333 0.081047 0.068788

2 0.299230 0.063465 0.034492 0.342787 0.349748 0.000690 0.104855 0.103963

3 0.277630 0.048469 0.020772 0.279547 0.423435 0.000533 0.117713 0.109531

4 0.295110 0.033367 0.044972 0.487990 0.250975 0.029015 0.082480 0.071201

1 0.288580 0.027600 0.060542 0.390746 0.308053 0.030271 0.097677 0.085110

2 0.283430 0.185936 0.033508 0.249273 0.340993 0.000657 0.096838 0.092795

3 0.253500 0.103581 0.016630 0.274061 0.414322 0.000475 0.105169 0.085762

4 0.255190 0.095325 0.053453 0.382647 0.284194 0.034745 0.082599 0.067036

37-081-0013 Guilford Mendenhall 0.5

37-057-0002 Davidson Lexington 0.5

37-035-0004 Catawba Hickory 0.5

Table 2-4: Quarterly PM2.5 Component-Specific Concentrations for Monitors in the North 

Carolina Nonattainment Areas 

AIRS ID County Site Name Quarter

FRM 

Mass

Blank 

Mass

NonBlank 

Mass Crustal EC OC SO4 NO3 NH4 PBW

1 13.94 0.50 13.44 0.33 0.80 6.30 3.35 0.64 1.12 0.89

2 15.54 0.50 15.04 0.62 0.56 5.52 5.25 0.01 1.55 1.53

3 19.80 0.50 19.30 0.55 0.49 6.38 7.58 0.01 2.18 2.10

4 13.27 0.50 12.77 0.26 0.62 6.21 3.08 0.53 1.13 0.94

1 14.50 0.50 14.00 0.30 0.70 7.04 3.40 0.45 1.13 0.96

2 16.43 0.50 15.93 1.01 0.55 5.46 5.57 0.01 1.67 1.66

3 18.91 0.50 18.41 0.89 0.38 5.15 7.79 0.01 2.17 2.02

4 14.20 0.50 13.70 0.46 0.62 6.69 3.44 0.40 1.13 0.98

1 11.67 0.50 11.17 0.31 0.68 4.37 3.44 0.34 1.09 0.95

2 13.40 0.50 12.90 2.40 0.43 3.22 4.40 0.01 1.25 1.20

3 17.59 0.50 17.09 1.77 0.28 4.68 7.08 0.01 1.80 1.47

4 11.72 0.50 11.22 1.07 0.60 4.29 3.19 0.39 0.93 0.75

37-081-0013 Guilford Mendenhall

37-035-0004 Catawba Hickory

37-057-0002 Davidson Lexington

2.2 Relative Response Factor Calculation 

The next step in the SMAT process is to use base year and future year modeling results to 

estimate a relative response factor (RRF) for each component of PM2.5 for each quarter. Simply 

put, the RRF is the quarterly average future year concentration near a monitor divided by the 

quarterly average base year concentration near the same monitor, or: 

Quarterly Mean Modeled Base Year Concentration "Near"Monitor "X"

Quarterly Mean Modeled Future Year Concentration "Near"Monitor "X" RRF =
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Instead of focusing on the individual cell containing the monitor, an array of cells that are “near” 

a monitor are considered in the attainment test. By sampling an array of cells from the modeling, 

the attainment test allows for variations in the model performance, as the peak concentrations 

may not occur in the grid cell that contains the monitor, but rather nearby the monitor. Table 2-5 

provides the USEPA's recommendations for defining “nearby” cells for grid systems having cells 

of various sizes. Since the modeling for the North Carolina attainment demonstration was 

preformed at a 12km grid resolution, a 3x3 grid array was used. 

Table 2-5: USEPA’s Recommendation for Defining “Near” Cells 

Size of Cell 

(km) 

Size of the Array of 

“Nearby” Cells 

<5 7 x 7 

>5-8 5 x 5 

>8-15 3 x 3 

>15 1 x 1 

For the PM2.5 SMAT, the RRF is calculated for each component, for each quarter. To 

accomplish this step, daily concentration for each component of PM2.5 is extracted from the 

base year and future year modeling output near the FRM monitoring sites for each day. The 

daily component concentrations from the 3x3 array are then averaged to develop a mean daily 

component mass for each day in both the base and future years. These mean daily component 

concentrations are then averaged for each quarter to develop base and future year quarterly mean 

component concentration. The future year quarterly mean component concentrations are then 

divided by their respective base year quarterly mean component concentrations to develop 

quarterly RRFs for each component of PM2.5. The quarterly RRFs for the FRM monitors in the 

North Carolina PM2.5 nonattainment areas are presented below in Table 2-6. In lieu of using 

RRFs, the estimated future mass of NH4 and PBW will be determined by the estimated future 

mass of SO4 and NO3, as was done in the CAIR SMAT tool, per the following equations: 

NH4 = DON * SO4 + 0.29*NO3 

PBW =(-0.002618) + (0.980314*NH4) + (-0.260011* NO3) + (-0.000784* SO4) + (-0.159452* 

(NH4^2)) + (-0.356957* NO3* NH4) + (0.153894* (NO3^2)) + (0.212891* SO4* NH4) + 

0.0444366* SO4* NO3) + (-0.048352* (SO4^2)) 
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Table 2-6: Quarterly Component RRFs for Monitors in the North Carolina Nonattainment 

Areas 

AIRS ID County Site Name Quarter Crustal EC OC SO4 NO3

1 0.999 0.775 0.881 0.886 0.942

2 1.150 0.789 0.952 0.761 0.704

3 1.218 0.815 0.963 0.633 0.605

4 1.037 0.744 0.897 0.810 0.867

1 1.014 0.782 0.899 0.836 0.938

2 1.146 0.788 0.961 0.749 0.690

3 1.193 0.819 0.971 0.632 0.712

4 1.061 0.746 0.911 0.782 0.877

1 1.037 0.785 0.901 0.831 0.932

2 1.169 0.789 0.958 0.727 0.721

3 1.221 0.818 0.967 0.618 0.748

4 1.076 0.753 0.914 0.778 0.889

RRF

37-035-0004 Catawba Hickory

37-057-0002 Davidson Lexington

37-081-0013 Guilford Mendenhall

2.3. Future Year Quarterly Concentration Calculation 

The next step in the SMAT process is to calculate future quarterly mean concentration estimates 

for each component of PM2.5. To accomplish this, the current quarterly mean component 

concentration (Step 1, Section 2.1.3) is multiplied by the component-specific RRFs obtained in 

Step 2 (Section 2.2). The quarterly component concentration estimates for the monitors in the 

North Carolina PM2.5 nonattainment areas are provided below in Table 2-7. 

Table 2-7: Blank Corrected Quarterly Component Future Concentrations Estimates for 

Monitors in the North Carolina Nonattainment Areas 

AIRS ID County Site Name Quarter Crustal EC OC SO4 NO3 NH4 PBW

1 0.326 0.621 5.553 2.969 0.606 1.007 0.792

2 0.714 0.438 5.256 3.994 0.008 1.183 1.161

3 0.673 0.397 6.148 4.797 0.006 1.379 1.335

4 0.271 0.458 5.567 2.493 0.462 0.921 0.764

1 0.306 0.551 6.330 2.842 0.425 0.962 0.807

2 1.159 0.433 5.245 4.171 0.008 1.250 1.235

3 1.064 0.313 4.995 4.927 0.007 1.370 1.297

4 0.485 0.460 6.090 2.691 0.349 0.895 0.766

1 0.320 0.531 3.933 2.861 0.315 0.917 0.793

2 2.804 0.341 3.079 3.200 0.006 0.909 0.875

3 2.162 0.233 4.531 4.375 0.006 1.111 0.993

4 1.151 0.452 3.923 2.482 0.346 0.734 0.595

37-081-0013 Guilford Mendenhall

37-035-0004 Catawba Hickory

37-057-0002 Davidson Lexington

2.4. Future Year Annual Average Estimate 

The final step in the SMAT process is to sum the quarterly mean components (from Step 3, 

Section 2.3) to get annual mean PM2.5 values. Table 2-8 displays the quarterly mean PM2.5 

values for the FRM sites in the North Carolina nonattainment areas. 
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Table 2-8: Quarterly Mean PM2.5 Mass Estimates for 2009 

AIRS ID County Site Name

NonBlank 

Mass 

Q1

NonBlank 

Mass 

Q2

NonBlank 

Mass 

Q3

NonBlank 

Mass 

Q4

37-035-0004 Catawba Hickory 11.874 12.754 14.734 10.936

37-057-0002 Davidson Lexington 12.222 13.501 13.972 11.737

37-081-0013 Guilford Mendenhall 9.669 11.214 13.410 9.683

The quarterly mean PM2.5 values are then averaged to produce a future year annual average 

PM2.5 estimate for each FRM site in the nonattainment area (Table 2-9). These values estimated 

annual PM2.5 values are then compared to the NAAQS (15.0 μg/m3

). Since the values at the 

FRM site in the nonattainment areas are < 15.0 μg/m3, the test is passed. 

Table 2-9: Estimated Annual Mean 2009 PM2.5 Mass Compared to the DVB 

AIRS ID County Site Name 2002 Annual 

DVB

2009 NonBlank Mass Blank Mass 2009 Annual 

DVF

37-035-0004 Catawba Hickory 15.137 12.575 0.050 13.075

37-057-0002 Davidson Lexington 15.509 12.858 0.050 13.358

37-081-0013 Guilford Mendenhall 13.095 10.994 0.050 11.494

3 Supplemental Analysis 

The Attainment Guidance asserts that all attainment demonstrations should be accompanied by 

supplemental analysis that further supports the modeling conclusions. This supplemental 

analysis can include additional analyses of air quality, emissions and meteorological data, and 

consider modeling outputs other than the results of the attainment test. If the attainment test 

results fall short of the standard, the results of corroboratory analyses may be used in a weight of 

evidence determination (WOE) to show that attainment is likely despite modeled results, which 

may be inconclusive. 

The Attainment Guidance defines the guidelines for supplemental analysis/WOE for the annual 

PM2.5 standard as follows: 

- Site with a DVF less than 14.5 μg/m3

 should submit basic supplemental analysis to 

confirm the outcome of the model attainment test. 

- Sites with a DVF between 14.5 and 15.5 μg/m3 should submit a weight of evidence 

demonstration to aggregate supplemental analysis to support the model attainment 

demonstration 

- Sites with a DVF greater than or equal to 15.5 μg/m3

 should consider additional control 

measure to ensure attainment, as more qualitative analysis is unlikely to attainment 
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9

All North Carolina PM2.5 nonattainment areas have DVFs lower than 14.5μg/m3, making the 

following section an examination of supplemental analysis to corroborate modeling results, 

rather than a WOE analysis to show attainment. In the following sections we explore 

refinements to the attainment test, additional modeling studies, and air quality and emissions 

trend as part of a supplemental analysis for the North Carolina PM2.5 nonattainment areas. 

3.1 Additional Air Quality Modeling 

The Attainment Guidance suggests several additional modeling exercises that can be performed 

as part of supplemental/WOE analysis. Suggestions include completing additional analysis on 

the air quality modeling preformed, or modeling alternative set ups and emissions as part of 

sensitivity. Each of theses items will be discussed in the following sections. 

3.1.1 Air Quality Modeling Metrics 

In Section 7.0 of the Attainment Guidance, various aspects of air quality models, modeled 

performance, and uncertainties associated with the length of modeled episodes and limited 

observational datasets are described. Section 7.1 suggests that some types of “absolute” 

modeling results may be used to assess general progress towards attainment from the baseline 

inventory to the projected future inventory. The Attainment Guidance goes on to describe 

several metrics that can be considered as part of this type of additional analysis, which include: 

1. Percent change in total amount of PM2.5 >= 15 μg/m3

 within the nonattainment area 

2. Percent change in number of grid cells >= 15 μg/m3 within the nonattainment area 

3. Percent change in grid cell-hours (days) >= 15 μg/m3

 within the nonattainment area 

4. Percent change in maximum modeled 24-hour PM2.5 concentrations within the 

nonattainment area 

As the US EPA notes in the Attainment Guidance, care should be taken in interpreting absolute 

metrics if the model evaluation shows a large under prediction or over prediction of ozone or 

PM2.5 concentrations, because under (over) prediction of observed concentrations will make it 

artificially easy (hard) to show progress towards absolute attainment levels. To better coincide 

with model performance evaluation results, the same subset of days from the modeling were 

used in the model performance evaluation were used to develop the air quality metrics. This 

subset of days included all days with an 24-hour PM2.5 concentration greater than 30 μg/m^3 at 

any of the monitoring sites in either nonattainment area, as well as the four days with the highest 

average daily values from each quarter. This selection process identified 28 days for 

presentation in this appendix, and coincides with the days used in the model performance 

evaluation (Appendix J) and in the model results section (Appendix K). A full listing of the 

days and the observed 24-hour PM2.5 concentrations from the monitors in the nonattainment 

areas can be found in either Appendix J or Appendix K. 

Because of the complexity of the model extraction for PM2.5, only the second metric is 

presented for supplemental analysis. The cell counts of modeling data was tallied from both the 

2002 baseline and the 2009 attainment year modeling run for the identified days. Data was 
Attainment Test 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L 

North Carolina Attainment Demonstration August 21, 2009 

10

extracted for only the grid cells that contained portions of the either of the nonattainment areas. 

Figure 3-1 highlights the 50 cells that encompass the North Carolina nonattainment areas. 

Figure 3-1: Area for which the air quality metrics were applied. 

The cell counts were binned based on concentration ranges of 15 μg/m3 intervals to help 

illuminate the change in severity on the days in North Carolina with the highest PM2.5 

concentrations. Figure 3-2 presents the cell count results both graphically and in tabular form. 

The graph clearly shows a striking increase in the number of days below 15 μg/m3

. By 2009 

only 41.57% of cells fall in the 0 –15 range, a substantial increase from the 17.21% in 2002. 

Raw cell counts shows a total of 341 cells shifted to the 0 – 15 μg/m3 range between 2002 and 

2009 (Table 3-1). 

Figure 3-2 also shows a decrease in the number of cell in the 15 – 30 μg/m3 bin (269 cell 

decrease) and the 30 - 45μg/m3

 bin (75 cell decrease). The number of cells in the 45 –60 range 

remains relatively constant from 2002 to 2009. A closer examination of the daily cell counts 

shows that all of the cells in the highest concentration category occur on the same day in both the 

2002 and 2009 modeling and are likely associated with a fire. Overall, the results from the air 

quality modeling metric are encouraging. The metric shows a substantial increase in the number 

of cells below 15 μg/m3

, and an increase in cells below 30μg/m3. 
Attainment Test 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L 

North Carolina Attainment Demonstration August 21, 2009 

11

Concentration 2002 2009

0 - 15 17.21% 41.57%

15 - 30 70.21% 51.00%

30 - 45 12.29% 6.93%

45 - 60 0.29% 0.50%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2002 2009

Modeling Year

Percentage of Nonattainment Area

45 - 60

30 - 45

15 - 30

0 - 15

Figure 3-2: Percentage of Cell in Nonattainment Areas within Concentration Categories 

for 2002 and 2009. Table of actual values is presented on the right. 

Table 3-1: Number of Cells within Concentration Bins. Increases (decreases) in the 

number of cells within the bins are noted by red (blue) coloration in the last column. 

Concentration 2002 2009 Difference

0 - 15 241 582 341

15 - 30 983 714 -269

30 - 45 172 97 -75

45 - 60 4 7 3

3.1.2 Other Modeling Results 

One way to acquire modeling sensitivity runs is to examine the modeling results from other 

Regional Planning Organizations or from EPA modeling studies. Other modeling studies may 

use different physical and chemical modeling options for their meteorological and air quality 

modeling runs, which would provide a comparison or sensitivity based on these different 

options. 

An air quality modeling exercise that contained results for North Carolina PM2.5 nonattainment 

area is the USEPA’s modeling for the Clean Air Interstate Rule (CAIR). The Technical Support 

Document for the final CAIR, March 2005, provided modeling results with and without the 

implementation for the CAIR. Differences between the USEPA’s modeling and the attainment 

demonstration are: 1) the meteorology was for 2001, 2) the DVB was the weighted design values 

for the 1999-2003 period and 3) the modeling results were for 2010. The DVF was calculated 

using the CAIR SMAT tool, so methodologies between the CAIR DVF and the values presented 

in Section 2.4 are the same. These modeling results are listed in Table 3-2 below. 
Attainment Test 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L 

North Carolina Attainment Demonstration August 21, 2009 

12

Table 3-2: US EPA CAIR Modeling Results 

Future Year Catawba County Davidson County

2010 14.07 14.36

2015 13.45 13.61

The USEPA’s results were for the highest monitor in a county where more than one monitor is 

located. The USEPA’s modeling results predicts that both the North Carolina nonattainment 

areas should be below the annual PM2.5 standard by 2010. Although this is one year later than 

the attainment year for these areas. The USEPA’s 2010 CAIR DVFs are 1 μg/m3 higher than 

what the NCDAQ is showing in the attainment demonstration, but still support that both the 

North Carolina nonattainment areas will attain the annual PM2.5 standard by the attainment year 

of 2009. 

3.2 Air Quality and Emission Trends Analysis 

Since the annual PM2.5 designation in 2002, annual average concentrations of PM2.5 have 

decreased. Values have hovered near the standard at the two nonattaining monitors for roughly 

the past 5 years, while the Mendenhall monitor has maintained values lower than the NAAQS. 

Table 3-3 provides the annual average data, with Figure 3-3 providing a graphical representation 

of the data, with preliminary 2007 annual average values. These preliminary 2007 values show 

the monitors are still trending towards the NAAQS. 

With the data in Table 3-3 and Figure 3-3, please note that the Mendenhall was not in operation 

from 1999 to 2001, as this site replaced the McLeansville site. Data from the McLeansville site 

has been substituted in place of the missing data in both Figure 3-3 and Table 3-3, as the two 

sites are within the required distance to be considered a continuous monitoring site. 

Table 3-3: Annual Average PM2.5 values for the past 10 years. 

Monitoring 

Site County AIRS ID Annual Averages 

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 

Hickory Catawba 3703500041 17.4 17.6 16.0 15.4 15.0 15.0 15.9 15.2 14.5 12.8 

Lexington Davidson 3705700021 17.3 18.0 16.5 15.9 15.2 15.2 15.4 15.1 14.6 13.7 

Mendenhall Guilford 3708100131 13.7 13.3 14.0 14.0 14.1 13.0 11.4 

         Average of 1st-4th Quarter For Each Year. Values in colored orange are in excess of the Annual PM2.5 NAAQS. 

Note: Mendenhall was not in operation from 1999 to 2001 

Note: There was an extended loss of monitoring data at the Mendenhall site during the 4th quarter of 

2006. The NCDAQ has performed an extensive data imputation study to estimate a 4th quarter 

average concentration such that an appropriate annual average concentration and design value could 

be calculated. This study, titled “Mendenhall PM2.5 Data Imputation for 4Q2006” can be found in 

Appendix C.3
Attainment Test 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L 

North Carolina Attainment Demonstration August 21, 2009 

13

Figure 3-3: Annual PM2.5 Average Values for the Monitors in the Hickory and Triad 

Nonattainment Areas. 

With the improvement in annual average PM2.5values, there has also been an improvement in 

PM2.5 design values. When one takes into account the period of record, PM2.5 design values 

have improved significantly over the last 6 years. Like with the annual averages, the three-year 

design values have also begun to hover near the level of the standard for both the Hickory and 

Lexington monitors in recent years. It is also important to note the Guilford County monitor 

(Mendenhall, 37-081-0013) monitor has also maintained design values that meet the NAAQS 

over the past 3 design value periods (See Table 3-4). 

Table 3-4: Three Year Design values for the Monitors in North Carolina’s PM2.5 

Nonattainment Areas. 

Monitoring 

Site County AIRS ID 

Design Values 

1999-

2001 

2000-

2002 

2001-

2003 

2002-

2004 

2003-

2005 

2004-

2006 

2005-

2007 

2006-

2008 

Hickory Catawba 3703500041 17.0 16.3 15.5 15.1 15.3 15.4 15.2 14.2 

Lexington Davidson 3705700021 17.2 16.8 15.8 15.4 15.2 15.2 15.1 14.5 

Mendenhall Guilford 3708100131 13.7 13.8 14.0 13.7 12.9 

         Average Over 12 Quarters. Negative & Underlined Indicate Altered Calculation 

Note: Both of the footnotes that apply to Table 3-3 are also applicable with this table.
Attainment Test 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L 

North Carolina Attainment Demonstration August 21, 2009 

14

When evaluating the trends in air quality values it is important to note that there are still 

significant sulfur dioxide (SO2) emission reductions that are expected between now and the 

attainment year in the utility sector. The Clean Smokestacks Act requires the two large North 

Carolina utilities to meet annual SO2 emission budgets for 2007 and a tighter budget for 2009. 

Units from maintained by both Duke Energy and Progress Energy units are still expected to have 

controls installed over the next two years. Table 3-5 lists the units that are near the PM2.5 

nonattainment areas and shows the year the controls are expected to come on line and the 

estimated amount of yearly SO2 emissions reductions. In total, over 250,000 tons of SO2 will be 

reduced annually in central North Carolina alone. As sulfates are one of the larger contributors 

to total PM2.5 in North Carolina, this substantial reduction in SO2 should result in significant 

reductions of the observed PM2.5 concentrations, which are already very close to the standard. 

Table 3-5: Projected emission reductions Near the North Carolina PM2.5 Nonattainment 

Areas. 

Total 2006 

Emissions

2009 Projected 

Emissions '07 Plan

2009 Projected Total 

Emissions '07 Plan

2009 Projected 

Reductions

Operator Facility Unit Technology 2007 Plan SO2 (Tons) SO2 (Tons) SO2 (Tons)

Allen 1 Scrubber 2009 1,585

Allen 2 Scrubber 2009 1,215

Allen 3 Scrubber 2009 11,543

Allen 4 Scrubber 2009 11,789

Allen 5 Scrubber 2009 5,996

Belews Creek 1 Scrubber 2008 5,632

Belews Creek 2 Scrubber 2008 4,385

Marshall 1 Scrubber 2007 1,909

Marshall 2 Scrubber 2007 1,916

Marshall 3 Scrubber 2007 3,495

Marshall 4 Scrubber 2006 3,241

Progress Energy Carolinas, Inc. Mayo 1 Scrubber 2009 24,499 9,406 9,406 -15,093

Roxboro 1 Scrubber 2008 742

Roxboro 2 Scrubber 2007 978

Roxboro 3 Scrubber 2008 1,102

Roxboro 4 Scrubber 2007 1,376

Duke Energy Carolinas, LLC

Duke Energy Carolinas, LLC

Duke Energy Carolinas, LLC

Progress Energy Carolinas, Inc.

10,017

32,128

10,561

94,627 4,198

85,094

45,442

95,364

-90,429

-74,533

-13,314

-85,347

3.3 Supplemental Analysis Conclusions 

After examining the totality of the modeling evidence, the North Carolina Division of Air 

Quality (NC DAQ) is confident the Hickory and Triad PM2.5 nonattainment areas should reach 

attainment status by the 2009 deadline. US EPA CAIR modeling corroborates NC DAQ 

modeling results suggesting both nonattainment areas should be below at least 14.5μg/m3

. In 

addition, current PM2.5 design values are near NAAQS, with substantial sulfate reductions 

anticipated in the vicinity of the nonattainment area over the next two years. NC DAQ feels 

these reductions will more than allow the PM2.5 nonattainment areas to achieve the NAAQS by 

2009. 

4 Unmonitored Area Analysis 

The modeled attainment test does not address future air quality at locations where there is not a 

PM2.5 monitor nearby. To guard against the possibility that air quality levels could exceed the 

standard in areas with limited monitoring, Section 3.4 of the Attainment Modeling Guidance

suggests that additional review is necessary, particularly in nonattainment areas where the PM2.5 

monitoring network just meets or minimally exceeds the size of the network required. This 
Attainment Test 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L 

North Carolina Attainment Demonstration August 21, 2009 

15

review is intended to ensure that a control strategy leads to reductions in PM2.5 and its constituent 

pollutants at other locations that could have baseline (and future) design values exceeding the 

NAAQS, were a monitor deployed there. The test is called an “unmonitored area analysis”. The 

purpose of the analysis is to use a combination of model output and ambient data to identify 

areas that might exceed the NAAQS if monitors were located there. 

The NCDAQ, along with Local and Tribal Programs, currently operates a network of 34 PM2.5

monitors. Twenty-nine of these monitors were established as State and Local Air Monitoring 

Stations (SLAMS). These SLAMS monitors were selected based on specific monitoring 

objectives (background concentration, area of highest concentration, high population, source 

impact, transport, and rural impact) as required by the USEPA and siting scales (micro, middle, 

neighborhood, urban, and regional) established by the USEPA. Of the remaining 8 monitors, 7 

are categorized as “Other” or “Special Purpose Monitors” that were established by NCDAQ to 

evaluate models, study PM2.5 formation and transport, and obtain a better understanding of PM2.5

in North Carolina. The remaining monitor is a Tribal monitor operated by the Eastern Band of 

Cherokee Nation. 

The NCDAQ believes that the density of its monitoring network more than adequately captures 

the full extent of the PM2.5 air quality concerns in North Carolina. With an average of one 

monitor per 3711 km2, this is one of the densest statewide PM2.5 monitoring networks in the 

southeast. A map of each PM2.5 monitor and its position relative to the NCDAQ/ASIP 12-km 

modeling grid is provided in Figure 4-1. As can been seen by the figure, the spatial coverage of 

the monitors, and their resulting “nearby” 3x3 arrays, covers the majority of the urban areas 

where PM2.5 tends to be higher. 

Figure 4-1: PM2.5 Monitors and with Respect to the VISTAS 12km Grid 
Attainment Test 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L 

North Carolina Attainment Demonstration August 21, 2009 

16

The adequacy of the NCDAQ PM2.5 monitoring network is further demonstrated when plotted 

against a projected spatial field of annual PM2.5 design values. Figure 4-2 presents the 2009 

future year PM2.5 design value modeling output from this attainment demonstration and the 

location of each PM2.5 monitor in and around North Carolina. This 2009 PM2.5 design value 

spatial field was created by the USEPA’s Modeled Attainment Test Software (MATS). It is 

clear from the MATS analysis that all of the regions of higher, yet attaining, PM2.5 design values 

have numerous representative PM2.5 monitors. There are not any identified PM2.5 hotspots that 

would require any additional monitoring considerations in North Carolina. 

 

Figure 4-2: PM2.5 Monitors and 2009 Modeled Attainment Spatial Field 

5 Attachments to Appendix L 

Attached to this Appendix is the following supporting documentation for Section 2.1: 

“Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by Application of the 

(Revised) Speciated Modeled Attainment Test (SMAT) Updated- 11/8/04” 

6 Reference 

Frank, N., 2006: “Retained Nitrate, Hydrated Sulfates, and Carbonaceous Mass in Federal 

Reference Method Fine Particulate Matter for Six Eastern U.S. Cities” J. Air Waste 

Mange. Assoc., 56, 500-511. 
Attachment L1 

Procedures for Estimating Future PM2.5 

Values for the CAIR Final Rule by 

Application of the (Revised) Speciated 

Modeled Attainment Test (SMAT)-

Updated- 11/8/04
1

Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule by

Application of the (Revised) Speciated Modeled Attainment Test (SMAT)

Updated- 11/8/04

I. Introduction

EPA has issued draft modeling guidance (EPA, 2001) that describes a procedure for

combining monitoring data with outputs from simulation models to estimate future

concentrations of PM2.5 mass. The guidance recommends that model predictions be used in a

relative sense to estimate changes expected to occur in each major PM2.5 species. The procedure

is referred to as the Speciated Modeled Attainment Test (SMAT). A preliminary version of

SMAT was applied in the Clean Air Interstate Rule (CAIR) proposal modeling (EPA, 2004) to

estimate future PM2.5 nonattainment in the Eastern United States. A revised version of the

SMAT technique has been applied in support of the CAIR final rule. Based on comments

received from the CAIR proposal, several improvements have been implemented. The revised

SMAT procedures are described below.

A. Default SMAT Procedures

The draft modeling guidance includes a sequence of key steps that are recommended for

processing PM2.5 ambient and modeling data. The following is a brief summary of those steps:

(1) Derive current quarterly mean concentrations for each of the major components of PM2.5. 

This is done by multiplying the monitored quarterly mean concentration of Federal

Reference Method (FRM) (EPA, 1997) derived PM2.5 by the monitored fractional

composition of PM2.5 species (at speciation monitor sites) for each quarter. (e.g., 20%

sulfate x 15 ug/m3 PM2.5 = 3 ug/m3 sulfate).

(2) For each quarter, apply an air quality model to estimate current and future concentrations

for each of the components of PM2.5. Take the ratio of future to current predictions for

each component. The result is a component-specific relative reduction factor (RRF).

(e.g., given model predicted sulfate for base is 10 ug/m3

 and future is 8 ug/m3 then RRF

for sulfate is 0.8).

(3) For each quarter, multiply the current quarterly mean component concentration (step 1)

times the component-specific RRF obtained in step 2. This leads to an estimated future

quarterly mean concentration for each component. (e.g., 3 ug/m3

 sulfate x 0.8 = future

sulfate of 2.4 ug/m3

).

(4) Average the four quarterly mean future concentrations to get an estimated future annual

mean concentration for each component. Sum the annual mean concentrations of the

PM2.5 components to obtain an estimated future annual concentration for PM2.5.

EPA is using the FRM data for nonattainment designations. Therefore it is critical that

CAIR SMAT Proceedures 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

North Carolina Attainment Demonstration 

 1 

 Attachment L1 

August 21, 2009
2

the speciated modeled attainment test described above uses FRM data as the base value for

projecting future PM2.5 concentrations. As can be seen from the list of steps, the modeled

attainment test is critically dependent on the availability of species component mass at FRM

sites. The modeling guidance recommends using ambient PM2.5 speciation data to estimate the

relative mass of PM2.5 components at each FRM site. The guidance further recommends using

the Interagency Monitoring of Protected Visual Environments (IMPROVE) procedure

(IMPROVE, 2000) for estimating reconstructed fine mass. In this procedure, the PM2.5 mass is

assumed to be composed of 6 species: ammonium sulfate, ammonium nitrate, organic carbon

mass, elemental carbon, crustal and un-attributed mass which is defined as the difference

between measured PM2.5 and the sum of the five component species. The relative proportion of

each of these 6 species was estimated in the CAIR proposal SMAT analysis. 

B. New Species Calculations and Definitions

Recent data analyses as well as a report submited by CAIR commenters (Glass, 2004)

have noted that the FRM monitors do not measure the same components and do not retain all of

the PM2.5 that is measured by routine speciation samplers and therefore cannot be directly

compared to speciation measurements from EPA’s Speciation Network (ESPN). By design, the

FRM mass measurement does not retain all ammonium nitrate and other semi-volatile materials

(negative sampling artifacts) and includes particle bound water associated with sulfates, nitrates

and other hygroscopic species (positive sampling artifacts). This results in concentrations and

percent contributions to PM2.5 mass which may be different than the ambient levels of some

PM2.5 chemical constituents. For the purposes of predicting changes in PM2.5 chemical

components on the PM2.5 mass, constructed PM2.5 mass should match the composition of mass

retained by the FRM. As such, we have made several revisions to the calculation and definition

of PM2.5 species used in SMAT.

The revised SMAT uses an FRM mass construction methodology which results in

reduced nitrates (relative to the amount measured by routine speciation networks), higher mass

associated with sulfates (reflecting water included in gravimetric FRM measurements) and a

measure of organic carbonaceous mass which is derived from the difference between measured

PM2.5 and its non-carbon components. This characterization of PM2.5 mass also reflects crustal

material and other minor constituents. The resulting characterization provides a complete mass

balance. It does not have any unknown mass which is sometimes presented as the difference

between measured PM2.5 mass and the characterized chemical components derived from routine

speciation measurements. The net difference between retained mass and measured mass for

individual PM2.5 chemical components is relatively small when expressed as ug/m3, but can be a

large percent for individual constituents. 

Below we describe an application of the revised SMAT procedures for a study domain

that extends over a large portion of eastern US. The study domain is defined for grids of

dimension 36 km X 36 km covering the area enclosed within -100 to -67 longitude and 25 to 49

latitude. Base case and future year model predictions are available for each FRM monitor (and

grid cell) that is contained within the domain.

CAIR SMAT Proceedures 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

North Carolina Attainment Demonstration 

 2 

 Attachment L1 

August 21, 2009
1

The CAIR proposal SMAT was based on 2001-2002 data (the last 3 quarters of 2001 and the 1st

quarter of 2002). There are 2 complete years of speciation data at a some sites, but we used only the

latest complete year of data (2002) for this analysis. There were many more speciation sites available in

2002 (compared to 2001) and we did not want to mix a single year of data at most sites with a two year

average at a few sites. That may have led to a regional bias in species composition.

3

II. PM2.5 Mass and Species Data Handling

Speciated PM2.5 data from both the IMPROVE and ESPN were used to derive mean

concentrations of each of six PM2.5 components. No attempt was made to resolve differences in

measurement and analysis methodology between the two networks. Since three (or more) years

of urban speciation data were not available, calendar year 2002 was used to best correspond1

 to

the available 5 years of FRM PM2.5 mass data (1999-2003). Quarterly average concentrations

between Jan 2002 to December 2002 were retained for sites that had at least 11 monitored values

per quarter for each of the major PM2.5 species. The quarters were defined as follows: Q1 = 

January - March 2002; Q2 = April - June 2002; Q3 = July - September 2002; and Q4 = October

- December 2002. Major species for ESPN include elemental carbon (EC), organic carbon (OC),

ammonium (NH4), sulfate (SO4), nitrate (NO3), and crustal material (which includes the five

trace elements aluminum, calcium, iron, silicon, and titanium). The major species for

IMPROVE are the same except for ammonium (NH4), which is not routinely measured in the

IMPROVE protocol.

All species were used as extracted directly from the Air Quality Subsystem (AQS) with

the exception of organic carbon in ESPN. Organic carbon in the ESPN was blank corrected

based on measurements from field blanks which indicate a positive bias. The blank corrections

were based on a draft report which examined the blank carbon data in the STN network

(Flanagan, 2003). The carbon corrections are shown below in Table 1. 

Table 1: Organic Carbon Blank Corrections

Sampler Type Organic Carbon

Correction

(ugC/m3)

URG MASS 0.29

R and P 2300 0.90

Anderson RAAS 1.19

R&P 2025 0.77

MetOne SASS 1.42

These sampler-specific, network-wide corrections were subtracted from daily

measurements of organic carbon and the results multiplied by 1.40 (to convert to organic carbon

mass) before aggregating to quarterly and annual levels.

CAIR SMAT Proceedures 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

North Carolina Attainment Demonstration 

 3 

 Attachment L1 

August 21, 2009
4

For both ESPN and IMPROVE data (for the year Jan 2002- Dec 2002), the following

quality checks were made to screen the raw data:

1. Any observations with one or more missing values of any of the major chemical species were

removed.

2. All observations on July 6-9, 2002 for the 10 states most affected by the July 2002 Quebec

Fires were removed. The 10 states were: DE, CT, VA, MD, NH, MA, NJ, VT, RI, and PA.

3. Only those sites that had a minimum of 11 observations for ALL the major species were

retained in the final database. 

These conditions result in the following final quarter-by-quarter number of observations and

sites from ESPN and IMPROVE for inclusion in the “SMAT” procedure for CAIR. 

Table 2: Number of Eastern sites and observations used in the SMAT analysis for 2002

January 2002—December 2002

ESPN IMPROVE

Total Number of

Obs.

Number of Sites Total Number of

Obs.

Number of Sites

Quarter 1 2022 98 1206 49

Quarter 2 2419 131 1131 47

Quarter 3 2844 145 1320 54

Quarter 4 2725 149 1637 58

CAIR SMAT Proceedures 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

North Carolina Attainment Demonstration 

 4 

 Attachment L1 

August 21, 2009
2

This QA step was not done for the IMPROVE network because IMPROVE sites are not colocated with FRM sites.

5

Figure 1- Speciation sites used in the revised SMAT analysis for the 4th quarter of 2002.

To further quality assure the ESPN data, the reconstructed fine mass (RCFM) was 

compared to measured FRM PM2.5 on a quarterly basis2

. To accomplish this QA check, daily

RCFM was calculated for each observation using the following equation:

RCFM = Sulfate + Nitrate + OCM + EC + Crustal + Ammonium

Quarterly average RCFM was calculated using the daily RCFM. These quarterly average RCFM

concentrations were then compared to quarterly average FRM PM2.5 measurements at co-located

sites.

Site-quarter combinations in the ESPN data were removed from the dataset when the

quarterly average RCFM was more than 30% higher or 30% lower than the quarterly average

CAIR SMAT Proceedures 

The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 

North Carolina Attainment Demonstration 

 5 

 Attachment L1 

August 21, 2009
3

The concentrations are interpolated to the CMAQ 36 km grid cells. Each grid cell has a unique

set of interpolated species concentrations. The species concentrations are not interpolated to the location

of each monitor within a grid cell. 

6

*

*

*

#

# = Center Grid-Cell “E”

* = Air Pollution Monitor

*

*

*

*

FRM PM2.5. This comparison resulted in the removal of 30 site-quarter records from the dataset.

 

B. Spatial Interpolation of Data

Since roughly 80% of the FRM sites do not have co-located speciation monitors, a spatial

interpolation methodology was developed to estimate component species mass at the FRM

locations that do not have co-located speciation data. The quarterly average species

concentrations at the IMPROVE and ESPN monitors were used to interpolate concentrations at

the PM2.5 monitoring sites3

. We previously used a Kriging methodology for SMAT, but have

now moved to a technique called Voronoi Neighbor Averaging (VNA) to produce the spatial

fields. We are using the revised interpolation technique in order to remain consistent with

calculations performed for the health benefits portion of the CAIR. For the benefits analysis,

interpolated PM2.5 species data is needed for the entire country (not just the East). We found

that VNA gave more spatially consistant results in the West compared to Kriging. We therefore

decided to use VNA for the entire country and for all analyses. 

All spatial interpolations were conducted using EPA’s environmental Benefits Mapping

and Analysis Program (BenMAP), about which information can be obtained at

http://www.epa.gov/ttn/ecas/benmodels.html (Abt, 2003). The VNA interpolation method is

contained within the BenMAP program. 

The first step in VNA is to identify the set of neighboring monitors for each of the

CMAQ grid cells in the Continental United States. The figure below presents nine grid cells and

seven monitors, with the focus on identifying the set of neighboring monitors for grid cell E.

In particular, BenMAP identifies the nearest monitors, or "neighbors," by drawing a

polygon, or Voronoi cell, around the center of each county. The polygons have the special

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weight1

1

30

1

30

1

45

1

45

1

55

= 0 35

+++

⎛

⎝

⎜ ⎞

⎠

⎟

= .

property that the boundaries are the same distance from the two closest points.

We then choose those monitors that share a boundary with the center of grid cell E. These are 

the nearest neighbors, and we use these monitors to estimate the air pollution level for this grid

cell.

To estimate the concentration of a species in each grid cell, BenMAP calculates an

inverse-distance weighted average of the concentrations from each neighboring monitor. The

further the monitor is from the grid cell center, the smaller the weight. 

The weight for the monitor 30 kilometers from the center of grid cell E is calculated as follows:

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The elemental carbon data appeared to have stronger urban gradients compared to other species. 

Therefore, an inverse distance weighting scheme was used to better maintain the observed gradients.

8

The weights for the other monitors would be calculated in a similar fashion. 

There are several variants to the general VNA method which can be used to allow for

differences in the spatial dynamics of different pollutants. These variants include distance

limited VNA, inverse distance squared weighting, and nested VNA. Distance limited VNA

functions the same as VNA with the exception that neighbors are constrained to be within a

given distance from the center of the grid cell. Inverse distance squared weighting uses the

inverse of the squared distance from the center of the grid cell in computing the weighted

average of neighboring monitors. Nested VNA allows for the use of different weighting

algorithms and distance limits depending on data availability. All three of these variants are

used in developing the interpolated species concentrations.

Because of the spatial dynamics of different PM species, different adjustments to the

generalized VNA method are applied for individual species. For the Eastern U.S., the quarterly

average concentrations for nitrates, sulfates, organic carbon mass (measured organic carbon

*1.4), and crustal species were interpolated using both IMPROVE and STN monitors with

inverse distance squared weighting. Eastern U.S. elemental carbon quarterly concentrations

were interpolated using both IMPROVE and STN monitors with inverse distance weighting4

. 

Degree of neutralization of sulfate (DON) was calculated at ESPN monitors (IMPROVE does

not collect ammonium data) and then interpolated using inverse distance squared weighting. 

 Interpolated spatial fields of quarterly average sulfate ion, “FRM” nitrate, elemental

carbon, organic carbon mass, crustal mass, and the degree of neutralization of sulfate (DON)

were created using VNA interpolation. Each of these fields were used in the calculation of

component mass as described in the procedures below. Figure 5 is an example of the resulting

interpolated surface using the first quarter data for nitrates and figure 6 shows the interpolated

fields for third quarter sulfate concentrations. 

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Figure 5. 1st quarter 2002 interpolated nitrate concentration.

Figure 6. 3rd quarter interpolated sulfate concentration

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III. Components of Measured PM2.5 Mass

A. Introduction

As stated in the section I, one of the goals of the revised SMAT methodology is to bettter

match the speciation data with the FRM PM2.5 retained mass. A series of adjustments and

calculations were performed in order to derive the estimated species compositions. Those

procedures are described below.

 

To represent composition of measured PM2.5 mass (for NAAQS implementation and

more precise representation of regulatory PM2.5), the following approach is used for revised

SMAT:

CFMFRM = [Ammoniated Sulfate Mass] + [Retained Nitrate Mass] + [Retained Carbonaceous

Mass] + [Crustal ] + [Other Components] [1]

In the above characterization, CFM equals constructed fine mass and all of the listed

chemical components reflect those retained during sampling and equilibration on the FRM’s

Teflon filter. Sulfate and nitrate mass include associated ammonium but which may be different

than assumed ammonium sulfate and ammonium nitrate compounds. Also, sulfates, nitrates and

carbonaceous mass includes particle bound water associated with these hygroscopic aerosols.

For these analyses, Crustal is intended to be a more general term that includes fine soil, and

oxides that result from other PM emissions.

B. Derivation of PM2.5 Species Used in Revised SMAT

1. Calculated adjusted nitrate by applying formula to daily average data 

Retained Nitrate Mass The first step in the procedure for identifying mass

components was to estimate the retained nitrate mass on the FRM filters. The FRM does not

capture all of the semi-volatile components of the ambient air, such as ammonium nitrate. The

retained amount of nitrate ion, however, can be accurately estimated by a simple thermodynamic

model that involves 24-hr ambient nitrate speciation concentrations (as measured by a standard

speciation sampler using a nylon filter preceded by a HNO3 denuder) together with hourly

ambient temperature and humidity. Atmospheric nitrates are higher during the cooler months.

Retention on the FRM is also higher during the cooler months and essentially all the nitrates are

lost during the summer. The retention does not appear to depend on ambient NH3 or HNO3.

More NO3 is retained at low temps and high humidity which varies by sampling location and

time of year.

Prediction of FRM Nitrates Because nitrate retention varies by site and season, an ammonium

nitrate equilibrium model is used to predict the amount of nitrates retained on the FRM teflon

filter. As used by Hering (Hering, 1999; Zhang, 1992), 

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 24

delta NO3 (ug/m3)= 745.7/TR* 1/24* E (Ki

1⁄2 ) [2]

 i=1

where TR is the reference temperature for the sampled air volume in degrees Kelvin and Ki is the

dissociation constant for ammonium nitrate evaluated at the ambient temperature for hour i. This

volatilization prediction characterizes depletion of some or all of the nitric acid and ammonia

vapors ahead of the filter and specifies a 3-5 degree increase in the filtration temperature above

ambient. 

This model is used to adjust 24-hr ESPN nitrate ion concentrations to estimate FRM NO3

(NO3FRM) as follows: 

NO3FRM = NO3ESPN - delta NO3 (ug/m3) [3]

For each hour of the day, the equilibrium dissociation constant for ammonium nitrate, Ki, was

calculated from hourly ambient temperature and hourly ambient relative humidity based on

Mozurkewich (Mozurkewich, 1993) and as applied by Chang et al.

When RH is less than deliquescence point of ammonium nitrate (61%), 

Ln K = 118.87 - (24084/T)-6.025 ln T, [4]

K in nanobars, T in Kelvins.

When RH is higher than 61%, K is replaced by 

K’=[P1-P2(1-a)+P3(1-a)2] (1-a)1.75*K [5]

where Ln P1, Ln P2, Ln P3 are specified as

Ln(P1) = -135.94 + 8763/T + 19.12ln(T)

Ln(P2) = -122.65 + 9969/T + 16.22ln(T)

Ln(P3) = -182.61 + 13875/T + 24.46ln(T)

Equation 4 assumes crystallization of ammonium nitrate when RH is less than 61%. 

Thus, predicted NO3 loss may be underestimated for situations where solids do not form on the

filter. For supersaturated solutions and with lower RH, the estimated dissociation for the solution

will be larger than K for the solid. However, there is little (or no) data that can be used to give a

reliable result for how much larger. 

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PLOT prediction_RH_high prediction_RH_low

0

1

2

3

4

5

6

7

8

9

10

temperature, deg F

-10 0 10 20 30 40 50 60 70 80

More loss at low humidity (blue curve)

 RH = 80% is illustrated with red curve

Figure 7- Potential NO3 loss as a function of temperature and relative humidity.

Based on equations [2]-[5], Figure 7 illustrates the potential nitrate loss as a function of

temperature and relative humidity. Temperature is presented as degrees F for more convenient

interpretation. It shows that at 50 deg F and RH of 80%, approximately 1.6 ug/m3 nitrate would

be lost. At RH less < 61% an additional 0.4ug/m3 could be lost. In both cases, the loss cannot

exceed the amount of ambient NO3, as depicted by the ESPN NO3. 

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When these predictions are compared with measured FRM nitrates at six eastern US

monitoring locations, the annual average prediction errors are <-0.3 - +0.1 ug/m3.

3. Estimated Ammonium associated with sulfates and retained nitrates and sulfates

 To determine the mass associated with nitrates, we first assume retained nitrate is

probably all ammonium nitrate in the Eastern US. Thus the ammonium associated with nitrates

can be derived directly from the measured or predicted NO3FRM as 

NH4NO3 = 0.29 * NO3FRM [6]

Similarly, the dry PM2.5 mass associated with nitrates is

[Retained dry FRM Nitrates] = 1.29 * NO3FRM [7]

FRM nitrates retains water. Discussion of hydrated nitrates (and sulfates) are discussed in the

next section.

The difference between total FRM NH4 (amount associated with nitrates and sulfates),

termed NH4FRM, and the ESPN NH4, termed NH4ESPN,, is needed to determine the ammoniated

form of sulfates as described by equation 4. Recent measurement study by Collett (Collett,

2004) shows that NH4 may not be completely retained during collection on nylon filters

preceded by a nitric acid denuder. During sampling conditions associated with nitrate

volatilization, ammonium nitrate dissociates but the HNO3 downstream of the denuder is

recaptured on the basic nylon media and the result is reported as particle nitrate. On the other

hand, the NH4+ volatilizes to gaseous NH3 and apparently passes thru the filter. At several FRM

study sites, the ESPN NH4 which is adjusted for evaporated NH4NO3 tends to more closely

corresponds to the measured NH4 from the FRM teflon filter. However, for other sites, the

measured ESPN NH4 appear to agree with FRM NH4. 

Because of uncertainty in retained FRM NH4, NH4adj is estimated as NH4 ESPN minus 1⁄2 the

amount that would have evaporated with lost NO3 as follows:

NH4adj = NH4 ESPN - 1⁄2 * 0.29 * (NO3 ESPN - NO3FRM) [8]

This essentially assumes that 50 % of the ammonium associated with lost nitrate is also lost. 

 4. Ammoniated Sulfate Mass.

The mass associated with sulfates is first estimated as its dry mass. All estimated sulfates are

assumed to be associated with ammonium, but the form of the sulfate compound and the amount

of ammonium must be estimated. The form of the ammoniated sulfate compound(s) and the

amount of associated ammonium, however, is somewhat uncertain. 

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Sulfates may not be fully neutralized in all geographic areas or seasons of the year. During

winter-time conditions, when nitrates are prevalent in the ambient aerosol, sulfates tend to be

fully neutralized and exist as ammonium sulfate. During the summer when sulfates are higher,

nitrates are lower and ammonia is less available for reaction with H2SO4 , the resulting aerosol

can be acidic and the form of sulfates can include bisulfate or even H2SO4. 

The amount of ammonium associated with the sulfate ion can be estimated as 

NH4(SO4) = NH4adj - 0.29 * NO3FRM, [9]

where 0.29 is the molar ratio of NH4 to NO3 and 

NH4FRM and NO3FRM reflect the amounts retained on the FRM filter.

The amount of NH4(SO4) is not allowed to exceed the fully neutralized amount of 0.375 multiplied

by the estimated sulfate ion concentration.

Because of uncertainties in NH4 speciation measurements, the spatially interpolated values of

NH4 are calculated by deriving the degree of sulfate neutralization (DON) from the estimated

NH4(SO4) as

DON = NH4(SO4) / SO4. [10]

Interpolated values of DON, sulfate and estimated FRM nitrate (adjusted nitrate) are then used to

estimate the adjusted ammonium at each FRM site as follows: 

 NH4FRM = DON * SO4 + 0.29*NO3FRM, [11]

 where: DON, SO4 and NO3FRM are the interpolated (kriged) quarterly average values at

each FRM site. NH4FRM is not a directly measured value, but is derived from the measurements

of NH4, SO4, and NO3. The interpolated DON values were used to estimate ammonium due to

the uncertainty of the ammonium measurements. The accuracy of the ammonium measurement

and the amount of ammonium that is retained on the FRM filter is uncertain. Use of the

smoothed, interpolated DON values allows for a relatively smooth field of ammonium

concentrations. 

5. Particle Bound Water 

Because ammoniated sulfate and ammonium nitrate are hygroscopic, the retained sulfate

and nitrate mass will include water. Particle bound water (PBW) is estimated using the Aerosol

Inorganic Model (AIM) (Clegg, 1998). PBW was derived from quarterly average FRM

concentrations of sulfate, ammonium, nitrate as describe above. Estimated hydronium ion, H+,

needed to achieve ionic balance was derived from the latter values. The model enables the

distribution of water and ions to be calculated between liquid, solid and vapor phases for specific

temperature and relative humidity conditions. Typical filter equilibration conditions of 35% RH

and 22 deg C (295 deg K) temperature were used. 

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Application of AIM at the specified FRM filter equilibration conditions show that PBW

is much more dependent on sulfate concentration compared to nitrate and that the relationship

varies somewhat by season to differentiate the relative amounts of sulfate and nitrate aerosol.

There is proportionally less estimated PBW water for wintertime aerosol which has higher NH4

and NO3 and lower SO4. 

After running the AIM model, it was determined that the particle bound water

concentrations are sensitive to the degree of neutralization of the sulfate particles (determined by

the relative concentration of NH4FRM). Due to the uncertainty in ammonium concentration

estimates, we used a relatively smooth field of interpolated ammonium concentrations as input to

AIM. This helped to smooth out some of the “bumpiness” in the water concentration

predictions. 

For computational convenience, a polynomial regression equation was fit to the

calculated water mass from AIM and the three input values that fed into AIM (sulfate, nitrate

and ammonium). The polynomial equation is then used in all SMAT analyses to estimate water. 

The equation is as follows:

PBW =(-0.002618) + (0.980314*nh4) + (-0.260011*no3) + (-0.000784*so4) 

 + (-0.159452*nh4**2) + (-0.356957*no3*nh4) + (0.153894*no3**2) 

 + (0.212891*so4*nh4) + (0.044366*so4*no3) + (-0.048352*so4**2) [12]

where nh4 = NH4FRM and no3 = NO3FRM

6. Other Non-Carbon PM2.5 Components (blank mass)

The other quantifiable components of PM2.5 mass include passively collected mass,

represented by the field blank concentration of 0.3-0.5ug/m3 (EPA, 2002). This appears to

constitute a contamination of the filter resulting from handling or contact with the FRM cassette.

This value is deemed to be an important constituent of PM2.5 mass (it is assumed to not be

dependent on pollutant emissions). A nominal blank mass value of 0.5 ug/m3 will be considered

in mass construction computations presented later. This value is assumed to remain constant

through time.

7. Calculation of Carbonaceous Mass 

Elsewhere, carbonaceous mass is estimated from blank corrected speciation data, where

organic carbonaecous mass is first estimated by multiplying the organic carbon concentrations

by 1.4 or alternative factors to account for the oxygen, hydrogen and other elements associated

with ambient carbon particles. To that amount is added the elemental carbon concentration. An

alternative approach to estimate carbon contribution to PM2.5 mass is used for revised SMAT

because of (1) many uncertainties in estimating carbonaceous mass from carbon measurements

(Turpin, 2001; Chow, 2004) (2) differences in carbon measurement protocol between urban and

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rural monitoring locations, (3) a relatively “bumpy” surface of urban carbon concentrations as

derived from these urban and rural organic carbon measurements and (4) lack of carbon

measurements at all FRM locations. The revised SMAT approach estimates carbon by mass

balance comparing precisely measured FRM PM2.5 mass (EPA, 2003) with the sum of its noncarbon components. The latter are sulfates, ammonium, nitrates, estimated particle bound water,

estimated crustal material plus 0.5 ug/m3 as discussed earlier. 

This approach estimates retained carbonacous FRM mass and explicitly accounts for the

following important and difficult to estimate carbon mass properties: (1) regional and urban-rural

differences in the mix of carbonaceous aerosols, i.e. the amount of oxygen, hydrogen, etc; (2)

retained water associated with hygroscopic carbon compounds (Saxena, 1996; Yua, 2004); (3)

volatile carbonaceous material measured by speciation samplers, but not retained in FRM mass; 

and (4) uncertainties associated with blank corrections of measured organic and elemental

carbon. 

Total Carbonaceous Mass by mass balance (TCMmb) is defined as , 

 TCMmb = PM2.5 - { [SO4] + [NO3FRM] + [NH4FRM] + [water] + [crustal material] + [0.5] } [13]

In this expression, all of the above quarterly average components represent the mass retained on

FRM teflon filters. 

The mass associated with organic compounds is defined as 

OCMmb= TCMmb - [EC] [14]

where EC is elemental carbon.

This approach completely accounts for FRM mass and OCMmb is often greater than the

amount that would be derived directly from speciation measurements. Because of uncertainties

in speciation measurements and their estimates from interpolated surfaces, a lower limit (floor)

for OCMmb was set so that the OCMmb was not unreasonably low. The floor was set so that

OCMmb could not be more than 30% lower than measured OCM. We used the Kriged

measured values of OCM to calculate the floor. The lower limit is equal to interpolated

(measured) OC * 1.4 * 0.7. If the OCMmb concentration was less than the lower limit, it was set

equal to the lower limit.

B. Summary of PM2.5 Composition Calculations

Equation 15 shows the final composition of PM species as they relate to the measured

FRM values for each quarter of 2002. Quarterly average FRM mass is equal to the sum of the

seven species plus blank mass.

PM2.5FRM = { [OCMmb] + [EC] + [SO4] + [NO3FRM] + [NH4FRM] + [water] + [crustal material]

+ [0.5] } [15]

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The measured OCM is only used to calculate the “floor” for OCMmb. 

17

The species data is generated in the following order:

1) Adjusted nitrate is calculated using hourly meteorology and 24-hour average nitrate

measurements.

2) Quarterly averages are calculated for adjusted nitrate, sulfate, elemental carbon, degree

of sulfate neutralization (DON), crustal mass , and measured OCM5

.

3) Quarterly average ammonium is calculated from the adjusted nitrate, sulfate, and DON

values.

4) Calculated ammonium, sulfate, and nitrate values are input into the polynomial water

equation to derive particle bound water concentrations.

5) Carbon mass by difference (OMCmb) is calculated from the PM2.5 mass, adjusted

nitrate, ammonium, sulfate, water, elemental carbon, crustal, and blank mass values.

6) The sum of the 7 species plus blank mass is equal to the FRM mass.

C. Calculation of Quarterly Species Fractions 

For each quarter at each FRM site, concentrations for each of the seven species (plus

blank mass) are combined with quarterly 2002 PM2.5 FRM averages to derive composition

fractions in the following manner:

First, the 0.5 ug/m3 of blank mass is subtracted from the FRM PM2.5 concentration.

PM2.5FRM-Blank= PM2.5FRM - 0.5 ug/m3 [16]

The blank mass is subtracted before species fractions are calculated because the blank

mass is held constant at 0.5 ug/m3 throughout the analysis. In the example below (table 3a), the

measured FRM mass for quarter 3 in 2002 is 22.5 ug/m3. The non-blank FRM mass is 22.0

ug/m3. The mass of the seven species add up to the non-blank mass. 

Table 3a

FRM

Mass

(ug/m3)

Blank

Mass

(ug/m3)

Non-blank

Mass

(ug/m3)

 Sulfate

(ug/m3)

Nitrate

(ug/m3)

Organic

aerosol

(ug/m3)

Elemental

Carbon

(ug/m3)

Water

(ug/m3)

Ammonium

(ug/m3)

Crustal

(ug/m3)

22.5 0 .5 22.0 8.5 1.1 5.2 0.9 2.3 3.3 0.7

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Next, species fractions are calculated for each quarter for each specie. In the example

below (table 3b), a fraction of non-blank mass is calculated for each of the seven species. Blank

mass remains fixed at 0.5 ug/m3. 

Table 3b

FRM

Mass

(ug/m3)

Blank

Mass

(ug/m3)

Non-blank

Mass

(ug/m3)

 %

Sulfate

%

Nitrate

%

Organic

aerosol

%

Elemental

Carbon

%

Water

%

Ammonium

%

Crustal

22.5 0 .5 22.0 38.6 5.0 23.6 4.1 10.5 15.0 3.2

The percentages in table 3b above are the relative composition for the 3rd quarter of 2002. 

For the purposes of this analysis, we are assuming that the relative specie composition for each

quarter of 2002 is representive of the 1999-2003 time period. 

IV. PM2.5 Design Values for Projecting to the Future

A. Defining “Current” Year FRM PM2.5 Values

The PM2.5 component species fractions are applied to “current” PM2.5 design values

which are then projected to the future. The CAIR proposal SMAT procedure followed the

recommendations in the current draft PM2.5 modeling guidance. The guidance recommends

projecting the highest of the three design values that straddle the base modeling emissions year. 

In this case the base emissions year is 2001. The three design value periods that straddle 2001

are 1999-2001, 2000-2002, and 2001-2003. The 2001-2003 data was not available when the

CAIR proposal was released, so the highest design value of the 2 available years; 1999-2001 or

2000-2002 were used in the CAIR proposal.

In the revised SMAT procedure we are proposing to revise the methodology to calculate

the base year design values for projections. We are proposing to use the average of the 3 design

value periods that straddle the emissions year. The average of the 3 design values is not a

straight five year average. It is, in effect, a weighted average of the annual averages. The design

value periods range from 1999-2003. In the average of 1999-2001, 2000-2002, and 2001-2003,

2001 is “weighted” 3 times, 2000 and 2002 are weighted twice, and 1999 and 2003 are weighted

once. This has the desired effect of weighting the projected PM2.5 values towards the middle

year of the five year period, which is the emissions and meteorology year (2001). The average

design value methodology also takes into account the emissions and meteorological variability

that occurs over the full 5 year period. The average weighted design value is thought to be more

representative of the 2001 emissions and meteorology period than the previous methodology of

choosing the highest single design value period. This value provides the “best estimate” current

year design value for use in future year model projections. It should be noted that in most cases,

the “average” design value will not be the same as the 2001-2003 design value that will be used

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Sites were considered to have complete data for the purposes of calculating a 3 year design value

if they had 12 complete quarters or were considered to have complete data through data substitution. If a

site was complete with data substitution and had 10 or 11 complete quarters, the quarterly design values

were calculated using only the complete quarters. Incomplete quarters were not used in the calculations. 

19

for the purpose of PM2.5 designations. The average design value may be higher or lower than the

2001-2003 value. 

 

There are several steps in the derivation of the average PM2.5 design values for

projections to the future. Quarterly average values are needed for each FRM site. The following

steps were used to derive the quarterly average FRM values.

1) The analysis began with quarterly average FRM data for all quarters from 1999-2003.

2) Completeness was defined as site quarters with11 valid samples per quarter. All site quarters

with less than 11 samples were removed.

3) A quarterly average 3 year design value was calculated for each design value period in which

a site had all 12 quarters with complete data6 (1999-2001, 2000-2002, and 2001-2003). This

results in four quarterly averages for up to three design value periods for each FRM site. Sites

had to have complete data for the latest design value period (2001-2003) to be considered in the

analysis. Since the PM2.5 designation process will use the 2001-2003 data, sites were not used in

the analysis if they did not have complete data (as defined in 2 above) for the 2001-2003 period.

4) The (up to) 3 quarterly design value periods were averaged together to get a single quarterly

average design value for each site. All complete design value data was used, provided that the

2001-2003 period was complete. A site did not have to have all 3 complete design periods. If

2001-2003 was the only complete period, then that was used as the average design value (even

though it isn’t truly an average). If 2 complete design value periods were available for a site

(1999-2001 and 2001-2003 or 2000-2002 and 2001-2003), then those 2 periods were averaged

together. 

The averaged quarterly average FRM design values were used as the “current” FRM

value for each monitoring site. The species fractions from the 2002 speciation data were used to

estimate the species concentrations for the current year FRM PM2.5 data. The percentage

compositions for 2002 are applied to the quarterly average design values as shown in table 4a.. 

In the example below, the average design value for the 3rd quarter for the site from table 3b is

20.3 ug/m3. This leads to the following concentrations of PM2.5 species:

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The DON was assumed to stay constant through time due to the uncertainty in the ammonium

measurements. The water calculation is sensitive to the ammonium (and therefore the DON value)

concentrations. Keeping the DON constant allows for the future year ammonium and water values to be

solely a function of the sulfate and nitrate concentrations. Otherwise, it is possible for sulfate and nitrate

to be reduced and water concentrations to increase. This may occur if sulfate becomes more neutralized

in the future. But it is somewhat illogical outcome (although scientifically possible) and is highly

dependent on an uncertain measurement (ammonium). Therefore we did not allow the DON value to vary

with time.

20

Table 4a. Calculation of the “current” species concentrations

Weighted

Avg.

FRM

Mass

(ug/m3)

Blank

Mass

(ug/m3)

Non-blank

Mass

(ug/m3)

 Sulfate

(ug/m3)

Nitrate

(ug/m3)

Organic

aerosol

(ug/m3)

Elemental

Carbon

(ug/m3)

Water

(ug/m3)

Ammonium

(ug/m3)

Crustal

(ug/m3)

20.3 0 .5 19.8 7.64 0.99 4.67 0.81 2.08 2.97 0.63

 This procedure is repeated for each PM2.5 site and quarter to complete the calculation of

current (or baseline) ambient concentrations used as the basis for future estimates of PM2.5 mass

and its components.

B. Estimating Future Year PM2.5

Future concentrations of PM2.5 component species are estimated by assuming that the

quarterly average component concentration will change in the same proportion as the model

predicted change. Model predicted changes in species concentrations (from a current year to a

future year) are used to calculate “relative reduction factors”. Relative reduction factors are

calculated for each grid cell and species as the ratio of the quarterly average future model

predictions to the current base model predictions. The relative reduction factor for each species

is then multiplied by the estimated current year ambient species mass for the site to estimate

future species concentrations. 

In the revised SMAT methodology, relative reduction factors are calculated for 5 species;

sulfate, nitrate, organic carbon mass, elemental carbon, and crustal mass. The future year

concentrations of the 5 components are calculated for each site quarter. The future year

ammonium concentrations are calculated from the sulfate, nitrate, and (current year) DON

values. Assuming that the DON is unchanged from the current year7, the ammonium is

calculated using the following formula:

NH4future = DON * SO4future + 0.29*NO3future, 

The NH4future, SO4future, and NO3future concentrations were then run through the polynomial

water equation to predict a future year water concentration. The future species concentrations at

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8

The blank mass value was held constant at 0.5 ug/m3, effectively giving it a relative

reduction factor of 1.0.

21

each FRM site were then summed over the seven species plus blank mass8

 to estimate the future

quarterly average PM2.5 concentration. The four quarterly values are then averaged to obtain

the estimated future annual average PM2.5 for each FRM site.

V. Summary

The results of the analysis at each of the FRM monitoring sites (with complete data) were 

used in the CAIR final rule modeling analysis. The revised SMAT technique has several

improvements over the original SMAT application in the CAIR proposal. One goal of the

revised SMAT methodology was to estimate the PM2.5 mass that is retained on the FRM filters. 

This provides a more unbiased estimate of future PM2.5 concentrations which are based on

current year FRM measurements. Averaging of multiple design value periods provides a “best

estimate” current year design value. Application of revised SMAT with interpolated spatial

fields allows us to take advantage of the measurements at each FRM site. In this way, a more

complete future year attainment/nonattainment picture can be derived by expanding the

predictions of future year design values to all FRM monitoring sites. 

Use of SMAT with Spatial Fields for SIPs

The details of this application of revised SMAT are specific to the short term use of the

FRM and speciation data (ESPN and IMPROVE) in estimating future year PM2.5 concentrations

for the CAIR. The use of a single year of speciation data interpolated to a modeling grid is

necessary at this time, due to the relatively sparse ambient data sets. The amount of available

ambient data will increase significantly in the future. When ambient data is needed for SIP

development, there will be at least 3 years of complete speciation data at hundreds of sites. In

many areas, the coverage of speciation data may be adequate so that interpolation of the data

through spatial fields is not necessary. It is likely that the routinely measured speciation data

will never be directly comparable with the FRM data, but our understanding of the biases,

artifacts, and sampling issues will continue to improve through time. This application should

serve as an example that can be replicated in the short term, but the techniques and assumptions

will likely evolve over the long term. 

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