# Appendix L Attainment TestAttainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 i (This page intentionally left blank)Attainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 ii 

### 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 ................................................................................................................... ................ 16Attainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 iii 

### 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 C oncentration 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 Re spect to the VISTAS 12km Grid ................................. 15 Figure 4-2: PM2.5 Monitors and 2009 M odeled 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 Concen trations for Monitors in the North Carolina Nonattainment Areas ........................................................................................................... ........... 3 Table 2-3: Quarterly Species Fracti ons from the CAIR SMAT Tool ............................................. 5 Table 2-4: Quarterly PM2.5 Com ponent-Specific Concentrations for Monitors in the North Carolina Nonattainment Areas .................................................................................................. ...... 5
Table 2-5: USEPA’s Recommendati on for Defining “Near” Cells ................................................ 6 Table 2-6: Quarterly Component RRFs for Monitors in the Nort h Carolina Nonatta inment Areas

............................................................................................................................... .......................... 7 Table 2-7: Blank Corrected Quar terly Component Future Concentr ations 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 2 009 PM2.5 Mass Compared to the DVB ............................. 8
Table 3-1: Number of Cells within Concentration Bins. Increases (decreas es) in the number of cells within the bins are noted by red (b lue) coloration in the last column. ................................. 11 Table 3-2: US EPA CAIR Modeling Results ............................................................................... 12 Table 3-3: Annual Average PM2.5 valu es for the past 10 years. ................................................. 12 Table 3-4: Three Year Design values for the Mo nitors in North Carolina’s PM2.5 Nonattainment Areas. ........................................................................................................................ .................... 13 Table 3-5: Projected emission reductions Near the North Carolina PM2.5 Nonattainment Areas.

............................................................................................................................... ........................ 14Attainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 1 1 Attainment Demonstration

This Appendix summarizes the procedures that were used to demonstrate attainment of the annual fine particulate matter (P M2.5) National Ambient Air Qua lity 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 M odels 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 am bient 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 m odeling 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 st ringent “weight of evid ence” determination to supplement the modeled attainment test and to fu rther support a demonstration of attainment of the NAAQS.

The modeled attainment test, additional corrobor ative analyses and weig ht of evidence, and unmonitored area analysis are described in furt her 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 dete rmine 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. Th e 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 f act that PM2.5 has many components. In the test, ambient PM2.5 is divided into major components, with a sepa rate relative response f actor (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 followi ng 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 stradd le 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 worksAttainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 2 out to a 5-year weighted average, with the base line year having the heav iest weight (e.g. {[2000] + 2*[2001] + 3*[2002] + 2*[ 2003] + [2004]}/9).

For the SMAT process, a mean PM2.5 DVB is de termined, 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: Ja nuary - March; Q2: April - June; etc.) as the NAAQS is calculated for a calendar year, and the quart ers need to fit evenly within a year. Also, calculating the attainment test on a quarterly basi s allows states to examine the differences in PM2.5 composition that occur durin g the different seasons.

Table 2-1 contains the quarte rly average PM2.5 concentration for the FRM monitors in the nonattainment areas. This quarterly data wa s 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 year s of data available, as the site came online late third quarter 2001. Per the Attainment Guidan ce, when only 3 years of data are available at a site, the baseli ne design value is then based on a single three-year design value. This is the case for quarters 1 th rough 3 at the Mendenhall site. Si nce there are four years of data for fourth quarter, then the baseline design va lue 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 Count y Site Name Yea r 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

Mendenhall Guilford DavidsonAttainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 3 Table 2-2: Observed Quarte rly 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 pa rticle bound water (PBW) 7. Mass associated with “other” primary inorganic particulate matter (Crustal) 8. And passively collected mass or th e mass of the blank filter (0.5 μ g/m 3 )

The second part of the process is to use th e quarterly mean PM2.5 DVBs (as calculated in Section 2.1.1) with speciated data to calculate the qua rterly 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 is sues were overcome to produce the speciated values needed for this at tainment 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 theref ore cannot be directly compared to speciation measurements from the Speciation Trends Network (STN). By design, the FRM mass measurement does not retain all ammonium nitrat e and other semi-volatile materials (negative sampling artifacts) and includes pa rticle 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 diffe rent than the ambient levels of some PM2.5 chemical constituents.Attainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 4 To resolve the differences between FRM and STN total mass, the US EPA has recommended using the “sulfate, adjusted nitrate, derive d water, inferred carbonaceous material balance approach” or SANDWICH approach. With the SANDWHICH approach, nitrate mass is adjusted to account for volat ilization based on hourly meteorol ogy parameters. Subsequently, quarterly average nitrate, sulfate, elemental car bon, 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, sulfat e mass, and DON of sulfate. Next the mass of particle bound water can be calcula ted from the previously obtain ed 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 calcul ated component mass (i.e. OC from mass balance ([OCMmb]) = PM2.5 FRM - {[EC] + [SO4] + [NO3] + [NH4] + [water] + [crustal material] + [passive mass]}).

2.1.2.2 Speciated Profiles

While the SANDWICH method reconciles the di fferences between FRM and STN, a lingering issue is not all FRM monitoring sites have co-lo cated STN monitors to provide speciated data. The US EPA Attainment Guidance suggests four meas ures 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 (f rom a different time period) 3. Use of interpolation technique s to create a spatial field using ambient speciation data 4. Use of interpolation technique s to create spatial fields, a nd gridded modeling outputs to adjust the species concentrations

Of the four methodologies, the US EPA recomme nds 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 developi ng software tool called “Modeled Attainment Test Software” (or MATS) that will perform the spatial analysis of described options number 3 and 4. However, th e PM2.5 portion of the MATS tool has not been released at this time. In trying to pursue th e US EPA recommended course, we have used the speciated profiles from the Clean Air Intersta te Rule (CAIR) SMAT tool, which is the predecessor for the MATS program , as an alternative.

The CAIR SMAT tool uses data from both th e Interagency Monitoring of Protected Visual Environments (IMPROVE) and the US EPA’s Speciation Network (ESPN) to derive mean concentrations for six PM2.5 components. Quar terly average concentrations between Jan 2002 to December 2002 were retained for sites that ha d at least 11 monitored values per quarter for each of the major PM2.5 species. Major speci es for ESPN include EC, OC, NH4, SO4, NO3, and crustal material (which incl udes 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 FR M monitoring sites usi ng a technique calledAttainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 5 Voronoi Neighbor Averaging (VNA). Attachment L1 to this appendix contains the document “Procedures for Estimating Future PM2.5 Values fo r the CAIR Final Rule by Application of the (Revised) Speciated Modeled Attainment Test (S MAT) Updated- 11/8/04”, which describes the interpolation process, and the data speciation proce ss in detail.

As a result of the CAIR SMAT process, quarter ly 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 quart erly concentrations for crustal, EC, OC, SO4, NO3, NH4 and PBW assuming a constant passive mass of 0.5 μ g/m 3

. Table 2-3: Quarterly Species Frac tions 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 0.5 Lexington Davidson 37-057-0002 37-035-0004 Catawba Hickory 0.5

Table 2-4: Quarterly PM2.5 Comp onent-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 f actor (RRF) for each component of PM2.5 for each quarter. Simply put, the RRF is the quarterly average future ye ar concentration near a monitor divided by the quarterly average base year concentr ation near the same monitor, or:

X" " Monitor Near" " ion Concentrat Year Base Modeled Mean Quarterly X" " Monitor Near" " ion Concentrat Year Future Modeled Mean Quarterly

= RRFAttainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 6 Instead of focusing on the individua l cell containing the monitor, an array of cells that are “near” a monitor are considered in the a ttainment test. By sampling an array of cells from the modeling, the attainment test allows for variations in th e 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 attainme nt demonstration was preformed at a 12km grid resoluti on, a 3x3 grid array was used.

Table 2-5: USEPA’s Recommendati on 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 ne ar the FRM monitoring sites for each day. The daily component concentrations from the 3x3 arra y are then averaged to develop a mean daily component mass for each day in bot h 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 quart erly 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. Th e quarterly RRFs for the FRM monitors in the North Carolina PM2.5 nonattainment areas are presen ted 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) + (0.15389 4* (NO3^2)) + (0.212891* SO4* NH4) + 0.0444366* SO4* NO3) + (-0.048352* (SO4^2))Attainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 7 Table 2-6: Quarterly Component RRFs for Moni tors in the North Carolina Nonattainment Areas

AIRS ID Count y 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 calcul ate future quarterly mean concentration estimates for each component of PM2.5. To accomplish th is, the current quarterly mean component concentration (Step 1, Section 2.1.3) is multiplie d by the component-specific RRFs obtained in Step 2 (Section 2.2). The quarterly component c oncentration estimates for the monitors in the North Carolina PM2.5 nonattainment area s 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 No rth Carolina nonattainment areas.Attainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 8 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 aver aged to produce a future year annual average PM2.5 estimate for each FRM site in the nonattainmen t area (Table 2-9). These values estimated annual PM2.5 values are then compared to the NAAQS (15.0 μ g/m 3 ). Since the values at the FRM site in the nonattainment areas are < 15.0 μ g/m 3 , 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 attain ment is likely despite modeled results, which may be inconclusive.

The Attainment Guidance defines the guideline s for supplemental analys is/WOE for the annual PM2.5 standard as follows:

- Site with a DVF less than 14.5 μ g/m 3 should submit basic supplemental analysis to confirm the outcome of the model attainment test.

- Sites with a DVF between 14.5 and 15.5 μ g/m 3 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 μ g/m 3 should consider additional control measure to ensure attainment, as more qual itative analysis is unlikely to attainmentAttainment Test The Hickory And Greensboro-Winston Salem-High Point, NC PM2.5 Appendix L North Carolina Attainment Demonstration August 21, 2009 9 All North Carolina PM2.5 nonattainment areas have DVFs lower than 14.5 μ g/m 3 , making the following section an examination of supplemen tal analysis to corroborate modeling results, rather than a WOE analysis to show attain ment. 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 fo r the North Carolina PM2.5 nonattainment areas.

3.1 Additional Air Quality Modeling

The Attainment Guidance suggests several additi onal modeling exercises that can be performed as part of supplemental/WOE analysis. Sugges tions include completing additional analysis on the air quality modeling preformed, or modeling a lternative 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 wi th the length of modeled episodes and limited observational datasets are described. Secti on 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 futu re 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 to tal amount of PM2.5 >= 15 μ g/m 3 within the nonattainment area 2. Percent change in number of grid cells >= 15 μ g/m 3 within the nonattainment area 3. Percent change in grid cell-hours (days) >= 15 μ g/m 3 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 und er prediction or over prediction of ozone or PM2.5 concentrations, because under (over) predic tion 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 sa me subset of days from the modeling were used in the model performance ev aluation were used to develop th e air quality metrics. This subset of days included a ll days with an 24-hour PM2.5 c oncentration greater than 30 μ g/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 us ed 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 concentra tions from the monitors in the nonattainment areas can be found in either Appendix J or Appendix K.

Because of the complexity of the model ex traction for PM2.5, only the second metric is presented for supplemental analysis. The cell co unts of modeling data wa s tallied from both the 2002 baseline and the 2009 attainment year mode ling run for the identified days. Data wasAttainment 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 th at contained portions of the eith er of the nonattainment areas. Figure 3-1 highlights the 50 cells that encompa ss 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 μ g/m 3 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 incr ease in the number of days below 15 μ g/m 3 . 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 μ g/m 3 range between 2002 and 2009 (Table 3-1).

Figure 3-2 also shows a decrease in the number of cell in the 15 – 30 μ g/m 3 bin (269 cell decrease) and the 30 - 45 μ g/m 3 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 concen tration 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 metr ic shows a substantial increase in the number of cells below 15 μ g/m 3 , and an increase in cells below 30 μ g/m 3

.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 YearPercentage of Nonattainment Area

45 - 60
30 - 45
15 - 30
0 - 15

Figure 3-2: Percentage of Cell in Nonattainme nt Areas within Concentration Categories for 2002 and 2009. Table of actual value s is presented on the right.

Table 3-1: Number of Cells within Concen tration 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 opti ons for their meteorological and air quality modeling runs, which would provide a comparis on or sensitivity based on these different options.

An air quality modeling exercise that contai ned results for North Carolina PM2.5 nonattainment area is the USEPA’s modeling for the Clean Air In terstate Rule (CAIR). The Technical Support Document for the final CAIR, March 2005, provi ded modeling results with and without the implementation for the CAIR. Differences betw een 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 re sults were for 2010. The DVF was calculated using the CAIR SMAT tool, so methodologies be tween the CAIR DVF and the values presented in Section 2.4 are the same. These modeling results are lis ted 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 CA IR 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 annua l PM2.5 standard by 2010. Although th is is one year later than the attainment year for these areas . The USEPA’s 2010 CAIR DVFs are 1 μ g/m 3 higher than what the NCDAQ is showing in the attainment demonstration, but still support that both the North Carolina nonattainment areas will attain th e 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 standa rd at the two nonattaining monitors for roughly the past 5 years, while the Mendenhall monitor ha s maintained values lower than the NAAQS. Table 3-3 provides the annual average data, with Figure 3-3 providing a gra phical representation of the data, with preliminary 2007 annual averag e values. These preliminary 2007 values show the monitors are still tren ding towards the NAAQS.

With the data in Table 3-3 and Figure 3-3, pleas e note that the Mendenhall was not in operation from 1999 to 2001, as this site replaced the McLean sville 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 4 th quarter of 2006. The NCDAQ has performed an extensiv e data imputation study to estimate a 4 th 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.3Attainment 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 PM2.5valu es, there has also been an improvement in PM2.5 design values. When one takes into acco unt the period of record, PM2.5 design values have improved significantly over the last 6 years. Like with the annual av erages, 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 main tained 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 reduc tions 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 emissi on budgets for 2007 and a tighter budget for 2009. Units from maintained by both Duke Energy and Pr ogress Energy units are still expected to have controls installed over the next two years. Table 3-5 lists th e units that are near the PM2.5 nonattainment areas and shows th e year the controls are expe cted to come on line and the estimated amount of yearly SO2 emissions reducti ons. 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 substan tial reduction in SO2 should result in significant reductions of the observed PM2.5 concentrations, whic h are already very clos e 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 SO 2 (Tons) SO 2 (Tons) SO 2 (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 an d Triad PM2.5 nonattainment areas should reach attainment status by the 2009 deadline. US EPA CAIR modeli ng corroborates NC DAQ modeling results suggesting bot h nonattainment areas shou ld be below at least 14.5 μ g/m 3 . In addition, current PM2.5 design values are near NAAQS, with substantia l 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 PM 2.5 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 PM 2.5 monitor nearby. To guard against the possibil ity that air quality le vels could exceed the standard in areas with limited monitoring, Se ction 3.4 of the Attainment Modeling Guidance suggests that additional review is n ecessary, particularly in nonattainment areas where the PM 2.5 monitoring network just meets or minimally exceed s the size of the network required. ThisAttainment 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 co ntrol strategy leads to reductions in PM 2.5 and its constituent pollutants at other locations that could have ba seline (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 combinati on 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 Progr ams, currently operates a network of 34 PM 2.5 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 concentrat ion, 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 PM 2.5 formation and transport, and ob tain a better unde rstanding of PM 2.5 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 mon itoring network more than adequately captures the full extent of the PM 2.5 air quality concerns in North Ca rolina. With an average of one monitor per 3711 km 2 , this is one of the densest statewide PM 2.5 monitoring networks in the southeast. A map of each PM 2.5 monitor and its position relativ e to the NCDAQ/ASIP 12-km modeling grid is provided in Figur e 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 PM 2.5 tends to be higher.

Figure 4-1: PM2.5 Monitors and with Respect to the VISTAS 12km GridAttainment 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 PM 2.5 monitoring network is furthe r demonstrated when plotted against a projected spatial field of annual PM 2.5 design values. Figure 4-2 presents the 2009 future year PM 2.5 design value modeling output from th is attainment demonstration and the location of each PM 2.5 monitor in and around North Carolina. This 2009 PM 2.5 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, PM 2.5 design values have numerous representative PM 2.5 monitors. There are not any identified PM 2.5 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 do cumentation for Section 2.1:

“Procedures for Estimating Future PM2.5 Values fo r 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 Ma tter 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- 11/8/041 

### Procedures for Estimating Future PM 2.5 

### 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 PM 2.5 mass. The guidance recommends that model predictions be used in a relative sense to estimate changes expected to occur in each major PM 2.5 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 PM 2.5 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 PM 2.5 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 PM
2.5 . This is done by multiplying the monitored quarterly mean concentration of Federal Reference Method (FRM) (EPA, 1997) derived PM 2.5 by the monitored fractional composition of PM 2.5 species (at speciation monitor sites) for each quarter. (e.g., 20% sulfate x 15 ug/m 3 PM 2.5 = 3 ug/m 3 sulfate).

(2) For each quarter, apply an air quality model to estimate current and future concentrations for each of the components of PM 2.5 . 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/m 3 and future is 8 ug/m 3 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/m 3 sulfate x 0.8 = future sulfate of 2.4 ug/m 3 ).

(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 PM 2.5 components to obtain an estimated future annual concentration for PM 2.5

. EPA is using the FRM data for nonattainment designations. Therefore it is critical that2 the speciated modeled attainment test described above uses FRM data as the base value for projecting future PM 2.5 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 PM 2.5 speciation data to estimate the relative mass of PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 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 PM 2.5 mass which may be different than the ambient levels of some PM 2.5 chemical constituents. For the purposes of predicting changes in PM 2.5 chemical components on the PM 2.5 mass, constructed PM 2.5 mass should match the composition of mass retained by the FRM. As such, we have made several revisions to the calculation and definition of PM 2.5 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 PM 2.5 and its non-carbon components. This characterization of PM 2.5 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 PM 2.5 mass and the characterized chemical components derived from routine speciation measurements. The net difference between retained mass and measured mass for individual PM 2.5 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.1 The CAIR proposal SMAT was based on 2001-2002 data (the last 3 quarters of 2001 and the 1 st 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 si ngle 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. PM 2.5 

### Mass and Species Data Handling

Speciated PM 2.5 data from both the IMPROVE and ESPN were used to derive mean concentrations of each of six PM 2.5 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 correspond 1 to the available 5 years of FRM PM 2.5 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 PM 2.5 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.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 582 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 4 th quarter of 2002.

To further quality assure the ESPN data, the reconstructed fine mass (RCFM) was compared to measured FRM PM 2.5 on a quarterly basis 2 . 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 PM 2.5 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 average3 The concentrations are interpolated to the CM AQ 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 PM 2.5 . 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 PM 2.5 monitoring sites 3 . We previously used a Kriging methodology for SMAT, but have now moved to a technique called Voronoi Nei ghbor 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 por tion 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 special7 weight 1 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 concen trations 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:4 The elemental carbon data appeared to have str onger urban gradients compared to other species. Therefore, an inverse distance weighting scheme wa s 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 weigh ting, 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 ST N monitors with inverse distance weighting 4

. 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 th e degree of neutralization of sulfate (DON) were created using VNA interpolation. Each of th ese 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.9 Figure 5. 1 st quarter 2002 interpolated nitrate concentration.

Figure 6. 3 rd quarter interpolated sulfate concentration10 

### III. Components of Measured PM 2.5 

### 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 PM 2.5 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 PM 2.5 mass (for NAAQS implementation and more precise representation of regulatory PM 2.5 ), the following approach is used for revised SMAT:

CFM FRM = [Ammoniated Sulfate Mass] + [Retaine d 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 PM 2.5 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)
,11 24 delta NO3 (ug/m3)= 745.7/T R * 1/24* 

### E ( K i 1⁄2 ) [ 2 ]
i=1 where T R is the reference temperature for the sampled air volume in degrees Kelvin and K i 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 (NO3 FRM ) as follows:

NO3 FRM = NO3 ESPN - delta NO3 (ug/m3) [3]

For each hour of the day, the equilibrium dissociation constant for ammonium nitrate, K i , 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’=[P 1 -P 2 (1-a)+P 3 (1-a) 2 ] (1-a) 1.75 *K [5]

where Ln P1, Ln P2, Ln P3 are specified as Ln(P 1 ) = -135.94 + 8763/T + 19.12ln(T) Ln(P 2 ) = -122.65 + 9969/T + 16.22ln(T) Ln(P 3 ) = -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.12 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 NO3.13 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 NO3 FRM as

NH4 NO3 = 0.29 * NO3 FRM [6]

Similarly, the dry PM 2.5 mass associated with nitrates is

[Retained dry FRM Nitrates] = 1.29 * NO3 FRM [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 NH4 FRM , and the ESPN NH4, termed NH4 ESPN, , 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 NH 4 ESPN minus 1⁄2 the amount that would have evaporated with lost NO3 as follows:

NH4 adj = NH 4 ESPN - 1⁄2 * 0.29 * (NO 3 ESPN - NO 3 FRM 

### ) [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.14 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) = NH4 adj - 0.29 * NO3 FRM , [9] where 0.29 is the molar ratio of NH4 to NO3 and NH4 FRM and NO3 FRM 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 su lfate 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:

NH4 FRM = DON * SO4 + 0.29*NO3 FRM , [11]

where: DON, SO4 and NO3 FRM are the interpolated (kriged) quarterly average values at each FRM site. NH4 FRM 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.15 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 NH4 FRM ). Due to the uncertainty in ammonium concentration estimates, we used a relatively smooth field of in terpolated 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 = NH4 FRM and no3 = NO3 FRM

6. Other Non-Carbon PM 2.5 Components (blank mass)

The other quantifiable components of PM 2.5 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 PM 2.5 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 PM 2.5 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 and16 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 PM 2.5 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 (TCM mb ) is defined as ,

TCM mb = PM 2.5 - { [SO4] + [NO3 FRM ] + [NH4 FRM ] + [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

OCM mb = TCM mb - [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 PM 2.5 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.

PM2.5 FRM = { [OCMmb] + [EC] + [SO4] + [NO3 FRM ] + [NH4 FRM ] + [water] + [crustal material] + [0.5] } [15]5 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 OCM 5

. 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 PM 2.5 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 PM 2.5 FRM averages to derive composition fractions in the following manner:

First, the 0.5 ug/m3 of blank mass is subtracted from the FRM PM 2.5 concentration.

PM2.5 FRM-Blank = PM2.5 FRM - 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 0.718 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 3 rd 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. PM 2.5 

### Design Values for Projecting to the Future

A. Defining “Current” Year FRM PM 2.5 Values

The PM 2.5 component species fractions are applied to “current” PM 2.5 design values which are then projected to the future. The CAIR proposal SMAT procedure followed the recommendations in the current draft PM 2.5 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 de sign 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 PM 2.5 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 used6 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 subs titution 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 PM 2.5 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 PM 2.5 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 data 6 (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 PM 2.5 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 PM 2.5 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 3 rd quarter for the site from table 3b is 20.3 ug/m3. This leads to the following concentrations of PM 2.5 species:7 The DON was assumed to stay constant through time due to the uncertainty in the ammonium measurements. The water calculation is sensitiv e to the ammonium (and therefore the DON value) concentrations. Keeping the DON constant allows fo r the future year ammonium and water values to be solely a function of the sulfate and nitrate concentrati ons. 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 PM 2.5 site and quarter to complete the calculation of current (or baseline) ambient concentrations used as the basis for future estimates of PM 2.5 mass and its components.

B. Estimating Future Year PM 2.5

Future concentrations of PM 2.5 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 year 7 , the ammonium is calculated using the following formula:

NH4 future = DON * SO4 future + 0.29*NO3 future ,
The NH4 future , SO4 future, and NO3 future concentrations were then run through the polynomial water equation to predict a future year water concentration. The future species concentrations at8 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 mass 8 to estimate the future quarterly average PM 2.5 concentration. The four quarterly values are then averaged to obtain the estimated future annual average PM 2.5 for each FRM site.


### V. Summary

The results of the analysis at each of the FR M 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 PM 2.5 mass that is retained on the FRM filters. This provides a more unbiased estimate of future PM 2.5 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 PM 2.5 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.22 References

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