1

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

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

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
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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.
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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 few sites, but we decided to use only

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

in 2002 and we did not want to use a single year of data at most sites and an average of two years of data

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

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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.
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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
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
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Figure 2- Third quarter 2002 interpolated sulfate concentration

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 STN monitors were used to interpolate concentrations at

the latitude and longitude of each of the PM2.5 monitoring sites. Kriging was used to perform the

interpolation using a software package developed by scientists at the National Center for

Atmospheric Research (NCAR) to perform generalized kriging and efficient spatial analysis of

large data sets. The Krig function in FIELDS was used to estimate the statistical parameters of

the spatial model using the Generalized Cross Validation (GCV) error as the criterion for

parameter estimation. Outputs from the Kriging process include the parameter estimates (range,

nugget and sill) along with predicted values at each of the PM2.5 monitor locations. Figure 2 is

an example of the resulting fitted surface using the third quarter data for sulfates and figure 3

shows the intrpolated fields for 1st quarter nitrate concentrations.. Locations of monitoring sites

are indicated by circles and concentrations are in ug/m3.
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Figure 3- First quarter 2002 interpolated “FRM” nitrate concentration

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

carbon, organic carbon mass (measured organic carbon *1.4), crustal mass, and the degree of

neutralization of sulfate (DON) were created using Kriging. Each of these fields were used in

the calculation of component mass as described in the procedures below. 

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
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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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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: 
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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. 
10

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

Based on equations [2]-[5], Figure 4 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. 

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]
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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. 

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
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by the estimated sulfate ion concentration.

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

NH4 are derived 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 ammonia 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. 

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
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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

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. 
3

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

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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]

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 OCM3.

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
15

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

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. 
4

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

16

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

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 data4 (1999-2001, 2000-2002, and 2001-2003). This
were calculated using only the complete quarters. Incomplete quarters were not used in the calculations. 

17

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:

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. 
5

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.

6

The blank mass value was held constant at 0.5 ug/m3, effectively giving it a relative

reduction factor of 1.0.

18

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 year5

, 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

each FRM site were then summed over the seven species plus blank mass6 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 Kriged spatial fields

allows us to take advantage of the measurements at each FRM site. In this way, a more complete

attainment/nonattainment picture can be derived by not limiting the predictions of future year

design values to only speciation 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 rule. The use of a single year of speciation data interpolated to a modeling grid is
19

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 match up directly with the FRM data, but our understanding of the biases, artifacts,

and sampling issues will continue to improve through time. And the monitoring protocols are

likely to change in the future. 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. 
20

References

Chang, Ming, Constantinos Sioutas, Seongheon Kim, Henry Gong Jr, William Linn, 2000:

“Reduction of nitrate losses from filter and impactor samplers by means of concentration

enrichment.” Atmospheric Environment, Vol 347, Pages 85-98.

Chow, Judith C., John G. Watson, L.-W. Antony Chen, W.Patrick Arnott, and Hans Moosmüller,

2004: “Equivalence of Elemental Carbon by Thermal/Optical Reflectance and Transmittance

with Different Temperature Protocols”, Environmental Science and Technology, in press. 

Clegg, S.L., P. Brimblecombe, and A. S. Wexler, 1998: “Aerosol Inorganics Model; A

Thermodynamic Model of the System H+ - NH4+ - SO42- - NO3- - H2O at Tropospheric

Temperatures.” J. Phys. Chem., 102A, 2137-2154. http://www.hpc1.uea.ac.uk/~e770/aim.html

Collett, Jeffrey, 2004. Technical note on IMPROVE study. 

Edgerton, SEARCH. FRM constructed Mass.

EPA, 1997: 40 CFR Part 50 National Ambient Air Quality Standards for Particulate Matter;

Final Rule, Federal Register / Vol. 62, No. 138 / Friday, July 18, 1997.

http://www.epa.gov/ttn/amtic/files/cfr/recent/pmnaaqs.pdf

EPA, Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional Haze

(Draft). January 2, 2001. Available on EPA’S TTN web page (www.epa.gov/scram)

EPA, Technical Support Document for the Interstate Air Quality Rule- Air Quality Modeling

Analyses. January 2004. Docket number OAR-2003-0053-0162.

EPA, 2003a. Federal Reference Method Quality Assurance Report.

EPA, 2002: 3 Year Quality Assurance Report- Calendar Years 1999, 2000, and 2001-PM2.5

Performance Evaluation Program.”

Flanagan, James, Peterson, Max, August 30, 2002: “Analysis of Speciation Network Carbon

Blank Data.” RTI International, Work Assignment 4-05 for contract 68-D-032, Draft Report.

Glass, Geoffrey, Sharon Douglas, Eric Edgerton, Ivar Tombach, March 29, 2004: “Comparison

of FRM Equivalent and Best Estimate Methods for Estimating Future-Year PM2.5 Design

Values.” Final Report. Docket number OAR-2003-0053-1073.

Hering, Susanne and Cass, Glen, 1999: "The Magnitude of Bias in Measurement of PM2.5

Arising from Volatilization of Particulate Nitrate from Teflon Filters.” JAWMA 49:725-733.

IMPROVE. 2000. Spatial and Seasonal Patterns and Temporal Variability of Haze and its

Constituents in the United States: Report III. Cooperative Institute for Research in the
21

Atmosphere, ISSN: 0737-5352-47.

Mozurkewich, Michael, 1993: “The dissociation constant of ammonium nitrate and its

dependence on temperature, relative humidity and particle size.” Atmospheric Environment,

Vol. 27, Pages 261-270.

NCAR Fields Manual. Home page of the Geophysical Statistic Project. URL address located:

http://www.cgd.ucar.edu/stats/Software/Fields/MAN.shtml

Saxena, P. and Hildemann, L, 1996: “Water Soluble Organics in Atmospheric Particles: A

Critical Review of the Literature and Application of thermodynamics to Identify Candidate

Compounds.” J. Atmos. Chem. 24:57-109

Turpin, Barbara J. and Ho-Jin Lim, 2001: “Species Contributions to PM2.5 Mass Concentrations:

Revisiting Common Assumptions for Estimating Organic Mass.” Aerosol Science and

Technology 35: 602-610.

Yua, Jian Zhen, Hong Yanga,Hongyi Zhanga,Alexis K.H.Lau, 2004: “ Size distributions of

water-soluble organic carbon in ambient aerosols and its size-resolved thermal characteristics.”

Atmospheric Environment 38:1061–1071

Zhang, Xinqiu and McMurry, Peter H, 1992: “Evaporative losses of fine particulate nitrates

during sampling.”Atmospheric Environment, Vol 26, Pages 3305-3312.