Appendix C. Speciated Linear PM10 Roll-Forward Modeling 

Report: In Support of the Northern Ada County PM10

Maintenance Plan Renewal
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Speciated Linear PM10

Roll-Forward Modeling Report

In Support of the Northern Ada County 

PM10 Maintenance Plan Renewal 

State of Idaho

Department of Environmental Quality

August 2012

Revised January 2013

Speciated Linear PM10

Roll-Forward Modeling Report

August 2012

Revised January 2013

Prepared by

Idaho Department of Environmental Quality

1410 North Hilton

Boise, Idaho 83706
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iii

Table of Contents

1 Introduction............................................................................................................................. 1

1.1 Background Information .................................................................................................. 1

1.2 Approach .......................................................................................................................... 1

2 Roll-Forward Model ............................................................................................................... 2

2.1 Assumptions..................................................................................................................... 3

2.2 Specific Considerations for Northern Ada County .......................................................... 4

3 Data ......................................................................................................................................... 4

3.1 Emissions Inventory......................................................................................................... 5

3.2 Ambient PM10 Concentrations......................................................................................... 8

3.2.1 PM10 Concentration Trends in Ada County.............................................................. 8

3.2.2 Exceptional Events.................................................................................................. 10

3.3 Speciation Data .............................................................................................................. 10

3.4 Background PM10 Concentration ................................................................................... 11

4 Procedures............................................................................................................................. 11

4.1 Determining the Design Value....................................................................................... 11

4.1.1 Design Values......................................................................................................... 12

4.1.2 Recent Data............................................................................................................. 13

4.2 Constructing Speciation Profiles.................................................................................... 14

4.2.1 Historical Speciation Data ...................................................................................... 14

4.2.2 Speciation for Winter.............................................................................................. 17

4.2.3 Speciation for Nonwinter Seasons.......................................................................... 19

5 Results................................................................................................................................... 22

5.1 NAAQS Attainment Demonstration .............................................................................. 22

5.2 Results for On-road Emission Conformity..................................................................... 26

5.3 Discussion ...................................................................................................................... 29

6 Sources of Uncertainties ....................................................................................................... 32

6.1 Model Assumptions........................................................................................................ 33

6.2 Bias from the Speciation Profiles................................................................................... 33

6.3 Effect of Wildfires and Windblown Dust ...................................................................... 33

6.4 Effects of Changing Nitrogen Oxides and Road-Dust Emissions.................................. 33

6.5 Nitrate Neutralization..................................................................................................... 34

7 Conclusions........................................................................................................................... 36

8 References............................................................................................................................. 38
iv

List of Tables

Table 1. Annual emissions.............................................................................................................. 6

Table 2. Average daily emissions for nonwinter seasons............................................................... 7

Table 3. Average daily emissions for winter seasons..................................................................... 7

Table 4. Growth rates of speciated annual and seasonal emissions, as percentage increases 

above 2008 levels................................................................................................................ 8

Table 5. Paved road dust emissions. ............................................................................................... 8

Table 6. The highest 24-hour average PM10 values measured at Boise Fire Station No. 5 in the 

past 11 years........................................................................................................................ 9

Table 7. The frequency of high PM10 events (24-hour average > 75 μg/m3), including 

exceptional events............................................................................................................. 10

Table 8. Background concentrations used for the roll-forward model. ........................................ 11

Table 9. Annual average PM10 concentrations ............................................................................. 12

Table 10. 24-hour design value (μg/m3) based on the highest observed PM10 24 hour average 

concentrations from 2007 through 2009. The design value is the third-highest value 

during the 3 years and is indicated in bold. ...................................................................... 13

Table 11. The highest 24-hour average PM10 values and annual average in 2010 and 2011 

(μg/m3).............................................................................................................................. 13

Table 12. Days with PM2.5 values 15 μg/m3or higher (after rounding) from 2006 through 

2009................................................................................................................................... 18

Table 13. Converted PM10 speciation for winter season............................................................... 18

Table 14. SANDWICH data from nonwinter seasons, 2006–2009. Sorted by total carbon mass 

(TCM). All values in μg/m3.............................................................................................. 20

Table 15. PM10 speciation profiles for nonwinter PM10 scenarios. .............................................. 20

Table 16. Predicted base-year (2008) design value speciation (μg/m3). ...................................... 22

Table 17. Background speciated PM10 concentrations (μg/m3).................................................... 22

Table 18. RRF values used for all species for future years in different scenarios........................ 23

Table 19. Predicted PM10 concentrations and species composition in future years (μg/m3)........ 24

Table 20. Total emissions from all categories used for conformity analysis................................ 27

Table 21. The motor vehicle PM10 emissions (road dust and on-road emissions) for 

conformity......................................................................................................................... 27

Table 22. RRFs used for conformity demonstration, for all emission categories......................... 28

Table 23. Summary of motor vehicle emissions........................................................................... 28

Table 24. PM10 attainment demonstration—PM10 and composition recomputed for on-road 

emission conformity purposes (μg/m3)............................................................................. 29
v

List of Figures

Figure 1. Flowchart for roll-forward modeling............................................................................... 2

Figure 2. 24-hour average PM10 trend in Ada County.................................................................... 9

Figure 3. Highest PM10 levels and annual and 24-hour averages—trend from 2005 through 

2009................................................................................................................................... 12

Figure 4. Procedures for constructing PM10 speciation profiles using SANDWICHed STN 

PM2.5 data.......................................................................................................................... 14

Figure 5. Wintertime absolute and relative speciated contributions to PM10 in Ada and 

Canyon Counties............................................................................................................... 16

Figure 6. Average composition of stagnation and high-winter scenarios..................................... 17

Figure 7. PM10 speciation for stagnation scenarios. ..................................................................... 19

Figure 8. PM10 speciation for high-winter scenarios. ................................................................... 19

Figure 9. PM10 speciation converted from the 8 highest nonwinter PM2.5 samples with highest 

carbon mass....................................................................................................................... 21

Figure 10. PM10 speciation converted from the 10 highest nonwinter PM2.5 samples with 

highest crustal mass. ......................................................................................................... 21

Figure 11. Predicted annual average PM10 concentrations. .......................................................... 24

Figure 12. Predicted 24-hour average PM10 concentrations for high-crustal scenario in 

nonwinter seasons. ............................................................................................................ 25

Figure 13. Predicted 24-hour average PM10 concentrations for high-carbon scenario in 

nonwinter season............................................................................................................... 25

Figure 14. Predicted 24-hour average PM10 concentrations for stagnation scenario in winter 

season................................................................................................................................ 26

Figure 15. Predicted 24-hour average PM10 concentrations for high-winter scenario in winter 

season................................................................................................................................ 26

Figure 16. Relative contributions of species for annual average PM10 model results. ................. 30

Figure 17. Relative contributions of species for high-crustal scenario in nonwinter seasons. ..... 31

Figure 18. Relative contributions of species for high-carbon scenario in nonwinter seasons...... 31

Figure 19. Relative contributions of species for the stagnation scenario in winter season. ......... 32

Figure 20. Relative contributions of species for high-winter scenario in winter season. ............. 32

Figure 21. Neutralization ratio of nitrate in Boise. ....................................................................... 35

Figure 22. Nitrate neutralization in Boise, Idaho—January 2009, ............................................... 35

Figure 23. Nitrate neutralization in Klamath Falls, Oregon, 2007–2009 ..................................... 36
vi

Acronyms and Abbreviations

μg/m3 micrograms per cubic meter

CFR Code of Federal Regulations

COMPASS Community Planning Association of Southwest Idaho

DEQ Idaho Department of Environmental Quality

EPA United States Environmental Protection Agency

ERG Eastern Research Group

FRM Federal Reference Method

IMPROVE Interagency Monitoring of Protected Visual Environments

MVEB motor vehicle emission budget

MOVES Motor Vehicle Emission Simulator—MOVES2010a

mph miles per hour

NAAQS National Ambient Air Quality Standard

NOx nitrogen oxides

PM particulate matter

PM10 particulate matter with an aerodynamic diameter of 10 micrometers or less

PM2.5 particulate matter with an aerodynamic diameter of 2.5 micrometers or less

RRF relative reduction factor

SANDWICH Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbonaceous Mass and 

Estimated aerosol acidity (H+)

SIP state implementation plan

STN Speciated Trends Network

TC total carbon

TCM total carbon mass

TEOM tapered element oscillating membrane

TVRDS Treasure Valley road dust study

VMT vehicle miles traveled 

VOC volatile organic compound

WSU Washington State University
vii

Chemical Symbols and Formulas 

Al203 aluminum oxide

Al aluminum

Ca calcium

CaO calcium oxide

Fe iron

Fe2O3 iron oxide

FeO iron monoxide

HNO3 nitric acid

MgO magnesium oxide

Na2O sodium oxide

NH3 ammonia

NH4NO3 ammonium nitrate

(NH4)2SO4 ammonium sulfate

NO nitric oxide

NO2 nitrogen dioxide

NO3 nitrate

NOx nitrogen oxides (= NO + NO2)

Si silicon

SiO2 silicon dioxide

SO2 sulfur dioxide

SO4 sulfate

Ti titanium

TiO2 titanium dioxide
viii

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1

1 Introduction

1.1 Background Information 

The Northern Ada County Maintenance Area was formally designated as a moderate PM10

nonattainment area upon passage of the 1990 Clean Air Act.

1

Idaho submitted the first PM10

attainment plan on November 14, 1991 (DEQ 1991). The United States Environmental 

Protection Agency (EPA) revised the PM10 National Ambient Air Quality Standard (NAAQS) in 

1997, and Idaho demonstrated to the EPA’s satisfaction compliance with the new standard

(Federal Register 62). The Northern Ada County PM10 SIP Maintenance Plan and Redesignation 

Request (Environ 2002) was submitted to EPA in September 2002, and EPA approved the plan 

in September 2003, restoring northern Ada County to attainment status for PM10.

In the period from 1992 through 2011, three exceedances were recorded; two resulted from an 

exceptional event clearly involving dust transport from the Black Rock Desert in northwest 

Nevada in February 2011 and the other was in 1997 and was agriculturally influenced. (DEQ is 

preparing exceptional event documentation for the February 2011 exceedances however this 

attainment demonstration does not rely on its approval.) Thus, the average number of 

exceedances in any 3 years was less than one per year. Based upon monitoring data, the area has 

clearly attained the 24-hour and annual PM10 NAAQS for all years since 1991. It should be 

noted, however, that the annual standard has been replaced by the annual PM2.5 standard and is 

no longer relevant; annual PM10 monitoring and modeling results are carried along in this report 

for information only.

The State of Idaho is submitting this analysis along with its maintenance plan to cover northern 

Ada County’s second 10-year maintenance period for PM10. This roll-forward modeling analysis 

has been conducted to demonstrate that the area will remain in compliance throughout the second 

10-year maintenance period. The modeling was also conducted for mobile source conformity to 

provide motor vehicle emission budgets for evaluating transportation plans through 2050.

1.2 Approach

Since the 3-year average 24-hour PM10 values have been significantly lower than the PM10

NAAQS (i.e., 150 μg/m3) in the last two decades in northern Ada County, the Idaho Department 

of Environmental Quality (DEQ) determined and EPA agreed that the roll-forward model is a 

proper tool to demonstrate compliance in future years. 

Linear roll-forward modeling is a relatively simple technique for evaluating the effect of 

emissions reductions or increases on future ambient concentrations of air pollutants. The model 

assumes that ambient concentrations above some regional background level are proportional to 

the estimated emissions of the local sources. By reducing the size of one or more of the local 

sources, the resulting reduction in ambient concentrations can be estimated. Since PM10 is 

composed of several different components (mainly geologic/crustal material, carbon mass, 

ammonium nitrate, and ammonium sulfate), roll-forward calculations can be performed on each 

of these species individually to more accurately evaluate different emission reduction or control 

options.

 

1

PM10 is particulate matter with an aerodynamic diameter of 10 micrometers or less.
2

Although the roll-forward model does not analyze the chemical reactions and dispersion of the 

pollutants, and it provides neither spatial nor temporal information for pollutant concentrations, it 

does provide a low-cost and relatively reliable approach to estimate the pollutant levels for 

worst-case meteorology, annual average conditions, and seasonal average conditions. The rollforward model can be used to safely estimate whether the PM10 concentrations can be maintained 

below the NAAQS in future years. 

To support this attainment demonstration, a high-quality, state implementation plan (SIP)-level 

emissions inventory was developed by Eastern Research Group (ERG) and Environ International 

Corporation (ERG and Environ 2010) for the base year 2008 and future years 2015 and 2023. 

The emissions inventory was developed originally using the MOBILE6 model for on-road 

mobile emissions. EPA released a new mobile source model, MOVES2010a (MOVES), after the 

emissions inventory was completed (EPA 2011b). DEQ replaced the MOBILE6 on-road 

emission estimates with MOVES results. While use of this new model is not yet required by 

EPA (until March 2013), DEQ made the change now so that future conformity determinations 

based on MOVES will be comparable to motor vehicle emission budgets established in this SIP 

renewal. DEQ also re-estimated road dust using the new EPA AP-42 approach (EPA 2011a). The 

updated MOVES modeling and road dust calculations are fully documented in Appendix E of the 

Northern Ada County Limited Maintenance Plan Renewal.

2 Roll-Forward Model 

Figure 1 shows the elements involved in roll-forward modeling. The governing equation of the 

roll-forward model is:

i i

b

i

b

i

f

f i

i

C bg bg

E

E

C  

















 ( )

(Equation 1)

where Ei, Ci, and bgi are the emissions, concentrations, and background concentrations, 

respectively, of component i. The superscripts f and b indicate future (controlled) and base cases. 

The ratio of future-year emissions and base-year emissions (E

f

/Eb) is defined as the relative 

reduction factor (RRF).

Figure 1. Flowchart for roll-forward modeling.

Emissions 

Inventory

Source

Profiles

Emission

Speciation

Speciated

Emission

Background

PM10

Concentration

Ambient PM10

Concentration

Roll-forward

Calculations

Predicted

Concentrations

and Control 

Strategy
3

The emission source profiles are needed when the emissions inventory only provides the 

information on total PM10 emissions. Because the current emissions inventory was speciated for 

secondary aerosol precursors, only limited source profiles were necessary for speciating the 

organic carbon and elemental carbon emissions from combustion sources. 

Ambient PM10 is primarily composed of five major components: geologic/crustal material, 

organic mass and elemental carbon (collectively carbon mass), ammonium sulfate, and 

ammonium nitrate. The geologic material component is estimated by summing the elements 

predominantly associated with soil plus oxygen for the normal oxides (Al2O3, SiO2, CaO, FeO, 

Fe2O3, and TiO2) plus a correction for other compounds such as MgO, Na2O, water, and 

carbonate (Sisler et al. 1996). The final equation for the geologic component of aerosol mass is:

[Geologic] = 2.20 [Al] + 2.49 [Si] +1.63 [Ca] +2.42 [Fe] + 1.94 [Ti] (Equation 2)

where all concentrations have units of mass per volume air (μg/m3). The components of these 

factors were confirmed in a comparison of local resuspended soils and ambient aerosols in the 

western United States (Cahill et al. 1981; Pitchford et al. 1981).

Based on the assumption that aerosol organic mass is 70% carbon (Watson et al. 1988), the 

organic mass component can be calculated from measured organic carbon as:

[Organic Mass] = 1.4 [OC] (Equation 3)

where [OC] is organic carbon.

Elemental carbon exists by itself in the aerosol such that:

[Elemental Carbon] = [EC]. (Equation 4)

In rural areas in the western United States, particulate sulfate and particulate nitrate are usually 

fully neutralized with ammonium. The equations for the sulfur and nitrate components of the 

aerosol are:

[Ammonium Sulfate] = 1.375 [SO4] (Equation 5)

[Ammonium Nitrate] = 1.29 [NO3]. (Equation 6)

The combination of the 5 individual components is frequently referred to as the reconstructed 

aerosol mass. 

2.1 Assumptions

Three key assumptions apply to current conditions in the Treasure Valley and are expected to 

remain relatively unchanged over the future-year modeling horizon planned for this attainment 

demonstration:

 The same primary emission components remain dominant.

 The same spatial and temporal emissions distributions are expected.

 The area is ammonia rich; the sulfate and nitrate are fully neutralized. This topic is 

discussed in more detail in section 6.5.

These conditions have not changed in the years that have passed since the last PM10 maintenance 

plan was submitted in 2002. Therefore, reviewing the historical data will aid in understanding the 

current situation and how to utilize the available data.
4

2.2 Specific Considerations for Northern Ada County

PM10 evaluation in the Treasure Valley has some unique characteristics due to the geographical 

location and meteorological conditions of the area. Cold, wet winters and hot, dry summers 

produce different patterns of emissions, transport, dispersion, and deposition. Natural events, 

such as wildfires and windblown dust storms, make significant contributions to the ambient PM10

concentrations, and while EPA does not intend for such events to influence SIP attainment 

demonstrations (EPA 1986), it is difficult to separate them completely in the observed data. 

An analysis of observed speciation data showed that the composition of ambient PM10 varies. In 

winter, defined here as November through February, the highest levels of secondary aerosols, 

especially ammonium nitrate, are recorded under the severe stagnation and high-moisture 

conditions. While the total PM10 can reach similar levels in a stagnant but drier event, the 

secondary aerosol concentration is typically much lower than in the wet, stagnant events. In hot, 

dry summer and fall periods, the high PM10 concentrations can be driven by carbon mass and/or 

crustal mass (geological material). For these reasons, the specific speciation profiles for the 

corresponding scenarios are needed to properly estimate potential PM10 concentrations under 

different conditions and evaluate the contributions from the major species. Effective control 

strategies can be considered from these estimates for these different events. 

DEQ has identified four speciation profile scenarios to characterize the conditions that dominate 

elevated PM10 events: (1) wintertime “stagnation” (severe stagnation events with wet 

conditions); (2) wintertime “high winter” (drier winter stagnation conditions); (3) high carbon 

mass events in nonwinter seasons; and (4) high crustal mass events in nonwinter seasons. An 

annual profile was also created. Although the highest PM10 value will be used for compliance 

purposes, emission changes and, if necessary, effective control strategies will be evaluated for all 

these different types of elevated PM10 scenarios.

Because no speciated PM10 data are available for recent years from Ada County, DEQ developed 

PM10 speciation profiles using speciated PM2.5 data (i.e., particulate matter with a diameter of 

2.5 micrometers or less). EPA SANDWICHed Speciated Trends Network (STN) PM2.5 

speciation data serves well for this purpose.2 Assuming the observed PM2.5 concentration is the 

fine portion of the PM10 on the same day, and particles in the coarse portion are all crustal mass, 

we can reasonably speciate the PM10 samples. 

We used all 4 years (2006–2009) of SANDWICHed STN data available at the 

St. Luke’s/Meridian site to construct the PM10 speciation profile

3 Data

The data required to conduct roll-forward modeling include base-year and projected future-years 

emissions data (i.e., emissions inventory data), ambient PM10 monitoring data, speciation data, 

and background PM10 data. All data must be speciated for major PM10 components 

(i.e., geological/crustal mass, carbon mass, ammonium sulfate, and ammonium nitrate) and the 

emissions inventory must include the precursor emissions (nitrogen oxides [NOx], SO2, and NH3) 

for the secondary aerosols. The PM10 data from 2007 through 2009 are used for the attainment 

demonstration because they make up the most recent complete data set at the time this analysis 

 

2

Sulfate, Adjusted Nitrate, Derived Water, Inferred Carbonaceous Mass and Estimated aerosol acidity (H+)
5

was initiated (including ambient concentrations, background, and speciation). The recently 

available 2010 ambient PM10 data and an exceedance observed in February 2011 are also 

analyzed and discussed but do not affect the conclusions drawn from the modeling results. 

3.1 Emissions Inventory

SIP-level emissions inventory data were developed by ERG and Environ (2010). The base year 

is 2008, and the projected years are 2015 and 2023. Summaries of the emissions are shown in 

Table 1 through Table 3, and future growth rates are summarized in Table 4. Total carbon was 

speciated by DEQ using speciation profiles from the Receptor Model Source Composition 

Library (EPA 1984). DEQ re-modeled the mobile emissions portion of the inventory using 

MOVES and re-estimated the road dust using the new AP-42 method (EPA 2011a).

ERG and Environ (2010) used the Treasure Valley Road Dust Study (TVRDS) (Etyemezian et al.

2002) for road dust emission factors, along with the 2008 vehicle miles traveled (VMT) based on 

the Community Planning Association of Southwest Idaho (COMPASS) travel demand model

projections. However, after the inventory was completed, DEQ became aware of a calibration 

problem associated with the paved road dust emission factors in the 2002 TVRDS, as described 

in Development of the Base- and Future-Year Mobile Source Emissions Inventory for the 

Treasure Valley, Idaho (DEQ 2012), included as Appendix E to the maintenance plan renewal 

documentation. As a result, DEQ determined that the most accurate paved road dust emissions 

estimates going forward for the current maintenance plan would result from applying EPA’s new 

paved road dust calculation method, released in January 2011 (EPA 2011a), along with locally 

measured silt loadings from the 2002 TVRDS (silt loadings were not affected by the emission 

factor calibration problem). Unpaved roadway emission estimates developed by ERG and 

Environ based on the TVRDS were not affected by the paved roadway calibration problem and 

remain unchanged.
6

Table 1. Annual emissions.

Year Source 

Type

NOx SO2 TC PM10 PM2.5

(tons per year)

2008

Point 356 66 77 169 143

Area 921 27 6522 19555 3551

On-Road 9775 67 384 413 330

Nonroad 2895 90 227 258 245

Biogenic 202 0 0 0 0

Total 14149 250 7210 20395 4269

2015

Point 356 66 68 169 143

Area 900 24 7806 21107 3651

On-Road 5857 33 246 283 193

Nonroad 1980 28 173 197 186

Biogenic 202 0 0 0 0

Total 9294 151 8293 21756 4173

2023

Point 391 72 75 186 157

Area 952 24 9776 25268 4073

On-Road 4306 42 229 285 157

Nonroad 1355 34 120 136 126

Biogenic 202 0 0 0 0

Total 7207 172 10199 25875 4512

Note: NOx = nitrogen oxides; SO2 = sulfur dioxide; TC = total carbon 
7

Table 2. Average daily emissions for nonwinter seasons.

Year Source 

Type

NOx SO2 TC PM10 PM2.5 

(tons per day)

2008

Point 0.96 0.16 0.21 0.46 0.38

Area 1.36 0.04 12.31 43.50 6.58

On-Road 28.47 0.20 1.01 1.09 0.86

Nonroad 10.06 0.31 0.82 0.93 0.89

Biogenic 0.70 0.00 0.00 0.00 0.00

Total 41.54 0.70 14.35 45.99 8.71

2015

Point 0.96 0.16 0.23 0.46 0.38

Area 1.33 0.03 15.10 45.63 7.11

On-Road 16.92 0.10 0.63 0.74 0.48

Nonroad 6.84 0.08 0.60 0.71 0.67

Biogenic 0.70 0.00 0.00 0.00 0.00

Total 26.75 0.37 16.56 47.54 8.64

2023

Point 1.05 0.17 0.24 0.51 0.42

Area 1.40 0.03 19.10 51.18 7.87

On-Road 12.35 0.12 0.58 0.75 0.37

Nonroad 4.55 0.10 0.39 0.47 0.44

Biogenic 0.70 0.00 0.00 0.00 0.00

Total 20.05 0.42 20.30 52.91 9.10

Note: NOx = nitrogen oxides; SO2 = sulfur dioxide; and TC = total carbon

Table 3. Average daily emissions for winter seasons.

Year Source 

Type

NOx SO2 TC PM10 PM2.5 

(tons per day)

2008

Point 1.00 0.22 0.22 0.45 0.39

Area 4.37 0.14 19.86 67.53 15.10

On-Road 24.00 0.16 1.12 1.19 0.98

Nonroad 5.55 0.17 0.66 0.42 0.40

Biogenic 0.30 0.00 0.00 0.00 0.00

Total 35.22 0.69 21.86 69.59 16.87

2015

Point 1.00 0.22 0.23 0.45 0.39

Area 4.26 0.12 22.74 74.93 14.88

On-Road 14.66 0.08 0.74 0.83 0.61

Nonroad 3.83 0.07 0.29 0.32 0.30

Biogenic 0.30 0.00 0.00 0.00 0.00

Total 24.05 0.50 24.00 76.54 16.18

2023

Point 1.10 0.24 0.25 0.50 0.43

Area 4.52 0.13 28.00 94.70 16.61

On-Road 10.91 0.10 0.71 0.84 0.53

Nonroad 2.81 0.09 0.20 0.23 0.21

Biogenic 0.30 0.00 0.00 0.00 0.00

Total 19.64 0.56 29.16 96.26 17.77

Note: NOx = nitrogen oxides; SO2 = sulfur dioxide; and TC = total carbon
8

Table 4. Growth rates of speciated annual and seasonal emissions, 

as percentage increases above 2008 levels.

NOx SO2 TC PM10 PM2.5

Annual

2008–2015 65.7% 60.3% 115.0% 106.7% 97.7%

2008–2023 50.9% 68.7% 141.4% 126.9% 105.7%

Nonwinter

2008–2015 64.4% 52.1% 115.4% 103.4% 99.3%

2008–2023 48.3% 60.1% 141.5% 115.0% 104.6%

Winter

2008–2015 68.3% 71.9% 109.8% 110% 95.9%

2008–2023 55.8% 80.6% 133.4% 138.3% 105.4%

Note: NOx = nitrogen oxides; SO2 = sulfur dioxide; and TC = total carbon

Table 5 shows the comparison of total PM10 and paved road dust emissions. The annual emission

of paved road dust was 36.8% of the total primary PM10 emission in 2008, and it is projected to 

increase to 51.2% in 2023. Thus, the paved road dust is the dominating specie and increases 

fastest in the future years, so it will greatly influence the roll-forward forecasting. 

Table 5. Paved road dust emissions.

Road Dust Emissions 

(% of Total PM10)

Total PM10 Emissions

2008 2015 2023 2008 2015 2023

Annual 

(tons per year)

7,501

(36.8%)

9,164

(42.1%)

13,243

(51.2%)

20,395 21,756 25,875

Nonwinter 

(tons per day)

10.4

(22.5%)

12.4

(26.1%)

17.9

(33.7%)

46.0 47.5 52.9

Winter 

(tons per day)

34.8

(49.9%)

43.1

(56.3%)

62.4

(64.8%)

69.6 76.5 96.3

3.2 Ambient PM10 Concentrations

Two PM10 tapered element oscillating membrane (TEOM) monitoring stations currently operate

in the Treasure Valley: one is at Boise Fire Station No. 5 in Ada County and the other is at the

Nampa Fire Station No. 1 in Canyon County. The measured values at the two stations are usually 

comparable; however, because some data are missing from the Nampa station and the Nampa 

site is outside of the Northern Ada County Maintenance Area, we only used data from the Boise

Fire Station No. 5 monitor.

This section discusses PM10 concentration trends in the past decade and exceptional events. It 

should be noted that this analysis began in 2010 when the latest approved data was from 2009. 

Since then, additional data for 2010 and 2011 has become available and while it does not 

materially change the conclusions, it is discussed in Section 4.1.2.

3.2.1 PM10 Concentration Trends in Ada County

Federal regulations state the following regarding PM10 NAAQS compliance:
9

The standards are attained when the expected number of days per calendar year with a 24-hour average 

concentration above 150 μg/m3, as determined in accordance with appendix K to this part, is equal to or 

less than one. (40 CFR 50.6)

No exceedances have been recorded in the Treasure Valley in the period from 1999 to 2009. The 

highest PM10 concentrations during the modeling period are considerably lower than the 

standards.

Table 6 shows the four highest values (with probably “exceptional events” included) at the Boise 

Fire Station No. 5 from 1999 through 2009. Figure 2 is a graphical presentation of the table and 

shows the declining trend of highest PM10 levels in Boise. 

Table 6. The highest 24-hour average PM10 values measured at Boise Fire Station No. 5 in the past 

11 years (exceptional events included). The 24-hour standard is 150 μg/m3.

Year PM10 Concentrations (μg/m3

)

Highest 2nd Highest 3rd Highest 4th Highest

1999 123 108 94 89

2000 95 92 88 81

2001 95 84 83 82

2002 150 91 89 85

2003 88 88 85 69

2004 70 57 53 51

2005 89 88 63 55

2006 97 89 69 69

2007 88 79 77 69

2008 92 91 75 68

2009 118 71 66 56

Figure 2. 24-hour average PM10 trend in Ada County.

Highest PM10 Values in Boise

Year 1999 to 2009

0

20

40

60

80

100

120

140

160

1998 2000 2002 2004 2006 2008 2010

Year PM10 Concentration (μg/m3)

1st-High

2nd High

3rd-High

4th-High

Linear (1st-High)

Linear (2nd High)

Linear (3rd-High)

Linear (4th-High)
10

Table 7 shows the frequency of elevated PM10 events (24-hour average > 75 μg/m3) in each year 

(1999–2009). The trend at this level is also declining.

Table 7. The frequency of high PM10 events (24-hour average > 75 μg/m3), including exceptional 

events.

24-Hour Average PM10 Concentrations (μg/m3)

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

7 5 4 4 3 0 2 2 3 3 1

Both data sets clearly indicate that the air quality due to PM10 in Boise has been consistently 

improving in the past decade. These data include some days of unofficial “exceptional events.” 

The higher levels on these days were caused by dust storms or wildfires. Because all PM10 values 

were lower than or equal to the standard (150 μg/m3), these exceptional events were not 

officially excluded. Due to the small number of these exceptional events, these higher values do 

not significantly affect the overall trend. Exceptional events are discussed in detail in the 

following section.

3.2.2 Exceptional Events

EPA guidance offers the following definition: 

...an exceptional event is defined as an event that is not expected to recur routinely at a given location, or 

that is possibly uncontrollable or unrealistic to control through the SIP process. (EPA 1986)

However, not all days strongly influenced by windblown dust or wildfires can be excluded based 

on the EPA guidance (e.g., if winds are high but less than 40 miles per hour [mph]). All days 

with high PM10 concentrations, even those that were obviously influenced by windblown dust or 

wildfire smoke were included in the design value determination. The effects on the modeling 

conclusions will be discussed in section 6.3. It should be noted here that DEQ does not expend 

the significant resources necessary to document and propose official exclusion of high PM10

samples that appear to meet the EPA guidelines for exclusion as exceptional natural events 

unless it is both an exceedance of the standard and would trigger a nonattainment designation or 

have other regulatory impact. However, in this document, DEQ generally refers to high values 

that would probably meet the guideline criteria for exceptional events as “exceptional events” 

even though they are not officially flagged or documented and approved by EPA as such. 

Including such values results in a conservative analysis, i.e. future predicted concentrations are 

expected to be even lower than presented here.

3.3 Speciation Data

The PM10 monitor currently in use is a TEOM continuous monitor located near downtown Boise 

at Fire Station No. 5. No speciated PM10 data are available for the most recent 10 years at Fire 

Station No. 5. To overcome this difficulty, we have used SANDWICH PM2.5 data (Frank 2006), 

which are collected at the St. Luke’s STN site. The SANDWICH technique is designed to

provide estimates of PM2.5 components as they might be measured by the PM2.5 Federal 

Reference Method (FRM). The data are available at EPA’s AirData website

(www.epa.gov/airdata). The averaged SANDWICHed speciation profiles of the highest PM2.5

days are converted to the PM10 speciation using the PM10 values of the same days. The details of 

the procedure are described in section 4.2. 
11

3.4 Background PM10 Concentration

Regional background concentrations must be known to estimate what fraction of the PM10 is due 

to emission sources within the Treasure Valley. The roll-forward model assumes that the 

difference between the total PM10 levels in the airshed and the background PM10 levels is 

proportional to the emissions generated within the airshed.

The IMPROVE (Interagency Monitoring of Protected Visual Environments) network was 

established to monitor visibility impairment from fine particles in Class I areas throughout the 

United States (Sisler et al. 1996).3IMPROVE stations typically measure the chemical 

composition of PM2.5 and light-absorbing properties of the aerosol. Some stations also feature a 

channel to measure PM10 concentrations, but chemical speciation is seldom done on these 

samples. The six IMPROVE stations closest to the Treasure Valley are Bridger Wilderness Area, 

Wyoming; Craters of the Moon National Monument, Idaho; Jarbidge Wilderness Area, Nevada; 

Salmon Wilderness Area, Idaho; Sawtooth Wilderness Area, Idaho; and Yellowstone National 

Park, Wyoming. The Jarbidge Wilderness Area station is located approximately 200 miles south 

of Boise and is the closest IMPROVE site to the Treasure Valley with a PM10 channel. Both 

PM10 and PM2.5 samples are collected at Jarbidge twice per week. Measurements from this 

relatively remote site are used to estimate the background PM10 concentrations in the Treasure 

Valley.

PM10 and PM2.5 mass and chemically speciated PM2.5 data are available for the Jarbidge 

Wilderness Area beginning in 1988. Average annual PM10 mass and an estimate of the average 

concentration of each major PM10 component (coarse particles [mostly geologic material], 

organic mass, elemental carbon, ammonium nitrate, and ammonium sulfate) were determined 

using 3-year average monitored data from Jarbidge. Average winter concentrations were also 

estimated using data for the three winters (December–February) in 2006, 2007, and 2008. 

The measured background concentrations are listed in Table 8. These values are elevation 

adjusted for consistency with temperature and pressure conditions in northern Ada County.

Table 8. Background concentrations used for the roll-forward model.

Time Period PM10 (2006–2008)

(μg/m3)

Annual 8.45

Winter Season 2.62

Nonwinter Season 11.3

4 Procedures

4.1 Determining the Design Value

Although only ambient PM10 data from a recent 3-year period are required for this modeling 

demonstration, DEQ analyzed data from 5 years to better understand the PM10 concentration 

trend and ensure the data set is a good representation of the true trend. 

 

3 The Clean Air Act defines Class I areas as certain national parks (over 6,000 acres), wilderness areas (over 

5,000 acres), national memorial parks (over 5,000 acres), and international parks that were in existence as of 

August 1977.
12

4.1.1 Design Values

Figure 3 shows the highest 24-hour average and annual average PM10 levels in the different 

seasons (winter season is defined as November through February). As expected, the annual and 

seasonal averages are stable with little variance, while the peak levels, especially during the 

winter season, vary more widely. 

Figure 3. Highest PM10 levels and annual and 24-hour averages—trend from 2005 through 2009. All 

(probable) exceptional events were excluded. 

Table 9 shows the annual average PM10 concentrations for 2005–2009. The data show that the 

annual average has been fairly stable. The annual design value would be 23.3 μg/m3based on 

2007–2009 data (the annual 50 μg/m3PM10 standard no longer exists, however this is provided 

for informational purposes).

Table 9. Annual average PM10 concentrations (exceptional events included).

Year Annual Average PM10 Concentration

(μg/m3)

2007 25

2008 23

2009 22

2007–2009 average 23.3

There are 931 total valid daily values in the three years from 2007 to 2009; therefore, the thirdhighest value during this 3-year period is the design value based on the EPA “table method” for 

determining PM10 design values (EPA 1987). The highest 24-hour average PM10 concentrations 

are listed in Table 10. The third-highest value in the three years is 90 μg/m3recorded in 2008. 

More recent data (2010 and partial 2011) became available at the time the analysis was finalized. 

Boise PM10

(Gust and wildfire events excluded) 

0

10

20

30

40

50

60

70

80

90

100

2005 2006 2007 2008 2009

Year

PM10(μg/m

3

)

Annual average

Winter average

Non-winter average

Winter highest (excludes

exceptional events)

Non-winter highest (excludes

exceptional events)
13

However, these data, which are discussed in the next section, do not affect the conclusions of this 

analysis. 

Table 10. 24-hour design value (μg/m3) based on the highest observed PM10 24 hour average 

concentrations from 2007 through 2009. The design value is the third-highest value during the 

3 years and is indicated in bold. 

Year First Second Third Fourth

2007 88 79 74 67

2008 91 90 74 67

2009 118 71 66 56

4.1.2 Recent Data

While too late to be included in the original modeling, more recent data became available for 

2010 and 2011. The 2010 and 2011 PM10 data from Boise Fire Station No. 5 have now been fully 

audited. The highest four PM10 values for 2010 and 2011 are listed in Table 11.

Table 11. The highest 24-hour average PM10 values and annual average in 2010 and 2011 (μg/m3).

Year First Second Third Fourth Annual

2010 95 55 51 45 18.1

2011 183 156 59 55 19.9

The 2010 highest value of 95 μg/m3—recorded on August 21, 2010—was determined to qualify 

as an exceptional event due to winds over 30 mph and gusts over 40 mph (however DEQ does 

not intend to pursue an EE concurrence for this event). The annual average is lower than the 

maximum value of 23 μg/m3in 2007–2009. In addition, two 2011 exceedances, 183 and 156 

μg/m3 were recorded on February 15 and 16, 2011 respectively, resulting from an extreme highwind dust event originating in northwest Nevada. DEQ has determined these two days qualify 

for exceptional event status.

5-year design values are the metric typically used in Maintenance Plan demonstrations. The 5-

year design value for the years 2007-2011 is the fifth highest value, 91 μg/m3, if the “table 

method” is used or the sixth highest value if the 40 CFR 51 Appendix W calculation method is 

used. 91 μg/m

3

is only 1 μg/m3higher than the design value of 90 μg/m3 based on 2007–2009 

data and would not significantly change the predictions nor the conclusions of this attainment 

demonstration analysis. Since the future year projections for attainment in 2023 (115.7 μg/m

3

, 

Table 19) and for conformity in 2050 (136 μg/m3, Table 24), are both well below the NAAQS 

(150 μg/m3), it can be seen that a 1 μg/m3increase will not bring the projection close to the 

standard, and is, in fact, well within the uncertainty of the original modeling based on a design 

value of 90 μg/m3. Thus, the original modeling described in this report is a sufficient 

demonstration of attainment in 2023 for SIP renewal purposes and in 2050 for conformity 

purposes. 

DEQ intends to seek Exceptional Event concurrence for the February 15 and 16, 2011 values, 

but does not anticipate, nor need concurrence as the attainment demonstration is sufficient 

without it (see previous paragraph). While the original modeling will not be updated, it may be 

concluded that the final design value used in this analysis (90 μg/m3) is more conservative than 
14

it would be using the final design value obtained after concurrence on the February 15 & 16, 

2011 values. This serves as strong “weight of evidence” support, resulting in a very conservative 

attainment demonstration.

4.2 Constructing Speciation Profiles

The procedures to construct PM10 speciation profiles and their relationship to the roll-forward 

model are presented in the Figure 4.

Figure 4. Procedures for constructing PM10 speciation profiles using SANDWICHed STN PM2.5

data.

4.2.1 Historical Speciation Data

Two types of high-PM scenarios in the Treasure Valley were identified in past studies: 

stagnation events and high-winter events. These terms were used in the 1995 SIP and 2002 

maintenance plan (Environ 2002). A stagnation event is a high-PM scenario with a severely 

stagnant atmosphere; very cold and wet conditions, such as snow cover on the ground; and a 

typical duration of a week or longer. The high-winter event is also a stagnant scenario but with 

relatively drier and warmer conditions than stagnation events. The highest PM levels were 

observed in the stagnation events, with high concentrations of secondary aerosols and relatively 

lower geological material due to the wet conditions. In recent years, the impact from secondary 

aerosols has been decreasing, making the contributions from secondary aerosols less important 

than 20 years ago. 
15

As mentioned above, geologic material, organic carbon, elemental carbon, ammonium nitrate, 

and ammonium sulfate are the major components of PM10 in the Treasure Valley. Filter analysis 

revealed that PM10 compositions were different depending on meteorological conditions. The 

winter season PM10 composition in the historical data (1988–1996) is shown in Figure 5. Figure 

6 shows the average percent composition of PM10 during stagnation and high-winter scenarios. 

PM levels were considerably higher during the stagnation scenario than the high-winter scenario. 

During the stagnation events, secondary aerosols (i.e., ammonium sulfate and ammonium nitrate) 

and organic mass dominate. Geologic material contributes a considerably higher fraction during 

the high-winter events.
16

Figure 5. Wintertime absolute and relative speciated contributions to PM10 in Ada and Canyon 

Counties. The labels on the x-axis show station and dates (Kuhns et. al. 1998).

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100% FSBC012888 MVBC012888 FSBC020989 MVBC020989 FSBC010491 MVBC010491 FSBC010791 MVBC010791 FSBC020493 FSBC122294 FSNA122294 LFBC122294 MVBC122294 FIME010395 FSBC010395 FSNA010395 LFBC010395 MVBC010395 FSBC111196 Percent of Mass

Stagnation High Winter

0

25

50

75

100

125

150

175

200 FSBC012888 MVBC012888 FSBC020989 MVBC020989 FSBC010491 MVBC010491 FSBC010791 MVBC010791 FSBC020493 FSBC122294 FSNA122294 LFBC122294 MVBC122294 FIME010395 FSBC010395 FSNA010395 LFBC010395 MVBC010395 FSBC111196 Concentration (ug/m

3

)

Geologic Organic Compounds

Elemental Carbon Ammonium Nitrate

Ammonium Sulfate Unknown
17

Figure 6. Average composition of stagnation and high-winter scenarios (from DEQ 2002)

4.2.2 Speciation for Winter

To characterize stagnation and high-winter scenarios in recent years, the 16 highest PM2.5 days in 

winter seasons from 2006 through 2009 with concentrations of 14 μg/m3or higher were selected, 

as shown in Table 12. The events were divided into two groups based on the order of secondary 

inorganic aerosol concentrations (secondary inorganic aerosol = sulfate mass + nitrate mass): the 

first group (stagnation scenario) includes the 8 days with the highest secondary aerosol 

concentrations, while the second group (high-winter scenario) includes the remaining 8 days.

Table 13 shows the converted PM10 speciation percentage composition for the winter season.

The “Crustal Mass” category defined in the SANDWICH data set is equivalent to the “Geologic” 

material in Figure 5 and Figure 6. Total carbon is defined as TCM (total carbon mass) in the 

SANDWICH data set, while it is separated into elemental and organic carbon mass in the past 

studies. A “passive artifact” related to passive adsorption or evaporation of semivolatile organic 

carbon matter on quartz fiber filters, shown in Tables 12 and 14, is typically assumed to be 

~ 0.5 μg/m3in the STN data. The “unknown artifact” in subsequent tables (Tables 15, 16, etc.)

results from this passive artifact in the sampling data.

Organic 

Compounds

21%

Elemental 

Carbon

5%

Ammonium 

Nitrate

37%

Ammonium 

Sulfate

16%

Unknown

12%

Geologic

9%

Stagnation

Geologic

36%

Organic 

Compounds

22%

Ammonium 

Nitrate

11%

Ammonium 

Sulfate

6%

Unknown

19%

Elemental 

Carbon

6%

High Winter
18

Table 12. Days with PM2.5 values 15 μg/m3or higher (after rounding) from 2006 through 2009 (from 

EPA AirData). All values in μg/m3.

Date

SANDWICH 

Sulfate 

Mass

SANDWICH 

Nitrate 

Mass

SANDWICH 

TCM

SANDWICH 

Crustal 

Mass

Passive

Artifact

PM2.5

FRM PM10

Winter Stagnation Scenario

1/25/2008 4.6 21.56 2.55 0.19 0.5 29.4 N/A

1/22/2009 4.15 21.49 3.9 0.35 0.5 30.4 N/A

12/7/2006 2.82 18.69 11.49 1 0.5 34.5 35.7

1/19/2008 1.11 11.04 4.45 0.4 0.5 17.5 26.5

12/30/2009 2.33 9.69 6.54 0.24 0.5 19.3 N/A

11/26/2008 1.98 9.85 15.94 0.73 0.5 29 35.5

1/30/2007 1.37 9.58 2.83 0.36 0.45 14.6 24

11/8/2007 2.02 8.1 8.89 1.4 0.5 20.9 42.3

Average 2.55 13.75 7.07 0.58 0.49 24.45 33

High-Winter Scenario

1/31/2009 1.35 8.17 5.95 0.43 0.5 16.4 24.3

11/27/2009 0.99 6.88 7.94 0.29 0.5 16.6 10.1

11/26/2007 1.84 5.43 8.77 0.26 0.5 16.8 28.7

12/1/2006 1.22 6 6.98 0.2 0.5 14.9 15.3

12/11/2008 0.96 5.17 10.16 0.71 0.5 17.5 31.4

11/8/2008 0.95 4.04 10.69 0.22 0.5 16.4 18.9

11/3/2009 1.51 3.22 9.2 0.37 0.5 14.8 25.8

11/17/2008 0.91 3.3 11.29 0.7 0.5 16.7 30.7

Average 1.22 5.28 8.87 0.40 0.50 16.26 23

Note: TCM = total carbon mass, FRM = Federal Reference Method

Table 13. Converted PM10 speciation for winter season.

Scenario Sulfate 

Mass

Nitrate 

Mass

Total 

Carbon 

Mass

Crustal 

Mass

Unknown

Artifact

Total 

PM10

Winter Stagnation 7.8% 41.9% 21.6% 27.2% 1.5% 100%

High-winter 5.3% 22.8% 38.3% 31.5% 2.2% 100%

Figure 7 and Figure 8 are the graphical presentations of the converted PM10 speciation profiles 

for both winter scenarios.
19

Figure 7. PM10 speciation for stagnation scenarios. The small differences from the table are due to 

rounding.

Figure 8. PM10 speciation for high-winter scenarios. The small differences from the table are due 

to rounding.

4.2.3 Speciation for Nonwinter Seasons

The magnitudes of the average highest PM10 values in winter and nonwinter seasons are 

comparable; however, the compositions of nonwinter and winter PM10 are very different. 

Ammonium nitrate is one of the most important components of PM10 in the winter stagnation 

scenario but is negligible in nonwinter due to its volatility at warmer temperatures. Although 

exceptional events (windstorms and wildfires) are excluded, the composition of nonwinter PM10

is more or less influenced by moderately high winds and/or suspected smoke from wildfires that 

Sulfate Mass

8%

Nitrate Mass

Total 42%

Carbon

22%

PM10

Crustal 

mass

27%

Unknown

1%

PM10 Speciation

Winter Stagnation Scenario

Sulfate Mass

5%

Nitrate 

Mass

23%

Total Carbon

38%

PM10

Crustal 

mass

32%

Unknown

2%

PM10 Speciation

High-winter Scenario
20

cannot be positively confirmed as “exceptional events” strictly following EPA’s guidelines 

(EPA 1986). 

Table 14 shows the SANDWICH data for nonwinter seasons. Table 15 shows the converted 

PM10 speciation percentage composition for the nonwinter season.

Table 14. SANDWICH data from nonwinter seasons, 2006–2009. Sorted by total carbon mass 

(TCM). All values in μg/m3.

Date

SANDWICH 

Sulfate

Mass

SANDWICH 

Nitrate 

Mass

SANDWICH 

TCM

SANDWICH 

Crustal Mass

Passive

Artifact

PM2.5

FRM

PM10

FRM

Nonwinter High Carbon

10/24/2008 1 2.46 13.88 0.96 0.5 18.8 39.7

10/14/2006 1.23 0.57 13.88 0.82 0.5 17 49

9/15/2007 1.18 0 13.68 1.44 0.5 16.8 43

10/31/2009 0.88 1.89 13.44 0.29 0.5 17 26.1

10/27/2008 1.98 0 12.68 1.54 0.5 16.7 40.8

7/29/2008 1.01 0 10.7 1.69 0.5 13.9 44.4

10/18/2008 0.84 0 10.52 0.65 0.5 12.5 31.6

7/23/2008 1.22 0 10.07 0.91 0.5 12.7 24.8

Average 1.17 0.62 12.36 1.04 0.50 15.68 37

Nonwinter High Crustal

10/30/2008 1.35 0 9.74 1.91 0.5 13.5 47.4

9/25/2009 0.86 0 9.19 1.95 0.5 12.5 52.8

4/18/2008 3.31 0 3.9 1.79 0.5 9.5 33.1

9/18/2008 1.99 0 3.74 2.66 0.5 8.9 39.5

9/9/2008 1.16 0 3.33 2.11 0.5 7.1 37.3

9/15/2008 1.59 0 3.04 2.97 0.5 8.1 41.1

5/18/2007 2.37 0 2.94 1.99 0.5 7.8 41.2

9/28/2009 1.36 0 2.44 2.59 0.5 6.9 59.3

3/20/2009 1.13 0 2.19 1.88 0.5 5.7 48.1

7/11/2008 0.58 0 1.44 1.78 0.5 4.3 40

Average 1.57 0.00 4.20 2.16 0.50 8.43 44

Note: TCM = total carbon mass, FRM = Federal Reference Method

Table 15. PM10 speciation profiles for nonwinter PM10 scenarios.

Scenario Sulfate 

Mass

Nitrate 

Mass

Total 

Carbon

Crustal 

Mass

Unknown

Artifact PM10

High Crustal 3.6% 0% 9.5% 85.8% 1.1% 100%

High Carbon 3.1% 1.64% 33.0% 60.9% 1.3% 100%
21

Figure 9 and Figure 10 show the graphical presentations of the PM10 speciation for the nonwinter 

scenarios with high carbon mass and high crustal mass. The highest carbon days are also the 

highest PM2.5 (but not PM10) days, indicating the strong influence of fires.

Figure 9. PM10 speciation converted from the 8 highest nonwinter PM2.5 samples with highest 

carbon mass. While the crustal mass is the dominating specie, carbon is an important specie in 

these samples. Small differences from the table are due to rounding.

Figure 10. PM10 speciation converted from the 10 highest nonwinter PM2.5 samples with highest 

crustal mass. The portion of crustal mass is significantly higher than in the cases shown in 

Figure 9. Small differences from the table are due to rounding.

Sulfate Mass

3%

Nitrate Mass

2%

Total Carbon

PM10 Crustal 33%

Mass

61%

Unknown

1%

PM10 Speciation

Nonwinter High Carbon Scenario

Sulfate Mass

4% Nitrate Mass

0%

Total Carbon

9%

PM10 Crustal 

Mass

86%

Unknown

1%

PM10 Speciation

Nonwinter High-crustal Scenario
22

5 Results

The speciated linear roll-forward model has demonstrated that PM10 levels in northern Ada 

County will remain well under the NAAQS in future years for all scenarios. 

5.1 NAAQS Attainment Demonstration

After the design values, background ambient concentrations, and RRFs are determined, the rollforward equation (Equation 1, section 2) is applied for each specie. The RRFs for each specie are 

computed by RRFi = Ei

f / Eib

, where Ei

f is the future emission total for each specie and Eib

is the 

base-year emission total for each specie. The total predicted PM10 concentration is equal to the 

sum of the predicted concentrations of all species. The sum of the species is slightly lower than 

the design value (up to 2 μg/m3) because the passive FRM sampling artifact (0.5 μg/m3) in 

SANDWICH data was omitted. This bias is corrected by adding an “unknown artifact” portion to 

the final results. 

Table 16 shows the speciated concentrations in the base year (2008) for the high-PM10 scenarios

discussed earlier, and Table 17 lists the background concentrations. Table 18 shows the final 

RRF values used in the roll-forward analysis. Background emissions are assumed to remain 

constant (RRF = 1), and the sulfate and nitrate components are held constant (RRF = 1) to 

account for any potential uncertainty in nitrate neutralization (see section 6.5).

Table 16. Predicted base-year (2008) design value speciation (μg/m3).

Sulfate 

Mass

Nitrate 

Mass

Total 

Carbon

Crustal 

Mass

Unknown

Artifact

Design 

Value

Annual average 1.07 1.19 3.76 16.14 1.17 23.33

Nonwinter high-crustal 3.21 0.14 8.58 77.18 0.88 90.00

Nonwinter high-carbon 2.81 1.48 29.71 54.80 1.20 90.00

Winter stagnation 7.10 37.73 19.41 24.51 1.25 90.00

High winter 4.80 20.51 34.49 28.32 1.87 90.00

Table 17. Background speciated PM10 concentrations (μg/m3).

Background PM10

Sulfate 

Mass

Nitrate 

Mass

Total 

Carbon

Crustal 

Mass

Total

PM10

Annual average 0.66 0.13 0.87 6.78 8.45

Winter average 0.34 0.14 0.24 1.89 2.62

Nonwinter average 0.82 0.13 1.18 9.17 11.30
23

Table 18. RRF values used for all species for future years in different scenarios.

Sulfate 

Mass

Nitrate 

Mass

Total 

Carbon

Crustal 

Mass

Annual

2015/2008 RRF 1.00 1.00 1.15 1.09

2023/2008 RRF 1.00 1.00 1.41 1.32

Nonwinter Day 

2015/2008 RRF 1.00 1.00 1.15 1.04

2023/2008 RRF 1.00 1.00 1.41 1.17

Winter Day

2015/2008 RRF 1.00 1.00 1.10 1.14

2023/2008 RRF 1.00 1.00 1.33 1.49

Background 

2015/2008 RRF 1.00 1.00 1.00 1.00

2023/2008 RRF 1.00 1.00 1.00 1.00

The roll-forward results are presented in Table 19. The predicted annual PM10 concentration is 

24.7 μg/m3in 2015 and 27.9 μg/m3in 2023, which are 50% and 56% of the NAAQS,

respectively. The highest predicted 24-hour average PM10 levels are 97.6 μg/m3in 2015 and 

115.7 μg/m3in 2023 (both in the high-winter scenario), which represent 65% and 77% of the 

NAAQS, respectively. Crustal mass is an important contributor in most scenarios including the 

annual average, while nitrate mass is the major contributor in the winter stagnation scenario and 

carbon-mass is the major contributor in the high-winter scenario. Nitrate and sulfate mass are

held constant in future years to allow for uncertainty in the linearity of nitrate neutralization. 

Section 6.5 discusses nitrate neutralization in more detail.
24

Table 19. Predicted PM10 concentrations and species composition in future years (μg/m3). When 

the RRFs for nitrate and sulfate mass are less than 1.0, their forecasted concentrations are kept at 

2008 levels (RRF = 1.0).

Year Sulfate Mass Nitrate Mass Total Carbon Crustal Mass Unknown

Artifact Total PM10

Annual

2008 1.1 1.2 3.8 16.9 0.4 23.3

2015 1.1 1.2 4.2 17.9 0.4 24.7

2023 1.1 1.2 5.0 20.2 0.4 27.9

Nonwinter High Crustal Day

2008 3.2 0.0 8.6 77.2 1.0 90.0

2015 3.2 0.0 9.7 80.1 1.1 94.1

2023 3.2 0.0 11.7 89.1 1.2 105.1

Nonwinter High Carbon Day

2008 2.8 1.5 29.7 54.8 1.2 90.0

2015 2.8 1.5 34.1 56.8 1.3 96.5

2023 2.8 1.5 41.5 62.8 1.5 110.1

Winter Stagnation Day

2008 7.1 37.7 19.4 24.5 1.2 90.0

2015 7.1 37.7 21.3 27.8 1.3 95.2

2023 7.1 37.7 25.8 35.6 1.5 107.7

High-winter Day

2008 4.8 20.5 34.5 28.3 1.9 90.0

2015 4.8 20.5 37.8 32.4 2.0 97.6

2023 4.8 20.5 45.9 42.0 2.4 115.7

Figure 11 through Figure 15 are the graphic presentations of the modeling results. 

Figure 11. Predicted annual average PM10 concentrations.

0

5

10

15

20

25

30

Sulfate

Mass

Nitrate

Mass

Total

Carbon

Crustal

Mass

Unknown PM10

Concentration (μg/m3

)

Predicted Annual PM10

2008

2015

2023
25

Figure 12. Predicted 24-hour average PM10 concentrations for high-crustal scenario in nonwinter 

seasons.

Figure 13. Predicted 24-hour average PM10 concentrations for high-carbon scenario in nonwinter 

season.

0

20

40

60

80

100

120

Sulfate

Mass

Nitrate

Mass

Total

Carbon

Crustal

Mass

Unknown PM10

Concentration (μg/m3

)

Predicted PM10

Nonwinter High Crustal Scenario

2008

2015

2023

0

20

40

60

80

100

120

Sulfate

Mass

Nitrate

Mass

Total

Carbon

Crustal

Mass

Unknown PM10

Concentration (μg/m3

)

Predicted PM10

Nonwinter High Carbon Scenario

2008

2015

2023
26

Figure 14. Predicted 24-hour average PM10 concentrations for stagnation scenario in winter 

season.

Figure 15. Predicted 24-hour average PM10 concentrations for high-winter scenario in winter 

season.

5.2 Results for On-road Emission Conformity

The modeling was also performed separately for the purpose of on-road emissions conformity. 

This process establishes motor vehicle emission budgets (MVEBs) consistent with the attainment 

demonstration that transportation planners may use to demonstrate conformity with the SIP and 

with the NAAQS. To provide a safety factor, the on-road mobile emissions used for the 2008–

2015 period are represented by 110% of the 2015 mobile emissions and 100% of 2008 emissions 

for all other categories. The emissions used for the 2015–2023 conformity demonstration period 

0

20

40

60

80

100

120

Sulfate

Mass

Nitrate

Mass

Total

Carbon

Crustal

Mass

Unknown PM10

Concentration (μg/m3

)

Predicted PM10

Winter Stagnation Scenario

2008

2015

2023

0

20

40

60

80

100

120

140

Sulfate

Mass

Nitrate

Mass

Total

Carbon

Crustal

Mass

Unknown PM10

Concentration (μg/m3

)

Predicted PM10

Winter High-winter Scenario

2008

2015

2023
27

represent 110% of 2023 on-road emissions and 100% of 2015 emissions for all other categories. 

The emissions used for the 2023–2050 conformity demonstration are 100% of projected on-road 

mobile emissions for 2050 with the other 2023 emission categories remaining unchanged. 

Because information is lacking for the period between 2023 and 2050, the MOVES on-road 

emission rates are kept at 2023 levels for the modeling demonstration and projected out to 2050 

using only the predicted VMT. This is conservative because the vehicle exhaust emission rates 

should continue to trend downward after 2023 as cleaner cars continue to be developed and 

introduced into the fleet.

Table 20 shows the estimated emissions used for PM10 attainment demonstration for conformity, 

while Table 21 shows the estimated motor vehicle emissions including road dust (paved and 

unpaved) and on-road emissions. Table 22 shows the RRF values used in the demonstration. The 

RRF values for NOx and SO2 are assigned to 1.0 if the predictions are decreasing. The RRF 

value for NOx for the 2023–2050 period in the table is greater than 1.0 because the on-road NOx

emission for 2050 is based on the VMT changes only. The real NOx emissions in the period are

expected to continue to decrease, but MOVES cannot predict to 2050 so forecasted VMT are 

used to conservatively extrapolate 2015 emissions to 2050. 

Table 20. Total emissions from all categories used for conformity analysis.

SO2 NOx

Total 

Carbon

Crustal

Mass

2008–2015

Annual (tons per year) 220 10,816 8,318 17,414

Nonwinter (tons per day) 0.60 29.2 16.6 40.1

Winter (tons per day) 0.62 27.3 24.1 64.7

2015–2023

Annual (tons per year) 164 8,175 10,222 22,690

Nonwinter (tons per day) 0.4 21.1 20.4 45.7

Winter (tons per day) 0.5 23.0 29.2 84.6

2023–2050

Annual (tons per year) 201 15,395 10,495 28,380

Nonwinter (tons per day) 0.5 43.8 21.1 55.3

Winter (tons per day) 0.6 40.0 30.0 108.2

Table 21. The motor vehicle PM10 emissions (road dust and on-road emissions) for conformity.

Paved Unpaved On-road Total PM10

2008–2015

Annual (tons per year) 10,080 926 311 11,317

Nonwinter (tons per day) 13.7 2.9 0.8 17.4

Winter (tons per day) 47.4 1.6 0.9 49.9

2015–2023

Annual (tons per year) 14,568 791 314 15,672

Nonwinter (tons per day) 19.6 2.5 0.8 22.9

Winter (tons per day) 68.6 1.3 0.9 70.9

2015–2050

Annual (tons per year) 19,550 1,795 604 21,949

Nonwinter (tons per day) 26.5 5.6 1.6 33.7

Winter (tons per day) 91.9 3.1 1.8 96.8
28

Table 22. RRFs used for conformity demonstration, for all emission categories. The RRFs for NOx

and SO2 are assigned to 1.0 if the predicted emissions are decreasing. The RRF for NOx is greater 

than 1.0 for 2023–2050 as opposed to 2008–2025 because the on-road emission estimate for 2050 

is based on the VMT increase only and the expected vehicle emission improvement is not 

considered. 

SO2 NOx

Total 

Carbon

Crustal

Mass

2008–2015

Annual 1.00 1.00 1.15 1.08

Nonwinter 1.00 1.00 1.16 1.07

Winter 1.00 1.00 1.10 1.23

2015–2023

Annual 1.00 1.00 1.42 1.41

Nonwinter 1.00 1.00 1.42 1.23

Winter 1.00 1.00 1.34 1.60

2023–2050

Annual 1.00 1.09 1.46 1.76

Nonwinter 1.00 1.05 1.47 1.48

Winter 1.00 1.14 1.37 2.05

Table 23 summarizes the annualized motor vehicle emissions resulting from the conformity 

attainment demonstration. Volatile organic compound (VOC) emissions are not used in the PM10

roll-forward modeling but are replicated from the mobile source emissions inventory (DEQ 

2012) for completeness. These values are recommended for use as the MVEBs. The PM10

emissions include paved road dust, unpaved road dust, and on-road primary PM10 emissions. 

Because the roll-forward model does not treat the nonlinear chemical processes and the 

forecasted NOx and VOC emissions are declining, the NOx and VOC emission budgets are kept 

at 2008 levels plus a 10% safety factor for both the 2008–2015 and 2015–2023 periods. The 

motor vehicle emissions in the last period (2023–2050) are forecasted 2050 emissions based only 

on VMT increases from 2015 to 2050. The emission estimates presented in Table 23 are 

calculated daily values (tons per day) based on the annual emissions.

Table 23. Summary of motor vehicle emissions. The listed emission rates are 110% of the highest

rates of the beginning or ending year (as applicable) for the 2008–2015 and 2015–2023 periods and 

100% for the 2023–2050 period.

Period PM10 NOx VOC

(tons per day)

2008–2015 31.0 29.5 12.6

2015–2023 42.9 29.5 12.6

2023–2050 60.1 34.2 17.2
29

Table 24 presents the forecasted PM10 levels using the estimated RRFs in Table 22. All futureyear predicted levels are below NAAQS. The high-winter scenario provides the highest 

predictions. 

Table 24. PM10 attainment demonstration—PM10 and composition recomputed for on-road 

emission conformity purposes (μg/m3). 

Year Sulfate 

Mass

Nitrate 

Mass

Total 

Carbon

Crustal 

Mass

Unknown

Artifact PM10

Annual

2008–2015 1.1 1.2 4.2 17.7 0.4 24.6

2015–2023 1.1 1.2 5.0 21.1 0.5 28.3

2023–2050 1.1 1.3 5.1 24.7 0.5 32.6

Nonwinter Season High Crustal Day

2008–2015 3.2 0.0 9.8 82.3 1.0 96.3

2015–2023 3.2 0.0 11.7 92.6 1.2 108.7

2023–2050 3.2 0.0 12.1 110.0 1.4 126.7

Nonwinter Season High Carbon Day

2008–2015 2.8 1.5 34.2 58.2 1.2 97.9

2015–2023 2.8 1.5 41.7 65.1 1.5 112.6

2023–2050 2.8 1.6 43.1 76.8 1.7 126.0

Winter Season Stagnation Day

2008–2015 7.0 37.7 21.3 29.6 1.4 97.1

2015–2023 7.0 37.7 25.9 38.2 1.7 110.4

2023–2050 7.0 42.9 26.6 48.3 1.9 126.6

Winter Season High-winter Day

2008–2015 4.7 20.5 38.0 34.3 1.9 99.5

2015–2023 4.7 20.5 46.0 45.3 2.6 119.1

2023–2050 4.7 23.3 47.3 57.9 2.9 136.1

5.3 Discussion

The model results clearly demonstrate that PM10 levels in northern Ada County will remain 

below NAAQS in the modeled period despite the predicted increase in county-level road dust 

emission rates. The design value is conservative because some high PM10 days influenced by 

windblown dust and wildfire smoke were included in the calculation. Figure 16 through Figure 

20 show the relative importance of the major species in the present and future years. Three 

species—crustal mass, carbon mass (total carbon), and ammonium nitrate (nitrate mass)—will 

continue to play important roles in future years. 

In the case of annual average PM10 contributions (Figure 16), the crustal mass is primarily 

responsible for the increase in PM10 concentration due to the road dust increase with VMT;

however, the relative contributions from all species remain virtually unchanged. A similar 

situation occurs for the high crustal mass scenario in nonwinter seasons (Figure 17), where the 

crustal mass is the only major contributor for PM10 throughout the modeling period. In the 

scenario of high carbon mass in nonwinter seasons (Figure 18), carbon mass and crustal mass 

together contribute more than 95%, with the crustal contribution decreasing as the carbon 

contribution increases. In the winter stagnation scenario (Figure 19), nitrate mass is the most 

important contributor but decreases in the future years, while crustal mass and carbon mass are 
30

predicted to increase. In the high-winter scenario (Figure 20), carbon plays the most important 

role, followed by crustal matter and nitrate. The importance of nitrate decreases with time as a 

result of tightening federal motor vehicle standards (however nitrate and sulfate are assumed 

constant in the modeling). In all scenarios and all time periods, the contribution of sulfate 

remains low and steadily decreasing.

The most noticeable feature of this analysis is that the contributions of inorganic secondary 

aerosols (ammonium nitrate and ammonium sulfate) will continue to decline in the winter 

season, which was historically the season in which most PM10 violations took place as a result of 

high secondary aerosols. Another major historical contributor for winter PM10 was carbon mass, 

which has been controlled by improved heating devices and through mandatory burning 

curtailment ordinances. The contributions from carbon mass will increase slightly due to 

population and VMT growth. The most important contributor is dust (crustal mass), which plays 

a major role both in winter and nonwinter seasons. The estimate of the dust contribution is 

conservative for various reasons, as discussed in section 6. 

Figure 16. Relative contributions of species for annual average PM10 model results. Crustal mass 

is the major contributor. The relative contributions from all species do not change significantly in 

the modeling period.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

2008 2015 2023

Relative Contributions of Species

Annual Average PM10

Sulfate Mass

Nitrate Mass

Total Carbon

Crustal Mass
31

Figure 17. Relative contributions of species for high-crustal scenario in nonwinter seasons. 

Crustal mass is the only major contributor. The relative importance of each contributor remains 

virtually unchanged.

Figure 18. Relative contributions of species for high-carbon scenario in nonwinter seasons. The 

crustal mass and carbon mass together contribute more than 95% to PM10 concentrations.

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

2008 2015 2023

Relative Contributions of Species

Nonwinter High Crustal Scenario

Sulfate Mass

Nitrate Mass

Total Carbon

Crustal Mass

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

2008 2015 2023

Relative Contributions of Species

Nonwinter High-carbon Scenarion

Sulfate Mass

Nitrate Mass

Total Carbon

Crustal Mass
32

Figure 19. Relative contributions of species for the stagnation scenario in winter season. Major 

contributors are nitrate mass, crustal mass, and carbon mass. The relative importance of nitrate 

mass decreases in the future years, while crustal mass and carbon mass play more import roles 

in future years.

Figure 20. Relative contributions of species for high-winter scenario in winter season. Similar as 

in the stagnation scenario, the relative impact from nitrate decreases in future years, while carbon 

mass, which plays a major role currently, will increase its impact in the future, as will the crustal 

mass.

6 Sources of Uncertainties

Several factors may produce uncertainties in the modeling results. Therefore, conservative 

assumptions are used, and the final modeled 24-hour average PM10 concentration in the future 

years is estimated to be conservative by over 20 μg/m3. These factors are discussed below.

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

2008 2015 2023

Relative Contributions of Species

Winter Stagnation Scenario

Sulfate Mass

Nitrate Mass

Total Carbon

Crustal Mass

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

2008 2015 2023

Relative Contributions of Species

Winter High-winter Scenario

Sulfate Mass

Nitrate Mass

Total Carbon

Crustal Mass
33

6.1 Model Assumptions

The basic assumption of roll-forward modeling is that the ambient pollutant concentration is 

proportional to the total emissions. The validity of this assumption depends on factors such as the 

domain size, spatial and temporal distribution of emission sources, and weather conditions. 

Therefore, we have to assume these conditions are not changing as mentioned in section 2.1. For 

the annual average, these assumptions are satisfied. For the 24-hour average, the unpredictable 

weather conditions may increase uncertainty, especially for secondary aerosol formation. 

However, since the PM10 concentration has remained at a low level over the past two decades, 

and the importance of the secondary aerosols has been decreasing and will continue to do so in 

future years, the uncertainty due to this factor is not of significant magnitude to affect future 

NAAQS compliance. 

6.2 Bias from the Speciation Profiles

The PM10 speciation profiles were constructed from PM2.5 STN data. Because PM2.5 and PM10

monitors are not located at the same site, this approach can introduce bias in the profiles. For the 

winter season, the highest PM2.5 samples were selected. The cut-off concentration was relatively 

low (15 μg/m3) due to the limited number of high days, so it is possible that the profiles are 

lower in nitrate mass but enhanced for crustal mass. Because the crustal mass is expected to 

increase and the nitrate mass is expected to decrease, this possible bias makes the predicted total 

PM10 potentially higher. For the nonwinter seasons, nitrate and sulfate mass have minimal 

impact; the major players are carbon mass and crustal mass, which are well separated into the 

fine and coarse portions. Therefore, we do not expect significant bias in the nonwinter profiles. 

6.3 Effect of Wildfires and Windblown Dust

The Treasure Valley is at times impacted by wildfires and windblown dust in summer and fall. 

Some high PM10 days that were obviously influenced by fire or dust storms could not be 

excluded by the EPA guidance and were used in the design value determination and construction 

of PM10 speciation profiles. The design value would be 71 μg/m3if only the exceedance days 

that appeared to be influenced by high windblown dust or wild fire smoke were excluded, which 

is 19 μg/m3lower than the design value used in the modeling. In addition, two types of 

profiles—high crustal mass and high carbon mass—were constructed and used for two types of 

nonwinter scenarios. The profiles are either enhanced in carbon mass or crustal mass due to the 

influences from fires or windblown dust. The model results have demonstrated PM10 levels well 

below the NAAQS in both cases. Because the crustal mass is the dominating specie in the 

emissions inventory, and it increases faster than other species in future years due to the growth in 

VMT, the model results are conservative due to the crustal-rich profiles.

6.4 Effects of Changing Nitrogen Oxides and Road-Dust Emissions

Nitrogen oxides (NOx) emissions are expected to decrease in the future years, and the importance 

of nitrate, the dominant specie in the winter fine particulate portion (PM2.5), will decline. 

However, while the total NOx emissions are expected to decrease, the total VMT continues to 

increase, and as a result, the emission of coarse particulates, mainly due to road dust, is expected 

to increase in future years. Because the roll-forward model’s forecasting is linearly proportional 
34

to the county-level emission rate, the predicted coarse portion of PM10 will increase accordingly. 

However, the road dust estimates depend on the total VMT, which is related more to expanding 

development in the valley rather than increased density in the urban area. Due to the fast 

deposition and reduced transport of the coarse particles, especially during stagnation scenarios, 

the coarse particle concentrations at hot spots (e.g., Boise Fire Station No. 5) should increase at a 

lower rate than the total emission growth rate. Thus, we expect the model to overestimate the 

coarse particulate increase at the compliance monitor in the downtown Boise area and any other 

areas with fully developed high-density traffic and home heating. In summary, we believe that 

this approach produces a conservative estimate of future PM10 concentrations. 

6.5 Nitrate Neutralization

The nitrate neutralization rate has an important role in determining the effectiveness of emission 

reduction and control strategies. In ammonia-limited areas, sulfate is neutralized first, followed 

by nitrate until insufficient ammonia remains, then excess nitrate will be observed. The 

occurrence of excess nitrate may potentially result in a slightly nonlinear response in the changes 

of nitrate aerosol due to changes in precursor emissions. In the ammonia-limited case, every 

mole of sulfate reduced by emissions control strategies will release up to two moles of 

ammonium ions that will be available to neutralize more than one mole of nitrate, resulting in 

higher ambient secondary aerosol concentrations. 

Excess nitrate has been found in some areas such as Klamath Falls, Oregon (Robert 

Kotchenruther, EPA Region 10, personal communication). More recently, excess nitrate was

observed by Washington State University (WSU), mainly as nitric acid (HNO3) absorbed onto 

aqueous aerosol mass in a small number of St. Luke’s (Meridian) samples during the 2008–2009 

winter PM2.5 aerosol study. However, the analysis of STN speciation data showed full 

neutralization of nitrate with very few exceptions, as shown in Figure 21. Figure 21 shows the 

highest concentrations of 20 samples (the rest of the data with smaller concentrations have 

similar character). Almost all days, except one sample (July 5, 2007), have a near 100% 

neutralization ratio. The lower ratio of July 5, 2007, was probably due to the high nitrate 

emissions from fireworks on the Fourth of July. 
35

Figure 21. Neutralization ratio of nitrate in Boise. (From STN data, 20 highest concentration 

samples). The single sample with incomplete neutralization (ratio < 80%) is July 5, 2007, a 

probable result of Independence Day fireworks.

Figure 22 shows the STN speciation data from the same period of the WSU secondary aerosol 

study mentioned above. This data set also shows full neutralization of nitrate. The difference 

between the WSU study and STN data might be due to the different measurement technologies. 

Secondary PM formation has the potential to react nonlinearly to controls on precursor gases 

such as NOx, SO2, and NH3. This nonlinearity stems from the complex interaction of these 

gaseous species with meteorology and the available oxidants (which are additionally influenced 

by NOx emissions). The measured full neutralization of NO3 and SO4 aerosols and likely excess 

NH3 in the Ada County airshed suggests that the nonlinear behavior due to potential excess 

nitrate will be mitigated. 

Figure 22. Nitrate neutralization in Boise, Idaho—January 2009,

Secondary Aerosols in Boise

20 Highest days from 2006-2009, STN data

0

5

10

15

20

25

30

Secondary Aerosol Concentration 

1 3 5 7 9 11 13 15 17 19

(μg/m3)

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

NH4+SO4+NO3

1.29*NO3+1.375*SO4

Neutralization Ratio

Secondary Aerosols in Boise 

Episode in January , 2009, STN data

0

5

10

15

20

25

30

1/1/20091/4/20091/7/2009

1/10/20091/13/20091/16/20091/19/20091/22/20091/25/20091/28/20091/31/2009

Date

Concentration 

(μg/m3)

0%

20%

40%

60%

80%

100%

120%

neutralization 

Ratio

NH4+SO4+NO3

1.29*NO3+1.375*SO4

Neutralization Ratio
36

For comparison, the Klamath Falls STN data are shown in Figure 23 with identical treatment of 

the neutralization ratio. Klamath Falls does show a lower neutralization ratio and therefore an 

excess of nitrate in most samples. The concentrations of secondary aerosols are much lower than 

those in the Boise region. Thus, based on the widely accepted STN monitoring methodology, 

which uses special nylon filters to chemically trap all the nitrate, we believe the Treasure Valley 

airshed does not have a significant excess nitrate problem that could result in a bias that could 

significantly affect future PM10 concentration projections. Nevertheless, in view of the 

uncertainty in nitrate neutralization, this roll-forward analysis very conservatively assumes no 

reduction in nitrate and sulfate precursor emissions in future years, even though new vehicle 

emission standards are projected to produce lower emissions. DEQ will continue to monitor the 

possible excess nitrate by evaluating STN data and emission data in future years.

Figure 23. Nitrate neutralization in Klamath Falls, Oregon, 2007–2009 (35 samples of highest 

secondary aerosol concentrations).

7 Conclusions

The roll-forward modeling has demonstrated that northern Ada County will meet the PM10 24-

hour NAAQS in future years up to 2023 with a large margin of safety. The model results for 

mobile emission conformity also show that the ambient PM10 concentrations will be below the 

NAAQS up to year 2050. The nitrate mass contributes much less to the total ambient PM10

compared to the early 1990s. The model predicted that this trend will continue into the next 

decade; however, nitrate mass is held constant in the analysis to ensure the result is conservative. 

Carbon mass will remain an important contributor in future years. Sulfate mass makes minimal 

contributions currently and even less in the future. Crustal mass will be the important contributor 

to ambient PM10 in all conditions and its relative importance will increase in winter due to the 

nitrate decrease. However, the model results are conservative since the design value was 

determined based on the high PM10 days that were influenced by windblown or wildfire smoke. 

Also, the projected future VMT increase will be spread out throughout the valley rather than 

0%

20%

40%

60%

80%

100%

120%

0

2

4

6

8

10

12

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Neutralization Ratio

Mass (μg/m3)

Sorted by totsl secondary Aerosol Mass

Klamath Falls (OR) Secondary PM2.5

Highest Secondary Days (2007-2009)

Sorted by total secondary aerosols

NH4+NO3+SO4 1.375*SO4+1.29*NO3 Neutralization Ratio
37

concentrated near the downtown Boise Fire Station No. 5 PM10 monitor. As a result, future 

concentrations at that monitor are not expected to increase with the same rate as the valley-wide 

VMT growth assumed in this simplified roll-forward model. This conclusion is reinforced by the 

steady decline in PM10 levels that we have seen over the past decade. 
38

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