# Appendix C. Speciated Linear PM 10 

# Roll

- Forward Modeling

Report: In Support of the Northern Ada County PM 10 

# Maintenance Plan Renewal

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- sided printing.

# S peciated Linear PM 10



# Roll

- Forward Modeling Report

In Support of the Northern Ada County

PM 10 

### Maintenance Plan Renewal



## State of Idaho Department of Environmental Quality

August 2012

Revised January 2013

# Speciated Linear PM 10



# Roll

- Forward Modeling Report

August 2012 Revised January 2013

Prepared by

Idaho Department of Environmental Qualit y

1410 North Hilton Boise, Idaho 83706

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- sided printing.

### 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 Consideration s for Northern Ada County

................................ .......................... 4

3 Data

................................ ................................ ................................ ................................ ......... 4

3.1 Emissions Inventory

................................ ................................ ................................ ......... 5

3.2 Ambient PM 10 

### Concentrations

................................ ................................ ......................... 8

3.2.1 PM 10 

### Concentration Trends in Ada County

................................ .............................. 8

3.2.2 Exceptional Events

................................ ................................ ................................ .. 10

3.3 Speciation Data

................................ ................................ ................................ .............. 10

3.4 Background PM 10 

### 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 Em issions

................................ .. 33

6.5 Nitrate Neutralization

................................ ................................ ................................ ..... 34

7 Conclusions

................................ ................................ ................................ ........................... 36

8 Refer ences

................................ ................................ ................................ ............................. 38

### iv



## List of Tables

Table 1. Annual emissions.

................................ ................................ ................................ ............. 6 Table 2. Average daily emissions for n onwinter 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 averag e PM 10 

### values measured at Boise Fire Station No. 5 in the past 11 years

................................ ................................ ................................ ........................ 9 Table 7. The frequency of high PM 10 

### events (24

- hour average > 75 μg/m 3 

### ), including exceptional events.

................................ ................................ ................................ ............ 10 Table 8. Background concentrations used for the roll

- forward model.

................................ ........ 11 Table 9. Annual average PM 10 

### concentrations

................................ ................................ ............. 12

Table 10. 24

- hour design value (μg/m 3 

### ) based on the highest observed PM 10 

### 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 PM 10 

### values and annual average in 2010 and 2011 (μg/m 3 

### ).

................................ ................................ ................................ ............................. 13 Table 12. Days with PM 2.5 

### val ues 15 μg/m 3 

### or higher (after rounding) from 2006 through

2009

................................ ................................ ................................ ................................ ... 18

Table 13. Converted PM 10 

### speciation for winter season.

................................ .............................. 18

Table 14. SANDWICH data from nonwinter seasons, 2006

– 2009. Sorted by total carbon mass (TCM). All values in μg/m 3



### . ................................ ................................ ............................. 20

Table 15. PM 10 

### speciation profiles for nonwinter PM 10 

### scenarios.

................................ .............. 2 0

Table 16. Predicted base

- year (2008) design value speciation (μg/m 3 

### ).

................................ ...... 22 Table 17. Background speciated PM 10 

### concentrations (μg/m 3 

### ).

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

....................... 23

Table 19. Predicted PM 10 

### concentrations and species composition in future years (μg/m 3 

### ).

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

............................... 27

Table 21. The motor vehicle PM 10 

### 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. PM 10 

### attainment demonstration

— PM 10 

### and composition recomputed for on

- road emission conformity pu rposes (μg/m 3 

### ).

................................ ................................ ............ 29

### v



## List of Figures

Figure 1. Flowchart for roll

- forward modeling.

................................ ................................ .............. 2 Figure 2. 24

- hour average PM 10 

### trend in Ada County.

................................ ................................ ... 9 Figure 3. Highest PM 10 

### levels and annual and 24

- hour averages

— trend from 2005 through

2009.

................................ ................................ ................................ ................................ .. 12 Figure 4. Procedures for constructing PM 10 

### speciation profiles using SANDWICHed STN

PM 2.5 

### data.

................................ ................................ ................................ ......................... 14 Figure 5. Wintertime absolute and relative speciated contributions to PM 10 

### in Ada and Canyon Counties.

................................ ................................ ................................ .............. 16 Figure 6. Average composition of stagnation and high

- winter scenarios

................................ ..... 17 Figure 7. PM 10 

### speciation for st agnation scenarios.

................................ ................................ ..... 19 Figure 8. PM 10 

### speciation for high

- winter scenarios.

................................ ................................ ... 19 Figure 9. PM 10 

### speciation converted from the 8 high est nonwinter PM 2.5 

### samples with highest

carbon mass.

................................ ................................ ................................ ...................... 21 Figure 10. PM 10 

### speciation converted from the 10 highest nonwinter PM 2.5 

### samples with highest crustal mass.

................................ ................................ ................................ ......... 21 Figure 11. Predicted annual average PM 10 

### concentrations.

................................ .......................... 24 Figure 12. Predicted 24

- hour average PM 10 

### concentrations for high

- crustal scenario in

nonwinter seasons.

................................ ................................ ................................ ............ 25 Figure 13. Predicted 24

- hour average PM 10 

### concentrations for high

- carbon scenario in

nonwinter season.

................................ ................................ ................................ .............. 25 Figure 14. Predicted 24

- hour average PM 10 

### concentrations for stagnation scenario in winter

season.

................................ ................................ ................................ ............................... 26 Figure 15. Predicted 24

- hour average PM 10 

### concentrations for high

- winter scenario in winter

season.

................................ ................................ ................................ ............................... 26 Figure 16. Relative contributions of species for annual average PM 10 

### model results.

................. 30 Figure 17. Relative contributions of speci es 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/m 3 

### micrograms per cubic meter

CFR Code of Feder al Regulations

COMPASS Community Planning Association of Southwest Idaho

DEQ Idaho Department of Environmental Quality

EPA U nited S tates Environmental Protection Agency

ERG Eastern Research Group

FRM Federal Reference Method

IMPROVE Interagency Monit oring 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

NO

x 

### nitrogen oxides

PM particulate matter

PM 10 

### particulate ma tter with an aerodynamic diameter of 10 micrometers or less

PM 2.5 

### particulate matter with an aerodynamic diameter of 2.5 micrometers or less

RRF relative reduction factor

SANDWICH S ulfate, A djusted N itrate, D erived W ater, I nferred C arbonaceous Mass and

Estimated aerosol acidity ( H +)

SIP state implementation plan

STN Speciated Trends Network

TC total carbon

TCM total carbon mass

TEOM t apered e lement o scillating m embrane

TVRDS Treasure Valley road dust study

VMT vehicle miles traveled

VOC volatile organic compound

WSU Washington State University

### vii



## Chemical Symbols and Formulas

Al 2 

### 0 3 

### aluminum oxide

Al aluminum

Ca calcium

CaO calcium oxide

Fe iron

Fe 2 

### O 3 

### iron oxide

FeO iron monoxide

HNO 3 

### nitric acid

MgO magnesium oxide

Na 2 

### O sodium oxide

NH 3 

### ammonia

NH 4 

### NO 3 

### ammonium nitrate

(NH 4 

### ) 2 

### S O 4 

### ammonium sulfate

NO nitric oxide

NO 2 

### nitrogen dioxide

NO 3 

### nitrate

NO

x 

### nitrogen oxides (= NO + NO 2 

### )

Si silicon

SiO 2 

### silicon dioxide

SO 2 

### sulfur dioxide

SO 4 

### sulfate

Ti titanium

TiO 2 

### titanium dioxide

### viii This page intentionally left blank for correct double

- sided printing.

### 1



# 1 Introduction

1.1 Background Information

The N orthern Ada County M aintenance A rea was formally designated as a moderate PM 10 

### nonattainment area upon passage of the 1990 C lean A ir A ct

. 1 

### Idaho submitted the first PM 10



### a ttainment p lan on November 14, 1991 (DEQ 1991). The U nited S tates Environmental Protection Agency ( EPA ) revised the PM 10 

### National Ambient Air Quality Standard ( NAAQS ) in

1997

, and Idaho demonstrated to the EPA’s satisfactio n compliance with the new standard (Federal Register 62)

. The Northern Ada County PM 10 

### SIP Maintenance Plan and Redesignation Request (Environ 2002) was submitted to EPA in September 2002 , and EPA approved the p lan in September 2003, restoring northern Ada County to attainment status for PM 10



### .

In the period from 1992 through 2011, t hree 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 o ther was in 1997 and was agricultural ly 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 y ears was less than one per year. Based upon monitoring data, the area has clearly attained the 24

- hour and annual PM 10 

### NAAQS for all years since 1991. It should be noted, however

, that the annual standard has been replaced by the annual PM 2.5 

### standard and is no longer relevant ; annual PM 10 

### monitoring and modeling results are carried along in this report

for information only

.

The State of Idaho is submitting this analysis along with its m aintenance p lan to cover n orthern Ada County ’s second 10

- year maintena nce period for PM 10



### . 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 mot or vehicle emission budgets for evaluating transportation plans through 2050

.



## 1.2 Approach

Since the 3

- year average 24

- hour PM 10 

### values have been significantly lower than the PM 10 

### NAAQS (i.e., 150 μg/m 3 

### ) in the last two decades in northern Ada County, the Idah o 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

emissi on s 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 PM 10 

### 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 PM 10 is particulate matter with an aerodynamic dia meter of 10 micrometers or less.

### 2

Although the roll

- forward model does not analyze the chemical reactions and dispersion of the pollutants, and i t 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 condi tions. The roll

forward model can be used to safely estimate whether the PM 10 

### concentrations can be maintained below the NAAQS in future years.

To support this attainment demonstration, a high

- quality, s tate i mplementation p lan (SIP)

- level

emission s inven tory 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 emission s inventory was developed originally using the MOBILE6 model for on

- road

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

emission s 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

EP A (until March 201 3 ), 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 (EP A 2011 a )

. 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

- forwa rd modeling. The governing equation of the

roll

- forward model is:

( Equation 1 )

where E i 

### , C i 

### , and bg i 

### 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 emission s and base

- year emission s ( E f 

### /E b 

### ) is define d as the r elative

r educti on f

actor (RRF).

Figure 1 . Flowchart for roll

- forward modelin g

.

Emission s Inventory

Source Profiles Emission Speciation

Speciated Emission

Background PM 10 Concentration

Ambient PM 10 Concentration Roll

- forward Calculations Predicted

Concentrations and Control Strategy

### 3

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

information on total PM 10 

### emissions. Because the current emission s inventory was speciated for secondary aerosol precursors, only limited source profiles were necessa ry for speciating the organic carbon and elemental carbon emissions from combustion sources.

Ambient PM 10 

### is primarily composed of five major components: geologic/crustal material, organic mass and elemental carbon (collectively carbon mass), ammonium sul fate, and ammonium nitrate. The geologic material component is estimated by summing the elements predominantly associated with soil plus oxygen for the normal oxides (Al 2 

### O 3 

### , SiO 2 

### , CaO, FeO,

Fe 2 

### O 3 

### , and TiO 2 

### ) plus a correction for other compounds such as MgO , Na 2 

### O, 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 a ir (μg/m 3 

### ). 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% ca rbon (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 Carbo n] = [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 [SO 4 

### ] ( Equation 5)

[Ammonium Nitrate] = 1.29 [NO 3 

### ]. ( 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 s ame primary emission components remain dominant

.

 The s ame spatial and temporal emission s distribution s 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 PM 10 

### 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

PM 10 

### evaluation in the Treasure Valley h as 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 wildfi res and windblown dust storms, make significant contributions to the ambient PM 10 

### 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 observ ed data.

An analysis of observed speciation data showed that the composition of ambient PM 10 

### 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 PM 10 

### 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 hi gh PM 10 

### 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 PM 10 

### 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 dom inate

elevated PM 10 

### 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 PM 10 

### 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 PM 10 

### scenarios

.

Because no speciated PM 10 

### data are available for recent years from Ada County, DEQ developed

PM 10 

### speciation profiles using speciated PM 2. 5 

### data (i.e., particulat e matter with a diameter of

2.5 micrometers or less). EPA SANDWICHed Speciated Tren ds Network (STN) PM 2. 5 

### speciation data serves well for this purpose. 2 

### Assuming the observed PM 2. 5 

### concentration is the fine portion of the PM 10 

### on the same day, and particles in the coarse portion are all crustal mass, we can reasonably speciate the PM 10 

### s amples.

We used all 4 years (200 6

– 2009) of SANDWICHed STN data available at the

St. Luke ’ s/Meridian site to construct the PM 10 

### speciation profile



# 3 Data

The data required to conduct roll

- forward modeling include base

- year and projected future

- years emissio ns data (i.e., emission s inventory data), ambient PM 10 

### monitoring data, speciation data, and background PM 10 

### data. All data must be speciated for major PM 10 

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

emission s inventory must include the precursor emissions ( nitrogen oxides [ NO

x 

### ] , SO 2 

### , and NH 3 

### ) for the secondary aerosols. The PM 10 

### data from 2007 through 2009 are used for the attainment demonstration because they make up the most recent complete dat a set at the time this analysis

2 S ulfate, A djusted N itrate, D erived W ater, I nferred C arbonaceous M ass and Estimated aerosol acidity ( H +)

### 5

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

available 2010 ambient PM 10 

### data and an exceedance observed in February 2011 are also analyzed and discussed but do not affect the co nclusions drawn from the modeling results.



## 3.1 Emission s Inventory

SIP

- level emission s inventory data were developed by ERG and Environ (2010). The base year is 2008, and the projected years are 2015 and 2023. S ummar ies of the emissions are shown in

Table 1 through Table 3 , and future growth rates are summarized in Table 4

. Total c arbon was speciated by DEQ using speciation profiles from the Receptor Model Source Composition Library (EPA 1984)

. DEQ re

- modeled t he mobile emissions portion of the inventory using

MOVE S and re

- estimated the road dust using the new AP

- 42 method (EPA 2011 a ).

ERG and Environ (2010) used the Treasure Valley Road Dust Study (TVRDS) ( Etyemezian et al. 2002) f or 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 cali bration 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 Emission s Inventory for the Treasure Valley, Idaho (DEQ 201 2 ) , included as A ppendix E to the m ain tenance p lan 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 appl ying EPA’s new paved road dust calculation method, released in J anuary 2011 (EPA 2011 a ), 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 NO
x SO 2 TC PM 10 PM 2.5 (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 25 8 245

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

Tota l 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: NO
x = nitrogen oxides; SO 2 = sulfur dioxide; TC = total carbon

### 7

Table 2

. Average daily e missions for nonwinter seasons

.

Year Source

Type NO
x SO 2 TC PM 10 PM 2.5 (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: NO
x = nitrogen oxides; SO 2 = sulfur dioxide; and TC = total carbon

Table 3

. Average daily e missions for winter seasons

.

Year Source

Type NO
x SO 2 TC PM 10 PM 2.5 (tons per day)

2008 Poi nt 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: NO
x = nitrogen oxides; SO 2 = sulfur dioxide; and TC = total carbon

### 8

Table 4 . Growth rates of specia ted annual and seasonal emissions

, as percentage increases above 2008 levels.

NO
x SO 2 TC PM 10 PM 2.5

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 %

20 08

– 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 % 10 5.4 %

Note: NO
x = nitrogen oxides; SO 2 = sulfur dioxide; and TC = total carbon



### Table 5 shows the comparison of total PM 10 

### and paved road dust emissions . The annual emission of paved road dust was 36.8 % of the total primary PM 10 

### emission in 2008, and i t is projected to

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

fastest in the future years, so it will greatly influence the r oll

- forward forecasting.

Table 5 . Paved road dust emissions

. Road Dust Emissions (% of Total PM 10 ) Total PM 10 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 PM 10 

## Concentrations

T wo PM 10 

### t aper ed e lement o scillating m embrane ( TEOM ) monitoring stations currently operat e 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, b ecause some data are missing from the Nampa station and the Nampa

site is outside of the N orthern Ada County M aintenance A rea , we only use d data from the Boise

Fire S tation No. 5 monitor

.

This section discuss es PM 10 

### conc entration 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 PM 10 

### Concentration Trends in Ada County

Federal r egulations state the following regarding PM 10 

### 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/m 3 , 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 PM 10 

### 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 throug h 2009. Figure 2 is a graphical presentation of the table and shows the declining trend of highest PM 10 

### levels in Boise.

Table 6 . The highest 24

- hour average PM 10 values measured at Boise F ire Station No. 5 in the past 11 years ( e xceptional events included) . The 24

- hour standard is 150 μ g/m 3

.

Year PM 10 Concentrations (μg/m 3 )

Highest 2nd High est 3rd High est 4th High est

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 PM 10 trend in Ada County

.

### 10

Table 7 shows the frequ ency of elevated PM 10 

### events (24

- hour average > 75 μg/m 3 

### ) in each year (1999

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

Table 7 . The frequency of high PM 10 events (24

- hour average > 75 μg/m 3 ), including exceptional

events.

2 4

- Hour Average PM 10 Concentrations ( μ g/m 3 )

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 PM 10 

### in Boise has been consistently improving in the past decade. These d ata include some days of unofficial “exceptional events.” The higher levels on these days were caused by dust storms or wildfires. Because all PM 10 

### values

were lower than or equal to the standard (150 μg/m 3 

### ), these exceptional events were not officially ex cluded. 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 defini tion:

...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 w indblown 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 PM 10 

### concentrations , even those that were obviously influenced by windblown dust or

wildfire smoke wer e 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 propos e official exclusion of high PM 10 

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

unless it is both an exceedance of the s tandard and would trigger a nonattainment designation or have other regulatory impact. Howe ver, 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 PM 10 

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

at Fire Station N o. 5. N o speciated PM 10 

### data are available for the most recent 10 years at Fire Station No. 5. To overcome this difficulty, we have used SANDWICH PM 2.5 

### data (Frank 2006), which are collected at the St. Luke’s STN site. The SANDWICH technique is designed to provide estimates of PM 2.5 

### components as they might be measured by the PM 2.5 

### Federal Reference Method (FRM). The data are available at EPA’s Air Data website (www.epa.gov/airdata) . The averaged SANDWICHed speciation profiles of the highest PM 2.5 

### days are c onverted to the PM 10 

### speciation using the PM 10 

### values of the same days. The details of the procedure are described in section 4.2

.

### 11



## 3.4 Background PM 10 

## Concentration

Regional background concentrations must be known to estimate what fraction of the PM 10 

### is due to emission sources within the Treasure Valley. The roll

- forward model assumes that the

difference between the total PM 10 

### levels in the airshed and the background PM 10 

### levels is proportional to the emissions generated within th e 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). 3 

### IMPROVE stations typically me asure the chemical composition of PM 2. 5 

### and light

- absorbing properties of the aerosol. Some stations also feature a

channel to measure PM 10 

### concentrations, but chemical speciation is seldom done on these samples. The six IMPROVE stations closest to the Tre asure 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 Wilderne ss Area station is located approximately 200 miles south

of Boise and is the closest IMPROVE site to the Treasure Valley with a PM 10 

### channel. Both

PM 10 

### and PM 2.5 

### samples are collected at Jarbidge twice per week. Measurements from this relatively remote sit e are used to estimate the background PM 10 

### concentrations in the Treasure Valley.

PM 10 

### and PM 2.5 

### mass and chemically speciated PM 2.5 

### data are available for the Jarbidge Wilderness Area beginning in 1988. Average annual PM 10 

### mass and an estimate of the aver age concentration of each major PM 10 

### 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 concentra tions 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 n orthern Ada County.

Table 8 . Background concentrations used for the roll

- forward model

.

Time Period PM 10 (2006

– 2008) (μg/m 3 )

Annual 8.45
Winter Season 2.62
Nonwinter Season 11.3



# 4 Pr ocedures

4.1 Determining the Design Value

Although only ambient PM 10 

### data from a recent 3

- year period are required for this modeling demonstration, DEQ analyzed data from 5 years to better understand the PM 10 

### concentration trend and ensure the data set is a go od 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 inter national 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 PM 10 

### levels in the different seasons (winter season is defined as November through February). As expected, the annua l and seasonal averages are stable with little variance, while the peak levels, especially during the winter season, vary more widely.

Figure 3 . Highest PM 10 levels and annual and 24

- hour averages

— trend from 2005 through 2009. All (probable) exceptional events were excluded.



### Table 9 shows the annual average PM 10 

### concentrations for 2005

– 2009. The data show that the annual average has been fairly stable. The annual design value would be 23.3 μg/m 3 

### based on 2007

– 2009 data (the annual 50 μg/m 3 

### PM 10 

### standard no longer exists, however this is provided for informational purposes)

.

Table 9 . Annual average PM 10 concentrations (exceptional events included)

.

Year A n n ual A verage PM 10 Conce ntration ( μ g/m 3 )

2007 25

2008 23

2009 22

2007

– 2009 average 23.3



### There are 931 total valid daily values in the t h ree years from 2007 to 2009; therefore, the third

highest value during this 3

- year period is the design value based on the EPA “table metho d” for determining PM 10 

### design values (EPA 198 7 ). The highest 24

- hour average PM 10 

### concentrations

are listed in Table 10

. The third

- highest value in the three years is 90 μg/m 3 

### recorded in 2008.

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

.

### 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/m 3 ) based on the highes t observed PM 10 24 hour average

concen trations from 2007 through 2009. The d esign 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 D ata

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

2010 and 2011 . The 2010 and 2011 PM 10 

### data from Boise Fire Station No. 5 have now been fully audited. The highest four PM 10 

### values for 2010 and 2011 are liste d in Table 11

.

Table 11 . The highest 24

- hour average PM 10 values and annual average in 2010 and 2011 (μg/m 3 )

.
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/m 3



### — 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). T he annual average is lower tha n the

maximum value of 23 μg/m 3 

### in 2007

– 2009. In addition, two 2011 exceedances, 183 and 156 μg/m 3 

### were recorded on February 15 and 16, 2011 respectively, resulting from an extreme high

wind dust event originating in northwest Nevada. DEQ has determined th ese 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/m 3 

### , 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/m 3 

### higher 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 at tainment 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/m 3 

### , Table 24), are both well below the NAAQS (150 μg/m 3 

### ), it can be seen that a 1 μg/m 3 

### increase will not b ring 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/m 3 

### . Thus, the original modeling described in this report is a sufficient

demonstration of attainment in 2023 f or 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/m 3 

### ) is more conservative than

### 14

it would be using the final design value obtained after concurrence on th e 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 PM 10 

### speciation profiles and their relationship to the roll

- forward model are presented in the Figure 4

.

Figure 4 . Procedures for constructing PM 10 speciation profiles using SANDWICHed STN PM 2

. 5 data

.



### 4.2.1 Historical Speciation Data

Two types of high

- PM scen arios in the Treasure Valley were identified in past studies:

s tagna tion events and h igh

- w inter 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 atmosp here ; 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 level s 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 f rom 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 PM 10 

### in the Treasure Valley. Filter analysis

revealed that PM 10 

### compositions were different depending on meteorological conditions. The

winter season PM 10 

### composition in the historical data (1988

– 1996) is shown in Figure 5

. Figure

6 shows the average percent composition of PM 10 

### 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 PM 10 in Ad a and Canyon
Counties. The labels on the x
- axis show station and dates ( Kuhns et. al. 1998)

.

### 17

Figure 6 . Average composition of stagna tion and high

- winter scenarios (from DEQ 2002)



### 4.2.2 Speciation for Winter

To characterize stagnation and high

- winter scenarios in recent years, the 1 6 highest PM 2.5 

### days in

winter seasons from 200 6 through 2009 with concentrations of 1 4 μg/m 3 

### or higher were selected,

as shown in Table 12

. The events were divided into two groups based on the order of secondary

in organic aerosol concentrations ( secondary in organic aerosol = sulfate mass + nitrate mass) : the first group (stagnation scenario) includes the 8 days with the highest secondary aerosol concentrations, while the second grou p (high

- winter scenario) includes the remaining 8 days.

Table 13 shows the converted PM 10 

### speciation percentage composition for the winter season.

T he “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/m 3 

### in the STN data. The “unknown artifact” in subsequent tables ( Tables 15, 16, etc. )

results from this passive a rtifact in the sampling data

.

### 18

Table 12 . D ays with PM 2. 5 values 15 μg/m 3 or higher (after rounding) from 2006 through 2009 (from

EPA Air Data)

. All values in μg/m 3

.

Date SANDWICH

Sulfa te
Mass SANDWICH

Nitrate

Mass SANDWICH

TCM SANDWICH

Crustal

Mass Passive

Artifact PM 2.5

FRM PM 10

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 0.9 6 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 = Fede ral Reference Method

Table 13 . Converted PM 10 speciation for winter season.

Scenario Sulfate

Mass Nitrate

Mass Total

Carbon

Mass Crustal

Mass Unknown

Artifact Total

PM 10

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

High

- wint er 5.3% 22.8% 38.3% 31.5% 2.2% 100%



### Figure 7 and Figure 8 are the graphical presentations of the converted PM 10 

### speciation profiles

for both winter scenarios.

### 19

Figure 7 . PM 10 sp eciation for stagnation scenarios. The small differences from the table are due to rounding.

Figure 8 . PM 10 speciation for high

- winter scenarios. The small differences from the table are due to rounding.



### 4.2.3 Speciation for Non w inte r Seasons

T he magnitudes of the average highest PM 10 

### values in winter and nonwinter seasons are comparable ; h owever
, the compositions of nonwinter and winter PM 10 

### are very different. Ammonium nitrate is one of the most important components of PM 10 

### in the w inter 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 PM 10 

### is more or less influenced by moderately high wind s and/or suspected smoke from wildfires that Sulfate Mass

8%

Nitrate Mass

42% Total

Carbon

22% PM 10 Crustal

mass

27% Unknown

1% 

# PM 10 

# Speciation
Winter Stagnation Scenario

Sulfate Mass

5%

Nitrate

Mass

23%

Total Carbon

38% PM 10 Crustal

mass

32% Unknown

2% 

# PM 10 

# 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

PM 10 

### speciation percentage composition for the nonwinter season.

Table 14 . SANDWICH data fro m nonwinter seasons , 2006

– 2009

. Sorted by total carbon mass (TCM). All values in μg/m 3

.

Date SANDWICH

Sulfate

Mass SANDWICH

Nitrate

Mass SANDWICH

TCM SANDWICH

Crustal Mass Passive

Artifact PM 2.5

FRM PM 10

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 . PM 10 speciation profiles for nonwinter PM 10 scenarios

.

Scenario Sulfate

Mass Nitrate

Mass Total

Carbon Crustal

Mass Unknown

Artifact PM 10

High Crustal 3.6 % 0% 9.5 % 85.8 % 1.1% 10 0%
High Carbon 3.1 % 1.64 % 3 3.0 % 60.9 % 1.3% 100%

### 21

Figure 9 and Figure 10 show the graphical presentations of the PM 10 

### speciation for the nonwinter scenarios with high carbon mass and high crustal mass. The highest carbon days are also the highest PM 2. 5 

### (but not PM 10 

### ) days, indicating the strong influence of fires.

Figure 9 . PM 10 speciation converted from the 8 highest nonwinter PM 2

. 5 samples with highest

carbon ma ss. 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 . PM 10 speciation converted from the 10 highest nonwinter PM 2. 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

33% PM 10 Crustal

Mass

61% Unknown

1% 

# PM 10 

# Speciation Nonwinter High Carbon Scenario

Sulfate Mass

4% Nitrate Mass

0%

Total Carbon

9%

PM 10 Crustal

Mass

86% Unknown

1% 

# PM 10 

# Speciation Nonwinter High

- crustal Scenario

### 22



# 5 Results

The speciated linear roll

- forward model has demonstrated that PM 10 

### le vels 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 RRF s are determined, the roll

forward equation ( Equation 1, sect ion 2) is applied for each specie

. The RRFs for each specie are computed by RRF i 

### = E i f 

### / E i b



### , where E i

f 

### is the future emission total for each specie and E i b 

### is the

base

- year emission total for each specie

. The total predicted PM 10 

### 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/m 3 

### ) because the passive FRM sampling artifact (0.5 μg/m 3 

### ) in

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

the final results.

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

- PM 10 

### scenarios

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

RRF values used in the roll

- forward analysis

. Background emissions are as sumed to remain constant (RRF = 1), and the sulfate and nitrate components are held constant (RRF = 1) to

account for a ny potential uncertainty in nitrate neutralization (see s ection 6.5 ).

Table 16

. Predicted base

- year (2008) design valu e speciation (μg/m 3 )

. Sulfate

Mass Nitrate

Mass Total

Carbon Crustal

Mass Unknown

A rtifact 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 s tagnation 7.10 37.73 19.41 24.51 1.25 90.00

H igh w inter 4.80 20.51 34.49 28.32 1.87 90.00

Table 17 . Background speciated PM 10 concentrations (μg/m 3 )

.

Background PM 10 Sulfate

Mass Nitrate

Mass Total

Carbon Crustal

Mass Total

PM 10 Annual average 0.66 0.13 0.87 6.78 8.45

Winter a verag e 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 RR F 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 .0 0 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 PM 10 

### concentration is

2 4.7 μg/m 3 

### in 2015 and 2 7.9 μg/m 3 

### in 2023, which are 50 % and 5 6% o f the NAAQS

, respectively. The highest predicted 24

- hour average PM 10 

### levels are 9 7.6 μg/m 3 

### in 2015 and

11 5.7 μg/m 3 

### in 2023 ( both in the h igh

- w inter scenario)

, which represent 6 5 % 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 s ulfate 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

. P redicted PM 10 concentrations and species composition in future years (μg/m 3 ). When

t he RRFs for nitrate and s ulfate mass are less than 1.0, the ir forecasted concentrations are kept at

2008 levels (RRF = 1.0)

.

Year Sulfate Mass Nitrate Mass Total Carbon Crustal Mass Unknown
Artifact Total PM 10

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 D ay

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 D ay

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 PM 10 concentrations. 0 5 10 15 20 25 30

Sulfate

Mass Nitrate

Mass Total

Carbon Crustal

Mass Unknown PM10 Concentration (μg/m 3 ) 

# Predicted Annual PM 10

2008

2015

2023

### 25

Figure 12 . Predicted 24

- hour average PM 10 concentrations for high

- crustal scenario in nonwinter

seasons.

Figure 13 . Predicted 24

- hour average PM 10 co ncentrations 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/m 3 ) 

# Predicted PM 10 

## 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/m 3 ) 

# Predicted PM 10 

## Nonwinter High Carbon Scenario

2008

2015

2023

### 26

Figure 14 . Predicted 24

- hour average PM 10 concentrations for s tagnation scenario in winter

season.

Figure 15 . Predicted 24

- hour average PM 10 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 emission s conformity. This process establishes motor vehicle emission budgets (MVEBs) co nsistent with the attainment

demonstration that transportation planners may use to demonstrate conformity with the SIP and with the NAAQS. To provide a safety factor, t he 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 f or 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/m 3 ) 

# Predicted PM 10 

## 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/m 3 ) 

# Predicted PM 10 

## 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 use d 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

emissio n 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 PM 10 

### attainment demonstration for conformity

, while Table 21 shows the estimated motor vehicle emission s including road dust (paved and unpaved) and on

- road emissions

. Table 22 shows the RRF values used in the demonstration. The

RRF values for NO

x 

### and SO 2 

### are assigned to 1.0 if the predictions are decreasing. The RRF

value for NO

x 

### for the 2023

– 20 50 period in the table is greater than 1.0 because the on

- road NO

x 

### emission for 2050 is based on the VMT changes only. The real NO

x 

### emission s in the period are expected to continue to decrease, but MOVE S cannot predict to 2050 so forecasted VMT are

used to conservatively extrapolate 2015 emissions to 2050

.

Table 20

. Total e missions from all categories used for conformity analysis

.

SO 2 NO
x T otal
C arbon Crustal

Mass

2008

– 2015
Annual (t ons p er y ear ) 2 20 10

, 81 6 8

, 31 8 17

, 414

Nonwinter ( t ons p er d ay ) 0.60 29.2 16.6 40.1
Winter (t ons p er d ay ) 0.62 27.3 24.1 64.7

2015

– 2023
Annual (t ons p er y ear ) 16 4 8

, 17 5 10

, 222 22

, 690
Nonwinter (t ons p er d ay ) 0.4 21.1 20.4 45.7
Winter (t ons p er d ay ) 0.5 23.0 29.2 84.6

2023

– 2050

A nnual (t ons p er y ear ) 201 15

, 39 5 10

, 495 28

, 380
Nonwinter (t ons p er d ay ) 0.5 43.8 21.1 55.3
Winter (t ons p er d ay ) 0.6 40.0 30.0 108.2

Table 21 . The motor vehicle PM 10 emissions ( road dust and on

- road emission s ) for conformity.

P aved Unpaved On

- road Total PM 10 2008

– 2015
Annual (t ons p er y ear ) 10

, 080 926 311 11

, 317 Nonwinter (t ons p er d ay ) 13.7 2.9 0.8 17.4 Winter (t ons p er d ay ) 47.4 1.6 0.9 49.9

2015

– 2023 Annual (t ons p er y ear ) 14

, 568 791 314 15

, 672
Nonwinter (t ons p er d ay ) 19.6 2.5 0.8 22.9 Winter (t ons p er d ay ) 68.6 1.3 0.9 70.9

2015

– 2050
Annual (t ons p er y ear ) 19

, 550 1

, 795 604 21

, 949 Nonwinter (t ons p er d ay ) 26.5 5.6 1.6 33.7
Winter (t ons p er d ay ) 91.9 3.1 1.8 96.8

### 28

Table 22 . RRF s used for c onformity demonstration , for all emission categories

. The RRF s for NO
x and SO 2 are assigned to 1.0 if the predicted emissions are decreasing. The RRF for NO
x is greater
than 1.0 for 2023

– 2050 as opposed to 2008

– 2025 because the on

- road emission estimate f or 2050
is base d on the VMT increase only and the expected vehicle emission improvement is not

considered.

SO 2 NO
x T otal
C arbon 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

Ann ual 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 1.0 0 1. 09 1.46 1.76

Nonwinter 1.0 0 1.0 5 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 PM 10 

### roll

- forward modeling but are replicated from the mobile source emission s inventory (DEQ 2012) for completeness. These val ues are recommended for use as the MVEBs

. The PM 10 

### emission s include paved road dust, unpaved road dust

, and on

- road primary PM 10 

### emissions.

Because the roll

- forward model does not treat the non linear chemical processes and the

forecasted NO

x 

### and VOC emiss ions are declining, the NO

x 

### and VOC emission budgets are kept at 2008 levels plus a 10% safety factor for both the 2008

– 2015 and 2015

– 2023 periods . T he

motor vehicle emission s in the last period (2023

– 2050) are forecasted 2050 emissions based only

on VMT i ncreases 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 emission s

. The listed e mission 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 PM 10 NO
x 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 PM 10 

### levels using the estimated RRFs in Table 22 . All future

year predicted levels are below NAAQS. The h igh

- w inter scenario pr ovides the highest predictions.

Table 24

. PM 10 attainment demonstration

— PM 10 and composition recomputed for on

- road emission conformity purposes (μg/m 3 ).

Year Sulfate

Mass Nitrate

Mass Total

Carbon Crustal

Mass Unknown

Artifact P M 10

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 1 26.0
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 1 10.4

2023

– 2050 7.0 4 2.9 26.6 48.3 1.9 1 26.6
Winter Season High

- winter Day

2008

– 2015 4.7 20.5 38.0 34.3 1.9 99.5

2015

– 2023 4.7 2 0.5 46.0 45.3 2.6 1 19. 1

2023

– 2050 4.7 2 3.3 47.3 57.9 2.9 13 6.1



## 5.3 Discussion

The model results clearly demonst rate that PM 10 

### 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 PM 10 

### days influenced by

windblown dust an d 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 (t otal carbon), and ammonium nitrate (nitrate mass)

— will continue to play important roles in future years.

In the case of annual average PM 10 

### contributions ( Figure 16 ), the crustal mass is primarily responsible for the increase in PM 10 

### 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 PM 10 

### through out the modeling period . In the scenario of high carbon mass in nonwinter seasons ( Figure 18 ), carbon mass and crustal mass together contribute more than 9 5% , 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

. T he importance of nitrate decreases with time as a result of tighte ning 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 PM 10 

### violations took place as a result of high secondary aerosols . Another major historical contributor for winter PM 10 

### 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 ma ss ), 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 PM 10 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 PM 10

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 PM 10 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

i n 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

S everal factors may produce uncertainties in the modeling results

. Therefore

, conservative assumptions are used

, and the final modeled 24

- hour average PM 10 

### concentration in the future

yea r s is estimated to be conservative by over 20 μg/m 3 

### . 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

propo rtional 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 men tioned 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 PM 10 

### 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 S peciation Profiles

The PM 10 

### speciation profiles were constructed from PM 2.5 

### STN data. Because PM 2.5 

### and PM 10 

### monitors are not located at the same site, this approach can introduce bias in the profiles. For t he winter season, the highest PM 2.5 

### samples were selected. The cut

- off concentration was relatively low (15 μg/m 3 

### ) 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

PM 10 

### potentially higher. For the nonwinter seasons, nitrate and sulfate mass have minimal impact; the major players are carb on mass and crustal mass, which are well separated into the fine and coarse portions. Therefore, we do not expect significant bias in the non winter 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 PM 10 

### 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 PM 10 

### speciation profiles. The design value would be 7 1 μg/m 3 

### if only the exceedance days that appeared to be influenced by high windblown dust or wild fire smoke w ere excluded, which

is 19 μg/m 3 

### lower 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 PM 10 

### levels we ll below the NAAQS in both cases. Because the crustal mass is the dominating specie in the

emission s 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 C hanging Nitrogen Oxides and Road

- D ust E missions

Nitrogen oxides (NO

x 

### ) emissions are expected to decrease in the future years, and the importance

of nitrate

, the dominant specie in the winter fine particulate portion (PM 2.5 

### )

, will decl ine. However, while the total NO

x 

### emission s 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 mode l’s forecasting is linearly proportional

### 34

to the county

- level emission rate, the predicted coarse portion of PM 10 

### will increase accordingly. However, the road dust estimates depend on the total VMT, which is related more to expanding development in the vall ey 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 in crease 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 hom e heating . In summary, we believe that this approach produces a conservative estimate of future PM 10 

### concentrations.



## 6.5 Nitrate Neutralization

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

reduction and co ntrol 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 emission s control strategies will release up to two moles of

ammonium ions that will be available to neutralize more tha n 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 (HNO 3 

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

– 2009

winter PM 2.5 

### 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

simi lar 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 differe nt measurement technologies.

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

such as NO

x 

### , SO 2 

### , and NH 3



### . This nonlinearity stems from the complex interaction of these gaseous species with meteorology and the ava ilable oxidants (which are additionally influenced by NO

x 

### emissions). The measured full neutralization of NO 3 

### and SO 4 

### aerosols and likely excess

NH 3 

### in the Ada C ounty airshed suggests that the nonlinear behavior due to potential excess

nitrate will be miti gated.

Figure 22 . Nitrate neutralization in Boise, Idaho

— January 2009

,

### 36

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

the neutralization ratio . Klamath Fal ls 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 PM 10 

### concentration projections. Neverthel ess, 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 l ower 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 (3 5 samples of highest

seco ndary aerosol concentrations)

.



# 7 Conclusion s

The roll

- forward modeling has demonstrated that northern Ada County will meet the PM 10 

### 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 PM 10 

### concentrations will be below the NAAQS up to year 20 50

. The nitrate mass contributes much less to the total ambient PM 10 

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

decade ; howeve r
, nitrate mass is held constant in the analysis to en sure 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 t he important contributor

to ambient PM 10 

### 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 PM 10 

### days that w ere 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 PM 2.5 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 PM 10 

### monitor. As a result, future

concentrations at that mon itor 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 PM 10 

### levels that we have seen over the past decade.

### 38



# 8 References

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“Revised Requirements for Designation of Reference and Equivalent Methods for PM2.5 and Ambient Air Quality Surveillance for Particulate Matte r, Final Rule.” 1997. Federal Register 62. July 18, 1997.

Cahill, T.A., L. Ashbaugh, R. Eldred, P. Feeney, B. Kusko, and R. Flocchini. 1981. “Comparisons between Size

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CFR (Code of Federal Regulation). 2006 . “ National Primary and Secondary Air Quality

Standards for PM10 .” 40 CFR 50.6.

DEQ (Idaho Department of Environmental Quality). 1991. Northern Ada County/Boise Particulate (PM

- 10) Air Quality Improvement Plan . Boise, ID: DEQ.

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DEQ (Idaho Department of Environmental Quality). 2012. Development of the Base

- and

Future

- Year Mobile Source Emission s Inventory for the Treasure Valley, Idaho

. Boise, ID: DEQ.

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Standards. EPA

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- 007.

EPA (US Environmental Protection Agency). 1987. PM 10 

### SIP Development Guidance

. Research Triangle Park, NC: EPA, Office of Air Quality Plan ning and Standards. EPA

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- 001

.

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. AP

- 42 Compilation of Air Pollutant

Emission Factors

. Available at: http://www.epa.gov/ttn/chief/ap42/index.h tml

.

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.

### 39

ERG and Environ (Eastern Resea rch Group and Environ International Corporation). 2010. 2008, 2015, and 2023 Emission Inventories for the Treasure Valley Airshed . Rancho Cordova,

CA: ERG and Environ.

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– 11.

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, and D. Riley. 1998. Analyzing Speciated Ambient

PM 10 

### Concentrations and Emissi ons in Ada and Canyon Counties, Idaho

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Sub

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Sisler, J., W. Malm, and K. Gebhart. 1996. “ Spatial and Seasonal Patterns and Long Term Variability of the Composition of the Haze in the United States.” CIRA internal

document. ISSN: 0737

- 5352

- 32.

US Congress. 1970 . Clean Air Act. 42 USC § 7401 et seq

.

Watson, J.G, J. Chow, L. Pritchett, W. Pierson , C. Frazier, R. Purcell, and I. Olmez. 1988. The

1987

– 88 Metro Denver Brown Cloud Study . Reno, NV: Desert Research Institute.

Doc. 8810 1F2.