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Idaho: West Silver Valley Nonattainment Area -

Area Designations for the 

2012 Primary Annual PM2.5 National Ambient Air Quality Standard

Technical Support Document

1.0 Summary

In accordance with Section 107(d) of the Clean Air Act (CAA), the Environmental Protection Agency (EPA)

must promulgate designations for all areas of the country. In particular, the EPA must identify those areas that 

are violating a National Ambient Air Quality Standard (NAAQS) or contributing to a violation of the NAAQS 

in a nearby area. The EPA is required to complete this process within 2 years of promulgating a new or revised 

NAAQS, or may do so within 3 years under circumstances not relevant to these designations.1 This technical 

support document (TSD) describes the EPA’s designation of one area in Idaho as nonattainment for the 2012 

primary annual fine particle NAAQS (2012 annual PM2.5 NAAQS).2

Consistent with section 107(d), states are required to submit area designation recommendations to the EPA for 

the 2012 annual PM2.5 NAAQS no later than 1 year following promulgation of the standard, or by December 13, 

2013. On December 6, 2013, Idaho recommended that all of Idaho be designated as “unclassifiable/attainment” 

for the 2012 annual PM2.5 NAAQS based on air quality data from 2010-2012. For two areas, West Silver 

Valley (Pinehurst, Idaho monitor) and Salmon, Idaho, the state relied on exceptional events as part of the 

recommendation of “unclassifiable/attainment.” In order to review the recommendation for the 2012 annual 

PM2.5 NAAQS based on air quality data from 2011-2013, the EPA worked closely with the Idaho Department 

of Environmental Quality (IDEQ) to complete the five factor analysis for the West Silver Valley area 

surrounding the violating monitor in Pinehurst. 

After considering the state’s recommendation and based on the EPA’s and the IDEQ’s technical analysis as 

described in this TSD, the EPA is designating the West Silver Valley area listed in Table 1 as nonattainment for

the 2012 annual PM2.5 standard and the remainder of the state as unclassifiable/attainment for the 2012 annual 

PM2.5 NAAQS based on air quality data from 2011-2013. Accounting for the EPA’s concurrence of exceptional 

events for Pinehurst and Salmon, the West Silver Valley area does not attain the standard while the Salmon area 

is attaining the standard. The EPA’s analysis of the exceptional events claims for both areas is discussed below. 

 

1 Section 107(d) of the CAA requires the EPA to complete the initial designation process within 2 years of promulgation of 

a new or revised NAAQS, unless the Administrator has insufficient information to make initial designation decisions in the 

2-year time frame. In such circumstances, the EPA may take up to 1 additional year to make initial area designation 

decisions (i.e., no later than 3 years after promulgation of the standard).

2 On December 14, 2012, the EPA promulgated a revised primary annual PM2.5 NAAQS (78 FR 3086, January 15, 2013). 

In that action, the EPA revised the primary annual PM2.5 standard, strengthening it from 15.0 micrograms per cubic meter 

(μg/m3) to 12.0 μg/m3.
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The EPA must designate an area nonattainment if it has an air quality monitoring site3that is violating the 

standard or if it has sources of emissions that are contributing to a violation of the NAAQS in a nearby area. 

Legal descriptions (e.g., county boundaries, townships and ranges) of West Silver Valley area are found below 

in the supporting technical analysis. As provided in CAA section 188(a), the EPA is initially classifying all 

nonattainment areas as “Moderate” nonattainment areas.

Table 1. Idaho’s Recommended Nonattainment Areas and EPA’s Designated Nonattainment Areas for 

the 2012 annual PM2.5 NAAQS

Area Idaho’s Recommended 

Nonattainment Counties EPA’s Nonattainment Counties

West Silver Valley, ID None Shoshone County (partial) – West 

Silver Valley

In their recommendation letter, Idaho recommended that the EPA designate as “unclassifiable/attainment” the 

entire State of Idaho and all Air Quality Control Regions based on 2010-2012 data. Based on 2011-2013 data, 

the Pinehurst, ID monitor exceeded the standard and led to the consideration of the area surrounding the 

monitor as nonattainment. With the exception of Shoshone County (partial county) identified in Table 1, the 

EPA agrees with Idaho’s recommendation, and the EPA is designating the remainder of Idaho as 

unclassifiable/attainment based on ambient monitoring data collected during the 2011-2013 period showing 

compliance with the 2012 annual PM2.5 NAAQS, and the EPA’s determination/assessment as outlined below 

that other areas within Idaho and Indian country are not likely contributing to nearby violations, as 

appropriate.4,5

2.0 Nonattainment Area Analyses and Boundary Determination

The EPA evaluated and determined the boundaries for the West Silver Valley, ID nonattainment area (to be 

referred to as the West Silver Valley nonattainment area) considering the specific facts and circumstances 

unique to the area. In accordance with CAA section 107(d), the EPA is designating as nonattainment not only 

 

3

In accordance with 40 CFR 50 Appendix N, PM2.5 measurements from the primary monitor and suitable collocated PM2.5 

FRM, FEM or ARMs may be used in a “combined site data record” to establish a PM2.5 design value to determine whether

the NAAQS is met or not met at a particular PM2.5 monitoring site.

4 Unless a state or tribe has specifically identified jurisdictional boundaries in their area recommendations, when 

determining “remainder of the state,” EPA will use Federal Information Processing Standard (FIPS) codes maintained by 

the National Institute of Standards and Technology (NIST), which are used to identify counties and county equivalents 

(e.g., parishes, boroughs) of the United States and its unincorporated territories (e.g., American Samoa, Guam, Northern 

Mariana Islands, Puerto Rico, and the US Virgin Islands). Available on EPA’s Envirofacts website at 

http://www.epa.gov/envirofw/html/codes/state.html.

5 EPA uses a designation category of "unclassifiable/ attainment" for areas that are monitoring attainment and for areas that 

do not have monitoring sites but which the EPA believes are likely attainment and does not include emissions sources that 

are contributing to nearby violations based on the five factor analysis and other available information.
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the area with the monitoring site that violates the 2012 annual PM2.5 NAAQS, but also those nearby areas with 

emissions sources that contribute to the violation in the violating area. Consistent with the EPA guidance,

6

after 

identifying each monitoring site indicating a violation of the standard in an area, the EPA analyzed those areas

with emissions contributing to that violating area by considering those counties in the entire metropolitan area 

(e.g., Core Based Statistical Area (CBSA) or Combined Statistical Area (CSA)) in which the violating 

monitoring site is located. The EPA also evaluated counties adjacent to the CBSA or CSA that have emissions 

sources with the potential to contribute to the violation. The EPA uses the CBSA or CSA as a starting point for 

the contribution analysis because those areas are nearby for purposes of the PM2.5 NAAQS. Based upon relevant 

facts and circumstances in each area, the designated nonattainment area could be larger or smaller than the 

CBSA or CSA. The EPA’s analytical approach is described in section 3 of this technical support document.

3.0 Technical Analysis 

In this technical analysis, the EPA used the latest data and information available to the EPA (and to the states 

and tribes through the PM2.5 Designations Mapping Tool7and the EPA PM Designations Guidance and Data 

web page8) and/or data provided to the EPA by states or tribes. This technical analysis identifies the area with a

monitoring site that violates the 2012 annual PM2.5 standard. The EPA evaluated this area and other nearby 

areas with emissions sources or activities that potentially contribute to ambient fine particle concentrations at 

the violating monitor in the area based on the weight of evidence of the five factors recommended in the EPA 

guidance and any other relevant information. This technical analysis in Factor 1 also explains EPA’s 

concurrence on requested exceptional events claims at both the Pinehurst and Salmon monitors.

The five analysis factors are:

Factor 1: Air Quality Data. The air quality data analysis involves examining available ambient PM2.5 air quality 

monitoring data at, and in the proximity of, the violating monitoring locations. This includes reviewing the 

design values (DV) calculated for each monitoring location in the area based on air quality data for the most 

recent complete three consecutive calendar years of quality-assured, certified air quality data in the EPA’s Air 

Quality System (AQS). In general, the EPA identifies violations using data from suitable Federal Reference 

Method (FRM), Federal Equivalent Method (FEM), and/or Approved Regional Method (ARM) monitors sited 

and operated in accordance with 40 CFR Part 58.9 Procedures for using the air quality data to determine whether 

a violation has occurred are given in 40 CFR part 50 Appendix N, as revised by a final action published in the 

 

6 EPA issued guidance on April 16, 2013, that identified important factors that EPA used to evaluate, in making a 

recommendation for area designations and nonattainment boundaries for the 2012 annual PM2.5 NAAQS. Available at

http://www.epa.gov/pmdesignations/2012standards/docs/april2013guidance.pdf.

7 EPA’s PM2.5 Designations Mapping Tool can be found at http://geoplatform2.epa.gov/PM_MAP/index.html.

8 EPA’s PM Designations Guidance and Data web page can be found at 

http://www.epa.gov/pmdesignations/2012standards/techinfo.htm.

9 Suitable monitors include all FEM and/or ARMs except those specific continuous FEMs/ARMs used in the monitoring 

agency's network where the data are not of sufficient quality such that data are not to be compared to the NAAQS in 

accordance with 40 CFR part 58.10(b)(13) and approved by the EPA Regional Administrator per 40 CFR part 58.11(e).
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Federal Register on January 15, 2013 (78 FR 3086).10 In addition to reviewing data from violating monitor sites, 

the EPA also assesses the air quality data from other monitoring locations to help ascertain the potential 

contribution of sources in areas nearby to the violating monitoring site. Examples include using chemical 

speciation data to help characterize contributing emissions sources and the determination of nearby 

contributions through analyses that differentiate local and regional source contributions.

Factor 2: Emissions and emissions-related data. The emissions analysis examines identified sources of direct 

PM2.5, the major components of direct PM2.5 (primary organic carbon/organic mass, elemental carbon, crustal 

material (and/or individual trace metal compounds)), primary nitrate and primary sulfate, and precursor gaseous 

pollutants (e.g., SO2, NOX, total VOC, and NH3). Emissions data are generally derived from the most recent 

National Emissions Inventory (NEI) (i.e., 2011 NEI version 1), and are given in tons per year. In some cases, 

the EPA may also evaluate emissions information from states, tribes, or other relevant sources that may not be 

reflected in the NEI. One example of such “other information” could include an inventory or assessment of 

local/regional area sources that individually does not meet the current threshold for reporting to the NEI but 

collectively contributes to area PM2.5 concentrations. Emissions data indicate the potential for a source to 

contribute to observed violations, making it useful in assessing boundaries of nonattainment areas.

Factor 3: Meteorology. Evaluating meteorological data helps to determine the effect on the fate and transport of 

emissions contributing to PM2.5 concentrations and to identify areas potentially contributing to the violations at 

monitoring sites. The Factor 3 analysis includes assessing potential source-receptor relationships in the area 

identified for evaluation using summaries of air trajectories, wind speed, wind direction, and other 

meteorological data, as available. To best account for the role of meteorology in driving source-receptor 

relationships, the IDEQ ran a woodsmoke box model for the portion of the West Silver Valley closest to 

Pinehurst and ran HYSPLIT to determine how important the local mountains are for wind flow. In addition, the 

IDEQ provided supplemental Calpuff modeling to more quantitatively identify the effect of prescribed burning 

and point sources on primary PM2.5 at the monitor.

Factor 4: Geography/topography. The geography/topography analysis includes examining the physical features 

of the land that might define the airshed and, therefore, affect the formation and distribution of PM2.5 over an 

area. Mountains or other physical features may influence the fate and transport of emissions and PM2.5

concentrations. Additional analyses may consider topographical features that cause local stagnation episodes via 

inversions, such as valley-type features that effectively “trap” air pollution, leading to periods of elevated PM2.5

concentrations. 

Factor 5: Jurisdictional boundaries. The analysis of jurisdictional boundaries identifies the governmental 

planning and organizational structure of an area that may be relevant for designations purposes. These 

jurisdictional boundaries provide insight into how the governing air agencies conduct or might conduct air 

quality planning and enforcement in a potential nonattainment area. Examples of jurisdictional boundaries 

 

10 As indicated in Appendix N to 40 CFR part 50, Interpretation of the National Ambient Air Quality Standards for PM2.5, 

section 3(a) indicates “Except as otherwise provided in this appendix, all valid FRM/FEM/ARM PM2.5 mass concentration 

data produced by suitable monitors that are required to be submitted to AQS, or otherwise available to EPA, meeting the 

requirements of part 58 of this chapter including appendices A, C, and E shall be used in the DV (design value)

calculations. Generally, EPA will only use such data if they have been certified by the reporting organization (as prescribed

by § 58.15 of this chapter); however, data not certified by the reporting organization can nevertheless be used, if the 

deadline for certification has passed and EPA judges the data to be complete and accurate.”
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include counties, air districts, areas of Indian country, CBSA or CSA, metropolitan planning organizations 

(MPOs), and existing nonattainment areas.

3.1 Area Background and Overview of West Silver Valley, Idaho

Figure 1a is a map of the EPA’s nonattainment boundary for the West Silver Valley nonattainment area. The 

map shows the location and design value of the ambient air quality monitoring location, county, topography, 

hydrologic unit codes (HUC), and jurisdictional boundaries. Figure 1b includes a larger geographic area to 

show the modeling domain used to analyze the regional air quality, and Figure 1c shows the Area of Analysis 

which included nearby monitors.

The violating monitor in the West Silver Valley is located in Shoshone County, a rural county in the northern 

panhandle of Idaho; the monitor is located in the city of Pinehurst. The area is sparsely populated and is home 

to rugged mountainous terrain. The city of Pinehurst itself is situated in the Silver Valley, known for its mining 

history. Pinehurst, Idaho historically has had elevated particulate matter levels in the 1990s and it is currently a 

PM10 nonattainment area although its PM10 design value is well below the standard. In 2009, EPA designated 

Shoshone County as unclassifiable/attainment for the 2006 24-hour PM2.5 standard based on the design value for 

the 2006-2008 period which was below the standard. In prior years, the area experienced violations of that 

standard. Residential wood combustion in the cold, winter months is most responsible for elevated particulate 

matter in the area, while prescribed burning in the late autumn and in the spring also contributes substantially. 

Smoke from wildfires can affect the area in the summer. Smoke from crop residue burning is a negligible 

contributor to PM2.5 in the West Silver Valley. For this designation process, the Area of Analysis was chosen to 

include all known sources that contribute to high PM2.5 levels at the Pinehurst monitor as well as a wider area to 

include a representative sample of nearby monitoring stations and potentially important sources. 
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Figure 1a. EPA’s Nonattainment Boundary for the West Silver Valley Area
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Figure 1b. Modeling Domain for the West Silver Valley Nonattainment Area (based on IDEQ modeling)
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Figure 1c. Area of Analysis for the West Silver Valley Nonattainment Area (PM2.5 Designations Mapping 

Tool)

The EPA must designate as nonattainment areas those areas that violate the NAAQS and nearby areas that 

contribute to the violation in the violating area. Shoshone County, Idaho shows a violation of the 2012 PM2.5

NAAQS, therefore a portion of Shoshone County is included in the nonattainment area. The EPA evaluated 

each county without a violating monitoring site located near the county with the violating monitoring site based 
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on the five factors and other relevant information and determined as outlined below that the neighboring 

counties did not contribute to the nearby violation. The following sections describe this five factor analysis 

process. While the factors are presented individually, they are not independent. The five factor analysis process 

carefully considers their interconnections and the dependence of each factor on one or more of the others.

Factor 1: Air Quality Data

All data collected during a particular year are important when determining contributions to an annual standard 

such as the 2012 annual PM2.5 NAAQS. Compliance with an annual NAAQS is dependent upon monitor 

readings throughout the year, including days with monitored ambient concentrations below the level of the 

NAAQS. For the 2012 annual PM2.5 NAAQS, the annual mean is calculated as the mean of quarterly means. A 

high quarter can drive the mean for an entire year, which, in turn, can drive an elevated 3-year DV. Although all 

data are important, seasonal or episodic emissions can provide insight as to relative contributors to measured 

PM2.5 concentrations. For these reasons, for the Factor 1 air quality analysis, the EPA assessed and 

characterized air quality at, and in the proximity of, the violating monitoring site locations first, by evaluating 

trends and the spatial extent of measured concentrations at monitors in the area of analysis, and then, by 

identifying the conditions most associated with high average concentration levels of PM2.5 mass in the area of 

analysis. 

In the case of the Pinehurst monitor, the EPA assessed air quality data on a seasonal and monthly basis.

11 PM2.5

in the West Silver Valley is characterized by various seasons. The monitor measures high values in the winter 

due to a combination of residential wood combustion, low wind speeds, and low mixing heights. In the 

springtime prescribed burning contributes to the elevated PM2.5 values. The monitor generally records lower 

values in the summer except for occasional impacts from wildfires, which can be Exceptional Events provided 

that an exceptional event demonstration is submitted that meets exceptional event criteria and timeframes as per 

Treatment of Data Influenced by Exceptional Events (Exceptional Events Rule or EER, 72 FR 13560). In the 

autumn season the monitor is again influenced by prescribed burning. Accordingly, the seasonality of PM2.5 in 

West Silver Valley is more accurately characterized when analyzed on a monthly basis as opposed to quarterly

basis. This is depicted in Figure 2a.

The EPA also identified the spatial extent of these high PM2.5 concentrations. The mass and composition at the 

design value location represents contributions from various emission sources including local, area-wide (which 

may comprise nearby urban and rural areas) and regional sources. To determine the source mix (by mass) at the 

design value monitoring site, the EPA examined the chemical composition of the monitored PM2.5 

concentrations (Figure 3) analyzed in the 2008 Neil Frank study12. The EPA compared the design value at the 

Pinehurst monitor to other sites in the region, including IMPROVE sites (Interagency Monitoring of Protected 

Visual Environments, i.e. visibility). (Table 2a and 2b). This comparison of local/area-wide PM2.5 data to 

 

11 Although compliance with the annual NAAQS depends on contributions from all days of the year, examining data on a 

monthly or seasonal basis can inform the relationship between the temporal variability of emissions and meteorology and 

the resulting PM2.5 mass and composition. In some areas of the country where there may be noticeable month-to-month 

variations in average PM2.5, the quarterly averages may not adequately represent seasonal variability. In these areas, air 

quality data may be presented by those months that best correspond to the local “seasons” in these areas. 

12http://www.epa.gov/airquality/particlepollution/designations/2006standards/techinfo/available_new_speciation_data_pm

2.5_naa.pdf
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regional data derives an “urban increment,” which helps differentiate the influence of more distant emissions 

sources from the influence of closer emissions sources, thus representing the portion of the measured violation 

that is associated with nearby emission contributions. For the West Silver Valley area, the urban increment was 

defined as the difference between the monthly averaged Pinehurst data and the average of the remote and rural

area values in Figure 2a and 2b.

13,14,15

PM2.5 Design Values and Total Mass Measurements – The EPA examined ambient PM2.5 air quality monitoring 

data represented by the DVs at the violating monitoring site and at other monitors in the area of analysis. The 

EPA calculated DVs based on air quality data for the most recent three consecutive calendar years of qualityassured, certified air quality data from suitable FEM/FRM/ARM monitoring sites in the EPA’s Air Quality 

System (AQS). For this designations analysis, the EPA used data for the 2011-2013 period (i.e., the 2013 design 

value), which are the most recent years with fully-certified air quality data. A monitor’s DV is the metric or 

statistic that indicates whether that monitor attains a specified air quality standard. The 2012 annual PM2.5

NAAQS is met at a monitoring site when the 3-year average annual mean concentration is 12.0 micrograms per 

cubic meter (μg/m3) or less (e.g., 12.1 μg/m3 or greater is a violation). A DV is only valid if minimum data 

completeness criteria are met or when other regulatory data processing provisions are satisfied (See 40 CFR part 

50 Appendix N). Table 2a identifies the current design value(s) (i.e., the 2013 DV) and the most recent two 

design values based on all monitoring sites in the area of analysis for the West Silver Valley, Idaho

nonattainment area.16 Where a county has more than one monitoring location, the county design value is 

indicated in red type. Table 2b identifies all of the additional monitors relied upon in this TSD, especially with 

regard to understanding the remote and rural background

Consideration of Exceptional Event Claims – As the EPA will be taking action on exceptional events in the

same Federal Register Final PM2.5 Annual Standard Area Designations, this TSD provides technical information 

for both the Pinehurst and Salmon exceptional event submission. The DVs for the Salmon 

unclassifiable/attainment area designation and West Silver Valley nonattainment area designation reflect the 

EPA’s concurrence on multiple exceptional event claims that affect the data used to calculate the DVs. In the 

state of Idaho’s area designations recommendation letter of December 6, 2013, Idaho recommended 

 

13 In most, but not all, cases, the violating design value monitoring site is located in an urban area. Where the violating 

monitor is not located in an urban area, the “urban increment” represents the difference between local and other nearby 

emission sources in the vicinity of the violating monitoring location and more regional sources.

14 Hand, et al. Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the United 

States: Report V, June 2011. Chapter 7 – Urban Excess in PM2.5 Speciated Aerosol Concentrations, 

http://vista.cira.colostate.edu/improve/Publications/Reports/2011/PDF/Chapter7.pdf

15 US EPA, Office of Air Quality Planning and Standards, December 2004. (2004) Area Designations for 1997 Fine 

Particle (PM2.5) Standards, Technical Support Document for State and Tribal Air Quality Fine Particle (PM2.5) 

Designations, Chapter 3, Urban Excess Methodology. Available at 

www.epa.gov/pmdesignations/1997standards/documents/final/TSD/Ch3.pdf

16 In certain circumstances, one or more monitoring locations within a monitoring network may not meet the network 

technical requirements set forth in 40 CFR 58.11(e), which states, “State and local governments must assess data from 

Class III PM2.5 FEM and ARM monitors operated within their network using the performance criteria described in table C4 to subpart C of part 53 of this chapter, for cases where the data are identified as not of sufficient comparability to a 

collocated FRM, and the monitoring agency requests that the FEM or ARM data should not be used in comparison to the 

NAAQS. These assessments are required in the monitoring agency's annual monitoring network plan described in 

§58.10(b) for cases where the FEM or ARM is identified as not of sufficient comparability to a collocated FRM....”
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unclassifiable/attainment area designations for the cities of Salmon (Lemhi County) and Pinehurst (Shoshone 

County). Data recorded at these monitors initially showed 2010-2012 design values for the monitors in these 

cities in violation of the 2012 annual PM2.5 NAAQS. However, Idaho contended that several days of flagged 

data should be excluded as exceptional events. 

On December 6, 2013, Idaho submitted an exceptional event demonstration supporting wildfire-based 

exceptional events claims for approximately 43 days at the Salmon monitor, and four days at the Pinehurst 

monitor. Idaho’s submission included an appendix of additional days that Idaho believed could qualify as 

exceptional events, but for which the state did not prepare or submit a technical demonstration. This 

submission was based on Idaho’s belief concerning the number of exceptional event claims necessary to result 

in a 2010-2012 DV that met the standard. However, these designations are based on 2011-2013 DVs, which 

incorporate the most recent valid data. Accordingly, on January 30, 2014, the EPA informed Idaho that in order 

to achieve a compliant 2011-2013 DV for the Salmon monitor Idaho would need to submit documentation 

supporting an additional six exceptional events claims for that monitor. On April 25, 2014, Idaho submitted an

addendum to its December 6, 2014 submission, with technical demonstrations supporting wildfire-based 

exceptional events for an additional 20 days at the Salmon monitor. 

On July 8, 2014, the EPA informed Idaho that it concurred on a number of days that Idaho claimed as 

exceptional events for the Salmon monitor, as well as all four days claimed as exceptional events for the 

Pinehurst monitor.17 The EPA concurred in a sufficient number of exceptional events claims to bring the 2011–

2013 DV at the Salmon monitor to the standard. For Pinehurst, however, as indicated below in Table 2a, the 

exceptional events claims were insufficient to bring the 2011-2013 DV into compliance with the standard. 

Furthermore, the number and magnitude of the remaining days flagged as potential exceptional events for the 

Pinehurst monitor would not have been sufficient to bring that monitor into compliance with the standard, even 

if Idaho had submitted additional technical demonstrations.

Table 2a. Air Quality Data collected in Area of Analysis (all DV levels in μg/m3)

City, County, State Monitor Site ID State Rec NA? 09-11 DV 10-12 DV 11-13 DV

St Maries, Benewah, 

Idaho 160090010 No 9 8.9 9.9

Spokane, Spokane, 

Washington 530630021 No 7.2 7.4 8.0

Pinehurst, Shoshone, 

Idaho 160790017 No 12 12.0 12.8*

Libby, Lincoln, 

Montana 300530018

- 11.4 11.5 11.4

Hamilton, Ravalli, 

Montana

300810007

- 7.8 11.0 11.2

Sandpoint, Bonner, 

Idaho** 160170005/3 No 3.7 3.6 4.0

Clearwater, Idaho No monitor - - - -

Coeur d Alene, 

Kootenai, Idaho** 160550003 No 4.3 3.9 4.1

 

17 Copies of the concurrence letter and analysis may be found in the docket.
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* The 2011–2013 DV for the Pinehurst monitor was derived from data that excluded the four exceptional event 

days discussed above. Had those exceptional events not been excluded, the 2011-2013 DV for this monitor 

would have been 12.9 μg/m3.

** Non regulatory monitor

Table 2b. Air Quality Data Collected at Special Purpose, Non-Regulatory Monitors (all DV levels in 

μg/m3)*

City, County, State Monitor Site ID State Rec NA? 09-11 DV 10-12 DV 11-13 DV

Plummer, Benewah, 

Idaho 160090011 No

Not 

determined

Not determined Not determined

Moscow, Latah, Idaho 160570005 No

Not 

determined Not determined Not determined

Spokane, Spokane, 

Washington 530630047 No

Not 

determined

Not determined 7.5

Colville, Stevens, 

Washington 530650004 No Not 

determined

Not determined 9.0

Welpinit, Stevens, 

Washington 530650002 No Not 

determined

Not determined 5.6

Pullman, Whitman, 

Washington 530750003 No Not 

determined

Not determined 5.8

Rosalia, Whitman, 

Washington 530750006 No Not 

determined

Not determined 4.7

LaCrosse, Whitman, 

Washington 530750005 No Not 

determined

Not determined 4.4

Thompson Falls, 

Sanders, Montana No monitor -

Not 

determined

Not determined Not determined

Cabinet Mts, Sanders, 

Montana IMPROVE -

Not 

determined

Not determined Not determined

Sula Pk, Ravalli, 

Montana IMPROVE -

Not 

determined

Not determined Not determined

* Monitors in this chart provided data for analyses in this TSD, primarily understanding the remote and rural 

background.

The Figure 1a and 1b maps, shown previously, identify the West Silver Valley, Idaho nonattainment area and

Pinehurst monitoring location with 2011-2013 violating DV. As indicated on the map, there is one violating 

monitoring location located in Pinehurst near the western border of Shoshone County, Idaho. It is located in the 

West Silver Valley and with the community of Kingston to the west and towns of Smelterville and Kellogg to 

the east. The West Silver Valley area historically has had elevated particulate matter levels; it is currently a 

PM10 nonattainment area (although attaining the standard). In 2009, EPA designated Shoshone County as 

unclassifiable/attainment for the 2006 24-hour PM2.5 standard based on the design value for the 2006-2008 

period which was below the standard. In prior years, the area experienced violations of that standard.

Residential wood combustion in the cold, winter months is most responsible for elevated particulate matter in 

the area, while prescribed burning in the late autumn and in the spring also contribute substantially. Smoke from 
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wildfires can affect the area in the summer. Smoke from crop residue burning is a negligible contributor to 

PM2.5 in West Silver Valley.

Seasonal variation can highlight those conditions most associated with high average concentration levels of 

PM2.5. Figure 2a and 2b show monthly mean PM2.5 concentrations for the 2008-2011 4-year period and the 

2011-2013 3-year period for the highest DV monitoring site within the area of analysis. The 2011-2013 data set

is not being used to establish the regional background as it is influenced by wildfire events. However, by 

comparing Figure 2a based on the 2008-2011 data and Figure 2b using 2011-2013 data with elevated wildfire 

values which EPA has concurred are exceptional events removed, it demonstrates that Figure 2a and the 2008-

2011 dataset is representative of years not affected by exceptional events and can be used as background for 

purposes of these analyses. This graphical representation is particularly relevant when assessing air quality data 

for an annual standard, such as the 2012 annual PM2.5 NAAQS, because, as previously stated, the annual mean 

is calculated as the mean of quarterly means and a high month or quarter can drive the mean for an entire year, 

which, in turn, can drive an elevated 3-year DV.

Figure 2a. West Silver Valley PM2.5 Monthly Means for 2008-2011, including Urban Increment

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12

Monthly Average PM2.5 Concentration, μg/m3

Month

Regional PM2.5 Background by Month, 2008-2011 

(Low Wildfire Years)

Pinehurst Avg

PM2.5

Rural Agricultural

Area

Remote Class I

Areas

Regional

Background (avg

of RuraL &

Remote)
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Figure 2b. West Silver Valley PM2.5 Monthly Means for 2011-2013, excluding state flagged days affected 

by wildfire smoke for comparative non-regulatory purposes

PM2.5 at the Pinehurst monitor is highest during wintertime months (October through March months 10-12, 1-3 

in Figure 2a) due to residential wood combustion. In April, August and September (months 4, 8, 9), prescribed 

burning elevates PM2.5 in West Silver Valley above the regional background. During the warmer months, PM2.5

levels are lower overall and are similar to the regional background with a minor component from smoke

generated by agricultural burning/crop residue burning and forestry burning (prescribed and wildfire). Note the 

high values in winter with a considerable urban increment and low values in summer with little to no urban 

increment from local emission sources. Figure 2a, along with subsequent speciation related analyses, clearly 

shows the importance of evaluating on a monthly basis as opposed to quarterly seasonal basis as otherwise it 

would not be possible to separate the impact from prescribed burning from that of residential wood combustion. 

PM2.5 Composition Measurements - To assess potential emissions contributions for each violating monitoring 

location, the EPA determined the various chemical species comprising total PM2.5 to identify the chemical 

constituents over the analysis area, which can provide insight into the types of emission sources impacting the 
Page 15 of 43

monitored concentration. Figure 3 illustrates the fraction of each PM2.5 chemical constituent at the Pinehurst

monitoring site based on a comparison to the aforementioned Neil Frank 2008 study.

Figure 3. West Silver Valley, Idaho Wintertime PM2.5 Constituents

As illustrated in Figure 3, the majority of the monitored PM2.5 at the site during the winter are carbon based, 

92%. During the winter the remaining 8% of PM2.5 are shown to be 5.2% from ammonium sulfate and 

ammonium nitrate and the other 2.8% from crustal and other fine components. This speciation information 

aligns with the preliminary analyses of monthly data that projected the bulk of emissions coming during wood 

heating season and prescribed burning season. To analyze contributions for the summer and in the absence of 

summertime speciation data in the West Silver Valley, we analyzed PM2.5 -speciated IMPROVE data from the 

nearby Sula Peak and Cabinet peak monitors. These monitors measure year-round and provide speciated data 

for the summer months when the urban increment is small. For the summer period of May through August

2008-2011 the monthly average background ammonium sulfate concentration was 0.6 μg/m3and ammonium 

nitrate was 0.1 μg/m3. It is unlikely for the ammonium sulfate and ammonium nitrate concentrations at the 

Pinehurst monitor to be much higher than these values because these components are regional in nature and 

tend to have few sharp gradients. Considering that the combined ammonium sulfate and ammonium nitrate is 

well less than 1 μg/m3and wintertime carbon concentration is near 20 μg/m3, we find that sources of organic 

Total Carbon

91.95%

Ammonium 

Sulfate

2.50%

Ammonium 

Nitrate

2.68%

Crustal

0.52%

Other Fine

2.35%

Percent of Mass on West Silver Valley High-PM2.5 Winter 

Filters 16 filters from 2004-2006 (N. Frank 2008)
Page 16 of 43

and elemental carbon are the primary driver of nonattainment of the annual PM2.5 standard in West Silver 

Valley. 

The EPA assessed seasonal and annual average PM2.5 constituents at monitoring sites within the area relative to 

monitoring sites outside of the analysis area to account for the difference between regional background 

concentrations of PM2.5, and the local concentrations of PM2.5, also known as the “urban increment.” This 

analysis differentiates between the influences of emissions from sources in nearby areas and in more distant 

areas on the violating monitor. Estimating the urban increment in the area helps to illuminate the amount and 

type of particles at the violating monitor that are most likely to be the result of sources of emissions in nearby 

areas, as opposed to impacts of more distant or regional sources of emissions. Figure 4 includes a plot showing 

the monthly average Pinehurst monitored values and the Pinehurst monthly average urban increment defined as 

the Pinehurst monthly average minus the average of the rural and remote site monthly average. For purposes of 

this TSD, the average of the rural and remote background values was chosen for determining the urban 

increment so that it would include sources such as agricultural burning in the estimate of local PM2.5. The urban 

increment is much higher in the winter than other seasons. The emission inventory presented below and the 

speciation data lend confidence that wintertime primary emissions of PM2.5 are the main sources contributing to 

the monitor’s highest PM2.5 values throughout the year. With regard to crop residue burning, according to the 

Idaho Open Burning of Crop Residue State Implementation Plan (SIP) revision in April 2008, estimates of 

PM2.5 contribution from crop residue burning (CRB) range from as low as 0.004 μg/m3seasonally to a slightly 

higher value of 0.17 μg/m3annually. For this analysis the more conservative 0.17μg/m3annual value was used.

Figure 4. West Silver Valley, Idaho Urban Increment Analysis 
Page 17 of 43

Factor 2: Emissions and emissions-related data

In this designations process, for each area with a violating monitoring site, the EPA evaluated the emissions 

data from nearby areas using emissions related data for the relevant counties to assess each county’s potential 

contribution to PM2.5 concentrations at the violating monitoring site or monitoring sites in the area under 

evaluation. Similar to the air quality analysis, these data were examined on a seasonal basis. The EPA examined

emissions of identified sources or source categories of direct PM2.5, the major components of direct PM2.5

(organic mass, elemental carbon, crustal material (and/or individual trace metal compounds)), primary nitrate 

and primary sulfate, and precursor gaseous pollutants (i.e., SO2, NOX, total VOC, and NH3). The EPA also 

considered the distance of those sources of emissions from the violating monitoring site. While direct PM2.5

emissions and its major carbonaceous components are generally associated with sources near violating PM2.5

monitoring sites, the gaseous precursors tend to have a more regional influence (although the EPA is mindful of 

the potential local NOX and VOC emissions contributions to PM2.5 from mobile and stationary sources) and 

transport from neighboring areas can contribute to higher PM2.5 levels at the violating monitoring sites. 

Emissions Data

For this factor, the EPA reviewed data from the 2011 National Emissions Inventory (NEI) version 1 (see 

http://www.epa.gov/ttn/chief/net/2011inventory.html). For each county in the area of analysis, the EPA

examined the magnitude of county-level emissions reported in the NEI. These county-level emissions represent 

the sum of emissions from the following general source categories: point sources, non-point (i.e., area) sources, 

nonroad mobile, on-road mobile, and fires. The EPA also looked at the geographic distribution of major point 

sources of the relevant pollutants.18 Significant emissions levels from sources in a nearby area indicate the 

potential for the area to contribute to monitored violations.

To further analyze area emissions data, the EPA also developed a summary of direct PM2.5, components of 

direct PM2.5, and precursor pollutants, which is available at 

http://www.epa.gov/pmdesignations/2012standards/docs/nei2011v1pointnei2008v3county.xlsx.

In addition, the IDEQ supplied the EPA with an updated annual emission inventory for direct PM2.5 and all 

precursors for Shoshone County and surrounding counties. They also gave an emissions inventory for the West 

Silver Valley nonattainment area, including direct PM2.5 and all precursors, for specific months when PM2.5 is 

high, namely the wintertime period and the late autumn, and for the area of the county including Pinehurst and 

its geographically-constrained airshed.

When considered with the speciation comparison analysis in Factor 1, evaluating the components of direct 

PM2.5 and precursor gases can help identify specific sources or source types contributing to elevated 

concentrations at violating monitoring sites and thus assist in identifying appropriate area boundaries. In 

general, directly emitted particulate organic carbon (POC) and VOCs19 contribute to PM2.5 organic mass (OM); 

directly emitted EC contributes to PM2.5 EC; NOX, NH3 and directly emitted nitrate contribute to PM2.5 nitrate 

mass; SO2, NH3 and directly emitted sulfate contribute to PM2.5 sulfate mass; and directly emitted crustal 

 

18 For purposes of this designations effort, “major” point sources are those whose sum of PM precursor emissions (PM2.5 + 

NOx + SO2 + VOC + NH3) are greater than 500 tons per year based on NEI 2011v1.

19 As previously mentioned, nearby VOCs are presumed to be a less important contributor to PM2.5 OM than POC. 
Page 18 of 43

material contribute to PM2.5 crustal matter. 20,21 The EPA believes that the quantities of those nearby emissions 

as potential contributors to the PM2.5 violating monitors are somewhat proportional to the PM2.5 chemical 

constituents in the estimated urban increment. Thus, directly emitted POC is more important per ton than SO2, 

partially because POC emissions are already PM2.5 whereas SO2 must convert to PM2.5 and not all of the emitted 

SO2 undergoes this conversion. 

Table 3a provides a county-level emissions summary (i.e., the sum of emissions from the following general 

source categories: point sources, non-point (i.e., area) sources, nonroad mobile, on-road mobile, and fires) of 

directly emitted PM2.5 and precursor species for the county with the violating monitoring site and nearby 

counties considered for inclusion in the West Silver Valley area. Table 3B summarizes the directly emitted 

components of PM2.5 for the same counties in the area of analysis for the West Silver Valley area. Table 3b

breaks down the direct PM2.5 emissions value from Table 3a into its components.

Table 3a. County-Level Emissions of Directly Emitted PM2.5 and Precursors (tons/year)

County, State Total NH3 Total NOx Total Direct 

PM2.5

Total 

SO2

Total VOC Total

Clearwater, ID 1,466 1,241 6,857 532 19,598 29,694

Kootenai, ID 543 6,668 2,240 220 8,902 18,573

Bonner, ID 471 3,702 2,271 212 7,265 13,921

Shoshone, ID 525 1,329 2,707 212 8,181 12,954

Benewah, ID 481 803 1,550 134 4,443 7,411

Latah, ID 817 1,596 1,412 81 2,837 6,743

Sanders, MT 402 1,464 1,217 107 3,375 6,565

Mineral, MT 118 1,426 483 62 1,614 3,703

Table 3b. County-Level Emissions for Components of Directly Emitted PM2.5 (tons/year) 22

County, State POM EC PSO4 PNO3 Pcrustal Residual Total Direct

Clearwater, ID 5,674 730 33 62 89 269 6,857

Shoshone, ID 2,239 300 14 25 30 100 2,707

Bonner, ID 1,590 256 19 17 178 211 2,271

Kootenai, ID 1,262 252 30 10 336 351 2,240

Benewah, ID 1,175 162 10 13 91 99 1,550

Latah, ID 686 115 12 6 314 279 1,412

Sanders, MT 935 148 6 11 44 72 1,217

 

20 See, Seinfeld J. H. and Pandis S. N. (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 

2nd edition, J. Wiley, New York. See also, Seinfeld J. H. and Pandis S. N. (1998) Atmospheric Chemistry and Physics: 

From Air Pollution to Climate Change, 1st edition, J. Wiley, New York.

21 USEPA Report (2004), The Particle Pollution Report: Current Understanding of Air Quality and Emissions through 

2003, found at: http://www.epa.gov/airtrends/aqtrnd04/pm.html.

22 Data are based on the 2011 and 2018 Emissions Modeling Platform Data Files and Summaries

(ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform) available at: http://www.epa.gov/ttn/chief/emch/index.html#2011

(accessed 02/26/14).
Page 19 of 43

County, State POM EC PSO4 PNO3 Pcrustal Residual Total Direct

Mineral, MT 356 72 4 3 11 37 483

Reviewing the county level emission data in Table 3a, Shoshone County is average when compared to the 

surrounding counties in the area of analysis. Shoshone is roughly in the middle (43.6% of the highest) in terms 

of total PM2.5 and precursor emissions. Table 3b provides more meaningful information with respect to the 

PM2.5 issue in the West Silver Valley. The Table 3b values in the Shoshone County, where the West Silver 

Valley resides, clearly identify that emissions of organic carbon (POM and EC) are much higher than that of

sulfates and nitrates. Overall, in all counties organic mass is by far the largest constituent of primary PM2.5 

emissions followed to a lesser extent elemental carbon and crustal matter. This is consistent with the speciation 

data in that both the county-level emissions and monitored PM2.5 are dominated by organic carbon. It 

furthermore supports the conclusion that secondary ammonium sulfate and ammonium nitrate are not large 

contributors to the violating monitor. 

Figure 5a-1. West Silver Valley, Idaho nonattainment Area Emissions Inventory, Annual PM2.5

547.4

426.7

25.5

11.3

10.2

8.1

7.687.2 6.0 4.7 1.8 1.5 0.6 0.6 0.5 0.3 0.1 0.1 0.10.0 0.0

0.0

0.0 RWC

Prescribed Burning

Unpaved Roads

OnRoad

Industrial Combustion

Point Sources

NonRoad

Paved Roads

Forest Wildfires

Residential Open Burning

Mining & Quarrying

Commercial Cooking

Commercial Combustion

Airport

Ag Tilling

Construction

Charcoal Grilling

Vehicle & Structure FIres

Livestock

West Silver Valley PM2.5 Annual TPY Locomotives
Page 20 of 43

Figure 5a-2. West Silver Valley, Idaho nonattainment Area Emissions Inventory, Annual NOx

421.8

56.3

52.2

35.2

21.9

9.4

7.7

5.2

4.8 1.6 0.8

0.5 0.1 0.1 0.0

0.0

OnRoad

RWC

Prescribed Burning

NonRoad

Point Sources

Industrial Combustion

Residential Combustion

Locomotives

Commercial Combustion

Residential Open Burning

Forest Wildfires

Airport

Charcoal Grilling

Cremation

Vehicle & Structure FIres

CRB

West Silver Valley NOX Annual TPY
Page 21 of 43

Figure 5a-3. West Silver Valley, Idaho nonattainment Area Emissions Inventory, Annual VOC

1,199.0

701.1

192.3

145.2

113.6

34.8

30.1

27.6

20.4

19.0

9.9

5.0 2.5 1.0 0.6 0.6 0.4 0.3 0.3 0.2

0.2

0.1

0.0

0.0

0.0

0.0

0.0

Prescribed Burning

RWC

NonRoad

OnRoad

Point Sources

Fuel Distribution

Consumer Products

Surface Coating

Forest Wildfires

Asphalt Application

Portable Fuel Containers

Degreasing

Residential Open Burning

Airport

Graphic Arts

Industrial Combustion

Residential Combustion

Commercial Combustion

Commercial Cooking

POTW

Locomotives

Vehicle & Structure FIres

Charcoal Grilling

Livestock

Cremation

West Silver Valley VOC Annual TPY
Page 22 of 43

Figure 5a-4. West Silver Valley, Idaho nonattainment Area Emissions Inventory, Annual SO2

33.0

10.0

7.6

4.5

1.1

1.0

0.5

0.3

0.2 0.1 0.1 0.0

0.0

0.0

Prescribed Burning

RWC

Point Sources

Industrial Combustion

OnRoad

Residential Combustion

Forest Wildfires

Commercial Combustion

Residential Open Burning

NonRoad

Airport

Locomotives

Cremation

CRB

West Silver Valley SO2 Annual TPY
Page 23 of 43

Figure 5a-5. West Silver Valley, Idaho nonattainment Area Emissions Inventory, Annual NH3

83.4

31.5

4.4

1.8

1.5

1.4

1.4 0.4 0.2 0.1 0.1 0.0 0.0 0.0 0.0

0.0

0.0

Prescribed Burning

RWC

OnRoad

Fertilizer Application

Residential Combustion

Forest Wildfires

Livestock

Residential Open Burning

Industrial Combustion

Surface Coating

NonRoad

POTW

Commercial Combustion

Graphic Arts

Locomotives

Landfills

CRB

West Silver Valley NH3 Annual TPY
Page 24 of 43

Figure 5b. West Silver Valley, Idaho nonattainment Area Emissions Inventory, Monthly, Wintertime

(December)

76.71

2.12

1.08

1.00

0.85

0.67

0.67

0.60

0.50

0.40

0.18 0.15 0.06 0.05 0.04 0.02 0.02 0.01 0.01 0.01

0.00

0.00

0.00

0.00

0.00

RWC

Unpaved Roads

NonRoad

Prescribed Burning

Industrial Combustion

Point Sources

OnRoad

Paved Roads

Residential Open Burning

Mining & Quarrying

Commercial Combustion

Commercial Cooking

Ag Tilling

Airport

Construction

Charcoal Grilling

Residential Combustion

Vehicle & Structure FIres

Locomotives

Livestock

Cremation

Ag Harvesting

West Silver Valley PM2.5 December Forest Wildfires
Page 25 of 43

Figure 5c. West Silver Valley, Idaho nonattainment Area Emissions Inventory, Monthly, Fall (October)

The West Silver Valley nonattainment area direct PM2.5 emissions data in Figures 5a-1, 5b, and 5c breakout 

direct PM2.5 by sources source category annually, in the wintertime (December), and in the autumn (October), 

respectively. Emissions in the autumn and winter are relevant because these seasons are when the urban 

increment is the highest. When reviewing the emissions inventory for the West Silver Valley nonattainment 

area within Shoshone County, the data shows in Figure 5a-1 that annually residential wood combustion (574.4 

tpy) and prescribed burning (426.7 tpy) are the major sources categories of direct PM2.5 with the next highest 

source category being only 25.5 tpy. For wintertime emissions, residential wood combustion is 76.7 tpm and 

the next highest sector is prescribed burning at only 2.12 tpm (December used as a representative month). For 

81.86

59.73

2.12

1.26

0.85

0.67

0.63

0.50

0.44

0.40 0.15 0.12 0.05 0.05 0.04 0.02 0.01 0.01 0.01 0.01

0.00

0.00

0.00

0.00

0.00

Prescribed Burning

RWC

Unpaved Roads

OnRoad

Industrial Combustion

Point Sources

Paved Roads

Residential Open Burning

NonRoad

Mining & Quarrying

Commercial Cooking

Commercial Combustion

Airport

Ag Tilling

Construction

Charcoal Grilling

Vehicle & Structure FIres

Locomotives

Livestock

Residential Combustion

Ag Harvesting

Cremation

West Silver Valley PM2.5 October TPM Forest Wildfires
Page 26 of 43

autumn emissions, residential wood combustion is 59.7 tpm and prescribed burning 35.6 tpm (October used as a 

representative month). This supports the conclusion that the residential wood combustion and prescribed 

burning source categories are the major sources of direct PM2.5 and that residential wood combustion is 

increased during the wintertime and that prescribed burning peaks during the fall. As seen in the chemical 

speciation comparative study depicted in Figure 3, the primary constituents at the Pinehurst monitor are in the 

form of total carbon and Table 3b breaks down the emissions into organic carbon and elemental carbon. The 

information in Figures 5a, 5b, and 5c, and in Tables 3a and 3b is consistent with the measured PM2.5 speciation 

in Figure 3. These emissions and subsequently identified constituents are characteristic of residential wood 

combustion that occurs during the wintertime months and prescribed burning during the early spring and early 

autumn months. As mentioned already, CRB was explicitly modeled by IDEQ as 0.17μg/m3annually.

With regard to precursor emissions in the West Silver Valley nonattainment area, the IDEQ also prepared 

annual emissions inventories for each PM2.5 precursor (Figures 5a-2 through 5a-5). For NOx, Figure 5a-2

identifies the top three source categories as being onroad, residential wood combustion, and prescribed burning. 

For VOCs, Figure 5a-3 identifies the top three source categories as being prescribed burning, residential wood 

combustion, and nonroad. For SO2, Figure 5a-4 identifies the top three source categories as being prescribed 

burning, residential wood combustion, and point sources. For NH3, Figure 5a-5 identifies the top source 

categories as being prescribed burning and residential wood combustion.

Point Sources

In addition to reviewing county-wide and nonattainment area emissions of PM2.5 and PM2.5 precursors, the EPA 

also reviewed emissions from point sources located in the area of analysis. The magnitude and location of these 

sources can help inform nonattainment boundaries. Table 5 provides facility-level emissions of direct PM2.5, 

components of direct PM2.5, and precursor pollutants (given in tons per year) from point sources located in the 

area of analysis for the West Silver Valley area. Table 5 also shows the distance from the facility to the 

Pinehurst monitor. The distance from the violating monitoring location is particularly important for directly 

emitted PM2.5. The influence of directly emitted PM2.5 on ambient PM2.5 diminishes more than that of gaseous 

precursors as a function of distance.23 Only six of the sources have total emissions of direct PM2.5 and 

precursors greater than 50 tons per year. Only one facility emits more than 100 tons per year of any pollutant.

As indicated in Figure 6, there are 11 point sources located in the Area of Analysis in Shoshone, Benewah, and 

Kootenai Counties. There are no major sources of PM2.5 near the monitor. All point sources within 20 miles of 

the violating monitor have fewer than six tons per year of direct PM2.5 emissions. The largest point sources for 

direct PM2.5 emit roughly 100 tons per year but are 22 and 34 miles from the violating monitor and are separated 

from the violating monitor by hills and mountains. 

 

23 Baker, K. R. and K. M. Foley. A nonlinear regression model estimating single source concentrations of primary and 

secondarily formed PM2.5. Atmospheric Environment. 45 (2011) 3758-3767.
Page 27 of 43

Table 5. NEI 2011 Point Source Emissions (tons/year) 

Distance NEI 2011 v1 Emissions - Tons/Year

County, 

State

Map 

Point

monitor 

(miles)

NH3 NOx PM2.5 SO2 VOC Tot

al

Shoshone, 

ID

P-1

Potlatch Land & Lumber – St. Maries Complex 35.5 - 36 28 1 - 65

Kootenai,

ID

P-2

Plummer Forest Products Inc. – Post Falls 54.3 - 80 19 2 - 101

Kootenai, 

ID

P-3

Idaho Forest Group LLC – Riley Creek-Chilco 57.8 - 65 20 7 -- 92

Kootenai, 

ID

P-4

TransCanada GTN Systems, Athol Station #5 35.7 - 72 1 1 - 74

Kootenai, 

ID

P-5

Rathdrum Power LLC 58.5 -99 66 

(PM10)

11 - 176

Benewah, 

ID

P-6

Stimson Lumber Co St. Maries 32.9 - - 3 - - 3

Shoshone, 

ID

P-7

Kellogg School District 391 7.8 - <1 <1 

(PM10)

<1 - 1

Shoshone, 

ID

P-8

North Fork Cedar, dba Kingston Cedar 5.1 - -

6 

(PM10)

- - 6

Shoshone, 

ID

P-9

Dave Smith Body Shop 3.8 - 4

<1 

(PM10)

<1 - 4

Shoshone, 

ID

P-10

Enyeart Cedar Products 5.2 - 12 1 

(PM10)

7 - 20

Shoshone, 

ID

P-11

Essential Metals Corp. 12.9 - 5 <1 - - 5

Shoshone, 

ID

P-12

Shoshone Funeral Service 8.9 - -

<1 

(PM10)

- - <1 

Benewah, 

ID

P-13 Potlatch Land & Lumber, LLC – St. Maries Complex on 

Coeur d’Alene Reservation*

22.0 - 96 97 13 68 274

Benewah, 

ID

P-14

Stimson Lumber Company – Plummer Operation* 33.7 - 99 101 12 41 253

Sources: IDEQ analysis. *Provided by IDEQ and CDA Tribe
Page 28 of 43

Figure 6. Point Sources in the Area of Analysis for the West Silver Valley Area.

Using the information in Table 5 and Figure 6, the IDEQ provided supplemental information regarding the 

impact of point sources in the West Silver Valley. The IDEQ performed Calpuff modeling and developed a tool 

to determine the impact of sources in the modeled domain around the violating monitor. More detailed 

information on source contribution and modeling can be found in the docket for the Federal Register Notice for 

this nonattainment area.
Page 29 of 43

Population density and degree of urbanization

In this part of the five factor analysis, the EPA evaluated the population and vehicle use characteristics and 

trends of the area as indicators of the probable location and magnitude of non-point source emissions that might 

contribute to the violations. Rapid population growth in a county on the urban perimeter signifies increasing 

integration with the core urban area, and indicates that it may be appropriate to include the county associated 

with area source and mobile source emissions as part of the nonattainment area. Table 6 shows the 2000 and 

2010 population, population growth since 2000, and population density for each county in the area.

Table 6a. Population Growth and Population Density, County.

County, 

State

Population 

2000

Population 

2010

% Change 

from 2000

Land 

Area (Sq. 

Miles)

Population 

Density 

(per Sq. 

Mile) %

Cumulative

%

Kootenai, ID 108,685 138,920 27.8 1,245 112 53 53

Bonner, ID 36,835 40,940 11.1 1,738 24 16 69

Latah, ID 34,935 37,298 6.8 1,077 35 14 83

Shoshone, ID 13,771 12,727 -7.6 2,634 5 5 88

Sanders, MT 10,227 11,397 11.4 2,762 4 4 92

Benewah, ID 9,171 9,281 1.2 776 12 4 96

Clearwater, ID 8,930 8,633 -3.3 2,461 4 3 99

Mineral, MT 3,884 4,216 8.5 1,220 3 1 100

Total 226,438 263,412

Source: U.S. Census Bureau population estimates for 2000 and 2010 

Table 6b. Population Growth, Cities in the Silver Valley.

County, 

State

Population 

2000

Population 

2010

% Change 

from 2000

Cataldo NA NA NA

Kingston NA NA NA

Pinehurst 1,619 1,615 -0.24

Smelterville 627 626 -0.16

Kellogg 2,120 2,113 -0.33

Wardner 188 188 0

Osburn 1,555 1,549 -0.39

Wallace 784 782 -0.26

Source: http://factfinder2.census.gov/

Within Shoshone County, there are pocket communities along the I-90 corridor and the county is overall 

sparsely populated at only 5 people per square mile. Surrounding counties population densities range from 3 –

35 people per square mile and overall populations ranged from 4,216 to 40,940 for entire counties. The outlier 

of the group was Kootenai at a still sparse 112 people per square mile and 138,920 people in the county. The 
Page 30 of 43

population in Shoshone County decreased 7.6% between 2000 and 2010. The only nearby CBSA/CSA was 

located in Kootenai County. 

There are several small towns along the Interstate 90 (I-90) corridor that bisect Idaho's panhandle along the 

Silver Valley. According to census data, the towns within the West Silver Valley nonattainment area range in 

size: from the largest, Kellogg with 2,113 residents, to the second-largest, Pinehurst with 1,615, and the 

smallest, Smelterville with 626. Just outside the nonattainment area the towns of Osburn, Wallace, and 

Wardner are very sparsely populated at 1,549, 782, and 188 respectively. The counties in the area of analysis 

have very low populations and very low density. 

As mentioned already, Pinehurst is located in a partially obstructed topographic bowl within the Silver Valley. 

The Silver Valley is also the location of the Bunker Hill Superfund site which is a large mining site that has 

been undergoing remediation since the 1980s. The mining site currently has only minor emissions associated 

with movement of product from the underground mining sites. The majority of the emissions as shown by the 

West Silver Valley specific emissions inventory and supporting speciation analysis is generated from residential 

wood combustion in close proximity to the violating monitor. With low employment rates and no major 

economic base, the use of woodstoves for inexpensive heating is common. Secondarily, emissions from 

prescribed burning generated in and around the Silver Valley are also expected to contribute to the exceedances 

at the violating monitor as shown by the discussion of Factor 4. With a very small and very sparse population, 

it is not projected that population changes will drive any major change in emissions in the West Silver Valley.
Page 31 of 43

Figure 7. 2010 County-Level Population in the Area of Analysis for the West Silver Valley Area.
Page 32 of 43

Traffic and Vehicle Miles Travelled

High vehicle miles travelled (VMT) and/or a high number of commuters associated with a county is generally 

an indicator that the county is an integral part of an urban area. Mobile source emissions of NOx, VOC, and 

direct PM2.5 may contribute to ambient particulate matter that contributes to monitored violations of the 

NAAQS in the area. In combination with the population/population density data and the location of main 

transportation arteries, an assessment of VMT helps identify the probable location of nonpoint source emissions

that may contribute to violations in the area. Table 7 shows 2011 VMT while Figure 8 overlays 2011 countylevel VMT with a map of the transportation arteries.

Compared to the surrounding counties, Shoshone County has only 7% of the total region-wide VMT based on 

the 2011 data. The majority of those miles are from cross state traffic in the I-90 corridor. Given the limited 

amount of VMT in Shoshone County, traffic and commuting patterns were not key factors in this area 

designation. The VMT information was sourced from the Federal Highway Administration

Table 7. 2011 VMT for the West Silver Valley Area.

County, State Total 2011 VMT Percent Cumulative %

Kootenai, ID 1,448,700,416 47 47

Bonner, ID 511,021,336 16 63

Latah, ID 342,088,444 11 74

Mineral, MT 235,209,168 8 82

Shoshone, ID 215,796,356 7 89

Benewah, ID 122,030,599 4 93

Sanders, MT 116,083,370 4 97

Clearwater, ID 87,808,049 3 100

Total 3,078,737,738 100

http://www.census.gov/hhes/commuting/data/commuting.html
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Figure 8. Overlay of 2011 County-level VMT with Transportation Arteries
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Figure 9. Source Category Contribution for West Silver Valley Area

Figure 9 summarizes the analysis of relevant county-level emission source categories and the contribution to the 

urban increment in the area. As already shown in this section and the prior sections of this TSD, residential 

wood combustion is the major contributor to the urban increment at 85.1% and prescribed burning at 8.9%. The 

remaining sources categories were all below 2.5% contribution: road dust – 2.3%, CRB contribution - 2.0%, 

residential open burning – 1.1%, point source 0.6%. The analysis in this section and the modeling discussion in 

the next section, Factor 3: Meteorology, supports the conclusion that residential wood combustion and 

prescribed burning are the primary contributors to violations at the Pinehurst monitor. The analysis in this 

section also provided support that point sources and CRB were not major sources of contribution to violations at 

the monitor. Review of population and VMTs also showed them to be not significant in terms of impact on the 

area designation process given that they were both small values.
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Factor 3: Meteorology

The EPA evaluated available meteorological data to determine how meteorological conditions, including, but 

not limited to, weather, transport patterns, and stagnation conditions, could affect the fate and transport of 

directly emitted particulate matter and precursor emissions from sources in the area of analysis. The EPA used 

three primary tools for this assessment: wind roses, woodsmoke box modeling provided by the IDEQ, and 

HYSPLIT back trajectories provided by the IDEQ. In addition, the IDEQ added supplemental reverse Calpuff 

modeling. When considered in combination with area PM2.5 composition and county-level and facility 

emissions source location information, the meteorological assessment tools helped identify the area that 

contributes to violations at the Pinehurst monitor. 

Wind roses are graphic illustrations of the frequency of wind direction and wind speed. Wind direction can 

indicate the direction from which contributing emissions are transported; wind speed can indicate the force of 

the wind and thus the distance from which those emissions are transported. The IDEQ constructed wind roses 

from hourly observations of wind direction and wind speed using 2009-2012 data from National Weather 

Service locations archived at the National Climate Data Center.24 When developing these wind roses, the IDEQ 

also used wind observations collected at meteorological sampling stations collocated at air quality monitoring 

sites, where these data were available. Figure 10a shows wind roses from data relevant in the West Silver 

Valley area.

 

24 ftp.ncdc.noaa.gov/pub/data/noaa or 

http://gis.ncdc.noaa.gov/map/viewer/#app=cdo&cfg=cdo&theme=hourly&layers=1&node=gis Quality assurance of the 

National Weather Service data is described here: http://www1.ncdc.noaa.gov/pub/data/inventories/ish-qc.pdf
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Figure 10a. Wind Roses in the Area of Analysis for West Silver Valley Area.

Steering winds in this region of Northern Idaho are westerlies, which often translates into south westerlies near 

the surface. Topography plays a role in channeling winds along valleys in particular meteorological situations. 

In West Silver Valley the predominant wind directions are southwesterly and westerly, but during pollution 

episodes the wind can also be southerly or northeasterly. During calm, wintertime weather conditions, cold 

surface temperatures, low wind speeds, and constrained vertical mixing develop in concert with each other. The 

deep, narrow mountain valley magnifies this effect relative to other nearby areas. The combination of these 

meteorological effects and the mountainous terrain confine the geographical area that could contribute 

emissions to the pollution episode in West Silver Valley. It is hypothesized that the particular terrain features 

around Pinehurst limit mixing in Pinehurst even more so than the rest of the West Silver Valley. In Figure 10b, 

the wintertime pollution rose supports the conceptual model for elevated PM2.5 in the West Silver Valley. Light 

westerly winds are often associated with high PM2.5 in West Silver Valley because the monitor is located on the 

east side of town. The light westerly winds collect wintertime residential wood combustion PM2.5 and carry it to 

the monitor to the east where it is constrained within the east side of the topographic bowl. At night cold air 

drainage winds follow the stream valleys from the higher terrain around West Silver Valley and into the river 

valley. These winds could bring smoke from smoldering prescribed burns from higher elevation into West 

Silver Valley. Stronger down-valley northeast winds clear the valley of pollution, while at other times weak 

northeast winds advect pollution to the monitor. As such, the nonattainment area boundary follows the local 
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hydrologic units in order to capture the emissions in the valley as well as that on the surrounding mountains that 

could drain at night into West Silver Valley. 

Figure 10b. Pollution Roses, 2011-2013 at the Pinehurst monitor in the West Silver Valley during the 

wintertime

In addition to wind roses, the IDEQ provided a wintertime woodsmoke model and HYSPLIT back trajectories 

for the autumn prescribed burning season. The IDEQ modeled woodsmoke in the towns of Pinehurst and 

Kellogg with a box model derived from the dimensions of the Silver Valley and climatological mixing heights 

during wintertime stagnation events. The simple model was able to account for greater than 90% of the 

wintertime urban increment. It corroborates our conceptual model of low wintertime wind speeds and low 

mixing heights trapping locally-emitted woodsmoke in the winter with little contribution from sources outside 

the immediate Silver Valley. The autumn HYSPLIT trajectories show how the mountainous terrain channels 

smoke from prescribed burning. The St. Joe Mountains acts as a topographic barrier between the burning in the 

south and the Pinehurst monitor. Smoke that breaches the topographical barriers around the Silver Valley would 

occur on days with very good mixing and thus not concentrate smoke in Pinehurst. However, smoke from 

prescribed burning in the nearby hills around Pinehurst can reach the valley floor during particular weather 

conditions. Under calm weather conditions, it is common for cold air to drain out of the hills and into the 

valleys during the evenings and nighttime hours. If a fire is still producing smoke into the evening, the smoke 

would drain to the valley floor and be trapped until at least the next morning. For the most important sources of 
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PM2.5 in the urban increment, woodsmoke and prescribed burning, meteorology during the winter and autumn 

suggest a boundary constrained by topography.

As supplemental information, the IDEQ also generated Calpuff 25 dispersion modeling to further illustrate how 

topography and meteorology limit the geographical extent of contributions to the violating monitor. While the 

EPA does not generally endorse this Calpuff modeling effort, EPA Region 10 worked closely with IDEQ to 

understand how the results inform our understanding of source contributions. To understand elevated emissions 

at the monitor, the IDEQ ran Calpuff modeling to turn the spatially-allocated and temporally-allocated emission 

inventory into contributions of PM2.5 at the violating monitor in West Silver Valley. The IDEQ ran a prescribed 

burning case for October 15th – November 15th, 2013 to represent the autumn prescribed burning season and a 

case for November 15th – December 15th, 2013 to represent the winter woodsmoke season. The results were 

apportioned to the urban increment in the appropriate months and analyzed for the annual average PM2.5. The 

results show that the source categories within the nonattainment area most available for control strategies –

residential wood combustion, prescribed burning, residential outdoor burning, point sources, and road dust –

account for about 95% of the annual urban increment. The Calpuff results are supportive of the conclusion 

relevant to boundary conditions derived from other analysis tools in this document that the vast majority of 

contribution to the urban increment is in the West Silver Valley.

These analyses support EPA’s conclusion that the boundary recommendation was appropriate and that no other 

nearby areas were contributing to the monitored violations. More detailed information on source contribution 

and modeling can be found in the docket for the Federal Register Notice for this nonattainment area. 

Factor 4: Geography/topography

To evaluate the geography/topography factor, the EPA assessed physical features of the area of analysis that 

might define the airshed and thus affect the formation and distribution of PM2.5 concentrations over the area. 

As mentioned, the West Silver Valley monitor is located within the city of Pinehurst which is located within the 

Silver Valley. Figures 11a and 11b depict West Silver Valley and the area immediately in its vicinity. The 

topography along with the wind rose, woodsmoke modeling, HYSPLIT trajectories, and supplemental Calpuff 

modeling all support that well over 90% of the emissions are generated in and around West Silver Valley. By 

far the highest PM2.5 values occur in the winter (Figure 2a and 4a), and these are concentrated on cold stagnant 

days. Cold temperatures promote the burning of wood for heat. Winds are light and strongly stable cold air 

pools in the Silver Valley. It is for this reason that the various analysis tools show very little influence on the 

West Silver Valley monitor from emission sources outside the valley. Even compared to the town of Kellogg a 

few miles upriver, West Silver Valley has lighter winds during these stagnant periods. 

While the nearby contribution of emissions is over 90%, the data support a small contribution of emissions from 

prescribed burning in the hills, mountains, and valleys that drain into West Silver Valley as a whole. Prescribed 

burning that occurs in the higher elevations outside of town would be transported into West Silver Valley if 

smoldering burns happen to coincide with meteorology conducive for cold air drainage winds. In order to 

capture this smaller source of contribution, the area boundary was drawn around the major topographic drainage 

features called hydrologic unit codes (HUCs), Figure 11c. Essentially, the HUCs are geographic based units 

that define watersheds and have been adapted for purposes of identifying a geographic boundary for the PM2.5

 

25 http://www.cleanairinfo.com/regionalstatelocalmodelingworkshop/documents/CALPUFF.pdf
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generated around the Pinehurst monitor. The West Silver Valley area includes the full HUC that directly drains 

into West Silver Valley, along with the two HUCs to the north and south east that also drain into West Silver 

Valley and the Silver Valley. Finally, a portion of a 4th HUC to the West of Pinehurst is included as it drains 

into the West Silver Valley as well. The full HUC was not used on the western boundary for jurisdictional 

reasons to be discussed in the following section.

Topography strongly dictates what emissions get funneled into West Silver Valley. It is equally important in 

limiting regional transport from outside of the non-attainment area. The St. Joe Mountains to the south of West 

Silver Valley and the rocky terrain in between provide a physical barrier between emissions from numerous 

burning activities to the south of the mountain range. In order for emissions from these burns to reach West 

Silver Valley, they would need to be lofted above the mountain range and then find their way back to the 

surface on the north side. This is not an impossible event, but generally if the smoke is lifted that high up it will 

both get diluted in the process and will be carried by the transport winds in an eastward direction. Nighttime 

drainage winds on the south side of the St. Joe Mountains would go in an opposite direction from West Silver 

Valley. The HYSPLIT trajectories and supplemental Calpuff modeling confirms this conceptual result by 

modeling a minimal impact in West Silver Valley from burning activities south of the mountain range.

Metropolitan areas outside the valley, such as Coeur D’Alene and Spokane, are unlikely to contribute to the 

violating monitor. Design values in Coeur D’Alene and Spokane are well below the annual standard (see Table 

2a) and so there is not much locally-generated pollution to transport to the West Silver Valley. During cold 

stagnant periods when PM2.5 is the highest, the mountainous terrain strongly limit the ability of the already 

weak transport winds to bring pollution from the metropolitan areas into the valley. In the autumn prescribed 

burning is a larger source of PM2.5 than the metropolitan areas, and at other times of the year the PM2.5 in 

Pinehurst is very close to remote background levels. 
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Figure 11a Pinehurst monitor within West Silver Valley Topography
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Figure 11b West Silver Valley Topography
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Figure 11c– Hydrologic Unit Code (HUC) boundaries in the West Silver Valley Area

Factor 5: Jurisdictional boundaries

In defining the boundaries of the West Silver Valley nonattainment area, the EPA considered existing 

jurisdictional boundaries, which can provide easily identifiable and recognized boundaries for purposes of 

implementing the NAAQS. Existing jurisdictional boundaries often signify the state and local governmental 

organization with the necessary legal authority for carrying out air quality planning and enforcement functions 

for the area. Examples of such jurisdictional boundaries include existing/prior nonattainment area boundaries 

for particulate matter, county lines, air district boundaries, township boundaries, areas covered by a 

metropolitan planning organization, state lines, and Reservation boundaries, if applicable. Where existing 

jurisdictional boundaries were not adequate or appropriate to describe the nonattainment area, EPA considered 

other clearly defined and permanent landmarks or geographic coordinates for purposes of identifying the 

boundaries of the designated areas.

For the majority of the boundary, the EPA relied on the geography based HUC features given that the majority 

of emissions were located close to the violating monitor in West Silver Valley and the minor contributions from 
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prescribed burning were facilitated by topography draining emissions towards the monitor within the boundary 

and otherwise providing a barrier for emissions from outside the valley. The EPA is using the Shoshone 

County boundary as the jurisdictional boundary on the west side of the nonattainment area boundary.

Conclusion for the West Silver Valley Area

Based on the assessment of the five factors described above, both individually and in combination, the EPA is 

designating the following partial county as the West Silver Valley nonattainment area because it is violating the 

2012 annual PM2.5 NAAQS: Shoshone County, Idaho – partial. When reviewing the first factor of the five 

factor analysis, Air Quality Data, the air quality monitoring site in West Silver Valley, Shoshone County 

indicate violations of the 2012 annual PM2.5 NAAQS based on the 2013 DVs. Air quality data from the 

surrounding area do not show a violation. Further review of Factor 1 – Air Quality Data, and Factor 2 

Emissions related data, identified that the nature of the violations was seasonal and had to be evaluated on a 

monthly basis. This analysis showed that the bulk of the high PM2.5 values were coming during the winter time 

and could be attributed to residential wood combustion, which historically has been a major source of elevated 

particulate matter in the area. The analysis also showed a much smaller but relevant contribution from 

prescribed burning in the shoulder months, April and October/November, of the heating season. A speciation 

analysis confirmed these assertions, and further evaluation of contributing sources from the area confirmed that 

emissions from major industrial sources and motor vehicles were not particularly contributing s to the problem, 

and more importantly, that close to 95% of the emissions were coming from within the West Silver Valley

nonattainment area located within Shoshone County, ID. Once the major sources of emissions had been 

identified and modelled, Factor 3- Meteorology and Factor 4 – Geography/topography, were utilized to ensure 

that the majority of contributing emissions were captured with the nonattainment area boundary. The final 

refinement of the West Silver Valley nonattainment area boundary came with consideration of Factor 5 –

Jurisdictional boundaries.