1

Source Contributions Analysis for the 

Pinehurst, Idaho Nonattainment 

Designation

1 Introduction

The purpose of this analysis is to determine the major source categories contributing to the Pinehurst, 

Idaho PM2.5 annual average concentrations, which currently exceed the Annual National Ambient Air 

Quality Standard (NAAQS) for PM2.5 (>12 μg/m3) based on the most recent 3 years of monitoring, and to 

determine what sources cause or contribute to the violation. A source’s location and its contribution to the 

monitored values are critical inputs in determining the boundaries of a nonattainment area. After 

identifying the major contributors on high PM2.5 days, the source locations, source emissions, wind 

patterns, and topographical influences can be used to identify the sources whose emissions cause or

contribute to most of the local-source (i.e. “Urban Increment”) PM2.5 experienced by the people in 

Pinehurst and the nearby Silver Valley. 

 

2 Pinehurst Winter Season PM2.5 Filter Speciation

2.1 Definition of Seasons. 

An examination of the PM2.5 concentrations measured on the filters collected from the Federal Reference 

Method (FRM) sampler in Pinehurst over the past 3 years (Figure 1) indicates that the highest 24-hour 

concentrations occur in the cooler season and, excluding days flagged for wildfire impacts, there are no 

filters with a PM2.5 concentration greater than 12 μg/m3during the period from April through September, 

as indicated by the “salmon colored” boxes with the last date and first date each year with an FRM filter 

above 12 μg/m3. As a result, while all months contribute to an annual average PM2.5 concentration, the 

April-September period is similar to background levels suggesting that controlling local sources will have 

very little effect in lowering the annual average during that period. Most of the high days are in the winter 

months, with a secondary peak around mid-October to mid-November when prescribed burning (Rx) 

activities are at their peak (refer to Section 5 for Rx burning patterns). Based on the PM2.5 temporal 

pattern and knowledge of predominant sources throughout the year, DEQ defines for the purpose of this 

analysis the “Low Season” as April 1 – September 30, the “Rx Season” as October 1 through November 

19, and the “Winter Season” as November 20 – March 31. Note, there appears to be a third cluster of 

elevated cool-season peaks each year in March, which may result from small private prescribed burners, 

but the burn databases don’t show permitted burning activity so March is retained as part of the Winter 

Season, even though those peaks may be influenced by private prescribed burning activity.
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Figure 1 Pinehurst PM2.5 Filters 2011-2013, showing the last and first dates of values above 12 μg/m3 (salmon 

colored boxes), and seasons defined as Low Season (April 1 -Sept 30), Winter Season (Nov 20 - March 31) and 

Prescribed Burning (Rx) Season (Oct 1 – Nov 19). 

2.2 Speciation of “cool season” PM2.5

The only speciation data available for Pinehurst was reported by Neil Frank in his 2008 study of archived 

filter speciation and SANDWICH analysis (Frank, 2008). Frank obtained 16 “cool season” Pinehurst 

filters from IDEQ for the period 2004-2006. The filters were all days with high PM2.5 concentrations 

ranging from 28 to 57 μg/m3, collected between October 27 and February 3, with four in what DEQ refers 

to in Figure 1 as the “Rx Season” and twelve in the Figure 1 “Winter Season”. No significant change in 

sources or burning practices has occurred since 2006 with the exception of a small number of woodstove 

replacements with EPA-certified stoves. The percent mass speciation from the Frank study is shown in 

Figure 2. The speciation analysis of archived filters as conducted in this study was not capable of directly 

analyzing organic carbon (OC) or elemental carbon (EC), however the total carbon mass can be 

determined by difference between the total filter mass and all the other species (shown in Figure 2). 

Frank showed this carbon mass estimation method to be highly correlated with the full speciation network 

samples which utilized a separate quartz filter with direct carbon analysis so this is a reasonable approach. 

In his study, Frank showed that during high-PM2.5 days in the Rx and Winter seasons in Pinehurst, the 

PM2.5 is composed almost entirely of carbon (92%), with less than 3% each of “other fine” mass,

ammonium nitrate, and ammonium sulfate, and only 0.5% of crustal/geologic sources such as paved and 

unpaved road dust.

Since the “Low Season” rarely has a day over 12 μg/m3(and no FRM filters in 2011-2013 other than 

wildfire influenced days) sources during that period do not contribute to pushing the 3-year annual Design 

Value (DV) over 12 μg/m3. In addition, the available speciation data indicates that only carbon sources 

contribute significantly to the high PM2.5 period from October through March when the emission 

inventory suggests that Rx and RWC are the predominant sources (Figure 3). This simplifies the list of 

potentially contributing sources during the cool period when PM2.5 levels are above 12 μg/m3. 
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Figure 2 Pinehurst Mass Speciation from 2004 - 2006 filters analyzed by EPA (Frank, 2008)

Figure 3 October and December PM2.5 Emission Inventory for western Silver Valley based on 2011 NEI. October 

represents the peak prescribed burning (Rx) season, while December represents the Winter Residential Wood 

Combustion (RWC) season. The EI data confirm that the carbon mass on the speciated filters (Figure 2) results 

primarily from Rx and RWC in October and primarily from RWC in December and the remainder of the cool season.
4

3 Regional Background PM2.5 and Urban Increment

The monthly averaged PM2.5 concentration at the Pinehurst monitor for the years 2008-2011 are shown in 

Figure 4 (red squares). These years are selected because they exclude the periods of heavy wildfire 

influence in 2007, 2012 and 2013. If heavy wildfire years had been included, they would have masked the 

normal temporal pattern and would not represent controllable sources in the local area. In order to 

understand the effect of local sources and meteorology in Pinehurst in comparison to the regional 

background, monthly-averaged background concentrations are also shown in Figure 4. The regional 

background sites appear to cluster into two distinct patterns: “Rural/Agricultural” sites and “Remote”

sites. The Rural/Ag monitors are at those sites in rural-agricultural areas in the region where the monitor 

is typically on the outskirts of town and typically where the terrain is relatively flat and well ventilated so 

wintertime inversions resulting from cold air pools do not develop as they do in Pinehurst. The monitors 

at Moscow, Idaho and at Pullman, Rosalia, LaCrosse and Welpinit, Washington represent the Rural/Ag

background. The Remote background is represented by the IMPROVE monitoring sites at two Class I 

wilderness areas in the region: the Cabinet Mountains and Sula Peak (representing the Selway-Bitterroot 

Wilderness), both near the Idaho-Montana border. The Remote background represents virtually no 

local/urban/roadway sources, while the Rural/Ag sites are expected to include some wintertime urban 

contribution and some agricultural influences. DEQ believes that the true background for Pinehurst 

would be somewhere between the Rural/Ag and the Remote background levels, so both are retained to 

provide a bounding estimate for the regional background.

The “Urban Increment” (UI) is defined as the concentrations occurring at the monitor of interest with the 

regional background subtracted. Thus, the UI represents the local/urban sources that are close enough to 

contribute to the Pinehurst monitor concentrations. Two versions of Urban Increment, one using Rural/Ag

background and one using the Remote background, are shown in Figure 5 along with the Pinehurst 

measured concentrations prior to removal of the background. 

Examination of the Pinehurst and regional background temporal patterns (Figure 4) reveals that Pinehurst 

is unique with greatly increasing PM2.5 levels in the cooler months. In addition, the Pinehurst and both 

regional background patterns reflect an increase in July through September, believed to result from 

residual wildfires (even in low-wildfire years) and perhaps some contribution from regional influences of

Crop Residue Burning (CRB) which occurs in Western Idaho, including on the Coeur d’Alene 

Reservation, and in Eastern Washington. Finally, the Pinehurst profile exhibits a prominent “bump” in its 

monthly pattern in October – November, which is not seen in the regional background. When the two 

regional background patterns are subtracted for each month from the Pinehurst PM2.5 values, the resulting 

Urban Increment does not exhibit the summer increase, but the October-November “bump” remains 

superimposed over the larger high-winter/low-summer pattern. These patterns correspond to the seasonal 

definitions identified in Section 2.1 above.
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Figure 4 Pinehurst Idaho PM2.5 (red squares) for 2008 - 2011, along with the Regional Background based on 

Rural/Agricultural sites (purple diamonds) and Remote sites in Class I areas (blue triangles).

Figure 5 Pinehurst Monthly averaged PM2.5 (red Squares) with Urban Increment based on subtraction of either the 

Rural/Ag regional background (green open squares) or the Remote regional background (open circles)

4 Residential Wood Combustion Temporal Patterns

Residential Wood Combustion (RWC) emissions are proportional to Heating Degree Days, which 

represent a surrogate for the heating load on the average household. The Heating Degree Days (HDD) for 

Kellogg Idaho are shown in Figure 6 (WRCC 2014). The overall pattern of the HDD monthly averages 

has generally the same shape and high-to-low-to-high annual pattern as the Pinehurst Urban Increment

(minus the Oct-Nov “bump”.) The shape of this annual pattern might also be similar to motor vehicle 

cold-start emissions which increase in the winter, however the December emissions inventory for western 

Silver Valley indicates that only 2% of PM2.5 emissions are from non-road and on-road vehicles 

combined, while 90% is from RWC. In addition, a majority of the minimal VMT in the valley comes 

from already “warmed up” freeway traffic passing through the valley on nearby Interstate-90, not coldstarts on local roads. Taken together, the speciation results, winter season emissions inventory, and the 

similarity between the HDD annual profile and the Pinehurst annual PM2.5 profile strongly suggest that 

RWC is a major contributor to the annual Pinehurst PM2.5 pattern and annual average concentration. 
6

Figure 6 Heating Degree Days for Kellogg Idaho, assuming a 65 degree F base temperature (WRCC, 2014)

5 Prescribed Burning Temporal Patterns

As indicated in Figure 1, the PM2.5 peaks in October through about Nov 20 are attributed in-part to 

prescribed burning (Rx) activity. The prescribed burning databases of the large burners, members of the 

Montana/Idaho Air-Shed Group (ASG), the smaller burners permitted by the Idaho Department of Lands 

(IDL), and a snapshot of all prescribed fires from the 2011 NEI are shown in Figure 7 through Figure 9, 

respectively. In all three cases, the peak burn season is primarily during October and the first half of 

November. There is also some smaller level of prescribed burning activity in May and late September, 

but due to better ventilation, the daily and monthly-averaged PM2.5 time series rarely show any significant 

Rx contribution “spikes” before October or after March. It appears that early March may experience 

some private burning activity by individuals, based on elevated PM2.5 values during the first two weeks of

March, similar to those we see in October (see Figure 1.) 

In summary, every Rx burn-permit database and inventory of larger burns indicates that the Rx emissions 

in the Pinehurst area and surrounding counties occurs primarily in October and November, with smaller 

levels of burning in May and September. In addition it is well known in the area that small private burners 

who prefer to not seek a permit, and whose burns are too small for satellite detection (NEI) begin to burn 

on October 20, the last day of the permit-required burn season and the 2 – 3 weeks after that date, before 

the weather deteriorates. This is a very active period for both permit burners (ASG & IDL) and nonpermitted burners (especially just after October 20). The PM2.5 time series chart (Figure 1) reveals a 

cluster of peaks during this period, as well as similar peaks in the 20-25 μg/m3range in early March of 

each year, which may also be due to nearby private burners.
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Figure 7 Prescribed burns completed by members of the Montana/Idaho Air Shed Group (MT/ID ASG) from 2011 

through 2013. Peak burn period is October 1 – mid-November, however fewer burns also occur in May and late 

September.

Figure 8 Prescribed burns and other outdoor burning activities receiving burn permits from the Idaho Department of 

Lands. These generally represent small to medium sized burns. The IDL permit period is May 1 through October 20

although prescribed broadcast and pile burns appear to peak from late September through about November 7.
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Figure 9 Permitted and satellite detected prescribed burning in the 2011 National Emission Inventory. The peak burn 

period is late September to November 20, however PM2.5 spikes above 12 μg/m3are not observed until October

(Figure 1)

6 RWC HDD Monthly Average PM2.5 Model and Contribution

An examination of the heating degree day (HDD) pattern (Section 4) suggested that home heating 

dominated by wood burning appliances drives the overall PM2.5 pattern in Pinehurst. Since RWC 

emissions are so strongly influenced by the HDD this parameter was used to develop simple regression 

models to simulate the RWC contribution. By developing such a model with the known/suspected months 

having Rx activity removed (October, November and March), the model could then be used (if highly 

correlated) to estimate the RWC-only contribution during those removed months. Once the RWC-only 

contribution is estimated, it can then be subtracted from the Pinehurst Urban Increment concentrations for 

those months to estimate the Rx contribution. The monthly average Pinehurst Urban Increment based on 

the Rural/Ag regional background was highly correlated with HDD (R2= 0.98), resulting in a predictive 

exponential relationship (Figure 10) called the Rural RWC-HDD Model (Equation 1);

Rural RWC-HDD Model UI(Rural) = 0.797 e0.003HDD Equation 1

where UI(Rural) is the Rural/Ag-based Urban Increment and HDD is the monthly average heating degree 

days (65oF basis). Similarly, the monthly average Pinehurst Urban Increment based on the Remote

regional background was also highly correlated with HDD (R2= 0.98), resulting in the predictive 

exponential relationship (Figure 11) termed the Remote RWC-HDD Model (Equation 2)

Remote RWC-HDD Model UI(Remote) = 1.663 e0.0024HDD Equation 2
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Figure 10 Best-fit exponential model relating monthly average Pinehurst Rural/Ag Urban Increment PM2.5

Concentrations to Heating Degree Days (HDD), with presumed Rx-influenced months (Mar, Oct, Nov) excluded. 

Figure 11 Best-fit exponential model relating monthly average Pinehurst Remote Urban Increment PM2.5

Concentrations to Heating Degree Days (HDD), with presumed Rx-influenced months (March, October and

November) excluded.

The Rural and Remote RWC-HDD models described above can be used to estimate the Pinehurst PM2.5

concentration due solely to RWC for each month of the year. If these RWC contributions are subtracted 

from the actual PM2.5 Urban Increment (with the same background basis), then an estimate is obtained for 

the portion of the UI not explained by the model in October, November and March. Since we know that
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the only other significant source during this period is prescribed burning, we can attribute these portions 

of the UI (essentially the Oct-Nov “bump” seen in Figure 1) to prescribed burning. The March and 

February differences may also be attributed to prescribed burning. Only the small March difference is 

believed to result from prescribed burning while the February difference is probably too early and may be 

associated with some unknown effect or may simply be caused by a less-than-perfect fit of the RWCHDD models. There are other small plus or minus differences in other months, but this “noise” in 

prescribed burning contributions is assumed to be zero for all months except March, October and 

November. After averaging the Rural/Ag and Remote cases together and subtracting the Rx component 

in those three months the annual RWC contributions can be estimated to be 8.5 μg/m3(Table 1).

Figure 12 Rural RWC-HDD model subtracted from Pinehurst Rural Urban Increment (UI) to estimate prescribed

burning contributions in March, October and November.

Figure 13 Remote RWC-HDD model subtracted from Pinehurst Remote Urban Increment (UI) to estimate prescribed

burning contributions in March, October and November.
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Table 1 Summary of Measured PM2.5 in Pinehurst, Regional Background, Urban Increment and Estimated RWC and 

Prescribed Burning Contributions, all averaged over Rural and Remote background cases.

Month

Pinehurst 

Measured 

PM2.5

Regional 

Background 

PM2.5 

Urban 

Increment PM2.5

Modeled 

RWC PM2.5

Contribution

Estimated 

Prescribed 

Burning PM2.5

Contribution*

1 24.4 2.3 22.1 21.43 0.00

2 19.3 2.4 16.9 13.06 0.00

3 13.1 2.4 10.6 9.67 0.97

4 7.7 2.9 4.8 5.41 0.00

5 5.7 2.9 2.8 3.06 0.00

6 4.5 2.8 1.7 1.89 0.00

7 6.4 4.6 1.8 1.40 0.00

8 7.2 5.8 1.4 1.44 0.00

9 7.2 5.0 2.2 2.35 0.00

10 15.6 4.4 11.1 5.90 5.24

11 16.9 3.5 13.4 12.13 1.27

12 24.4 3.0 21.3 24.12 0.00

Est. Annual 

Average 

(μg/m3): 

12.7 3.5 9.2 8.5 0.62

Percent of 

Annual (%): 100% 28% 72% 67% 5%

Note, Pinehurst measured = Regional Background + Urban Increment. Urban Increment is assumed to be approximately = RWC 

+ Prescribed Burning contributions. Other sources are minor (See Section 2.2) and are assumed to be largely included in the 

Regional Background for the purposes of this analysis. * Small positive or negative differences outside the Rx months of March, 

October and November were attributed to “noise” and are shown as 0.00.

7 Prescribed Burning Contribution

As shown in Table 1, the contribution of prescribed burning to the Pinehurst air shed reaches a peak in 

October, when it is estimated to average 5.24 μg/m3or approximately 33% of the average October 

measured PM2.5 (15.6 μg/m3) in Pinehurst. However, since the prescribed burning contributions only 

occur for around 3 months, and the RWC contributions are predominant through winter, spring and fall,

the prescribed burning sources only contribute about 0.62 μg/m3or 7% to the annual PM2.5 Urban 

Increment while RWC contributes approximately 8.5 μg/m3or 93% of the annual Urban Increment when 

averaged over the entire year. Under the approximation that all other sources are minor (or are included in 

the regional background), the RWC and Rx together contribute 9.2 μg/m3of the total 12.7 μg/m3 DV. 

8 Relative influence of geographic/terrain influences on 

Pinehurst/Silver Valley PM2.5 concentrations. 

The topography of the Pinehurst area and the larger Silver Valley largely determines the transport and 

buildup of pollutants influencing the Pinehurst monitor. The complex terrain surrounding Pinehurst itself 

is shown in Figure 14, while the larger east-west running Silver Valley is shown in Figure 15. During 

calm wind/high pressure weather conditions, cold air pools in the valleys. This results in stagnant air that

prevents air pollutant emissions from dispersing well. The transport winds are effectively channeled by 

the Silver Valley, as indicated by a frequency map of HYSPLIT back-trajectories shown in Figure 16.
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This back-trajectory frequency plot was computed using 1.33 km WRF output grids from the University 

of Washington in a stand-alone version of HYSPLIT (March 2014 release) (Draxler and Hess, 1997, 

1998). The plot is based on the period from October 15 to November 15, 2013 the peak season for 

prescribed burning in the region. 

Figure 14 Complex Terrain in the immediate vicinity of Pinehurst Idaho (monitor location is red circle). Drainage 

winds follow the Pine Creek drainage roughly south to north through Pinehurst and east to west through the Silver 

Valley. The Pine Creek valley is ~ 2.5 km across while the main Silver Valley can range up to ~ 5 km across, with

vertical relief up to 1240m from the Pinehurst monitor (680m) to Kellogg Peak (~1920m) just south of Kellogg in the 

lower right corner of the map.(Google Maps).

Figure 15 Complex terrain in the area surrounding Pinehurst (red circle). The Silver Valley runs east-west along I-90 

just north of Pinehurst then arcs southwestward following the Coeur d’Alene River past Rose Lake. Winds tend to 

follow the Coeur d’Alene River and Silver Valley east to west, but the Pine Creek drainage winds generally flow south

to north at the monitor. The St. Joe Mountains to the south (yellow dashed line is the highest ridge) tend to limit 

transport from the St. Joe River drainage south of the Silver Valley. (Google Maps).
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Figure 16 Frequencies of HYSPLIT back-trajectories (Draxler and Hess, 1997, 1998) for the period October 15 -

November 15, 2013, the peak season for prescribed burning in the region. Pinehurst is at the red square and the 

boundaries of Shoshone, Benewah and Kootenai counties are shown as grey lines. From 1% to 10% of the winds 

arriving at Pinehurst travel through the green contour area along the Silver Valley, while the blue area represents 

1/10th of that frequency (0.1 to 1%) and the white areas 1/100th of that frequency (<0.1%). 

The HYSPLIT back-trajectory frequency plot shown in Figure 16 supports the conclusion above that the 

St. Joe Mountains effectively separate much of the prescribed burning activity to the south from reaching 

the Silver Valley in any significant concentrations (or at least with 1/10th the frequency of occurrence

found in the Silver Valley. If the prescribed burning annual contribution at Pinehurst is only 0.62 μg/m3

or 7% of the annual PM2.5 Urban Increment, then prescribed burns in areas with 1/10th of that potential for 

impacting the monitor (blue area in Figure 16) are not likely to be required within the nonattainment 

boundaries. On the other hand, Figure 16 also suggests that the prescribed burning sources along the 

Coeur d’Alene River Valley and adjacent drainages into that valley have the greatest likelihood of 

influencing the monitor at Pinehurst and the people of the western Silver Valley.

Even more localized than prescribed burning emissions, the RWC emissions are expected to concentrate 

in the immediate populated areas where the wood burning occurs (near Pinehurst, Smelterville and 

Kellogg) and where the stagnant conditions occur with greatest frequency and intensity in the cooler 

months. The extremely low population outside the Pinehurst/Smelterville/Kellogg area is believed to limit 

the RWC impacts to that immediate area of the Silver Valley. 

9 Spatial distribution of prescribed burning sources

The prescribed burning sources from multiple permit databases and the 2011 NEI are shown in Figure 17.

The greater density of burns located south of the St. Joe Mountains and around Lake Coeur d’Alene is 

clearly seen. However, as discussed in Section 8 both topographic barriers and distance make those burns 

much less likely to influence the Pinehurst monitor than those in and near the Silver Valley. 
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Figure 17 Prescribed burning source locations based on multiple data sources for burn permits, satellite detections 

and slash pile creation from logging practices under the Forest Practices Act. Data for the NEI represents 2011, but 

other datasets include up to 3 years of burns, to assure that the relative density of burns was robust.

10 Selection of areas to include in the Nonattainment Area

The goal recommended by EPA in accounting for emissions contributing to non-attaining monitors such 

as in Pinehurst, is to account for nearly all sources that contribute to PM2.5 at the violating monitor in 

Pinehurst. By doing that, we can be sure that control measures identified in the SIP will be effective in

returning the area to healthy air quality conditions (i.e. an annual PM2.5 NAAQS below 12 μg/m3).

As discussed above, the area most likely to influence PM2.5 concentrations in Pinehurst and the populated 

portion of the Silver Valley are those within the green “>1%” frequency contour in the back-trajectories 

plot in Figure 16, and within that area, the portion of the Valley nearest to the Pinehurst-Kellogg area will 

naturally have the greatest contribution. Since RWC contributes approximately 8.5 μg/m3or 93% of the 

annual Urban Increment (9.2 μg/m3), RWC alone nearly satisfies the goal of capturing the vast majority 

of emissions that contribute to PM2.5 at the Pinehurst monitor. A summation of the prescribed burning 

emissions within the green “>1%” contour indicates that 50% of the burn emissions for all datasets 

combined are within the Shoshone County portion of the “>1%” contour (green) with 50% in the 

Kootenai County portion of the “>1%” contour. Adding the 50% Shoshone county portion of prescribed 

burning (= 3.5%) to the Pinehurst / Smelterville / Kellogg residential wood combustion accounts for 

96.5% of the contribution at the monitor. Capturing the remaining 3.5% would involve adding Kootenai 

County and potentially Benewah County thereby doubling or tripling the pool of stakeholders and County 

officials subjected to and involved in the non-attainment process. In addition, since the prescribed fires 

closest to the Pinehurst monitor will have greater relative contribution than those more distant in Kootenai 

County, the total portion of captured prescribed burning emissions are expected to be even greater than 

the 96.5% assumed here based on burn locations alone.
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In conclusion, this analysis demonstrates that the western end of the Silver Valley from Kellogg to 

Cataldo (on the Shoshone County-Kootenai county boundary) should be included in the nonattainment 

area, along with the terrain-defined areas that drain into that portion of the Silver Valley. Capturing the 

drainage area is critical because any burns within a hydrologic drainage flowing into the western Silver 

Valley will cause smoke to drain down into the populated areas. Thus, the hydrologic units connected to 

the Silver Valley and located north of the St. Joe Mountains, east of the Kootenai County border, and 

west of Osburn are recommended for inclusion in the nonattainment area. All other areas in the domain, 

south of the St. Joe Mountains and west of the Shoshone-Kootenai county line contribute only about 3.5% 

or less and are not significant contributors to PM2.5 in Pinehurst.

11 Supporting Q/d Analysis

The previous sections provide a rationale for selecting a portion of Shoshone County, approximately the 

area shown in the right portion of the red box shown in Figure 18 below, to capture the prescribed burning 

that contributes to the Pinehurst/Silver Valley high PM2.5 levels in excess of the 12 μg/m3 NAAQS. 

Summed together the RWC emissions and the Shoshone County prescribed burn emissions were 

estimated in the previous section to contribute over 96.5% of the annual PM2.5. The burns south of the red 

box are south of the St. Joe Mountain range and are geographically separated from the Silver Valley and 

the majority of the winds that approach the Pinehurst monitor (Figure 16). Most of the prescribed burns 

within the red rectangle are in the Kellogg-to-Cataldo portion of western Silver Valley in Shoshone 

County, however there are also burns located in the Kootenai County portion of the Silver Valley, most of 

them much further away near Lake Coeur d’Alene on the western edge of the red rectangle. 

In order to better assess the relative contributions of the burns in the Kootenai portion of the rectangle in 

comparison to those in the Shoshone portion, a “Q/d” analysis was performed for all the burns in the 

larger red rectangle. This is a well-known method for screening sources based on the magnitude of their 

emissions (Q) and their distance to the receptor or monitor (d). Q/d (tons/km) was computed for each 

burn in the red rectangle and the Q/d’s were summed for each portion of the red rectangle. The Q/d’s for 

the Kootenai portion of the red rectangle sum to 0.60 tons/km while the Q/d’s for the Shoshone County 

portion of the rectangle sum to 1.18 tons/km, or when considered together, the Shoshone burns contribute 

77% of the total Q/d sum, and Kootenai sources contribute only 23%. Q/d sums do not represent 

predicted concentration impacts but are a surrogate indicator that DEQ believes provides reasonable 

relative information on contributions of different areas. Thus, to again estimate source area contributions, 

if RWC contributes 93% of the annual PM2.5 Urban Increment at Pinehurst, based on the PM2.5

monitoring pattern, and if all prescribed burns contribute 7% together, then 77% of that 7% (=5.4%), 

when added to the 93% RWC contribution results in an estimate of 98% of the Urban Increment which 

can be accounted for by just adding the Shoshone portion of the prescribed burns in the red rectangle to 

the RWC contribution. The more distant burns in the Kootenai side of the rectangle (near Coeur d’Alene) 

are not likely to add significant PM2.5 at the Pinehurst monitor due to their even greater distance and their 

locations outside the “>0.1%” (blue) HYSPLIT frequency contours (Figure 16.)
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Figure 18 Prescribed burn locations (red box) approximately within and surrounding the areas of most frequent back 

trajectories approaching the Pinehurst PM2.5 monitor (refer to Figure 16). The southern edge of the box is aligned 

with the ridge of the St. Joe Mountains, which serves to limit the winds and smoke emissions south of there that can 

reach the Silver Valley isolating the fires south of the box geographically from Pinehurst and the Silver Valley.
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12 References

Draxler, R.R., and G.D. Hess, 1997: Description of the HYSPLIT_4 modeling system. NOAA Tech. 

Memo. ERL ARL-224, NOAA Air Resources Laboratory, Silver Spring, MD, 24 pp.

Draxler, R.R., and G.D. Hess, 1998: An overview of the HYSPLIT_4 modeling system of trajectories, 

dispersion, and deposition. Aust. Meteor. Mag., 47, 295-308.

Frank, Neil, 2008. Limited 2004-06 Speciation Data Derived from FRM Filters for Twenty Areas, Office 

of Air Quality Planning and Standards, United States Environmental Protection Agency, Internal 

Report, September 18, 2008.

WRCC, 2014. Western Regional Climate Center, Desert Research Institute, Kellogg Idaho, 

Heating Degree Days. Accessed April 2014 from http://www.wrcc.dri.edu/cgibin/cliMAIN.pl?idkell.