1 

# Source Contributions Analysis for the
Pinehurst , Idaho Non a ttainment Designation

1 Introduction

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

, Idaho PM 2.5 a nnual average concentrations, which currently exceed the Annual Nation al Ambient Air Quality Standard (NAAQS) for PM 2.5 (> 12 μg/m 3 ) 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

monitor ed values a

re critical input s in determining the boundaries of a nonattainment area. After identifying the major contributors on high PM 2.5 days, the source locations , source emissions

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

contribut e to most of the local

- source (i.e. “Urban Increment”) PM 2.5 experienced by the people in

Pinehurst and the nearby Silver Valley

.



## 2 Pinehurst Winter Season PM 2.5 

## Filter Speciation

2.1 Definition of Seasons.

An examination of the PM 2.5 concentrations measured on the filter s 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 PM 2.5 concentration greater than 12 μg/m 3 during 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/m 3

. As a result, while all months contribute to an annual average PM 2.5 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 seco ndary peak around mid

- October to mid

- November when prescribed burning (Rx)

act ivities are at their peak (refer to Section 5 for Rx burning patterns )

. Based on the PM 2.5 temporal pattern and knowledge of predominant sources throughout the year, DEQ defines for the purpose of this analy s is 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

- s eason 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.2

Figure 1 Pinehurst PM 2.5 Filters 2011

- 2013, showing the last and first dates of values above 12 μg/ m3 (sal mon 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 1 9).



### 2.2 Speciation of “cool season” PM2.5
The only speciation data available f or Pinehurst was reported by Nei l 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 PM 2.5 concentrations ranging from 28 to 57 μg/m 3 , 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 p ercent 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 anal ysis so this is a reasonable approach. In his study, Frank showed that during high

- PM 2.5 days in the Rx and Winter seasons in Pinehurst, the

PM 2.5 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/m 3 (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 PM 2.5 period from October throug h March when the emission inventory suggests that Rx and RWC are the predominant sources ( Figure 3 ) . This simplif ies the list of potentially contributing sources during the cool period when PM 2.5 levels are above 12 μg/m 3

.3 Figure 2 Pinehurst Mass Speciation from 2004

- 2006 filters analyzed by EPA (Frank, 2008)

Figure 3 October and December PM 2.5 Emission Inventory for western Silver Valley based on 2011 NEI. October

repres ents the peak prescribed burni ng (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 ) r esults primarily from Rx and RWC in October and primarily from RWC in December and the remainder of the cool season.4



## 3 Regional Background PM 2.5 

## and Urban Increment

The monthly averaged PM 2.5 concentration at the Pinehurst monitor for the years 20 08

- 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 t he 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

. T he 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 r ural

- a gricultural 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 . Th e 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 Mount ains and Sula Peak (representing the Selway

- Bitterroot

Wildernes s), 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

contri butio n and some a gricultural influence s. 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 “Ur ban 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 Incr e ment, 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 PM 2.5 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 W estern Idaho, including on the Coeur d’Alene

Reservation

, and in E astern Washington. Finally, the Pinehurs t profile exhibits a prominent “bump” in its monthly pattern in October

– November, which is not seen in the regional background. W hen the two regional background patterns are subtracted for each month from the Pinehurst PM 2.5 values, the resulting

Urban I ncrement 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.5

Figure 4 Pinehurst Idaho PM 2.5 (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 PM 2.5 (red Squares ) with Urban Increment based on subtraction of either the Rural/Ag regional background ( g reen open squares) or the Remote regional background ( o pen 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 PM 2.5 emissions are from non

- road and o n

- 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 nearb y Interstate

- 90, not cold

starts on local roads. T aken together, th e speciation results, winter season emissions inventory, and the similarity between the HDD annual profile and the Pinehurst annual PM 2.5 profile strongly suggest that RWC is a major contributor to the annual Pinehurst PM 2.5 pattern and annual average conc entration.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 PM 2.5 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/Idah o 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 PM 2.5 time series rarely show any significant

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

some private burning activity by individuals , based on elevated PM 2.5 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 Nove mber, 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 las t day of the permit

- required burn season and the 2

– 3 weeks after that date, before

the weather deteriorates . T his is a very active period for both permit burners (ASG & IDL) and non

permitted burners (especially just after October 20 )

. The PM 2.5 time ser ies chart ( Figure 1 ) reveals a cluster of peaks during this period, as well as similar peaks in the 20

- 25 μg/m 3 range in early March of each year

, which may also be due to nearby private burners.7

Figure 7 Prescribed burns completed by members of the Montana/Idaho Air Shed Group (M T /I D 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 p rescribed broadcast and pile burns appear to peak from late September through about November 7.8

Figure 9 Permitted and satellite de tected p rescribed burning in the 2011 National Emission Inventory. The p eak burn period is late September to November 20, however PM 2.5 spikes above 12 μg/m 3 are n ot observed until October ( Figure 1 )



## 6 RWC HDD Monthly Average PM 2.5 

## 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 PM 2.5 pattern in Pinehurst. Since RWC emissions are so strongly influenced by the HDD th is parameter w as u sed to develop simple regression

model s 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 t he 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 (R 2 = 0.9 8 ), resulting in a predictive exponential relationship ( Figure 10 ) called the Rural RWC

- HDD M odel ( Equation 1 ) ;

Rural RWC

- HDD Model UI(Rural) = 0.797 e 0.003HDD Eq uation 1

where UI(Rural) is the Rural/Ag

- based Urban Increment and HDD is the monthly average heating degree days (65 o F basis). Similarly, the monthly average Pinehurst Urban Increment based on the Remote regional background was also highly correlated with HDD (R 2 = 0.98), resulting in the predictive exponential relationship ( Figure 11 ) termed the Remote RWC

- HDD M odel (Equ ation 2 )

Remote RWC

- HDD Model UI(Remote) = 1.663 e 0.0024HDD Equation 29

Figure 10 Best

- fit exponential model relating monthly average Pinehurst Rural/Ag Urban Increment PM 2.5 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 PM 2.5 Concentrations to Heating Degree Days (HDD), with presumed Rx

- influenced months (Mar ch , Oct ober and
Nov ember ) excluded.

The Rural and Remote RWC

- HDD models describ ed above can be used to estimate the Pinehurst PM 2.5 concentration due solely to RWC for each month of the year. If these RWC contributions are subtracted

from the actual PM 2.5 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 . S ince we know that10

the only other significant source during this period is prescribed burni ng, 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 . O nly the small March difference is believed to result from prescribed burning while the February difference is probably too early and may be

a ssociated with some unknown effe ct or may simply be caused by a less

- than

- perfect fit of the RWC

HDD models. The re 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 an d November. After averaging the Rur al/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/m 3 ( 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.11

Table 1 Summary of Measured PM 2.5 in Pinehurst, Regional Background, Urban Increment and Estimated RWC and Prescribed Burning Contributions , all a veraged over Rural and Remote b ackground cases.

Month Pinehurst

Measured
PM 2.5 Regional

Background
PM 2.5 Urban

Increment PM 2.5 Modeled

RWC PM 2 .5 Contribution Estimated

Prescribed Burning PM 2.5 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/m 3 ): 12.7 3.5 9.2 8.5 0.62

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

Note, Pinehurst measured = Regional Background + Urb an 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 5.2 4 μ g/m 3 or approximately 33% of the average October

measured PM 2.5 (15.6 μg/m 3 ) in Pinehurst. However, since the prescribed burning contributions only occur for around 3 months, and the RWC contribution s are predominant through winter

, spring and fall

, the prescribed burning sources only contribute about 0.62 μg/m 3 or 7 % to the annual PM 2.5 Urban

Increment w hile RWC contributes approximately 8.5 μg/m 3 or 9 3 % of the annual Urban Increment when averaged over the entire year. Under th e approximation that all other sources are minor (or are included in the regional background)

, the RWC and Rx together contribute 9.2 μg/m 3 of the total 12.7 μg/m 3 DV

.



## 8 Relative influence of geographic/terrain influences on Pinehurst/Silver Valley PM 2.5 

## concentrations.

The topography of the P inehurst area and the larger Silver Valley largely determines the transport and buildup of pollutants influencing the Pinehurst m onitor. The complex terrain surrounding Pinehurst itself

is shown in Figure 14 , while the larger east

- west running Silver Valley is shown in Figure 15

. Du ring

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 Sil ver Valley, as indicated by a frequency map of H YSPLIT back

- trajectories shown in Figure 16

.12

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 e ast to w est through the Silver
V alley. 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
lo wer 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 the n 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. T he St. J oe 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).13

Figure 16 Frequencies of HYSPLIT b ack

- 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

. F rom 1% to 10% of the winds arriving at Pinehurst travel through the green contour area along the Silver Valley , while the blue area represents 1/10 th of that frequency (0.1 to 1%) and the white area s 1/100 th of that frequency (<0.1%)

.

The HYSPLIT back

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

St. Joe Mountains ef

fectively separate much of the p rescribed burning activity to the south from reaching the Silver Valley in any significant concentrations (or at least with 1/10 th the frequency of occurrence found in the Silver Valley. If the prescribed burning annual contribution at Pinehurst is only 0.62 μg/m 3

or 7% of the annual PM 2.5 Urban Increment

, then prescribed burn s in areas with 1/10 th of th at 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 sou rces 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 extr emely 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 d istribution 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

. H owever , as discussed in Section 8 both topographic barriers and distance make those burns much less likely to influence the Pinehurst m onitor than those in and near the Silver Valley.14

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 Non a ttainment Area

The goal recommended by EPA in accounting for emissions c ontributing to non

- attaining monitors such as in Pinehurst, is to account for nearly all sources that contribute to PM 2.5 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 PM 2.5 NAAQS below 12 μg/m 3 )

. As discussed above, the area most likely to influence PM 2.5 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/m 3 or 93% of t he annual Urban Increment (9.2 μg/m 3 ) , RWC alone nearly satisfies the goal of capturing the vast majority

of emissions that contribute to PM 2.5 at the Pinehurst monitor

. A sum mation 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

burni ng (= 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 tr ipling 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 Kooten ai 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.15

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 th e 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 onl y about 3.5%

or less and are not significant contributors to PM 2.5 in Pinehurst.



## 11 Supporting Q/d Analysis

The previous sections provide a rational e for selecting a portion of Shoshone County, approximately the area shown in the right portion of the red bo x shown in Figure 18 below , to capture the prescribed burning that contributes to the Pinehurst/Silver Valley high PM 2.5 levels in excess of the 12 μg/m 3 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 PM 2.5. 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

- t o

- 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 rectangl e.

In order to better asses s 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

- know n 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 Koote nai 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 PM 2.5 Urban Increment at Pinehurst , based on the PM 2.5 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 9 8 % 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 . T he more distant burns in the Kootenai side of the rectangle (n ear Coeur d’Alene)

are not likely to add significant PM 2.5 at the Pinehurst monitor due to their even greater distance and their locations outside the “>0.1%” (blue) HYSPLIT frequency contours ( Figure 16 .)16

Figure 18 Prescribed burn locations (red box) approximately within and surrounding the areas of most frequent back trajectories approaching the Pinehurst PM 2.5 monitor (refer to Figure 16 ). The southern edge of the box is aligned
with the ridge of the St. Joe Mountains, which serve s to limit the winds and smoke emissions south of there that can reach the Silver Valley isolatin g the fires south of the box geographically from Pinehurst and the Silver Valley.17



## 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/cgi

bin/cliMAIN.pl?idkell

.