1 

# Dispersion Modeling E stimate s of R elative
Source C ontributions to the Pinehurst
Monitor

1 Introduction

Most annual PM 2.5 non

- attainment areas (NAA) proposed in the United States are in the E astern United States and are typically composed of one whole count y, or multiple counties, sometimes with a partial county in addition. EPA NAA designation tools tend to support selection of whole
counties or groups of counties as non

- attainment areas, supported by multiple monitors exceeding the NAAQS , widely distribut ed population and emission sources and relatively flat terrain which facilitates transport and mixing of pollutants across wide areas.

In the W est, very low population density is topographically separated with dramatically varying wind and stagnation pat terns. As a result, wintertime Residential Wood Combustion (RWC)
emissions can accumulate in small isolated mountain valleys while near

- pristine conditions occur in near proximity

. Figure 1 demonstrates that moun tain terrain can cause extreme variation in
winter season PM 2.5 concentrations over relatively short distances

. This figure shows the mountainous terrain in the area of Shoshone County in Idaho, and Lincoln and Sanders c ount ies
in Montana. The 2008

– 2011 4

- year average winter season (De c

- Jan

- Feb) PM 2.5 concentration is shown in parentheses after each site name. It is clear than the terrain

- isolated mountain towns of Pinehurst and Libby can have extremely high wintertime concentrations approaching twice th e annual standard w hile the Cabinet Mountain I MPROVE monitoring site, in a nearby valley half

way between these two towns experiences very pristine winter

- time fine particle (PM 2.5 ) concentrations just over 1 μg/m 3 for the same winter period averaged over 4 years

. This is clear
evidence that u nlike the E ast ern U.S. where whole counties or groups of counties are typically proposed as n on

- attainment a reas , the geographically distinct and isolated airsh eds of the West

may justify partial

- count y non

- attainment areas defined by terrain barr iers which isolate the
sources that primarily contribute to the problem

. In view of this problem, and the challenges of applying the EPA NAA designation tools to mountainous locations in the West, DEQ offered to the EPA supplem ental analyses that account for the extreme terrain conditions in the Pinehurst area. A terrain map of the Pine Creek Valley containing Pinehurst and the adjacent east

- west trending Silver Valley of the Coeur d’Alene
River and Interstate

- 90 is shown in Fig ure 2. The valleys vary from 2. 5 to 5 km across and up to 12 4 0m in elevation difference from valley floor to ridge tops (e.g. Pinehurst to Kellogg peak

. ) It
is clear from Figure 2 that valley drainage flows could vary by > 90 o over the 1 km distance between Pinehurst and the main Silver Valley. Thus, w hile DEQ has meteorological data at the Pinehurst monitor, it clearly represents only the valley flows of the Pine Creek drainage and not
the over

- riding synoptic flow directions nor the east

- west Silver Valley drainage flows. DEQ
believes AERMOD is incapable of addressing the complex flows in this area and a suitable2

alternative near

- field model is not readily available

. Nevertheless, a high resolution CALPUFF analysis was used in “no obs” mode with the stated goal not of computing absolute concentrations, but as a relative tool that attempts to take into account complex flows and elucidate relative impac ts from different source types and source areas in a semi

- qualitat ive weight

- of

- evidence analysis.

Figure 1 Extreme differences in the Winter

- time average PM 2.5 in adjacent mountainous c ounties. The December

- January

February average for 2008

- 2011 is shown for Pinehurst ID, Libby MT, and Thom pson Falls MT, with the near pristine Cabinet

M ountains site in the center. A 12

- month average d (winter

- time) variation of up to 22.1 μg/m 3 over 39 miles represents a severe concentration gradient that is likely unique to the mountainous western U.S. and d emonstrates that terrain should be used to define non

- attainment areas in the western mountains rather than county lines.

Figure 2 Complex terrain in Pinehurst/Silver Valley area . The red circle is the Pinehurst monitor. The sou th

- to

- north flowing Pine Creek drainage which flows into Pinehurst is ~ 2.5 km across while the eas t

- to

- west draining main Silver Valley can range up to 5km across, with vertical relief of up to 1240m between Pinehurst (680m) and Kellogg Peak (1920m) in th e lower right hand corner. (Google Maps).3



## 2 Model Selection
DEQ ’s model selection for this weight

- of

- evidence analysis was driven by an extremely short timeframe, and a need to account for very complex t errain

.
When the EPA decided to consider 2013 data in the PM 2.5 annual NAAQS designations, bringing
Pinehurst into non

- attainment after the deadline for non

- attainment designations i n December 2013, DEQ was advised that it would have approximately 1

- 2 month s to provide input to the designation process

. Nevertheless, DEQ wanted to develop the best available scientific analyses such that EPA could choose the most appropriate boundaries possible for the non

- attainment
area. The DEQ has provided a qualitative supplemental analysis ( DEQ 2014) in which the

m easured monthly averaged PM 2.5 pattern wa s analyzed temporally to estimate RWC and Rx

contr ibutions to the Urban Increment. In addition a back

- trajectory f requency map was included to demonstrate that areas outside the Silver Valley have little connection to the Pinehurst
monitor. In this study, DEQ provide s additional supplemental weight

- of

- evidence modeling to show how terrain limits the geographic extent of source contributions to the monitored values at
Pinehurst.

In view of the extremely short sched ule, DEQ used a n existing CALPUFF

- based “reverse modeling” tool that the agency had available from previous airshed assessments

. This tool uses CALPUFFv6.112/CALMETv6.211/CALPOSTv6.131 (Scire, 1990) and no significant secondary
aerosol was considered in th e analysis. When used in conjunction with emission information it can provide useful information about how much sources in a geographic region contribute to
PM 2.5 at the Pinehurst monitor. While with sufficient lead time DEQ has ideas on how to further imp rove the tool for potential future applications, DEQ believes this approach adequately accounts for complex valley meteorology (based on past experience) and provides useful relative
contribution information not otherwise available for near

- field, complex terrain conditions.

The CALPUFF modeling domain ( Figure 3 ) includes the neighboring counties of Kootenai and Benewah, including most of the Coeur d’Alene Tribal area in Benewah County and extends west
of the Idaho

- Washington border.

Archived meteorological model outputs at 1.33 km resolution from the Weather Research Forecast model (WRF) were obtained from the University of Washington Department of Atmospheric Sciences, and nested down in CALMET to 250 meters resol ution in an attempt to capture the influence of the Pine Creek valley in which Pinehurst lies, approximately 2. 5 km from ridge to ridge, and the main Silver Valley of the Coeur d’Alene River with approximately a 5 km dimension from ridge to ridge.4

Figure 3 CALPUFF modeling domain.



## 3 Modeling Approaches

3.1 Point Source (Forward) Modeling Point source modeling was conducted using CALPUFF v6.112 following Idaho and other state ’ s configurations used to conduct BART regional haze mode ling

. The point source modeling was
done to examine whether the few point sources in neighboring counties have a significant
contribution to the PM 2.5 at the Pinehurst Monitor. Point sources were modeled explicitly to predict their impact at the Pinehurs t monitor. This approach was the traditional “forward modeling” approach in which each source is explicitly modeled. Approximately one

- month average concentrations were computed for the period s October 15

– November 15 and
November 16

– December 15, 2013 the episodes of highest Rx and RWC impacts in the 3 year Design Value period 2011

– 2013.



### 3.2 “Reverse Modeling” of area source categories RWC, Rx and ROB When seeking to understand the relative contribution of different source areas to a single monitor, D EQ utilizes an approach we call “reverse modeling”. Reverse modeling is best explained in comparison to traditional point source “forward modeling”. In traditional modeling, a source is located at a single location and pollutant impacts are computed over a usually uniform “receptor grid”. The spatial nature of the impacts can then be visualized by contouring the concentration isopleths or lines of equa l concentration or equal impact due to the single source.

In the “reverse model” approach, there is a sing le receptor at which concentrations are computed, in this case the Pinehurst monitor, and a rectangular grid is still used, but in this case an identical “unit emission” source is located at each grid point and the model determines the impact at the (Pineh urst monitor) receptor for each possible source location (at the grid points). Thus the5

values at each grid point represent the concentration that the “unit emission” source would have
on the Pinehurst monito r if located at that grid point, and if results are contoured, the contours represent, for one source configuration/unit emission rate, the lines of equal source impact at the receptor. Such a “reverse model” contour map elucidates areas of relative impact at the monitor

the primary goal of a non

- attai nment boundaries analysis. This process was repeated for three types of source configurations: a ) a typical residential wood combustion chimney, b) a small ( plume rise Class 1) prescribed fire, and c) a somewhat larger ( plume rise Class 2) prescribed
fir e. In addition, the RWC configuration was used to approximate residential outdoor burning (ROB), as typically occurs in “burn barrels” or backyard yard waste piles.

While the relative “reverse model” results represented the primary goal of this analysis

, i .e. to delineate the transport corridors into the Pinehurst area, th ese results assume a constant source is located at each grid point and don’t account for variations in source strength or source magnitude. A dense source area will have a greater impact a t the Pinehurst monitor than an area
with a similar “dispersion factor” but lower source density . To take the analysis one step further, the actual source locations and emission rates (Q j ) were multiplied by the “reverse model” dispersion factor ( DF ) surfa ce in the Geographic Information System (GIS) according to the equation;

C R = DF i x Q j Equation 1

Where Q

j is the emission rate for individual sources (g/s); DF i is the dispersion factor for that source type at that location; and C R is the concentrati on contributed by that source at the receptor location, in this case, the location of the Pinehurst monitor. It now becomes clear that the DFs from the “reverse modeling” approach, when multiplied by the source term (Q i ) at each location provides an equiv alent concentration contribution value that would result from the traditional modeling approach. (Q i includes emissions rates and in addition, for RWC, the number of households at each location.) The advantage of this approach is that the dispersion model ing can be accomplished once providing an understanding of the DF variation by itself, while allowing multiple source strength/source density scenarios to be applied to the DF surface within GIS, without explicitly re

- running the dispersion model. In addit ion, with the results aggregated within the GIS system we have the ability to accumulate the results by geographic area to explore each area’s relative contribution to the Pinehurst monitor.



### 3.3 Other minor source categories In addition to the source categ ories modeled in this study, DEQ has made rough estimates of other minor categories such as Crop Residue Burning (CRB) and paved/unpaved road dust.

Crop Residue Burning. DEQ modeled the Crop Res idue Burning throughout Idaho in a 200 8
S tate I mplementation Plan (SIP) revision addressing Crop Residue Burning ( DEQ 200 9 ) . This
SIP revision formed the basis for Idaho’s current comprehensive Crop Residue Burning management program and was approved by the EPA in 2008 (FR44915)

. In that study, the highest modeled 24

- hour contribution at Pinehurst (~ 10 μg/m 3 ) resulting from the 2005 CRB

season w as found to match the measured maximum concentration very closely on a large burn day for the region , giving confidence in the long term average . The annual CRB contribution6

estimated in that SIP for Pinehurst was 0.17 μg/m 3 . Since CRB burning activity levels have not
exceeded those 2005 levels in recent years, DEQ believes 0.17 μg/m 3 is a conservative estimate for the CRB impacts at Pinehurst. The July through September peri od is somewhat elevated in Pinehurst, as well as all the regional background sites, believed to result from wildfires and potentially some regional CRB activity, and as a result this minor increase in the Pinehurst annual average does not appear in the Urb an Increment but is considered part of the regional background. Since the maximum (2005) CRB contribution in Pinehurst is so low and since Idaho’s CRB program has since been improved under the 2008 SIP revision and approved by EPA, DEQ does not recommend c onsidering it in the current nonattainment designation.

Geologic Sources, primarily paved and unpaved road dust. As a result of the previous PM 10 SIP all the roads in Pinehurst and nearly all those in the western Silver Valley are paved. While the
emissio n inventory indicates significant unpaved road emissions for Shoshone County , these are based on many miles of lightly travelled mountain roads geographically isolated from the Pinehurst/Silver Valley area. This assumption is confirmed by the 2004

- 2006 “c ool season” filter speciation conducted by N. Frank (2008). Based on 16 high

- PM 2.5 filters, the geologic/crustal component was 0.2 μg/m 3 or approximately 0.5% of the total average filter mass. Based on this speciation data, road dust is assumed to contri bute 0.2 μg/m 3 , an insignificant amount to the annual average PM 2.5 violation.



## 4 Point Sources
Point sources present in Shoshone, Kootenai and Benewah counties were modeled, based on their emissions the 2011 National Emission Inventory, with updates where appropriate

, based on emissions documented in their permits. Point source stack parameters were obtained from the permits. Both filtered and condensable PM 2.5 emissions were modeled as primary PM 2.5 and where only PM 10 emissions were identified for combu stion sources, the entire emission rate was assumed to be primary PM 2.5 . Major sources in all three counties were modeled, while all sources in the Silver Valley were modeled, regardless of magnitude. The point emissions and stack parameters modeled are shown in Table 1

. A number of sources in Kootenai County were outside the originally selected modeling domain. To conservatively account for these sources, their location in the model was moved inside the domain, bringing them closer to the Pinehurst monitor. Point source modeling results indicated that when all modeled source impacts are accumulated, a total contribution at the Pinehurst monitor for the Oct ober

- Nov ember modeling period is projected to be 0.03 μg/ m 3 and for the Nov ember

- December winter episode is projected
to be 0.02 μg/m 3 . For both periods, the point source contribution at the Pineh urst monitor is not significant and the annual average contribution may be conservatively estimated to be less than 0.03 μg/m 3 since improved ventilation in the warmer months is expected to reduce the point source impact.7

Table 1 Nationl Emission Inventory NEI 2011 v1 Point Source Emissions (tons/year) with permit based updates*

County, Sta te Map

Point Source Name Distance
to Monitor, miles NH 3 NOx PM 2.5 SO 2 VOC Total

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 (PM 10 ) 11

- 176

Benewah, ID P

- 6 Stimson Lumber Co St. M aries 32.9

- - 3

- - 3

Shoshone , ID P

- 7 Kellogg School District 391 7.8

- <1 <1 (PM 10 ) <1

- 1

Shoshone , ID P

- 8 North Fork Cedar, dba Kingston

Cedar 5.1

- - 6 (PM 10 ) - - 6

Shoshone , ID P

- 9 Dave Smith Body Shop 3.8

- 4 <1 (PM 10 ) <1

- 4

Shoshone , ID P

- 10 Enyeart Cedar Products 5.2

- 12 1 (PM 10 ) 7

- 20

Shoshone , ID P

- 11 Essential Metals Corp. 12.9

- 5 <1

- - 5

Shoshone , ID P

- 12 Shoshone Funeral Service 8.9

- - <1 (PM 10 ) - - <1

Benewah, ID P

- 13 Potlatch Land & Lumber, LLC

–

St. Maries Complex on Coeur d’A lene Reservation* 22.0

- 96 97 13 68 274

Benewah, ID P

- 14 Stimson Lumber Company

–

Plummer Operation* 33.7

- 99 101 12 41 253

*Based on Air Quality Permit information provided by CDA Tribe.



## 5 Residential Woodstove Sources

Residential wood combustion was modeled using the “reverse model” for the entire CALPUFF domain, then, the results were multiplied by the number of households at each location to arrive at a relative contribution for each populated area.

RWC emission estimates

. Residential wood burning emissions are based on the 199 2 Silver Valley wood burning survey developed during the Pinehurst PM 10 SIP effort (DEQ 1992.) Wood burning device types in Pinehurst and the remainder of the Silver Valley were updated based on the results of multiple woodst ove change

- out programs initiated since 199 2 which served to
reduce the number of non

- certified stoves in Pinehurst and to a lesser extent, the rest of the valley. Key values used in estimating emissions for th is modeling for Pinehurst and the
remainder o f the Silver Valley are summarized in Table 2

. The remainder of Shoshone County and the surrounding counties were assigned the same emissions rates as the “Silver Valley excluding Pinehurst”. RWC was modeled for t he October 15

– November 15, 2013 prescribed8

burning season, and Nov ember 16

- December 15 , 2013 a representative portion of the wood heating season.

ROB emission estimates. The residential outdoor burning (ROB) emissions were obtained from
the 2011 Nationa l Emission Inventory and were assigned to households in the modeling domain similarly to the RWC emissions. Separate dispersion modeling was not conducted, but rather considering the very small proportion of ROB emissions, the RWC plume rise treatment/sour ce configuration and “reverse model” DF “surface” ( Figure 11 ) was considered an adequate surrogate for estimating ROB the contribution.

Table 2 Residential Wood Combustion Source Parameters for Pinehurst and the remainder of the Silver Valley. All other areas are assigned the same parameters as the Silver Valley Excluding Pinehurst.

Location Pinehurst Silver Valley Excluding Pinehurst Percent of Wood Burning Households per Households 58.55% 62.32% Percent of Devices Burned Each Day per Household 70.85% 75.41% Typical Woodstove Emission rate (grams per day) for 10/15

– 11/15/2013 period (26.0 HDD) 155.8 156.4 Typical Woodstove Emission rate (grams per day) for 11/16

– 12/15/2013 period ( 40.1 HDD) 240.3 241.2

Source Characteristics . The source configuration for modeling RWC emissions were b ased on a study by Omni Environ mental (Li, 2007) and are summarized in Table 3 . The diurnal emissions prof ile from the AIRPACT4 CMAQ modeling system operated by Washington State University is used to temporally allocated RWC emissions for the average day during the modeled episodes. The average day temporal emissions profile is shown in Figure 4

. The source configuration ( Table 3 ) and diurnal profile ( Figure 4 ) used for RWC sources was used to approximate ROB
sources as well. Since muc h of the yard burning occurs in the evening when people return from work, t he RWC dirunal profile is a reasonable approximation for such a small emission category.

Table 3 Residential Wood Combustion Source Parameters (Li, 2007)

Par ameter Value Stack Height, m 4 Stack Diameter, m 0.2 Exit velocity, m/s 1 Exit Temperature, o K 473 Initial sigma

- y, m 2 Initial sigma

- z, m 2 Momentum Flux Factor, (0

– 1) 09

Figure 4 Residential Wood Combustion hourly pro file averaged over weekend and weekday profiles.


## 6 Prescribed Burning Sources

Prescribed (Rx) burning is known to cause elevated PM 2.5 concentrations at the Pinehurst monitor during the fall, typically from Oct ober 1 through mid

- November, but peaking aroun d and just after October 20. DEQ utilized all available prescribed burning datasets to assure that the spatial distribution of all types of prescribed burns is well characterized.

Prescribed Burn Data Sets Used

. Prescribed burns ar e identified through the use of multiple data bases to assure that all major classes of prescribed burning, from the largest agency/industrial burns to the smallest private individual burns are included. Burn records for 1 to 3 years are identified then divided by the number of y ears to assure that annual average burning activity is
determined.

 Idaho

- Montana Air Shed Group (ASG). The ASG membership includes major state and federal agencies and large industrial burners who work together to burn on days with good ventilation and to minimize their smoke impacts. These burns are well documented by date and location.

 Idaho Department of Lands (IDL). The IDL burn permitting program operates from April 1 to October 20 and typically includes private and industrial burners who apply for a permit within the permit season, and receive a 10 day window to complete their burns. Locations are accurately known but burn dates are only estimated to the midpoint of their
10

- day burn window.

 2011 NEI prescribed b urns. The 2011 NEI database (v2) for prescribed burns which includes permit

- based burns from the ASG and IDL and unidentified satellite detected burns that are not wildfires are included, however all NEI burns that coincide with ASG
or IDL burns are removed to assure that no burns are double

- counted.

 CDA Tribe prescribed burns . The Coeur d’Alene Tribe data for prescribed burns from
2011

– 2013 were obtained (Les Higgins, personal communication). These burns were located only by proximity to a named road or geographic features, and those t hat could not be specifically located were assigned a single location in the eastern portion of the
Tribal area and modeled as one burn to be conservative.

 Forest Protective Act Compliances Database s (FPA ). The FPA “Compliance” data for
the Mica Forest Pr otection D istrict and a portion of the Cataldo Forest Protection D istrict10

were provided by those districts to allow DEQ to identify the areas of small private burners that are not captured by the ASG and IDL permit processes and that are too small
to be de tected by satellite (as for the NEI) . FPA “Compliance” records are associated with logging operations of any size and don’t identify burn dates, but allow logging and management of the resulting slash within a two year period. FPA locations coinciding
with ASG and IDL permits and NEI burn locations were removed to avoid double counting. In addition, FPA Compliance records that indicate slash is being managed by chipping are also removed as no burning is involved at those locat ions. The remainder of
the FP A compliance locations are assumed to represent the small private burners that wait until after the IDL permit season ends on October 20 (and prior to April 1) to accomplish their burns. The FPA databases provide an acreage and location so that these
small er burns can be quantified and then assumed to be spread evenly throughout the
October 20

– November 15 period . Many of these burns likely occur in the spring, so this is a conservative assumption for the fall burning season.

Emission Rates Used

. Emis sion rates are included in the 2011 NEI database , however the other datasets do not provide emission estimates. Emissions in the Shoshone County area vary with fuel loading, fuel moisture and acres burned, so rather than utilize default emission factors, the 2011 NEI prescribed fire emissions, based on the SMARTFIRE system with local fuel loadings,
moisture

, etc. were used. The NEI emission rates per acre burned were averaged separately for broadcast fires, slash pile fires and landing fires and the resulting emission factors were applied to the corresponding fire types in the other datasets. Landing fires are larger slash accumulations from logging operations in which trees are transported to one location for de

- limbing, while piles are hand or machine piled slash from trees felled in the immediate vicinity. NEI

- derived overall
emission factors for northern Idaho conditions are summarized in Table 4 and total emissions in the domain for the modeling period are summari zed in Table 5

.

Table 4 Emission factors for the Northern Idaho area derived from SMARTFIRE

- based 2011 NEI prescribed burns. These factors are applied to the same type/size of prescribed f ires in the other datasets which do not report emissions (IDL, FPA, CDA Tribe)

Effective Emission Factors from 2011 NEI Prescribed F ires for the Northern Idaho Area

Fire type/size Effective Emission Factor (tons per acre burned) Broadcast fires > 300 a cres (n=3) 0.229 Broadcast fires < 300 acres (n=349) 0.176 Landing fires (n=19) 0.211 Pile Fires (n=9) 0.21611

Table 5 Prescribed burning emission totals by data source for modeling domain (tons).

Burn Database Total Tons for Model Period (31d) % of

Total

ASG 2455 73.0%

IDL 641 19.1%

FPA

- Mica FPD 146 4.4%

FPA

- Cataldo FPD 7 0.2%

CDA Tribe 114 3.4%

Total 3362 100.0%

Rx s ource configuration and Plume Rise Class. The size and intensit y of prescribed fires determine the plu me rise. Source release characteristics for the prescribed fires were determined following the scheme used by Air Sciences/Environ for prescribed fires in the DEASCO3 modeling project completed by the Western Air Partnership (Air Sciences, 2013; Air Scienc es, 2013b; Environ 2012). These methods are derived from the WRAP 2002 regional haze modeling effort ( Air Sciences 2005 ), but were updated for the DEASCO3 project, designed to predict the effects of fire on ozone concentrations. This procedure was selecte d because it is perhaps the most widely peer reviewed wildfire and prescribed fire modeling effort in recent years with both modeling and fire/smoke behavior experts providing input

. Using the
DEAS CO3 approach, fire consumption and the flame

- phase fire co nsumption rates were estimated for each prescribed fire and the fires were then categorized by size and potential for plume rise vertical loft

. The DEASCO3 approach identifies the fraction of the smoke /PM 2.5 that remains in the surface layer, due to cool s moldering portions of the fire and the fraction of the
fire that rises aloft based on the size and flame

- phase consumption i ndex (FPCI) for the fire. The FPCI quantifies the flame

- phase combustion but then adjusts it by divid ing by the square root of
the acres burned to better capture the intensity of the burn:

FPCI = Flaming Phase Consumption/(acres) 0.5 Equation 2

This approach utilizes only the heat from the hotter flame phase in determining the plume rise of the aloft plume, and the fraction of smok e aloft versus the fraction in the surface layer. In DEQ ’s
CALPUFF modeling , each prescribed fire was simulated as two volume sources distributed, per Environ (2012) as a surface layer portion and an aloft portion based on the hour of day ( see Environ ( 20 12 ) charts duplicated in Figure 5 and Figure 6 below for the surface source fractions
and upper and lower elevation of each aloft plume. ) Plume rise is represented in CALP UFF by
volume sources placed at these ( Figure 5 and Figure 6 ) vertical elevations and additional plume
rise is not simulated as the plume /puffs travel and disperse downwind

.12

Figure 5 Plume vertical distribution and fraction of emissions in the surface layer for Class 1 fires (Environ, 2012)

Figure 6 Plume vertical distribution and fraction of emissions in the surface layer for Class 2 fires (Environ, 2012)

Figure 7 Diurnal variation in Fuel Consumption and Plume Rise Buoyancy Efficiency, a plume rise parameter.13

Figure 8 Mean Maximum Mixing Depths for October, sho wing a climatological average MMD for the Pinehurst region of

Nor thern Idaho around 800m (Holzworth, 1964)
Nearly all the prescribed fires were identified as Class 1 or Class 2 fires except for a very small number of larger burns which were identified with in the Class 3 plume rise bin, with plume rise elevations reaching 1000

– 3500m (Environ 2012) . However, since these prescribed burns are occurring in October, and the maximum mean mixing depth for northern Idaho in October is approximately 80 0 m ( Figure 8 ) , and since this height approximately matches the upper elevation of Class 2 plumes ( Figure 6 )

, DEQ simplified the modeling by assuming the larger fires behaved according to plume rise Class 2 characteristics at this time of year , i.e. limited by the lower mixing depths . T his approximation is somewhat conservative for less than 1% of the prescribed fires simulated, but it reduced the computational time by 33% which was cri tical with the short time frame available for this analysis.

This modeling exercise focused on the monthly average relative contributions resulting from the size and location of the known burns, and short term impacts were not assessed for this annual

st andard

- based analysis. To assure relative contributions are robust and not subject to day

- to day wind direction variations, but rather to the period

- averaged winds, the burn emissions for each source were divided by 31 and were modeled at the resulting per iod

- averaged emission rate for every day of the episode.14



## 7 Results

Point Sources. As indicated in the introduction, the goal of this analysis is to provide a relative
indication of the PM 2.5 contrib utions at the Pinehurst monitor for different areas f rom near to distant from the monitor. T hese results are considered to be only semi

- quantitative, however the variations in relative contribution vary by orders of magnitude and since they appear to reflect the Silver Valley flows, they nevertheless convey useful information. The point source modeling
results are shown for t he two modeling periods in Figure 9 and Figure 10 below. Only very small sources are present in the Sil ver Valley itself and the larger distant sources have very little impact at the Pinehurst monitor

, approximately 0.03 μg/m 3 ( period average ) for the highest of the two modeling periods, October 15

– November 15, 2013 ( Figure 9 ). This is less than one

quarter of one percent of Pinehurst’s annual average. The annual contribution is likely to be of the same magnitude or lower than this value, since the warmer months likely result in better ventilation. Therefore, DEQ concludes that point sources contribute insignificantly to the Pinehurst annual average

.

Figure 9 Point source relative contribution for period 10

- 15

- 13 to 11

- 15

- 13 . Pinehurst is the square city property line in the center of th e map where the actual contribution is 0.03 μg/m 3

.15

Figure 10 Point source relative contribution for period 1 1

- 1 6

- 13 to 1 2

- 15

- 13 . Pinehurst is the square city property line in the center of the map where the actual contribution is 0.03 μg/m 3

.


### Residential Wood Combustion

. The “reverse modeling” co ntours for the RWC source
category are shown in Figure 11 below. The values in this image are different than the traditional “forward model” resu lts such as the point source results ( Figure 9 and Figure 10 ) in
that each grid value represents the contribution at the Pinehurst monitor that would be caused by a single wood burning household, on average, at that grid location on the map. With this type of result, one only needs to multiply a given value on the map by the number of households at that location (or small town, for example) to obtain an estimate of the actua l contribution at the
Pinehurst monitor. In order t o take these results one step further, this “reverse model” surface was multiplied in GIS (Equation 1) by the number of households present at each location to provide an estimate of the actual contributio n at Pinehurst due to any town or other cluster of
residential households.16



### Residential Open Burning The RWC dispersion factor surface ( Figure 11 ) was also used to estimate the relative impact of
ROB sources. The RO B emissions were obtained from the 2011 NEI an d are allocated to households similarly to the RWC emissions.

Figure 11 Residential Wood Combustion Rela tive Impact locations based on DEQ "Reverse Modeling" tool. 10

- 15

- 13 to 11

- 15

- 13 . Residential open burning sources are assumed to follow the same dispersion patterns in this analysis.



### Prescribed Burning. Similar to the RWC “reverse modeling” results, the prescribed burning (Rx) source con tributions for Class 1 plume rise category of fires and the Class 2 category were compiled and are presented graphically in Figure 12 , showing the October 15 to November 15, 2013 period when prescribed burning activity is greatest

. The Class 1 plume rise c ase is on the left and the results for Class 2 fires are shown on the right. For both periods it is clear that the nearest prescribed fires yield the greatest potential contributions to the Pinehurst monitor for those fires in the immediate valleys drainin g into and surrounding Pinehurst, resulti ng in the high est contributions and only minor portions of Kootenai County reach contribution levels approaching 1% of the highest areas nearest the Pinehurst monitor. As with the RWC “reverse model” maps, the “dis persion factor” value (DF i ) at any location on these maps can be multiplied17

by the emissions in tons for any individual burn (as a 30

- day average), to estimate the actual contribution at the Pinehurst monitor. Thus, when the spatially distributed database of prescribed burns in the Class 1 and Class 2 categories are multiplied by the appropriate surface for smaller fires (Class 1 plume rise) or larger fires (Class 2 plume rise) , the actual estimated contributions from any area of burning can be semi

- quanti tatively assessed in at least a relative

sense.


### Application of Source Emissions (Q i 

### ) The final step in DEQ ’s weight

- of

- evidence evaluation of areas contributing to the Pinehurst monitor involved applying the household density map and the
combined burn dat abase to the “reverse model” results shown in Figure 11 and Figure 12 to provide semi

- quantitative relative contributions for each area , as shown in Equation 1 . The
modeli ng domain was divided up into areas or “boxes” of potential interest, for example reflecting terrain barriers, jurisdictional boundaries, distance and source density to explore relative contributions. This was done because while the potential impact of a given area is of partial interest, the source density at that location must also be used to inform the relative impact for that area at the Pinehurst monitor. The modeling contribution areas or “boxes” used in this
exercise are shown in Figure 13 and the relative contributions for each area are summed in GIS
over each box (∑(DF i x Q j )

. The contributions for each “ box ” were ranked from the largest to smallest contribution then summed until a majority of the potentia l modeled emission source
areas were accounted for.

The largest amount of contribution came from the area immediately surrounding the monitor in Pinehurst, Box 1 (91.1%) resulting from the predominant contribution of RWC in the immediate
Pinehurst area c losest to the monitor. Box 2 provided the next largest contribution amounting to 2.1% additional contribution, resulting in a total for the first two boxes of 93.2%. The more distant Boxes such as Box 12, 19, 20, 15, 18 all had less than 0.01 g/m 3 contrib uti on to the annual average at Pinehurst (~ 0.1%)

. The remaining significant contributions come from Box es 10 and 11, also selected because of their proximity to Pinehurst in the adjacent Silver Valley

. Prescribed burns are known to occur in these areas, b ased on the 2011 NEI, however incomplete
burn data was available for these areas

. U ncertainty in the potential burn activity in boxes 10 and 11 is an additional consideration indicating that they should considered for inclusion in the
nonattainment area. T his would assure that all prescribed burns in the same drainages as
Pinehurst and the western Silver Valley are addressed . All remaining “boxes” provide insignificant contribution at around one half of one percent, or lower.

The cumulated PM 2.5 contribut ion due to boxes 1,2,10,11 and 9 results in 8.42 μ g/m 3 or approximately 94. 4 % of the total domain wide emissions . The assumed geologic/road dust (RD) contribution of 0.2 μg/m 3 is included in this sum because only local roads contribute minor amounts of “ge ologic” dust within the immediate valleys near the monitor, as reflected in the speciation data ( DEQ 2014)

. DEQ believes 94.4% is a sufficient amount of controllable emission contributions on which to base an effective attainment demonstration and a quick return from
“n on a ttainment ” status.18

Figure 12 Prescribed burning r ela tive i mpact locations for Class 1 Plume rise case (left) and Class 2 Plume Rise case (right) based on DEQ "Reverse Modeling" tool. 10

- 15

- 13 to 11

- 15

- 13 m odeling period.19

Figure 13 Arbitrary "contribution areas" selected for relative comparisons of the potential for different areas to impact the Pinehurst monitor (Pinehurst is the square in Area or "Box" 1. ) Vertical line left of Box 2 is the Kootenai

- Shoshone county line.

Table 6 summarizes how the boxes were summed to find the percent contributions from the
various boxes

. Table 6 identifies that the 94. 4 % of emissions contributing to the annual average
at the Pinehurst monitor were within the central group of boxes in Shoshone County (1,2,9,10,11) and the remainder of the sources contributing modeled Pinehurst impact s ( 5. 6 % ) were diffusely spread over the remaining modeling domain. These results represent 8.42 μ g/m 3 resulting from the nearby boxes (1,2,9,10,11) and 0.3 μ g/m 3 resulting from the boxes in the
remainder of the domain

. This is approximately consistent with the conclusions from Urban
In crement/RWC

- HDD/Hysplit analysis provided by DEQ earlier ( DEQ 2014) in which the urban
increment is 9.2 μ g/m 3 ( the small difference is due to other small source categories not included in the modeling )

.20

Table 6 Area (Box Co ntrib ution) totals for Figure 12 boxes 1,2,9,10 and 11 versus remainder of the modeling domain.

Area Included Annual Contribution from RWC + Rx + ROB + RD ( g/m 3 ) Percent of Modelled Emissions (%) RWC + Rx + ROB + RD

Boxes 1, 2, 9, 10, 11 8.42 94.4%

Rest of modeling domain (Boxes 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 17, 18 , 19, 20, 21) 0.30 3.4%
Subtotal 8.72

Remainder of s ources that can be estimated, but are mostly in areas not proposed for inclusion in NAA Crop Residue Burning : 0.17 1.9%
Point Sourc es : 0.03 0.3%

Total Modeled Annual Contribution 8.92 100%

Estimated Urban Increment ( DEQ 2014) 9.2

Note, RWC is residential wood combustion, Rx is prescribed burning, ROB is residential open burning and RD is road dust.



### Estimated Contributions for EPA

- Proposed NAA Boundaries Following DEQ ’s estimation ( Table 6 ) of the RWC, Rx and ROB contributions to the Pinehurst annual average PM 2.5 in the central group of Figure 13 “ boxes”, EPA determined that the topographic barriers to transport from more distant valleys prevents significant contribution from
more distant sources at the Pinehurst monitor but that cold air drainage from mountain valleys contiguous to the Pine Creek a nd west Silver Valley have significant potential to contribute to
the PM 2.5 at the monitor. As a result, EPA determined that the hydrologic units draining into the western Silver Valley and Pine Creek valleys should be included in the non

- attainment area to assure that prescribed burning does not contribute to high PM 2.5 24

- hour events nor to annual

average levels above 12 g/m 3 , and that the residential wood burning in the populated portions of the western Silver Valley and Pine Cr eek valley contribute as well. As a result, EPA proposed a
terrain

- based non

- attainment boundary following watershed boundaries as shown in Figure 14

. Based on the proposed boundary, DEQ re

- accumulated the relative contributions within th e EPA proposed boundary. The final annual average contributions for the EPA’s proposed boundary are
94.5% similar to the “box

- based” contributions (94. 4 %) for boxes 1,2,9,10,11 shown in Table 6

. Based on the EPA

- pr oposed boundaries, DEQ believes that 94.5% of the potentially controllable
source contribution to the Pinehurst monitor will be captured in the NAA, that this is a sufficient basis for a successful attainment demonstration, and that adding additional mount ainous terrain with little geographic or meteorologic al connection to the western Silver Valley will contribute no significant additional reductions to the annual average PM 2.5 for the citizens of Pinehurst and the nearby western portion of the Silver Vall ey.21

Figure 14 Proposed Western Silver Valley Non

- Attainment Area. Pinehurst is square area in center of the NAA.22



## References
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, 2005. “2002 Fire Emission Inventory for the WRAP Region

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- Attainment Designation

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, Victor S. Li, Environment Canada, Ontario, Environmental Protection Operations Division, 4905 Dufferin St., Toronto, Ontario, Canada, M3H 5T4. Presented at Emission Inventories: "Integration, Analysis, and
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