1

Dispersion Modeling Estimates of Relative 

Source Contributions to the Pinehurst 

Monitor

1 Introduction

Most annual PM2.5 non-attainment areas (NAA) proposed in the United States are in the Eastern 

United States and are typically composed of one whole county, 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 distributed population and emission sources and relatively flat 

terrain which facilitates transport and mixing of pollutants across wide areas. 

In the West, very low population density is topographically separated with dramatically varying 

wind and stagnation patterns. 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 mountain terrain can cause extreme variation in 

winter season PM2.5 concentrations over relatively short distances. This figure shows the 

mountainous terrain in the area of Shoshone County in Idaho, and Lincoln and Sanders counties

in Montana. The 2008 – 2011 4-year average winter season (Dec-Jan-Feb) PM2.5 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 the 

annual standard while the Cabinet Mountain IMPROVE monitoring site, in a nearby valley halfway between these two towns experiences very pristine winter-time fine particle (PM2.5) 

concentrations just over 1 μg/m3for the same winter period averaged over 4 years. This is clear 

evidence that unlike the Eastern U.S. where whole counties or groups of counties are typically 

proposed as non-attainment areas, the geographically distinct and isolated airsheds of the West

may justify partial-county non-attainment areas defined by terrain barriers 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 supplemental 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 Figure 2. The valleys vary from 2.5 to 5 km across and up to 

1240m 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 >90oover the 1 km distance 

between Pinehurst and the main Silver Valley. Thus, while 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 suitable 
2

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 impacts from different source types and source areas in a semi-qualitative 

weight-of-evidence analysis.

Figure 1 Extreme differences in the Winter-time average PM2.5 in adjacent mountainous counties. The December-JanuaryFebruary average for 2008 - 2011 is shown for Pinehurst ID, Libby MT, and Thompson Falls MT, with the near pristine Cabinet 

Mountains site in the center. A 12-month averaged (winter-time) variation of up to 22.1 μg/m3over 39 miles represents a severe 

concentration gradient that is likely unique to the mountainous western U.S. and demonstrates 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 south-to-north flowing Pine 

Creek drainage which flows into Pinehurst is ~ 2.5 km across while the east-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 the 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 terrain. 

When the EPA decided to consider 2013 data in the PM2.5 annual NAAQS designations, bringing 

Pinehurst into non-attainment after the deadline for non-attainment designations in December 

2013, DEQ was advised that it would have approximately 1 - 2 months 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 

measured monthly averaged PM2.5 pattern was analyzed temporally to estimate RWC and Rx 

contributions to the Urban Increment. In addition a back-trajectory frequency map was included 

to demonstrate that areas outside the Silver Valley have little connection to the Pinehurst 

monitor. In this study, DEQ provides 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 schedule, DEQ used an 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 the analysis. When used in conjunction with emission information it 

can provide useful information about how much sources in a geographic region contribute to 

PM2.5 at the Pinehurst monitor. While with sufficient lead time DEQ has ideas on how to further 

improve 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 resolution 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 modeling. The point source modeling was 

done to examine whether the few point sources in neighboring counties have a significant 

contribution to the PM2.5 at the Pinehurst Monitor. Point sources were modeled explicitly to 

predict their impact at the Pinehurst 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 periods 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, DEQ 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 equal concentration or equal impact due to the single source.

In the “reverse model” approach, there is a single 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 

(Pinehurst monitor) receptor for each possible source location (at the grid points). Thus the 
5

values at each grid point represent the concentration that the “unit emission” source would have 

on the Pinehurst monitor 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 monitorthe primary goal of a non-attainment 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 

fire. 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, these 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 at 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 (Qj) were multiplied by the “reverse model” 

dispersion factor (DF) surface in the Geographic Information System (GIS) according to the 

equation;

CR = DFi x Qj Equation 1

Where Qj is the emission rate for individual sources (g/s); DFi is the dispersion factor for that 

source type at that location; and CR is the concentration 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 (Qi) at each location 

provides an equivalent concentration contribution value that would result from the traditional 

modeling approach. (Qi includes emissions rates and in addition, for RWC, the number of 

households at each location.) The advantage of this approach is that the dispersion modeling 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 addition, 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 categories 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 Residue Burning throughout Idaho in a 2008

State Implementation Plan (SIP) revision addressing Crop Residue Burning (DEQ 2009). 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/m3) resulting from the 2005 CRB 

season was 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 contribution
6

estimated in that SIP for Pinehurst was 0.17 μg/m3. Since CRB burning activity levels have not 

exceeded those 2005 levels in recent years, DEQ believes 0.17 μg/m3is a conservative estimate 

for the CRB impacts at Pinehurst. The July through September period 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 Urban 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 considering it in the current nonattainment designation.

Geologic Sources, primarily paved and unpaved road dust. As a result of the previous PM10 SIP 

all the roads in Pinehurst and nearly all those in the western Silver Valley are paved. While the 

emission 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 “cool season” 

filter speciation conducted by N. Frank (2008). Based on 16 high-PM2.5 filters, the 

geologic/crustal component was 0.2 μg/m3or approximately 0.5% of the total average filter 

mass. Based on this speciation data, road dust is assumed to contribute 0.2 μg/m3, an 

insignificant amount to the annual average PM2.5 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 PM2.5 emissions were modeled as primary PM2.5 and 

where only PM10 emissions were identified for combustion sources, the entire emission rate was 

assumed to be primary PM2.5. 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 October-November modeling 

period is projected to be 0.03 μg/m

3

and for the November-December winter episode is projected 

to be 0.02 μg/m3. For both periods, the point source contribution at the Pinehurst monitor is not 

significant and the annual average contribution may be conservatively estimated to be less than

0.03 μg/m3since 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, 

State

Map 

Point Source Name

Distance 

to 

Monitor, 

miles NH3 NOx PM2.5 SO2 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 

(PM10)

11 - 176

Benewah, 

ID P-6 Stimson Lumber Co St. Maries 32.9 - - 3 - - 3

Shoshone

, ID P-7 Kellogg School District 391 7.8 - <1 <1 

(PM10)

<1 - 1

Shoshone

, ID P-8

North Fork Cedar, dba Kingston 

Cedar 5.1 - -

6 

(PM10)

- - 6

Shoshone

, ID P-9 Dave Smith Body Shop 3.8 - 4

<1 

(PM10)

<1 - 4

Shoshone

, ID P-10 Enyeart Cedar Products 5.2 - 12 1 

(PM10)

7 - 20

Shoshone

, ID P-11 Essential Metals Corp. 12.9 - 5 <1 - - 5

Shoshone

, ID P-12 Shoshone Funeral Service 8.9 - -

<1 

(PM10)

- - <1

Benewah, 

ID P-13

Potlatch Land & Lumber, LLC –

St. Maries Complex on Coeur 

d’Alene Reservation*

22.0 - 96 97 13 68 274

Benewah, 

ID P-14 Stimson Lumber Company –

Plummer Operation* 33.7 - 99 101 12 41 253

*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 1992 Silver 

Valley wood burning survey developed during the Pinehurst PM10 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 woodstove change-out programs initiated since 1992 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 this modeling for Pinehurst and the 

remainder of 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 the October 15 –November 15, 2013 prescribed
8

burning season, and November 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 National 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/source 

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 based on a 

study by Omni Environmental (Li, 2007) and are summarized in Table 3. The diurnal emissions 

profile 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 much of the yard burning occurs in the evening when people return from 

work, the RWC dirunal profile is a reasonable approximation for such a small emission category.

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

Parameter Value

Stack Height, m 4

Stack Diameter, m 0.2

Exit velocity, m/s 1

Exit Temperature, oK 473

Initial sigma-y, m 2

Initial sigma-z, m 2

Momentum Flux Factor, (0 – 1) 0
9

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

6 Prescribed Burning Sources

Prescribed (Rx) burning is known to cause elevated PM2.5 concentrations at the Pinehurst 

monitor during the fall, typically from October 1 through mid-November, but peaking around 

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 are 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 years 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 burns. 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 that 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 Databases (FPA). The FPA “Compliance” data for 

the Mica Forest Protection District and a portion of the Cataldo Forest Protection District 
10

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 detected 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 locations. The remainder of 

the FPA 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 

smaller 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. Emission 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 summarized 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 fires in the other datasets which do not report emissions (IDL, FPA, CDA Tribe)

Effective Emission Factors from 2011 NEI Prescribed 

Fires for the Northern Idaho Area

Fire type/size

Effective 

Emission Factor 

(tons per acre 

burned)

Broadcast fires > 300 acres (n=3) 0.229

Broadcast fires < 300 acres (n=349) 0.176

Landing fires (n=19) 0.211

Pile Fires (n=9) 0.216
11

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 source configuration and Plume Rise Class. The size and intensity of prescribed fires 

determine the plume 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 Sciences, 

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 selected 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 

DEASCO3 approach, fire consumption and the flame-phase fire consumption 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/PM2.5 that 

remains in the surface layer, due to cool smoldering portions of the fire and the fraction of the 

fire that rises aloft based on the size and flame-phase consumption index (FPCI) for the fire. The 

FPCI quantifies the flame-phase combustion but then adjusts it by dividing 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 smoke 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 (2012) 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 CALPUFF 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, showing a climatological average MMD for the Pinehurst region of 

Northern 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 within 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 800 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. This approximation is somewhat conservative for less than 1% of the prescribed

fires simulated, but it reduced the computational time by 33% which was critical 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 

standard-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 period-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 PM2.5 contributions at the Pinehurst monitor for different areas from near to 

distant from the monitor. These 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 the two modeling periods in Figure 9 and Figure 10 below. Only very small 

sources are present in the Silver Valley itself and the larger distant sources have very little 

impact at the Pinehurst monitor, approximately 0.03 μg/m3(period average) for the highest of 

the two modeling periods, October 15 – November 15, 2013 (Figure 9). This is less than onequarter 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 the map where the actual contribution is 0.03 μg/m3.
15

Figure 10 Point source relative contribution for period 11-16-13 to 12-15-13. Pinehurst is the square city property line in the 

center of the map where the actual contribution is 0.03 μg/m3.

Residential Wood Combustion. The “reverse modeling” contours for the RWC source 

category are shown in Figure 11 below. The values in this image are different than the 

traditional “forward model” results 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 actual contribution at the 

Pinehurst monitor. In order to 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 contribution 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 ROB emissions were obtained from the 2011 NEI and are allocated to 

households similarly to the RWC emissions.

Figure 11 Residential Wood Combustion Relative 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 contributions 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 case 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 draining into and surrounding Pinehurst, resulting in the 

highest 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 “dispersion factor” value (DFi) at any location on these maps can be multiplied 
17

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-quantitatively assessed in at least a relative 

sense. 

Application of Source Emissions (Qi) 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 database 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 

modeling 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 (∑(DFi x Qj). The contributions for each “box” were ranked from the largest to 

smallest contribution then summed until a majority of the potential 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 closest 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/m3contribution to the 

annual average at Pinehurst (~ 0.1%). The remaining significant contributions come from Boxes 

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, based on the 2011 NEI, however incomplete 

burn data was available for these areas. Uncertainty 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. This 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 PM2.5 contribution due to boxes 1,2,10,11 and 9 results in 8.42 μg/m3or 

approximately 94.4% of the total domain wide emissions. The assumed geologic/road dust (RD) 

contribution of 0.2 μg/m3is included in this sum because only local roads contribute minor 

amounts of “geologic” 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 

“nonattainment” status. 
18

Figure 12 Prescribed burning relative impact 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 modeling 

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 impacts (5.6%)

were diffusely spread over the remaining modeling domain. These results represent 8.42 μg/m3

resulting from the nearby boxes (1,2,9,10,11) and 0.3 μg/m3resulting from the boxes in the 

remainder of the domain. This is approximately consistent with the conclusions from Urban 

Increment/RWC-HDD/Hysplit analysis provided by DEQ earlier (DEQ 2014) in which the urban 

increment is 9.2 μg/m3(the small difference is due to other small source categories not included 

in the modeling).
20

Table 6 Area (Box Contribution) 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/m3)

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 sources that can be estimated, but are mostly in areas not proposed 

for inclusion in NAA

Crop Residue Burning: 0.17 1.9%

Point Sources: 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 PM2.5 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 and west Silver Valley have significant potential to contribute to 

the PM2.5 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 PM2.5 24-hour events nor to annual 

average levels above 12 g/m3, and that the residential wood burning in the populated portions of 

the western Silver Valley and Pine Creek 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 the 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-proposed 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 mountainous terrain 

with little geographic or meteorological connection to the western Silver Valley will contribute 

no significant additional reductions to the annual average PM2.5 for the citizens of Pinehurst and 

the nearby western portion of the Silver Valley.
21

Figure 14 Proposed Western Silver Valley Non-Attainment Area. Pinehurst is square area in center of the NAA.
22

References

Air Sciences, 2013. DEASCO3 Project updates to the fire plume rise methodology to model 

smoke dispersion, Prepared by: Matt Mavko, Air Sciences Inc. and Ralph Morris, ENVIRON

Project 178-14, December 3, 2013.

Air Science, 2013b. “DEASCO3 2008 Emissions Inventory Methodology.” Prepared for the 

Joint Fire Sciences Program. Air Sciences, Inc., Project No. 178. September 2013. 

http://wraptools.org/pdf/ei_methodology_20130930.pdf.

Air Sciences, 2005. “2002 Fire Emission Inventory for the WRAP Region—Phase II.” Prepared 

for the Western Governors’ Association/Western Regional Air Partnership. Air Sciences, Inc. 

Project 178. July 22, 2005. http://www.wrapair.org/forums/fejf/tasks/FEJFtask7PhaseII.html.

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.

Environ 2012, Final Technical Memorandum No. 5: Fire Emissions, Subject: Wildfires, 

Prescribed Burns and Agricultural Burning Emissions. Technical Memorandum to T. Moore 

from Morris, R. and E. Tai, C. Loomis, Z. Adelman, Western Regional Air Partnership (WRAP), 

April 27, 2012. 

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.

Holzworth, G. C., 1964: Estimates of mean maximum mixing depths in the contiguous United 

States. Mon. Weather Rev. 92: 235-242.

DEQ 1992. Pinehurst Particulate (PM-10) Air Quality Improvement Plan, Idaho Department of 

Environmental Quality, February 5, 1992. Includes wood burning survey by ES Field Services.

DEQ 2006. Open Burning of Crop Residue State Implementation Plan (SIP) Revision, September 

2009, Accessed from https://www.deq.idaho.gov/media/347121-crb_sip.pdf.

DEQ 2014. Source Contributions Analysis for the Pinehurst, Idaho Non-Attainment Designation.

Prepared by Idaho Department of Environmental Quality, 2014.

Li, 2007. Conventional Woodstove Emission Factor Study, 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 

Communications", Raleigh, May 14 - 17, 2007.

Scire, J.S., D.G. Strimaitis and R.J. Yamartino, 1990. Model Formulation and User’s Guide

for the CALPUFF Dispersion Model. Prepared for the California Air Resources Board by Sigma

Research Corporation, Concord, MA.