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PM10 Dispersion Modeling for Treasure Valley, Idaho
Darko Koracin a , Domagoj Podnar a , Judith Chow a , Vlad Isakov b , Yayi Dong c ,
Alison Miller c & Mike McGown c
a Desert Research Institute , Reno , Nevada , USA
b DynTel , Research Triangle Park , North Carolina , USA
c Idaho Department of Health and Welfare , Division of Air Quality , Boise , Idaho , USA
Published online: 27 Dec 2011.
To cite this article: Darko Koracin , Domagoj Podnar , Judith Chow , Vlad Isakov , Yayi Dong , Alison Miller & Mike
McGown (2000) PM10 Dispersion Modeling for Treasure Valley, Idaho, Journal of the Air & Waste Management Association,
50:8, 1335-1344, DOI: 10.1080/10473289.2000.10464174
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TECHNICAL PAPER
Koracin et al.
ISSN 1047-3289 J. Air & Waste Manage. Assoc. 50:1335-1344
Copyright 2000 Air & Waste Management Association
PM10 Dispersion Modeling for Treasure Valley, Idaho
î
Darko Koracin, Domagoj Podnar, and Judith Chow
Desert Research Institute, Reno, Nevada
Vlad Isakov
DynTel, Research Triangle Park, North Carolina
Yayi Dong, Alison Miller, and Mike McGown
Idaho Department of Health and Welfare, Division of Air Quality, Boise, Idaho
ABSTRACT
needed to eliminate the simulated exceedances in all
The recorded exceedances of the 24-hr PM10 National Am-
bient Air Quality Standard (NAAQS) in Treasure Valley,
Idaho, have been associated with prolonged stagnation
periods during the winter. A comprehensive modeling
study of PM10 impact in Treasure Valley was performed
to support the State Implementation Plan (SIP). The study
included base-year and short-term episodic conditions.
The ISCST3 (Industrial Source Complex Short Term 3)
model, using the base-year meteorology and gridded
emissions of mobile sources, point sources, and wood
burning as input, generally agreed well with measure-
ments in both temporal patterns and annual averages.
The WYNDvalley model was evaluated using monitor-
ing data and was used to simulate the PM10 impact for
episodic exceedances during stagnant winter conditions.
An emission inventory was prepared for a base year
(1995) and then extrapolated to the years 2000, 2005,
2010, and 2015 in order to determine air quality plan-
ning requirements. According to the simulations using
base-year emissions and meteorology, exceedances are
not expected. However, exceedances at some stations
could be expected using projected emissions and episodic
meteorology. Results from emission control strategies we
developed indicate that mobile-source emissions have
the most significant impact; reduction of 25% would be
projected years.
INTRODUCTION
The U.S. Environmental Protection Agency (EPA) declared
the part of Ada County, Idaho, north of the Boise baseline
a moderate particulate non-attainment area. Ada County
is situated in a region of complex terrain known as Trea-
sure Valley. It is bordered by a mountain range to the north
and a sequence of terrain branches to the south (Figure 1).
Exceedances of the 24-hr PM10 National Ambient Air Qual-
ity Standard (NAAQS) were recorded in the city of Boise.
These exceedances have been associated with prolonged
stagnation periods during the winter. As cold air enters the
airshed following the passage of a cold front, a shallow
inversion is formed in the basin; an emission-filled air mass
stabilizes as warm air moves over the region.
Wolyn and McKee1 included the Boise area in their
study of deep stable layers in the intermountain west-
ern United States. They studied Boise radiosounding
records from 1959 through 1983 and estimated that in
December and January, about 15% of days had deep
stable layers. These stable and stagnant conditions are
highly favorable to the pooling of the surrounding air
toward the valley floor, and they commonly appear be-
tween the passages of strong weather systems. With only
11 in. of precipitation annually, these periods between
fronts can be of extended duration. The primary mecha-
nism for the transport and buildup of pollutants in Boise
appears to be the light, geographically induced drain-
age. Consequently, pollutants tend to pool in low spots
during these stable and stagnant conditions. Dispersion
is limited to weak diurnal up- and down-valley drifting
of pollutants with buildup over time. Due to emission
sources within this complex terrain and the frequency
of stable and stagnant conditions, daily exceedances of
IMPLICATIONS
This study investigates the appropriateness of using U.S.
Environmental Protection Agency (EPA)-regulatory and
EPA-recommended dispersion models to assess PM10
concentrations in complex terrain. An evaluation of model
predictions using monitoring data led to a validated model
that was then used to determine the most efficient con-
trol strategy for the SIP.
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Koracin et al.
Figure 1. Topography of the Boise area.
the PM10 NAAQS (150 µg/m3) during the winter season
have been recorded.
Figure 2 shows the locations of the stations measur-
ing PM10 in Treasure Valley. The map in Figure 2 is ex-
tracted from the topographic map of Boise, Idaho
(1:100,000 scale). The names of the stations and the length
of their recorded measurements are as follows:
•
•
•
Fire Station No. 5 (FS5)–Recorded data exist from
January 1986 to December 1996;
Liberty Street Fire Station (LFS)–Recorded data
exist from February 1989 to December 1996;
Mountain View Elementary School (MVS)–Re-
corded data exist from January 1986 to Decem-
ber 1996;
Figure 2. Map of Boise with locations of air monitoring stations indicated.
1336 Journal of the Air & Waste Management Association
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Koracin et al.
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•
•
Meridian (MER)–Recorded data exist from Janu-
ary 1992 to December 1996;
in Koracin et al.2 The main objectives of this study are
•
to evaluate selected EPA-regulatory and EPA-
alternative dispersion models for a base year and
episodes of elevated PM10 concentrations using
estimated emissions; and
Nampa (NAM)–Recorded data exist from July
1993 to December 1996 (not shown in Figure 2;
located 5 km south and 29 km west of FS5); and
Cloverdale (CLO)–Recorded data exist from Au-
gust 1995 to December 1996 (not shown in Fig-
ure 2; located 24 km south and 7 km west of FS5).
•
•
to develop strategies to control emissions by us-
ing the evaluated models and emissions projected
to future years.
A list of recorded PM10 exceedances is shown in Table 1.
Four episodes of elevated PM10 concentration have been
selected for data analysis and modeling:
(1) December 11, 1985–January 1, 1986;
(2) January 7–15, 1986;
DISPERSION MODELS
Based on recommendations from the EPA, Region 10, the
IDHW-DEQ selected the ISCST3 (Industrial Source Com-
plex Short Term 3) model4 for base-year simulations and
the WYNDvalley model5–7 for modeling episodes of el-
evated PM10 concentrations, including days with
exceedances.
(3) January 20–29, 1988; and
(4) January 4–7, 1991.
These episodes were considered the most extreme
cases of the meteorological conditions conducive to the
development of elevated PM10 concentrations. Meteoro-
ISCST3 Model
logical and monitoring conditions for these episodes are
î
ISCST3 is a steady-state, Gaussian dispersion model de-
signed for assessing pollutant concentrations from a vari-
ety of sources. According to the EPA Guideline on Air Quality
Models,4 the ISCST3 model (classified as an “A” group pre-
ferred model) is recommended for industrial source com-
plexes, rural or urban areas, flat or rolling terrain, transport
distances less than 50 km, 1 hr to annual averaging time,
and continuous pollutant emissions. ISCST3 can be used
to model primary pollutants from point, line, area, and
volume sources. ISCST3 contains algorithms for effects
such as building downwash and pollutant removal (wet
scavenging and dry deposition), and is capable of model-
ing either gaseous species or PM.
described in detail by Koracin et al.2 Figure 3 illustrates
specifics of atmospheric and monitoring data during the
one of the episodes (January 1991). The figure empha-
sizes that meteorological conditions characterized by low
temperature (Figure 3a), low wind speeds (Figure 3b), and
high humidity (Figure 3c) were associated with elevated
PM10 concentrations (Figure 3d). An examination of the
nocturnal radiosonde (at 0500 LST) indicates that the
episodes occurred during strong surface inversions with
high atmospheric stability and a vertical potential tem-
perature gradient frequently in excess of 20 K/kmwithin
the first kilometer (Figure 4). During the first episode, the
potential temperature gradient reached as high as 35 K/ km.
This extreme atmospheric stability appears to be a domi-
nant process that prevents vertical mixing and ventila-
tion of the pollutants from the Boise basin.
WYNDvalley Model (Version 3.11)
The basic assumption of Gaussian models is that the in-
tensity of dispersion is inversely proportional to wind
speed. Since increased PM10 concentrations occur during
stable, low-wind conditions, the use of ISCST3 might not
be appropriate. One alternative (or complementary) ap-
proach is to use the WYNDvalley model.5–7 The
WYNDvalley model does not rely on the Gaussian assump-
tion and can be used for low-wind situations. According
to the EPA Guideline on Air Quality Models,4 the
WYNDvalley model (classified as a
To support a State Implementation Plan (SIP), the
Idaho Department of Health and Welfare–Division of En-
vironmental Quality (IDHW-DEQ) initiated observational
and modeling studies, including the present one, to pro-
vide a basis for control of emissions for this area.3 A com-
prehensive PM10 observational and modeling study was
conducted by Desert Research Institute; details are given
“B” group alternative model) is rec-
ommended on a case-by-case basis
Table 1. Daily exceedances of PM10 in Ada County, Idaho, with associated meteorological data.
to estimate concentrations during
Year
Month
Day
Site
Total PM10 Avg. Temp Avg. WS
Avg. WD
Precip.
valley stagnation periods of 24 hr
or longer. WYNDvalley is a five-
layer, Eulerian-grid dispersion
model that permits its users flexibil-
ity in defining the borders around
the areas to be modeled, the bound-
ary conditions at these borders, the
(µg/m3)
(oC)
(mph)
(deg)
(in. per day)
1986
1988
1991
1991
Jan
Jan
Jan
Jan
14
28
7
FS5
FS5
FS5
MVS
314
165
173
164
–10
–2
5.5
5.2
5.9
5.9
175
214
244
244
0.00
0.00
0.09
0.09
–8
7
–8
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Koracin et al.
Boise–91–Jan–Temp
Day in January 1991
Boise–91–Jan–WS
Day in January 1991
Boise–91–Jan–RH
Day in January 1991
Boise–91–Jan–PM10
(d)
Day in January 1991
Figure 3. Time series of surface temperature (a), wind speed (b), relative humidity (c), and PM10 concentrations (d) at Boise for January 1991.
1338 Journal of the Air & Waste Management Association
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Koracin et al.
Figure 4. Vertical gradient of the potential temperature from the radiosonde data from Boise for January 1991.
intensities and locations of emission sources, and the
winds, diffusivities, and deposition processes that affect
the dispersion of atmospheric tracers.
developed by System Applications International.8,9 We
selected all regulatory default options as input for these
simulations. More details on the model input and setup
î
can be found in Koracin et al.2 A comparison of the simu-
lated and measured annual averages of PM10 concentra-
tions is shown in Table 2. Simulated concentrations
compare well at the MVS, MER, and NAM stations. Model
overestimation at the FS5 and LFS stations is possibly due
to overestimation of emissions near these two sites. Un-
derestimation of the model results at CLO is possibly due
to a relatively large background value that was not in-
cluded in the simulations.
MODEL EVALUATION
For the sake of completeness of the input meteorological
and monitoring data, we have selected 1995 as a base year.
The emission inventory was developed on a 2 km x 2 km
grid for both Ada and Canyon counties.8,9
For model evaluation, we have extracted meteorologi-
cal and emission data for January 1995. Both ISCST3 and
WYNDvalley models were then applied to Ada and Can-
yon counties with the same input data and compared to
monitoring data. All optional model parameters were set
to the default values. The results of the comparison of
simulation results with measurements at six sites are
shown in Figure 5. In most cases, ISCST3 reproduces quite
well the order of magnitude and provides similar average
values. WYNDvalley overestimates measured values in
most of the cases; however, in spite of overestimation,
WYNDvalley generally better represents the trend of
A comparison of 24-hr average concentrations as pre-
dicted by ISCST3 and as measured for the base year 1995
was performed for all receptor sites (Figure 6). Similar to
what was seen with the annual averages, ISCST3 was able
to reproduce the correct order of magnitude of measured
PM10 concentrations and, to some extent, temporal varia-
tions for most of the receptors. The only significant dif-
ferences between the modeled and measured PM10
concentrations are at FS5. As mentioned earlier, this might
indicate that the emissions near FS5 are generally overes-
timated.
higher measured values. A more complete model evalua-
î
tion is presented in Koracin et al.2
BASE YEAR AND PROJECTED YEAR SIMULATIONS
(ISCST3 MODEL)
We performed simulations of PM10 dispersion using me-
teorological data collected at the Boise airport in 1995,
emission data for the same year, and emission estimates
for projected years (2000, 2005, 2010, and 2015) that were
MODELING OF THE EPISODES (WYNDvalley)
WYNDvalley modeling was performed for the four epi-
sodes of elevated PM10 concentrations indicated in the
Introduction. Modeling results for the first episode will
not be reported at this time, as a final validation of the
monitoring data is not available. The actual meteorology
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Koracin et al.
Dispersion Model Simulation (ISC/WV): Idaho-1995aa
Dispersion Model Simulation (ISC/WV): Idaho-1995aa
Day in January 1995
Day in January 1995
Dispersion Model Simulation (ISC/WV): Idaho-1995aa
Dispersion Model Simulation (ISC/WV): Idaho-1995aa
Day in January 1995
Day in January 1995
Dispersion Model Simulation (ISC/WV): Idaho-1995aa
Dispersion Model Simulation (ISC/WV): Idaho-1995aa
Day in January 1995
Day in January 1995
Figure 5. Comparison of PM10 concentrations as simulated by ISCST3 and WYNDvalley for January 1995 vs measurements at FS5, LFS, MVS,
MER, NAM, and CLO using annual averaged emissions. Default options were used in all runs.
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Koracin et al.
Table 2. Comparison of annual (8760 hr) average PM10 concentration values (µg/m3) as simu-
lated by ISCST3 vs measurements for 1995.
and later. According to “roll-back” and receptor
modeling,10 there is a significant portion of second-
ary aerosols in PM10 during stagnation episodes.
Some of the adjusted model’s underprediction can
be viewed as measuring only primary aerosols.
Year
Site
X-coord (m)
Y-coord (m)
Conc. (µg/m3)
Predicted
Measured
FS5
LFS
563204.00
559731.00
558976.00
550339.00
534585.00
556368.00
4829782.00
4829657.00
4831638.00
4829673.00
4824729.00
4805687.00
71.2
40.4
36.3
22.4
40.0
8.3
36.6
29.1
26.2
29.8
39.9
27.0
CONTROL STRATEGIES
WYNDvalley, with adjusted emissions and base-year
meteorology, did not simulate exceedances in any
of the projected years. However, WYNDvalley simu-
lations with adjusted emissions and 1991 episodic
meteorology did simulate exceedances in 2010 and
later years. We applied several control strategies to
MVS
MER
NAM
CLO
1995
eliminate the exceedances.
for each episode and the “worst-case” emissions were used
as inputs. Figures 5 and 7 show comparisons between the
modeled and measured values for all available data. It can
be seen that the modeled values are mostly greater than
the measured, but for some points a fairly good agree-
ment exists. WYNDvalley generally overestimated mea-
sured data for both the base-year month (see Model
Evaluation) and the historical episodes of elevated PM10
concentrations. However, in order to use dispersion mod-
eling for the projected years and develop control strate-
gies, any dispersion model a priori has to conform to
measurements. We followed IDHW-DEQ’s suggestion, af-
ter their discussions with EPA officials (Region 10), to re-
duce the estimated emissions and bring WYNDvalley
results in closer agreement with measured data. After that,
WYNDvalley was used with input episodic meteorology
and projected emissions to develop control strategies.
Adjustments were made to mobile emissions to re-
duce the emissions by 45% for days without precipita-
tion and 98% for days with precipitation. Reduction for
“dry” days was calculated by dividing the number of “wet”
days by the total number of days in the winter period.
Dry days are defined as those with less than 0.01 inches
of precipitation. Reduction for wet days follows the prin-
ciple that no road dust will be emitted on days with pre-
cipitation and the fact that tailpipe emissions contribute
to only 2% of total mobile emissions. The effect of pre-
cipitation on other types of sources was included implic-
itly in the models by means of the parameterization of
wet deposition. Also, in order to conform to model con-
ditions, dry deposition velocity was set to 0.005 m/s prior
to exercising the control strategies for the projected years’
emissions. In particular, the adjustments were mainly re-
stricted to the results from FS5, where most of the mea-
sured exceedances occurred.
Mobile Emissions
Mobile emissions were further reduced by 25% as com-
pared with the base year; as a result, exceedances were
eliminated in 2010 and later years. This reduction was
applied to both tailpipe and road-dust emissions.
Point Sources
Because the projected point-source emissions are signifi-
cantly large, we used an 80% reduction to investigate the
effect on exceedances. Even with this large and unrealis-
tic reduction, the exceedances were not removed. This
suggests that the model is quite insensitive to point-source
emission, the monitoring network is not appropriately
set up in the vicinity of point sources, and/or the meteo-
rology was not favorable to transport from point sources
to the given receptors.
Wood Burning
Wood-burning emissions are generally small as compared
with, for example, mobile-source emissions. However, in
some cases when the mobile-source emissions are reduced
due to precipitation (rain or snow), wood-burning emis-
sions might be important. Application of a 10% reduc-
tion in wood-burning emissions as a control strategy
yielded insignificant change in PM10 concentrations at
receptor sites.
Figure 8 shows the efficiency of these control strate-
gies in eliminating PM10 exceedances by 2015. The results
assume a background PM10 concentration of 10.75 µg/m3.
The figure shows that the most efficient reductions can
be accomplished by reducing mobile source emissions.
CONCLUSIONS
A comprehensive modeling study focusing on both an-
nual and episodic impacts of PM10 in Ada and Canyon
counties in Idaho was performed to support the SIP. The
ISCST3 model was able to reproduce the correct order of
magnitude of PM10 concentrations and also temporal
After these adjustments, results for the projected years’
emissions with meteorology for January 1995 indicate that
exceedances are not expected. However, using the pro-
jected years’ emissions and the meteorology of the 1991
episode, exceedances could be expected at FS5 in 2010
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ISCSTS Simulation: Idaho-1995aa
ISCSTS Simulation: Idaho-1995aa
Julian Day
Julian Day
ISCSTS Simulation: Idaho-1995aa
ISCSTS Simulation: Idaho-1995aa
Julian Day
Julian Day
ISCSTS Simulation: Idaho-1995aa
ISCSTS Simulation: Idaho-1995aa
Julian Day
Julian Day
Figure 6. Comparison of PM10 predicted by ISCST3 vs measurements for 1995 for FS5, LFS, MVS, MER, NAM, and CLO, respectively.
1342 Journal of the Air & Waste Management Association
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Koracin et al.
variations at four of the six receptors. Overestima-
tion of PM10 concentrations at the remaining two sta-
tions shows that the emissions in their vicinity are
most likely overestimated and/or the input meteo-
rology in this complex terrain is not well represented
by a single station and relatively simple dispersion
models. Simulations of emissions predicted for 2000,
2005, 2010, and 2015 indicate that annual
exceedances of the 24-hr PM10 NAAQS can be expected
at four of the six receptors. The ISCST3 model con-
firmed compliance with the NAAQS with regard to
annual average for most of the receptors. The
WYNDvalley model results with all the default op-
tions generally overestimated the measured data;
however, the model results did resemble the observed
temporal variation in the measured data. In order to
examine the WYNDvalley model’s performance prior
to its use in simulations of projected years and con-
trol strategies, we performed a series of sensitivity tests.
As can be expected, the model was primarily sensi-
tive to the magnitude of predicted emissions. The
PM10 Concentrations predicted by WYND valley vs measurements
Measured (µg/m3)
Figure 7. Comparison of measured and modeled PM10 concentrations for
the WYNDvalley default run for all episodes.
Figure 8. WYNDvalley-modeled PM10 concentrations for the year 2015 at six receptor sites with the following control strategies applied: (clockwise
from top left) 25% mobile-source emissions reduction, 80% industrial emissions, and 10% wood-burning emissions.
Volume 50 August 2000
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Koracin et al.
4. Guideline on Air Quality Models; EPA-450/2-78-027R; U.S. Environmen-
tal Protection Agency: Research Triangle Park, NC, 1995.
5. Harrison, H. An Eulerian-Grid Air-Quality Dispersion Model with
Versatile Boundaries, Sources, and Winds. In Proceedings of the APCA-
EPA Specialty Conference on Receptor Models in Air Resource Management;
San Francisco, CA, 1988.
6. Harrison, H. TracerRose: A Hybrid Source-Receptor Model to “Finger-
Point” Significant Sources. In Proceedings of PNWIS/A&WMA; Novem-
ber 1990.
7. Harrison, H. A Users’ Guide to WYNDvalley: An Eulerian-Grid Air-Qual-
ity Dispersion Model with Versatile Boundaries, Sources, and Winds—Ver-
sion 3.11; WYNDsoft Inc.: Mercer Island, WA, 1992.
8. Development of Base and Future Year Emission Inventories for Area Sources,
Non-Road Mobile Sources, and On-Road Motor Vehicles for the Northern
Ada County PM10 Nonattainment Area; Systems Applications Interna-
tional; DEQ: Boise, ID, 1997.
agreement between the model results and measurements
was improved by varying the wind speed and dry deposi-
tion velocity. To confirm the WYNDvalley results with
measurements, adjustments were made on the emission
inventory and wind data based on suggestions from the
IDHW-DEQ. The WYNDvalley simulations for projected
years and meteorology during exceedance episodes indi-
cate that exceedances can be expected at the central sta-
tion after 2010.
The efficiency of the proposed control strategies was
investigated by simulating reductions in the projected
emission inventory. Reduction of mobile-source emissions
appears to be the most efficient control strategy; a 25%
reduction in mobile-source emissions eliminates the
exceedances in all projected years. A possible reduction
of 10% in wood burning created only minor reductions
in the simulated PM10 concentrations. Point-source emis-
sions are very large in projected years; however, applying
even an 80% reduction in 2010 and later years could not
eliminate the exceedances. The reasons for this might in-
clude model insensitivity to point-source emissions, in-
adequate siting of monitoring stations, or meteorology
that is not favorable to transport from the point sources
to the given receptors.
9. Development of Base and Future Year Emission Inventories for Industrial
Sources for the Northern Ada County PM10 Nonattainment Area; DEQ:
Boise, ID, 1998.
10. Kuhns, H.; Green, M.; Watson, J.; McGown, M.; Riley, D. Analyzing
Speciated Ambient PM10 Concentrations and Emissions in Ada and Can-
yon Counties, Idaho; Technical Report; Desert Research Institute: Reno,
NV, June 1998.
Secondary aerosols are important contributors to am-
bient PM10 concentrations under severe stagnation con-
ditions. The model results suggest the importance of
additional research on this issue (including chemical trans-
formations) and the appropriateness of possible control
strategies for these events.
î
About The Authors
Dr. Darko Koracin is an associate professor with over ten
years’ experience in atmospheric and dispersion modeling.
Dr. Judith Chow is a research professor with over 22 years’
experience in conducting air-quality studies and perform-
ing statistical analyses of monitoring data. They are cur-
rently with the Division of Atmospheric Sciences at the
Desert Research Institute, 2215 Raggio Parkway, Reno, NV
89512.
ACKNOWLEDGMENTS
This study was supported by the Idaho Department of
Health and Welfare (Contract number IQC040200).
î
Dr. Vlad Isakov is a former graduate student of Dr.
Koracin’s and is currently employed as a senior scientist at
DynTel, P.O. Box 12804, Research Triangle Park, NC 27709.
REFERENCES
î
Mr. Domagoj Podnar is a doctoral student working with Dr.
1. Wolyn, P.; McKee, T. Deep Stable Layers in the Intermountain West-
î
Koracin.
ern United States; Monthly Weather Review 1989, 117.
2. Koracin, D.; Isakov, V.; Podnar, D. PM10 Dispersion Modeling for Trea-
sure Valley, Idaho; Prepared for Alison Miller & Yayi Dong, Division of
Environmental Quality, Idaho Department of Health and Welfare;
DRI: Reno, NV, 1998.
3. Northern Ada County/Boise PM10 Air Quality Improvement Plan; Idaho
Department of Health and Welfare–Division of Environmental Qual-
ity; DEQ: Boise, ID, 1991.
Dr. Yayi Dong, Ms. Alison Miller, and Mr. Mike McGown
work for the Idaho Department of Health and Welfare, Divi-
sion of Environmental Quality, 1410 North Hilton, Boise, ID
83706-1255.
1344 Journal of the Air & Waste Management Association
Volume 50 August 2000