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Modeling Assessment of 2017 Prescribed
Grassland Burning in the Flint Hills Region

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EPA-454/R-21-004
June 2021
Modeling Assessment of 2017 Prescribed Grassland Burning in the Flint Hills Region
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
U.S. Environmental Protection Agency
Region 7
Air and Radiation Division
Lenexa, KS

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1. BACKGROUND
Prescribed and wild grassland burning results in emissions of pollutants that can react in the atmosphere
to become particulate matter less than 2.5 microns in diameter and ozone (Baker et al., 2019), both of
which have known negative health effects in humans (Reid et al., 2016) and are regulated under the
Clean Air Act with National Ambient Air Quality Standards. Other pollutants emitted by grassland fires
are toxic (e.g., formaldehyde, mercury) or have negative ecological impacts (e.g., ammonia, S02, NOX)
and potentially contribute to impaired visibility in downwind areas including National Parks and National
Monuments.
The Flint Hills ecoregion contains approximately 5 million acres of grassland (Baker et al., 2019). Each
year when weather is conducive, hundreds of thousands of acres are burned with prescribed fire to
minimize the encroachment of woody growth, maintain the ecosystem for native species, and to
promote agribusiness (Towne and Craine, 2016; Weir and Scasta, 2017). A fire interval of 1 to 3 years is
considered necessary to minimize encroachment of woody vegetation (Ratajczak et al., 2016).
There are over 5 million acres of grassland in the Flint Hills region (Baker et al., 2019) which must be
burned every 1 to 3 years to meet ecosystem resilience (Ratajczak et al., 2016), however only a fraction
of the region is burned on this frequency (Baker et al., 2019). Prescribed fire activity in this region is
largely done within a few weeks period from late March to mid-April each year (Baker et al., 2019). The
large amount of activity on a given day has led to downwind areas exceeding the level of the 03
National Ambient Air Quality Standard (NAAQS). The state of Kansas implemented a smoke
management plan in 2010 to encourage prescribed burning practices that minimize the emissions of
pollutants that can react in the atmosphere to form 03 (Kansas Department of Health and Environment,
2010). If prescribed fire was done over the longer dormant season rather than focusing on a few weeks
in the early spring season more acres could be burned, and air quality impacts would likely be less on
any given day (Weir and Scasta, 2017).
Complex photochemical grid models have been used to demonstrate grassland burning impacts on
secondarily formed pollutants (e.g. 03, PM2.5, regional haze) to support retrospective regulatory
assessments (Baker et al., 2016; Kansas Department of Health and Environment, 2012). Photochemical
grid models have historically underestimated regional PM2.5 organic aerosol (Wagstrom et al., 2014),
which is the largest component of biomass burning PM emissions. Photochemical models have been
shown to replicate downwind surface level elevated PM2.5 impacts for large western wildfires (Baker et
al., 2016; Baker et al., 2018), but systematically over-predict at monitors downwind of grassland burning
in the Flint Hills region (Baker et al., 2016). These models tend to systematically overestimate 03 at
monitors downwind of both wild (Baker et al., 2016; Baker et al., 2018) and grassland fires (Baker et al.,
2016).
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In March and November 2017, a field study took place in the Flint Hills region to better understand
prescribed grassland fire fuels, emissions, and their impacts on air quality in the ecoregion (Whitehill et
al., 2019). The study included an extensive cataloging of fuel characteristics surveyed during individual
burns and ceilometer deployment to assess smoke plume heights. In this work, we use the unique
dataset collected during the 2017 Flint Hills study to constrain and evaluate simulations of the observed
burns in the Community Multi-scale Air Quality (CMAQ) modeling system. We also evaluate local to
regional scale model-predicted smoke transport using other datasets including satellite imagery and
meteorological observations from surface and sonde measurements. The combination of co-located fuel
data and atmospheric observations collected during the 2017 Flint Hills study provides an opportunity to
assess air quality model skill in representing different scales of prescribed grassland fire smoke transport
in the region.
Here, the Community Multiscale Air Quality Model (CMAQ) was applied for specific prescribed grassland
fires in the Flint Hills region in 2017 instrumented with surface and remotely sensed measurements and
entire prescribed burning seasons to illustrate how well the model predicts regional air quality
compared with routine surface monitors and space-based air quality products. The influence of grid
resolution was explored for the 2017 case studies and a common regulatory assessment grid resolution
was used for the longer-term seasonal evaluation. The entire record of prescribed fires at Konza Prairie
in 2017 and 2018 are compared with the HMS satellite detection product to evaluate burn detection
location accuracy for these types of grassland fires.
2. METHODS
Field measurements were made near multiple prescribed grassland fires at Konza Prairie in March and
November of 2017 and at Tallgrass Prairie in November of 2017 (Whitehill et al., 2019). Table 1 shows
the date, size, fuel load, and fuel moisture measured for prescribed burns at both locations. Dominant
fuel types were identified, and fuel load was estimated for these prescribed fires as part of the field
study effort. Fuel moisture and other meteorological data were determined using nearby routine
measurement networks. As part of this field study, a ceilometer was operated continuously at Konza
Prairie during March, a period of increased fire activity in the region (Baker et al., 2019), to capture local
to regional scale prescribed grassland fire impacts. The ceilometer was operated again in November
2017 at Konza Prairie for a similar objective then deployed at Tallgrass Prairie to capture local scale
impacts by being placed immediately adjacent and downwind of a single large prescribed fire
(11/15/17). A second ceilometer was operated during November 2017 at the Tallgrass Prairie Visitor
Center upwind of the prescribed fire to provide an estimate of incoming aerosol and the surface mixing
layer height.
Model configuration and application
Models were applied for the entirety of the March and November 2017 periods coincident with a field
study ceilometer deployment in the region. The extent of the continental scale 12 km domain, 4 km
domain covering the Kansas and northern Oklahoma, and inner 1 km domains over a portion of the Flint
Hills are shown in Figure 1. The springtime 1 km modeling domain was centered over the Konza Prairie
Biological Station and was extended in the November modeling simulation to encompass the Tallgrass
Prairie National Preserve.
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Figure 1. Spatial extent covered by the 12, 4, and multiple 1 km model domains with gradients
representing grassland coverage. The Flint Hills ecoregion is shown with the dashed black outline.
Ceilometer placement at Konza Prairie and Tallgrass Prairie (co-located RAWS surface meteorological
site) are also shown.
Meteorological model simulations over the Flint Hills region were completed using version 3.9 of the
Weather Research and Forecasting (WRF) model with the Advanced Research dynamical core
(Skamarock et al., 2005). Table 1 summarizes the WRF model set-up, where the model configuration
consisted of three one-way nested domains with horizontal grid spacing of 12, 4 and 1 km and 35
vertical levels. The vertical atmosphere was resolved using 35 vertical layers with the lowest 12 levels
within approximately 1 km of the surface
WRF physics options used in the simulations include the Rapid Radiative Transfer Model longwave and
shortwave radiation schemes (lacono et al., 2008) and the Thompson (Thompson et al., 2008)
microphysics scheme. The Teidke cumulus parametrization (Tiedtke, 1989) was applied in the 12-km
domain and no cumulus parametrization was used in the inner 4 km and 1 km domains. Each simulation
used the Mellor-Yamada-Janjic (MYJ) PBL scheme and Monin-Obukhov (Janjic Eta) similarity surface
layer scheme. The United States Geological Survey (USGS) land use data set was used along with the
NOAH land surface model to simulate soil moisture and temperature (Niu et al., 2011). The 12 km
resolution North American Mesoscale model (NAM) operational analyses was used to initialize
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atmospheric and land surface variables as well as provide the lateral boundary conditions. WRF
simulations were executed using 5.5 day overlapping run segments.
Table 1. Configuration options selected for the WRF model applications.
Model Feature
Description
Version
3.9
Boundary Conditions
NAM Analysis
Vertical Levels
35
Model Top
50 hPa
Domains
3 one-way nests
Resolution
12 km, 4 km, 1 km
Number of Grid Cells
12 km (471 x 312), 4 km (271 x 325), 1 km (100 x

140 - spring; 161 x 233 - November)
Timestep (s)
36,12, 3
Planetary Boundary Layer
Mellor-Yamada-Janjic (MYJ)
Surface Layer
Monin-Obukhov (Janjic Eta)
Land Surface
NOAH
Land Use
USGS
Radiation
RRTMG
Microphysics
Thompson
Cumulus
Tiedke, 1989; None for 4 km or 1 km domains
Gases and aerosol were predicted with the Community Multiscale Air Quality (CMAQ) version 5.3 model.
The Carbon Bond version 6r3 was used to represent gas phase chemistry (Emery et al., 2015),
ISORROPIA II inorganic particulate partitioning (Fountoukis and Nenes, 2007), and aqueous phase
chemistry (Fahey et al., 2017). Biomass burning consists of high molecular weight compounds and has
extremely low volatility (Washenfelder et al., 2019) so organic aerosols were treated as non-volatile.
Some anthropogenic and biogenic VOC yield secondary organic aerosol and were treated as semi-
volatile (Carlton et al., 2010).
Fire location and timing were based on HMS fire detections coupled with burn area products reconciled
with the SmartFire2 (SF2) system. Emissions were based on fuel type and loading from the Fuel
Characteristic Classification System (FCCS) Fuel Loading Module version 2 and fuel consumption from
the CONSUME module of the BlueSky Framework version 3.5.0 (rev 38169) (Larkin et al., 2014). The Fire
Emission Production Simulator Module (FEPS) version 2 and fuel moisture estimates from the Wildland
Fire Assessment System (WFAS), each of which are also part of the same BlueSky system, were used in
the process of calculating emissions from the fires.
Plume rise was estimated with the modified Briggs approach, which has been used extensively to
estimate local to regional scale smoke transport from prescribed fires in the Flint Hills region (Baker et
al., 2016), Pacific northwest (Zhou et al., 2018), and wildfires in California (Baker et al., 2018) and
Arizona (Baker et al., 2016). Plume height is a function of the buoyant heat flux, which is based on acres
burned, fuel loading, and the duration of the fire (Zhou et al., 2018). Prescribed burns measured as part
of the field study were modeled using actual burn unit size in addition to the default approach.
The emissions modeling process used here assigns the same allocation of daily emissions to specific
hours of the day for all prescribed fires. The default profile applied to prescribed fire in SMOKE allocates
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most emissions in hours 11 am to 6 pm (Baker et al., 2016), which generally matches prescribed burning
activity in this region (Baker et al., 2019) but does not accurately reflect start time and duration of
individual fires that typically burn for less than 4 to 6 hours (Brey et al., 2017). Prescribed burns
measured as part of the field study were modeled using actual start and end times in addition to a
second simulation that used the default hour of the day allocation profile (Figure Al).
CMAQ was applied using the SF2/BlueSky system and separate sensitivity simulation where prescribed
burns measured as part of the field study were modeled using actual acres burned, fuel loading, fuel
consumption, and timing information. A third simulation without prescribed grassland fires was done to
compare against the runs with prescribed fire to isolate the impacts of these emissions on predicted air
quality (Kelly et al., 2015).
Surface and Sonde Measurements
The Flint Hills WRF simulations were evaluated using surface meteorological observations obtained from
NOAA's Meteorological Assimilation Data Ingest System (MADIS) dataset. Meteorological measurements
were compared with model estimates of 2 m temperature and humidity, and 10 m winds (Figure 1).
Additionally, a Remote Automated Weatherstation (RAWS) located atTallgrass Prairie provided solar
radiation, fuel moisture, and precipitation measurements along with surface temperatures, humidity
and winds. Vertical profiles of temperature and wind from rawinsondes released daily in the morning
and evening from the Topeka airport were used to evaluate the model's ability to reproduce the vertical
structure of the boundary layer.
Remote Sensing Measurements and Imagery
Vaisala ceilometers (CL31 and CL51) were episodically deployed at locations in the Flint Hills ecoregion
in March and November 2017 (Figure 1) to provide a near-continuous (approximately 36 s interval)
backscatter profile measurements up to 4.5 km. BL-View software was used to calculate aerosol layer
heights and cloud base heights from the backscatter profiles. The aerosol layer determination is based
on a gradient method that identifies steep decreases within the backscatter profile, with the first
negative gradient maximum identified as the boundary layer height. Boundary layer heights were
temporally aggregated to 5 min averages and periods were excluded from this analysis when the
standard deviation exceeded 0.20 km or relative standard deviation (standard deviation divided by the
mean) exceeded 20% (Knepp et al., 2017). A Campbell Scientific ceilometer was operated during the
November 15 burn atTallgrass Prairie and located upwind of the prescribed fire.
Satellite true color imagery products from Moderate Resolution Imaging Spectroradiometer (MODIS)
provide information about clouds and smoke in the region (www.airnowtech.com). Images from the
terra satellite represent ~10:30 am local time and aqua satellite images represent ~1:30 pm local time.
Level 1 and 2 measurements of AOD were obtained from the Aerosol Robotic Network (AERONET;
aeronet.gsfc.nasa.gov) site located at Konza Prairie.
Field measurements: biomass type, biomass sampling
Biomass was collected from 1 m2 sized clip plots at Konza Prairie Biological Station burn units in March
(N=3) and November (N=l). A total of 10 replicants were collected, dried, and weighed from 3 large burn
treatment areas and 5 replicants for a smaller treatment area. An average was calculated for each of the
burn treatments. Dominant vegetation species were identified by an ecologist. Fuel moisture was
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collected hourly at a RAWS located at Tallgrass Prairie (https://mesowest.utah.edu/cgi-
bin/droman/meso_base.cgi?stn=TGSKl&time=GMT).
3. RESULTS & DISCUSSION
Fire location and detection, fuel type, fuel loading
A total of 13 prescribed fires (Table 2) were observed as part of the field study, each with different
timing and size. The modeling system was able to correctly place most of the prescribed fires observed
during the field study based on satellite fire detections on days without cloud cover (9 days). Only 3 of
the prescribed fires were not detected by satellite on cloud-free days and each of these totaled less than
100 acres and were not active during the overpass time of a polar orbiting satellite. Missed detection of
prescribed fire will result in an underestimate of emissions unless other sources of information provide
the location, date, and area burned. Regional prescribed fire was minimal during the fall period,
although large prescribed fires occurred at Tallgrass Prairie on November 13th (~2000 acres) and 15th
(~1000 acres). Cloudy conditions on November 13th precluded satellite detection. It is possible more
prescribed fire activity happened in the spring but was not detected by satellites due to extensive
periods of cloud cover over the region.
Table 2. Burn unit and fuel information for prescribed fires at Konza Prairie and Tallgrass Prairie in

2017 observed as part of the field study. All fuel moisture measurements were from a Tallgrass Prairie
monitor location.



















Measured
FCCS Total
Percent

WFAS 10-
WFAS
WFAS
Percent







Total Fuel
Fuel
Difference
Measured
hr Fuel
Live Fuel
Duff Fuel
Different



Burn Unit
Approx.
Approx.
HMS
Loadi ng
Loading
in PM
Daily Avg. Fuel
Moisture
Moisture
Moisture
in PM
Site
Burn
Date
Size (acres)
Start Time
End Time
detected
(tons/acre) (tons/acre)
emissions
Moisture {%)
(%)
(%)
(%)
emission;

Konza
1
3/15/2017
111
13:30
17:15
Cloud
3.10
3.96
-8%
116
60
130
150
-14%

Konza
2
3/16/2017
84
10:50
12:00
No
2.36
4.08
-48%
90
60
130
150
48%

Konza
3
3/16/2017
205
13:00
14:30
Yes
2.78
4.08
-38%
90
60
130
150
48%

Konza
4
3/17/2017
11
15:00
16:00
Cloud
3.10
3.96
-8%
128
60
130
150
-15%

Konza
5
3/17/2017
16
16:40
17:15
Cloud
3.10
4.08
-31%
128
60
130
150
40%

Konza
6
3/20/2017
294
10:45
15:30
Yes
2.68
4.08
-41%
90
60
130
150
48%

Konza
7
3/20/2017
335
10:45
15:30
Yes
2.68
4.08
-41%
90
60
130
150
48%

Konza
8
3/20/2017
299
10:45
15:30
Yes
2.36
4.08
-48%
90
60
130
150
48%

Konza
9
3/20/2017
148
10:45
15:30
Yes
2.36
4.08
-48%
90
60
130
150
48%

Konza
10
11/10/2017
27
13:50
14:45
No
4.97
4.08
10%
101
60
130
150
46%

Konza
11
11/10/2017
26
15:30
16:15
No
4.97
4.08
10%
101
60
130
150
46%

Tallgrass
12
11/13/2017
1,960
10:30
16:30
Cloud
2.40
4.08
-47%
193
60
130
150
6%

Tallgrass
13
11/15/2017
938
12:00
16:00
Yes
2.40
4.08
-47%
175
60
130
150
21%
Fuels at each location were largely cheatgrass (Bromus tectorum), Indian grass (Sorghastrum nutans),
big bluestem (Andropogon gerardii), and little bluestem (Schizachyrium scoparium). The FCCSfuel type
matched these at each burn unit (Table 2). Burns 1 and 4 were categorized as "old field grassland"
resulting in a slightly different fuel loading and type (more shrubs) than the other locations. Fuel loading
estimated by FCCS and field-based data for each burn are shown in Table 2. Fuel loading estimates
based on FCCS for these prescribed fires tended to be higher (35% on average) than the field data but
varied for each burn unit (-18% to 73%).
Figure 2 shows satellite burn detections and burn areas at Konza Prairie for multiple days from 2017.
The prescribed fires during 2017 were often partially or fully obscured by cloud cover. The March 16 and
20 burns had clear skies. Extensive prescribed fire activity across the region on April 11 may have
obscured the actual fire from detection and resulted in many false detections outside the burn unit.
Some of the missed prescribed burns may be the result of size and timing of the fire missing satellites
with higher resolution detection products. Only one of the 2 days of prescribed fire at Tallgrass Prairie
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was captured by satellite due to extensive cloud cover on the first day (Figure 2). A similar analysis was
done for burn days at Konza Prairie in 2018 to capture additional periods of clear skies (Figure A2). Most
of the 2018 prescribed fires at Konza Prairie were detected with a least one satellite detection within a
km of the actual burn unit (Figures LOCATION-KONZA).
Figure 2. Satellite detected fires based on the HMS system (red) and burn areas (green outline) by day
at Konza Prairie (blue outline) for 2017. Gray shading represents grassland coverage at 1 km grid
spacing.
2/6/2017	3/9/2017	3/10/2017
3/15/2017	3/16/2017	3/20/2017
4/11/2017		4/13/2017		11/10/2017
o Actual fire burn unit
• HMS fire detect
O AMERIFLUX
+ CASTNET
Fuel moisture
Fuel moisture was estimated for 10-hr fuel (shrubs), live fuel (grasses), and duff (litter) as 60, 130 and
150 (% of 1,000 hr fuel) at each of the burns by the WFAS system. Daily average fuel moisture data
measured at Tallgrass Prairie is provided in Table 2 for each prescribed fire. Fuel moisture can exceed
100% due to water in the fuel that may weigh more than the dry fuel. The WFAS climatological data
tended to be higher for live fuel and duff than measured during the spring and lower in the fall for the
days that were part of the field study at Konza Prairie and Tallgrass Prarie. Applying CONSUME with the
day specific fuel moisture resulted in drier fuel, more consumption, and higher emissions (40 to 48%)
during the spring burn units dominated by grass and duff. Burn units dominated by shrubs had less
consumption and emissions because measured fuel moisture levels for 10-hr fuel were higher than the
WFAS estimate. The larger burn units at Tallgrass Prairie had much less change in fuel consumption and
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emissions (increases of 6 to 21%) compared to the smaller units at Konza Prairie despite the higher fuel
moisture measured compared to WFAS. For the prescribed fires considered here, fuel loading
overestimates by FCCS and fuel moisture overestimates from WFAS tend to compensate in terms of
resulting emissions, which would decrease with more accurate fuel loading information (by 44% on
average) and increase for these particular burns based on the fuel being drier which would typically lead
to more consumption and emissions (by 39% on average).
Timing
The approximate start and end times for specific fires were recorded for 5 separate prescribed fires
from 2017 (Figure 3). These prescribed fires ranged in size (26 to 1,960 acres), start time (mid-morning
to late afternoon), and duration (less than an hour to 6 hours). All fires finished burning before dusk. A
strong linear relationship (r2=.88, residual error=0.88 and p=0.0058) exists between the fire duration and
acres burned (Figure 2). Including fuel loading results in a slightly better estimate of total fire duration
(r2=.97, residual error=0.46, p=0.0058). The sample size is small (N=5), but these data suggest that fire
duration could be estimated based on acres burned and fuel loading. Satellite data indicate prescribed
burns in this region typically start mid-morning or after lunch (Baker et al., 2019) and last up to 6 hours
(Brey et al., 2017).
Figure 3. Default (red trace) and actual prescribed grassland fires included in a field study at Konza
Prairie and Tallgrass Prairie in 2017. The inset shows area burned and burn duration for a subset of
the prescribed fires with well characterized start and end times.
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Since prescribed fires in this region are often short in duration and small in size, satellite products may
not always have start and end time estimates. This means that the linear equations could provide an
estimate of duration when coupled with a generic start time of late morning for longer fires and early
afternoon for shorter fires. Currently, emissions are allocated to daytime hours, with most between 11
am and 6 pm (Figure Al) (Baker et al., 2016). The default assumption for prescribed fire duration was
longer than even the largest fires observed during the field study. Overestimating burn duration will
result in lower hourly heat flux and less plume buoyancy, which can result in less realistic plume heights
(Zhou et al., 2018). In the future, fire-specific temporal profiles could be incorporated into an emissions
processing system by using the fire size and time of detect for those fires detected by satellite.
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November 15, 2017 case study (Vertical plume placement)
Multiple ceilometers were operated at Tallgrass Prairie on November 15, 2017. One ceilometer was
placed to capture smoke impacts immediately downwind of the prescribed fire and the other upwind at
the Tallgrass Prairie Visitors Center to characterize aerosol inflow and aerosol layer structure (Figure 4).
The second ceilometer operated upwind and shows very little aerosol loading in the area. Based on the
vertical backscatter gradient at the upwind ceilometer, the data suggest that the surface mixing layer
was between 1000 and 1200 meters during the prescribed fire (Figure 5). The prescribed fire started
around 12:30 pm along the southern edge of the burn unit which was close to the downwind
ceilometer. The fire progressed to the north while winds transported smoke plumes toward the south,
which is a prescribed fire approach sometimes called a backfire that is intended to maintain a slow rate
of fire spread (Wade, 2013).
Figure 4. Satellite detected fires by the HMS system (dots) matched with November 13 (green) and 15
(yellow) burn units at Tallgrass Prairie for 2017. Picture, wind flow, fire progression, and ceilometer
placement are shown for the November 15 prescribed burn.
Smoke from the prescribed fire was evident in the ceilometer backscatter profile with near-ground
plumes at the beginning of the burn when the fire was closest to the ceilometer (less than 1.5 km) to
larger plumes that were sometimes decoupled from the surface toward the end of the burn when the
fire was furthest (1.5 to 3.5 km) from the ceilometer (Figure 5). Smoke plumes extended from the
surface to approximately 125 meters during the first half hour of the fire (12:35 to 1:15 pm), 250 meters
during the next half hour (1:15 to 1:45 pm), and 500 meters between 1:45 and 2:15 pm. The smoke
extended from the surface to top of the surface mixing layer at 2:30 and afterwards smoke was often
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decoupled from the surface and plume heights were variable. Smoke plumes likely did riot extend above
the surface mixing layer at the ceilometer location, which may be due to the close proximity of the
measurements to the fire activity. Most of the smoke plumes showed fairly uniform levels of aerosol
backscatter from bottom to top and were rarely mixed from the surface to the top of the boundary
layer.
Figure 5. Ceilometer profile of aerosol backscatter measured upwind (bottom panel) [Atijand
downwind (top panel) of the November 15, 2017 prescribed fire at Tallgrass Prairie. Ceilometer
estimated surface mixing layer height (dashed line) also shown.
12	13	14	15	16
Hour of the day (local time)
11	12	13	14	15	16	17
Hour of the day (local time)
The 1 km model predicted plume from this prescribed fire is shown in Figure 6. The model provides
hourly output that generally corresponds to the timing of the fire and estimates smoke plumes that are
fairly well mixed from the surface to the top of the modeled boundary layer. The timing of the smoke
from this fire starts too early and extends too late in the evening when the default temporal profile is
used instead of actual timing information about the prescribed fire (Figure 6). Using the default profile
results in very high surface concentrations in the early evening after the fire was over and places
emissions in the free troposphere which will result in unrealistic downwind impacts locally and
regionally.
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Figure 6. November 15, 2017 prescribed fire PM2.5 modeled at 1 km grid resolution using the actual
start and end time (top) and the default temporal profile (bottom). Modeled planetary boundary
layer height (solid line) and ceilometer estimated surface mixing layer height (dashed line) also
shown.
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Since very little aerosol was present in the atmosphere, the smoke plumes provided the strongest
vertical aerosol gradient which meant the ceilometer estimates of mixing layer height generally
correspond to some part of the smoke plumes. The algorithm used by the ceilometer to estimate
boundary layer tops sometimes correspond to plume edges, but often represented the middle of
plumes while some of the densest plumes were not identified at all with the ceilometer mixing layer
height algorithm (Figure A3).
March 16 and 17, 2017 case study
Prescribed fire impacts were modeled at the ceilometer location before dawn on March 16 from activity
in southern Kansas and northeast Oklahoma late in the previous day (March 15). A 205 acre prescribed
fire was set in the early afternoon at Konza Praire on March 16 and lasted until mid-afternoon (Table 2;
burn 3). This fire was located to the southeast of the ceilometer location. Winds were steady from the
south, which transported smoke from this prescribed fire to the north, but not directly toward the
ceilometer (Figure 7). The model predicted some impacts from this prescribed fire at the ceilometer
location when applied at 4 km resolution, but clear separation exists between the smoke plume and
ceilometer location at 1 km resolution. Consistent with other studies (Baker et al., 2014), model
prediction of peak impacts in the domain increased (usually at the source location) when these fires
were modeled at finer grid resolution.
The modeling system shows impacts at the ceilometer location from prescribed fires at Konza Prairie on
March 17, 2017. These modeled prescribed fires totaled approximately 30 acres and were set during the
late afternoon to early evening time period. This prescribed fire was not included in the standard
SmartFire2/BlueSky approach due to cloud cover, but also likely would not be detected due to the
relatively small size and late in the day timing. Other prescribed fires in the region (located to the north)
impacted the ceilometer location between noon and 6 pm. Agricultural tilling activity occurred in fields
located immediately north of the ceilometer during March 17 and may have contributed brief periods of
increased aerosol backscatter during the afternoon and evening (Figure 8). Afternoon elevated periods
11

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of AOD were also observed at Konza Prairie and often match up to coincident increases in aerosol
backscatter observed by the ceilometer.
Figure 7. Daily total forward trajectory weighted fire detections for March 16, 2017 (top left),
modeled PM2.5 from the prescribed fire at Konza Prairie at 1 pm local time (top right), photograph of
the same prescribed fire at 2 pm local time taken from the east of the fire looking toward the
northwest (bottom left), and vertical cross-sections of modeled PM2.s using actual and default timing
information at the ceilometer location (bottom right).
g x Ceilometer location
7 " > Camera location
03/16:08 03/16:13 03/16:18 03/16:08 03/16:13 03/16:18
12

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Figure 8. Aerosol backscatter measured by a ceilometer at Konza Prairie on March 17, 2017. Modeled
planetary boundary layer height and ceilometer estimated surface layer mixing height (black) also
shown. The grey box indicates the period and vertical extent of model predicted smoke impacts from
prescribed fire. Agricultural tilling activity was observed upwind near the ceilometer on this day.
a)
"O

Modeled smoke impacts near ceilometer
Aqua satellite visible image
Topeka sounding

KMML
• Ceilometer mixing layer estimate
1 Sill x WRF 4km mixing layer
WRF 1km mixing layer
o ><®
<§>
ft
*
i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i i i i r
03/17:00 03/17:03 03/17:06 03/17:09 03/17:12 03/17:15 03/17:18 03/17:21 03/18
00
The smoke plume on March 16th was close to the ground at the flame front due to strong southerly
winds consistently at ~15 mph and became well mixed through the boundary layer immediately
downwind. Further downwind, smoke was evident toward the top of the surface mixing layer with less
smoke near the surface. On March 17th, upper-level clouds and light northerly winds resulted in a stable
plume updraft immediately over the location of active burning with the densest smoke nearest the
surface and becoming less opaque as distance from the ground increased (Figure 9). When these
prescribed fires were modeled with realistic timing information, the model did well to capture these
plumes with a well-mixed plume on the 16th and a plume with highest concentration of smoke near the
surface on the 17th of March (Figure 4 and 5). The prescribed fire plume is well mixed throughout the
modeled surface mixing layer when using actual start and end times for the fire which results in higher
estimated heat flux and higher plume rise compared to using the default temporal profile for prescribed
fire.
13

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Figure 9. Daily total forward trajectory weighted fire detections for March 17, 2017 (top left),
modeled PM2.s from prescribed fires at 5 pm local time (top right), photograph of the Konza Prairie
prescribed fire at 5 pm local time taken from the north of the fire looking toward the south (bottom
left), and vertical cross-sections of modeled PM2.5 using actual and default timing information at the
ceilometer location (bottom right).
~i	1	1—	r~
-97 -96 -95 -94
1
600
500
-	400
-	300
-	200
100
0
a _
x Ceilometer location
> Camera location
/
-1	1	r~
10	20	30
40	50	60	70
i Modeled BL estimate
Ceilometer BL estimate
03/17:12 03/17:15 03/17:18
—i—i—i—i—i—T
03/17:12 03/17:15 03/17:18
0
Lig/m3
March 18, 2017 case study
Numerous prescribed fires were detected by satellite on March 18, 2017 and many were large enough
to be seen with satellite imagery (Figure 10). As winds shifted from northerly to easterly during the day
on March 18 and then southeasterly overnight smoke from afternoon prescribed fires to the north and
east of Konza Prairie was transported to the ceilometer location. Smoke impacts persisted into the next
morning (March 19). The modeling system shows a large impact of prescribed fire smoke during this
period at the ceilometer location. The modeled impacts are well mixed from the surface through the
surface mixing layer and up to clouds located about 2000 meters above ground level and generally
coincide with the timing of elevated aerosol measured by the ceilometer. Vertical mixing is similar
between the 12 and 4 km simulations. Regional smoke predictions were similar spatially between 12 and
4 km simulations but tended to be systematically lower at 12 km, especially near large isolated fires
which is consistent with grid-based model system representation of primarily emitted pollutants (Baker
etal., 2014).
14

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Figure 10. March 18, 2017 modeled PM2.5 from prescribed fires at 7 pm local time at 12 km resolution
(left) and 4 km resolution (right).
¦S !1 i
+

T
¦II
—1—
-100
—r~
100
—r~
150


x
%
i
x Ceilometer location
+ Topeka met station
120
- 90
- 60
- 30
-1- 0
ug/m3
March 20, 2017 case study
Satellite imagery shows multiple prescribed fires near Konza Prairie on March 20, 2017 (Figure 11). An
analysis of MODIS imagery burn scars from the prescribed fire complex to the northeast
(Westmoreland) suggests these fires burned approximately 550 acres. The smoke plume from this
complex and the prescribed fires at Konza Prairie are similar on visible images from the 1:30 pm Aqua
satellite (Figure 11). Neither were visible on the 10:30 am terra satellite image meaning both started
after the morning satellite overpass. The satellite image (Figure 11) of the Konza Prairie smoke plumes
indicate a plume width of approximately 6 to 7 km downwind which compare well to the plume width
predicted by the modeling system at 1 km resolution. The length of the Konza Prairie smoke plume on
the satellite image is slightly longer than 40 km at 1:30 pm. This distance coupled with average winds of
20 km/hr suggest a start time between 11 and 11:30 am which matches well with the actual 10:45 am
start time of the burn.
The Westmoreland complex plume width was approximately 3 km wide at 10 km downwind and 7 km
wide at 20 km downwind. The full plume extent is difficult to discern on the satellite image but a 30 km
total length with 20 km/hr winds suggests a start time around noon, which cannot be verified since
those fires were not part of the field study. The modeling system predicts a plume width of
approximately 4 to 7 km for the Westmoreland plume and suggest this fire and other regional
prescribed fires impacted the ceilometer location.
15

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Figure 11. March 20, 2017 true color satellite image from the aqua satellite at approximately 1 pm
local time showing visible smoke plumes, fire detections, and clouds (left) and modeled PM2.s from
the prescribed fires at 1 pm local time (right).
9 -
x Geitamatsr totalbri
WasTnardarid
ccnipiei jl
X
_
Karura Pran«^ra
I	1	I	1	1	1	
io	ao	3d	m	fio	m	fti
Local to Regional Scale Transport
Modeling system skill in representing local to regional scale plume transport of PM2 5 depends on many
variables, most notably meteorology. Modeled winds and temperature at the surface and aloft were
compared to routine measurements to evaluate smoke transport. Table 3 shows aggregated model
performance metrics over all monitors in the model domain for temperature, water mixing ratio, wind
speed, and wind direction for the spring and fall time periods. Aggregate performance for temperature
(mean error < 2 degrees K) and water mixing ratio (mean error < 1 g/kg) at both grid resolutions and
seasons was comparable to other continental scale WRF simulations (US EPA, 2011; UNC, 2015). Winds
were slightly over-predicted (mean bias between 1.5 and 1.75 mph) compared to observation data
(Table 3) and slightly worse than aggregate continental scale WRF performance shown in other studies
(US EPA, 2011; UNC, 2015).
The model does well in both seasons capturing synoptic and diurnal variability of solar radiation,
temperature, wind speed, and wind direction at Tallgrass Prairie (Figures A4 and A5). Daytime solar
radiation is well characterized but the model tends to under-estimate solar radiation on cloudy days
(Figures A4 and A5). The model did well replicating temperature, wind speed, and wind direction during
periods of increased prescribed fire activity (Figure A4 and A5).
16

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Table 3. Aggregate model performance metrics for routine surface measurements and model
predictions from the 4 and 1 km WRF simulations.
Spring	November
4km	1km 4km 1km
# of sites
330
3
330
9
# of hourly model prediction-observation




456,256
4,243
28,346
2,112
pairings
2m Temperature Mean Bias (K)
-0.4
-0.17
-0.4
-0.45
2m Temperature Error (K)
1.65
1.71
1.47
1.5
2m Mixing Ratio Bias (g/kg)
0.22
0.37
0.18
-0.24
2m Mixing Ratio Error (g/kg)
0.73
0.7
0.53
0.56
10m Wind Speed Bias (m/s)
0.71
0.71
0.78
0.68
10m Wind Speed RMSE
1.87
1.85
1.66
1.5
10m Wind Direction Bias (°)
4.7
-0.75
4.52
1.1
10m Wind Direction Error (°)
19.43
19.14
15.82
16.2
Model predicted surface boundary layer heights generally follow synoptic and diurnal patterns
estimated by the BL-view software (Figure 12 and 8). However, WRF often predicted a nocturnal stable
layer (NSL) that was much closer to the surface which BL-View was unable to detect from the ceilometer
backscatter profile (Figures 3 and S4). The inability of the ceilometer to resolve this feature of the NSL
may be the result of low aerosol content (i.e. clean conditions) in the NSL or that WRF is predicting the
NSL too close to the surface. However, the ceilometer did appear to capture the residual aerosol layer
during the night and day time hours above the NSL and convective boundary layer.
Figure 12. Aerosol backscatter measured by a ceilometer at Konza Prairie during November 2017.
Modeled planetary boundary layer height and ceilometer estimated surface layer mixing height
(black) also shown. Models and data do not suggest prescribed grassland fire impacts during this
period.
11/06:18 11/07:02 11/07:10 11/07:18 11/08:02 11/08:10 11/08:18 11/09:02 11/09:10 11/09:18 11/10:02 11/10:10 11/10:18
17

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4. CONCLUSIONS, CURRENT AND FUTURE WORK
Accurate representation of surface and aloft winds and smoke plume size compared with visible satellite
images suggest the modeling system shows skill toward capturing local to regional scale smoke plume
transport from prescribed grassland fire. The use of more realistic start and end times for prescribed fire
resulted in a better match against smoke plumes measured with a ceilometer. Within day timing can to
some degree be approximated based on total acres burned and where available improved with accurate
fuel consumption information. Cloudy conditions and smaller prescribed fires outside the timing of polar
orbiting satellites result in small fires not being captured in the modeling system. A simple prescribed
fire tracking system linking burn unit specific information (e.g., area burned, fuel types, and fuel
loading), day of the burn, and approximate start and end times would be valuable toward better
representation of prescribed grassland fires in air quality modeling systems. Predicted 03 impacts were
higher in March compared to November for a prescribed fire with identical size and emissions both
locally and regionally while PM2.5 impacts were comparable regionally, which is generally consistent with
weather being more 03 conducive during the March period. However, additional work is needed to
understand how well the modeling system characterizes the chemical evolution of secondarily formed
pollutants in grassland fire plumes from the fire location to regional scales. Further, more studies are
needed comparing air quality impacts of prescribed grassland fires in this region during different times
of year.
This report of the 2017 field study in the Flint Hills has provided a basis for the tools and techniques
available to further analyze the prescribe burning and smoke impact in Eastern Kansas. A limitation to
the 2017 study was the limited timeframe that the ceilometer was deployed, and thus it did not provide
an ideal dataset of the prescribed burning throughout the predominate burn period (i.e., early April).
Since the 2017 field study, a Vaisala CL-51 ceilometer has permanently been placed at Konza Prairie
(https://alg.umbc.edu/kpks-archive-calendar/). It has been in operation since March of 2020 and has
provided data and visualization for two springtime Flint Hills prescribed burn seasons (2020 and 2021),
along with showing smoke transport from Western U.S. wildfires in the summer of 2020 and long-range
transport of Saharan dust from west Africa. The type details provided by the Konza Prairie ceilometer
are highlighted in Figure 13 and Figure 14.
Figure 13 shows the ceilometer backscatter during the March 31, 2020 Flint Hills prescribed burn event.
Smoke from burning is seen on the backscatter and the evolution of the daytime mixed layer is evident,
with smoke being mixed within the growing afternoon planetary boundary layer. The ceilometer shows
the destruction of the daytime mixed layer and the transition to the more stratified nighttime boundary
(between 1:00 and 3:00 UTC). Figure 14 shows that most of the smoke from daytime burning sits above
the nighttime boundary layer, detached from the surface. Also evident is smoke from evening or
nighttime burning that is "trapped" within the stable nighttime boundary (3:00 UTC).
18

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Figure 13. Aerosol backscatter from the CL-51 ceilometer at Konza Prairie showing smoke during a
prescribed fire event on March 31, 2020.
Vaisala CL51-KPKS
15000
10000
5000
18:00
21:00
0
00:00
03:00 06:00
09:00 12:00 15:00
March 31 2020 (UTC)
00:00
Figure 14 provides an example of the transport of long-range smoke from wildfires from the Western
U.S. The September 18, 2020 ceilometer backscatter indicates an atmospheric layer of smoke between
1,000 and 3,000 meters above the surface. The smoke layer tends to be transported within the free
atmosphere above the boundary layer, limiting the smoke impact on the surface. The daytime heating
may be able to mix the smoke within the afternoon boundary layer starting at 21:00 UTC.
Figure 14. Aerosol backscatter from the CL-51 ceilometer at Konza Prairie showing the Western U.S.
smoke transport on September 18, 2020.
15000
10000
5000
0
00:
Vaisala CL51 - EPA Region 7
00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00
September 18 2020 (UTC)
19

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In March of 2020, a Pandora ground-based spectrometer Pandora was installed at Konza Prairie (Figure
15). Pandora provides high-quality spectrally resolved direct sun/lunar or sky scan radiance
measurements in the UV and visible wavelengths. The Pandora radiance measurements provide real-
time data of key air quality relevant pollutants, which can be compared to similar measurements from
satellites. These observations include total column 03, NO2, and formaldehyde (HCHO).
20

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Pandora column N02 combined with the ceilometer vertical aerosol backscatter and mixed layer height
measurements will aid in the characterization of prescribed burning in the Flint Hills Region. By
capturing the total column of pollution as opposed to routine surface measurements, Pandora can
better track local to regional scale smoke plumes, especially when the smoke is decoupled from the
surface. The Pandora is also valuable for direct comparison with satellite based N02 column
measurements to validate and improve retrievals for unique areas like the central U.S. where prescribed
burning provides an episode large N02 signal compared to relatively sparse emissions sources nearby.
This data collected at Konza Prairie site from the Pandora and ceilometer will further aid in the
development, validation, and use of the air quality modeling system that are described in this Report.
These include but are not limited to: model profile of fire timing and duration, modeled vertical plume
height, modeled secondary chemistry for prescribed burning, and the air quality impacts of prescribed
fires during different seasons (i.e., a spring vs fall burn).
Contributors to this report
Kirk Baker, Lance Avey, Andy Hawkins , Lara Reynolds, Chris Allen, Barron Henderson, Brian Gullett,
Amara Holder, Matt Landis, Russell Long, George Pouliot, Jeff Vukovich, Venkatesh Rao, Norm Possiel,
Chris Misenis, Nathaniel Bunsell, Joe Wilkins, and Patrick O'Neal.
Disclaimer
The views expressed in this article are those of the authors and do not necessarily represent the views
or policies of the U.S. Environmental Protection Agency.
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2018. Modeling crop residue burning experiments to evaluate smoke emissions and plume transport.
Science of The Total Environment 627, 523-533.
23

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APPENDIX A
24

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Figure Al. Temporal profile used to allocate daily wildland (wild and prescribed) fire emissions to
specific hours of the day.
0.14
Hour of the day (local time)
25

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Figure A2. Satellite detected fires based on the HMS system (red) and burn areas (green outline) by day
at Konza Prairie (blue outline) for 2018. Gray shading represents grassland coverage at 1 km grid
spacing.
2/14/2018	3/1/2018	3/8/2018	3/9/2018
3/30/2018	4/17/2018	4/19/2018	4/23/2018
26

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Figure A3. Ceilometer profile of aerosol backscatter measured upwind and downwind of a prescribed
fire at Tallgrass Prairie in 2017 (top). The same prescribed fire PM2.5 modeled at 1 km grid resolution
using the actual start and end time and the default temporal profile.
Modeled plume using default temporal profile
¦ Modeled boundary layer estimate
+ Ceilometer plume estimate
	 -V
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* -*
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2000 -
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5 750 -
500 -
250 -
0 -
1
2000
1750
1500
E" 1250
"O 1000
if 750
500
250
Ceilometer downwind
27

-------
Figure A4. Observations and model predictions (local time) at Tallgrass Prairie for a period of March
2017. Measured fuel moisture is also shown. HMS fire detections are shown in the top panel based on
fire radiative power when that data was provided and simply as a detection (orange square) for
detections without fire radiative power information.
I 8
(£ 00
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+
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03/13:00 03/14:00 03/15:00 03/16:00 03/17:00 03/18:00 03/19:00 03/20:00 03/21:00 03/22:00
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	1	1		r	1—	1	1	1	1	r~
03/13:00 03/14:00 03/15:00 03/16:00 03/17:00 03/18:00 03/19:00 03/20:00 03/21:00 03/22:00
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03/16:00 03/17:00
03/18:00
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03/19:00
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03/20:00
I
03/21:00
03/22:00
28

-------
Figure A5. Observations and model predictions (local time) at Tallgrass Prairie for a period of November
2017. Measured fuel moisture is also shown. HMS fire detections are shown in the top panel based on
fire radiative power when that data was provided and simply as a detection (orange square) for
detections without fire radiative power information.
+ Detection reporting FRP

Detection not reporting FRP

- . 1
i
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11/08:12 11/09:12 11/10:12 11/11:12 11/12:12 11/13:12 11/14:12 11/15:12 11/16:12
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+ Modeled 1km

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11/16:12
i	r
11/08:12 11/09:12
n	r
11/10:12 11/11:12
11/12:12	11/13:12	11/14:12
1	T
11/15:12 11/16:12
11/08:12 11/09:12 11/10:12 11/11:12 11/12:12 11/13:12 11/14:12 11/15:12 11/16:12
N •
NW #
N

jr^

^sw



1	r
11/08:12 11/09:12
11/10:12
i	r
11/11:12 11/12:12
11/13:12
i	i	r
11/14:12 11/15:12 11/16:12
29

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Figure A6. The distribution of temperature, wind speed, solar radiation, and fuel moisture made at
Tallgrass Prairie by month for the years 2002 to 2018. The solar radiation panel only includes data
collected between 8 am and 8 pm.
Tallgrass Prairie
SB

3
-9-
t T i
0
E
0
Month
Tallgrass Prairie
6 7
Month
"i	1	r
10 11 12
Tallgrass Prairie

~T
-1	1	1—
10 11 12
5 7
Month
Tallgrass Prairie
mm
~r
~r
—i	1	1—
10 11 12
6 7
Month
30

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APPENDIX B
Images from each of the prescribed burns at Konza Prairie and Tallgrass Prairie in 2017.
31

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United States	Office of Air Quality Planning and Standards	Publication No. EPA-454/R-21-004
Environmental Protection	Air Quality Assessment Division	June 2021
Agency	Research Triangle Park, NC

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