Int. J. Environment and Pollution, Vol. 52, Nos. 3/4, 2013
Prescribed burn smoke impact in the Lake Tahoe
Basin: model simulation and field verification
Tom Malamakal, L-W. Antony Chen*,
Xiaoliang Wang, Mark C. Green,
Steven Gronstal, Judith C. Chow and
John G. Watson
Division of Atmospheric Sciences,
Desert Research Institute,
2215 Raggio Parkway, Reno, NV 89512, USA
and
Atmospheric Science Program,
University of Nevada,
1664 N. Virginia St., Reno, NV 89557, USA
E-mail: Tom.Malamakal@dri.edu
E-mail: Antony.Chen@dri.edu
E-mail: Xiaoliang.Wang@dri.edu
E-mail: Mark.Green@dri.edu
E-mail: Steve.Gronstal@dri.edu
E-mail: Judy.Chow@dri.edu
E-mail: John.Watson@dri.edu
*Corresponding author
Abstract: Smoke dispersion modelling based on the Fire Emission Production
Simulator and the Hybrid Single Particle Lagrangian Integrated Trajectory
(FEPS-HYSPLIT) model was applied to prescribed burns in the Lake Tahoe
Basin (LTB) during fall 2011. This, in conjunction with measurements at
sources and real-time ambient PM2.5 monitoring around LTB, served to
evaluate the prescribed burning impacts on air quality. For a given combustion
efficiency, in-plume measurements suggest FEPS to underestimate PM2.5
emission factors by up to six-fold, though FEPS agrees relatively well with
laboratory combustion of dry fuels. Prescribed burns in LTB were mostly
< 100 acres; time series analysis and model prediction (with 2 km or 12 km
spatial resolution) suggest generally small effects on PM2.5 exposure of local
communities due to careful selection of the burn windows. In regard to a few
scenarios where significant impact (≥ 2 μg/m3 hourly) is predicted, the model
with 2 km resolution shows smoke arrival times more consistent with ambient
observations. However, uncertainties in the model predictions should be
reduced further by acquiring more accurate burn records and measuring
markers specific to biomass burning at the monitoring sites.
Keywords: smoke forecast; biomass burning; emission model; PM2.5 emission
factor; WRF; IMPROVE network; USA.
Reference to this paper should be made as follows: Malamakal, T.,
Chen, L-W.A., Wang, X., Green, M.C., Gronstal, S., Chow, J.C. and
Watson, J.G. (2013) ‘Prescribed burn smoke impact in the Lake Tahoe Basin:
model simulation and field verification’, Int. J. Environment and Pollution,
Vol. 52, Nos. 3/4, pp.225–243.
Copyright © 2013 Inderscience Enterprises Ltd.
225
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T. Malamakal et al.
Biographical notes: Tom Malamakal received his MS in Atmospheric Science
from the University of Nevada, Reno in 2013. His research interests involve
atmospheric transport of chemicals and environmental impact assessment. He is
currently a research staff member at the Desert Research Institute (DRI).
L-W. Antony Chen received his PhD in Chemical Physics from University
of Maryland in 2002. Since 2004, he has been a Research Faculty at
Desert Research Institute (DRI), Nevada System of Higher Education. His
research focuses on aerosol measurement techniques, combustion aerosol
characterisation, and source apportionment/modelling of air pollutants for
health and climate studies.
Xiaoliang Wang received his PhD in Mechanical Engineering from University
of Minnesota-Twin Cities in 2006. He was a Senior Development Engineer at
TSI Inc. for four years, where he invented and developed the DustTrak DRX
Aerosol Monitor that was used to monitor aerosol concentrations in this study.
He has been a research faculty at Desert Research Institute, Nevada System of
Higher Education since 2009. His research interests include physical and
chemical characterisation of aerosols, pollution source characterisation, and
aerosol instrument development.
Mark C. Green received his PhD in Atmospheric Sciences from the University
of California at Davis in 1990. He is a Research Professor in the Division of
Atmospheric Sciences at Desert Research Institute where he has been since
1990. He is also the Director of the Graduate Program in Atmospheric Sciences
at the University of Nevada, Reno. His research focuses on aerosol
measurement and data analysis and the relationship between meteorology and
air pollution.
Steven Gronstal received his MS in Physics from Creighton University in 2003.
He is currently pursuing his PhD in Atmospheric Sciences at the University of
Nevada, Reno, while conducting research with the Environmental Analysis
Facility at the Desert Research Institute. His research focuses on aerosol
measurement techniques, stack sampling methods, and CFD modelling of
aerosols.
Judith C. Chow is the Nazir and Mary Ansari Chair in Entrepreneurialism and
Science and a Research Professor in the Division of Atmospheric Sciences at
the Desert Research Institute. She received her Doctor of Science degree in
Environmental Health Science and Physiology from Harvard University. She
has over 38 years of experience conducting air quality studies and performing
statistical data analysis. As the leader of DRI’s Environmental Analysis Facility
(EAF), she develops and applies advanced analytical methods to characterise
suspended atmospheric particles.
John G. Watson is a Research Professor in the Division of Atmospheric
Sciences at the Desert Research Institute. He received his PhD in
Environmental Sciences from Oregon Graduate Institute (now Oregon Health
and Science University). He has over 41 years of experience in physics,
environmental sciences, air quality network design and measurement, and
source/receptor modelling. He is known for formulating conceptual models as
well as organising and planning large-scale, multi-year air quality studies in the
USA.
Prescribed burn smoke impact in the Lake Tahoe Basin
1
227
Introduction
Good air quality and visibility are important assets of the Lake Tahoe Basin (LTB),
located along the California (CA) and Nevada (NV) state border, as a major tourist
attraction in the Sierra Nevada mountain range. The annual PM2.5 (airborne fine
particulate matter with aerodynamic diameter ≤ 2.5 μm) concentrations measured at
South Lake Tahoe and Bliss State Park, characteristic urban and rural environment of the
basin, respectively, are 9.0 and 3.5 μg/m3 (for 2000–2004, see Green et al., 2012), well
below the US National Ambient Air Quality Standard (NAAQS) of 12 μg/m3 (US EPA,
2013). Major sources of air pollutants, including PM2.5, PM10, nitric oxide (NO), nitrogen
dioxide (NO2), carbon monoxide (CO), and ozone (O3), within the basin were determined
to be:
1
biomass burning
2
on-road engine exhausts
3
off-road engine exhausts
4
road dust
5
natural dust (Kuhns et al., 2004; Green et al., 2012).
Long-range transport of air pollution from outside the basin does not contribute
significantly on average (Gertler et al., 2006).
The LTB (~25 × 25 km2 in size) contains one of the visibility Class I areas, the
Desolation Wilderness, which is subject to the regulation of the Clean Air Visibility Rule
(CAVR, see US EPA, 2005). The rule requires the visibility in Class I areas to return to
their ‘natural’ conditions by 2065. The state of CA also established an 8 hr visibility
standard equating to an extinction coefficient (bext) of 70 Mm–1, roughly extinction of 7%
incident light per kilometer, for LTB. Despite compliance with all NAAQS/CA standards
and a generally improving visibility condition with respect to annual median bext
measured at Bliss State Park, bext on the 20% worse visibility days have been increasing
(visibility decreasing) since 1990. The trend was mostly attributed to an increased
wildfire activity (Chen et al., 2011; Green et al., 2012).
Increasing wildfire frequency and intensity have been reported across the Western US
due to regional climate change (Westerling et al., 2006). On the other hand, there were
episode days occurring in fall through spring with no clear evidence of wildfire impact
(e.g., Chen et al., 2011). These episodes might reflect the influence of prescribed burning
and/or residential wood combustion (RWC).
Prescribed burning had been conducted year-around inside and outside LTB to
maintain natural succession of plant communities and to reduce wildfire danger. In 2011,
> 2000 acres of wildland were fire-treated within 20 km of Lake Tahoe, and roughly 20%
of them belonged to broadcast/understory burns of natural vegetation (the rest being pile
burns, per communications with fire agencies listed in Figure 1). Although these
prescribed burns were practised under strict guidelines, they could still impact visibility
and human exposure of air pollutants causing short-term, acute effects and/or long-term
background change. The importance of prescribed burning contributions to air pollutants
is increasing as regulations over other sources such as motor vehicles, road dust, and
RWC have been implemented and shown measurable effectiveness in reducing emissions
from these sources over the last two decades (Chen et al., 2011).
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T. Malamakal et al.
Figure 1
Locations of the five ambient air monitoring sites (blue balloons) during the fall 2011
study: 1) Incline Village [119°57’24.19”W; 39°15’1.49”N; 58 m above lake level
(ALL)]; 2) Tahoe City (120°8’55.74”W; 39°9’57.63”N; 12 m ALL); 3) Bliss State Park
(120°6’8.73”W; 38°58’33.63”N; 206 m ALL); 4) South Lake Tahoe (119°58’13.99”W;
38°56’42.26”N; 5 m ALL); 5) Cave Rock (119°56’54.83”W; 39°2’37.12”N; 1 m ALL)
(see online version for colours)
Notes: Also shown are the three prescribed burns (stars) measured as part of the study at
Skyland (SKY), Donner Memorial State Park (DSP), and Tunnel Creek (TNC).
Solid circles indicate other known prescribed fire events carried out by Lake
Tahoe Basin Management Unit (red), California State Park (pink), Nevada
Division of Forestry (green), and North Lake Tahoe Fire Protection Department
(yellow) during September–November, 2011.
Smoke transport simulation is useful to assess the impact of prescribed burns and develop
control strategies, thus contributing to an integrated approach to address air quality and
deposition issues in LTB (Gertler et al., 2009). The simulation could be achieved by
interfacing the Fire Emission Production Simulator (FEPS) to the Hybrid Single Particle
Lagrangian Integrated Trajectory (HYSPLIT) model run with high-resolution
meteorological data (Goodrick et al., 2012). This paper presents the first application of
FEPS-HYSPLIT modelling for prescribed burns in LTB. In-plume measurements were
conducted to evaluate FEPS emission estimates, while overall model performance (with
different spatial resolutions) was verified with ambient monitoring data in cases where
significant smoke impacts were predicted. The potential effects of prescribed burning on
LTB air quality and future research needs are discussed.
2
Technical approaches
This study focuses on the fall 2011 prescribed burning season (September–November,
2011). The period featured above-average temperatures and below-average precipitation
in Northern NV, leading to relatively dry fuel conditions. This, combined with the
abundance of growth due to an abnormally wet spring earlier in the year and sufficient
Prescribed burn smoke impact in the Lake Tahoe Basin
229
ignition sources, led to an active wildfire season (Nevada Climate Office, 2012). Based
on satellite imaginary, i.e., the MOderate Resolution Imaging Spectroradiometer
(MODIS) fire product (see http://modis-fire.umd.edu), large wildfires that clearly
impacted LTB air quality during the study period include Idaho’s Salmon-Challis Forest
Fire (9/9–9/10) and CA’s Humboldt-Toiyabe Forest (Buckeye) Fire (9/27–9/29).
Moreover, Figure 1 shows locations of 60 prescribed burns conducted in 29 days during
this period.
2.1 Ambient monitoring network
Five monitoring stations were established around the Lake Tahoe (Figure 1) for fall 2011.
The Incline Village (NV), Tahoe City (CA), and South Lake Tahoe (CA) sites were
located near the centre of the respective township representing community exposure to air
pollutants. The Bliss State Park site is 206 m above Lake Tahoe and has been part of the
Interagency Monitoring of PRotected Visual Environments (IMPROVE) network since
1990. It represents the Desolation and Mokelumne Wilderness Class I areas according to
the CAVR. Cave Rock is a pier located on the east shore of Lake Tahoe. The site is
considered rural but could be influenced by emissions from boats and US-50 traffic
(~50 m above it). Chen et al. (2011) described the site characteristics in greater detail.
Each site was equipped with a DustTrak 8520 (TSI Inc., Shoreview, MN) to
continuously measure PM2.5 concentration at 1 min time resolution. All sites except the
Bliss State Park also contained two MiniVol samplers (AirMetrics, Eugene, Oregon) to
collect weekly integrated PM2.5 samples. One MiniVol used a Teflon-membrane filter for
gravimetric mass and elemental composition measurement. The other MiniVol used a
quartz-fibre filter followed by citric acid-impregnated cellulose filter to quantify water
soluble ions as well as organic carbon (OC), elemental carbon (EC), and ammonia (NH3).
Detailed chemical analysis methods and quality assurance/quality control for filter
samples can be found in Chow et al. (2002) and Chow and Watson (2012) while the
PM2.5 chemical composition will be presented in other papers. The DustTrak data
correlated well with collocated MiniVol gravimetric PM2.5 mass across all four sites
(r2 = 0.91) though they generally overestimated PM2.5 concentrations by two-fold. A
calibration curve was established for all DustTraks using the regression coefficients.
After the correction, 24-hr average DustTrak PM2.5 concentrations at the Bliss State Park
agreed well (within ±10%) with collocated measurements made independently as part of
the IMPROVE network (Green et al., 2012).
2.2 Source measurement
Source measurements were conducted for three prescribed burn events (Table 1 and
Figure 1) out of > 50 known burns in the basin using an in-plume monitoring system
(Wang et al., 2012). The in-plume system was generally located downwind just a few
meters outside the fire perimeter, though it might be moved to better capture the smoke if
the prevailing wind direction changed during the experiment. The sample inlet was
located ~3 m above the ground. In addition to PM2.5 [quantified by a DustTrak DRX
(Wang et al., 2009), TSI Inc., Shoreview, MN), carbon dioxide (CO2), CO, reactive
nitrogen oxides (NOx), and black carbon (BC) concentrations were monitored in real time
at 1- to 10-sec resolution by non-dispersive infrared sensor (SBA-4, PP Systems,
Amesbury, MA), electrochemical detector (Model 350S, Testo Inc., Sparta, NJ),
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chemiluminescence analyzer (Model 400/401, 2B Technologies Inc., Boulder, CO), and
micro-aethalometer (AE51, Magee Scientific, Berkeley, CA), respectively. Background
concentrations were taken before the burn and subtracted from the in-plume
measurements. The DustTrak has a precision of ~±20% and ±5% for 1-sec and 1-min
averaging time, respectively (Wang et al., 2012). The 1-min maximum DustTrak
concentration observed was ~50 mg/m3, well below its 150 mg/m3 measurement limit.
Time-integrated filter samples were acquired every hour for analysis of PM2.5 mass
(gravimetry) and chemical composition. The DustTrak data were normalised to the
corresponding gravimetric mass on filters, with normalisation factors of 0.3 to 0.6.
Table 1
Prescribed burns measured with an in-plume air monitoring system
Burn
Burn location
ID
Date
Burn type
Burn
area
(acres)
Fuel moisture
Burn
Ignition
start time duration*
(hours)
(LST)
Fuel
load†
(tons/
acre)
SKY
39°01’02”N 06/11/2011 Understory/
119°57’00”W
piles
27
10 (pile)–100%
1100
3
28
DSP
39°18’29”N 10/03/2011 Understory
120°14’43”W
9
70–100%
1100
5
35.6
TNC
39°13’45”N 10/20/2011 Understory
119°53’20”W
7
90–100%
1200
4
16.3
Notes: SKY and TNC were burns conducted within the basin and DSP was located outside the basin
(Figure 1).
*Duration of active ignition by fire agencies. Fire continues after ignition.
†Estimated from agencies’ report and Fuel Characteristic Classification System (FCCS);
canopy and duff excluded.
The fuel-based emission factors were calculated from carbon balance following Nelson
(1982), Andreae and Merlet (2001), and Chen et al. (2007):
EFi = CMF fuel ×
( CMFCO
2
× ρCO2
ρi
+ CMFCO × ρCO
(1)
+CMFVOCs × ρVOCs + CMFPM × ρPM )
where
•
EFi: emission factor of pollutant i (e.g., g/kg)
•
ρi: concentration of pollutant i measured above background levels (e.g., μg/m3)
•
CMFfuel: carbon mass fraction of fuel
•
CMFi: carbon mass fraction of species i, including CO2, CO, volatile organic
compounds (VOCs), and PM2.5.
The combustion efficiency (CE), i.e., the fraction of carbon in the fuel ending up in CO2,
would be:
CE =
CMFCO2 × ρCO2
CMFCO2 × ρCO2 + CMFCO × ρCO + CMFVOCs × ρVOCs + CMFPM × ρPM
(2)
Prescribed burn smoke impact in the Lake Tahoe Basin
231
Since the contribution of VOCs to carbon is generally minor, the VOCs term in equaitons
(1) and (2) was ignored. Chen et al. (2010) suggested that CMFfuel of 0.49 is reasonable
for a mixture of forest fuels in LTB, and filter analysis showed an average CMFPM of
0.58. EFi was calculated for each minute as well as the filter integrating periods. It should
be noted that the in-plume system was not always in the plume, and our measurements
were not always representative of the entire smoke plume. Nonetheless, the
measurements contrast between field and laboratory observations and help determine the
range of emission factors and combustion efficiencies that can be compared with default
model parameters.
2.3 Fire Emission Production Simulator
FEPS, developed by the US Forest Service (Anderson et al., 2004), is an integrated
platform for modelling wild- or prescribed fire emissions. FEPS contains default values
for fuel loading and fraction of fuel consumed specific to most vegetation type/moisture
content combinations in the USA. Based on the fuel information and user supplied hourly
burned area, FEPS calculates fuel consumption by flaming, short-term smouldering
(STS), and long-term smouldering (LTS) phases. Flaming combustion with high CE
produces relatively complete oxidation converting fuel carbon to CO2, while smouldering
smoke contains higher fractions of partially reduced compounds such as CO, VOCs, and
PM (Crutzen and Andreae, 1990).
Dynamic emission factors, modulated by CE (i.e., based on the relative strength of
flaming, STS, and LTS at each hour), are used to determine the hourly emission rates:
ERi = CR × EFi = CR × ( ai − bi × CE )
(3)
where
•
ERi: emission rate of pollutant i (e.g., g/hour)
•
EFi: emission factor of pollutant i (e.g., g/kg)
•
CR: fuel consumption rate (e.g., kg/hr)
•
ai, bi: emission factor constant and coefficient for pollutant i (default ai = 67.4 g/kg
and bi = 66.8 g/kg for PM2.5).
Outputs from FEPS also include hourly heat release (kJ/hr) from the fire, which is useful
for calculating the plume height.
Fuel loading was determined from FEPS by matching the burn area with a Fuel
Characteristic Classification System (FCCS; Ottmar et al., 2007) map of 1 km × 1 km
resolution for the USA, supplemented by field observation and/or report from fire
agencies. The agencies’ reports were especially important for piled slash burns. They also
provided information such as fire shape (e.g., rate of progress), ignition time, and fuel
moisture content. Field measurements of fire emissions were made in a few burns (i.e.,
burns in Table 1), but however these burns did not appreciably impact any of our ambient
monitoring sites based on observation and modelling of smoke transport. Even with all
the available information, temporal fuel consumption could still be quite uncertain due to
the dynamic nature of fire. EFPM 2.5 (i.e., PM2.5 emission per unit mass of fuel burned)
232
T. Malamakal et al.
could also be biased as they were mostly based on laboratory studies (Yokelson et al.,
2013).
2.4 HYSPLIT dispersion model
The HYSPLIT model was developed and maintained by the NOAA Air Resource
Laboratory (ARL) (Draxler and Hess, 1998; Draxler, 1999; Draxler and Rolph, 2013).
The dispersion of a pollutant can be calculated by assuming either puff or particle
dispersion, while a mixed mode of top-hat horizontal puff and vertical particle
distribution was used in this study. This approach takes advantage of the greater accuracy
of the particle mode in vertical dispersion parameterisation and an expanding number of
puffs to represent the pollutant distribution as the spatial coverage of the pollutant
increases over time (Draxler and Hess, 1998). A prescribed fire is simulated as a point
source centred at the burn area typically of 2 to 100 acres, with hourly PM2.5 emission
rates determined from FEPS. The plume rise is computed assuming an air parcel’s rise
based on the buoyancy terms (Briggs, 1969; Arya, 1999) using the fire heat release also
provided by the FEPS. This approach is generally compatible with the US Forest
Service’s BlueSky smoke modelling framework (Larkin et al., 2009).
HYSPLIT can run with multiple nested data grids of a wide range of spatial
resolution. The model was adapted to meteorological fields from the North American
Mesoscale (NAM) model (12 km resolution, 43 vertical levels from surface to ~100 mb)
provided by ARL and Weather Research and Forecasting (WRF) model (2 km resolution,
32 vertical levels from surface to 100 mb, updated every 12 hours) provided by the
California and Nevada Smoke and Air Committee (CANSAC) for the period of
September–November, 2011. The WRF model is particularly suitable for modelling
complex terrain in Sierra Nevada to capture complex flow and meteorological parameters
critical in dispersion calculations (Lu et al., 2012).
3
Results and discussion
3.1 Diurnal variation of PM2.5
Site-specific PM2.5 diurnal patterns were determined from medians of all hourly data
(Figure 2) as a guide to distinguish repetitive sources’ (e.g., motor vehicles, RWC, and
cooking) contribution from potential prescribed burning impact. Individual days with
diurnal profile substantially different from this pattern and/or with extreme PM2.5
concentrations (> 95 percentile) were subject to further investigation using prescribed
burn records. Field observations and model simulations suggest that effect of individual
controlled burns is usually inhomogeneous over the basin and varies rapidly in time (Cliff
and Cahill, 2000; Cahill, 2009). Large-scale wildfires outside the basin produce more
uniformly high PM2.5 concentrations. Exclusion of days influenced by known wildfires
(i.e., 9/9–10/2011 and 9/27–29/2011) does not change the diurnal patterns significantly.
The traffic influences are most clear at Tahoe City with distinguishable morning
[0700–0800 Local Standard Time (LST)] and evening PM2.5 peaks (1600–1700 LST)
corresponding to the rush hours (Figure 2). The morning rush hour is visible at the urban
South Lake Tahoe site but the evening peak appears to be delayed until ~2000 LST. This
likely reflects a combined effect of traffic emissions, commercial activities (e.g.,
Prescribed burn smoke impact in the Lake Tahoe Basin
233
restaurants, stores, etc.) as well as RWC towards winter. RWC, but not traffic, may also
influence the Incline Village site. Cave Rock at the lake level and Bliss State Park
~200 m above the lake without significant sources nearby served as the low and elevated
background sites, respectively. The cold lake surface develops a strong (~10°C), shallow
(~30 m) inversions at all times throughout the year (Cahill, 2009). While little diurnal
variation of PM2.5 was observed at Bliss, a mid-afternoon peak at Cave Rock is consistent
with downward mixing by turbulence when PM2.5 concentrations aloft (e.g., 30 m) are
higher than those at the surface (e.g., Grivas et al., 2008). PM2.5 levels appear relatively
uniform across the basin during 1300–1700 LST, suggesting extensive horizontal mixing
besides vertical mixing in the afternoon.
Figure 2
Diurnal variation of PM2.5 at five ambient monitoring sites in the Lake Tahoe Basin,
based on medians of hourly PM2.5 measurement (from corrected DustTrak data) during
September–November, 2011 (see online version for colours)
Note: All times are in LST.
3.2 Prescribed burning emission characteristics
Figure 3(a) to Figure 3(b) show the time series of in-plume measurements for the TNC
burn as an example. An initial flaming combustion phase was identified during the first
~15 minutes of burning, as CE generally exceeded 0.9 with the highest PM2.5 and BC
concentrations measured. BC is known to be generated mostly from flaming combustion
(Kuhlbusch and Crutzen, 1995; Chen et al., 2006). The EFPM 2.5 during the period was
below 30 g/kg. Following the flaming phase was a transition period of ~30 minutes
featuring CE of 0.8 to 0.9 and much higher EFPM 2.5 (up to 90 g/kg). BC concentrations
and emission factors remained high, but there were apparently increasing influences of
smouldering combustion. One hour after ignition, CE fluctuated between 0.75 and 0.85
with more variable EFPM 2.5 while EFBC decreased gradually [Figure 3(b)]. The later
period would likely be dominated by smouldering emissions (i.e., STS + LTS).
234
Figure 3
T. Malamakal et al.
Time series of in-plume measurements (1-min time resolution) during the Tunnel Creek
burn on 10/20/2011 for (a) carbon (i.e., C) concentrations and combustion efficiency
(b) PM2.5 concentrations and emission factors (see online version for colours)
Notes: Total C emission includes carbon in CO2, CO, and BC. Arrows indicate the
ignition time. Flaming, transition, and smouldering phases are separated
empirically (see text for details). All times are in LST.
Fire-integrated combustion efficiencies and emission factors are presented in Table 2.
FEPS confirms the increasing importance of fuel consumption by STS and LTS and
decreasing values of CE as the burns progress. Based on the default flaming, STS, and
LTS CE coefficients (i.e., 0.90, 0.76, and 0.76), FEPS calculates the fire-integrated CE to
be 0.87, 0.83, and 0.84 for the SKY, DSP, and TNC burns, respectively, lower than the
measured values of 0.92 for SKY and 0.86 for both DSP and TNC. Providing that
CE > 0.9 is observed during the prescribed burns, a higher flaming CE may need to be
implemented in FEPS.
Table 2
Fire integrated combustion efficiencies and emission factors (see Table 1 for burn
details)
Burn ID
CE
MCE*
EFCO2 (g/kg)
EFCO (g/kg)
EFPM 2.5 (g/kg)
SKY
0.92
0.94
1,654
61.5
21.7
DSP
0.86
0.89
1,545
124.6
26.5
TNC
0.86
0.89
1,543
117.6
32.6
Note: *MCE: modified combustion efficiency, i.e., ignoring both the VOCs and PM
terms in equation (2).
EFPM 2.5 is suggested to anti-correlate with CE since incomplete combustion causes
higher particulate emission. The relationship should also depend on the fuel type (Janhäll
et al., 2010). Our field measurements from SKY, DSP, and TNC prescribed burning
events show a wide range of EFPM 2.5 for any measured CE. They can be bounded,
however, between two extremes (Figure 4). The default FEPS equation, equation (3),
Prescribed burn smoke impact in the Lake Tahoe Basin
235
closely describes the lower bound EFPM 2.5 . The upper bound can be parameterised by
using ai and bi of 431.8 and 429.7 g/kg, respectively in equaiton (3), as derived from the
highest 10th percentile data with respect to the EFPM 2.5 / (1 − CE ) ratio.
Figure 4
EFPM 2.5 -CE relationship (1-min data) over three prescribed burning events (i.e., SKY,
DSP, and TNC) (see online version for colours)
Notes: The lower bound of distribution is the FEPS default parameterisation while the
upper bound is determined from the top 10th percentile of data with respect to the
EFPM 2.5 / (1 − CE ) ratio. Blue triangles and green circles indicate data from
laboratory combustion of wet and dry fuels, respectively (Chen et al., 2010).
The FEPS default algorithm underestimates the observed PM2.5 emissions in most cases.
The bias could be up to a factor of 6. A recent laboratory combustion study using the
forest fuels from LTB (Chen et al., 2010) highlights the sensitivity of EFPM 2.5 to fuel
moisture content. The EFPM2.5 -CE relationship for wet fuels across several species
(pines, bitterbrush, manzanita, etc.) and types (stems, leaves, duffs, etc.) in that study
agrees better with the upper bound in Figure 4. For dry fuels (< 10% moisture content),
however, it is closer to the lower bound and FEPS estimates. Providing that most
laboratory studies focus on dried fuels (e.g., Hays et al., 2002; McMeeking et al., 2009),
extrapolating the laboratory data to predict wildland fire emissions would require some
positive adjustments, especially for smouldering combustion with low CE.
Other factors such as wind and scale of burn could also impact the emissions of
prescribed burning (Yokelson et al., 2013). For a similar range of fire-integrated CE
[0.86–0.92, or modified combustion efficiency (MCE) 0.89–0.94, see Table 2], Burling
et al. (2011) report EFPM 2.5 of 10 to 25 g/kg for conifer forest understory burns, slightly
lower than observed in this study (22 to 33 g/kg).
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T. Malamakal et al.
3.3 Smoke impact scenarios
Although multiple agencies carried out prescribed burning in LTB (Figure 1), strict
guidelines have been used for smoke management. Preferred weather conditions include
an unstable boundary layer (i.e., no inversion) with moderate winds to carry smoke
outside the basin. Burns usually begin in the late morning and continue through early
afternoon to take advantage of the strong turbulent updraft in the afternoon. While the
ignition and flaming phase stop completely in 2 to 5 hours, smouldering can continue
overnight and even into the next day. Clear impact of prescribed burning smoke on
ambient air quality is not often observed and/or reported. Out of 60 burns in Figure 1, the
FEPS-HYSPLIT model (with WRF meteorology) noted only a few scenarios (Table 3)
where smoke was transported over one or more of our ambient monitoring sites
producing ≥ 2 μg/m3 impact on hourly PM2.5 concentrations.
Table 3
Prescribed burns with potential impacts on air quality during the fall 2011 monitoring
period
Ignition
start time
(LST)
Burn
duration*†
(hours)
10 (pile)–100%
0700
4
32
10 (pile)–100%
1000*
5
39°15’51”N 10/19/2011 Understory
119°58’35”W
8
90–100%
1000
7
39°08’02”N 10/18/2011
120°13’04”W
2
10%
1000*
2
Date
Burn type
Burn area
Fuel moisture*
(acres)
Burn ID
Burn location
Angora1
38°52’21”N 10/20/2011 Understory/
120°03’02”W
piles
32
Angora2
39°52’25”N 11/02/2011 Understory/
120°03’01”W
piles
North1
West1
Piles
Notes: *Estimated values.
†Assuming linear progression of fire.
Angora Fire (6/24–7/2/2007) was the largest wildfire within LTB in the last decade,
burning 3100 acres of forest south of Lake Tahoe (Safford et al., 2009). Since the Angora
Fire, the Lake Tahoe Basin Management Unit (LTBMU) has been carrying out
prescribed burns in the Angora area to clean out debris to reduce future fire danger.
Model simulations for two of the burns on 10/20/2011 (Angora1) and 11/2/2011
(Angora2) predict smoke transport to northeast [Figure 5(a) to Figure 5(b)]. Five to eight
hours after ignition the smoke starts to impact the air quality of South Lake Tahoe and
Cave Rock as well as Bliss to a lesser degree. The smoke appears to move along the
eastern side of the basin with horizontal transport limited by the mountain ridges.
The North Lake Tahoe Fire Protection Department (NLTFPD) and Nevada Division
of Forestry (NDF) have consistently conducted small-scale (< 10 acres) prescribed burns
on the north shore of Lake Tahoe. Due to the proximity to the burn areas, Incline Village
can sometimes experience the smoke. Figure 5(c) shows an example occurring on
10/19/2011 (i.e., North1). The smoke is transported southeast, covering Incline Village
and Cave Rock in a few hours, though the impacts are not severe. Prescribed burns on the
western bank had been carried out by LTBMU and CA State Park from time to time. The
smoke could be transported northeast impacting Tahoe City and Incline Village, as
demonstrated in Figure 5(d) (i.e., burn West1). These burns are usually very small in size
(< 5 acres).
Prescribed burn smoke impact in the Lake Tahoe Basin
Figure 5
237
FEPS-HYSPLIT (WRF 2 km meteorological data) simulated surface PM2.5
concentrations (μg/m3, 0 to 25 m AGL average in log scale) during four prescribed
burning events at (a) 1300 LST, 10/20/2011 (b) 2000 LST, 11/2/2011 (c) 1600 LST,
10/19/2011 (d) 1500 LST, 10/18/2011 (see Table 3 for details) (see online version
for colours)
Note: The diamond symbol indicates the prescribed burn location while the numbers
mark the ambient monitoring stations as in Figure 1.
3.4 Verification of model simulation
On 11/2/2011, the South Lake Tahoe site recorded a PM2.5 peak of 37.4 μg/m3 at
0700 LST, well above the typical levels defined by the 10th and 90th percentile of the
diurnal data (Figure 6). A simultaneous peak was also observed at Cave Rock. These
238
T. Malamakal et al.
peaks correspond well with the maximum impact of the prescribed burn by LTBMU on
11/2/2011 (Angora2), as simulated by FEPS-HYSPLIT with the WRF meteorological
data. The model, however, predicts lower concentrations and does not capture a
secondary peak at both sites around midnight of 11/3/2011. This is consistent with
underestimation of fuel loading and/or smouldering emission factors, though certainly the
unaccounted PM2.5 could result from other sources. The late afternoon PM2.5 highs on
11/3/2011 at both sites also deviate from the normal diurnal pattern. There are no
prescribed burning records for that day but a continuous operation of the fire crew in
adjacent plots (i.e., unrecorded burns) is possible, according to LTBMU. The model does
indicate transport of smoke from the previous day’s burn over the two sites through
the evening of 11/3/2011. Snowfall began ~1900 LST on 11/3/2011, which likely
extinguished the combustion and removed particles from the air.
Figure 6
Measured hourly PM2.5 concentrations at, (a) site 4 (South Lake Tahoe) (b) site 5 (Cave
Rock), compared with FEPS-HYSPLIT predicted smoke PM2.5 concentrations at the
sites originating from the 11/2 prescribed burn by LTBMU (Angora2, see Table 2)
(see online version for colours)
(a)
(b)
Notes: Models are based on WRF or NAM meteorological data. Arrows indicate the
ignition time. The shaded region indicates the range of diurnal variations defined
by the 10th and 90th percentile of diurnal data at the site. All times are in LST.
Using NAM instead of WRF data causes a shift of modelled smoke arriving time. This is
especially clear for Cave Rock [Figure 6(b)]. The mountain barrier consistently shown in
Figure 5(a) to Figure 5(b) is not resolved in the simulation with NAM (12 km resolution),
which predicts the plumes to extend eastward compared to the WRF simulation causing
changes in the smoke forecast.
The 10/20 burn (Angora1, see Figure 7) is an example where smoke impact could
show up one and half days after ignition. FEPS assumes smouldering long (> 2 days)
after the active ignition period. The late night PM2.5 peak on 10/22/2011 at South Lake
Tahoe is consistent with the model predicted strong impact from the 10/20 burn in the
Angora area [Figure 7(a)]. However, the PM2.5 highs are within the typical range of
diurnal pattern, and no such effect is predicted with the NAM data. Less confidence could
be placed on asserting the influence of this burn. The model also predicts small impact on
the other sites relative to the site-specific diurnal variations [e.g., Figure 7(b)].
Prescribed burn smoke impact in the Lake Tahoe Basin
Figure 7
239
Measured hourly PM2.5 concentrations at, (a) site 4 (South Lake Tahoe) (b) site 3 (Bliss
State Park), compared with FEPS-HYSPLIT predicted smoke PM2.5 concentrations at
the sites originating from the 10/20 prescribed burn by LTBMU (Angora1, see Table 2)
(see online version for colours)
(a)
(b)
Notes: Models are based on WRF or NAM meteorological data. Arrows indicate the
ignition time. The shaded region indicates the range of diurnal variations defined
by the 10th and 90th percentile of diurnal data at the site. All times are in LST.
Figure 8
Measured hourly PM2.5 concentrations at, (a) site 1 (Incline Village) (b) site 2 (Tahoe
City), compared with FEPS-HYSPLIT predicted smoke PM2.5 concentrations at the
sites originating from (a) 10/19 prescribed burn by NLTFPD and (b) 10/18 prescribed
burn by LTBMU (North1 and West1, respectively, see Table 2) (see online version
for colours)
(a)
(b)
Notes: Models are based on WRF or NAM meteorological data. Arrows indicate the
ignition time. The shaded region indicates the range of diurnal variations defined
by the 10th and 90th percentile of diurnal data at the site. All times are in LST.
Figure 8(a) and Figure 8(b) show the smoke impact at Incline Village (10/19 burn, i.e.,
North1) and Tahoe City (10/18 burn, i.e., West1). The burning contribution to PM2.5 is
generally minor, according to the simulations. It would have been larger if higher
emission factors, as suggested by Figure 4, were used in FEPS. Estimating the
contribution of the 10/19 burn to Incline Village is challenging since the distance
between the burn plot and monitoring site is close to the model grid size and because of
difficulties to model boundary layer movement (Pournazeri et al., 2012). Nonetheless,
smoke impact is expected when westerly or northwesterly winds prevail, which happened
during the first five hours of this prescribed burning and after midnight of 10/21/2011.
There is an unusually high afternoon PM2.5 peak at Tahoe City on 10/18/2011
240
T. Malamakal et al.
[Figure 8(b)], consistent in time with the transport of smoke from the burn area to the
monitoring site. The predicted smoke peak, however, appears to be off by a few to
several hours.
4
Conclusions
A smoke dispersion model such as HYSPLIT can be a simple yet effective tool to
predict/evaluate the influence of prescribed burning in sensitive environmental zones
such as Lake Tahoe. In this approach, particles (i.e., PM2.5) are treated as inert tracer
without chemical interaction with other atmospheric constituents. The model grid
resolution should be adequate to reflect the topography while incurring manageable
computation times. Burns could be modelled individually with the effects being
accumulated.
The emission information was found to be the most challenging part of the study.
Prescribed burns were not always recorded with complete information such as fuel load,
moisture content, ignition time, rate of progress, and burn duration that are required by
the FEPS to calculate temporally-resolved (e.g., hourly) PM2.5 and heat release. Some
known burns are completely missing in the records. Since field observations could not be
made for each of the burns, inaccurate burning information leads to uncertainties in
evaluating smoke transport and the overall effect of prescribed burning on air quality.
Measurements at three prescribed burn events show increasing fractions of
smouldering combustion and decreasing CE as the burn progress. EFPM 2.5 generally
increases with the decreasing CE, but with a wide range of EFPM2.5 / (1 − CE ) ratio. FEPS,
in its default setting, often reports lower CE than measured values. It also underestimates
the PM2.5 emission factors for a given CE by up to a factor of 6. The
high-end emission factors measured in this study are consistent with those from
laboratory combustion of relatively moist fuels. Since most laboratory studies focus on
dry fuels, emission models based on laboratory data may need to be positively adjusted
for real-world burns.
During our three-month observing period in fall 2011, serious and obvious impact
from a known prescribed burn on air quality (e.g., causing short-term PM2.5
concentrations several times the three-month averages or exceeding the 24-hr PM2.5
standard) at one or more of the ambient monitoring sites around Lake Tahoe was rare.
This should be the case if the prescribed burning guidelines are strictly followed by all
the agencies. A few scenarios with potential impacts were identified by the
FEPS-HYSPLIT model. Model predicted smoke contributions were consistent with
elevated ambient PM2.5 concentrations in two cases, and a higher model resolution
generally produce more accurate smoke arrival times. In other cases, the model
performance was difficult to evaluate due to low predicted smoke contributions relative
to the typical ambient PM2.5 level. Future studies may include hourly measurement of
more specific biomass burning markers, such as water soluble potassium and/or
levoglucosan, at the ambient monitoring sites to reduce the ambiguity in model
evaluation and facilitate development of optimal modelling parameters.
Prescribed burn smoke impact in the Lake Tahoe Basin
241
Acknowledgements
This project was funded by the USDA Forest Service through the Southern Nevada
Public Land Management Act (SNPLMA Round 11). The authors thank John
Washington from LTBMU and Roland Shaw from NDF for their assistance in the field
and providing the burn information. The authors also appreciate Tim Brown for
providing WRF data, Keith Szelagowski for organising the database, and Steve Kohl for
laboratory analysis. The reviewers’ comments are greatly appreciated. The conclusions
are those of the authors and do not necessarily reflect the views of the sponsoring
agencies.
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