Combustion efficiency and emission factors for US wildfires

used by air regulatory agencies to understand and to predict the impact of fires on air quality. Fire emission factors (EF), which quantify the amount of pollutants released per mass of biomass burned, are essential input for the emission models used to develop EI. Over the past decade substantial progress has been realized in characterizing the composition of fresh biomass burning (BB) smoke and in quantifying BB EF. However, 10


Introduction
Biomass burning (BB, defined here as the open burning of biomass which includes wildfires and prescribed fires in forests, savannas, grasslands, and shrublands, and agricultural fire such as the burning of crop residue) is a major source of global trace gases and particles (van der Werf et al., 2010;Wiedinmyer et al., 2011). In terms of 5 total global source BB emissions are estimated to account for 40 % of carbon monoxide (CO), 35 % of carbonaceous particles, and 20 % of nitrogen oxides (NO x ) (Langmann et al., 2009). The contribution of BB in the conterminous US to global BB emissions is minor (van der Werf et al., 2010;Wiedinmyer et al., 2011). However; in the US wildland fires (defined here as BB excluding agricultural fires) have a significant impact 10 on air quality and present major challenges to air regulatory agencies responsible for achieving and maintaining compliance with federal National Ambient Air Quality Standards (NAAQS; USEPA, 2012c) for ozone (O 3 ) and fine particulate matter (PM 2.5 ) and Regional Haze Regulations (USEPA, 1999). Because O 3 is a secondary pollutant resulting from complex chemistry, quantifying the contribution of wildfires to O 3 related air 15 quality degradation is difficult. A thorough review of regulatory issues associated with wildfire O 3 production is provided by Jaffe and Wigder (2012). Acute impacts of wildfires and prescribed fires on PM 2.5 levels in urban areas have been reported in numerous studies (DeBell et al., 2004;Liu et al., 2009;Sapkota et al., 2005) and documented by air regulatory agencies (USEPA, 2012b). Wildland fires have also been identified as 20 important contributors to visibility reduction in areas protected by the Regional Haze Rule (Brewer and Moore, 2009).
While prescribed burning (fires intentionally ignited for land management purposes) dominates fire activity in the southeastern US (∼ 75 % of area burned between 2002-2010(NIFC, 2012)), wildfires are dominant in the western US (defined here as: Introduction Conditions during the western US wildfire season, low fuel moistures and highintensity fire fronts, are favorable for the consumption of CWD and duff and these fuels may comprise a significant portion of total fuel consumed in a fire event (Campbell et al., 2007;Reinhardt et al., 1991). Conversely, prescribed burning is generally characterized by low-intensity fire when the moisture of CWD and duff are moderate to high 5 (Finney et al., 2005;Hardy, 2002), conditions which minimize consumption of these fuels relative to fine fuels. Thus, wildfires might be expected to burn with more smoldering combustion than prescribed fires and have higher EF for species associated with smoldering combustion (and lower EF for species related to flaming combustion). This reasoning suggests EF based on prescribed fires may not be appropriate for modeling 10 emissions from wildfires.
We present smoke emissions data from airborne field measurements of fires that occurred in conifer dominated montane forests of the western US during the 2011 wildfire season. We report our measurements of modified combustion efficiency (MCE) and EF for CO 2 , CO, and CH 4 and compare these with previous field studies of temperate for- 15 est fires. The MCE measured in our field study are used to estimate wildfire EF for 14 additional species using previously published EF-MCE relationships. This new EF dataset for western US wildfires is compared with a recent review article and a national emissions inventory. We also examine MCE and fuel consumption data from previous studies of 18 prescribed fires to gain insight into regional MCE trends and to identify Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | emission datasets. The fire activity and meteorological conditions associated with the fires on each day of sampling is provided in Table 1. All fires in this study were sampled using a US Forest Service Cessna 206 aircraft equipped with atmospheric chemistry instrumentation as described in Sect. 2.2. 5 All 4 fires occurred in high elevation mixed conifer forests of Lodgepole Pine, Douglas-Fir, Englemann Spruce, and Subalpine Fir. The vegetation involved was determined from a combination of ICS-209 reports (NWCG, 2012) and geospatial overlays of the incident fire perimeters (MTBS, 2012) and a US Forest Service Forest Type map (Ruefenacht et al., 2008;USDA, 2012a). Fire elevation was obtained from geospatial over- 10 lays of the incident fire perimeters and a digital elevation map (LANDFIRE, 2012).

Hammer Creek Fire
The (above mean sea level). The incident management team reported the fire was burning in "mature timber with moderate to heavy dead standing and dead down" trees and also in the area of a previous burn with "moderate to heavy component of dead/down fuel" (Carbonari, 2011b Carbonari, 2011a). With the exception of a ∼ 70 ha pocket, the area of the Big Salmon Lake Fire had not been significantly impacted by fire in over 25 yr (MTBS, 2012). An aerial forest health survey conducted in 2010 found ∼ 10 % of the area burned by the Big Salmon Lake fire area was impacted by mortality due to beetles (USDA, 2012b). CRDS technique, the gas sample flows through an optical cavity with partially reflecting mirrors. Light of a specific wavelength from a continuous wave laser is injected into the optical cavity through one of the partially reflecting mirrors. While the laser is on, light builds up in the optical cavity. The laser is abruptly turned off and the decay of light intensity is monitored with a photodetector after the light exits the cavity through a sec-10 ond partially reflecting mirror. The measured light decay is used to determine the optical absorbance of the gas sample and provide a mixing ratio measurement of a particular gas species. A specific gas is measured by scanning a continuous wave laser over an individual spectral line of the targeted gas. The G2401-m analyzer used in this study scans lasers over the individual spectral lines of CO 2 , CO, CH 4 , and H 2 O at wave-15 lengths between 1560 nm and 1650 nm. The precise wavelengths used for monitoring are considered proprietary information and would not be released by the instrument's manufacturer. The analyzer tightly controls the gas sample pressure and temperature at ±0.005 • C and ±0.0002 atm to provide stable, well-resolved spectral features and ensure high precision measurements. The data acquisition rate was 2 s. 20 Frequent, in-flight, calibrations using 3 standard gases were used to maintain accuracy of the CRDS measurements and quantify the measurement precision. The in-flight standards were gas mixtures of CO 2 , CO, and CH 4 in Ultrapure air and included or were cross-calibrated against two NIST-traceable gas mixtures (concentration in ppm ± reported analytical uncertainty: CO 2 = 351 ± 4 and 510 ± 5; Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | CO = 0.092 ± 0.0092 and 3.03 ± 0.06; CH 4 = 1.493 ± 0.015 and 3.03 ± 0.03) (Scott-Marrin, Inc., Riverside, CA, USA). In the laboratory, a five point calibration, using an additional high span CO standard and Ultrapure air were used to ensure linearity of the CO calibration between the instrument limit of detection (∼ 0.030 ppm CO, defined as the 15 s standard deviation while measuring a calibration standard) and 10 ppm.
In recent years, the CRDS technique has been successfully used for high accuracy/high precision measurements of CO 2 , CH 4 , and CO from airborne platforms (Beck et al., 2012;Chen et al., 2010a). To our knowledge, our study is the first to employ this technique for the in-situ measurement biomass burning emission factors. 10 Emissions were measured by sampling the smoke above the flame front and up to 40 km downwind at elevations between 300 m a.g.l. (above ground level) and plume top. It is not unusual for wildfires in complex forested terrain to spread unevenly across the landscape due to changing weather conditions and variability in fuels and terrain resulting in a burn with mixed severity (Arno, 1980;Hudec and Peterson, 2012;Schwind, 15 2008). The wildfires sampled in this study had active fire occurring, often discontinuously along a large portion of the fires' perimeters. Active fire was also typically scattered throughout the perimeter interior as areas unburned or lightly burned during progression of the initial fire front burned/re-burned. Pockets of vigorous fire activity within the perimeter appeared to entrain and loft smoke from the surrounding smoldering fu-20 els. A typical sample run began a few km upwind of the fire in smoke free air, providing a background sample, and then penetrated the smoke plume immediately downwind of the fire front. Often, after passing over a segment of the fire front the sample run would continue to sample smoke in the plume for a several km downwind. Sample runs often encountered multiple smoke plumes as interior regions of the perimeter with active fire 25 were transected. Smoke sampling also included level elevation transects perpendicular to the direction of the smoke transport at distances of 2-40 km downwind of the fire front. Sampling in this second mode typically crossed the entire width of the plume 42 Introduction and provided measurements of background air on one or both ends of the sample run. The extensive downwind transects of smoke emissions obtained in this study may be used for the validation of smoke dispersion models. However, the focus of this paper is limited to EF.

5
Multiple smoke samples were collected on each day of fire sampling. For each smoke sample the excess mixing ratio (EMR) of compound X, ∆X, was calculated for each 2 s data point by subtracting the average background (X background ) for that sample run (∆X = X smoke − X background ). Sample emission factors for the each compound X, EFX (grams of species X emitted per kilogram dry fuel burned), was calculated from the 10 2 s ∆X using the carbon mass balance method (Yokelson et al., 1999) following two approaches. Approach 1 (Eq. 1) used the integrated ∆X for each plume sample while, the second approach (Eq. 2) used emission ratios determined from linear regression fits, with the intercept forced to 0, of ∆X vs. ∆CO or ∆CO 2 using the 2 s data points. The emission ratio of CH 4 to CO 2 , ∆CH 4 /∆CO 2 , was calculated as the product of 15 ∆CH 4 /∆CO × ∆CO/∆CO 2 . In Eq. (1) ∆C i are the excess mass mixing ratios of carbon (C) in each species. In Eqs.
(1) and (2) MM X is the molar mass of X (g mole −1 ), 12 the molar mass of carbon (g mole −1 ), and F C is the mass fraction of carbon in the dry biomass, assumed to be 0.50. We assumed F C = 0.50 based on studies which found that F C ranged between 0.45 and 0.55 for the vegetation types involved in this 20 study (Burling et al., 2010). The majority of carbon mass (> 95 %) in biomass smoke is contained in CO 2 , CO, and CH 4 , therefore our neglect other carbon-containing species in the carbon mass balance method over estimates the EF by ∼ 5 % ( The chemical composition of emissions from biomass fires are related to the com-5 bustion characteristics of the fire, in particular the relative amounts of flaming and smoldering combustion. Some species are emitted almost exclusively by flaming or smoldering, while the emissions of others are significant from both processes. Flaming combustion produces the gases CO 2 , NO, NO 2 , HCl, SO 2 , HONO and N 2 O (Burling et al., 2010;Lobert, 1991) and black carbon particles (Chen et al., 2007;McMeeking et al., 10 2009). The species CO, CH 4 , NH 3 , many NMOC, and primary organic aerosol (OA) are associated with smoldering combustion (Burling et al., 2010;McMeeking et al., 2009). Several NMOC have been linked with both flaming and smoldering combustion (Burling et al., 2010;Lobert, 1991;Yokelson et al., 1996). Modified combustion efficiency (MCE; Eq. 3) is used to characterize the relative 15 amount of flaming and smoldering combustion (Akagi et al., 2011;Ward and Radke, 1993). Laboratory studies have shown MCE is ∼ 0.99 for pure flaming combustion (e.g. fine fuels completely engulfed in flame, (Chen et al., 2007;Yokelson et al., 1996)), while the MCE for smoldering combustion varies over ∼ 0.65-0.85, with 0.80 being a typical value (Akagi et al., 2011). Since many species are predominantly emitted dur-20 ing either flaming or smoldering combustion, the EF of many compounds correlate with MCE. Laboratory studies of the combustion of fine fuels (Burling et al., 2010;Christian et al., 2003;McMeeking et al., 2009;Yokelson et al., 1997) and recent field measurements of emissions from prescribed fires (Burling et al., 2011)  of compounds, we have calculated MCE for all fresh smoke samples. But, we note that two laboratory studies of pure smoldering combustion of duff, organic soils, and CWD found poor correlation between MCE and EF (Bertschi et al., 2003;Yokelson et al., 2007a). However, since the combustion of CWD and duff in the natural environment is dependent on fuel bed characteristics such as the loading and arrangement of CWD 5 and the presence of fine dead wood and litter (Albini et al., 1995;Ottmar et al., 1989), laboratory studies may not be a good proxy for these fuels. Nonetheless, it is possible that wildland fires involving a large component of CWD and duff may not show a strong MCE-EF relationship.
10 3 Results and discussion

Emission measurements
Fire perimeters, area of active burning, and region of smoke sampling from a representative fire-day, the Saddle Complex on 24 August, are shown in Fig. 1. The perimeters, as observed via airborne IR sensor on the evenings (23 and 24 August), indicate that 15 on 24 August the fire growth occurred mostly on the west and east ends, with some minor growth along the northern and southern edges. In addition to the active fire fronts on the perimeter we also observed many pockets of burning scattered within the perimeter while sampling on the afternoon of 24 August. The MODIS burn scar data (RSAC, 2012b) and active fire detections (RSAC, 2012a) for that day captured some of 20 this activity (Fig. 1). On this day, fresh smoke samples were obtained along the northern edge of the fire perimeter. Winds were from the WSW and the initial portion of our sampling runs captured emissions emanating from the within the western area of the perimeter just downwind as they reached neutral buoyancy. The runs proceeded to the ENE sampling smoke above the fire front on the northern perimeter and then continued downwind with the plume that entrained smoke from across the fire complex. CRDS measurements for a fresh smoke sample run on 24 August (Table 2, sample SC2402) are shown in Fig. 2. The dashed line in each plot marks the background mixing ratios measured upwind of the fire. The background mixing ratios for this sample 5 (CO 2 = 382.56 ppm, CH 4 = 1.856 ppm, and CO = 0.110 ppm) were typical of the background for all fire-days. EF, MCE, and average ∆X for all 63 fresh smoke samples are given in Table 2. The EF in Table 2 were calculated from integrated ∆X using Eq. (1) (Sect. 2.4). The fire-day average EF calculated using Eqs. (1) and (2) agreed within 10 %. Some plume samples were taken significant distances downwind of the source. In particular, on 17 August, samples were taken 40 km downwind of the Big Salmon Lake Fire. The afternoon atmospheric sounding at Great Falls, Montana (NOAA, 2012) on this day indicated the transport winds were ∼ 11 m s −1 implying a smoke age of ∼ 60 min for these samples. However, since CO 2 , CO, and CH 4 are fairly non-reactive in the atmosphere (CO, the 15 most chemically reactive of the 3 gases, has a lifetime > 30 days with respect to chemical reaction; Seinfeld and Pandis, 2006) the age will not impact the measured EF for these gases.
The Big Salmon Lake Fire and the Saddle Complex were sampled on multiple days and as mentioned previously we have treated these sampling days as separate fires, 20 identifying each as a "fire-day". We believe this treatment is justified given the complex terrain, heterogeneous fuels, and the inter-day variability in metrological conditions and observed fire behavior (see Table 1). Furthermore, one day is an appropriate temporal scale for atmospheric chemical modeling applications since most biomass burning emission inventories provide estimates on a daily basis, from which models 25 then create an hourly profile based on assumptions about diurnal fire behavior cycles. Our study average values (average of the 9 fire-day values) for MCE, EFCO 2 , EFCO, and EFCH 4 are 0.883, 1596 g kg −1 , 135 g kg −1 , 7.30 g kg −1 , respectively. Figure 3a- fire-day average values of EFCO and EFCH 4 are confined to a fairly narrow span of 26 % and 21 % of the study average, respectively, and the standard deviations are only ∼ 10 % of the study average (Table 2). This muted inter-fire-day variability supports the idea that the dataset average values are more broadly representative of wildfire season forest fires in the western US. We note that despite the limited span of MCE and 5 EFCH 4 observed in our study, our measurements are sufficiently precise to reveal an MCE-EFCH 4 relationship. CH 4 is produced by smoldering combustion processes, and as expected, EFCH 4 has a strong inverse correlation with MCE ( Fig. 3d; r = −0.87, p-value = 0.002). 10 We compare our results with previous field studies of emissions from fires in temperate conifer dominated forests in the US and southern British Columbia, Canada: the airborne studies of Burling et al. (2011, hereafter B11), Hobbs et al. (1996, hereafter H96) and Radke et al. (1991, here after R91) (Burling et al., 2011;Hobbs et al., 1996;Radke, 1991), and the tower based study of Urbanski et al. (2009, hereafter Table A1 in U09 (EB1, EB2, FL5, SC9, FS1, ICI3) in SE since these fires were, in fact conifer/hardwood understory burns. They were listed as grassland/shrub in U09 since the fuel consumed was overwhelmingly grass and shrubs in the understory. In this sense these fires were very similar to the southeast burns studied in B11. We assigned the 6 North Carolina fires of B11 to SE and the 2 Sierra Nevada fires to NW. 5 The H96 and R91 prescribed fires were included in the NW set. First we compare our wildfire results (WF) with the prescribed fire data. We include the North Fork Prescribed Fire in our WF results since it burned during the wildfire season. Fire average MCE, EFCO, and EFCH 4 from this study, B11, U09, H96, and R91 are shown in Fig. 4a-c. Figure 4a-c includes the wildfire measurements of H96 and 10 R91, however in the ensuing discussion WF refers strictly to the results of our study (Table 2). On average the fires sampled in our study burned with a lower combustion efficiency compared to the prescribed fires. The data show a clear trend in average MCE across categories:

Comparison with other studies
There is no overlap of the WF MCE with those of the SE and SW fires. The MCE of B11's Shaver 15 fire (0.885) and the average MCE of H96 (0.877) are both close to the WF average MCE. These four prescribed fires involved heavy loads of down dead wood due to logging in the case of H96 and pine beetle activity in the Shaver fire (see Sect. 3.3). There is also a pronounced trend across categories for EFCO (WF (135) > NW (111) > SW (88) > SE (76)) and EFCH 4 (WF (7.30) > NW (6.29) > SW (3.32) > SE (2.13)). This work 20 does not report EFPM 2.5 ; however, we note that the EFPM 2.5 of B11 and U09 exhibit a similar trend (NW (18.0) > SW (14.5) > SE (12.6)).
There is limited temperate forest wildfire data with which we can compare our measurements. Figure 4a shows  information on the Corral-Blackwell Complex, but do note that it was sampled "during smoldering combustion". The MCE of smoldering combustion has been found to range from ∼ 0.65-0.85, but typically being near 0.80 (Akagi et al., 2011). Using a ground based FTIR, B11 measured post-flame front emissions from nearly pure smoldering combustion of dead tree stumps for a prescribed at Camp Lejeune, NC (The ground-5 based measurements of pure smoldering are not included in the B11 results discussed thus far and reproduced in Fig. 4). Since CWD can smolder for an extended period of time and can comprise a large share of fuel consumed in western forest fires (Brown et al., 1991;Reinhardt et al., 1991) observed active flaming combustion on all days for all fires, usually included torching of tree crowns, and the emissions we measured originated from both active flaming and post-flame front smoldering combustion (see Fig. 1 and related discussion). The Silver and Myrtle/Fall Creek wildfires sampled by R91 occurred in southwestern Oregon, which has a Mediterranean climate, and they burned in different vegetation 20 and elevation than the wildfires sampled in our work. The Silver Fire burned in Douglas-Fir/Tanoak/Pacific Madrone forest between elevations of 75 m and 1500 m with an average of 800 m. R91 described the vegetation involved in the Myrtle/Fall Creek wildfires as "standing pine, brush, and Douglas-fir" and they burned at elevation of 300 m and 900 m (average = 615 m). The different vegetation involved in the R91 fires may ex-25 plain the relatively high MCE they measured compared to the wildfires sampled in this work. The vegetation involved in the Silver Fire was determined from a combination of geospatial overlays of the fire perimeters (MTBS, 2012) and an existing vegetation map (LANDFIRE, 2012) and literature (Thompson and Spies, 2010  tracted all wildfires in forest ecosystems (fires type = "WF" and "canopy" > 0) in the western US and then from the extracted fires calculated effective EF for species X as the sum of emissions of X for all fires divided by the sum of fuel consumed for all fires. The NEI effective MCE was based on ∆CO/∆CO 2 calculated from the effective EF and molecular masses of CO and CO 2 (∆CO/∆CO 2 = MM CO 2 /MM CO × EFCO/EFCO 2 ).

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For most species our measured and estimated wildfire EF are substantially larger than those in A11. This is expected given our wildfire MCE is much lower than their temperate forest MCE. The ratio of wildfire EF to the recommendations of A11, R WF/A , were 1.9 for CH 4 , ∼ 3 for phenol and furan, and 4.1 in the case of glycolaldehyde. Wildfire EF for PM 2.5 , methanol and acetic acid were also markedly higher, with R WF/A = 2.0, 15 1.6 and 1.9, respectively. For a few compounds the EF were little changed, and EFH-COOH is actually lower than A11. For NO x (NO + NO 2 ), a product of flaming combustion, R WF/A was 0.77. We note that the nitrogen content of fuel also plays an important role in the emissions of both NO x and NH 3 (Burling et al., 2010 Andreae and Merlet, 2001). If the wildfires sampled in our study are representative more generally of wildfires in western US forests, then use of EF based on temperate forest prescribed fires will significantly underestimate PM 2.5 and NMOC emissions. Because large wildfires dominate fire burned area, fuel consumption, and emissions in the western US (Urbanski et al., 2011), this has important implications 20 for the forecasting and management of regional air quality. The western US wildfire PM 2.5 emissions reported in the most recent national emission inventory is based on an effective EFPM 2.5 that is approximately a factor of 2 lower than that expected based on our wildfire field measurements and published EFPM 2.5 -MCE relationships. Given the magnitude of biomass consumed by western US wildfires, the failure to use wildfire 25 appropriate EFPM 2.5 has significant implications for the forecasting and management of regional air quality. The contribution of wildfires to NAAQS PM 2.5 and Regional Haze may be underestimated by air regulatory agencies. This is especially true considering Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | that compared with anthropogenic and biogenic emission sources, wildfire emissions are highly concentrated both temporally and spatially (Urbanski et al., 2011).

MCE, EF, and fire characteristics
The MCE we measured for wildfires are significantly lower than those reported in the literature for prescribed fires in temperate conifer forests. There are also distinct regional 5 differences in the published prescribed fire MCE (Fig. 4a). Factors that affect the combustion process, in particular environmental conditions (e.g. wind speed, topography) and fuel characteristics (e.g. moisture, chemistry, soundness of dead wood, geometry and arrangement of fuel particles) (Ottmar, 2001;Sandberg et al., 2002) will also influence MCE. Fine fuels, those with high surface to volume ratios, such as grasses, 10 conifer needles, and fine woody debris (diameter < 7.6 cm) have a tendency to burn by flaming combustion with a high MCE (Chen et al., 2007;Ottmar, 2001;Sandberg et al., 2002;Yokelson et al., 1996). Smoldering combustion, which has a lower MCE, is more prevalent in CWD, duff, and organic soils (Bertschi et al., 2003;Burling et al., 2011;Ottmar, 2001;Sandberg et al., 2002;Yokelson et al., 1997). Reviews of field studies 15 show that fires in ecosystems dominated by fine fuels such as grasslands and savannas burn with a higher MCE than forest fires (Akagi et al., 2011;Andreae and Merlet, 2001;Urbanski et al., 2009). In addition to fuel geometry and arrangement, recent laboratory studies suggest a linkage between fuel moisture and MCE, with MCE tending to increase with decreasing fuel moisture for a constant fuel type and fuel mass (Chen ). An analysis of emission field measurements for multiple biomes found evidence that the spatio-temporal variability in MCE could be partially attributed to fraction of tree cover and monthly precipitation (van Leeuwen and van der Werf, 2011), the later which is presumably a surrogate for fuel moisture.
Considering the influence of fuel moisture and the tendency of certain fuel types to 25 favor flaming or smoldering combustion, one might expect higher fuel moisture and/or the involvement of heavy fuels (CWD and duff) to result in fires with lower MCE. However, the combustion completeness of CWD and duff increases with decreasing fuel 53 Introduction  (Albini and Reinhardt, 1997;Brown et al., 1991;Ottmar et al., 2006;Ottmar, 2001), while that of fine woody debris, grasses, and litter is relatively insensitive to moisture once ignition is achieved (Ottmar et al., 2006;Ottmar, 2001). Because the moisture contents of different types of fuel particles respond to environmental conditions with different time-lags, there can be a large difference in the moisture content of fuel bed components. The moisture content of fine fuels like cured grasses, litter, and small twigs (< 0.64 cm diameter) adjusts to environmental conditions with a time-lag on the order of 1 h (these are often referred to as 1-h fuels; (Bradshaw et al., 1984)). In contrast, CWD and duff respond with a time-lag of around 1000 h (1000-h fuels; Bradshaw et al., 1984;Brown et al., 1985;Harrington, 1982). Therefore, at a given forest 10 stand, under conditions typical of a springtime prescribed burn, consumption of heavy fuels may be minimal due to the high fuel moisture content of these components. However, at the same site under wildfire conditions, when the moisture content of heavy fuels is low, these components may comprise the majority of fuel consumed. Thus, despite the lower fuel moisture during the wildfire season, one might expect a fire with 15 lower MCE compared with a springtime prescribed fire in the same forest stand due to the greater involvement of heavy fuels which favor smoldering combustion processes. We believe the relatively low MCE of the wildfires and the general trend in MCE across regions is partially attributable to the differential involvement of heavy fuels. The Big Salmon Lake, Hammer Creek, Saddle Complex wildfires and the North Fork Pre-20 scribed fire involved significant areas of dead standing and dead down trees (Sect. 2.1). The 6 SE understory conifer fires reported in B11 occurred under conditions of high duff moisture and the fuels burned were predominantly shrubs, litter, grass, and fine woody debris (B11; Reardon, 2012). Pre and post fuel loading measurements taken at two of the B11 NC sites (the two Camp Lejeune burns) indicate CWD and duff were < 15 % 25 of the fuel mass consumed (Reardon, 2012). While the SE burns of B11 involved predominantly fine fuels, their Sierra Nevada burns (Turtle burn and Shaver burn) involved moderate to heavy loadings of dead wood. At the Turtle burn site litter and 1-h dead wood comprised only ∼ 1/3 of the surface dead fuel loading (Gonzalez, 2009 primary fire carrier was expected to be dead woody fuels and pockets of chaparral were not expected to burn except where covered with pine needle drape (Gonzalez, 2009). The site of the Shaver burn had dead woody fuel loadings of up to 28 kg m −2 due to mountain pine beetle activity and the lack of previous fire (B11). Perhaps coincidently, the MCE measured for the Shaver burn (0.885) was roughly equal to the 5 average MCE (0.883) of the wildfires studied in this work which also burned in forests with areas of standing dead trees and heavy down dead wood. In contrast to the B11 SE burns, which were characterized by high fuel moistures, the region of the Shaver and Turtle burns experienced only ∼ 0.50 cm of precipitation in the 27 days preceding the burns, and none in the two weeks prior (WFAS, 2012).
Consequently, at the time of the Shaver and Turtle burns, the heavy fuels had fairly low moisture content (1000-h = 18 %, (WFAS, 2012)) and likely comprised a significant portion of the fuel mass consumed. This comparison of the B11 prescribed fires and the wildfires suggests the presence of heavy fuels (CWD and duff) and conditions favorable for their burning results in fires with a greater fraction of smoldering combustion, a lower 15 MCE, and higher emissions of species associated with smoldering.
Given the lack of fuel consumption data for the wildfires and all but 2 of the B11 prescribed fires our argument is highly speculative. However, fuel consumption data is available for 13 prescribed fires from U09 and for the 3 prescribed fires of H96. To test our argument that the consumption of heavy fuels favors lower MCE we compared the 20 ratio of heavy fuel consumption to total fuel consumption (HFF) versus MCE for the 18 prescribed fires with fuel consumption data (see Appendix A for details). The results, plotted in Fig. 5, show a strong negative correlation between HFF and MCE (r = −0.83, p-value = 1.7e-5), as heavy fuels comprise a larger fraction of the total fuel consumed the fire average MCE decreases.

25
The analysis presented in Fig. 5 indicates the consumption of heavy fuels favors smoldering combustion, a finding consistent with previous ground based studies of prescribed burns in logging slash and guidelines for smoke management (Ottmar, 2001;Sandberg et al., 2002). However, we emphasize that our conclusion is based on a small sample size and involves significant uncertainty regarding the representativeness of emission sampling. The fuel consumption measurements quantify the fuel consumed over the entire life of the burn. Since smoldering combustion may continue for many hours after the active flame front has passed (Ottmar, 2001;Sandberg et al., 2002) it is unlikely the emissions sampling is properly weighted for smoldering emissions.

5
Due to this temporal mismatch between emissions and fuel sampling it is possible the contribution of smoldering emissions may be underrepresented in the MCE and EF measurements. Further, given the variability in fuel loading and fire characteristics (spread rate, ignition method) the degree of sampling bias with respect to smoldering emissions may vary among burns. For these reasons the data may not be suitable for 10 predicting MCE. Nonetheless, the analysis identifies relative heavy fuel consumption as a driver of fire average MCE and provides an explanation for the differences in MCE measured for temperate forest fires. van Leeuwen and van der Werf (2011) developed a global, biome-independent MCE model. This continuous MCE model, a multivariate regression of field measured MCE 15 versus coarse-scale (monthly, 0.5 • × 0.5 • ) environmental parameters, was driven primarily by monthly precipitation and fraction tree cover (FTC), and explained about 34 % of the variability in the field measured MCE. They were unable to account for fuel composition due to lack of consistent data, but suggested it may be a crucial factor driving the MCE variability not captured by their analysis. The authors also explored biome 20 stratified emissions data and highlighted a strong negative correlation between MCE and FTC for fires in Australian Savannas and deforestation fires in Brazil. If the loading of CWD is proportional to FTC, then the heavy fuel combustion-MCE dependence we have identified may help explain their observed FTC-MCE relationship. Since their model is biome-independent and aggregates across grasslands, savannas, and forests

Conclusions
Over 8 days in August of 2011 we sampled emissions from 3 wildfires and a prescribed fire that occurred in mixed conifer forests of the northern Rocky Mountains. We measured MCE and EF for CO 2 , CO, and CH 4 using a CRDS gas analyzer. We believe this study may be the first to apply in-flight CRDS technology to characterize 5 the emissions from open biomass burning in the natural environment. The combustion efficiency, quantified by MCE, of the fires sampled in this work was substantially lower than the average MCE measured in previous field studies of prescribed fires in similar forest types (conifer dominated temperate forests) and that reported in recent review articles of biomass burning emissions. In comparison to previous field pled in this work burned in areas reported to have moderate to heavy components of standing dead trees and dead down wood due to insect activity and in the case of one fire, a previous burn. Of previously published field measurements of prescribed fires the few with MCE similar to that measured in our study also burned in forests with heavy loadings of large dead wood and/or duff. 20 Fuel consumption data was not available for any of the fires sampled in this study; however, it was available for 18 prescribed fires reported in the literature. For these 18 fires we found a significant negative correlation between MCE and the ratio of heavy fuel (CWD and duff) consumption to total fuel consumption. This observation suggests the comparatively low MCE measured for the fires in our study results from the avail-25 ability of heavy fuels and conditions that facilitate combustion of these fuels (e.g. low moisture content). More generally, our measurements and the comparison with previous studies indicate that fuel composition is an important driver of EF variability. Considering the accumulation of heavy fuels in western US forests due to factors such as fire exclusion and insect induced mortality (see for example Klutsch et al., 2009), the MCE and EF measured in this study and those we have estimated based on EF-MCE relationships, may be representative of wildfires in forests across the western US. The temperate forest EF reported in the literature are based on fires which burned 5 with higher combustion efficiency (i.e. a lower relative fraction of smoldering combustion) than the wildfires sampled in our study. Because the EF of many smoldering combustion species have a strong negative correlation with MCE, the EF found in the literature may significantly underestimate the true EF for smoldering species for fires with combustion characteristics similar to the wildfires measured in this work. EF-MCE 10 linear relationships from the literature and our study average MCE were used to estimate wildfire EF for 14 species. If the MCE of the fires sampled in this work are representative of the combustion characteristics of wildfires in western US forests, this analysis indicates that the use of literature EF will result in a significant underestimate of wildfire PM 2.5 and NMOC emissions. The most recent national emission inventory 15 reports western wildfire emissions of PM 2.5 based on an effective EFPM 2.5 that is less than half that estimated in this study. Given the magnitude of biomass consumed by western wildfires, the failure to use wildfire appropriate EFPM 2.5 has significant implications for the forecasting and management of regional air quality. The contribution of wildfires to NAAQS PM 2.5 and Regional Haze may be underestimated by air regulatory 20 agencies.

ACPD
Our study sampled 4 fires over 8 days for a total of 9 fire-day observations. The fires burned in similar environments: high elevation, mixed conifer forest of Lodgepole Pine, Douglas-Fir, Engelmann Spruce, and Subalpine Fir with significant insect induced tree mortality and moderate to heavy loadings of standing dead and down dead wood. Our 25 measured MCE and EF and the EF estimated from EF-MCE relationships may not be applicable to all wildfires in western US forests. The presence of heavy loadings of standing dead trees and dead down wood may have been the main factor driving the MCE and EF of these fires. Additionally, our wildfire measurements did not include ACPD Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | fires in ponderosa pine dominated forests which are characterized by lower loadings of CWD (Graham et al., 1994). Other forests types or forests with a different disturbance history may not have similar loadings of heavy fuels and the therefore the MCE and EF (measured and estimated) reported here may not be applicable. Future emission studies focusing on other regions (e.g. Southern Rocky Mountains), forest types (e.g. 5 ponderosa pine dominated), and forests with different disturbance histories are needed to better quantify PM 2.5 and NMOC emissions from wildfires in the western US.

Appendix A
Qualitative reports indicate the low MCE fires sampled in our study involved significant consumption of CWD and standing dead trees. In contrast, previous studies of 10 prescribed burns in the southeastern US (B11, U09) measured relatively high MCE and mostly anecdotal observations suggested these fires consumed mostly fine fuels with the consumption of CWD and duff being minimal. This pattern is not unexpected since fine fuels have a tendency to burn by flaming combustion, while CWD and duff favor smoldering combustion processes (Sandberg et al., 2002). Using previous stud- 15 ies of 18 prescribed burns for which detailed fuel consumption data was available, we tested for a relationship between fire average MCE and the composition of fuel consumed. Specifically we tested for a significant correlation between the relative amount of flaming and smoldering combustion, quantified by MCE, and the relative amount of heavy fuel and fine fuel consumption. The later fire characteristic was quantified with 20 the heavy fuel fraction (HFF), defined as the sum of CWD and duff fuel loading consumed divided by the sum of total fuel loading consumed. HFF is given by equation A1 where C i is consumption (kg m −2 ) of fuel component i and fine fuels includes grasses, shrubs, foliage, litter, and fine woody debris (small diameter (< 7.62 cm) dead wood):  Bradshaw, L. S., Deeming, J. E., Burgan, R. E., and Cohen, J. D.: The 1978 National Fire-Danger Rating System: Technical Documentation, General Phys., 11, 12197-12216, doi:10.5194/acp-11-12197-2011  Chem. Phys., 10, 6617-6625, doi:10.5194/acp-10-6617-2010, 2010b. Christian, T., Kleiss, B., Yokelson, R., Holzinger, R., Crutzen, P., Hao, W., Saharjo, B., and  Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | USEPA: Regional haze regulations; final rule, US Federal Register, vol. 64, 126, 199940 CFR 51, 1999