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. 2022 Sep 22;49(18):e2022GL099175. doi: 10.1029/2022GL099175

Understanding the Evolution of Smoke Mass Extinction Efficiency Using Field Campaign Measurements

Pablo E Saide 1,2,✉, Laura H Thapa 1, Xinxin Ye 1, Demetrios Pagonis 3,4, Pedro Campuzano‐Jost 3,4, Hongyu Guo 3,4, Melinda L Schuneman 3,4, Jose‐Luis Jimenez 3,4, Richard Moore 5, Elizabeth Wiggins 5, Edward Winstead 5, Claire Robinson 5, Lee Thornhill 5, Kevin Sanchez 5, Nicholas L Wagner 4,6, Adam Ahern 4,6, Joseph M Katich 4,6, Anne E Perring 4,7, Joshua P Schwarz 6, Ming Lyu 4,6, Christopher D Holmes 8, Johnathan W Hair 5, Marta A Fenn 5,9, Taylor J Shingler 5,9
PMCID: PMC9788259  PMID: 36591326

Abstract

Aerosol mass extinction efficiency (MEE) is a key aerosol property used to connect aerosol optical properties with aerosol mass concentrations. Using measurements of smoke obtained during the Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) campaign we find that mid‐visible smoke MEE can change by a factor of 2–3 between fresh smoke (<2 hr old) and one‐day‐old smoke. While increases in aerosol size partially explain this trend, changes in the real part of the aerosol refractive index (real(n)) are necessary to provide closure assuming Mie theory. Real(n) estimates derived from multiple days of FIREX‐AQ measurements increase with age (from 1.40 – 1.45 to 1.5–1.54 from fresh to one‐day‐old) and are found to be positively correlated with organic aerosol oxidation state and aerosol size, and negatively correlated with smoke volatility. Future laboratory, field, and modeling studies should focus on better understanding and parameterizing these relationships to fully represent smoke aging.

Keywords: mass extinction efficiency, smoke, refractive index, mass scattering efficiency, wildfire, aging

Key Points

  • Aerosol mass extinction efficiency can increase up to a factor of 2–3 after 1 day of aging

  • Changes in both particle mean diameter and real part of the refractive index are needed to fully explain the changes

  • Changes in the estimated real part of the refractive index are correlated to organic aerosol oxidation state, volatility, and particle size

1. Introduction

Smoke from wildfires can have a wide range of impacts, including adverse effects on human health (O’Dell et al., 2021; C. E. Reid et al., 2016), reduction in visibility (Spracklen et al., 2007), and climate implications (Randerson et al., 2006). The severity of wildfires has been predicted to increase with climate change (Jin et al., 2015; Spracklen et al., 2009), and recent wildfire seasons across the world are confirming this by being devastating by many measures (Deb et al., 2020; Higuera & Abatzoglou, 2021).

Aerosol optical properties play an important role in observing and quantifying smoke and its impacts. For instance, satellite‐derived aerosol optical depth (AOD), as well as aerosol extinction and backscattering coefficient can be used to provide constraints on smoke emissions (Ichoku & Ellison, 2014; Peterson et al., 2021; Saide et al., 2015) and to estimate surface PM2.5 when surface networks are not available (Cheeseman et al., 2020; Mirzaei et al., 2020). Also, increasingly common low‐cost sensors quantify PM2.5 by measuring light scattering (Delp & Singer,  2020), which can be useful to extend the coverage of reference networks (Forehead et al., 2020). These examples show that, while optical properties are sampled, many times the quantity of interest is a measure of smoke mass (e.g., concentrations or emissions). Thus, a key parameter to understand is the efficiency at which a unit of aerosol mass concentration produces aerosol extinction (scattering + absorption) or scattering, generally referred to as mass extinction efficiency (MEE) and mass scattering efficiency (MSE). Laboratory and field measurements have found a large range in mid‐visible MSE for fresh and aged smoke (1.5–6 m2/g for wavelengths on the 532–550 nm range), with values being correlated with changes in aerosol mean diameter as expected from Mie theory (Kleinman et al., 2020; Laing et al., 2016; Levin et al., 2010; J. S. Reid et al., 1998; J. S. Reid, Eck, et al., 2005). Aerosol size increase with age is the result of a combination of coagulation and gas‐to‐particle conversion of semi‐volatile material (Martins et al., 1996; J. S. Reid, Koppmann, et al., 2005), likely primarily coagulation (Hodshire et al., 2021; June et al., 2022).

The complex refractive index is an intrinsic property of the aerosols that describes how light interacts with the material in terms of the scattering (real part) and absorption (imaginary part) (Moosmüller et al., 2009), and thus also influences MEE and MSE. Numerous laboratory and field estimates of the real refractive index have been made for biomass burning. Average real refractive index in mid‐visible wavelengths for ambient wildfire smoke has been found to be in the 1.52–1.55 range, while the whole range of smoke measurements tend to be within 1.42–1.61 (Aldhaif et al., 2018; Bian et al., 2020; Espinosa et al., 2017). Similar averages and ranges are found in laboratory studies (Levin et al., 2010; Sumlin et al., 2018; Womack et al., 2021). While these studies sampled a mixture of smoke ages, the evolution of refractive index and dependence on other smoke aerosol intrinsic properties was not explored. While there is a relevant body of literature studying changes of real refractive index of organic aerosol (other than smoke) with aging (Moise et al., 2015 and references therein), there is limited literature on the topic for smoke aerosols.

Here we use observations from the 2019 NOAA‐NASA FIREX‐AQ field campaign to assess the evolution of MEE with smoke age. FIREX‐AQ occurred during a below‐average (compared to recent years) fire season, which enabled tracking of individual plumes for much longer time periods and without plumes of interest mixing with smoke from other fires. In the following, we describe the observations and computational methods, analyze and discuss results, and provide future directions.

2. Materials and Methods

2.1. Observations

FIREX‐AQ measurements on board the DC‐8 research aircraft for western US wildfires were used in this study. Aerosol mass concentrations were obtained from a high‐resolution time‐of‐flight aerosol mass spectrometer (AMS for short) (Guo et al., 2021 and references therein) and the NOAA Single Particle Soot Photometer (SP2, Lamb et al., 2018). AMS was also used to quantify bulk organic aerosol (OA) properties such as the oxygen to carbon (O:C) ratio (Canagaratna et al., 2015), oxidation state (OSc), density (Kuwata et al., 2012), and volatility, the latter defined as the mass fraction remaining (MFR) of organic aerosol after a thermodenuder set at 40°C and ∼70 s residence time (see details in Text S1 in Supporting Information S1).

Aerosol optical properties were measured by two teams. The NASA team measured aerosol scattering and absorption coefficients using a nephelometer (at 450, 550, and 700 nm wavelengths) and a particle soot absorption photometer (at 470, 532, and 660 nm wavelengths), respectively, which were then combined to calculate the aerosol extinction coefficient at 532 nm wavelength (Ziemba et al., 2013). The NOAA team used a cavity ringdown spectrometer (Langridge et al., 2011) to measure extinction at 532 nm wavelength. The NOAA measurements also included extinction measurements after a thermodenuder set at 250°C and 1–2 s residence time that allowed the computation of extinction fraction remaining (EFR), another proxy for aerosol volatility. All these were taken at dry conditions. Additionally, OA concentrations and NASA extinction and scattering coefficients were corrected for in‐cabin evaporation of OA as described in Text S1 in Supporting Information S1.

Aerosol size distributions were measured by multiple instruments but the main one used here is the LAS (Moore et al., 2021). A second LAS measured behind a thermodenuder set at 350°C and 1–2 s residence time, which was used to compute the volume fraction remaining (VFR) as an additional metric for aerosol volatility. Additional details on these observations, on other observations, on measurement uncertainties, on smoke age estimates, and on plume identification are described in Text S1 in Supporting Information S1.

2.2. Optical Properties Calculations

For a given wavelength, MEE is defined as the dry extinction coefficient (σ ext) per unit of mass concentration (M) of an aerosol population:

MEE=σextM (1)

Equivalently, MSE is defined as replacing σ ext by the scattering coefficient (σ scat) in Equation 1. These quantities can be estimated directly from FIREX‐AQ observations using aerosol extinction/scattering coefficients and aerosol mass concentrations from AMS and SP2.

One can discretize the aerosol population by size bins (e.g., those measured by sizing instruments) and using Mie theory assuming spherical and internally mixed particles can estimate σ ext (Bohren & Huffman, 1983) as:

σext=π4N∑idi2αinifi (2)

where N is the total aerosol number concentration, d i is the diameter of size bin i, αini is the extinction efficiency at diameter d i with aerosol refractive index n i, and f i is the fraction of aerosol number at size bin i, which we refer to here as the aerosol size distribution shape. Similarly, aerosol mass concentration can be computed by discretizing the aerosol size distribution and estimating volume assuming spherical aerosols, and converting to mass using aerosol density per size bin (ρi):

M=π6N∑idi3ρifi (3)

Substituting Equations 2 and 3 into 1, one obtains:

MEE=32∑idi2αinifi∑idi3ρifi (4)

This shows that, while MEE can be estimated by two extensive aerosol variables, in theory it is independent of aerosol mass concentrations and is a function of the aerosol size distribution shape, refractive index, and density. Smoke tends to be dominated by accumulation mode aerosol and, in this size range, MEE generally increases with aerosol size and refractive index (Figure S1 in Supporting Information S1). Also, MEE is inversely proportional to aerosol density (Equation 4).

Since MEE, aerosol density, and the size distribution shape were reported from FIREX‐AQ observations, the real part of the aerosol refractive index can be estimated numerically using Equation 4 and assuming refractive index and aerosol density don't change across aerosol size for each sample time. This assumption is typically used when estimating refractive index (Hand & Kreidenweis, 2002), and we believe it is reasonable as most of the aerosol mass and volume for FIREX‐AQ smoke is in the accumulation mode (Moore et al., 2021) and aerosol modes are usually assumed to be internally mixed. Also, this requires assuming an imaginary part of the refractive index, which is estimated from composition measurements using a volume mixing rule assuming imaginary refractive index for black carbon (BC) equal to 0.71 (Bond & Bergstrom, 2006) and zero for the rest of the species. While this assumption does not include brown carbon (BrC) absorption, the contribution of BrC is expected to be low at mid‐visible wavelengths (Zeng et al., 2022). Alternatively, MSE can also be used for this estimation by replacing extinction efficiency with scattering efficiency, which reduces the dependency on aerosol absorption assumptions. Aerosol scattering at 550 nm was used in the calculations, and thus all estimated real(n) values in this study are representative of the mid‐visible. Note that the use of aerosol size distribution shape in the computations reduces uncertainties due to loses aerosol sizing instruments have at large concentrations (Liu et al., 2017; Nault et al., 2018).

3. Results

Results are shown for multiple fires and days which are described in Text S2 in Supporting Information S1 along with smoke characteristics that provide context for this analysis. Smoke is separated into three age categories (<2, 2–10, and 10–50 hr old), by OA aerosol concentrations, and by day of emission. Reasons for these choices are outlined in Text S2 in Supporting Information S1.

3.1. MEE Evolution

Figure 1a shows how smoke MEE evolves with age (colors) and organic aerosol concentration (x‐axis) for smoke emitted on 7 August by the Williams Flats fire. The lowest values of MEE are found for <2‐hr old and dense smoke (>500 μg/sm3) with mean and medians in the range of 2.5–3.2 m2/g, which increase to 3.5–4 m2/g for >2‐hr old smoke (Figure 1a). Similar trends (but variations in MEE magnitude) are found on other days for fires where comparable OA concentration levels were measured for sustained periods of time (Figures S4a and S4c in Supporting Information S1). On the other hand, the largest values for MEE are found for one‐day‐old smoke emitted on 7 August (Figure 1a), reaching mean and median values between 6 and 7 m2/g for OA in the 20–500 μg/m3 range. Corresponding mean and median MSE values are in the 5.5–6.2 m2/g range (Figure 1b), which is in the upper end of results from previous field and lab studies (see Section 1). One‐day‐old smoke MSE is also within literature values. Assuming that smoke sampled had similar emission time, this implies that the MEE of smoke emitted on 7 August changed by a factor of 2–3 over the course of 1 day. This result is relatively insensitive to the corrections to account for OA evaporation and to which aerosol extinction instrument is used to compute MEE (Figures 1a, 1c, and 1d). One‐day‐old smoke (paired with fresh smoke sampling) was measured in two other instances during FIREX‐AQ, but neither of them showed MEE values over 5 m2/g. (Figures S4b and S4d in Supporting Information S1). Also, MEE enhancements between <2‐hr old and 1‐day old smoke on these other days were more moderate, with maximum values of a factor ∼2 increase for both days.

Figure 1.

Figure 1

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Box and whisker plots showing smoke properties (y‐axis) as a function of organic aerosol (OA) concentration (x‐axis) and age (colors) for Williams Flats fire smoke emitted on 7 August. Center solid lines indicate the median, circles represent the mean, boxes indicate upper and lower quartiles, and whiskers show the upper and lower deciles. Number of 1‐s data points for each age and OA concentration range is shown on top of each panel using the corresponding box and whisker color. The unit “μg/sm3” stands for μg/m3 at standard temperature and pressure conditions.

As discussed in Section 2.2, MEE is a function of size distribution shape, aerosol density, and refractive index. Assuming log‐normal aerosol distributions, the size distribution shape is determined by the mean diameter and the geometric standard deviation. Figures 1a and 1g shows similarities between the evolution of MEE and the volumetric geometric mean diameter (VGMD), both showing a tendency to increase with age and consistent with the literature (see Section 1). While this can explain some of the MEE changes, we note a tendency of VGMD to increase with OA concentration for the more fresh and dense plumes (OA > 500 μg/m3 for 2–10 hr old, OA > 2,000 μg/m3 for <2 hr old), while MEE remains roughly constant. The increasing VGMD at higher OA concentrations and the increasing VGMD with smoke age are robust because the same trends are captured by other aerosol size measurements (Figure S5 in Supporting Information S1). On the other hand, OA density decreases with OA concentration for fresh smoke above 500 μg/m3 (Figure 1h), so it also does not explain the trends in MEE (as aerosol density is inversely proportional to MEE). MEE calculated using observed size distribution shape, a fixed real refractive index of 1.53 for OA, and Mie calculations (Equation 4), can explain only 10% and 37% of the variability (based on R 2) of the observed MEE when using reported and constant OA densities, respectively, and results in a narrower range of mean and median values (5–7 m2/g, Figures 1e and 1f). Thus, we infer it is highly likely that smoke refractive index is changing to account these unexplained changes in MEE. As seen in Figures 1i–1k, smoke properties such as OA O:C ratio, oxidation state and volatility change significantly with OA concentration and age for smoke emitted on Aug 7th, which further support potential changes in smoke refractive index.

3.2. Real(n) Evolution

Working under the hypothesis that the smoke refractive index can evolve with age, we used the method described in Section 2.2 to estimate the real refractive index for smoke sampled from multiple days and multiple fires. Single‐scattering albedo means and medians for smoke from all fires investigated here were in the range of 0.90–0.97 (i.e., absorption accounts for 3%–10% of extinction), implying that MEE is more sensitive to variations of the real (scattering) part of the refractive index (real(n)) and thus it is the parameter that we estimated. This estimation assumes particles are spheres, which is thought to be generally valid for smoke (Martins et al., 1998), and that we assess it produces errors within an acceptable range considering other uncertainties of this study (Text S3 and Figures S6 and S7 in Supporting Information S1). Figure 1l shows the estimated real(n) for the most extreme day of the Williams Flats fire. There is a tendency for decreasing estimated real(n) with increasing OA concentrations above 500 μg/m3, which results in low (1.37–1.44) values for the highest concentrations of fresh smoke. On the other hand, high values (1.52–1.56) are found for highest concentrations of one‐day‐old smoke. The latter are closer to the values often found in literature for both smoke and more aged secondary organic aerosol (see Section 1). On the other hand, estimated real(n) for fresh smoke at high concentrations is either in the lower end of ranges found in the literature or slightly below. We hypothesize that differences with previous field work can be attributed to previous estimates of real refractive index being not separated by age or OA concentrations, and the tendency of opportunistically sampling plumes that might not correspond to smoke emitted on the same day. Differences with laboratory work could include the sampling at different environmental conditions, especially much lower temperature that can affect the organic aerosol partitioning (see discussion on following paragraphs). Changing the assumptions on aerosol density and how aerosol size is estimated result in similar real(n) increasing trends with age and decreasing trend with OA above 500 μg/m3 (Figure S8 in Supporting Information S1), making these trends be the most robust feature of our results. Figure 2 shows the data sorted by emission date to explore the fire‐to‐fire and day‐to‐day variations of the estimated real(n) along with potential explanatory variables. This figure shows that the low estimated real(n) for <2 hr smoke tends to be consistent across fires and days, with mean and median values on the 1.40–1.45 range, and slightly lower weighted means as values tend to decrease for large OA concentrations (Figure 1l). Also, estimated real(n) generally increases with age, reaching mean and median values on the range of 1.5–1.54 for one‐day‐old smoke.

Figure 2.

Figure 2

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Box and whisker plots as in Figure 1 showing smoke properties as a function of day of emission (x‐axis) and age (colors) for data from seven flights. The dates correspond to smoke from the following fires: 25 July to Shady, 29 July to Tucker, and 3 August , 6 August, and 7 August to Williams Flats. “x”s denote averages weighted by organic aerosol (OA) concentrations. OA concentrations were restricted to above 20 μg/m3.

As expected from OA oxidation, there is generally an increasing degree of oxygenation with age for a given day of emission (based on O:C ratio or carbon oxidation state (Kroll et al., 2011), Figures 1i and 1j and 2c). However, the magnitude and rate of increase of the degree of oxygenation can vary substantially for the same age category among days. These differences are likely explained by differences in smoke concentrations (Figure 2b) as a thicker plume will obscure sunlight and potentially reduce photochemistry. Additionally, under thick smoke the aircraft can access plumes with reduced photochemistry, which in turn can result in sampling of larger gradients (Decker et al., 2021; Xu et al., 2021). For instance, two‐to ten‐hour‐old smoke emitted on 7 August has a lower O:C ratio compared to the other days as it dilutes slower, and <2 hr smoke emitted on 6 August tends to have larger O:C ratio than the other days as concentrations were much lower. The same trend is also found for one‐day‐old smoke. Additionally, the fire on 7 August exhibited the formation of pyro cumulus creating the possibility for smoke and clouds to interact, which could change OA aging (Ervens et al., 2018). While other differences such as fuel type and flame temperature could also play a role (Cappa et al., 2020), this is less likely as modified combustion efficiency (i.e., a proxy the for relative amounts of flaming and smoldering combustion) from fresh smoke was similar across days (means and medians in the 0.89–0.91 range).

Different measures of smoke volatility are shown in Figures 2d–2f. OA MFR is the more direct measure of volatility, but the sample size was low (due to long integration time and less frequent measurements) and was not available on all days. However, we note that while VFR and EFR show lower corresponding values than OA MFR (which is expected due to the lower operating temperature in the AMS thermodenuder), the trends between different days and age categories are similar and thus can be used to make inferences when OA MFR is missing (see additional verification of this approach on Text S4 in Supporting Information S1). In theory, OA oxidation through addition of functional groups leads to lower volatility, however other processes can either reverse this trend (e.g., fragmentation) or decrease volatility without changes on oxidation (e.g., oligomerization) (N. M. Donahue et al., 2012). Thus, while we do observe a certain degree of correlation between oxidation and the volatility measures (e.g., R 2 between O:C ratio and VFR is 0.77 for means stratifying by age, OA concentration, and day of emissions as in Figures 1 and 2), there are clear differences in some of their trends. For instance, O:C ratio decreases with OA concentration for fresh smoke while volatility tends to be nearly constant (Figures 1i and 1k). Another difference is the very low volatility (high OA, VFR, and EFR) shown for one‐day‐old smoke emitted on 7 August compared to any other emission day and ages (Figures 2d–2f), while this smoke has a similar or even lower O:C ratio than one‐day‐old smoke from 6 August (Figure 2c). Figure S9 in Supporting Information S1 shows that indeed day‐old smoke emitted on 7 August is less volatile than fresh smoke and not just a reflection of differences in partitioning due to lower OA concentrations. However, we are unable to verify this trend with OA MFR measurements as one‐day‐old smoke OA MFR was only available for smoke emitted on 7 August.

Various levels of correlation are apparent when comparing the estimated real(n) to O:C ratio, smoke volatility, and aerosol diameter (Figures 1g, 1i, 1j, and 1l, and  2a and 2e–2g). For averages stratified by age, OA concentration, and day of emissions (i.e., circles from Figure 1 but for all days of emission) for the Williams Flats fire, O:C ratio, VFR, and diameter explain 58%, 59%, and 29% of the variability in estimated real(n), respectively (scatterplots and fits in Figure S10 in Supporting Information S1). The multi‐linear regression of all these variables explains 63% of the variability. On the other hand, mean O:C ratio, VFR, and diameter by age and emission day (i.e., Figures 2c, 2e, and 2g circles) are able to explain 71%, 96%, and 72% of the variability of mean estimated real(n) (all p‐values < 0.05), with the multi‐linear regression explaining 96% of the variability. There is a tendency of oxidation to better explain real(n) trends for fresh smoke, while volatility and diameter better explain those for one‐day‐old smoke. For instance, Figures 1g, 1i, 1j, and 1l shows very similar decreasing trend in O:C ratio and estimated real(n) for fresh smoke as OA concentrations increase, which is not shown by volatility or diameter. On the other hand, the large increase in estimated real(n) for 7 August one‐day‐old smoke is only captured by the volatility measures and diameter (Figure 2). Estimated real(n) and O:C ratio shown in Figure 2 can be plotted in a scatter plot (Figure S11 in Supporting Information S1) as has been done in studies summarizing literature of OA aging (Figure 8 in Moise et al., 2015), showing that smoke real(n) follows similar trends to that of the oxidation products of azelaic acid (a biogenic molecule used as a proxy for oxidized OA, Cappa et al., 2011) but with a larger range of values. Increasing values of real refractive index with increase in particle size has also been generally found in studies on SOA production from various VOC precursors (Kim et al., 2014; Kim & Paulson, 2013), similar to what is shown here.

These results indicate that there is promise in predicting smoke real(n) based on OA oxidation and volatility, and on particle size. A modeling framework that independently tracks OA volatility and oxidation such as the two‐dimensional volatility basis set (2‐D‐VBS, Chuang & Donahue, 2016; N. M. Donahue et al., 2011; N. M. Donahue et al., 2012) or simplifications of it (Koo et al., 2014) might be suitable for this purpose. However, such a model would have to first accurately predict the oxidation state and volatility of smoke. In this regard, recent model developments show promise as they are able to track smoke OA oxidation with photochemical aging at high accuracy based on laboratory data (Cappa et al., 2020). Smoke aging in the dark has been found to be an important source of oxidized aerosol (Kodros et al., 2020). This is likely important for wildfire smoke as emissions tend to concentrate in the late afternoon and evening and extreme fire behavior often results in overnight burning (Peterson et al., 2015), thus this needs to be considered when developing models and designing future laboratory studies. As shown earlier, proper representation of aerosol size is also important for resolving MEE. Thus, given the correlation of diameter and refractive index, evaluating and developing models to resolve particle diameter (e.g., June et al., 2022) can also support parameterizations of real refractive index evolution.

Perhaps the most challenging feature for future models to represent would be the strong decrease in volatility for 7 August emitted smoke that is not explained by oxidation changes. While we are currently not able to fully explain this behavior, we can provide potential hypotheses for future work to explore. Observed smoke emitted on Aug 7th was characterized by having the highest OA concentrations and being emitted (and sampled) at higher altitudes (Figure S2 in Supporting Information S1) and thus lower temperature (T) than smoke emitted on other days (mean Ts for 3, 6, and 7 August are 281, 285, and 270 K, respectively). Due to the semi‐volatile nature of the smoke, these conditions would lead to a large fraction of the higher volatility organics to be in the particle phase even after 2 hours of since emission, while they are mostly in the gas phase for emissions on the other days (Figure S12 in Supporting Information S1). Thus, differences between products of gas phase oxidation and condensed phase reactions (e.g., heterogeneous uptake of oxidants, reactions among organics, and acid base reactions) could be playing a role in the resulting volatility. Oligomerization is a process where small reactive organic molecules (monomers) can create higher carbon number association products (oligomers) with significantly lower volatility and negligible changes to oxidation state, and it's thought to occur mostly in the condensed phase (De Haan et al., 2009; Kalberer et al., 2004; Tolocka et al., 2004). Additionally, the fire on 7 August exhibited the formation of pyro cumulus. Previous studies have found that aqueous chemistry can promote oligomerization reactions in ways that are not accessible to gas‐phase processing only (Altieri et al., 2008; Boone et al., 2015), providing additional pathways for oligomerization to occur. Thus, conditions on 7 August could be favoring the occurrence of oligomerization resulting in the large decreases in smoke volatility.

4. Conclusions and Future Directions

MEE is an important aerosol optical property to understand in the context of smoke evolution. The FIREX‐AQ field campaign sampled isolated wildfire smoke from multiple fires with ages ranging from below 1 hr up to over one‐day‐old which allows a detailed study of MEE progression. We find that for the day of more extreme fire development of the Williams Flats fire, MEE shows the largest changes, with mean MEE in the range of 2.5–3.2 m2/g for thick smoke younger than 2 hr, up to values of 6–7 m2/g for 1‐day‐old smoke. Fresh smoke MEE shows similar behavior for other days and other fires, but such high MEE values for one‐day‐old smoke are not reproduced any other day and are in the upper end of values found in the literature. Increases in aerosol size with age partially explain these trends but are insufficient to fully explain the observed variability and range of MEE. Thus, we conclude that changes in the smoke refractive index are needed to explain the trends in MEE. Using observations and Mie theory, we derive expected values of the real part of the smoke refractive index (real(n)) to match the observed MEE. We find that estimated real(n) depends strongly on aerosol mass concentration, age, and day of emission. Mean extreme values of 1.37–1.44 for high OA concentrations (>500 μg/m3) of <2 hr old smoke, and 1.52–1.56 for one‐day‐old smoke >50 μg/m3 are found, with the former being in the lower range or below previous studies and the latter closer to averages found in the literature. Considering all fires and OA concentrations >20 μg/m3, average estimated real(n) increases from 1.40–1.45 to 1.5–1.54 from <2 hr of age to 1‐day‐old smoke. Additionally, derived real(n) is correlated with increases of OA oxidation and aerosol size, and decreases of smoke volatility, to a point where the mean behavior of real(n) by emission day and age category can be well explained by these variables. While there are multiple uncertainties inherent to this analysis (e.g., instrument uncertainty, inlet evaporation, assumption of aerosol density, Mie theory assumptions applied to smoke aerosol), the trends found appear robust and warrant further investigation.

These results have multiple implications. Extreme fire behavior is more often being observed in recent fire seasons. For instance, the day the Williams Flats showed the more extreme behavior (7 August) it burned ∼5,200 Ha, while large non‐complex fires in 2020 such as Creek, Cameron Peak, Dolan, East Troublesome, and Holiday Farm burned similar or larger areas on multiple days. Additionally, fires are expected to inject more smoke at greater heights as wildfire activity intensify (Wilmot et al., 2022). Thus, the conditions generating large differences in MEE and real(n) between fresh and aged smoke found here for smoke emitted on 7 August are expected to happen more often. However, given the limited sample of large fires studied here, this needs to be confirmed by future research. A review of smoke forecasting systems showed that only a minority of them track aerosol size, and little to none track changes of OA refractive index with age (Ye et al., 2021). Thus, this increases uncertainties when constraining smoke aerosol mass using satellite AOD retrievals as proper parameterization of aerosol optical properties is required. Future studies need to evaluate the capability and performance of atmospheric composition models to reproduce smoke size changes with age, but also parameterization development is needed to fully characterize the changes of refractive index with age. Low‐cost sensors measuring surface PM2.5 generally convert aerosol scattering into PM2.5 mass concentrations, and thus an assessment of their calibration and conversion accuracy under different smoke concentrations and ages is needed. Additionally, we expect this work to motivate laboratory studies that would try to replicate the conditions generating low real(n), and the low volatility associated to high real(n), to understand the chemical process behind them and support model development.

Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

Supporting information

Supporting Information S1

Click here for additional data file. (6.1MB, pdf)

Acknowledgments

We acknowledge Charles Brock for provided insightful comments in the preparation of the manuscript. This work has been supported by the following grants: NASA 80NSSC18K0629, NASA 80NSSC18K0630 NASA 80NSSC20K1650, NASA 80NSSC19K0124, NOAA NA18OAR4310107, NSF 2013461.

Saide, P. E. , Thapa, L. H. , Ye, X. , Pagonis, D. , Campuzano‐Jost, P. , Guo, H. , et al. (2022). Understanding the evolution of smoke mass extinction efficiency using field campaign measurements. Geophysical Research Letters, 49, e2022GL099175. 10.1029/2022GL099175

Data Availability Statement

Flight observational data from FIREX‐AQ, along with the DIAL‐HSRL data during the field campaign are archived by NASA/LARC/SD/ASDC (https://doi.org/10.5067/SUBORBITAL/FIREXAQ2019/DATA001, https://www-air.larc.nasa.gov/missions/firex-aq/). The database for database of tri‐axial ellipsoid optical properties was provided by Prof. Ping Yang (Texas A&M University).

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information S1

Click here for additional data file. (6.1MB, pdf)

Data Availability Statement

Flight observational data from FIREX‐AQ, along with the DIAL‐HSRL data during the field campaign are archived by NASA/LARC/SD/ASDC (https://doi.org/10.5067/SUBORBITAL/FIREXAQ2019/DATA001, https://www-air.larc.nasa.gov/missions/firex-aq/). The database for database of tri‐axial ellipsoid optical properties was provided by Prof. Ping Yang (Texas A&M University).

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