Optical properties of biomass burning aerosol during the 2021 Oregon fire season: comparison between wild and prescribed fires

Abstract

The Mt. Bachelor Observatory (MBO, 2.76 km a.s.l.) was frequently impacted by biomass burning (BB) smoke in 2021, an extreme forest fire year in the state of Oregon. We used measurements of fine particulate matter (PM₁) and dry aerosol scattering and absorption coefficients (σ_scat and σ_abs) to determine intensive aerosol optical properties for 27 BB events observed at MBO from April to September 2021. Measurements of carbon monoxide (CO), carbon dioxide (CO₂), and total oxidized nitrogen (NO_y) were also made during the study period. Four BB events originated from prescribed fires (PFs) between April and May, and 23 events originated from wildfires (WFs) between June and September. On average, the ΔPM₁/ΔCO normalized enhancement ratio (NER) was higher for the PF events, which we propose is due to more efficient organic aerosol condensation during quick plume dilution at colder ambient temperatures. At the same time, the PF events exhibited significantly higher Δσ_abs/ΔCO NERs at 652 nm, indicating a larger black carbon component. We attribute this to more efficient combustion, as supported by higher modified combustion efficiency (MCE) as well as higher ΔNO_y/ΔCO NERs for the PF events. Median mass scattering efficiencies (MSE; σ_scat/PM₁) ranged from 3.3 to 7.4 m² g⁻¹ (at 530 nm) across all biomass burning events, with no significant difference between WF and PF events. We found MSE to be positively correlated with plume concentration (ΔPM₁) and negatively correlated with the scattering Ångström exponent, suggesting that fast coagulation in dense smoke drives size distributions towards larger particles with greater scattering efficiency.

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Environmental significance

Prescribed burning is a forest management technique that decreases fuel loads in forests, thereby limiting future area burned by wildfire. Typically conducted outside of the summer wildfire season under controlled conditions, smoke from prescribed fires can have different air quality impacts than wildfire smoke. This study characterizes the optical properties of biomass burning aerosol in both wild and prescribed fire smoke observed at the Mt. Bachelor Observatory in central Oregon. We find that smoke from prescribed fires was more absorbing, indicating a larger black carbon component that likely resulted from more efficient combustion. Further, we show a dependence of mass scattering efficiency on smoke plume concentration, which can aid data interpretation from aerosol light scattering instruments during extreme smoke events.

1 Introduction

Aerosols emitted from biomass burning (BB) have a significant impact on air quality^1,2 and global climate.³ BB aerosols can scatter and absorb sunlight, which alters Earth's radiation balance.^4,5 These radiative effects can be significant both from dense, freshly emitted smoke and from aged smoke diluted into the background troposphere.⁶ In the western US, several factors, including climate-driven fuel aridity and high fuel loading from historical fire suppression, have led to increased wildfire activity in recent years.⁷ Wildfires and associated aerosol emission are projected to intensify under future climate change,^8,9 although this trend can be slowed by proactive measures such as prescribed fire or other forest management activities.¹⁰ Prescribed fire types include understory (the fire consumes surface fuels but not large trees), pile (burning of previously gathered biomass piles resulting from logging or other fuel management activities), and broadcast (burning in areas with little to no canopy present). Fuel availability is substantially reduced in areas treated with prescribed fire, resulting in slower spread and less damage from future wildfires.^11,12

Over the past two decades, area burned by wildfire in the western US exceeded the area treated with prescribed fire by over an order of magnitude.¹³ This contrasts with the southeastern US, where the majority of BB is prescribed.^1,14 Though necessary for restoring fire-adapted ecosystems, deployment of prescribed fire in the western US is often faced with resource and risk-related barriers. Prescribed burning is usually done outside of the summer wildfire season, when fuel moisture is high enough to prevent uncontrolled fire activity. Wind speeds and direction are also taken into account to minimize smoke intrusion into populated areas.¹⁵ In this study, we analyze observations of BB aerosol from wild and prescribed fires in the state of Oregon, US, in 2021. Prescribed fires in Oregon are regularly ignited from October to May, with an average of ∼700 km² ignited per year between 2010 and 2020.¹⁶ The Pacific Northwest region of the US experienced an extreme heat wave event in late June 2021, setting all-time high temperature records and worsening drought conditions in many parts of Oregon.¹⁷ The ensuing 2021 wildfire season burned 3354 km² in Oregon, compared to the 2011–2020 average of 2677 km² burned per year,¹⁸ contributing to significant regional aerosol pollution.

BB aerosol mass is mostly organic, with smaller contributions from black carbon (BC) and inorganic material (e.g., nitrate, sulfate, ammonium, chloride).^19–21 BC is the main absorptive component of BB aerosol, although a portion of organic carbon (OC) known as brown carbon (BrC) also has absorptive properties. BC exhibits mostly uniform absorption in the visible spectrum, while BrC absorption is more significant at shorter wavelengths.^22,23 The BC/OC emission ratio varies widely based on combustion conditions, commonly described using the modified combustion efficiency (MCE), the ratio of CO₂ enhancement to the sum of CO and CO₂ enhancements in smoke (see Section 2.5). MCE typically spans the range of 0.7–1.0, with higher values implying flaming combustion that efficiently converts fuel to CO₂.^24,25 Smoldering combustion (MCE < 0.9) emits less BC and more OC relative to flaming combustion (MCE > 0.9).^26,27

Secondary chemical transformations have competing effects on BB aerosol absorption following emission. Absorption by BC can be enhanced via internal mixing with OC, which forms organic coatings that refract light into the BC particle core (sometimes referred to as the lensing effect).^28,29 The BrC fraction of OC can also grow after emission through the photooxidation (via OH radical) of aromatic volatile organics and the addition of absorbing carbonyl functional groups to non-aromatic compounds.^22,30 Simultaneous fragmentation reactions and photobleaching (via prolonged solar irradiation), however, can lead to a net decrease in BrC absorption as smoke ages.^31,32 In the absence of sunlight, oxidation by the nitrate radical at night or in dense smoke forms absorbing nitroaromatics and organonitrates, which enhance BrC absorption during dark aging.^33–36

Gas/aerosol repartitioning can also significantly affect the optical properties of BB aerosol in the hours after emission.^37,38 Evaporation during plume dilution decreases total aerosol mass, although evaporated semi-volatile compounds can re-condense onto existing particles or facilitate new particle formation via oxidation. Primary gas-phase compounds emitted from combustion can also condense into the aerosol phase, forming secondary organic aerosol (SOA). These processes happen simultaneously, and often balance one another out, leading to little to no net change in aerosol mass over the first ∼24 h of aging.³⁹ In contrast, evaporation-driven mass loss dominates in older smoke.⁴⁰ Condensation and coagulation processes can shift aerosol size distributions towards larger particles, which have higher scattering efficiencies. During the first day of aging, enhancement in scattering with time has a more significant effect on total light extinction than simultaneous enhancement in absorption through BC coating and secondary BrC formation.³⁸ The loss rate of absorbing aerosol through photobleaching, volatilization, cloud processing, and other processes varies on time scales from days to weeks, and the resulting changes in optical properties of aged smoke are poorly constrained in global and regional models.⁴¹

Ground-based studies often sample BB smoke opportunistically, such that the sampled plumes have undergone varying extents of dilution and aging prior to measurement. Dilution extent can be accounted for by normalizing any change in a measured property by a non-reactive co-emitted species like CO. This method, however, does not account for dilution-driven changes in gas-particle equilibrium. Denser plumes from larger fires have slower dilution rates which favor coagulation and condensation, while less concentrated smoke from smaller fires is expected to have faster rates of dilution and evaporation, producing more SOA precursors.^42–44

This study examines aerosol optical properties of BB plumes with varied dilution extents during transport to the Mt. Bachelor Observatory, a remote, high-altitude site in central Oregon. Our goals are to evaluate enhancements in submicron particulate matter (PM₁) and its intensive optical properties (Ångström exponents, single scattering albedo, mass scattering and absorption efficiencies) in the context of fire type (wild vs. prescribed), ecoregion, and post-emission smoke processing. We find that BB aerosol was generally more absorbing (at all visible wavelengths) when MBO was impacted by prescribed fire smoke compared to wildfire smoke, which we attribute to more efficient combustion (i.e., more flaming than smoldering). We evaluate multiple methods for estimating combustion conditions to support our findings. Given the large range of aerosol mass loadings measured at MBO, we stress the importance of considering plume concentration when examining intensive properties of BB aerosol.

2 Methods

2.1 Mt. Bachelor Observatory

The Mt. Bachelor Observatory (MBO) is located on the summit of an isolated volcanic peak in central Oregon (43.98°N, 121.69°W, 2.76 km a.s.l.). Near-continuous measurements of aerosol scattering and absorption coefficients, CO, and CO₂ have been in operation since 2004. In 2021, we added PM₁ mass concentration and total oxidized reactive nitrogen (NO_y) to the suite of measurements. Located within the Deschutes National Forest and 31 km east of Bend, Oregon (population: 100 000), MBO has proven to be well situated to observe locally emitted and long-range transported smoke plumes.^{21,40,45–49} Mt. Bachelor is home to a ski resort with all electric lifts, and the only emissions at the summit come from occasional passes of snow-grooming equipment. These emissions are easily identified by short (<10 min) spikes in all measured species during the winter-spring ski season, and the affected periods have been removed from the data used in this analysis.

2.2 CO, CO₂, meteorology, and NO_y

CO and CO₂ were measured with a Picarro G2302 cavity ring-down spectrometer. Calibrations were performed every 8 h using three different National Oceanographic and Atmospheric Administration (NOAA) calibration gas standards, which are referenced to the World Meteorological Organization's mole fraction calibration scale.⁵⁰ Total uncertainty based on the precision of calibrations was ±4% for CO and ±2% for CO₂. Basic meteorological parameters (temperature, pressure, relative humidity (RH), and wind speed) were also measured continuously as detailed by Ambrose et al.⁵¹

NO_y was measured using a commercial trace-level chemiluminescence detector (Thermo 42iQTL) with a custom molybdenum converter inlet. The inlet converter was reclaimed from another NO_x instrument (Thermo 17i) and heated to 350 °C by an Omega CN616A temperature controller. This technique has been previously shown to reduce various NO_y species to NO with near-unit efficiency (e.g., peroxy acyl nitrate, ethyl nitrate, N₂O₅, and HONO).^52–54 The NO_y sample did not pass through a filter before conversion so that particulate nitrate species would also be converted.⁵⁵ Certain NO_y species with greater thermal stability (e.g., HNO₃) may be converted less effectively, leading to an underestimate of total NO_y. We assume that this bias is small given that PAN and other organic nitrates account for the majority of NO_y in aged wildfire smoke,^46,56,57 and these species are readily reduced over heated molybdenum. We calibrated the NO chemiluminescence detector twice during the campaign using a multipoint standard addition method with an NO gas standard (4.52 ppmv from Airgas, Tooele, UT). Total uncertainty based on accuracy and precision errors was ±20%. Precision uncertainty between BB events was ±15%, as estimated from the standard deviation of NO_y measurements when the instrument was sampling zero air (generated by a Sabio Model 1001 zero air generator) over a 60 min period.

2.3 Aerosol measurements and derived optical properties

Dry (RH < 35%) submicron aerosol scattering coefficients (σ_scat), absorption coefficients (σ_abs), and mass concentration (PM₁) were measured using similar instruments and inlet configuration as described by Laing et al.⁴⁸ An in-line 1 μm impactor was located upstream of all aerosol instruments, which were housed in an instrument room that was temperature-controlled at 20 ± 3 °C, typically 5–10 °C warmer than the ambient temperature. Because the last ∼2.5 m of the inlet line was also located in the instrument room, the ambient RH of the sample airstream was reduced to <35% (measured by the nephelometer) before reaching the aerosol instruments.

PM₁ mass concentrations were measured with a Grimm 1.109 optical particle counter (OPC). The OPC counts particles down to an optical diameter of 0.25 μm in 31 size bins, 11 of which are under the 1 μm size cut. A lognormal distribution of particle count is assumed based on the measured size distribution, which corrects for the unmeasured fraction of particles smaller than 0.25 μm. After assuming spherical particles, the volume distribution is converted to mass concentration using a constant density factor chosen by the manufacturer by reference to gravimetric PM measurements. The Grimm OPC has been designated as a US EPA federal equivalent method (FEM) for measuring PM_2.5 mass concentrations. We note, however, that this conversion to mass concentration does not account for potential changes in BB aerosol density and refractive index. While these properties show little variation across replicate laboratory burns,⁵⁸ there is evidence that both aerosol density and refractive index can change with smoke age.⁵⁹ The OPC was factory-calibrated prior to deployment with polydisperse dolomite dust. Uncertainty based on flow rate and accuracy errors is estimated by the manufacturer at ±5%, although the true uncertainty of our PM₁ measurement is likely larger considering the above approximations. We acknowledge that these assumptions cause our calculations involving OPC data to be difficult to directly compare to other studies which employed filter-based or laser-induced incandescence methods to measure mass concentration.

A TSI 3563 integrating nephelometer measured σ_scat at 450, 550, and 700 nm. Data reduction and uncertainty analysis for the nephelometer was conducted per Anderson and Ogren.⁶⁰ Total uncertainty for σ_scat measurements was ±15–20% during BB events. A continuous light absorption photometer (CLAP) measured σ_abs at 467, 528, and 652 nm. The CLAP is a NOAA-built instrument that is based on the design of the 3λ particle soot absorption photometer (PSAP). Both the CLAP and the PSAP use the same 47 mm diameter glass-fiber filters, except the CLAP consecutively samples on eight spots of one filter compared to the single-spot sampling method of the PSAP, allowing the instrument to run unattended for longer periods before the filter needs manual replacement.⁶¹ Correction factors for aerosol scattering and filter loading derived by Bond et al.⁶² were applied to CLAP data. Uncertainty calculations were based on those used in a previous study at MBO for measurements with a PSAP.⁶³ Combining sources of uncertainties^62,64–66 yielded a total uncertainty of ±30–40% for σ_abs during BB events. Precision uncertainty for σ_scat and σ_abs between BB events, as estimated from the standard deviation of measurements over a 60 min window with no smoke influence at MBO, was <10%. We use the precision uncertainty when comparing individual BB events analyzed in this study, whereas total uncertainty is more appropriate for comparing our results with other studies that used different measurement methods.⁶⁴ All aerosol data (σ_scat, σ_abs, and PM₁) were averaged at 5 min intervals and corrected to standard temperature and pressure (STP; T = 273.15, P = 101.325 kPa).

For filter-based absorption photometers like the CLAP or the PSAP, instrument noise varies with filter transmittance (Tr), the ratio of light intensity measured through a loaded filter vs. through a clean filter. For this study, the CLAP was programmed to rotate to the next sampling spot once the Tr of the previous spot dropped to 0.4. If the instrument had rotated through the eight available spots of a single filter, sampling continued on the last spot until the filter was manually replaced. Due to the remote location of MBO, filters were not always replaced immediately after a major BB event, so in some cases, Tr dropped below 0.4 during several smoke-impacted periods. We base our treatment of σ_abs data collected with low Tr on the analysis of Luoma et al.,⁶⁷ who found that PSAP response remained linear even with Tr < 0.4 when the Bond et al.⁶² correction scheme was applied (see Table 3 and Fig. 5 in Louma et al.⁶⁷). We therefore chose to include CLAP data with low Tr in our analysis while noting that the total σ_abs uncertainty may be as high as ±60% during these periods due to scattering and loading artifacts beyond those accounted for by the correction scheme.^68–70 Because of this high uncertainty, the absorption Ångström exponent (eqn (4)) was not calculated for five wildfire (WF) events during which minimum Tr dropped below 0.1 (see Table S1†). We emphasize that these events occurred during a limited interval (August 18–September 5, 2021) of the full six-month study period (April–September), and Tr remained well above 0.4 during most of the remaining BB events.

Single-scattering albedo (SSA) is defined as

We calculated the SSA at 528 nm for each BB event by inserting the slope of the reduced major axis (RMA) regression of σ_absvs. σ_scat into the right-hand side of eqn (1). R² values between σ_abs and σ_scat were >0.80 for all events. We adjusted the 550 nm σ_scat value to 528 nm by assuming a power law relationship between scattering and wavelength:

where SAE is the scattering Ångström exponent calculated with the 450–550 nm σ_scat pair:

We calculated the absorption Ångström exponent (AAE) with the 467–652 nm σ_abs pair:

We use the median 5 min value calculated for the BB event time range rather than the mean when comparing Ångström exponents between events to minimize the influence of outlier values, which can result from measurement error associated with a low absorption signal.⁷¹

Mass scattering efficiency (MSE) was calculated as the ratio of σ_scat at 550 nm to PM₁ mass concentration at every 5 min data point, with the median value being used to compare across BB events. Analogously, the mass absorption efficiency (MAE) was calculated as the ratio of σ_abs at 528 nm to PM₁. Uncertainties in Ångström exponents, MSE, and MAE were calculated by adding the uncertainties of each measurement used in the respective calculation in quadrature. Uncertainty in SSA was calculated by adding in quadrature the uncertainties in σ_scat, σ_abs, and the standard deviation of the slope of the RMA regression of σ_absvs. σ_scat. Precision and total uncertainties in aerosol optical properties for each BB event are listed in Table S1.†

2.4 Biomass burning event identification

Previous studies at MBO have developed a set of criteria to identify smoke-impacted periods. Laing et al.⁴⁸ identified periods with σ_scat (at 550 nm) ≥ 20 Mm⁻¹ and CO ≥ 150 ppbv for at least 1 h as BB events, if back trajectory analysis also indicated transport from known fires. Wigder et al.⁴⁵ and Baylon et al.⁴⁶ used similar criteria with data collected at MBO. For this analysis, we modified these criteria given that 2021 was an extremely active fire year for the region: 23% of 1 h-averaged data met the criteria used by Laing et al.⁴⁸ from April to September, and 46% from July to September alone. Considering the variability in background concentrations during smoke-impacted periods (peaks in σ_scat and CO in the July–September period often coincided with already elevated background σ_scat and CO), we identified BB events as periods when σ_scat550 and CO rose by 50 Mm⁻¹ and 50 ppbv, respectively, above the local background and stayed above these thresholds for at least 1 h. We excluded several “smoky periods” where these thresholds were exceeded for more than 20 h, as these events were influenced by multiple sources with large variability in transport times to MBO, and hence less relevant for comparison with shorter duration events. We also required σ_scat to remain strongly correlated with CO (R² ≥ 0.70) for the duration of each event.

We used the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model⁷² accessed from AirNow-Tech Navigator⁷³ to determine the source of each BB event and to estimate transport time to MBO. Back trajectories were calculated in reference to meters above model ground level (a.m.g.l.) using 12 km resolution meteorological data from the North American Mesoscale Forecast System (NAM). The summit of Mt. Bachelor is ∼1500 m above the surrounding terrain, so trajectories were initiated at a starting height of 1500 a.m.g.l. with a new trajectory generated every hour for the duration of each event. In cases where the source fire could not be clearly identified, additional trajectories were initiated at 500 m a.m.g.l. to reflect daytime upslope transport to MBO. We referred to the NOAA Hazard Mapping System (HMS) Fire and Smoke Product⁷⁴ and the NASA Fire Information for Resource Management System⁷⁵ to locate active fires in regions under the back trajectory path. Events were identified as prescribed fires (PFs) if the hotspot location and timing matched the information listed on US Forest Service regional fire agencies' webpages: Central Oregon Fire⁷⁶ for PFs in the Deschutes National Forest, and South Central Oregon Fire Management Partnership⁷⁷ for PFs in the Fremont-Winema National Forest. Locations of wildfire events were confirmed using the US Forest Service Incident Information System.⁷⁸

2.5 Normalized enhancement ratios and modified combustion efficiency

Normalized enhancement ratios (NER; ΔX/ΔY) for each event were calculated as the slope of the RMA regression of species X vs. species Y at 5 min averages. Changes in background concentrations during plume transport can impact NERs calculated with the RMA slope (or a different regression method), particularly when a plume rises from the boundary layer and dilutes in the free troposphere.⁷⁹ Uncertainty associated with a changing background is reduced when the species of interest are significantly enhanced above the local background, and when the correlation between the two species is strong.^21,47 Given that our criteria for BB events required σ_scat and CO to be elevated above the local background by at least 50 Mm⁻¹ and 50 ppbv, respectively, we use the RMA method to calculate all NERs. NERs were not calculated when the R² value between the two species X and Y was less than 0.50.

Modified combustion efficiency (MCE) is defined as:

Like SSA, MCE was calculated by inserting the slope of the RMA regression of CO vs. CO₂ into the right-hand side of eqn (5), only for events where CO and CO₂ were well-correlated (R² ≥ 0.50). Calculating the MCE with aged smoke is prone to additional uncertainties given that CO₂ is not always clearly enhanced during BB events and there are significant variations in background CO₂ due to upslope/downslope winds at MBO.^47,80 We discuss these uncertainties and compare MCE to other proxies of combustion conditions (Δσ_abs/ΔCO, ΔNO_y/ΔCO) in Section 3.2. Uncertainties for all NERs and MCE were estimated in the same way as for SSA: each measurement uncertainty was added in quadrature with the standard deviation of the slope of the RMA regression of the two species in question.

2.6 Hourly fire radiative power product

Fire radiative power (FRP), defined as the rate at which an active fire is emitting radiative energy, is a remote sensing metric proportional to the rates of fuel consumption and smoke emission.^81,82 In this study we use hourly 3 km gridded FRP from the Regional ABI and VIIRS Fire Emissions (RAVE) product.⁸³ RAVE fuses observations from the Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R Series (GOES-R) with Visible Infrared Imaging Radiometer Suite (VIIRS) observations aboard the Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 satellites. A dynamic cloud correction is applied to prevent both the underestimation of FRP under partial cloud cover and the overestimation of FRP in grids with high cloud fractions, as burning conditions under clear vs. cloudy skies are not the same (see details in Li et al.⁸³). We use the RAVE FRP product to contextualize MBO observations by identifying the source fire for each BB event (Section 2.4) and summing FRP observations across the fire area for each hour that the fire was active (i.e., FRP > 0). We then calculate the mean and median FRP (in MW) across the entire fire lifecycle (Table S2†). We also estimate an area-weighted FRP density (in MW km²) by dividing FRP observations by the total area with FRP > 0 for a given fire. Given that several events were influenced by multiple fires with variable transport times (Table 1), we use FRP to examine broad differences in fire intensity between fire type (prescribed vs. wild) and ecoregion (Section 3.1) rather than comparing the FRP of individual events.

Table 1 Overview of 2021 biomass burning events. All BB events originated from forest fires in Oregon Cascades mountain range. For the event type column, PF = prescribed fire and WF = wildfire. All BB events originated from forest fires in the Oregon Cascades mountain range. Transport time was estimated from HYSPLIT back trajectories. ΔPM₁ was calculated as the difference between the 5 min maximum and minimum PM₁ values during the event time range

Event number	Date and time (local time, UTC-8)	Duration (h)	Type	East/West slope of Oregon Cascades?	Forest type	Estimated transport time (h)	ΔPM1 (μg m^−3)
1	4/14/21 13:30–4/14/21 17:15	3.75	PF	East	Ponderosa pine	1 ± 1	156
2	4/21/21 12:30–4/21/21 15:30	3	PF	East	Ponderosa pine	2 ± 1	106
3	4/28/21 18:00–4/28/21 21:15	3.25	PF	East	Ponderosa pine	5 ± 1	32
4	5/5/21 16:00–5/5/21 20:45	4.75	PF	East	Ponderosa pine	7 ± 1	80
5	6/20/21 7:45–6/20/21 22:30	14.75	WF	East	Ponderosa pine	19 ± 9	58
6	7/5/21 20:45–7/5/21 22:45	2	WF	West	Douglas-fir	5 ± 1	123
7	7/6/21 9:45–7/7/21 00:45	15	WF	West	Douglas-fir	17 ± 6	38
8	7/9/21 18:45–7/9/21 23:15	4.5	WF	West	Douglas-fir	4 ± 1	194
9	7/10/21 18:45–7/10/21 21:45	3	WF	West	Douglas-fir	5 ± 1	111
10	7/13/21 15:45–7/13/21 20:45	5	WF	East	Ponderosa pine	7 ± 1	53
11	7/17/21 16:30–7/17/21 19:00	2.5	WF	West	Douglas-fir	4 ± 1	16
12	8/15/21 10:30–8/15/21 23:45	13.25	WF	West	Douglas-fir	6 ± 3	173
13	8/18/21 23:15–8/19/21 01:30	2.25	WF	West	Douglas-fir	3 ± 1	448
14	8/20/21 9:45–8/20/21 15:00	5.25	WF	West	Douglas-fir	7 ± 1	51
15	8/22/21 13:15–8/22/21 19:00	5.75	WF	West	Douglas-fir	3 ± 1	184
16	8/25/21 2:45–8/25/21 22:30	19.75	WF	West	Douglas-fir	6 ± 3	268
17	8/26/21 12:00–8/26/21 20:15	8.25	WF	West	Douglas-fir	2 ± 1	172
18	8/29/21 19:00–8/29/21 20:45	1.75	WF	West	Douglas-fir	6 ± 1	276
19	8/30/21 10:30–8/30/21 19:00	8.5	WF	West	Douglas-fir	2 ± 1	124
20	9/3/21 9:00–9/3/21 11:15	2.25	WF	West	Douglas-fir	11 ± 6	96
21	9/3/21 14:00–9/4/21 09:00	19	WF	West	Douglas-fir	6 ± 6	347
22	9/4/21 9:45–9/4/21 22:45	13	WF	West	Douglas-fir	4 ± 3	353
23	9/5/21 7:45–9/5/21 23:45	16	WF	West	Douglas-fir	3 ± 1	447
24	9/11/21 22:15–9/12/21 06:30	8.25	WF	West	Douglas-fir	6 ± 3	74
25	9/12/21 13:00–9/12/21 18:30	5.5	WF	West	Douglas-fir	3 ± 1	43
26	9/17/21 1:30–9/17/21 06:00	4.5	WF	West	Douglas-fir	2 ± 1	147
27	9/17/21 13:45–9/17/21 22:45	9	WF	West	Douglas-fir	3 ± 1	128
PF mean ± standard deviation		3.7 ± 0.8				3.8 ± 2.8	94 ± 52
WF mean ± standard deviation		8.2 ± 5.7				5.8 ± 4.4	170 ± 128

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3 Results and discussion

3.1 Overview of 2021 biomass burning events

Table 1 shows a summary of the 27 BB events identified between April and September 2021. Time series of data collected at MBO during this study period are shown in Fig. 1, and the suspected source fire locations are shown in Fig. 2. Events 1–4 were associated with prescribed fires (PFs) that burned in ponderosa pine-dominated forests on the eastern slopes of the Oregon Cascades mountain range in April and May. HYSPLIT back trajectories show that Events 1 and 2 originated southwest of Sisters, OR, 30 km northeast of MBO. Event 3 originated 70 km south of MBO. The sources of Events 1–3 were within the Deschutes National Forest. Event 4 originated near Chiloquin OR, 140 km south of MBO, with a longer estimated plume transport time of ∼7 h compared to 1 h for Event 1, 2 h for Event 2, and 5 h for Event 3. The source of this event started as the North 2 PF in the Fremont–Winema National Forest on May 4, before being converted to wildfire status (renamed the Meadow Fire) the next day when fire behavior escalated beyond what was planned for. Conversion to wildfire status was done to bring in fire suppression aircraft, and the fire never breached the original perimeter intended for prescribed burning.⁷⁷ In this study we consider Event 4 as a PF event given that it originated from the eastern Oregon Cascades and outside of the summer wildfire season, making this event similar to Events 1–3 in seasonality and ecoregion.

Fig. 1 Time series of 5 min CO, CO₂, NO_y, PM₁, σ_scat (550 nm), and σ_abs (528 nm) measurements from MBO during the April and September 2021 study period. The periods shaded in blue and red show periods when MBO was impacted by PF and WF smoke, respectively. NO_y data was not collected between May 2 and July 21. PM₁, σ_scat, and σ_abs data were not collected between July 31 and August 13. No σ_abs data was collected between September 6 and September 15.

Fig. 2 Fire hotspots in Oregon between (a) April 13 and May 6, 2021, and (b) June 19 and September 18, 2021, colored by their VIIRS fire radiative power (FRP). Note the log scale of the FRP coloring. Suspected source fires are circled and labeled by event number. Note that the circles sometimes include more than one fire. The black triangle shows the location of MBO. Forest type data from the US Forest Service National Forest Type Dataset.⁸⁵

The remaining events originated from wildfires (WFs) between June and September. Two WF events originated from the eastern Cascades: Event 5 (S-503 fire, Warm Springs Reservation, 120 km north of MBO) and Event 10 (Grandview fire, Deschutes National Forest/Crooked River National Grassland, 50 km northeast of MBO). The remaining WFs originated from fires in Douglas-fir-dominated forests of the western Cascades, including the Jack fire (Umpqua National Forest, ∼100 km southwest of MBO, source of Events 6–9, and 11) and the Bull Complex fire (Mt. Hood National Forest, ∼100 km northwest of MBO, source of Events 13 and 14). Events 12, 15–27 were influenced by multiple fires in southwestern Oregon, namely the Middle Fork Complex (∼60 km west of MBO), Rough Patch Complex (∼90 km southwest of MBO), Devils Knob Complex (∼130 km southwest of MBO), and Skyline Ridge Complex fires (∼170 km southwest of MBO). We note that other large wildfires burned in eastern Oregon in 2021 (e.g., the 1674 km² Bootleg fire),⁸⁴ but persistent westerly wind during the summer resulted in the majority of WF events at MBO to originate from southwestern Oregon. Based on HYSPLIT back trajectories, the mean (±standard deviation) estimated transport time for the WF events was 5.8 ± 4.4 h, compared to 3.8 ± 2.8 h for PF events (Table 1). Several WF events had significantly longer transport times, namely Event 5 (19 ± 9 h), Event 7 (17 ± 6 h), and Event 20 (11 ± 6 h). One WF event that originated from Northern California on June 30, 2021, was excluded from this analysis given that its longer transport time (∼48 h) deemed it less comparable to the Oregon BB events, which were all transported to MBO in approximately 24 h or less. HYSPLIT back trajectory plots for each event are included in the ESI.†

The hourly RAVE FRP product (Section 2.6) revealed substantial differences in the duration and intensity of the sampled fires. Notably, there was active FRP data for 3 days for PFs 1 and 4, and just 1 day for PFs 2 and 3, while the WFs remained active for an average of 30 days (maximum 51 days for the Middle Fork Complex). FRP statistics derived from the RAVE product are listed in Table S2.† The longer duration and larger area of the WFs resulted in much larger total fire radiative energy (FRE; the time-integral of FRP), and consequently much larger total emissions than the PFs. We constructed diurnal cycles of FRP for each sampled fire based on the average FRP at each hour that the fire was active (Fig. S1†). The diurnal cycles show that the PFs were active only during the day, while both eastern and western Cascades WFs remained active at night with peak FRP occurring in the afternoon. Despite the smaller absolute FRP of the PFs, medians of area-weighted FRP density revealed that the PFs burned at comparable intensity to the western Cascades fires (8.84 ± 1.78 vs. 9.13 ± 2.23 MW km⁻²). The two eastern Cascades WFs (S-503 and Grandview fires) burned at the highest median and mean FRP density (Table S2†), which underscores the importance of ecoregion on fire behavior. We discuss the potential causes of these differences in fire behavior (and resulting smoke properties observed at MBO) in Section 3.2.

3.2 Influence of combustion conditions on aerosol absorption

We tested the statistical significance of differences in PF and WF event properties using one-way analysis of variance (ANOVA) (Table 2). At the 95% confidence level, the Δσ_abs/ΔCO normalized enhancement ratio (NER) at all three of the measured σ_abs wavelengths was significantly higher for PF events, especially at 652 nm (Δσ_abs652/ΔCO; P = 0.008). Considering that brown carbon (BrC) mostly absorbs at shorter wavelengths, the Δσ_abs652/ΔCO NER can be used as a surrogate for the black carbon (BC) NER, assuming minimal contribution from BrC species that also absorb in the red and near-infrared.^86,87 Higher mean Δσ_abs652/ΔCO during PF events suggests greater emission of BC (relative to fuel burned) compared to the WF events, which is also supported by observed differences in mass absorption efficiency (MAE; σ_abs/PM₁) and the absorption Ångström exponent (AAE; eqn (4)). The AAE describes the wavelength dependence of absorption, with BC exhibiting an AAE of ∼1 due to nearly uniform absorption in the 0.4–1 μm wavelength range, while BrC exhibiting an AAE > 2 due to greater absorption at shorter wavelengths.^28,71,88 The PF events had significantly lower mean AAE (1.93 ± 0.28) than WF events (2.71 ± 0.63) (P = 0.029), indicating a larger BC component (Fig. 3).

Table 2 Mean ± standard deviation of estimated transport time, ΔPM₁, MCE, ΔNO_y/ΔCO, and intensive aerosol properties derived for the four prescribed fire (PF) events and 23 wildfire (PF) events observed at MBO in 2021. P values were obtained by one-way ANOVA. Individual aerosol optical properties and their uncertainties for each BB event are included in Table S1

	Prescribed fire (PF) events	Wildfire (WF) events	P value
Number of events	4	23
Estimated transport time (h)	3.8 ± 2.8	5.8 ± 4.4	0.373
ΔPM1 (μg m^−3)	94 ± 52	170 ± 128	0.254
MCE	0.92 ± 0.05	0.89 ± 0.03	0.159
ΔNOy/ΔCO (pptv ppbv^−1)	6.90 ± 3.20	4.18 ± 2.56	0.087
ΔPM1/ΔCO (μg m^−3 ppbv^−1)	0.30 ± 0.10	0.16 ± 0.04	<0.001
Δσscat550/ΔCO (Mm^−1 ppbv^−1)	1.44 ± 0.40	1.17 ± 0.33	0.160
Δσabs467/ΔCO (Mm^−1 ppbv^−1)	0.19 ± 0.07	0.11 ± 0.06	0.031
Δσabs528/ΔCO (Mm^−1 ppbv^−1)	0.15 ± 0.06	0.08 ± 0.05	0.018
Δσabs652/ΔCO (Mm^−1 ppbv^−1)	0.10 ± 0.04	0.05 ± 0.03	0.008
SSA528	0.94 ± 0.03	0.91 ± 0.04	0.052
AAE467–652	1.93 ± 0.28	2.71 ± 0.63	0.029
SAE450–550	1.82 ± 0.08	1.84 ± 0.28	0.905
MSE550 (m^2 g^−1)	3.86 ± 0.38	4.91 ± 1.10	0.075
MAE528 (m^2 g^−1)	0.40 ± 0.12	0.30 ± 0.07	0.043

---

Fig. 3 Median absorption Ångström exponents (AAE) and scattering Ångström exponents (SAE) for 2021 BB events at MBO, with classification categories from Cappa et al.⁷¹ Each point corresponds to a single BB event and is colored by the Δσ_abs652/ΔCO NER. Error bars show the precision uncertainty of the medians. Triangles are prescribed fire (PF) events. Square points are wildfire (WF) events that originated from the western Oregon Cascades. The two diamond-shaped points correspond to WF events 5 and 10, which originated from the eastern Cascades.

Higher Δσ_abs652/ΔCO NERs, higher MAE, and lower AAE in the PF events we sampled suggests more flaming combustion with enhanced BC emission. The modified combustion efficiency (MCE; eqn (5)) was indeed higher for PF events (0.92 ± 0.05) vs. WF events (0.89 ± 0.03), however, this difference was not significant at the 95% confidence level (P = 0.159) (Table 2). MCE may be a compromised metric of combustion conditions at MBO due to its large uncertainty in aged smoke. MBO experiences a strong diurnal variation in CO₂, with varying free tropospheric and boundary layer influence (Fig. S2†), which can lead to an underestimation of the true ΔCO₂ since a portion of the CO₂ emitted by a fire is lost via boundary layer transport and mixing.^47,79,80 Given the well documented relationship between combustion efficiency and BC emission for a variety of biomass types,^89–92 the Δσ_abs652/ΔCO NER may be better suited for evaluating the influence of combustion efficiency on aerosol properties observed at MBO than MCE.

We also tested the ΔNO_y/ΔCO enhancement ratio as a proxy for combustion conditions, given that more nitrogen oxides (NO_x) are emitted from flaming combustion relative to smoldering.^93–95 NO_x emitted from BB is efficiently converted into other forms of NO_y such as peroxy acyl nitrates and other organic nitrates within hours after emission,⁵⁷ so the total ΔNO_y/ΔCO ratio is expected to be conserved over the first day of aging, decreasing slightly with the formation and deposition of nitric acid (HNO₃) and particulate nitrate.⁵⁶ The PF events had higher mean ΔNO_y/ΔCO than WF events (Table 2), but, like MCE, this difference was not significant (P = 0.087). ΔNO_y/ΔCO NERs were, however, well-correlated with Δσ_abs652/ΔCO (Pearson's correlation coefficient r = 0.86; Fig. S3†), confirming that more oxidized nitrogen is emitted alongside BC during flaming combustion.

Fig. 4 shows event-averaged single scattering albedo (SSA; eqn (1)) at 528 nm plotted against our three proxies of combustion conditions: MCE, Δσ_abs652/ΔCO, and ΔNO_y/ΔCO. The SSA is a key intensive optical property used to calculate the net radiative effect of BB aerosol,⁵ and laboratory studies have observed a sharp drop in the SSA of primary BB particles produced from high MCE fires.^26,27,92,96 While the SSA measured during laboratory burns can range from 0.2 to 1, ambient BB smoke typically has a much narrower SSA range (0.8–1) due to photochemical processing and mixing.⁹⁷ Previous field measurements and laboratory aging experiments have found SSA to increase rapidly with smoke age due to OA condensation/SOA formation, which leads to larger particles with higher scattering efficiency.^37,38,98,99 The range of event-averaged SSA in our study was relatively tight (0.86–0.97), indicating that substantial secondary processing has occurred during transport to MBO. That said, negative correlations were still observed between SSA and our three proxies for combustion conditions, the strongest being the correlation between SSA and Δσ_abs652/ΔCO (r = −0.82), which shows that some degree of variation in SSA can be explained by combustion conditions even in smoke sampled several hours after emission. Both MCE and ΔNO_y/ΔCO had similarly strong correlations with SSA (r = −0.71).

Fig. 4 Single-scattering albedo (SSA) at 528 nm plotted as a function of (a) modified combustion efficiency (MCE), (b) Δσ_abs652/ΔCO, and (c) ΔNO_y/ΔCO NERs. Error bars show precision uncertainty. Pearson's correlation coefficient (r) is included for each relationship. Data are fit with reduced major axis (RMA) regressions with shading representing the 95% confidence interval of the fits. Events are shaped according to their origin in the same way as in Fig. 3.

More flaming combustion during the PFs vs. WFs—as reflected by higher average MCE, Δσ_abs652/ΔCO, and ΔNO_y/ΔCO—can be attributed to differences in seasonality and fuel type. Summertime WFs can have a high intensity flaming front, but residual smoldering continues well after their perimeter has been contained. This results in a lower average combustion efficiency compared to shorter duration spring/fall PFs that are extinguished completely after the desired perimeter has burned.¹⁰⁰ Environmental conditions during the western United States WF season are more favorable for low MCE fires due to the lower moisture content of coarse ground layer fuels (dead wood) and duff that burn primarily by smoldering.^101,102 Conversely, PFs outside of the WF season occur when fuel moisture is high, favoring the consumption of finer fuels (grasses, foliage, and fine woody debris) which burn mostly by flaming combustion.¹⁰³ Indeed, PFs in central Oregon are only ignited if fuel moisture is above a predetermined threshold.¹⁵ Specific prescribed burning practices can also impact MCE: Zhang et al.¹⁰⁴ observed lower organic carbon/elemental carbon emission ratios (indicative of more flaming combustion) in pile burning vs. understory prescribed burning near Lake Tahoe, CA. These conditions suggest that PFs are prone to emit more BC relative to fuel burned compared to WFs, although this is highly dependent on the ecoregion in which PFs are occurring. May et al.⁹⁰ reported a mean BC emission ratio relative to CO of 6.5 ng m⁻³ ppbv⁻¹ from two PFs in coniferous forests of the California Sierra Nevada mountains, which is almost double the mean BC emission ratio from four California WFs (3.3 ng m⁻³ ppbv⁻¹) measured by Sahu et al.¹⁰⁵ Pratt et al.¹⁰⁶ reported BC emission ratios of up to 18 ng m⁻³ ppbv⁻¹ for a sagebrush PF in Wyoming.

The PF events analyzed in this study burned in ponderosa pine-dominated forests of the eastern Oregon Cascades, while most WF events originated from Douglas-fir-dominated forests of the western Cascades (Fig. 2). Forests of the eastern Cascades have more open canopies and less fuel moisture averaged across all seasons compared to forests of the western Cascades, where fuels are dry enough to burn only during the summer wildfire season.¹⁰⁷ In a laboratory study, Turn et al.¹⁰⁸ found that the PM carbon fraction from combustion of dried ponderosa pine slash contained ∼15% more BC than from dried Douglas-fir slash. The two WF events in our study that originated from the eastern Cascades (Events 5 and 10) were clustered in a similarly low AAE range as the PF events (Fig. 3) and had higher Δσ_abs652/ΔCO and lower SSA than most western cascades WF events (Fig. 4) (MCE and ΔNO_y/ΔCO data were not available for Events 5 and 10). As mentioned in Section 3.1, the source fires of Events 5 and 10 also exhibited the highest mean and median FRP density out of all fires sampled (Table S2†). When the 27 BB events are separated by ecoregion (the four PF events and two eastern Cascades WF events vs. 21 western Cascades WF events), the SSA is significantly lower (P < 0.001) for the eastern Cascades events (Table S3†). This indicates that the observed differences in aerosol properties between PF and WF events are likely driven by a combination of burning practices as well as the characteristic fuel type unique to the two ecoregions from which the BB events originated.

3.3 Normalized ΔPM₁/ΔCO enhancement ratios were larger for prescribed fires than wildfires

Observed smoke concentration ranged from relatively dilute (absolute PM₁ enhancement (ΔPM₁) < 50 μg m⁻³) to extremely dense (ΔPM₁ > 400 μg m⁻³) across the 27 BB events (Table 1). ΔPM₁ was calculated as the difference between the 5 min maximum and minimum PM₁ value during the event time range. While ΔPM₁ was generally lower for PF events (mean ± 1σ: 94 ± 52 μg m⁻³) than WF events (170 ± 128 μg m⁻³), the mean ΔPM₁/ΔCO NERs for PF events were significantly higher (0.30 ± 0.10 μg m⁻³ ppbv⁻¹) than WF events (0.16 ± 0.04 μg m⁻³ ppbv⁻¹) (P < 0.001; Table 2). The ΔPM₁/ΔCO NER is driven by both combustion conditions and post-emission processing. A well-established relationship exists between total PM emission and MCE, such that smoldering combustion emits more PM (relative to fuel burned) than flaming combustion.^109–111 Since we observed higher average MCE in PFs than WFs, as well as other metrics that indicate a greater degree of flaming combustion in these fires (Section 3.2), ΔPM₁/ΔCO NERs for PFs may be biased high relative to WFs due to a smaller CO/CO₂ emission ratio, however, no relationship was observed between ΔPM₁/ΔCO and MCE. This suggests that the higher observed ΔPM₁/ΔCO for PFs is likely due to efficient secondary chemistry conducive to PM mass gain.

Following emission, PM mass gain through organic aerosol (OA) condensation is expected to largely balance mass loss through evaporation at least over the first day of aging.³⁹ Our result can be compared to that of Selimovic et al.,¹¹² who reported a ∼35% higher ΔPM_2.5/ΔCO NER from a PF event compared to WF events sampled in Montana during summer 2018. PM₁ typically accounts for more than 80% of PM_2.5 in BB smoke.¹¹³ Smoke from the PF sampled by Selimovic et al.¹¹² was aged ∼3 h compared to the more dilute, aged WF smoke (up to several days old), so the larger ΔPM_2.5/ΔCO NER was likely driven by less evaporative mass loss compared to older WF smoke. In our study, the sources of PF events were similarly distant from MBO compared to WF events (Fig. 2), and smoke during PF events was, on average, only ∼2 h younger than WF smoke (Table 1). This suggests that PM mass loss through evaporation is unlikely to be the dominant driver of the observed differences in ΔPM₁/ΔCO.

Ambient temperature may affect the ΔPM₁/ΔCO NER given that warmer temperatures can slow OA condensation and enhance evaporation.¹¹⁴ As discussed by Selimovic et al.¹¹² and Jaffe et al.,¹¹⁵ ΔPM/ΔCO NERs derived from ground-based measurements of smoke are typically lower than those from smoke sampled at higher altitudes from aircraft or mountain-top sites like MBO. The 4 PF events analyzed in our study were sampled in April–May 2021, when the average ambient temperature at MBO was lower (3.7 ± 6.6 °C during PF events) compared to summertime WF events (11 ± 4.0 °C). Fig. 5 shows the relationship between ΔPM₁/ΔCO and temperature for all 27 BB events. Event 1 was by far the coldest (−6.0 °C) and considered an outlier in terms of temperature, while also having the largest observed ΔPM₁/ΔCO NER (0.40 μg m⁻³ ppbv⁻¹). Event 2 arrived at MBO one week after Event 1 (Table 1), and, despite also being a PF from the same area (Fig. 2), displayed a more moderate ΔPM₁/ΔCO of 0.28 μg m⁻³ ppbv⁻¹. Other aerosol properties, including σ_scat and σ_abs NERs and Ångström exponents, were very similar between the two events (Table S1†), yet the average temperature during Event 2 was ∼12 °C warmer. This suggests that more pronounced evaporative PM₁ mass loss has occurred during the warmer event despite comparable primary emissions.

Fig. 5 Relationship between ΔPM₁/ΔCO (±precision uncertainty) and average ambient temperature (±standard deviation) for BB events observed in 2021. Each point corresponds to a single BB event and is colored by the absolute PM₁ enhancement (ΔPM₁). Events are shaped according to their origin in the same way as in Fig. 3.

Differences in plume concentration may have also influenced the normalized PM₁ enhancement. Hodshire et al.¹¹⁶ sorted smoke data from the 2013 BBOP aircraft campaign by percentiles of ΔCO and showed that the less concentrated plume edges had higher rates of evaporation and photochemistry, and slower rates of coagulation than the denser plume cores. This result is similar to the observations of Garofalo et al.,¹⁹ as well as the ground-based study of Lee et al.¹¹⁷ It follows that more efficient SOA formation is expected in dilute vs. concentrated plumes since evaporated compounds can act as SOA precursors and recondense.¹¹⁸ Given that the mean ΔPM₁ was lower in PF vs. WF events, the PF events can be thought of as smaller, quicker-diluting plumes that are prone to faster secondary chemistry and hence, a more efficient increase in secondary PM mass. Chemical changes indicative of SOA formation have been observed within the first several hours after emission,³⁸ which is consistent with our observations of relatively fresh (<24 h transport time) plumes.

The mean Δσ_scat550/ΔCO NER was also higher in PF events (1.44 ± 0.40 Mm⁻¹ ppbv⁻¹) compared to WF events (1.17 ± 0.33 Mm⁻¹ ppbv⁻¹) (Table 2). Though not significantly different (P = 0.160), this supports our interpretation that smoke from PFs experienced more efficient SOA formation during transport to MBO due to the temperature and dilution effects discussed above, since σ_scat of BB aerosol increases as particles get more oxidized.^22,98,119 Like ΔPM₁/ΔCO, no relationship was observed between Δσ_scat550/ΔCO and MCE, indicating that the scattering component, and hence the majority of the normalized PM₁ enhancement, is more strongly driven by secondary processes rather than combustion conditions. Our observation of “smokier” (=higher ΔPM₁/ΔCO) plumes from PFs is perhaps surprising given the more efficient combustion of these fires. We stress, however, that this result does not necessarily contradict the findings of Liu et al.⁹⁶ and May et al.,⁹⁰ who reported lower PM emission factors from both southeastern US and western US montane PFs compared to western US WFs. Instead, our results underscore the effects of post-emission processing on the observed ΔPM₁/ΔCO NER in smoke lofted to the high altitude of MBO (2.76 km a.s.l.), which can mask the fuel and combustion efficiency-driven differences in initial PM emission. Total PM emission from WFs is still much larger than from PFs given more area burned and higher rates of fuel consumption.¹

3.4 Drivers of mass scattering efficiency

The mass scattering efficiency (MSE; σ_scat/PM₁) is a function of particle size, shape, density, and refractive index.¹²⁰ Median MSE values ranged from 3.3 to 7.4 m² g⁻¹ across the 27 BB events, with no significant difference between PFs and WFs at the 95% confidence level (Table 2). This range is higher than previous observations of MSE at MBO (2.5–4.8 m² g⁻¹),^47,48 although MSEs > 6 m² g⁻¹ at visible wavelengths have also been observed in BB smoke elsewhere.^59,120–122 Because of the uncertainties and assumptions associated with using OPC data to estimate MSE (see Section 2.3), we caution against making direct comparisons between our MSE estimates and those made with more accurate mass concentration measurements or calculated using Mie theory. That said, we found robust relationships that explain some of the observed variability in MSE, which is important to understand given that this property is used to convert remote sensing observations of aerosol optical properties to mass concentrations,¹²³ as well as a correction factor for ground-based light scattering instruments, including low-cost sensors.¹²⁴

Mie theory predicts that MSE will increase as mean particle diameter grows towards the measurement wavelength, and decrease if the particles continue to grow further.¹²⁵Fig. 6 shows MSE (at 550 nm) plotted as a function of SSA and the scattering Ångström exponent (SAE). We observed a positive relationship (r = 0.56) between MSE and SSA, though there is a lot of variance in MSE within the 0.93–0.97 SSA range of most BB events. A positive relationship is expected given that higher SSA means more SOA accumulation, leading to larger particles that are more efficient scatterers. This can happen rapidly after emission: Kleinmann et al.³⁸ observed an increase in MSE by over 50% in ambient BB plumes within 2 h after emission, despite minimal net change in PM₁ mass. A similarly rapid increase in MSE was observed in FIREX-AQ data.⁵⁹

Fig. 6 Mass scattering efficiency (MSE; σ_scat/PM₁) at 550 nm plotted as a function of (a) single scattering albedo (SSA) at 528 nm, and (b) the scattering Ångström exponent (SAE). Each event is colored by the absolute PM₁ enhancement (ΔPM₁). Error bars show precision uncertainty. Data are fit with reduced major axis (RMA) regressions with shading representing the 95% confidence interval of the fits. Events are shaped according to their origin in the same way as in Fig. 3. Pearson's correlation coefficients (r) are shown for the plotted relationships. The r values for MSE vs. ΔPM₁ and SAE vs. ΔPM₁ are 0.69 and −0.44, respectively.

We also observed a negative correlation between MSE and SAE (r = −0.73). The SAE describes the wavelength dependence of scattering and is inversely related to particle size.⁷¹ The mean SAE of PF events (1.82 ± 0.08) was very similar to the mean SAE of WF events (1.84 ± 0.02), suggesting that any effects of different combustion conditions on particle size are overshadowed by post-emission processing.^27,126 Lower SAE and higher MSE was generally observed in denser plumes (ΔPM₁ coloring in Fig. 6), which suggests that plume concentration may be a key driver of particle size, and by extension, MSE. Previous studies have concluded that coagulation has a larger effect on particle growth than OA evaporation/SOA formation, and coagulation rates are faster in denser plumes compared to more dilute plumes.^43,44,127 Using data from a scanning mobility particle sizer (SMPS) deployed at MBO during summer 2015, Laing et al.⁴⁸ found both MSE and geometric mean particle diameter to be strongly correlated with plume concentration. Our results suggest that about 50% of the observed variability in MSE is explained by particle size (based on the R² between SAE and MSE), and that particle growth towards the measurement wavelength of 550 nm is more efficient in denser plumes. Additional potential drivers of MSE include changes in refractive index and aerosol density,⁵⁹ which we are unable to quantify in the current study but warrant further investigation.

While we did not collect SMPS data in 2021, the relatively low SAE of several events are indicative of larger particles. The median SAE was less than 1.5 for two WFs (Events 12 and 22), which suggests a mixture of BC, BrC, and dust per the AAE/SAE classification scheme suggested by Cappa et al.⁷¹ (Fig. 3). Intense pyro-convective winds can uplift fine mineral dust that mixes with fire emissions,¹²⁸ and several field studies of aerosol composition reported mixed dust/smoke transport from western US coniferous forest fires.^129–131 It is therefore possible that some dust may have been mixed with smoke during several WFs observed in our study, which would result in higher MSE, although this cannot be confirmed without aerosol composition measurements. The lowest median SAE value (1.35 for Event 20) lies on the higher end of SAE previously reported for dust mixtures,⁷¹ so the events with lower SAE may as well have been influenced by larger BrC/BC particles rather than fine dust. While we use SAE as a surrogate for particle size, it is important to note that SAE is not a direct measure of particle size and can be influenced by other factors such as BC coating thickness.¹³²

4 Conclusions

We characterized intensive aerosol optical properties during 27 BB events observed at the Mt. Bachelor Observatory (MBO) in 2021. Four of the 27 events originated from springtime (April–May) prescribed fires (PFs) in central and south-central Oregon. The remaining 23 events originated from summertime (June–September) Oregon wildfires (WFs). Δσ_abs/ΔCO normalized enhancement ratios (NERs) were, on average, significantly larger for the PF events, especially at 652 nm, which indicates a larger BC component in PF smoke. This is attributed to more efficient combustion (i.e., more flaming vs. smoldering), as supported by higher average modified combustion efficiency (MCE) and higher average ΔNO_y/ΔCO NERs for the PF events. While most WF events originated from Douglas-fir-dominated forests of the western Oregon Cascades, the four PF events and two of the WF events originated from ponderosa pine-dominated forests of the eastern Oregon Cascades. Similarities in aerosol properties between the PFs and the eastern Cascades WFs show that regional fuel differences greatly influence combustion conditions and resulting BB aerosol absorption, and these fuel-related differences remain discernible even after several hours of photochemical processing. While the PFs burned at higher MCE, the mean ΔPM₁/ΔCO NER was significantly larger for the four PF events compared to the 23 WF events. Given that the transport times to MBO of all 27 events were less than 1 day, this suggests that more efficient OA condensation/SOA formation enhanced the ΔPM₁/ΔCO NER during the PF events, as enabled by colder ambient temperatures and lower plume concentrations compared to WF events. Despite the higher mean ΔPM₁/ΔCO NERs, PFs had much lower fuel combustion rates than WFs as indicated by lower FRP, resulting in lower total PM emissions. Should prescribed burning become more frequent in ponderosa pine-dominated forests of the Pacific Northwest, regional models and air quality forecasts should consider greater BC emission per unit of fuel burned relative to summertime wildfires in Douglas-fir-dominated forests.

Plume concentrations played a role in the observed range of mass scattering efficiencies (MSEs), with extremely concentrated WF events (ΔPM₁ > 400 μg m⁻³) exhibiting MSEs upwards of 6 m² g⁻¹, mostly likely driven by efficient particle coagulation. The large range of MSEs observed in our study of regional smoke highlights the complex effects of rapid secondary processing on particle light extinction, which is relevant for predicting and validating air quality forecasts for smoke events. Oregon's Department of Environmental Quality (DEQ) currently estimates PM levels across the state by applying an empirical conversion to light scattering measurements.^15,133 This method can introduce significant bias to the PM mass concentration estimate given that the MSE can vary substantially during moderate vs. extreme smoke events. To fully assess the variability of MSE, analyses of future fire seasons at MBO would benefit from measurements of mass concentration that do not rely on assumptions of constant density and refractive index, as well as additional observations of aerosol size distributions and composition.

Data availability

Data collected at the Mt. Bachelor Observatory are stored at the University of Washington ResearchWorks Archive. More information on data availability can be found at: https://blogs.uw.edu/djaffe/data/.

Author contributions

AM, NB, NWM, DJ, and JLF carried out the field study. NB compiled and quality-controlled the dataset. AM, RvG, and NB performed the analysis. AM wrote the paper with contributions from all co-authors.

Conflicts of interest

The authors declare that they have no conflict of interest.

Acknowledgements

The Mt. Bachelor Observatory is supported by the National Science Foundation (grant #AGS-1447832) and the National Oceanic and Atmospheric Administration (contract #RA-133R-16-SE-0758). A. Marsavin and J. L. Fry acknowledge additional support from the Dreyfus Foundation, the Paul K. Richter and Evalyn Elizabeth Cook Richter Memorial Fund, and the Reed College Class of ‘21 award. The authors gratefully acknowledge S. Kondragunta, F. Li, and C. Xu for providing the reprocessed RAVE FRP product, as well as the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT transport model used in this publication. We thank J. Ewing for help with construction of the external converter for our NO_y instrument. We thank the staff of the Mt. Bachelor Ski Area for their support.

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Footnotes

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d2ea00118g

‡ Current address: Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA.

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