# Airborne Observations Constrain Heterogeneous Nitrogen and Halogen Chemistry on Tropospheric and Stratospheric Biomass Burning Aerosol

Zachary C J Decker, Gordon A Novak, Kenneth Aikin, Patrick R Veres, J.

Andrew Neuman, Ilann Bourgeois, T. Paul Bui, Pedro Campuzano-jost, Matthew M Coggon, Douglas A Day, et al.

► To cite this version:

Zachary C J Decker, Gordon A Novak, Kenneth Aikin, Patrick R Veres, J. Andrew Neuman, et al. Airborne Observations Constrain Heterogeneous Nitrogen and Halogen Chemistry on Tropospheric and Stratospheric Biomass Burning Aerosol. Geophysical Research Letters, 2024, 51 (4), pp.e2023GL107273. 10.1029/2023GL107273. hal-05121232

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<figure><img src="image_1.png" /></figure>

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# Geophysical Research Letters®

## RESEARCH LETTER

10.1029/2023GL107273

**Key Points:**

* $ClNO_2$ formation is active on biomass burning (BB) particles but decreases with transport to the upper troposphere and lower stratosphere (UTLS)

* $N_2O_5$ uptake coefficients are low on young BB smoke and increase with transport through a PyroCB and UTLS aging

* $N_2O_5$ uptake coefficients on aged BB particles in the UTLS are significantly lower than those used in model parameterizations

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

S. S. Brown and Z. C. J. Decker,
steven.s.brown@noaa.gov;
ZacharyCJDecker@gmail.com

**Citation:**

Decker, Z. C. J., Novak, G. A., Aikin, K., Veres, P. R., Neuman, J. A., Bourgeois, I., et al. (2024). Airborne observations constrain heterogeneous nitrogen and halogen chemistry on tropospheric and stratospheric biomass burning aerosol. *Geophysical Research Letters*, *51*, e2023GL107273. https://doi.org/10.1029/2023GL107273

Received 19 NOV 2023
Accepted 21 JAN 2024

**Author Contributions:**

**Data curation:** Demetrios Pagonis,
Pamela S. Rickly

## Airborne Observations Constrain Heterogeneous Nitrogen and Halogen Chemistry on Tropospheric and Stratospheric Biomass Burning Aerosol

<figure><img src="image_2.png" /></figure>

Zachary C. J. Decker¹,²,³,⁴ ID, Gordon A. Novak¹,², Kenneth Aikin¹,² ID, Patrick R. Veres¹,⁵ ID,
J. Andrew Neuman¹,², Ilann Bourgeois¹,²,⁶, T. Paul Bui⁷ ID, Pedro Campuzano-Jost²,³ ID,
Matthew M. Coggon¹,² ID, Douglas A. Day²,³ ID, Joshua P. DiGangi⁸ ID, Glenn S. Diskin⁸ ID,
Maximilian Dollner⁹ ID, Alessandro Franchin¹,²,¹⁰ ID, Carley D. Fredrickson¹¹ ID, Karl D. Froyd¹,² ID,
Georgios I. Gkatzelis¹,²,¹² ID, Hongyu Guo²,³, Samuel R. Hall¹⁰ ID, Hannah Halliday⁸ ID,
Katherine Hayden¹³, Christopher D. Holmes¹⁴ ID, Jose L. Jimenez²,³ ID, Agnieszka Kupc¹,²,⁹ ID,
Jakob Lindaas¹⁵ ID, Ann M. Middlebrook¹, Richard H. Moore⁸ ID, Benjamin A. Nault¹⁶ ID,
John B. Nowak⁸ ID, Demetrios Pagonis²,³,¹⁷ ID, Brett B. Palm⁵,¹¹ ID, Jeff Peischl¹,² ID, Felix M. Piel¹⁸,¹⁹ ID,
Pamela S. Rickly¹,²,²⁰ ID, Michael A. Robinson¹,²,³ ID, Andrew W. Rollins¹ ID, Thomas B. Ryerson¹ ID,
Gregory P. Schill¹ ID, Kanako Sekimoto²¹, Chelsea R. Thompson¹ ID, Kenneth L. Thornhill²,⁸,²² ID,
Joel A. Thornton¹¹ ID, Kirk Ullmann¹⁰ ID, Carsten Warneke¹ ID, Rebecca A. Washenfelder¹,²³ ID,
Bernadett Weinzierl⁹ ID, Elizabeth B. Wiggins⁸ ID, Christina J. Williamson¹,²,²⁴,²⁵ ID,
Edward L. Winstead⁸,²² ID, Armin Wisthaler¹⁸,¹⁹ ID, Caroline C. Womack¹,², and
Steven S. Brown¹,³ ID

¹NOAA Chemical Sciences Laboratory (CSL), Boulder, CO, USA, ²Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA, ³Department of Chemistry, University of Colorado Boulder, Boulder, CO, USA, ⁴Now at Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), Villigen, Switzerland, ⁵Now at National Center for Atmospheric Research, Boulder, CO, USA, ⁶Now at Université Savoie Mont Blanc, INRAE, CARRTEL, Thonon-les-Bains, France, ⁷NASA Ames Research Center, Moffett Field, CA, USA, ⁸NASA Langley Research Center, Hampton, VA, USA, ⁹Faculty of Physics, Aerosol Physics and Environmental Physics, University of Vienna, Vienna, Austria, ¹⁰Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA, ¹¹Department of Atmospheric Sciences, University of Washington, Seattle, WA, USA, ¹²Now at Institute of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, Jülich, Germany, ¹³Air Quality Research Division (AQRD), Environment and Climate Change Canada, Toronto, ON, Canada, ¹⁴Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA, ¹⁵Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA, ¹⁶Center for Aerosol and Cloud Chemistry, Aerodyne Research, Inc., Billerica, MA, USA, ¹⁷Now at Department of Chemistry and Biochemistry, Weber State University, Ogden, UT, USA, ¹⁸Institute for Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria, ¹⁹Department of Chemistry, University of Oslo, Oslo, Norway, ²⁰Now at Colorado Department of Public Health and Environment, Denver, CO, USA, ²¹Graduate School of Nanobioscience, Yokohama City University, Yokohama, Japan, ²²Science Systems and Applications, Inc. (SSAI), Hampton, VA, USA, ²³Now at Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA, ²⁴Now at Finnish Meteorological Institute, Helsinki, Finland, ²⁵Now at Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland

**Abstract** Heterogeneous chemical cycles of pyrogenic nitrogen and halides influence tropospheric ozone and affect the stratosphere during extreme Pyrocumulonimbus (PyroCB) events. We report field-derived $N_2O_5$ uptake coefficients, $\gamma(N_2O_5)$, and $ClNO_2$ yields, $\phi(ClNO_2)$, from two aircraft campaigns observing fresh smoke in the lower and mid troposphere and processed/aged smoke in the upper troposphere and lower stratosphere (UTLS). Derived $\phi(ClNO_2)$ varied across the full 0–1 range but was typically <0.5 and smallest in a PyroCB (<0.05). Derived $\gamma(N_2O_5)$ was low in agricultural smoke (0.2–3.6 $\times 10^{-3}$), extremely low in mid-tropospheric wildfire smoke (0.1 $\times 10^{-3}$), but larger in PyroCB processed smoke (0.7–5.0 $\times 10^{-3}$). Aged biomass burning aerosol in the UTLS had a higher $\gamma(N_2O_5)$ of 17 $\times 10^{-3}$ that increased with sulfate and liquid water, but that was 1–2 orders of magnitude lower than values for aqueous sulfuric aerosol used in stratospheric models.

**Plain Language Summary** The injection of reactive material into Earth's atmosphere from fires affects atmospheric composition at regional and hemispheric scales. Reported stratospheric ozone depletion during extreme events, such as the 2020 Australian wildfires, illustrates one example of fire impacts and the role of heterogeneous (gas-particle) processes. We report field quantification of rates and product yields from

© 2024 His Majesty the King in Right of Canada and The Authors. Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
airborne observations of smoke. Extremely slow heterogeneous reaction rates on young smoke increase with transport and aging, but upper atmospheric values are still a factor of 10 slower than parameterizations used in stratospheric models. Heterogeneous production of ClNO₂, a major lower atmospheric chlorine activation pathway, may be active on biomass burning aerosol in the upper atmosphere.

# 1. Introduction

Biomass burning (BB) impacts global atmospheric chemical processes and is increasing regionally due to climate-change-induced trends in fire weather (Jones et al., 2022). Fires emit nitrogen oxides (NO + NO₂ = NOₓ), volatile organic compounds (VOCs) and aerosol that affect tropospheric oxidants (Koss et al., 2018). Roughly 10% of global inorganic chloride enters the atmosphere by BB (Wang et al., 2019), and a small fraction of this may be subsequently activated to inorganic chlorine radicals. The co-emission of NOₓ and VOCs enhances tropospheric O₃ globally on a scale comparable to, or greater than, urban pollution (Bourgeois et al., 2021; Xu et al., 2021).

Large wildfires can form smoke-filled thunderstorm clouds called pyrocumulonimbus (PyroCB) towers (Peterson et al., 2021, 2022) that loft pyrogenic emissions to the upper troposphere/lower stratosphere (UTLS). Aerosol injection from the 2019–2020 Australian New Year fires altered the partitioning of total reactive chlorine (Clₓ) and nitrogen (NOᵧ) species and led to stratospheric O₃ loss (Bernath et al., 2022; Solomon et al., 2022, 2023; Strahan et al., 2022). This O₃ depletion results from heterogeneous reactions on the particulate surface area of injected BB material. One of the major heterogeneous reactions is the uptake of N₂O₅, which in stratospheric models produces exclusively nitric acid, HNO₃ (Küll et al., 2002; Zambri et al., 2019).

$$N_2O_{5(g)} + H_2O_{(l)} \rightarrow 2HNO_3(g) \quad (R1, \gamma)$$

The uptake coefficient, γ, is the probability for reactive uptake upon a gas-particle collision (Ravishankara, 1997). Production of N₂O₅ occurs primarily in the absence of sunlight due to the photochemical instability of its nitrate radical (NO₃) precursor (Brown & Stutz, 2012). Reaction 1 influences NOₓ and O₃ in both the stratosphere and troposphere by altering the partitioning of reactive nitrogen and the availability of NOₓ (Dentener & Crutzen, 1993; Solomon, 1999).

Tropospheric observations have shown substantial yields of nitryl chloride, ClNO₂, from chloride-containing aerosol (McDuffie et al., 2018b), represented below as reaction with HCl.

$$N_2O_{5(g)} + HCl_{(aq)} \rightarrow ClNO_2(g) + HNO_3(g) \quad (R2, \phi)$$

Subsequent photolysis of ClNO₂ produces Cl. The yield, φ, for R2 is the molar ratio of ClNO₂ produced per N₂O₅ reacted. Due in part to the lack of chloride partitioning to highly-acidic stratospheric aerosol, R2 has been considered an unimportant contribution to stratospheric halogen activation (Solomon, 1999) despite its prevalence in the troposphere. Figure 1 illustrates BB emissions to different regions of the atmosphere together with the emissions and heterogeneous chemistry described above with results described below.

There is considerable uncertainty caused by a lack of experimentally-derived rates and yields related to N₂O₅ heterogeneous chemistry on BB particles (Solomon et al., 2022; Strahan et al., 2022; Yu et al., 2021). Current models assume BB particles are similar to volcanic particles but are unable to reproduce the remote sensing observations of Clₓ and NOᵧ, suggesting substantial differences in N₂O₅ heterogeneous chemistry. Values of γ (N₂O₅) and φ(ClNO₂) for BB aerosol are poorly constrained. To our knowledge there no field-derived values. There exist limited BB laboratory studies on γ(N₂O₅) or φ(ClNO₂), which suggest γ(N₂O₅) and φ(ClNO₂) can be altered by aerosol organic content and inorganic salts (e.g., nitrate and chloride) (Ahern et al., 2018; Goldberger et al., 2019; Jahl et al., 2021). Current tropospheric and stratospheric models used to study BB impacts are poorly constrained for γ(N₂O₅) and have not considered ClNO₂ formation.

Here, we present aircraft observations of ClNO₂ and N₂O₅ and field-derived values for γ(N₂O₅) and φ(ClNO₂) in BB smoke. The analysis combines model and parameterization methods with aircraft observations from two field campaigns: the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Illustration of biomass burning sources and emission to different regions of the atmosphere together with the heterogeneous chemistry of N<sub>2</sub>O<sub>5</sub> and ClNO<sub>2</sub>. Arrows on the right-hand side illustrate trends in heterogeneous parameters, γ (N<sub>2</sub>O<sub>5</sub>) and φ(ClNO<sub>2</sub>), determined from aircraft observations in this work.</figcaption></figure>

(Warneke et al., 2023) and the 2017–2018 Atmospheric Tomography Mission (ATom) (Thompson et al., 2022). We derive γ(N<sub>2</sub>O<sub>5</sub>) and φ(ClNO<sub>2</sub>) for fresh montane- and agricultural-fueled smoke emissions in the troposphere and a PyroCB injection of smoke to the upper troposphere from observations of daytime N<sub>2</sub>O<sub>5</sub> and ClNO<sub>2</sub> during FIREX-AQ. We derive γ(N<sub>2</sub>O<sub>5</sub>) for stratospheric BB-influenced aerosol from ATom. Derived N<sub>2</sub>O<sub>5</sub> uptake coefficients are considerably lower than current model parameterizations. Halogen activation through ClNO<sub>2</sub> from NO<sub>x</sub> and particulate chloride (pCl<sup>-</sup>) is prevalent in low altitudes and possible, yet unquantified, at high altitude. We discuss the factors that govern this heterogeneous chemistry in young and aged smoke.

## 2. Data and Methods

### 2.1. Observations

FIREX-AQ was a large-scale field research campaign focusing on wildfire smoke plumes in the western U.S. and prescribed agricultural burning smoke plumes in the southeastern U.S. during the summer of 2019. We use observations from the NOAA Chemistry Twin Otter and NASA DC-8 aircraft (see Text S1.1 in Supporting Information S1). The NASA Atmospheric Tomography (ATom) mission was a large-scale research campaign focusing on remote tropospheric, UTLS, and stratospheric air (Thompson et al., 2022). Data here are from September–October 2017 (ATom-3) and April–May 2018 (ATom-4) (Text S1.2 in Supporting Information S1). Table S1 in Supporting Information S1 lists instrumentation used in this analysis. In situ observations from ATom and FIREX-AQ are available as a merged data set and found in Wofsy et al. (2018) and Warneke et al. (2023), respectively. See further details in Supporting Information S1.

### 2.2. Models and Parameterizations

Two models are used: an iterative 0-D box model constrained to crosswind transects of wildfire plumes sampled during FIREX-AQ (Decker, Robinson, et al., 2021) (Text S1.3 in Supporting Information S1) and an iterative diel model constrained to observations for each parcel of sampled air (McDuffie et al., 2018b) above an arbitrary elevation cutoff of 6 km during ATom-3 and ATom-4 (Text S1.3 in Supporting Information S1). Calculated φ(ClNO<sub>2</sub>) uses a parameterization determined from laboratory experiments (Bertram et al., 2009; Roberts et al., 2009) (Text S1.4 in Supporting Information S1). See further details in the SI.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (a) NASA DC-8 flight tracks colored and sized by CINO₂ mixing ratio for the Williams Flats fire plume on Aug 3. The inset map shows the approximate location of sampling in Washington State. (b) Observations of CO (gray), pCl⁻ (pink), and jNO₃ (black) and (c) N₂O₅ (red), CINO₂ (yellow) and P(NO₃) (blue) for a subset of crosswind plume transects. (d and e) Histogram of N₂O₅ and CINO₂ NEMRs from all montane (d) and agricultural (e) fires. Box plots show 10th, 25th, 50th, 75th, and 90th percentiles.</figcaption></figure>

# 3. Results and Discussion

## 3.1. CINO₂ and N₂O₅ Observations

During FIREX-AQ the NOAA I⁻ CIMS aboard the DC-8 measured mixing ratios of N₂O₅ and CINO₂ up to 19 and 3 parts per trillion by volume (pptv), respectively, in near-field (<5 hr transport time) daytime plumes. Figure 2a shows the flight track of the NASA DC-8 aircraft sampling the Williams Flats fire on 3 August colored and sized by observed CINO₂. Both N₂O₅ and CINO₂ exhibit clear enhancements despite significant photolysis rates of NO₃ (jNO₃) (Figures 2b and 2c). These enhancements are associated with CO, a smoke tracer, and rapid (>1 ppbv hr⁻¹) NO₃ production, P(NO₃) = k[NO₂][O₃], where k is the bimolecular rate coefficient for reaction of NO₂ with O₃.

Median jNO₃ at the center of wildfire and agriculture plume transects (0.14 and 0.19 s⁻¹ respectively) presented here were 15%–30% lower than values outside of plumes (0.16 and 0.20 s⁻¹ respectively). In large wildfire plumes jNO₃ attenuation was a factor of 10 or more (Figures 2b and 2c), but small agricultural plumes exhibited no attenuation (Figure S1 in Supporting Information S1). Previous analyses of FIREX-AQ plumes found that NO₃ photolysis and reaction with NO are not major NO₃ loss pathways regardless of time of day (Decker, Robinson, et al., 2021) and that NO₃ chemistry is active regardless of the location at plume center or edge (Decker, Wang, et al., 2021). Rapid P(NO₃) together with large concentrations of highly reactive VOCs and aerosol surface area control NO₃ and N₂O₅ chemistry. Plumes with measurable daytime N₂O₅ provide measures of NO₃ reactivity and N₂O₅ heterogeneous uptake for these species that are otherwise important only at night in non-fire environments.

The Normalized Excess Mixing Ratio (NEMR, see Text S1.5 in Supporting Information S1) measures the above background enhancements of a compound x relative to the smoke tracer CO (Table S2 and Figures S2–S4 in Supporting Information S1). The median N₂O₅ NEMR was 1.0 pptv ppmv⁻¹ CO for both agricultural- and montane-fueled fire groups (Figures 2d and 2e). The CINO₂ NEMRs, by contrast, differ by a factor of ~6 between montane (0.3 pptv ppmv⁻¹) and agricultural (1.8 pptv ppmv⁻¹) fuels. Agricultural and grass burning emits more Cl⁻ per kg of fuel burned (emission factor) when compared to temperate and boreal forest burning (Akagi
<figure><img src="image_5.png" /><figcaption>**Figure 3.** (a) Calculated CINO₂ NEMRs from agricultural-fueled fires versus plume age. Each marker is the Normalized Excess Mixing Ratio of a single plume transect. Dashed lines show biexponential fits (see text). (b) Parametrized φ(CINO₂) of agricultural smoke. (c) Box model results (lines) compared to observations (markers) of N₂O₅ and CINO₂ from the July 29 North Hills smoke plume with γ(N₂O₅) = 10⁻⁴. The hashed area shows changes to volatile organic compounds (N₂O₅) or yield (CINO₂) that encompass observational uncertainty. Sensitivity to an aerosol surface area factor of 2 increase, considered the maximum increase in surface area from water corrections, is shown by a transparent blue area. The apparent discontinuity of N₂O₅ in the model is due to a reduction in the photolysis rate at sunset (2.5 hr of age). (d) Parameterized φ(CINO₂) for montane smoke (filled bars) and transect center observations used in the box model (empty bars). Box model derived φ(CINO₂) is shown as horizontal ranges in black. The black marker indicates the average of the five modeled plumes sampled in the lower troposphere. The range on the model-derived φ(CINO₂) shows the range of the five modeled plumes. Note that within observation uncertainty the full range is 0–1.</figcaption></figure>

et al., 2011; Liu et al., 2016; May et al., 2014). Despite considerable variability, the greater median CINO₂ NEMR for agricultural-fueled fires is consistent with the observed differences in particulate chloride (pCl⁻). DC-8 and Twin Otter observations of the above background pCl⁻ show that agricultural and grass smoke contains roughly 16× more pCl⁻ by mass than montane smoke (Text S2 and Figure S5 in Supporting Information S1). The majority of pCl⁻ is from KCl emissions, which is quantitatively detectable in the AMS.

## 3.2. Montane and Agricultural Smoke

To derive γ(N₂O₅) from agricultural smoke, we use the calculated NEMR of CINO₂ as a function of the physical plume age (Text S1.1 in Supporting Information S1) shown in Figure 3a. We combine the calculated CINO₂ NEMRs with the relationship between γ(N₂O₅) and φ(CINO₂) below to estimate a γ(N₂O₅).

$$ \gamma(\text{N}_2\text{O}_5) = 4 \times \frac{k_{\text{N}_2\text{O}_5}}{c \times \text{SA}} \qquad (1) $$

$$ \phi(\text{CINO}_2) = \frac{k_{\text{CINO}_2}}{k_{\text{N}_2\text{O}_5}} \qquad (2) $$

Here c is the mean molecular speed of N₂O₅ and SA is the aerosol surface area density. Data are separated into low and high NEMR groups, although we are unable to identify any metric that differentiates the two groups. The
biexponential fit represents first-order formation ($k_{CINO_2}$) and photolytic loss ($j_{CINO_2}$) of ClNO₂. Constraining the fit to an observed median photolysis rate of $j_{CINO_2} = 3.3 \times 10^{-4} s^{-1}$ (Figure S6a in Supporting Information S1) we find $k_{CINO_2} = 2.0–5.8 \times 10^{-4} s^{-1}$. Aerosol surface area can vary widely across a plume transect and therefore we chose a range (2–11 $\times 10^3 \ \mu m^2 cm^{-3}$) of observed SA representative of most observations in Figure 3a (Figure S6b in Supporting Information S1) and present a sensitivity analysis to this choice in Figure S6c of Supporting Information S1. Finally, we use a median observed temperature of 296 K to find $\gamma(N_2O_5) \times \varphi(CINO_2) = 0.3–4.7 \times 10^{-3}$.

To estimate $\varphi(\text{CINO}_2)$ we use a laboratory-based parameterization based on observed pCl⁻ and calculated liquid water content, hereafter referred to as parameterized $\varphi(\text{CINO}_2)$ (Section 2.2, Text S1.4 in Supporting Information S1). Figure 3b shows parameterized $\varphi(\text{CINO}_2)$ for all 1 Hz agriculture smoke observations, with median $\varphi(\text{CINO}_2)$ of 0.72. When considering only observations in Figure 2a, used to determine $\gamma(\text{N}_2\text{O}_5)$, the median is 0.77. Previous field comparisons have shown that parameterized $\varphi(\text{CINO}_2)$ is likely an upper limit to actual values (McDuffie et al., 2018a), which may, in part, be caused by the assumption that chloride is homogeneously distributed across surface area (Jeong et al., 2023; McNamara et al., 2020; Royer et al., 2021). Therefore, the derived $\gamma(\text{N}_2\text{O}_5)$ is a lower limit range of $0.2–3.6 \times 10^{-3}$.

Montane smoke plumes included several cross-wind transects downwind, which allows for $\gamma(\text{N}_2\text{O}_5)$ and $\varphi(\text{CINO}_2)$ determination in individual plumes using a constrained 0-D chemical box model (Decker, Robinson, et al., 2021) (see Text S1.3 in Supporting Information S1). Model input values of $\gamma(\text{N}_2\text{O}_5)$ were varied between $10^{-4}$ and $10^{-1}$ to minimize the difference between the model and observations of N₂O₅. The modeled N₂O₅ is sensitive to NO₃⁻ loss to reactions with VOCs. The model uses VOC emissions from laboratory burn emissions inventories, and these are also varied to improve the agreement between modeled and observed N₂O₅. A comparison of modeled and observed VOCs shows that the majority of the observation-model comparisons remain within the observation uncertainty. Lastly, $\varphi(\text{CINO}_2)$ is varied between 0 and 1. Figures S7–S12 in Supporting Information S1 show complete model and observation comparisons.

Figure 3c shows a representative model to observation comparison for N₂O₅ and ClNO₂. In all model runs, a $\gamma(\text{N}_2\text{O}_5)$ of $10^{-4}$ (one order of magnitude precision, see Figure S7 in Supporting Information S1) best reproduces N₂O₅ observations. In these five cases, values of $\gamma(\text{N}_2\text{O}_5) \ge 10^{-3}$ cannot recreate the N₂O₅ observations without near or complete removal of VOCs, and values of $\gamma(\text{N}_2\text{O}_5) < 10^{-4}$ require $\varphi(\text{CINO}_2) > 1$ to reproduce ClNO₂.

The box model derived $\varphi(\text{CINO}_2)$ ranges from 0.18 to 0.80 but spans the entire 0–1 range when considering the ClNO₂ observational uncertainty (Figure S13 in Supporting Information S1). The average model-derived $\varphi(\text{CINO}_2)$ is 0.45 (Figure 3d, black marker). The average of transect-center-parameterized $\varphi(\text{CINO}_2)$ is 0.65, similar to the average of all parameterized $\varphi(\text{CINO}_2)$ of 0.62. Parameterized $\varphi(\text{CINO}_2)$ exceeds the box model, similar to previous field derivations (McDuffie et al., 2018a), although >90% of parameterized $\varphi(\text{CINO}_2)$ lies within the box model derived range (Figure 3d). The derived $\varphi(\text{CINO}_2)$ of agricultural smoke is generally greater than montane smoke, consistent with the greater pCl⁻ in the former. Plots of $\varphi(\text{CINO}_2)$ against the OA:pCl⁻ or O:C ratio do not show clear dependences for the small number of determinations (Figure S14 in Supporting Information S1).

Values of $\gamma(\text{N}_2\text{O}_5)$ derived here are smaller than values determined in urban air ($\gamma(\text{N}_2\text{O}_5) 10^{-3}–10^{-1}$) (Brown & Stutz, 2012; McDuffie et al., 2018b) and comparable to or lower than a limited number of laboratory studies. A chamber study of pyrogenic aerosol for a wire grass fuel ($2.8–6 \times 0.6 \times 10^{-3}$) and a long leaf pine needle fuel ($2.5–3.2 \times 0.4 \times 10^{-3}$) (Goldberger et al., 2019) are similar to our agricultural fuels result. A flow-tube study of pyrogenic aerosol identified an increase of $\gamma(\text{N}_2\text{O}_5)$ for high-chloride-containing BB fuels at relative humidity (RH) >80% (Jahl et al., 2021). This is similar to the average RH (70%) for the agricultural smoke plumes here (Figure S15 in Supporting Information S1) and consistent with the observation of greater pCl⁻ (Figure S5 in Supporting Information S1) and larger $\gamma(\text{N}_2\text{O}_5)$ values (Figure 3) compared to montane smoke.

### 3.3. PyroCB Processed Smoke

The DC-8 sampled a PyroCB event produced from the Williams Flats fire on August 8 that reached 6–10 km above sea level, or 5.6 to 1.6 km below the mean tropopause height. Three visually distinct plumes were observed, and therefore we separate our analysis by plume number and transect number defined by Peterson et al. (2022).
<figure><img src="image_6.png" /><figcaption>**Figure 4.** NASA DC-8 observations in a PyroCB on Aug 8 5.6 to 1.6 km below the mean tropopause height. Panels (a and b) show plume 2 observations only (transects 4–7). (a and c) Observations of N₂O₅ (red), CINO₂ (yellow) and P(NO₃) (blue), CO (gray), pCl⁻ (pink), and jNO₃ (black). (b and d) NEMRs of N₂O₅ and CINO₂. Markers and colors indicate the plume number, and white numbers indicate the transect number. The thick black line indicates the limit of detection. E. Transect center observations of N₂O₅ (black) for plume 2 compared to the model N₂O₅ for a range (7 × 10⁻⁴–5 × 10⁻³) of γ(N₂O₅). (f) Transect center observations of CINO₂ (black) for plume 2 compared to the model-derived CINO₂. Solid color is the result of a φ(CINO₂) = 0.05 and a γ(N₂O₅) = 1–2 × 10⁻³ and the hashed area shows a range of possible φ(CINO₂).</figcaption></figure>

Observed P(NO₃) and N₂O₅ (Figure 4a) demonstrate the potential for heterogeneous chemistry in the PyroCB injection to the upper atmosphere. Calculated N₂O₅ NEMR increases with calculated physical plume age when separated by plume number (Figure 4b). Enhancement of pCl⁻ (Figure 4c) demonstrates the potential for CINO₂ production. However, observations of CINO₂ remained at or below the 1 Hz I⁻ CIMS detection limit of 0.05 pptv in Figures 4a and 4d, limiting the ability to quantify its production. Figure S16 in Supporting Information S1 shows that the CINO₂ signal within all PyroCB smoke observations (average ± 1-σ of 0.03 ± 0.10 pptv) is statistically significantly greater (*p* < 0.001) than signal outside of the plume (average ± 1-σ of 0.02 ± 0.06 pptv), but the data do not allow quantification of the amount of CINO₂ within the PyroCB.

Aerosol data are unavailable for plume 3, and plume 1 did not have sufficient semi-Lagrangian crosswind transects required to constrain the model. Therefore, the box model is used to derive γ(N₂O₅) and to place an upper limit on φ(CINO₂) for plume 2 only. The model derived γ(N₂O₅) = 0.7–5.0 × 10⁻³ (Figure 4e), which is a factor of 7–50 × greater than the γ(N₂O₅) values from plumes produced by the same fire but sampled in the lower troposphere.

The model predicts φ(CINO₂) < 0.05 to match observations at or below the detection limit (or 0.05 at the LoD), although φ(CINO₂) may be up to 0.23 within the 1-σ determined CINO₂ measurement uncertainty (15% + 0.05 pptv). The average parameterized φ(CINO₂) (0.53) is also lower than tropospheric smoke from the same fire (Figure S17 in Supporting Information S1) as a result of increased calculated liquid water fraction (LWF, Figure
<figure><img src="image_7.png" /><figcaption>**Figure 5.** (a) Comparison of the model-derived γ(N₂O₅) from FIREX-AQ and ATom. Markers show FIREX-AQ results, and the histograms show ATom BB-related γ(N₂O₅). The box and whisker plots show the ATom BB-related (brown) and all of the ATom (gray) results from the UTLS. (b) Log γ(N₂O₅) versus aerosol sulfate fraction for FIREX-AQ and ATom. (c) γ(N₂O₅) parametrization from Burkholder et al. (2020) for aqueous sulfate particles used in stratospheric models compared to results from this work. Marker size represents the interquartile range of temperature and sulfate fraction in this work.</figcaption></figure>

S18 in Supporting Information S1) in the PyroCB. The presence of sufficient pCl⁻ for average parameterized φ(CINO₂) > 0.5 suggests that CINO₂ production may occur in PyroCB transported smoke, even if it was observed only at the detection limit in this daytime flight.

### 3.4. Aged UTLS Pyrogenic Aerosol

Observations from the ATom campaign provide N₂O₅ observations in the UTLS. We separate our analysis into pyrogenic-influenced and background. Air parcels with >75% of aerosol number concentration containing pyrogenic markers (see SI) are defined as pyrogenic-influenced while all others are considered background. The pyrogenic aerosol is estimated to have a physical age of >15 days. Unlike FIREX-AQ, during ATom the DC-8 did not conduct targeted sampling of plumes to constrain a semi-Lagrangian box model. Instead, a diel model built on the framework of previous model determinations of γ(N₂O₅) in the lower troposphere (McDuffie et al., 2018b) is constrained to chemical observations (see Text S1.3 in Supporting Information S1).

The diel model predicts the median γ(N₂O₅) from all background UTLS samples (N = 3,483) is 2.9 × 10⁻² as shown in Figure 5a (gray box and whiskers). The pyrogenic-influenced aerosol has a median γ(N₂O₅) of 1.7 × 10⁻² (Figure 4a, brown) which is significantly different (*p* < 0.001) than the background aerosol. We also consider a smaller subset of pyrogenic influenced aerosol from ATom previously identified by Katich et al. (2023) to have originated from PyroCB influence. The resulting γ(N₂O₅) of 2.5 × 10⁻² is significantly (*p* = 0.01) less than background UTLS aerosol, and greater than our selection of pyrogenic influenced aerosol (Figure S19 in Supporting Information S1). Overall, the model predicts that pyrogenic aerosol has a lower rate of N₂O₅ uptake than background UTLS aerosol, yet substantially greater than pyrogenic aerosol in young tropospheric plumes.

The differences in γ(N₂O₅) across agricultural, montane, PyroCb, and UTLS data are associated with increased aerosol sulfate fraction. Figure 5b shows a positive trend in log(γ(N₂O₅)) as a function of aerosol sulfate fraction distribution. The median sulfate fraction was 1%, 5%, 9%, and 42% in recently-emitted montane, PyroCB, agricultural and aged stratospheric BB aerosol, respectively. Laboratory studies suggest organic coatings inhibit N₂O₅ uptake, which is generally dependent on the organic layer composition and RH (Gaston et al., 2014). Conversely, increasing sulfate fraction is associated

with increasing γ(N₂O₅), potentially due to increased aerosol hygroscopicity (McDuffie et al., 2018b). Sulfate in tropospheric BB plumes arises from oxidation of pyrogenic SO₂ (Rickly et al., 2022), whereas pyrogenic-influenced aerosol in the UTLS takes up sulfate during aging.

Current stratospheric models of BB impacts on stratospheric processes (Strahan et al., 2022; Yu et al., 2021) use a γ(N₂O₅) based on purely aqueous sulfate aerosol. However, Figure 5c shows γ(N₂O₅) values from BB influenced aerosol are a factor of 10–100 lower than pure aqueous sulfate particles (Burkholder et al., 2020). BB particles are expected to condense organics from low-volatility VOC oxidation products (Palm et al., 2020), forming organic layers that will likely reduce γ(N₂O₅) relative to pure sulfate aerosol. Indeed, evidence of organic markers on stratospheric aerosol was found in some studies of stratospheric BB aerosol (Bernath et al., 2022; Katich et al., 2023), and BB aerosol markers are used here, by definition, to separate BB aerosol from background aerosol.

The results here indicate γ(N₂O₅) values increase for BB particles transported from the troposphere into the UTLS, but never reach values used in stratospheric models. Some of this transport occurs through PyroCB events. Injection of the chloride-containing aerosol observed in montane smoke or repartitioning of gas phase HCl to particulate organics or reduced nitrogen (Solomon et al., 2023) may result in a non-zero CINO₂ yield, thus introducing chlorine activation pathways currently not considered. Observations presented here cannot quantify
CINO₂ production on BB particles transported through a PyroCB but demonstrate potential for this process. Observations of diffuse BB influenced particles in the UTLS from ATom do not have reliable CINO₂ measurements, such that we are unable to assess CINO₂ production on aged, dilute UTLS BB influenced particles. Concentrated BB plumes transported to the stratosphere through PyroCB events, such as the 2020 Australian fires, should have heterogeneous chemistry similar to that observed here. Recent analysis of high-altitude aircraft data suggest a ubiquitous influence of such events on stratospheric aerosol composition (Katich et al., 2023).

## 4. Conclusions

These results have implications for heterogeneous reactions on smoke aerosol and for smoke injection to the upper atmosphere.

Uptake coefficients for N₂O₅ determined from in situ observations are lower on BB aerosol than current model parameterizations. Figure 1 illustrates the observed trends in uptake coefficients from the lower to the upper atmosphere. The γ(N₂O₅) on dilute smoke-impacted particles derived in this study is already lower than model parameterizations but likely represents an upper limit for more concentrated smoke such as the 2020 Australian wildfires. We therefore suggest that models of the smoke impact to the UTLS will require revised parameterizations with reduced uptake coefficients.

The recent results of Solomon et al. (2023) shows that chloride uptake by the organic phase of smoke aerosol increases heterogeneous reaction rates of halogen-containing species, thereby activating chlorine radicals that participate in ozone destruction cycles. Our results demonstrate that N₂O₅ uptake on chloride-containing smoke particles produces CINO₂ in the lower atmosphere and has the potential to do so in the upper atmosphere. We suggest that CINO₂ formation from N₂O₅ uptake on smoke particles injected into the stratosphere during large PyroCB events may be a component of smoke-induced halogen activation cycles that influence stratospheric ozone.

## Data Availability Statement

The aircraft data used in the study are publicly available at https://www-air.larc.nasa.gov/missions/firex-aq/.

## Acknowledgments

We thank Charles A. Brock for particle sizing data from ATom. We thank William H. Brune and Alexander B. Thames for OH/HO₂ data from ATom. We thank Daniel M. Murphy for PALMS particle type data from ATom. ZCJD, GAN, KA, IB, PCJ, MMC, DAD, AF, JLJ, DP, JP, PR, MAR, CRT, CW, and CCW, were supported by the NOAA cooperative agreement NA17OAR4320101. PCJ, HG, BAN, DP, DAD, and JLJ acknowledge support from NASA Earth Sciences Division (Grants NNX15AH33A, 80NSSC18K0630, and 80NSSC21K1451). C.D.F., B.B.P., and J. A.T. acknowledge support from the NOAA OAR Climate Program Office (Grant NA17OAR4310012) CDF acknowledges support from the Future Investigators in NASA Earth and Space Science and Technology (FINESST) Grant 80NSSC20K1612. SRH and KU acknowledge support from NASA Earth Sciences Division (Grants 80NSSC18K0638 and NNX15AG71A). Participation in ATom Mission flights by G.P.S., K.D.F., and D.M.M. was supported by NOAA climate funding (no. NNH15AB12I).

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