Ozone chemistry in western U.S. wildfire plumes

et al

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, eabl3648 (2021) 8 December 2021

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ATMOSPHERIC SCIENCE

Ozone chemistry in

western U.S. wildfire plumes

Lu Xu

*†, John D.

Crounse

, Krystal T.

Vasquez

, Hannah

Allen

, Paul O.

Wennberg

1,3

Ilann Bourgeois

4,5

, Steven S.

Brown

4,6

, Pedro

Campuzano-Jost

5,6

, Matthew M.

Coggon

4,5

James H.

Crawford

, Joshua P.

DiGangi

, Glenn S.

Diskin

, Alan

Fried

, Emily M.

Gargulinski

Jessica B.

Gilman

, Georgios I.

Gkatzelis

4,5

‡, Hongyu

Guo

5,6

, Johnathan W.

Hair

, Samuel R.

Hall

Hannah A.

Halliday

§, Thomas F.

Hanisco

, Reem A.

Hannun

11,12

, Christopher D.

Holmes

L. Gregory Huey

, Jose L.

Jimenez

5,6

, Aaron

Lamplugh

4,5

, Young Ro

Lee

, Jin

Liao

11,15

Jakob Lindaas

||, J. Andrew Neuman

4,5

, John B.

Nowak

, Jeff

Peischl

4,5

, David A.

Peterson

Felix Piel

18,19,20

, Dirk

Richter

, Pamela S.

Rickly

4,5

, Michael A.

Robinson

4,5,6

, Andrew W.

Rollins

Thomas B.

Ryerson

¶, Kanako

Sekimoto

, Vanessa

Selimovic

, Taylor

Shingler

Amber J.

Soja

7,9

, Jason M.

St.

Clair

11,12

, David J.

Tanner

, Kirk

Ullmann

, Patrick R.

Veres

James Walega

, Carsten

Warneke

, Rebecca A.

Washenfelder

, Petter

Weibring

Armin Wisthaler

18,20

, Glenn M.

Wolfe

11,12

, Caroline C.

Womack

4,5

, Robert J.

Yokelson

Wildfires are a substantial but poorly quantified source of tropospheric ozone (O

). Here, to investigate the highly

variable O

chemistry in wildfire plumes, we exploit the in situ chemical characterization of western wildfires

during the FIREX-AQ flight campaign and show that O

production can be predicted as a function of experimen-

tally constrained OH exposure, volatile organic compound (VOC) reactivity, and the fate of peroxy radicals. The O

chemistry exhibits rapid transition in chemical regimes. Within a few daylight hours, the O

formation substantially

slows and is largely limited by the abundance of nitrogen oxides (NO

x

). This finding supports previous observations

that O

formation is enhanced when VOC-rich wildfire smoke mixes into NO

x

-rich urban plumes, thereby deteriorating

urban air quality. Last, we relate O

chemistry to the underlying fire characteristics, enabling a more accurate repre

sentation of wildfire chemistry in atmospheric models that are used to study air quality and predict climate.

INTRODUCTION

Wildfires emit large quantities of reactive trace species to the atmo-

sphere, including primary pollutants, as well as precursors for the

production of O

and particulate matter (

). The number and size

of wildfires are predicted to increase as a result of historical fire sup-

pression practices and ongoing climate change (

). This threatens to

offset some of the improvements in air quality in the United States

over the past few decades, particularly during fire season (

formation depends on the mix of initial emissions and the

postemission atmospheric processing, both of which are highly

variable (Fig. 1). As a result, O

formation observed in previous field

studies exhibits substantial fire-to-fire variability (

). Numerous

studies have investigated O

chemistry in wildfire plumes using

atmospheric models of different dynamical and chemical complexity

(

–

), but accurate simulation of wildfire chemistry has proved

challenging. Several hypotheses have been proposed to explain the

model deficiencies, such as uncertain emission inventories, inaccu-

rate description of oxidation chemistry, and difficulties in modeling

plume dispersion. O

production from wildfire emissions remains

as a major uncertainty in assessing the tropospheric O

burden (

The in situ observations of a suite of trace species made during

the Fire Influence on Regional to Global Environments and Air

Quality (FIREX-AQ) campaign (Supplementary Materials, section S1)

enable a detailed diagnosis of key variables controlling O

forma-

tion, including oxidant sources, volatile organic compound (VOC)

emissions, and the chemistry of NO

and peroxy radicals (RO

; the sum

of hydroperoxy radical and organic peroxy radical) (Fig. 1). These

variables depend on fire conditions, undergo rapid transitions in

Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA.

Division of Chemistry and Chemical Engineering, California Insti-

tute of Technology, Pasadena, CA, USA.

Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA.

NOAA Chemical Sciences

Laboratory, Boulder, CO, USA.

Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USA.

Department of Chemistry,

University of Colorado Boulder, Boulder, CO, USA.

NASA Langley Research Center, Hampton, VA, USA.

Institute of Arctic and Alpine Research, University of Colorado

Boulder, Boulder, CO, USA.

National Institute of Aerospace, Hampton, VA, USA.

Atmospheric Chemistry Observations & Modeling Laboratory, National Center for Atmo-

spheric Research, Boulder, CO, USA.

Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA.

Joint Center for Earth

Systems Technology, University of Maryland, Baltimore County, Baltimore, MD, USA.

Department of Earth, Ocean, and Atmospheric Science, Florida State University,

Tallahassee, FL, USA.

School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA.

Universities Space Research Association, Columbia,

MD, USA.

Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA.

U.S.

Naval Research Laboratory, Monterey, CA, USA.

Department of

Chemistry, University of Oslo, Oslo, Norway.

IONICON Analytik GmbH, Innsbruck, Austria.

Institut für Ionenphysik und Angewandte Physik, Universität Innsbruck,

Innsbruck, Austria.

Graduate School of Nanobioscience, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama, Kanagawa, Japan.

Department of Chemistry

and Biochemistry, University of Montana, Missoula, MT, USA.

*Corresponding author. Email: lu.xu@noaa.gov (L.X.); wennberg@caltech.edu (P.O.W.)

†Present address: NOAA Chemical Sciences Laboratory, Boulder, CO, USA, and Cooperative Institute for Research in Environmental Sciences, University of Colorado,

Boulder, CO, USA.

‡Present address: Institute for Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich GmbH, Jülich, Germany.

§Present address: U.S.

Environmental Protection Agency, Research Triangle Park, NC, USA.

||AGI/AAAS Congressional Science Fellow.

¶Present address: Scientific Aviation, Boulder, CO, USA.

The Authors, some

rights reserved;

exclusive licensee

American Association

for the Advancement

of Science. No claim to

original U.S. Government

Works. Distributed

under a Creative

Commons Attribution

NonCommercial

License 4.0 (CC BY-NC).

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chemical regimes, and hence profoundly influence the O

chemistry

during smoke transport. Building upon our systematic evaluation

of O

chemistry, we provide a parameterization to estimate the O

formation from temperate wildfires.

During FIREX-AQ, the NASA DC-8 aircraft sampled fires rep-

resentative of those in the major ecosystems in the western United

States in July and August 2019. Figure 2B shows one example flight

track that involves multiple crosswind transects of a fire plume at

different distances downwind. Previous analyses of aircraft-based

observations typically studied the plume evolution in a pseudo-

Lagrangian framework. Such analysis is often complicated by the fact

that fire conditions change over time and by aircraft navigation ar-

tifacts, such as missing the dense plume in some crosswind tran-

sects (Supplementary Materials, section S1). Here, to investigate the

chemistry in a way that mitigates some of the challenges associated

with fluctuations in fire emissions, we apply single transect analysis

(STA) that examines the differences in the plume composition across

each crosswind transect. Because of the high aerosol optical extinc-

tion in the center of large smoke plumes, the center experiences

substantially lower actinic flux and photolysis rates than the edges

at a given altitude. This provides a different extent of photochemical

processing and, in particular, a range of time-integrated exposure of

emissions to hydroxyl radicals (i.e., OH exposure) between the plume

center and edges (Fig. 2A as an example). Since a single transect

samples smoke emitted at similar times, the assumption of stationary

fire conditions is often better satisfied in STA than traditional pseudo-

Lagrangian analysis. Spatial variability in fire emissions and complex

plume structure can still complicate the STA, so transects suitable

for the STA are scrutinized by a set of stringent criteria (Supple-

mentary Materials, section S4).

The STA is combined with a conceptual model (fig. S13 and

Supplementary Materials, section S5) to investigate the daytime

118.6

118.4

118.2

118

117.8

117.6

Longitude

47.6

47.7

47.8

47.9

48.1

48.2

48.3

Latitude

Williams

Flats

Fire

B

0.5

1.5

2.5

OH exposure (

molecules cm

012

OH exposure

(

molecules cm

47.85

47.9

47.95

48.05

48.1

0.02

0.04

x

CO (ppb/ppb)

47.85

47.9

47.95

48.05

48.1

Latitude

A

x

OH exposure

Wind direction

Fig. 2. Single transect analysis (STA) examines the differences in plume composition across individual transects of the wildfire plumes.

In (

), the flight track on

3 August 2019 is colored by OH exposure, which is lower in plume center than edges, as a result of high aerosol optical extinction in plume center. In (

), the dilution-corrected

formation (i.e.,



CO) is illustrated in one near-field transect.

Fig. 1. Simplified scheme to illustrate the factors influencing O

formation in wildfire plumes.

Wildfires emit oxidant precursors, NO

, and an enormous diversity of

VOCs. In the near field, OH produced via photolysis of HONO initiates VOC oxidation, which proceeds in the presence of NO

and leads to efficient O

formation. After a

few hours, the HONO has been consumed and NO

has been both diluted sufficiently and converted to PANs and

−

such that the O

formation slows by several orders

of magnitude. In this simplified scheme, the width of arrows having the same color represents the relative importance of competing pathways.

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chemical closure of odd oxygen [O

= O

+ NO

+ HNO

+ particulate

nitrate + peroxyacylnitrates (PANs)]. O

accounts for the inter-

conversion between O

and other O

species (

). The instanta-

neous production rate of O

can be expressed by the product of

three terms: VOC reactivity (VOCR), OH concentration, and the

fraction of peroxy radicals that react with NO (

RO2 + NO

) (i.e., Eq. 1).

VOCR is a condensed parameter summarizing several properties of

individual VOCs (Eq.2), including the VOC concentration ([VOC

]),

the reaction rate coefficient of the VOC with OH (

OH + VOC

), the

number of peroxy radicals produced from the oxidization of each

VOC

molecule to its first-generation closed-shell products (



), and

the alkylnitrate branching fraction of the VOC

-derived RO

+ NO

reaction (



). More details about VOCR are described in the Supple-

mentary Materials, section S7.

Integrating Eq. 1 from the fresh (i.e., lowest OH exposure) to the

aged portion (i.e., highest OH exposure) across each plume transect

(i.e., Eq. 3) reflects the predicted O

formation based on the obser-

vationally constrained VOCR, OH exposure, and RO

chemistry.

To account for dilution and background contributions, excess mix-

ing ratios (i.e., the difference between smoke and background air,

denoted as



in Eq. 3) were normalized to



[CO], which is a stable

plume tracer. The predicted O

production can be compared to the

direct measurement of the same transect (i.e., left hand side of Eq. 3),

providing a diagnostic of chemical closure, enabling constraints on

the sources and sinks of O

. This analysis is denoted as O

chemical

closure analysis.

[

]

─

VOCR

· [OH

] ·

+NO

(1)

VOCR =

∑

OH+VO

· [VO

] ·



· (1

−



)

(2)

(



[

]

─



[ CO]

)

aged

−

(



[

]

─



[ CO]

)

fresh

∫

fresh

aged

VOC R

[OH]

─



[CO]

[OH]

+NO

([OH ]

)

(3)

RESULTS

Variables influencing O

formation

OH exposure

The OH exposure is estimated from the observed ratio of phenol to

benzene (eq. S6 and Supplementary Materials, section S3), both of

which are emitted in high yields in wildfires. Phenol reacts with OH

∼

20 times faster than benzene, so their ratio serves as a measurement

of photochemical processing in the absence of other substantial sinks

or sources. The OH exposure is highly correlated with the nitrous

acid (HONO) loss. Figure 3A shows the measurements on the

3 August 2019 flight as an example. Before 90% of HONO is lost,

the OH exposure correlates with the lost HONO whose photolysis

accounts for >50% of the total HO

production rate (Supplementary

Materials, section S3). HONO photolysis is thus a critical OH source

in wildfire plumes, consistent with a recent study by Peng

et al

. (

After HONO is depleted, the OH exposure continues to increase

because of the photolysis of O

and aldehydes, albeit at a much

slower rate, indicating lower [OH] (figs. S5 and S6).

VOC reactivity

The approximately 80 quantified VOCs are classified into seven

structural categories. Figure 3B shows the relative contribution to

total VOCR of each category averaged from transects included in

the O

chemical closure analysis. On average, oxygenated VOCs

(OVOCs) are the largest contributor, together accounting for about

one-third of VOCR.

The OVOCs are predominantly small aldehydes,

including formaldehyde and acetaldehyde (fig. S21). Alkanes and

alkenes are the second largest contributors to VOCR.

The historically

overlooked furans also play an important role in wildfire plumes,

contributing about one-fifth of VOCR, consistent with recent find-

ings from lab studies (

). While oxygenated aromatics, primarily

guaiacol, catechol, and creosols, account for only one-tenth of total

VOCR, their oxidation contributes a much larger fraction of the

secondary organic aerosol (SOA) formed [

∼

60% as found in (

)].

The relative importance of each VOC category to total VOCR

changes with OH exposure. An example transect is shown in fig. S22.

Many of the primary emissions, including alkenes, furans, and

oxygenated aromatics, are rapidly oxidized, and their importance

0.2

0.4

0.60

f

HONO,lost

0.5

1.5

2.5

OH exposure

(

molecules cm

A

0.2

0.4

0.6

0.8

HONO

Linear fit (

= 0.41)

B

OVOCs

36%

Alkanes/alkenes

23%

Furans

20%

Oxygenated

aromatics

10%

Reduced aromatics 2%

Fig. 3. Production and fate of OH.

(

) shows that the OH exposure correlates with the amount of HONO loss [

HONO,lost

= 1 − (



HONO/



CO)/(



HONO/



CO)

max

] for the

3 August 2019 Williams Flats Fire. The correlation indicates that OH is produced mainly by HONO photolysis in the near field. The color represents the relative contribution

of HONO photolysis to total HO

production rate (denoted as

HONO

). (

) shows that OVOCs, alkanes/alkenes, and furans are the major contributors to total VOCR based

on the average of transects included in the O

chemical closure analysis.

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decreases with increasing OH exposure. In contrast, small aldehydes

have substantial secondary sources, and, as a result, their contribu-

tion to the total VOCR increases over time. The VOCR of longer

lived compounds, such as CO, remains relatively constant.

chemistry

is produced via the reaction of RO

with NO.

There are, howev-

er, a number of processes that can compete with this reaction. Thus,

to understand O

formation in wildfire plumes, knowing the RO

fate is critical. With direct measurements of organic hydroperoxides

(ROOH) and hydroxynitrates (RONO

) from the OH-initiated

oxidation of small alkenes (i.e., ethene and propene), we are able to

provide the first experimental constraint on RO

fate in wildfire

plumes. We probe the competition between RO

+ NO and RO

reactions and thereby estimate the fraction of RO

that reacts

with NO (

RO2 + NO

). Figure 4 shows the evolution of propene-

derived ROOH and RONO

in two transects with different NO levels.

In the transect shown in Fig. 4A, where [NO] is above 500 parts per

trillion by volume (pptv), only RONO

is produced, as the RO

NO reaction outruns the RO

+ HO

reaction. In the transect shown

in Fig. 4B, [NO] is below 500 pptv and reaches as low as 50 pptv. As

a result of the low [NO], both ROOH and RONO

are produced,

suggesting that RO

+ HO

and RO

+ NO reactions are competi-

tive. H

, which is a product of HO

+ HO

reaction, shows a similar

trend as ROOH in these two transects (fig. S24).

Measurement imprecision precludes the estimate of a pointwise

RO2 + NO

across each transect, so we apply Eq. 4 to calculate transect-

averaged

RO2 + NO

using the transect-integrated production of

RONO

(i.e.,

RONO2

; eq. S29) and ROOH (i.e.,

ROOH

; eq. S30).

RO2 + NO

is calculated from both ethene and propene systems, and

they are consistent within 10% (fig. S25). Figure 5A shows the evo-

lution of

RO2 + NO

for the Williams Flats Fire sampled on two different

days. On both days, the

RO2 + NO

decreases with downwind distance,

illustrating the transition of RO

fate from an RO

+ NO–dominated

regime to a mixed regime with increasing importance of RO

+ HO

The change rate of

RO2 + NO

varies between fires. On 7 August 2019,

the

RO2 + NO

decreases from 1 to 0.7 after the smoke travels from 25 to

100 km. On 3 August 2019, the

RO2 + NO

decreases more rapidly

with downwind distance, and it reaches

∼

60% at 45 km (estimated

transport time

∼

3 hours). Such difference is likely caused by fire

strength and fuel consumption. The fire on 7 August 2019 is the most

intense fire sampled during FIREX-AQ, with the fire radiative power

(FRP) up to 4.4 × 10

MW and 72.3 km

daily area burned. The fire

on 3 August 2019 has lower intensity (i.e., peak FRP

∼

1.5 × 10

MW) and

smaller daily burned area (43.2 km

). It takes more time for the NO

concentration in intense fires to decline to a level where RO

+ HO

reactions can become competitive. Note that over 90% of fires around

the world have FRP <100 MW (

), so that the transition of

RO2 + NO

can occur rapidly. More importantly, a large fraction of wildfire

VOCs is oxidized in the mixed regime. As shown in Fig. 5B, for both

fires,

∼

70% of the VOCR remains when

RO2 + NO

decreases to 0.6

+ NO

· [NO]

────────────────────

+ NO

· [NO

] +

· [H O

]

RON



RON

─

RON



RON

ROOH



ROOH

(4)

This regime transition is a result of [NO

] decrease, which is

caused primarily by dilution with ambient air and by chemical loss

of NO

. The major NO

oxidation products are PAN and nitrate

(

−

= HNO

+ particulate nitrate). Together, they account for

nearly all of NO

oxidation products, NO

(= NO

− NO

− HONO)

(fig. S27). The fractions of PAN and nitrate in total reactive oxidized

nitrogen (NO

) increase with OH exposure as a result of NO

con-

version (Fig. 6A), consistent with previous studies (

Because nitrate is a permanent NO

sink but PAN is a temporary

reservoir, the NO

loss pathways affect O

formation in the

long-range transport of wildfire plumes. To investigate the compe-

tition between NO

loss pathways, we use STA.



PAN/



CO and



CO correlation slopes (fig. S28) give the relative fraction of

Fig. 4. The measurements of ROOH and RONO

from propene oxidation are used to diagnose the RO

fate.

The ROOH is not produced in the transect with high [NO]

(

) but produced in the transect with low [NO] (

). The signals of both RONO

and ROOH are divided by the branching ratio of the corresponding RO

reaction (i.e.,



). The

ROOH signal is multiplied by a factor of 4 to be shown in the same scale as RONO

. The shaded area represents the 25th to 75th percentile. ppb, parts per billion.

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loss to PAN (denoted as

PAN

) as the smoke chemically evolved

from the photochemical condition in plume center to that in plume

edge across individual transects.

PAN

is different from



PAN/



as the latter is an accumulative property that depends on initial

emissions and the integral of NO

loss over time. Figure 6B shows

PAN

for each transect of several fires as a function of smoke age.

Despite fire-to-fire variability,

PAN

is 0.2 to 0.4 at a smoke age of

0.5 hour and rapidly increases to 0.8 to 1 at 2 hours. This trend sug-

gests that the major NO

oxidation product transitions from

−

to PAN after

∼

2 hours of transport.

This transition is mainly driven by the change in [CH

CHO]/[NO

which increases with smoke age (fig. S30) and reflects the fact that

is chemically lost to other NO

species, but CH

CHO has sub-

stantial production from VOC oxidation. Larger [CH

CHO]/[NO

]

favors the PAN formation by producing more acetyl peroxy radical

(Supplementary Materials, section S8). Therefore, fire conditions

that affect the [CH

CHO]/[NO

], or broadly the [VOCs]/[NO

], alter

the partitioning between NO

species and, as a result, downwind O

formation. Figure S33 shows that the plateau value of



PAN/



from different fires negatively correlates with the modified combustion

100

Downwind distance (km)

0.5

0.6

0.7

0.8

0.9

+ NO

A

3 August 2019

7 August 2019

01234

Fire radiative power (

MW)

0.6

0.70

VOCR/

max

VOCR/

0.5

0.6

0.7

0.8

0.9

+ NO

B

3 August 2019

7 August 2019

02468

CO (ppt/ppb)

Fig. 5. The RO

fate transitions from an RO

+ NO–dominated regime to a mixed regime with increasing importance of RO

+ HO

(

) The

RO2 + NO

decreases as

smoke transports in the William Flats Fire sampled on 2 different days. The data points are colored by the fire radiative power (FRP) measured at the estimated time of

smoke emission. (

) A large fraction of VOCs is oxidized in the mixed regime. The max



VOCR/



CO is represented by the average



VOCR/



CO of observations with the

top 1% [CO] during the fire sample. The downwind distance is estimated on the basis of the aircraft position and the burned area. The dashed lines are provided as a

visual aid. The ellipses represent the uncertainty range.

Fig. 6. The evolution of the partitioning of NO

species.

(

) shows measurements of the 3 August 2019 Williams Flats Fire. As smoke ages, the NO

and HONO emitted

from fires are converted to PAN and NO

−

. (

) shows that the fraction of NO

loss to PAN (

PAN

) across each transect increases with smoke age, which results from evolving

CHO/NO

as discussed in the text. Each data point represents one transect, and the transects from the same fire sampling patterns have the same color. The black line

is provided as a visual aid. The numbers in parentheses represent the index of a set of crosswind transects in a flight.

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efficiency (MCE). This observation is consistent with the finding from

STA that higher emission ratios of [CH

CHO]/[NO

] (associated

with lower MCE; fig. S38) favors NO

loss to PAN.

chemical closure analysis

We now return to the conceptual model (Eq.3) to test the chemical

closure of O

in wildfire plumes. The O

production (denoted as

) across each transect is predicted on the basis of the three key

chemical variables: OH exposure, VOCR, and RO

fate. Then, this

prediction is compared to the measured

calculated as a sum of

the measured individual O

species (Eq.3). On the basis of a set of

stringent criteria (Supplementary Materials, section S4), 25 transects,

for which the

and RO

fate can be quantified with high confi-

dence, are selected for this comparison. As shown in Fig. 7, the cor-

relation between the observed and predicted

is quite strong (

0.64). On average, the predicted

is higher than the measured

by 12%, well within the analysis and measurement uncertainties

(Supplementary Materials, section S9). Overall, the use of the con-

ceptual model and the comprehensive measurements of VOCs in

FIREX-AQ enables remarkably good prediction of O

production.

Such agreement suggests that the majority of VOCs contributing to

formation are quantified during FIREX-AQ, at least in the early

stage of the wildfire plumes. This provides confidence in the char-

acterization of fire emissions during FIREX-AQ, which will serve as

a foundation for future use in chemical transport models (CTMs).

Furthermore, as the conceptual model solely based on gas phase

chemistry is sufficient to account for the measured O

production

here, we suggest that the role of heterogeneous loss of O

and HO

is likely minor in wildfire plumes, a hypothesis often invoked when

models overpredict the measured O

(

Parameterization of the O

+ NO

production

The chemistry and dynamics described in this study occur on spa-

tial scales smaller than those used in even modestly high-resolution

CTMs. Thus, there is a need to parameterize the near-field chemistry

to properly capture the oxidation chemistry. Here, we focus on O

and NO

, as they are critical air pollutants. The production of O

and NO

across individual transects, which is represented by the

difference in



+ NO



CO between aged and fresh smoke, is

denoted as

+ NO

ranges from 0 to 0.06 and exhibits a

positive relationship with the span of OH exposure (



OH exposure)

across individual transects (

= 0.47; Fig. 8A). This trend implies more

+ NO

production as plumes age in the near field, consistent with

previous observations (

). In addition to OH exposure, the

+ NO

positively correlates with MCE (

= 0.23; Fig. 8B). Higher MCE

indicates more flaming combustion, which usually leads to higher

emissions and lower VOC emissions, together leading to a higher

/VOCR (

). The

+ NO

does increase with NO

/VOCR,

as shown in fig. S34. Overall, the positive relationship between

+ NO

and MCE suggests that the formation of O

+ NO

in fresh

wildfires in the western United States is generally NO

limited.

As the O

+ NO

formation depends on several variables, we de-

velop a statistical model based on multivariate adaptive regression

splines (

) to attribute such dependence (Supplementary Materials,

section S10). We examine the relationship between

+ NO

of each

transect and a number of variables (MCE,



OH exposure, VOCR,

/VOCR, and RO

fate) using stepwise forward selection. The

final model form is Eq. 5 [the units of

+ NO

and OH exposure

are parts per billion (ppb)/ppb and 10

molecules cm

−3

s, re-

spectively]. The model captures 56% of the measurement variance

(Fig. 8C)

× max(0,

MCE −

)

× (OH exposure)

0.0036 ± 0.0028;

0.46 ± 0.16

0.916 ± 0.002;

0.014 ± 0.0019

(5)

The terms

× max(0, MCE −

) in Eq. 5 are interpreted as

the MCE-dependent primary emission ratio (ER) of NO

to CO, i.e.,

ER(NO

), because O

+ NO

is essentially all NO

when there is no

chemical aging of fire emissions. To examine this interpretation, we

compare the field-derived ER(NO

) to that measured in the FIREX

FireLab 2016 study, where fuel complexes important for western

U.S. ecosystems were burned. Figure 8D compiles the ER(NO

)

from lab fuel types that are relevant to FIREX-AQ fires (table

S7). The empirical parameterization reasonably predicts the nearly

constant ER(NO

) when MCE is <0.92 and slightly overpredicts the

rising ER(NO

) as MCE increases above 0.92. One factor that com-

plicates this comparison is the fuel dependence of ER(NO

), which

shows larger variability as MCE increases. In comparison to indi

vidual fuel types (fig. S36), the empirical parameterization reason-

ably predicts the ER(NO

) of douglas fir, Engelmann spruce, and

subalpine fir, but slightly overpredicts for fuels like ponderosa pine

and manzanita. Among all 253 transects in FIREX-AQ, more than

90% of transects have MCE less than 0.92 (fig. S2), a range where the

field-derived parameterization performs accurately, and the ER(NO

)

is largely independent of fuel type (fig. S36). Therefore, this field-derived

parameterization is a reasonable approximation of the subgrid scale

+ NO

production for CTMs without an accurate emissions in-

ventory and fuel characteristics.

The other term in Eq. 5 (

× OH exposure) is interpreted as the

+ NO

formation during plume aging. This linear dependence of

+ NO

production on OH exposure is likely confined to the near

field of wildfire plumes (i.e., maximum OH exposure used to con-

strain the parameterization is 2.5×10

molecules cm

−3

s, which is

roughly 7 hours transport time) before the RO

chemistry transitions

.020

.04

0.06

0.08

0.1

0.02

0.04

0.06

0.08

0.1

1:1

1:1.5

1.5:1

1:2

2:1

Each transect

York fit

0.5

1.5

2.5

3.5

Transect median VOCR/NO

(

molecule

)

Slope = 1.12

0.26

Intercept = 0.001

0.01

= 0.64

Fig. 7. The predicted and measured O

production show reasonable agreement.

The ellipses represent the uncertainty range (Supplementary Materials, section S9).

The slope and intercepts are obtained from a York fit.

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et al

Sci. Adv.

7

, eabl3648 (2021) 8 December 2021

SCIENCE ADVANCES

|

RESEARCH ARTICLE

7 of 10

to HO

-dominated reactivity. We compile the literature values of



CO from boreal and temperate wildfires over a wide range of

plume ages in fig. S35 and find that the aircraft-based observations



CO in the free troposphere typically reach a maximum

value of 0.1 at 3 to 5 days downwind, which is only about twice the

value after 7 hours of aging observed in this study. The



CO is

relatively constant afterward and even shows a decreasing trend in

some plumes that are

∼

10 days old. This observation suggests that

the major fraction of O

in wildfire plumes in the free troposphere

is produced in the near field, consistent with the analysis above that

the wildfire plumes quickly run out of NO

and then the reaction of

with RO

efficiently competes with NO.

DISCUSSION

Uncertainties in emissions characterization and oxidation chemistry

are long-standing challenges in understanding O

production in

wildfire plumes. The agreement between the measured and predicted

production in this study indicates that the oxidation of VOCs

has been accurately captured by the comprehensive suite of analytical

instruments deployed here. This chemical closure provides confidence

in diagnosing the key chemical variables influencing O

formation.

These variables undergo rapid transition in chemical regimes. HONO

photolysis is the major source of OH in the near field. Once the

primary HONO is consumed, the rate of photochemistry in the plume

decreases quickly. O

formation also slows because of the changing

fate of RO

radicals. Given the high VOC/NO

produced in the fire,

the RO

fate transitions within a few hours from an RO

+ NO–

dominated regime to a mixed regime with increasing importance of

the RO

+ HO

reaction. A large fraction of VOCs is oxidized in the

mixed regime. The changing RO

fate affects not only O

formation

but also SOA formation. To estimate SOA formation in wildfire plumes,

previous studies have used high NO

SOA yields from chamber ex-

periments (

). The SOA yields of aromatics, which are critical

SOA precursors in wildfire plumes, are generally higher under low

condition than high NO

condition (

). Therefore, the

estimated SOA formation in some previous studies may be biased

low if the rapid transition to low NO

chemistry is not represented

accurately.

The O

chemistry in temperate wildfire emissions is generally in

the NO

-limited regime. Thus, fire conditions that influence the NO

emissions and sinks critically determine the O

formation. Wildfires

with higher MCE have higher emission ratios of HONO and NO

which tend to increase O

formation. On the other hand, higher MCE

CD

0.5

1.5

OH exposure (

molecules cm

0.02

0.04

0.06

0.08

+ NO

(ppb/ppb)

A

= 0.47

0.85

0.9

0.95

MCE

0.85

0.9

0.95

MCE

0.02

0.04

0.06

0.08

+ NO

(ppb/ppb)

= 0.23

B

0.5

OH exposure

Fig. 8. Parameterization of the O

+ NO

production.

The measured production of O

+ NO

) across individual transects exhibits positive correlation with the

span of OH exposure (



OH exposure) and MCE, as shown in (

) and (

), respectively. Thirty-nine transects are selected for this analysis (Supplementary Materials, section S4).

(

) Comparison between predicted and measured P

+ NO

for individual transects. (

) The emission ratios (ERs) of NO

to CO derived from the field [i.e.,

×max(0,MCE-

)]

and measured in the 2016 FIREX FireLab are plotted as a function of MCE.

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