Organic nitrate chemistry and its implications for nitrogen budgets in an isoprene- and monoterpene-rich atmosphere: constraints from aircraft (SEAC4RS) and ground-based (SOAS) observations in the Southeast US

Organic nitrate chemistry and its implications for nitrogen

budgets in an isoprene- and monoterpene-rich atmosphere:

constraints from aircraft (SEAC

RS) and ground-based (SOAS)

observations in the Southeast US

J. A. Fisher

1,2

D. J. Jacob

3,4

K. R. Travis

P. S. Kim

E. A. Marais

C. Chan Miller

K. Yu

L. Zhu

R. M. Yantosca

M. P. Sulprizio

J. Mao

5,6

P. O. Wennberg

7,8

J. D. Crounse

A. P.

Teng

T. B. Nguyen

7,a

J. M. St. Clair

7,b

R. C. Cohen

9,10

P. Romer

B. A. Nault

10,c

P. J.

Wooldridge

J. L. Jimenez

11,12

P. Campuzano-Jost

11,12

D. A. Day

11,12

W. Hu

11,12

P. B.

Shepson

13,14

F. Xiong

D. R. Blake

A. H. Goldstein

16,17

P. K. Misztal

T. F. Hanisco

G. M. Wolfe

18,19

T. B. Ryerson

A. Wisthaler

21,22

, and

T. Mikoviny

Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong,

NSW, Australia

School of Earth and Environmental Sciences, University of Wollongong, Wollongong, NSW,

Australia

Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University,

Cambridge, MA, USA

Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA

Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA

Geophysical Fluid Dynamics Laboratory/National Oceanic and Atmospheric Administration,

Princeton, NJ, USA

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

USA

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

USA

Department of Chemistry, University of California at Berkeley, Berkeley, CA, USA

Department of Earth and Planetary Science, University of California at Berkeley, Berkeley, CA,

USA

Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA

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

CO, USA

Department of Chemistry, Purdue University, West Lafayette, IN, USA

Correspondence to: J.A. Fisher (jennyf@uow.edu.au).

NASA Public Access

Author manuscript

Atmos Chem Phys

. Author manuscript; available in PMC 2018 April 19.

Published in final edited form as:

Atmos Chem Phys

. 2016 ; 16(9): 5969–5991. doi:10.5194/acp-16-5969-2016.

NASA Author Manuscript

Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette,

IN, USA

Department of Chemistry, University of California Irvine, Irvine, CA, USA

Department of Environmental Science, Policy, and Management, University of California at

Berkeley, Berkeley, CA, USA

Department of Civil and Environmental Engineering, University of California at Berkeley,

Berkeley, CA, 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

Chemical Sciences Division, Earth System Research Lab, National Oceanic and Atmospheric

Administration, Boulder, CO, USA

Department of Chemistry, University of Oslo, Oslo, Norway

Institute for Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, Austria

Now at Department of Environmental Toxicology, University of California at Davis, Davis, CA,

USA

Now at Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center,

Greenbelt, MD, USA and Joint Center for Earth Systems Technology, University of Maryland

Baltimore County, Baltimore, MD, USA

Now at Department of Chemistry and Biochemistry and Cooperative Institute for Research in

Environmental Sciences, University of Colorado, Boulder, CO, USA

Abstract

Formation of organic nitrates (RONO

) during oxidation of biogenic volatile organic compounds

(BVOCs: isoprene, monoterpenes) is a significant loss pathway for atmospheric nitrogen oxide

radicals (NO

), but the chemistry of RONO

formation and degradation remains uncertain. Here

we implement a new BVOC oxidation mechanism (including updated isoprene chemistry, new

monoterpene chemistry, and particle uptake of RONO

) in the GEOS-Chem global chemical

transport model with

∼

25 × 25 km

resolution over North America. We evaluate the model using

aircraft (SEAC

RS) and ground-based (SOAS) observations of NO

, BVOCs, and RONO

from

the Southeast US in summer 2013. The updated simulation successfully reproduces the

concentrations of individual gas- and particle-phase RONO

species measured during the

campaigns. Gas-phase isoprene nitrates account for 25-50% of observed RONO

in surface air,

and we find that another 10% is contributed by gas-phase monoterpene nitrates. Observations in

the free troposphere show an important contribution from long-lived nitrates derived from

anthropogenic VOCs. During both campaigns, at least 10% of observed boundary layer RONO

were in the particle phase. We find that aerosol uptake followed by hydrolysis to HNO

accounts

for 60% of simulated gas-phase RONO

loss in the boundary layer. Other losses are 20% by

photolysis to recycle NO

and 15% by dry deposition. RONO

production accounts for 20% of the

Fisher et al.

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NASA Author Manuscript

net regional NO

sink in the Southeast US in summer, limited by the spatial segregation between

BVOC and NO

emissions. This segregation implies that RONO

production will remain a minor

sink for NO

in the Southeast US in the future even as NO

emissions continue to decline.

1 Introduction

Nitrogen oxide radicals (NO

≡

NO + NO

) are critical in controlling tropospheric ozone

production (

Monks et al., 2015

, and references therein) and influencing aerosol formation

(

Rollins et al., 2012

;

Ayres et al., 2015

;

Xu et al., 2015

), with indirect impacts on

atmospheric oxidation capacity, air quality, climate forcing, and ecosystem health. The

ability of NO

to influence ozone and aerosol budgets is tied to its atmospheric fate. In

continental regions, a significant loss pathway for NO

is reaction with peroxy radicals

derived from biogenic volatile organic compounds (BVOCs) to form organic nitrates (

Liang

et al., 1998

;

Browne and Cohen, 2012

). NO

loss to organic nitrate formation is predicted to

become increasingly important as NO

abundance declines (

Browne and Cohen, 2012

), as

has occurred in the US over the past two decades (

Hidy et al., 2014

;

Simon et al., 2015

Despite this increasing influence on the NO

budget, the chemistry of organic nitrates

remains the subject of debate, with key uncertainties surrounding the organic nitrate yield

from BVOC oxidation, the recycling of NO

from organic nitrate degradation, and the role

of organic nitrates in secondary organic aerosol formation (

Paulot et al., 2012

;

Perring et al.,

2013

). Two campaigns in the Southeast US in summer 2013 provided datasets of

unprecedented chemical detail for addressing these uncertainties: the airborne NASA

SEAC

RS (Studies of Emissions and Atmospheric Composition, Clouds, and Climate

Coupling by Regional Surveys;

Toon et al., 2016

) and the ground-based SOAS (Southern

Oxidants and Aerosols Study). Here we use a

∼

25 × 25 km

resolution 3-D chemical

transport model (GEOS-Chem) to interpret organic nitrate observations from both

campaigns, with focus on their impacts on atmospheric nitrogen (N) budgets.

Nitrogen oxides are emitted from natural and anthropogenic sources primarily as NO, which

rapidly achieves steady state with NO

. Globally, the dominant loss pathway for NO

reaction with the hydroxyl radical (OH) to form nitric acid (HNO

). In the presence of

VOCs, NO

can also be lost by reaction with organic peroxy radicals (RO

) to form peroxy

nitrates (RO

) and alkyl and multifunctional nitrates (RONO

) (

O'Brien et al., 1995

Their daytime formation temporarily sequesters NO

, facilitating its export to more remote

environments (

Horowitz et al., 1998

;

Paulot et al., 2012

;

Mao et al., 2013

). RO

species

are thermally unstable at boundary layer temperatures and decompose back to NO

on a

time scale of minutes, except for the longer-lived peroxyacylnitrates (PANs) (

Singh and

Hanst, 1981

). RONO

species can dominate NO

loss when BVOC emissions are high and

emissions are low (

Browne and Cohen, 2012

;

Paulot et al., 2012

;

Browne et al., 2014

)

and may be more efficient for reactive N export than PANs (

Mao et al., 2013

). The amount

of NO

sequestered by RONO

depends on the interplay between BVOC and NO

emissions, the RONO

yield from BVOC oxidation, and the eventual RONO

fate.

RONO

chemistry and impacts are illustrated schematically in Fig. 1, starting from reaction

of NO

with BVOCs (mainly isoprene and monoterpenes) to form RONO

. The RONO

Fisher et al.

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NASA Author Manuscript

yield (

) from isoprene oxidation by OH has been inferred from laboratory and field

experiments to be 4-15% (

Tuazon and Atkinson, 1990

;

Chen et al., 1998

;

Sprengnether et

al., 2002

;

Patchen et al., 2007

;

Perring et al., 2009a

;

Paulot et al., 2009

;

Nguyen et al., 2014

;

Xiong et al., 2015

). Models have shown nearly this full range of yields to be compatible with

RONO

observations, depending on the chemical mechanism assumed. For example, two

models using different isoprene reaction schemes both successfully reproduced observations

from a 2004 aircraft campaign (ICARTT) - one assuming a 4% molar yield (

Horowitz et al.,

2007

) and the other assuming an 11.7% molar yield (

Mao et al., 2013

). The RONO

yield

from monoterpene oxidation by OH is even more uncertain. Laboratory measurements exist

only for

-pinene, and these show divergent results: 26% (

Rindelaub et al., 2015

), 18%

(

Nozière et al., 1999

), and 1% (

Aschmann et al., 2002

, a lower limit due to significant wall

losses). RONO

yields remain a significant uncertainty in BVOC oxidation schemes, with

implications for their impacts on NO

sequestration.

The fate of RONO

is of central importance in determining whether sequestered NO

returned to the atmosphere or removed irreversibly. Many first generation RONO

(i.e.,

those formed from NO reaction with BVOC-derived peroxy radicals) have a short lifetime

against further oxidation to form a suite of second generation RONO

(

Beaver et al., 2012

;

Mao et al., 2013

;

Browne et al., 2014

), especially if they are produced from di-olefins such

as isoprene or limonene. Laboratory studies indicate little NO

release during this process

(

Lee et al., 2014

); however, NO

can be recycled by subsequent oxidation and photolysis of

second generation species (

Müller et al., 2014

). Estimates of the NO

recycling efficiency,

defined as the mean molar percentage of RONO

loss that releases NO

, range from <5% to

>50% for isoprene nitrates (INs) (

Horowitz et al., 2007

;

Paulot et al., 2009

), and best

estimates depend on assumptions about the IN yield (

Perring et al., 2009a

). NO

recycling

efficiencies from monoterpene nitrates (MTNs) have not been observed experimentally, but

model sensitivity studies have shown a 14% difference in boundary layer NO

between

scenarios assuming 0% versus 100% recycling (assuming an initial 18% MTN yield,

Browne et al., 2014

). Uncertainty in the NO

recycling efficiency has a bigger impact on

simulation of NO

and ozone than uncertainty in the RONO

yield (

Xie et al., 2013

Organic nitrates are more functionalized and less volatile than their BVOC precursors and

are therefore more likely to partition to the particle phase. In the Southeast US,

Xu et al.

(2015)

recently showed that particulate RONO

(pRONO

) make an important contribution

to total organic aerosol (5-12%), consistent with in situ observations from other

environments (

Brown et al., 2009

2013

;

Fry et al., 2013

;

Rollins et al., 2012

2013

Chamber experiments have shown high mass yields of aerosol from NO

-initiated oxidation

of isoprene (15-25%;

Ng et al., 2008

;

Rollins et al., 2009

) and some monoterpenes (33-65%;

Fry et al., 2014

). There is evidence that RONO

from OH-initiated oxidation also form

aerosol, although with lower yields, possibly via multi-functionalized oxidation products

(

Kim et al., 2012

;

Lin et al., 2012

;

Rollins et al., 2012

;

Lee et al., 2014

). pRONO

are

removed either by deposition or by hydrolysis to form HNO

(

Jacobs et al., 2014

;

Rindelaub

et al., 2015

). Both losses augment N deposition to ecosystems (

Lockwood et al., 2008

Aerosol partitioning competes with photochemistry as a loss for gas-phase RONO

with

impacts for NO

recycling. Partitioning also competes with gas-phase deposition, and

because lifetimes against deposition are much longer for organic aerosols than for gas-phase

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NASA Author Manuscript

precursors (

Wainwright et al., 2012

;

Knote et al., 2015

), this process may shift the enhanced

N deposition associated with RONO

(

Zhang et al., 2012

;

Nguyen et al., 2015

) to

ecosystems further downwind of sources.

The 2013 SEAC

RS and SOAS campaigns provide a unique resource for evaluating the

impact of BVOC-derived organic nitrates on atmospheric NO

. Both campaigns provided

datasets of unprecedented chemical detail, including isoprene, monoterpenes, total and

particle-phase RONO

, and speciated INs; during SOAS these were further augmented by

measurements of MTNs. Continuous measurements from the SOAS ground site provide

high temporal resolution and constraints on diurnal variability (e.g.,

Nguyen et al., 2015

;

Xiong et al., 2015

). These are complemented by extensive boundary layer profiling across a

range of chemical environments from the SEAC

RS airborne measurements (

Toon et al.,

2016

). Combined, the campaigns covered the summer period when BVOC emissions in the

Southeast US are at a maximum (

Palmer et al., 2006

). These data offer new constraints for

testing models of organic nitrate chemistry, with implications for our understanding of NO

ozone, and aerosol budgets in BVOC-dominated environments worldwide.

We examine here the impact of BVOC oxidation on atmospheric NO

, using the 2013

campaign data combined with the GEOS-Chem model. The version of GEOS-Chem used in

this work represents a significant advance over previous studies, with higher spatial

resolution (

∼

25 × 25 km

) that better captures the spatial segregation of BVOC and NO

emissions (

Yu et al., 2016

); updated isoprene nitrate chemistry incorporating new

experimental and theoretical findings (e.g.,

Lee et al., 2014

;

Müller et al., 2014

;

Peeters et

al., 2014

;

Xiong et al., 2015

); addition of monoterpene nitrate chemistry (

Browne et al.,

2014

;

Pye et al., 2015

); and consideration of particle uptake of gas-phase isoprene and

monoterpene nitrates. We first evaluate the updated GEOS-Chem simulation using SOAS

and SEAC

RS observations of BVOCs, organic nitrates, and related species. We then use

GEOS-Chem to quantify the fates of BVOC-derived organic nitrates in the Southeast US.

Finally, we investigate the impacts of organic nitrate formation on the NO

budget.

2 Updates to GEOS-Chem simulation of organic nitrates

We use a new high resolution version of the GEOS-Chem CTM (

www.geos-chem.org

)

v9-02, driven by assimilated meteorology from the NASA Global Modeling and

Assimilation Office (GMAO) Goddard Earth Observing System Forward Processing

(GEOS-FP) product. The model is run in a nested configuration (

Wang et al., 2004

), with

native GEOS-FP horizontal resolution of 0.25° latitude by 0.3125° longitude over North

America (130-60°W, 9.75-60°N). Boundary conditions are provided from a 4° × 5° global

simulation, also using GEOS-Chem. The native GEOS-FP product includes 72 vertical

layers of which

∼

38 are in the troposphere. Temporal resolution of GEOS-FP is hourly for

surface variables and 3-hourly for all others. Our simulations use a time step of 5 minutes

for transport and 10 minutes for emissions and chemistry.

GEOS-Chem has been applied previously to simulation of organic nitrates in the Southeast

US (e.g.,

Fiore et al., 2005

;

Zhang et al., 2011

;

Mao et al., 2013

Mao et al. (2013)

recently

updated the GEOS-Chem isoprene oxidation mechanism to include explicit production and

Fisher et al.

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NASA Author Manuscript

loss of a suite of second generation isoprene nitrates and nighttime oxidation by nitrate

radicals. While their updated simulation showed good agreement with aircraft observations

from the 2004 ICARTT campaign over the eastern US, we find that the more detailed

chemical payloads available during SOAS and SEAC

RS highlight deficiencies in that

mechanism, resulting in large model biases in RONO

A major component of this work is modification of the organic nitrate simulation in GEOS-

Chem. Our focus here is on the BVOC-derived nitrates for which field measurements are

newly available. GEOS-Chem simulation of PANs was recently updated by

Fischer et al.

(2014)

and is not discussed here. Our improvements to the RONO

simulation are detailed

below and include updates to isoprene oxidation chemistry, addition of monoterpene

oxidation chemistry, and inclusion of aerosol uptake of RONO

followed by particle-phase

hydrolysis. Other updates from GEOS-Chem v9-02 and comparison to Southeast US

observations are presented in several companion papers.

Kim et al. (2015)

describe the

aerosol simulation and

Travis et al. (2016)

the gas-phase oxidant chemistry. Constraints on

isoprene emissions from satellite formaldehyde observations are described by

Zhu et al.

(2016)

. The low-NO

isoprene oxidation pathway and implications for organic aerosols are

described by

Marais et al. (2016)

. Finally,

Yu et al. (2016)

evaluate the impact of model

resolution and spatial segregation of NO

and BVOC emissions on isoprene oxidation. Our

simulation is identical to that used in

Travis et al. (2016)

Yu et al. (2016)

, and

Zhu et al.

(2016)

2.1 Isoprene oxidation chemical mechanism

The basic structure of the GEOS-Chem isoprene oxidation mechanism is described by

Mao

et al. (2013)

, with updates to low-NOx pathways described and validated by

Travis et al.

(2016)

. All updates to the isoprene oxidation mechanism are provided in

Travis et al. (2016)

Tables S1 and S2. Figure 2 shows our updated implementation of OH-initiated isoprene

oxidation in the presence of NO

leading to isoprene nitrate (IN) formation. Isoprene

oxidation by OH produces isoprene peroxy radicals (ISOPO

) in either

- or

-hydroxy

peroxy configurations depending on the location of OH addition. In the presence of NO

ISOPO

reacts with NO to either produce NO

(the dominant fate;

Perring et al., 2013

) or

form INs, with the yield of INs (

) defined as the branching ratio between these two

channels. Early laboratory measurements of

suggested an IN yield between 4.4 and 12%

(

Tuazon and Atkinson, 1990

;

Chen et al., 1998

;

Sprengnether et al., 2002

;

Patchen et al.,

2007

;

Paulot et al., 2009

;

Lockwood et al., 2010

). More recent experiments indicate

continuing uncertainty in

, with a measured yield of

= 9 ± 4% from the Purdue Chemical

Ionization Mass Spectrometer (CIMS;

Xiong et al., 2015

) and

= 13 ± 2% from the Caltech

−

Time-of-Flight CIMS (CIT-ToF-CIMS; Teng et al., in preparation), despite excellent

agreement during calibrated intercomparison exercises using one isoprene nitrate isomer

(4,3 ISOPN). The sensitivity of the CIT-ToF-CIMS is similar for all isomers of ISOPN (

Lee

et al., 2014

), while the Purdue instrument is less sensitive to the major isomer (1,2 ISOPN)

(

Xiong et al., 2015

). Here, we use a first generation IN yield of

= 9%, which we find

provides a reasonable simulation of the SOAS observations and is also consistent with the

SOAS box model simulations of

Xiong et al. (2015)

. We discuss the model sensitivity to the

choice of

in Sect. 3.

Fisher et al.

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For the oxidation of isoprene by OH, the mechanism described in

Mao et al. (2013)

assumed

a first generation IN composition of 40%

-hydroxyl INs (

-ISOPN) and 60%

-hydroxyl

INs (

-ISOPN). However, new theoretical constraints show that under atmospheric

conditions, (

-channel peroxy radicals are only a small fraction of the total due to fast

redissociation of peroxy radicals that fosters interconversion between isomers and tends

towards an equilibrium population with more than 95%

-isomers (

Peeters et al., 2014

Using a simplified box model based on the extended Leuven Isoprene Mechanism LIM1, we

found

-isomers were 4-8% of the total peroxy pool in representative Southeast US

boundary layer conditions (temperature

∼

295-300 K, ISOPO

lifetime

∼

20-60 seconds). In

what follows, we use an IN distribution of 90%

-ISOPN and 10%

-ISOPN. Our box

modeling suggests 10% is an upper limit for the

-ISOPN pool; however, we maintain this

value as it allows improved simulation of species with predominantly

-pathway origins,

including glyoxal and the second generation INs propanone nitrate (PROPNN) and ethanal

nitrate (ETHLN).

First generation ISOPN isomers formed via OH oxidation of isoprene have a short

photochemical lifetime against atmospheric oxidation (

Paulot et al., 2009

;

Lockwood et al.,

2010

;

Lee et al., 2014

). Here we use updated reaction rate constants and products from

Lee

et al. (2014)

that increase the

-ISOPN+OH reaction by roughly a factor of two and

decrease ozonolysis by three orders of magnitude (relative to the previous mechanism based

Lockwood et al., 2010

;

Paulot et al., 2009

). Changes in

-ISOPN reaction rate constants

are more modest but in the same direction. For both isomers, reaction with OH forms a

peroxy radical (ISOPNO

) along with a small (10%) yield of isoprene epoxy diols (

Jacobs et

al., 2014

). Rate constants and products of the subsequent oxidation of ISOPNO

to form a

suite of second generation INs follow the

Lee et al. (2014)

mechanism. We explicitly

simulate methylvinylketone nitrate (MVKN) and methacrolein nitrate (MACRN), which are

primarily from the

-pathway; PROPNN and ETHLN, which are primarily from the

pathway (and NO

-initiated oxidation); and C

dihydroxy dinitrate (DHDN), formed from

both isomers (

Lee et al., 2014

Isoprene reaction with NO

is the dominant isoprene sink at night and can also be significant

during the day (

Ayres et al., 2015

), producing INs with high yield (

Perring et al., 2009b

;

Rollins et al., 2009

). This reaction can account for more than 20% of isoprene loss in some

environments (

Brown et al., 2009

) and may explain 40-50% of total RONO

in the Southeast

(

Mao et al., 2013

;

Xie et al., 2013

). The mechanism used here is identical to that described

Mao et al. (2013)

. Reaction of isoprene with NO

forms a nitrooxy peroxy radical

(INO

). Subsequent reaction of INO

with NO, NO

, itself, or other peroxy radicals forms a

first generation C

carbonyl nitrate (ISN1) with 70% yield, while reaction with HO

forms a

nitrooxy hydroperoxide (INPN) with 100% yield. In this simplified scheme, we do not

distinguish between

- and

- isomers for ISN1 and INPN, nor do we include the C

hydroxy nitrate species recently identified in chamber experiments (

Schwantes et al., 2015

Mao et al. (2013)

lumped all second generation nitrates derived from ISN1 and INPN into a

single species (R

), but here we assume that the lumped species is PROPNN on the basis

of recent chamber experiments that show PROPNN to be a high-yield photooxidation

product of INs from NO

-initiated oxidation (

Schwantes et al., 2015

). This effectively

assumes instantaneous conversion of INs to PROPNN, a simplification that results in a shift

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in the simulated diurnal cycle of PROPNN (see Sect. 3). We do not include here the nitrooxy

hydroxyepoxide product recently identified by

Schwantes et al. (2015)

Possible fates for second generation INs include further oxidation, photolysis, uptake to the

aerosol phase followed by hydrolysis (Sect. 2.3), and removal via wet and dry deposition.

Müller et al. (2014)

show that photolysis is likely significantly faster than reaction with OH

for carbonyl nitrates (e.g., MVKN, MACRN, ETHLN, PROPNN) due to enhanced

absorption cross sections and high quantum yields caused by the proximity of the carbonyl

group (a strongly absorbing chromophore) to the weakly-bound nitrate group. Here we

increase the absorption cross sections of the carbonyl INs following the methodology of

Müller et al. (2014

, Sect. 2). Briefly, we first use the PROPNN cross section measured by

Barnes et al. (1993)

to calculate a wavelength-dependent cross section enhancement ratio

(

), defined as the ratio of the measured cross section to the sum of the IUPAC-

recommended cross sections for associated monofunctional nitrates and ketones. We then

calculate new cross sections for ETHLN, MVKN, and MACRN by multiplying

by the

sum of cross sections from appropriate monofunctional analogues (Table S5). The new cross

sections are 5-15 times larger than in the original model, which used the IUPAC-

recommended cross section of the monofunctional analogue tert-butyl nitrate for all

carbonyl nitrates (

Roberts and Fajer, 1989

). For all species, we calculate photolysis rates

assuming unity quantum yields, whereby the weak O–NO

bond dissociates upon a

rearrangement after photon absorption to the carbonyl chromophore (

Müller et al., 2014

Peak midday photolysis rates now range from

∼

3 × 10

−5

−1

(PROPNN) to

∼

3 × 10

−4

−1

(MACRN).

Removal by dry deposition has been updated based on new observations from the SOAS

ground site. The dry deposition calculation is now constrained to match observed deposition

velocities for ISOPN, MVKN, MACRN, and PROPNN (

Nguyen et al., 2015

;

Travis et al.,

2016

), with all other RONO

deposition velocities scaled to that of ISOPN. Wet scavenging

of gases is described in

Amos et al. (2012)

and has been modified here to use the same

Henry's Law coefficients as for dry deposition. Aerosol partitioning is described in Sect. 2.3

below.

2.2 Monoterpene oxidation chemical mechanism

Monoterpene chemistry is not included in the standard GEOS-Chem gas-phase chemical

mechanism. Here we implement a monoterpene nitrate scheme developed by

Browne et al.

(2014)

that was built on the RACM2 chemical mechanism (

Goliff et al., 2013

) and evaluated

using aircraft observations over the Canadian boreal forest (

Browne et al., 2014

). Our

implementation is summarized in Fig. 3 and described briefly below, with the full

mechanism available in the Supplement (Tables S1-S3) and at

http://wiki.seas.harvard.edu/

geos-chem/index.php/Monoterpene_nitrate_scheme

. We include two lumped monoterpene

tracers: API representing monoterpenes with one double bond (

-pinene,

sabinene, and Δ-3-carene) and LIM representing monoterpenes with two double bonds

(limonene, myrcene, and ocimene). Combined, these species account for roughly 90% of all

monoterpene emissions (

Guenther et al., 2012

), and we neglect other terpenes here. During

the day, LIM and API are oxidized by OH to form peroxy radicals. Subsequent reaction with

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NO forms first generation monoterpene nitrates with a yield of 18% (

Nozière et al., 1999

These can be either saturated (MONITS) or unsaturated (MONITU), with precursor-

dependent partitioning as shown in Fig. 3. For all subsequent discussion, we refer to their

sum MONIT = MONITU + MONITS.

At night, both LIM and API react with NO

to form a nitrooxy peroxy radical that either

decomposes to release NO

or retains the nitrate functionality to form MONIT. The

branching ratio between these two fates is 50% nitrate-retaining for LIM + NO

(

Fry et al.,

2014

) and 10% nitrate-retaining for API + NO

(

Browne et al., 2014

). The 10% nitrate yield

from API + NO

is on the low end of the observed range (

Fry et al., 2014

), so simulated

pinene-derived MONIT should be considered a lower bound. In

Browne et al. (2014)

, the

API + NO

reaction used the

-pinene + NO

rate constant from the Master Chemical

Mechanims (MCMv3.2). We have updated this rate constant to

API

+NO

= 8.33 ×

−13

490/

, a rough average of the MCMv3.3

- and

-pinene values, as API comprises

both

- and

-pinenes (the dominant API components, present in roughly equal amounts

during both SEAC

RS and SOAS). API and LIM also react with O

, but this reaction does

not lead to RONO

formation.

We do not distinguish between OH-derived and NO

-derived MTN species. MONIT are

subject to removal via wet and dry scavenging, aerosol uptake, photolysis, ozonolysis

(MONITU only) and oxidation by OH. Here, we also add MONIT reaction with NO

with

the same rate constant as used for nighttime isoprene nitrates. The products of MONIT

oxidation are currently unknown; here we follow

Browne et al. (2014)

and assume oxidation

produces a second generation monoterpene nitrate (HONIT) that undergoes dry deposition,

photolysis, and oxidative loss. In our simulation, HONIT is also removed via aerosol uptake

(Sect. 2.3).

2.3 Aerosol partitioning of RONO

Evidence from laboratory and field studies suggests aerosol uptake is a potentially

significant loss pathway for gas-phase RONO

(e.g.,

Day et al., 2010

;

Rollins et al., 2010

;

Darer et al., 2011

;

Fry et al., 2013

2014

). In particular, BVOC oxidation by NO

radicals

has been shown to result in high organic aerosol yields (

Ng et al., 2008

;

Fry et al., 2009

;

Rollins et al., 2012

). Recent work from SOAS highlighted the role of the monoterpenes +

reaction, with an estimated 23-44% yield of organic nitrate aerosol (

Ayres et al., 2015

)

that can explain roughly half of nighttime secondary organic aerosol production (

Xu et al.,

2014

). Isoprene + NO

results in smaller but still significant yields;

Xu et al. (2014)

estimate

that isoprene was responsible for 20% of nighttime NO

-derived organic aerosol observed

during SOAS. Organic nitrate aerosol yields from daytime oxidation by OH are lower but

non-negligible. At Bakersfield, for example,

Rollins et al. (2013)

found 21% of RONO

partitioned to the aerosol phase during the day, and that these could explain 5% of the total

daytime organic aerosol mass.

Aerosol partitioning of RONO

has not previously been considered in GEOS-Chem. Here

we add this process using a reactive uptake coefficient (

) parameterization. Our

parameterization was designed to provide a necessary sink for gas-phase RONO

species

(overestimated in earlier iterations of our model), and therefore makes a number of

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simplifying assumptions. In particular, we do not allow pRONO

to re-partition to the gas

phase (likely to impact the more volatile isoprene-derived nitrates), and uptake coefficients

are defined to fit the measurements of gas-phase species. More accurate simulation of

organic nitrate aerosols would require additional updates that take into account vapor

pressure differences between species (as done recently by

Pye et al., 2015

) and incorporate

new findings from SOAS (

Ayres et al., 2015

;

Lee et al., 2016

). For our simulation, we apply

reactive uptake to all BVOC-derived RONO

except PROPNN and ETHLN, which lack

hydroxyl groups and are therefore expected to be significantly less soluble. We assume an

uptake coefficient of

=0.005 for isoprene nitrates (from both daytime and nighttime

chemistry) and

=0.01 for all monoterpene nitrates (Table S4). Our isoprene nitrate uptake

coefficient is in the middle of the range predicted by

Marais et al. (2016)

using a mechanistic

formulation, and is a factor of 4 lower than the upper limit for ISOPN inferred by

Wolfe et

al. (2015)

using SEAC

RS flux measurements. Although simplified, we find this

parameterization provides a reasonable fit to the SEAC

RS and SOAS observations of

individual gas-phase RONO

species measured by the CIT-ToF-CIMS and total pRONO

measured by an Aerosol Mass Spectrometer (AMS) (see Sect. 3 and 4).

After partitioning to the aerosol, laboratory experiments have shown that pRONO

can

hydrolyze to form alcohols and nitric acid via pRONO

+ H

→

ROH + HNO

. Some

pRONO

species hydrolyze rapidly under atmospherically-relevant conditions, while others

are stable against hydrolysis over timescales significantly longer than the organic aerosol

lifetime against deposition (

Darer et al., 2011

;

Hu et al., 2011

;

Liu et al., 2012

;

Jacobs et al.,

2014

;

Rindelaub et al., 2015

). Lifetimes against hydrolysis inferred from bulk aqueous and

reaction chamber studies range widely from minutes (

Darer et al., 2011

;

Rindelaub et al.,

2015

) to a few hours (

Liu et al., 2012

;

Lee et al., 2016

) to nearly a day (

Jacobs et al., 2014

Here we apply a bulk lifetime against hydrolysis for the entire population of pRONO

(similar to

Pye et al., 2015

). In other words, our implementation of aerosol partitioning

involves a two-step process of (1) uptake of gas-phase RONO

to form a simplified non-

volatile pRONO

species, with rate determined by

, followed by (2) hydrolysis of the

simplified pRONO

species to form HNO

, with rate determined by the lifetime against

hydrolysis. These steps are de-coupled, and we do not include any dependence of

on the

hydrolysis rate (unlike the more detailed formulation of

Marais et al. (2016

)). In subsequent

sections, we compare the simplified pRONO

formed as an intermediate during this process

to total pRONO

derived from observations. The assumption of a single hydrolysis lifetime

overestimates the loss rate of non-tertiary nitrates (

Darer et al., 2011

;

Hu et al., 2011

) and

may lead to model bias in total pRONO

, particularly in the free troposphere where the

longer-lived species would be more prevalent (see Sect. 4).

We assume here a bulk lifetime against hydrolysis of 1 h, which we found in preliminary

simulations to provide a better simulation of pRONO

than longer lifetimes. Our 1 h bulk

hydrolysis lifetime is shorter than the 2-4 h lifetime found in recent analysis of SOAS data

and laboratory experiments (

Boyd et al., 2015

;

Lee et al., 2016

;

Pye et al., 2015

) - likely

reflecting the simplifying assumptions of our uptake parameterization. In any case, the

choice of hydrolysis lifetime does not affect the concentration of gas-phase RONO

species

(because pRONO

cannot re-partition to the gas phase in the model), and we find this value

provides a reasonable match to AMS measurements of total pRONO

at the surface during

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SOAS and SEAC

RS (see Sect. 3 and 4). Impacts on HNO

are minor: compared to a

simulation without hydrolysis, our simulation with a 1 h lifetime against hydrolysis

increased boundary layer HNO

by 20 ppt, or 2.4%.

3 BVOCs and organic nitrates in the Southeast US

We evaluate the updated GEOS-Chem simulation using Southeast US measurements of

isoprene, monoterpenes, and a suite of oxidation products from two field campaigns in

summer 2013. SEAC

RS was a NASA aircraft campaign that took place in August-

September 2013 (

Toon et al., 2016

). All observations discussed in this work were taken

onboard the NASA DC-8 (data doi: 10.5067/Aircraft/SEAC

RS/Aerosol-TraceGas-Cloud),

which was based in Houston, Texas with an

∼

8-hour flight range. SOAS was a ground-based

campaign that took place in June-July 2013 at the Centreville monitoring site near Brent,

Alabama (32.903°N, 87.250°W).

3.1 Isoprene and monoterpenes

Understanding BVOC sources and chemistry was a primary goal of SEAC

RS, resulting in a

large number of boundary layer flights over regions of enhanced biogenic emissions (

Kim et

al., 2015

). Isoprene and monoterpene distributions in Southeast US surface air (80-94.5°W,

29.5-40°N, and below 1 km) measured by PTR-MS are shown in Fig. 4, and their campaign-

median vertical profiles are shown in Fig. 5(b,c). Whole Air Sampler (WAS) measurements

of isoprene and

-pinene +

-pinene (Fig. S1) are similar, but with more limited sampling

than the PTR-MS. All observations have been averaged to the spatial and temporal

resolution of the model.

The SOAS site is located at the edge of a mixed coniferous and deciduous forest (

Nguyen et

al., 2015

). SOAS observations of isoprene and monoterpenes, measured by PTR-ToF-MS

and averaged to hourly mean values, are shown in Fig. 6. Both species display a clear

diurnal cycle with peak isoprene during day, reflecting the light- and temperature-dependent

source, and peak monoterpenes at night. For monoterpenes, the figure also shows the sum of

-pinene +

-pinene as measured by 2D-GC-FID, which indicates that these are the

dominant monoterpenes.

Figures 4, 5, and 6 compare observed BVOCs from both campaigns to the GEOS-Chem

simulation, sampled to match the observations. Similar figures for NO

can be found in

Travis et al. (2016)

and in Fig. S2. Model bias relative to observations is quantified using the

normalized mean bias

NMB = 100 % × [∑

(

–

)/∑

(

)]

, where

and

are the

observed and modeled values and the summation is over all hours (SOAS) or unique

gridbox-timestep combinations along the flight tracks (SEAC

RS). BVOC emissions are

from MEGANv2.1 (

Guenther et al., 2012

) and have been decreased by 15% for isoprene and

doubled for monoterpenes to better match aircraft (isoprene, monoterpene) and satellite

(formaldehyde) observations (

Kim et al., 2015

;

Zhu et al., 2016

). With these scalings

applied, simulated surface isoprene and monoterpenes overestimate somewhat the SEAC

data (Fig. 4, mainly due to a few simulated high-BVOC events), but the medians are well

within the observed variability (Fig. 5). Model high bias above 500 m is likely caused by

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excessive vertical mixing through the simulated boundary layer (

Travis et al., 2016

Relative to the SOAS data, simulated monoterpenes are biased low by a factor of two, while

isoprene falls within the interquartile range of the measurements. The opposite sign of the

SOAS monoterpene bias relative to the more spatially representative SEAC

RS data

suggests a low bias in MEGANv2.1 monoterpene emissions that is unique to the Centreville

gridbox; errors in vertical mixing may also contribute. For isoprene, the model reproduces

both the observed nighttime decline and the subsequent morning growth with a small delay

(

∼

1 hour).

The observed declines in isoprene at night (Fig. 6) and above the boundary layer (Fig. 5)

reflect its short lifetime against oxidation. We find in the model that OH oxidation accounts

for 90% of isoprene loss (

Marais et al., 2016

), but only 65% of monoterpenes loss (with

responsible for most of the rest). For isoprene, the subsequent fate of the peroxy

radicals (ISOPO

) has been evaluated in detail by

Travis et al. (2016)

, who also present an

in-depth analysis of the NO

budget and impacts on ozone. They show that on average 56%

of ISOPO

reaction during SEAC

RS is with NO, and that there is large spatial variability in

this term that is accurately reproduced by the high-resolution GEOS-Chem simulation. Here

we focus exclusively on this pathway and the resultant formation of RONO

from both

isoprene and monoterpenes.

3.2 First generation RONO

Observed near-surface mixing ratios of first generation isoprene nitrates (ISOPN) during

SEAC

RS are shown in Fig. 7 and are generally well represented by GEOS-Chem (

= 0.61;

NMB = -0.6%). ISOPN vertical profiles in Fig. 5e indicate a rapid decline from the

boundary layer to the free troposphere, reflecting the short atmospheric lifetime (2-4 h in our

simulation; Table 1). Comparing the lowest altitude SEAC

RS observations to the SOAS

median from the CIT-ToF-CIMS (black triangle) indicates an apparent vertical gradient from

the surface to

∼

500 m. This could be caused by spatial variability between the campaigns, or

could reflect rapid dry deposition of ISOPN with limited vertical mixing. GEOS-Chem does

not simulate this SOAS-SEAC

RS difference, possibly due to overly strong vertical mixing

through the modeled boundary layer as identified by

Travis et al. (2016)

from model

comparison to SEACIONS ozonesonde observations.

During SOAS, ISOPN was measured simultaneously by the CIT-ToF-CIMS (

Crounse et al.,

2006

;

Nguyen et al., 2015

) and the Purdue CIMS (

Xiong et al., 2015

), and Fig. 6 shows the

diurnal cycles from both. Median ISOPN from the Purdue CIMS is a factor of two higher

than that from the CIT-ToF-CIMS during daylight hours, with the most significant

differences in mid-late morning. In both datasets, ISOPN peaks around 10:00 am local time,

is elevated until early evening, and declines to a pre-dawn minimum. Simulated ISOPN from

GEOS-Chem is in good agreement with the Purdue CIMS measurements except in the

afternoon when modeled ISOPN shows a broad peak (rather than the observed decline)

coincident with simulated peak isoprene (Fig. 6). After

∼

7:00 pm, the model captures the

observed timing of the nighttime ISOPN decline seen in both datasets, as well as the rapid

morning growth seen in the Purdue CIMS measurements.

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As described in Sect. 2.1, there is considerable uncertainty in the ISOPN yield. We find here

that a 9% yield provides the best simulation of the ensemble of SEAC

RS and SOAS

observations, given experimental constraints on oxidative loss rates (

Lee et al., 2014

) and

dry deposition fluxes (

Nguyen et al., 2015

). Using model sensitivity studies, we found that

applying a lower yield of 7% improved the agreement with the CIT-ToF-CIMS during

SOAS, but worsened agreement with the other datasets and is inconsistent with the yields

from laboratory experiments (Teng et al., in preparation). We also tested a higher yield of

12%, and found the model overestimated observed SEAC

RS and SOAS ISOPN (from both

instruments) unless we invoked much larger aerosol uptake and/or added another ISOPN

sink. ISOPN sinks (especially aerosol uptake) remain poorly constrained, and the uncertain

parameter space describing these processes likely contains multiple solutions that fit the

observations equally well (i.e., a higher yield could be accommodated by faster ISOPN loss

to aerosol).

Our finding that GEOS-Chem can reproduce the Purdue CIMS ISOPN observations using a

9% ISOPN yield is consistent with the box model of

Xiong et al. (2015)

. The chemical

mechanisms used in both studies are similar. In both simulations, modeled ISOPN was

overestimated unless an extra sink was included (also consistent with

Wolfe et al., 2015

who inferred a missing sink based on SEAC

RS flux measurements). While we assumed this

sink was due to aerosol uptake,

Xiong et al. (2015)

invoked enhanced ISOPN photolysis.

They argued that models typically underestimate the ISOPN absorption cross section by not

taking into account the combined influence of the double bond and hydroxyl group in the

ISOPN structure (Fig. 2).

Xiong et al. (2015)

were better able to reproduce the observed

ISOPN morning peak and afternoon decline when they increased the MCMv3.2 photolysis

rate constant by a factor of 5. Including both faster ISOPN photolysis and uptake to the

aerosol phase could be a means to accommodate a higher initial ISOPN yield, such as the

12-14% yield inferred from laboratory experiments with the CIT-ToF-CIMS (Teng et al., in

preparation), although both sinks remain unverified. The nature of the sink has implications

for NO

recycling from isoprene nitrates (photolysis recycles NO

while uptake removes it),

and this remains a source of uncertainty in our estimates of the impacts of RONO

on the

budget.

Even more uncertain than ISOPN are the first generation monoterpene nitrates (MONIT).

MONIT in GEOS-Chem is a lumped species that represents the sum of monoterpene nitrates

from both daytime OH-initiated and nighttime NO

-initiated oxidation (Sect. 2.2). The

nighttime oxidation cascade involves a diversity of reactants (including NO, HO

, NO

, and

other peroxy radicals) and produces a diversity of monoterpene nitrate species (

Lee et al.,

2016

) that we do not distinguish here. In the model, most MONIT is produced from the

-initiated chemistry, resulting in mean MONIT concentrations of 30-60 ppt at night and

∼

10-20 ppt during the day.

During SOAS, two monoterpene nitrates were measured by the CIT-ToF-CIMS: C

and C

. We find that simulated MONIT shows the same diurnal pattern as the sum

of the two measured species (with peak concentrations at night) but is a factor of 2-3 higher

(Fig. S3).

Pye et al. (2015)

similarly found simulated MONIT was a factor of 7 higher than

observations using a version of the CMAQ model with explicit MONIT chemistry. The

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higher modeled values in both studies presumably reflect inclusion in modeled MONIT of

many species that were not measured by CIT-ToF-CIMS (including several identified during

SOAS by

Lee et al., 2016

), as well as biases in the model mechanisms (most of the rate

constants and products have not been measured). NO

-initiated monoterpene oxidation is

particularly uncertain and is likely too strong in GEOS-Chem, as indicated by large

nighttime MONIT overestimates (Fig. S3) combined with monoterpene underestimates (Fig.

6). Simulated nighttime peak values of NO

-derived isoprene nitrates (ISN1) during SOAS

are also up to a factor of 2 higher than the observations reported by

Schwantes et al. (2015)

This suggests that model biases in nighttime PBL heights and associated vertical mixing

may also contribute to simulated nighttime overestimates for some RONO

species.

3.3 Second generation RONO

and pRONO

First generation ISOPN and MONIT undergo further oxidation to form a suite of second

generation RONO

species that retain the nitrate functionality (Figs. 2, 3). Four of these

species (MVKN, MACRN, PROPNN, and ETHLN) were measured by the CIT-ToF-CIMS,

with vertical profiles shown in Fig. 5 (f-h) and spatial distribution shown in Fig. 7. The

model provides a good simulation of SEAC

RS MVKN+MACRN but underestimates the

variability of PROPNN and ETHLN. In contrast, all three species show positive mean model

biases relative to the SOAS surface observations. The model tends to overestimate PROPNN

and ETHLN at night but underestimate them during the day (Fig. S3), reflecting the

assumption in our mechanism that PROPNN is produced at night during NO

-initiated

isoprene oxidation. In reality, the nighttime chemistry produces INs that only photo-oxidize

to PROPNN after sunrise (

Schwantes et al., 2015

). This missing delay between nighttime

addition and subsequent daytime photo-oxidation likely also explains the model bias

relative to the SEAC

RS observations, which mostly took place during daytime. Additional

simplifications in the NO

-initiated chemistry could also contribute to the biases, and

preliminary simulations conducted with the AM3 model show that including more details of

this chemistry improves model ability to match observed PROPNN (Li et al., in preparation).

Some of the bias may also be due to error in the assumed distribution between

- and

channel OH-initiated oxidation, as both PROPNN and ETHLN are produced by the latter

channel only.

The full time series of first and second generation INs measured at Centreville during SOAS

are shown in Fig. 8. We also include the time series of observed particulate RONO

(pRONO

) estimated from AMS measurements (

Fry et al., 2013

;

Ayres et al., 2015

;

Lee et

al., 2016

; Day et al., in preparation) and of

ANs, the sum of all RONO

species (including

pRONO

) as measured by thermal dissociation laser-induced fluorescence (TD-LIF;

Day et

al., 2002

). Despite the biases identified above, the simulation captures the temporal

variability in gas-phase, particulate, and total RONO

observed over the 6-week campaign,

with correlation coefficients of

∼

0.6-0.7. Low observed and modeled values for all species

in early July (days 185-189) indicate suppressed BVOC emissions caused by low

temperatures (

Marais et al., 2016

). The model underestimates both pRONO

and

ANs at

night (Fig. S3), suggesting that hydrolysis of particulate monoterpene nitrates should be

slower than assumed here (Sect. 2.3). Afternoon overestimates of pRONO

relative to the

AMS observations (Fig. S3) are coincident with the peak in isoprene nitrates (Fig. 6),

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suggesting overly strong partitioning to the aerosol phase likely due to our assumption of

irreversibility (Sect. 2.3).

3.4 RONO

-HCHO relationship

The relationship between organic nitrates and formaldehyde (HCHO), a high-yield product

of the ISOPO

+ NO reaction, provides an additional test of the model chemistry and in

particular the IN yield. Daytime isoprene oxidation in the presence of NO

co-produces

HCHO and INs, resulting in an expected strong correlation between these species (

Perring et

al., 2009a

). When INs dominate total RONO

, the correlation should also be strong between

HCHO and

ANs, and this relationship has previously been used to constrain the IN yield

when IN measurements were not available. For example, HCHO and

ANs measurements

from the 2004 ICARTT aircraft campaign showed moderate correlation with

∼

0.4-0.6

(

Perring et al., 2009a

;

Mao et al., 2013

). However, linking the HCHO-

ANs correlation to

the IN yield is complicated by the contribution to

ANs from other RONO

sources (e.g.,

monoterpene nitrates, anthropogenic nitrates, etc.). During SEAC

RS, a better constraint can

be obtained directly from the HCHO-IN relationship. Figure 9 shows the correlation

between HCHO and the sum of ISOPN, MVKN, and MACRN (we exclude PROPNN and

ETHLN to avoid the biases identified previously). The figure shows the observed slope of

0.027 (ppt IN) (ppt HCHO)

−1

is reproduced by the model but with more scatter in the

simulation (

∼

0.5) than in the observations (

∼

0.7). The similarity of the observed and

simulated relationships in Fig. 9 lends confidence to the IN mechanism used here, at least

for the

-peroxy channel.

4 Total alkyl and multifunctional nitrates (

ANs)

4.1 Speciated versus total RONO

SEAC

RS represents one of the first airborne campaigns to make measurements of

individual BVOC-derived RONO

species. Without these speciated measurements, previous

model evaluations of isoprene nitrate chemistry have relied on TD-LIF observations of

ANs (total RONO

), with the assumption that gas-phase INs account for the majority of

ANs (

Horowitz et al., 2007

;

Perring et al., 2009a

;

Mao et al., 2013

;

Xie et al., 2013

Figure 10a compares the TD-LIF

ANs measurement (solid line) to the sum of explicitly

measured gas-phase RONO

species and total pRONO

(dashed line, combined CIT-ToF-

CIMS, WAS, and AMS measurements) during SEAC

RS. The figure shows a large gap

between measured

ANs and the total of speciated RONO

(including both gas-phase and

aerosol contributions), especially near the surface (

ANs = 409 ppt, total speciated RONO

= 198 ppt). Figure 10a also shows the median surface

ANs measured during SOAS (198

ppt; black triangle). As for SEAC

RS, SOAS total speciated RONO

is much lower (82 ppt)

when calculated from the CIT-ToF-CIMS and AMS measurements. The gap is smaller, but

still exists, when calculated using ISOPN from the Purdue CIMS (total RONO

= 102 ppt)

or pRONO

from the TD-LIF (total RONO

= 139 ppt). An independent thermal

dissociation instrument operated by the SouthEastern Aerosol Research and Characterization

(SEARCH) Network also measured

ANs at the SOAS site and showed values that were 80

ppt higher than measured by the TD-LIF (but generally well correlated, with slope close to 1

and

∼

0.8).

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