acp-16-5969-2016.pdf

Atmos. Chem. Phys., 16, 5969–5991, 2016

www.atmos-chem-phys.net/16/5969/2016/

doi:10.5194/acp-16-5969-2016

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

Jenny A. Fisher

1,2

, Daniel J. Jacob

3,4

, Katherine R. Travis

, Patrick S. Kim

, Eloise A. Marais

, Christopher Chan

Miller

, Karen Yu

, Lei Zhu

, Robert M. Yantosca

, Melissa P. Sulprizio

, Jingqiu Mao

5,6

, Paul O. Wennberg

7,8

John D. Crounse

, Alex P. Teng

, Tran B. Nguyen

7,a

, Jason M. St. Clair

7,b

, Ronald C. Cohen

9,10

, Paul Romer

Benjamin A. Nault

10,c

, Paul J. Wooldridge

, Jose L. Jimenez

11,12

, Pedro Campuzano-Jost

11,12

, Douglas A. Day

11,12

Weiwei Hu

11,12

, Paul B. Shepson

13,14

, Fulizi Xiong

, Donald R. Blake

, Allen H. Goldstein

16,17

, Pawel K. Misztal

Thomas F. Hanisco

, Glenn M. Wolfe

18,19

, Thomas B. Ryerson

, Armin Wisthaler

21,22

, and Tomas 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

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

Correspondence to:

Jenny A. Fisher (jennyf@uow.edu.au)

Received: 18 January 2016 – Published in Atmos. Chem. Phys. Discuss.: 4 February 2016

Revised: 27 April 2016 – Accepted: 29 April 2016 – Published: 17 May 2016

Published by Copernicus Publications on behalf of the European Geosciences Union.

5970

J. A. Fisher et al.: Organic nitrate chemistry in an isoprene- and monoterpene-rich atmosphere

Abstract.

Formation of organic nitrates (RONO

) during ox-

idation of biogenic volatile organic compounds (BVOCs:

isoprene, monoterpenes) is a significant loss pathway for

atmospheric nitrogen oxide radicals (NO

), but the chem-

istry of RONO

formation and degradation remains uncer-

tain. Here we implement a new BVOC oxidation mecha-

nism (including updated isoprene chemistry, new monoter-

pene chemistry, and particle uptake of RONO

) in the GEOS-

Chem global chemical transport model with

∼

25 km

resolution over North America. We evaluate the model us-

ing aircraft (SEAC

RS) and ground-based (SOAS) observa-

tions of NO

, BVOCs, and RONO

from the Southeast US

in summer 2013. The updated simulation successfully re-

produces 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 con-

tributed by gas-phase monoterpene nitrates. Observations in

the free troposphere show an important contribution from

long-lived nitrates derived from anthropogenic VOCs. Dur-

ing 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 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

≡

) are critical in

controlling tropospheric ozone production (Monks et al.,

2015, and references therein) and influencing aerosol for-

mation (Rollins et al., 2012; Ayres et al., 2015; Xu et al.,

2015), with indirect impacts on atmospheric oxidation ca-

pacity, 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 de-

rived from biogenic volatile organic compounds (BVOCs) to

form organic nitrates (Liang et al., 1998; Browne and Co-

hen, 2012). NO

loss to organic nitrate formation is pre-

dicted to become increasingly important as NO

abundance

declines (Browne and Cohen, 2012), as has occurred in the

US over the past 2 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 de-

bate, with key uncertainties surrounding the organic nitrate

yield from BVOC oxidation, the recycling of NO

from or-

ganic nitrate degradation, and the role of organic nitrates in

secondary organic aerosol formation (Paulot et al., 2012; Per-

ring et al., 2013). Two campaigns in the Southeast US in

summer 2013 provided data sets of unprecedented chemical

detail for addressing these uncertainties: the airborne NASA

SEAC

RS (Studies of Emissions and Atmospheric Compo-

sition, Clouds, and Climate Coupling by Regional Surveys;

Toon et al., 2016) and the ground-based SOAS (Southern Ox-

idants and Aerosols Study). Here we use a

∼

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) bud-

gets.

Nitrogen oxides are emitted from natural and anthro-

pogenic sources primarily as NO, which rapidly achieves

steady state with NO

. Globally, the dominant loss path-

way for NO

is 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 multifunc-

tional 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 timescale 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 NO

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 schemati-

cally in Fig. 1, starting from reaction of NO

with BVOCs

(mainly isoprene and monoterpenes) to form RONO

. The

RONO

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). Mod-

els have shown nearly this full range of yields to be com-

patible with RONO

observations, depending on the chem-

ical mechanism assumed. For example, two models using

different isoprene reaction schemes both successfully repro-

duced 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 ex-

ist only for

-pinene, and these show divergent results: 26 %

Atmos. Chem. Phys., 16, 5969–5991, 2016

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J. A. Fisher et al.: Organic nitrate chemistry in an isoprene- and monoterpene-rich atmosphere

5971

BVOC

RONO

1−α

Photolysis,

Oxidation

Aerosol

Uptake

pRONO

N deposition

Hydrolysis

HNO

N deposition

Reactive

N export

(+ oxidant)

Figure 1.

Schematic representation of organic nitrate chemistry and impacts. Organic nitrates are shown in blue, NO

and processes that

recycle NO

are shown in red, and nitrogen deposition is shown in orange. Symbols courtesy of the Integration and Application Network,

University of Maryland Center for Environmental Science (ian.umces.edu/symbols/).

(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 im-

pacts on NO

sequestration.

The fate of RONO

is of central importance in determining

whether sequestered NO

is returned to the atmosphere or re-

moved irreversibly. Many first generation RONO

(i.e., those

formed from NO reaction with BVOC-derived peroxy radi-

cals) 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. Lab-

oratory studies indicate little NO

release during this process

(Lee et al., 2014); however, NO

can be recycled by subse-

quent oxidation and photolysis of second generation species

(Müller et al., 2014). Estimates of the NO

recycling ef-

ficiency, defined as the mean molar percentage of RONO

loss that releases NO

, range from

5 % to

50 % for iso-

prene 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 % vs. 100 % recycling (assuming an initial 18 %

MTN yield, Browne et al., 2014). Uncertainty in the NO

re-

cycling 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 envi-

ronments (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 iso-

prene (15–25 %; Ng et al., 2008; Rollins et al., 2009) and

some monoterpenes (33–65 %; Fry et al., 2014). There is ev-

idence that RONO

from OH-initiated oxidation also form

aerosol, although with lower yields, possibly via multifunc-

tionalized oxidation products (Kim et al., 2012; Lin et al.,

2012; Rollins et al., 2012; Lee et al., 2014). pRONO

are re-

moved either by deposition or by hydrolysis to form HNO

(Jacobs et al., 2014; Rindelaub et al., 2015). Both losses aug-

ment 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. Par-

titioning also competes with gas-phase deposition, and be-

cause lifetimes against deposition are much longer for or-

ganic aerosols than for gas-phase precursors (Wainwright

et al., 2012; Knote et al., 2015), this process may shift the en-

hanced 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 pro-

vided data sets of unprecedented chemical detail, including

isoprene, monoterpenes, total and particle-phase RONO

and speciated INs; during SOAS these were further aug-

mented by measurements of MTNs. Continuous measure-

ments from the SOAS ground site provide high temporal res-

olution 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 chemi-

cal 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 world-

wide.

We examine here the impact of BVOC oxidation on at-

mospheric 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 previ-

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Atmos. Chem. Phys., 16, 5969–5991, 2016

5972

J. A. Fisher et al.: Organic nitrate chemistry in an isoprene- and monoterpene-rich atmosphere

ous studies, with higher spatial resolution (

∼

25 km

)

that better captures the spatial segregation of BVOC and

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 ni-

trate 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 im-

pacts 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 Assim-

ilation Office (GMAO) Goddard Earth Observing System

Forward Processing (GEOS-FP) product. The model is run

in a nested configuration (Wang et al., 2004), with a 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

◦

global sim-

ulation, also using GEOS-Chem. The native GEOS-FP prod-

uct includes 72 vertical layers of which

∼

38 are in the tropo-

sphere. Temporal resolution of GEOS-FP is hourly for sur-

face variables and 3-hourly for all others. Our simulations

use a time step of 5 min for transport and 10 min for emis-

sions 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) re-

cently updated the GEOS-Chem isoprene oxidation mecha-

nism to include explicit production and loss of a suite of sec-

ond 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

highlight deficiencies in that mechanism, resulting in large

model biases in RONO

A major component of this work is modification of the or-

ganic 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 de-

tailed below and include updates to isoprene oxidation chem-

istry, addition of monoterpene oxidation chemistry, and in-

clusion 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 oxi-

dant chemistry. Constraints on isoprene emissions from satel-

lite formaldehyde observations are described by Zhu et al.

(2016). The low-NO

isoprene oxidation pathway and im-

plications 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 emis-

sions 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-NO

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 pro-

duce NO

(the dominant fate; Perring et al., 2013) or form

INs, with the yield of INs (

) defined as the branching ra-

tio between these two channels. Early laboratory measure-

ments of

suggested an IN yield between 4.4 and 12 % (Tu-

azon and Atkinson, 1990; Chen et al., 1998; Sprengnether

et al., 2002; Patchen et al., 2007; Paulot et al., 2009; Lock-

wood et al., 2010). More recent experiments indicate contin-

uing uncertainty in

, with a measured yield of

4 %

from the Purdue Chemical Ionization Mass Spectrometer

(CIMS; Xiong et al., 2015) and

2 % from the Cal-

tech CF

−

Time-of-Flight CIMS (CIT-ToF-CIMS; Teng

et al., 2016), despite excellent agreement during calibrated

intercomparison exercises using one isoprene nitrate isomer

(4,3 ISOPN). The sensitivity of the CIT-ToF-CIMS is sim-

ilar 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.

For the oxidation of isoprene by OH, the mechanism de-

scribed in Mao et al. (2013) assumed a first generation IN

composition of 40 %

-hydroxyl INs (

-ISOPN) and 60 %

-hydroxyl INs (

-ISOPN). However, new theoretical con-

straints 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 interconver-

Atmos. Chem. Phys., 16, 5969–5991, 2016

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J. A. Fisher et al.: Organic nitrate chemistry in an isoprene- and monoterpene-rich atmosphere

5973

OH,

MACR

MVK

HCHO

Other

products

-hydroxyl

peroxy

radical

and

isomers

-hydroxyl

peroxy

radical

and

isomers

90%

10%

ISOPO

=9%

91%

=9%

91%

ONO

-hydroxyl

isoprene

nitrate

and

isomers

-ISOPN

ONO

-hydroxyl

isoprene

nitrate

and

isomers

-ISOPN

OH,

90%

10%

isoprene

epoxy

diols

ONO

-ISOPNO

•

OH,

90%

10%

ONO

•

-ISOPNO

NO,

methylvinylketone

nitrate

MVKN

methacrolein

nitrate

MACRN

ONO

diydroxy

dinitrate

DHDN

propanone

nitrate

PROPNN

ethanal

nitrate

ETHLN

NO,

21%

(NO)

20%

(HO

)

30%

(NO)

23%

(HO

)

27%

(NO)

21%

(NO)

44%

(NO)

28%

(HO

)

26%

(NO)

16%

(HO

)

ONO

Figure 2.

Schematic of the formation of isoprene nitrates (INs) from OH-initiated isoprene oxidation as implemented in GEOS-Chem. The

isomers shown are indicative as the mechanism does not distinguish between isomers (except for

- vs.

-configurations). For ISOPNO

oxidation, only IN products are shown, along with their yields from both NO and HO

pathways. Small yields (

10 %) of MVKN and

MACRN from

-ISOPNO

are not shown.

sion between isomers and tends towards an equilibrium pop-

ulation with more than 95 %

-isomers (Peeters et al., 2014).

Using a simplified box model based on the extended Leu-

ven 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 s). 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

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Atmos. Chem. Phys., 16, 5969–5991, 2016

5974

J. A. Fisher et al.: Organic nitrate chemistry in an isoprene- and monoterpene-rich atmosphere

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 at-

mospheric 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 2 and de-

crease ozonolysis by 3 orders of magnitude (relative to the

previous mechanism based on Lockwood et al., 2010; Paulot

et al., 2009). Changes in

-ISOPN reaction rate constants

are more modest but in the same direction. For both iso-

mers, reaction with OH forms a peroxy radical (ISOPNO

)

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

cobs et al., 2014). Rate constants and products of the sub-

sequent 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 primar-

ily from the

-pathway (and NO

-initiated oxidation); and

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 (Per-

ring et al., 2009b; Rollins et al., 2009). This reaction can

account for more than 20 % of isoprene loss in some en-

vironments (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 by Mao et al. (2013). Reaction of isoprene with

forms a nitrooxy peroxy radical (INO

). Subsequent re-

action of INO

with NO, NO

, itself, or other peroxy radi-

cals forms a first generation C

carbonyl nitrate (ISN1) with

70 % yield, while reaction with HO

forms a C

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 in the sim-

ulated 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 de-

position. Müller et al. (2014) show that photolysis is likely

significantly faster than reaction with OH for carbonyl ni-

trates (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 car-

bonyl 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 mono-

functional nitrates and ketones. We then calculate new cross

sections for ETHLN, MVKN, and MACRN by multiplying

by the sum of cross sections from appropriate mono-

functional analogs (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

analog 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–

bond dissociates upon a rearrangement after photon ab-

sorption to the carbonyl chromophore (Müller et al., 2014).

Peak midday photolysis rates now range from

∼

−

(PROPNN) to

∼

−

(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 deposi-

tion 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 coeffi-

cients 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 im-

plement a monoterpene nitrate scheme developed by Browne

et al. (2014) that was built on the RACM2 chemical mecha-

nism (Goliff et al., 2013) and evaluated using aircraft obser-

vations 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 Sup-

plement (Tables S1–S3) and at http://wiki.seas.harvard.edu/

geos-chem/index.php/Monoterpene_nitrate_scheme. We in-

clude two lumped monoterpene tracers: API representing

monoterpenes with one double bond (

-pinene,

sabinene, and

-3-carene) and LIM representing monoter-

penes 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

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J. A. Fisher et al.: Organic nitrate chemistry in an isoprene- and monoterpene-rich atmosphere

5975

-pinene

sabinene

-3-carene

API

Limonene + myrcene + ocimene

LIM

•

MONITS

=12%

=18%

MONITU

=6%

MONITU

MONITS

MONITU

•

ONO

DAY:

NIGHT:

DAY:

NO,

=7%

Nitroxy peroxy

radical

=3%

90%

82%

MONITS

MONITU

NIGHT:

NO,

=35%

Nitroxy peroxy

radical

=15%

50%

•

•

ONO

Peroxy

radical

Peroxy

radical

82%

Figure 3.

Simplified representation of the formation of monoterpene nitrates (MTN) from monoterpene oxidation as implemented in GEOS-

Chem. For each lumped species, only one indicative form is shown.

neglect other terpenes here. During the day, LIM and API are

oxidized by OH to form peroxy radicals. Subsequent reaction

with NO forms first generation monoterpene nitrates with a

yield of 18 % (Nozière et al., 1999). These can be either satu-

rated (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

or retains the nitrate functionality to form MONIT.

The branching ratio between these two fates is 50 % nitrate-

retaining for LIM

(Fry et al., 2014) and 10 % nitrate-

retaining for API

(Browne et al., 2014). The 10 % ni-

trate yield from API

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

reaction used the

-pinene

rate con-

stant from the Master Chemical Mechanism (MCMv3.2).

We have updated this rate constant to

API

−

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 gen-

eration monoterpene nitrate (HONIT) that undergoes dry de-

position, 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 ox-

idation by NO

radicals has been shown to result in high or-

ganic 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

re-

sults in smaller but still significant yields; Xu et al. (2014)

estimate that isoprene was responsible for 20 % of night-

time 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 pa-

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Atmos. Chem. Phys., 16, 5969–5991, 2016

5976

J. A. Fisher et al.: Organic nitrate chemistry in an isoprene- and monoterpene-rich atmosphere

rameterization 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 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 de-

fined to fit the measurements of gas-phase species. More ac-

curate simulation of organic nitrate aerosols would require

additional updates that take into account vapor pressure dif-

ferences 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 ex-

pected to be significantly less soluble. We assume an uptake

coefficient of

0.005 for isoprene nitrates (from both day-

time and nighttime chemistry) and

0.01 for all monoter-

pene nitrates (Table S4). Our isoprene nitrate uptake coeffi-

cient 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 sim-

plified, 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 spectrome-

ter (AMS) (see Sects. 3 and 4).

After partitioning to the aerosol, laboratory experiments

have shown that pRONO

can hydrolyze to form alcohols

and nitric acid via pRONO

→

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 aque-

ous 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 hydroly-

sis for the entire population of pRONO

(similar to Pye et al.,

2015). In other words, our implementation of aerosol parti-

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

RONO

to form a simplified non-volatile pRONO

species,

with a rate determined by

, followed by (2) hydrolysis of

the simplified pRONO

species to form HNO

, with a 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

, partic-

ularly 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 bet-

ter 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 parame-

terization. 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

SOAS and SEAC

RS (see Sects. 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 us-

ing Southeast US measurements of isoprene, monoter-

penes, 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 dis-

cussed in this work were taken onboard the NASA DC-

8 (data doi:10.5067/Aircraft/SEAC4RS/Aerosol-TraceGas-

Cloud), which was based in Houston, Texas, with an

∼

8 h

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. 5b,

c. Whole air sampler (WAS) measurements of isoprene and

-pinene

-pinene (Fig. S1 in the Supplement) are sim-

ilar, 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 observa-

tions 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

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J. A. Fisher et al.: Organic nitrate chemistry in an isoprene- and monoterpene-rich atmosphere

5977

DC-8 OBSERVATIONS

1000

2000

3000

4000

ppt

Isoprene

GEOS-CHEM

Monoterpenes

100

200

300

400

ppt

Figure 4.

Observed (left) and simulated (right) mixing ratios of isoprene and monoterpenes below 1 km during the SEAC

RS aircraft

campaign (12 August–23 September 2013). The GEOS-Chem model has been sampled along the aircraft flight tracks, and the observations

binned to the spatial and temporal resolution of the model. The normalized mean bias of the simulation relative to the PTR-MS measurements

in the lowest 500 m is

34 % for isoprene and

3 % for monoterpenes.

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 grid box–time step combi-

nations 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 monoter-

penes to better match aircraft (isoprene, monoterpene) and

satellite (formaldehyde) observations (Kim et al., 2015; Zhu

et al., 2016). With these scalings applied, simulated sur-

face isoprene and monoterpenes overestimate somewhat the

SEAC

RS 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 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

2, 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

data suggests a low bias in MEGANv2.1 monoterpene emis-

sions that is unique to the Centreville grid box; errors in ver-

tical mixing may also contribute. For isoprene, the model re-

produces both the observed nighttime decline and the subse-

quent morning growth with a small delay (

∼

1 h).

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 monoterpene loss (with NO

responsible for

most of the rest). For isoprene, the subsequent fate of the per-

oxy radicals (ISOPO

) has been evaluated in detail by Travis

et al. (2016), who also present an in-depth analysis of the

budget and impacts on ozone. They show that on aver-

age 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 iso-

prene nitrates (ISOPN) during SEAC

RS are shown in Fig. 7

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Atmos. Chem. Phys., 16, 5969–5991, 2016