Introduction
Organic material comprises 20–90 % of the mass in atmospheric particles
smaller than 1 micrometer (Jimenez et al., 2009; Zhang et al., 2007). Most
of this small organic particulate material is secondary organic aerosol
(SOA), and the major fraction of SOA globally is formed from the oxidation
of biogenic volatile organic compounds (BVOCs) released by vegetation
(Hallquist et al., 2009). BVOCs are emitted by plants primarily for
defensive purposes (Dudareva et al., 2006; Kesselmeier and Staudt, 1999).
BVOC emission rates and emission profiles (i.e., the types of compounds
emitted) can change significantly when plants are exposed to biotic and
abiotic stressors (Holopainen, 2004; Peñuelas and Staudt, 2010; Pinto et
al., 2010). It follows then that plant stress exposure associated with
climate change could have significant impacts on SOA formation, and thus
could lead to a climate feedback because atmospheric aerosols play an
important role in the global radiation budget.
Potential climate change feedbacks resulting from the processes linking
naturally produced aerosols and the rest of the Earth system have been
summarized by Carslaw et al. (2010). These processes include the production
of secondary sulfate aerosol from phytoplankton emissions, physical
processes that contribute to dust entrainment, and the formation of biogenic
SOA from terrestrial plant emissions. Their simulations estimated that the
radiative forcing resulting from these feedbacks could produce positive
radiative perturbations up to 1 W m-2 by the end of the 21st
century, amplifying the expected effects of climate change (Carslaw et al.,
2010). Another review focused on feedbacks between the terrestrial biosphere
and climate, and also included a discussion of the biogenic SOA formation
process (Arneth et al., 2010). They estimated that climate feedbacks with
the terrestrial biosphere could result in positive radiative perturbations
of up to 1.5 W m-2 K-1 by the end of the 21st century. Both
reviews make clear that more work is required to fully understand these
feedbacks, stating that the current level of scientific understanding for
them is poor (Carslaw et al., 2010) and very low (Arneth et al.,
2010). Despite the uncertainty in these projections, the assessments of both
papers are in stark contrast to the previously held assumption that the
overall contribution of vegetation to the changing climate system is to act
as a sink for increasing CO2 (Magnani et al., 2007). Carslaw et al. (2010)
listed several research topics that need to be addressed in order to reduce
the uncertainty in these predictions; resolving BVOC responses to climate
change stressors and investigating the subsequent impact on biogenic SOA
formation was included as a high priority for future research projects.
Most studies of how BVOC emissions respond to stressors have focused solely
on the BVOC emissions themselves. Using these results to infer overall
impacts on climate requires highly uncertain assumptions about how different
mixtures of BVOCs could impact SOA yields and chemical composition. A few
studies have examined SOA formation from real plant emissions more directly.
Joutsensaari et al. (2005) were the first to report SOA formation in a
laboratory chamber from the oxidation of real plant emissions. They used a
methyl jasmonate (MeJA) treatment to induce emissions in order to investigate the
role of inducible plant volatiles in particle nucleation and growth. Other
studies have focused on SOA production and chemical composition from BVOC
emissions under baseline conditions, rather than looking at potential
feedbacks between stressors and climate (Hao et al., 2011; Kiendler-Scharr
et al., 2009; Mentel et al., 2009; VanReken et al., 2006). Our own recent
work showed that SOA can also form from BVOCs emitted from leaf litter, and
that this aerosol is chemically very similar to SOA produced from live tree
emissions (Faiola et al., 2014). BVOC emissions from leaf litter were also
found to respond to external environmental drivers, raising the possibility
of additional pathways for climate feedbacks to occur.
Recently, there have been two studies that compared biogenic SOA yields for
baseline emission versus stressed conditions. Lang-Yona et al. (2010)
examined the effect of increased temperature on holm oak (Quercus ilex) emissions and
subsequent SOA formation, finding that increased temperature led to
heightened BVOC emissions and increased SOA production. The BVOC profile was
slightly altered with increasing temperature, but this did not impact the
resultant SOA mass yields. In another study, Mentel et al. (2013)
investigated the impact of herbivory, drought, and heat stress on biogenic
SOA yields. They found that the measured impact on SOA formation was
different for different stressors. For example, infestation by the aphid
Cinara pilicornis resulted in emissions of large organic compounds that had higher SOA yields
than the baseline emissions (33 % stress yield vs. 4–6 % baseline
yield). However, if the plants were experiencing both herbivory and drought
stress concurrently, emissions of small six-carbon green leaf volatiles
increased, which reduced biogenic SOA yields. These results suggest that
climate change could have significant impacts on biogenic SOA formation, and
furthermore, that multiple stressors can interact to change the SOA
formation potential of BVOC emissions in a different way than a single
stressor in isolation. These previous plant stress SOA formation studies
provide valuable insight into the potential impacts of climate change
stressors on biogenic SOA yields. However, to date there have been no
in-depth analyses to investigate how plant stress may affect biogenic SOA
composition, which would have implications for aerosol radiative properties
and cloud forming potential. The research described in this paper addresses
these gaps in our current understanding of the variability in biogenic SOA
composition – including a discussion of inter- and intra-plant species
variability as well as a first look at some impacts of herbivore stress on
biogenic SOA composition.
Methods
Description of dual chamber system and operation
The experiments presented here were performed using the Biogenic Aerosol
Formation Facility at Washington State University. This dual chamber
facility uses emissions from living vegetation as the precursor volatile organic compound (VOC) source
for SOA formation. This is in contrast to other systems that have
historically used commercially obtained pure compounds as a proxy for
biogenic emissions. The facility includes a dynamic plant enclosure and an
aerosol growth chamber. The plant enclosure is a rectangular 0.3 × 0.3 × 0.3 m fluorinated ethylene propylene (FEP) Teflon film dynamic enclosure where sapling trees are stored. A
full description of the plant enclosure and the on-line analytical gas
chromatography (GC) system used to measure BVOC emissions is provided in a
separate paper that focuses on the impacts of herbivory stress on plant
emissions (Faiola et al., 2015). The current paper focuses specifically on
the composition of biogenic SOA formed from the oxidation of the plant
emissions.
The aerosol growth chamber operation and SOA generation methods are similar
to those described by Faiola et al. (2014). Chamber dimensions were 1.6 × 2.2 × 2.2 m. All aerosol growth experiments were conducted with the
chamber using a batch mode approach. Oxidation of SOA precursors was
initiated with ozone that was generated with an Enaly model HG-1500 ozone
generator. The chemistry in this chamber is best described as dark
ozone-initiated chemistry because the chamber was not equipped with UV
lights and no OH scavenger was used. Most experiments were un-seeded.
Experiments where 50 nm ammonium sulfate seed particles were used are marked
with an asterisk in the experiment summary table (Table 1). When used, seed
particles were produced from a TSI constant output atomizer (model 3076) and
then size selected with a differential mobility analyzer (DMA, TSI, Inc.).
Temperature and relative humidity in the aerosol growth chamber were not
controlled, but were monitored using a Vaisala HMP110 humidity and
temperature probe. Nitrogen oxides were not measured, but the aerosol
chamber likely contained some NOx due to soil emissions from the plant pots
(Davidson and Kingerlee, 1997).
Tree description and experimental design
Six different coniferous plant species were used as emissions sources to
generate biogenic SOA in this study: ponderosa pine (Pinus ponderosa), bristlecone pine
(Pinus aristata), blue spruce (Picea pungens), western redcedar (Thuja plicata), grand fir (Abies grandis), and Douglas fir
(Pseudotsuga menziesii). All tree species are commonly found in the western mountain ranges of
North America. Saplings were 1–3 years old at the time of the experiments.
All specimens were obtained from the University of Idaho forest nursery, and
were stored outdoors at the Washington State University greenhouse facility
when they were not being used for experiments. Greenhouse staff cared for
the specimens, providing regular watering and fertilization.
Plants were transported to the laboratory at least 36 h before the first
aerosol growth chamber experiment to allow time for acclimation to
laboratory conditions. Three to nine saplings of the same species were
placed in the plant enclosure (the number depended on the size of the
plants). The only exceptions to this were four experiments performed using a
combination of Abies grandis and Pseudotsuga menziesii specimens rather than just a single plant species
(referred to as mix experiments). One day before an aerosol growth
experiment, the aerosol growth chamber was cleaned with 1 ppm ozone and
flushed with zero air for at least 18 h until ozone concentrations were
less than 20 ppb (Model 1008-PC ozone monitor, Dasibi) and particle number
concentrations were less than 10 cm-3 (Model 3771 condensation particle
counter, TSI, Inc.). Zero air was generated with a pure air generator (AADCO
model 737). Chamber flushing was stopped on the morning of the experiment,
at which point the chamber was operated in batch mode. Biogenic VOC
emissions were pumped from the plant enclosure to the aerosol growth chamber
for 3 h (flow = 9.5 L min-1) using a chemically resistant vacuum pump
(KNF Laboport model UN810 FTP) through Perfluoroalkoxy alkanes (PFA) lines heated to 80 ∘C.
Lines were heated to minimize losses of lower-volatility compounds. During
chamber loading, a fan inside the chamber was used to facilitate mixing.
When VOC loading was complete, the oxidation chemistry was initiated by
rapidly introducing 130 ppb ozone to the aerosol growth chamber. The mixing
fan was turned off immediately following oxidant addition to reduce particle
wall loss. Particle growth and composition were then monitored for the next
6–8 h. This process was repeated with the same batch of trees twice in
1 week – once before treatment was applied and again after the treatment.
The treatment was either a stress application or a negative control. Both
treatments are described in detail in the next section. The time required to
observe maximum plant response to treatment can vary (Copolovici et al.,
2011). Consequently, some of the post-treatment aerosol growth experiments
were performed the day after treatment, and some were performed on the same
day as the treatment.
A list of all experiments with the experiment ID, date, and treatment
approach is provided in Table 1. The naming convention for the experiment ID
is plant species type + experiment number + experiment type.
For example, PA-1-Pre stands for Pinus aristata, first experiment, pre-treatment, and
PA-1-Post stands for Pinus aristata, first experiment, post-treatment. One
pre-treatment aerosol growth experiment performed with Picea pungens specimens on 14 May 2013 did not produce enough particle mass for aerosol mass spectrometer (AMS) analysis; therefore, it has been
removed from this table and will not be considered further. There was also
one SOA growth experiment performed that used a single-component standard,
MeJA, as the precursor compound to generate SOA, rather
than using real plant emissions. For this experiment, a 95 % MeJA standard
solution (Sigma-Aldrich part no. 392707-5ML) was introduced into the aerosol
growth chamber using a dynamic dilution system (Faiola et al., 2012).
Experiment summary. Asterisks on experiment ID indicate that seed
was used; MeJA is methyl jasmonate. The last column provides the IDs
of the corresponding BVOC experiments, where available, to facilitate
cross-referencing with the companion paper (Faiola et al., 2015). n/a means “not applicable”.
SOA
Tree type (Species)
Common name
Experiment
Date
Treatment
BVOC
experiment ID
type
approach
experiment
ID
PPo-1-Pre*
Pinus ponderosa
Ponderosa Pine
Baseline
31 July 2012
–
–
PPo-1-Post*
MeJA
2 August 2012
exogenous,
–
day before
PPo-2-Pre
Baseline
23 October 2012
–
–
PPo-2-Post
MeJA
25 October 2012
exogenous,
–
day before
PA-1-Pre*
Pinus aristata
Bristlecone Pine
Baseline
9 October 2012
–
–
PA-1-Post
MeJA
18 October 2012
exogenous,
–
day before
PA-2-Pre
Baseline
23 April 2013
–
–
PA-3-Pre
Baseline
21 May 2013
–
PA-E
PA-3-Post
MeJA
23 May 2013
foliar, day before
PA-E
PA-4-Pre
Baseline
28 May 2013
–
PA-C
AG-1-Pre
Abies grandis
Grand Fir
Baseline
25 June 2013
–
AG-E
AG-1-Post
MeJA
27 June 2013
foliar, day before
AG-E
AG-2-Pre
Baseline
3 September 2013
–
–
AG-2-Post
MeJA
5 September 2013
foliar, same day
–
AG-3-Pre
Baseline
10 September 2013
–
–
AG-3-NC
Negative
12 September 2013
foliar, same day
–
TP-1-Pre
Thuja plicata
Western redcedar
Baseline
14 August 2013
–
–
TP-1-Post
MeJA
16 August 2013
foliar, same day
–
TP-2-Pre
Baseline
21 August 2013
–
–
TP-2-NC
Negative
23 August 2013
foliar, same day
–
TP-3-Pre1
Baseline
17 September 2013
–
TP-E
TP-3-Pre2
Baseline
19 September 2013
–
TP-E
TP-3-Post
MeJA
22 September 2013
foliar, same day
TP-E
PPu-1-Post
Picea pungens
Blue Spruce
MeJA
16 May 2013
foliar, day before
PP-E1
PPu-2-Pre
Baseline
16 July 2013
–
PP-E2
PPu-2-Post
MeJA
18 July 2013
foliar, day before
PP-E2
PM-1-Pre
Pseudotsuga menziesii
Douglas fir
Baseline
27 August 2013
–
–
PM-1-Post
MeJA
29 August 2013
foliar, same day
–
PM-2-Pre
Baseline
24 September 2013
–
PM-E
PM-2-Post
MeJA
26 September 2013
foliar, same day
PM-E
Mix-1-Pre
Mix-Abies grandis and
Grand Fir and
Baseline
24 July 2013
–
–
Mix-1-Post
MeJA
26 July 2013
foliar, same day
–
Mix-2-Pre
Pseudotsuga menziesii
Douglas fir
Baseline
30 July 2013
–
–
Mix-2-NC
Negative
1 August 2013
foliar, same day
–
MeJA SD
n/a
n/a
Standard
8 May 2014
n/a
–
Stress treatment
Herbivory stress was simulated by exposing plants to MeJA. This compound is
a plant stress hormone with the chemical formula C13H20O3 that
is used in plant–plant communication for defensive purposes (Cheong and
Choi, 2003). Plants emit MeJA into the gas phase, where it induces the
jasmonic acid defense pathway in neighboring plants (Farmer and Ryan,
1990) – a biochemical pathway that leads to changes in the VOCs
produced and emitted from those plants. Consequently, exposing plants to
MeJA alters BVOC emission rates, their chemical profile, and their
concentrations in storage pools (Martin et al., 2003; Rodriguez-Saona et
al., 2001). For the 2012 experiments, MeJA was introduced using an exogenous
treatment where 20 µL of a 9 : 1 diluted ethanol : MeJA solution was
applied to a cotton swab and placed in the biogenic emissions enclosure with
the plants, following the methods of Rodriguez-Saona et al. (2001). The 2013
experiments used a foliar application of 10 mM MeJA in nanopure water,
following the approach of Martin et al. (2003). The plant foliage was
sprayed with 200 mL of this solution. The negative control treatment was a
foliar application of 200 mL of nanopure water rather than the MeJA
solution.
The revised MeJA treatment employed in 2013 was intended to promote a
maximal herbivory stress response. The goal was to allow us to investigate
an upper limit of the potential impacts of herbivory on biogenic SOA
composition, something that has not been reported previously. The foliar
MeJA stress treatment elevates BVOC emissions and typically leads to much
larger mass loadings relative to the pre-treatment experiments. Importantly,
the purpose of these experiments was not to quantify changes to the amount
of SOA formed under stressed conditions. Rather, this research seeks to fill
in current gaps in knowledge by investigating changes to biogenic SOA
composition due to stress.
A number of the post-treatment aerosol growth experiments were performed the
same day as the foliar MeJA application. In these cases, MeJA solution
remained present on the plants in the plant chamber while the aerosol
chamber was being loaded. The vapor pressure of MeJA at 23 ∘C is
1.28 × 10-4 mmHg (Acevedo et al., 2003), which corresponds to an
effective saturation concentration (C*) of 1500 µg m-3. This
puts MeJA at the lower end of the intermediate volatility range (C* range of
1000–100 000 µg m-3) approaching the semi-volatile range (C*
range of 0.1–1000 µg m-3) (Robinson et al., 2007). To compare,
the vapor pressure of alpha-pinene, a typical monoterpene, is 4 orders of
magnitude greater, nearly 3 mmHg at 20 ∘C. Even with MeJA's low
vapor pressure, some of the compound sprayed on the trees would volatilize
and be subsequently pumped into the aerosol growth chamber. This MeJA could
act as an SOA precursor in addition to the VOC emissions from the plant.
Consequently, there are two types of post-treatment SOA in these
experiments: pure plant emission post-treatment SOA and plant emission + MeJA post-treatment SOA. This latter SOA could still be considered a type of
stress SOA because plants do emit significant quantities of plant hormones
in forests when exposed to stressed conditions (Karl et al., 2008). The role
of plant hormones in SOA formation has typically been ignored in plant SOA
experiments. Recently, Richards-Henderson et al. (2014) demonstrated that
aqueous phase oxidation of MeJA had an SOA mass yield of 68 %, suggesting
that this is a compound that warrants further investigation.
Analytical instrumentation
SOA particle number size distributions were measured with a scanning
mobility particle sizer (SMPS, custom built with major components from TSI,
Inc.) described previously by Faiola et al. (2014) and Mwaniki et al. (2014). Aerosol mass spectra were continuously measured using a high-resolution time-of-flight aerosol mass spectrometer (HR-AMS, Aerodyne
Research, Inc.) described in detail elsewhere (Canagaratna et al., 2007;
DeCarlo et al., 2006). Briefly, the HR-AMS collimates sub-micron particles
into a narrow beam with an aerodynamic lens. The particle beam is directed
onto a vaporizer plate held at 600 ∘C that volatilizes all
non-refractory components. The volatilized fragments are then ionized with a
tungsten filament with 70 eV electron impact ionization. These mass
fragments are introduced to a Tofwerk high-resolution time-of-flight mass
spectrometer where they are separated by size and quantified. The HR-AMS was
operated with 1 to 4.5 min sample averaging, alternating between
general mass spectrometer (MS) mode and particle time-of-flight (p-ToF)
mode. Only v-mode data were used in this study because pre-treatment
experiments often did not have sufficient signal for w-mode data to be used.
Ionization efficiency calibrations were performed using the brute force
single particle technique with monodisperse ammonium nitrate particles
generated with a constant output atomizer (TSI Model 3076).
AMS data analysis
The goal of this research was to compare the aerosol mass spectra between
SOA formed from the oxidation of emissions from different types of trees and
between SOA formed under pre-treatment vs. post-treatment conditions. In the
past, unit-mass resolution (UMR) data from the Aerodyne HR-AMS has been
normalized to the sum of the organic mass to compare spectra between
different experiments with different mass loadings (Sage et al., 2008). One
way these UMR spectra can be quantitatively compared is to calculate the
square of the Pearson correlation coefficient (r2), called the
coefficient of determination, between the two spectra (Kiendler-Scharr et
al., 2009). Using this approach, Kiendler-Scharr and colleagues observed
clear differences between biogenic SOA and other types of organic aerosol
including biomass burning organic aerosol (r2=0.44–0.51), diesel
exhaust organic aerosol (r2=0.44–0.51), and ambient hydrocarbon-like
organic aerosol in Pittsburgh (r2=0.16–0.41). For the comparisons
presented here, only those m/z that contributed to 90 % of the HR-AMS UMR
organic signal in any of the experiments was used to calculate the
correlations. The m/z values used in the UMR analysis are listed in the
Supplement.
The composition of organic aerosol can also be described through the use of
elemental analysis (Aiken et al., 2008). Results of such analyses are
presented on a Van Krevelen diagram with axes of hydrogen to carbon (H : C)
and oxygen to carbon (O : C) ratios. In general, laboratory SOA generation
studies produce aerosol that is less oxidized than those found in the
ambient atmosphere (Kroll and Seinfeld, 2008). However, laboratory chamber
studies have also shown a wide variability in elemental ratios that are
dependent on the precursor compounds used to generate the aerosol (Chhabra
et al., 2010; Ng et al., 2010). Consequently, differences in the precursor
compounds from different sources of BVOCs (e.g., different trees, or
pre-treatment versus post-treatment emissions, or the presence of near
semi-volatile plant hormones) could produce differences in biogenic SOA
composition that would occupy different locations in Van Krevelen space.
Experiment conditions. The n.r. stands for not recorded. Particle
volume was calculated from SMPS measurements.
Experiment
Biogenics
Biogenics
Aerosol
Aerosol
Ozone at
Ozone at
Max Particle
Elemental
ID
chamber
chamber
chamber
chamber
experiment
experiment
volume
analysis
RH (%)
temp (K)
RH (%)
temp (K)
start (ppb)
end (ppb)
(µm3 m-3)
PPo-1-Pre
n.r.
n.r.
n.r.
n.r.
70
n.r.
6.24
UMR
PPo-1-Post
n.r.
n.r.
n.r.
n.r.
50
33
6.33
HR
PPo-2-Pre
97 %
301
17 %
298
460
415
21.33
UMR
PPo-2-Post
91 %
300
20 %
298
130
99
11.64
UMR
PA-1-Pre
69 %
300
12 %
299
90
60
25.76
HR
PA-1-Post
79 %
300
12 %
298
130
107
8.19
UMR
PA-2-Pre
88 %
300
19 %
298
255
174
3.48
UMR
PA-3-Pre
96 %
299
26 %
296
126
90
2.7
UMR
PA-3-Post
90 %
300
21 %
298
148
105
6.02
UMR
PA-4-Pre
90 %
300
21 %
299
126
93
3.6
UMR
AG-1-Pre
100 %
299
26 %
297
114
9
156.87
HR
AG-1-Post
96 %
302
26 %
301
126
15
172.45
HR
AG-2-Pre
96 %
304
29 %
302
116
5
17.32
HR
AG-2-Post
97 %
303
26 %
302
124
34
150.33
HR
AG-3-Pre
87 %
305
23 %
303
151
37
36.78
HR
AG-3-NC
86 %
306
20 %
304
146
49
41.73
HR
TP-1-Pre
92 %
306
20 %
305
129
37
3.24
HR
TP-1-Post
97 %
305
25 %
304
129
26
123.98
HR
TP-2-Pre
85 %
303
21 %
302
138
42
13.56
HR
TP-2-NC
94 %
303
24 %
302
144
42
15.65
HR
TP-3-Pre1
99 %
300
26 %
299
128
25
1.78
UMR
TP-3-Pre2
97 %
300
32 %
299
123
3
2.98
UMR
TP-3-Post
96 %
300
25 %
299
116
53
61.93
HR
PPu-1-Post
99 %
298
24 %
298
275
21
104.59
HR
PPu-2-Pre
97 %
304
22 %
303
142
23
16.28
HR
PPu-2-Post
98 %
305
21 %
304
115
21
33.69
HR
PM-1-Pre
96 %
303
20 %
302
195
29
4.06
HR
PM-1-Post
96 %
303
22 %
303
222
16
160.78
HR
PM-2-Pre
98 %
300
33 %
298
212
71
58.84
UMR
PM-2-Post
95 %
300
26 %
298
109
37
118.71
HR
Mix-1-Pre
87 %
306
20 %
305
136
17
5.88
UMR
Mix-1-Post
85 %
306
18 %
304
140
6
133.68
HR
Mix-2-Pre
82 %
303
17 %
302
125
15
21.98
HR
Mix-2-NC
88 %
302
22 %
301
144
22
24.23
HR
Some of the baseline aerosol growth experiments had low HR-AMS signals
(< 10 µg m-3 of organic aerosol). Consequently, the
high-resolution data were screened to ensure adequate signal-to-noise, s/n, for
further HR analysis. All elemental ratios presented from the HR analysis had
a relative standard deviation less than 10 %. For the experiments with low
s/n, elemental ratios of O : C and H : C were parameterized with
UMR data, using the fractions of m/z 44 (f44) and of m/z
43 (f43) to the total organic signal as described by Aiken et al. (2008) and Ng et al. (2011), respectively. The approach used to calculate
elemental ratios (UMR vs. HR) for each experiment is summarized in Table 2
along with other important experimental conditions. Substantial revisions to
the Aiken et al. (2008) approach to elemental analysis have recently been
proposed by Canagaratna et al. (2015). These revisions have not been
incorporated into this analysis. A major motivation for performing elemental
analysis in this work was to make comparisons with previously published results,
which used the earlier methods. Another objective was to compare results
between the different experiments conducted here; the new approach will
affect all ratios similarly and thus will not influence the conclusions
from these comparisons. Other technical considerations related to the HR
data analysis are described in more detail in the Supplement.
Scatter plots comparing the normalized spectra of all three paired
pre-treatment/negative-control experiments. The markers denote the m/z.
Only the 89 UMR m/z signals used in the correlation analyses are plotted.
The x axis is the percent contribution to total organic mass for the
pre-treatment experiment and the y axis is the percent contribution to
total organic mass for the paired negative control experiments. The dashed
gray 1 : 1 lines are shown for reference. AG-3 is an Abies grandis
experiment. TP-2 is a Thuja plicata experiment, Mix-2 is a mixed
Abies grandis and Pseudotsuga menziesii experiment.
Correlations (r2) between the negative control spectra and the
pre-treatment spectra are shown in the boxes on each plot.
We cannot rule out the presence of NOx in the reaction chamber because the
plant chamber contained saplings potted in soil – microbial activity in
soil can be a source of NOx. While NOx was not measured directly, we could
observe that the contribution of nitrogen-containing peaks to total organic
signal was low – N : C ratios ranged from 0.004 to 0.011. Consequently, the
nitrogen-containing signals were not the focus of the analysis presented in
this paper.
Results and discussion
Our analysis of the SOA composition in these experiments show definite
inter- and intra-species variability, but the differences are generally
subtle. In this section, we first present the paired pre-treatment and
negative control experiments to demonstrate the reproducibility of the
chamber system and provide context for the variability that was observed in
other experiments. Next, a summary of all experiments is presented with a
discussion of the inter- and intra-species variation, followed by a
discussion of the post-treatment aerosol spectra. In this section, we
present the first aerosol mass spectra generated from SOA produced via the
gas-phase oxidation of the plant hormone, MeJA. Finally, we present results
of the SOA elemental analysis using a Van Krevelen plot and discuss the
inter-species variability along with implications for stress effects on SOA
composition.
Negative controls
Three sets of paired pre-treatment/negative control experiments were
performed for which AMS measurements are available – one with grand fir
(Abies grandis), one with western redcedar (Thuja plicata), and one with a mix of grand fir (Abies grandis) and
Douglas fir (Pseudotsuga menziesii). Negative controls refer to experiments where the plants were
sprayed with water instead of the MeJA solution. Scatter plots comparing the
normalized UMR organic spectra between the pre-treatment SOA and the
corresponding paired negative control SOA are shown in Fig. 1. The signal
at m/z 28 was removed to avoid air interferences in the UMR spectra
comparisons. The coefficient of determination (r2) for each comparison
is shown, calculated from the square of the Pearson product moment
correlation coefficient. All paired negative control experiments were very
similar, with r2 greater than or equal to 0.990. The reproducibility of
the high correlations between these paired experiments suggests that any
correlations less than 0.99 that were observed in other experiments do truly
reflect differences in SOA mass spectra. Based on these results, we
considered any correlations lower than 0.90 to indicate potentially
noteworthy differences between SOA mass spectra.
UMR comparisons
Correlations (r2) comparing SOA organic UMR spectra from all biogenic
aerosol growth experiments are summarized in Fig. 2. Correlations ranged
from 0.503 to 0.999. In general, the pre-treatment aerosol mass spectra from
all tree types had higher correlation values with respect to each other than
they did with respect to post-treatment aerosol mass spectra. One
pre-treatment experiment, AG-1-Pre (no. 9), stands out clearly with lower
correlation values when compared to all other spectra. During this
experiment, plants may have been exposed to an unidentified stress before
transport to the laboratory (Faiola et al., 2015). This pre-treatment
experiment will be referred to as the unidentified stress (UNID) stress experiment and will be
discussed in detail in a later section. Other than the AG-1-Pre spectrum, all
other pre-treatment SOA spectra had correlations ranging from 0.806 to 0.997
when compared to each other across all tree types.
Summary of all comparisons between biogenic SOA spectra. Each
number on the x and y axes refers to a single SOA growth experiment. The
legend provides a key to match the axis number with its corresponding
experiment ID. Details of each experiment are listed by experiment ID in
Tables 1 and 2. The color scale denotes the strength of correlation between
the two spectra. Due to air interferences, m/z 28 was removed from the
spectra for all comparisons. The figure was organized by year followed by
experiment type (pre-treatment, post-treatment, negative control) followed
by tree type. NC is negative control.
Most of the weakest correlations (excluding the AG-1-Pre spectrum) were found
between comparisons that included the 2013 post-treatment experiments
(nos. 21–30 in Fig. 2). Specifically, the following experiments had the
lowest correlations when compared to other SOA spectra: AG-2-Post (no. 23),
TP-1-Post (no. 24), TP-3-Post (no. 25), PM-1-Post (no. 28), PM-2-Post
(no. 29), and Mix-1-Post (no. 30). This list includes all the experiments
where the MeJA treatment and the aerosol growth experiment occurred on the
same day (Table 1). In contrast, when these six aerosol spectra were
compared to one another, each comparison had r2 greater than or equal to
0.95. This suggests that the MeJA and its oxidation products may have
contributed substantially to SOA formation. This hypothesis and its
environmental implications are explored in detail in a later section on MeJA
SOA.
To further investigate trends in the SOA spectra correlations, all
comparisons were classified by the type of comparison and binned into six
different ranges of correlation values: < 0.6000, 0.6000–0.6999,
0.7000–0.7999, 0.8000–0.8999, 0.9000–0.9499, and 0.9500–0.9999. The results
of this analysis are presented in Fig. 3. The top bar in the figure shows
the results from all types of biogenic SOA comparisons using real plant
emission as the VOC precursor for SOA formation (N=561 total comparisons).
This classification did not include any comparisons with the MeJA standard
SOA spectrum. Nearly 50 % of all comparisons with biogenic SOA had an
r2 greater than or equal to 0.90. The rest of the comparison types were
organized reading top to bottom from the highest to the lowest number of
correlations that fell within the 0.9500–0.9999 correlation bin. Comparisons
between experiments performed different years and comparisons with the
standard MeJA spectrum are discussed in the Supplement (Fig. S2).
All three comparisons of the paired pre-treatment/negative control SOA
spectra were in the highest correlation bin with an r2 greater than or
equal to 0.95. The fifteen comparisons between the post-treatment SOA
spectra where the MeJA treatment occurred the same day as the SOA growth
experiment (SD, PostT) were all greater than or equal to 0.90. The
pre-treatment SOA comparisons were more heavily weighted toward the higher
correlation values than the all comparisons category, with nearly 80 %
of the r2 values greater than or equal to 0.90. Additionally, the
pre-treatment SOA spectra were more similar to one another than the
post-treatment spectra were to one another. This suggests there was more
variability in VOC emissions post-treatment than there was pre-treatment
between the different tree types. The negative control spectra tended to be
more similar to the pre-treatment SOA than the post-treatment SOA with
nearly 80 % of comparisons with r2 greater than or equal to 0.90 for
the former and only ∼ 30 % of comparisons with r2
greater than or equal to 0.90 for the latter. SOA spectra from the same tree
type were more heavily weighted toward the higher correlation bins than SOA
spectra generated from different tree types.
Distribution of correlations classified by type of comparison.
x axis is the % of total occurrences within a given correlation range for
each experiment classification. Each horizontal bar denotes the type of
comparison where the N value in parentheses refers to the total number of
comparisons within that classification. PreT is pre-treatment;
PostT is post-treatment; NC is negative control; SD,PostT is post-treatment
where treatment and SOA growth experiment occurred on the same day; UNID
stress is unidentified stress.
Thirteen paired pre-treatment/post-treatment experiments were performed. Six
of these had GC-MS-FID data available to investigate whether or not a plant
response to the stress treatment had occurred. For several of the intended
pre-treatment/post-treatment comparisons, there were no differences in the
BVOC profile between the pre- and post-treatment experiment. These paired
experiments were excluded from our comparisons after also verifying that the
stress treatment had not produced any significant differences in the SOA
mass spectra (PPo-2, r2=0.92; PPu-2, r2=0.98; PA-3,
r2=0.97). All other pre-treatment/post-treatment comparisons were
included in the analysis even if the BVOC profile only changed minimally
after treatment (PPo-1) or if there were confounding winter dormancy effects
on emissions (PA-1). All of the comparisons without GC data to confirm plant
stress response were included in the analysis. Eight of the 10 remaining
pre-treatment/post-treatment comparisons had r2 between 0.7 and 0.8999,
substantially lower than the negative control spectra comparisons. This
suggests there were small, but possibly significant, differences between the
SOA generated under the baseline emissions scenario and the SOA generated
under the herbivore-stress emissions scenario. A potential plant stress AMS
marker in the post-treatment SOA is discussed further in Sect. 3.4.2.
The weakest correlations between biogenic SOA spectra (excluding the MeJA
single-component standard spectra comparisons) were observed for comparisons
with the UNID stress SOA from experiment AG-1-Pre (no. 9). All SOA spectra
comparisons with the UNID stress spectrum had correlations less than 0.90
and nearly 80 % of the comparisons had r2 less than 0.70. Due to the
dissimilar nature of this SOA spectrum relative to others, we have included
a detailed description of the spectral characteristics of this SOA in a later section (Sect. 3.4).
Post-treatment aerosol mass spectra
Thirteen post-treatment SOA experiments were completed for this study. Of
those, three were performed in 2012 with the exogenous MeJA treatment. The
PPo-1 experiment exhibited small, likely insignificant, differences between
the pre- and post-treatment SOA that could have been due to natural
variation in plant emissions. The BVOC profile indicated that any stress
response was weak if it existed at all. The other two experiments performed
in 2012 may have had a confounding stress effect due to pulling the plants
out of dormancy. For these reasons, the 2012 post-treatment experiments will
not be the focus of this discussion of post-treatment SOA here. Of the 10
post-treatment SOA experiments performed in 2013 with the foliar MeJA
application, four were performed the day after treatment and six were
performed the same day as treatment.
The four experiments that were performed the day after MeJA treatment were
PPu-1-Post, PPu-2-Post, AG-1-Post, and PA-3-Post. Based on the BVOC
profiles, none of the post-treatment experiments where the treatment was
performed on a different day than the aerosol growth experiment would work
as good candidates for identifying a biogenic stress SOA marker in the AMS
spectra. The AG-1-Post SOA spectrum shows confounding effects on the SOA spectra from an apparent unidentified stress, as discussed in Section 3.4. The PPu-1-Post and PPu-2-Post both appear to be representative
of a stress condition for Picea pungens based on the BVOC profiles presented in Faiola et
al. (2015). The stress response for the PPu-1 experiment in particular was
very high. However, the PPu-1-Pre experiment did not produce enough SOA mass
to perform AMS analysis, so there is no baseline Picea pungens SOA spectra for
comparison. The BVOC results from the PPu-2-Pre experiment suggest these
plants may also have been stressed before being brought into the laboratory;
their BVOC profile closely resembled the post-treatment Picea pungens BVOC profile from
the previous experiment. So, no baseline Picea pungens SOA spectra were acquired for
comparison with the post-treatment SOA spectra for these two experiments.
Finally, no stress response was observed during the PA-3 experiment based on
the BVOC profile. Consequently, this post-treatment SOA spectrum could not
be used to identify a stress biogenic SOA marker either.
The remaining six post-treatment SOA spectra were AG-2-Post, PM-1-Post,
PM-2-Post, TP-1-Post, TP-3-Post, and Mix-1-Post. Where BVOC data were
available (PM-2 and TP-3), it suggested there was an identifiable plant
stress response due to the foliar MeJA stress treatment (Faiola et al.,
2015). These six spectra also stand out distinctly on the correlation
summary figure because they had lower correlations with other spectra than
observed for most of the other SOA spectra comparisons (Fig. 2). However,
the influence of the MeJA and its oxidation products needs to be accounted
for when interpreting these spectra. A discussion of these results is
provided in the next section.
Methyl jasmonate SOA
The aerosol mass spectrum of SOA generated from the oxidation of the
single-component MeJA standard is shown in Fig. 4. To the authors'
knowledge, this is the first description of SOA generated from the plant
hormone, MeJA, from ozone-initiated gas-phase oxidation. The dominant
fragments in the normalized mass spectrum were m/z 28, 29, and 44. The
standard MeJA SOA had more of the highly oxidized m/z 44 and less m/z 43
than observed in typical biogenic SOA generated from chamber experiments.
Additionally, there were small, but observable, peaks at m/z 131 and m/z 157
that were not typical of the other biogenic SOA spectra generated in the
work presented here. The lowest correlations between all SOA spectra
acquired throughout these experiments were observed between biogenic SOA
generated from real plant emissions and SOA derived from the oxidation of
the MeJA single-component standard. This is shown in the bottom three
horizontal bars on Fig. S2 in the Supplement. The most
similar spectra to the MeJA standard were those from the post-treatment SOA
where treatment was applied the same day as the SOA growth experiment (SD,
PostT). However, even these correlations were all less than or equal to
0.8999. All other comparisons between biogenic SOA spectra and
single-component MeJA standard spectra had r2 less than 0.80.
The possible influence of MeJA and its oxidation products on SOA composition
could have significant atmospheric implications because plant hormones can
be emitted from forests at rates as high as monoterpenoids when plants
experience stressed conditions in the natural environment (Karl et al.,
2008). For the experiments where the MeJA foliar application occurred on the
same day as the aerosol growth experiments (referred to herein as same-day
experiments), the estimated amount of MeJA vapor transported to the aerosol
growth chamber was between 30 and 70 % of the total monoterpenoid
concentrations. This value was estimated based on the saturation vapor
pressure of MeJA, with the range reflecting variations in monoterpenoid
emission rates from experiment to experiment.
Normalized mass spectra of SOA generated from the oxidation of a
MeJA standard. The x axis shows the m/z value and the y axis denotes the
percent contribution of each m/z to the total organic mass. The chemical
structure and molecular formula of MeJA is shown on the figure.
Corrected 2013 same-day post-treatment SOA
The relative contribution of MeJA to the six same-day post-treatment SOA
spectra was estimated by generating a series of linear combinations of
different relative amounts of the normalized pre-treatment SOA spectra and
the normalized MeJA standard SOA spectra. For each series, an optimized
linear combination was determined based on identifying the combination
spectra that had the strongest correlation with the paired post-treatment
SOA spectra. The results of this analysis for all six same-day experiments
are presented in Fig. 5. In each of the six experiments, the optimized
linear addition spectrum occurred when the contribution of the pre-treatment
spectrum was between 40 and 60 % of the combination spectrum. Thus, MeJA
and its oxidation products were estimated to contribute between 60 and
40 % of the SOA mass in the same-day post-treatment spectra. The
optimized combination spectrum was then subtracted from the normalized
post-treatment spectra to define a residual spectrum for each
experiment. This residual should be more representative of the influence of
stress-induced emissions on post-treatment spectra, having removed the
presumed direct effect of the MeJA present.
Results from the linear addition optimization for all six
experiments where the post-treatment aerosol growth experiment was performed
the same day the foliar MeJA treatment was applied. The x axis denotes the
fraction of the pre-treatment experiment that was included in the linear
addition of the pre-treatment and MeJA standard SOA spectra. The y axis is
the correlation of the linear addition spectra with the paired
post-treatment SOA spectra. The fraction of pre-treatment SOA included in
the linear addition spectra that produced the highest correlation with the
paired post-treatment is shown in the box on each graph.
All six of these residual spectra are shown in Fig. 6. Only the positive
values are shown to focus on the m/z fragments that were remaining after
subtracting off the optimized linear addition of the paired pre-treatment
and MeJA standard spectra. The residual spectra were generally very similar
to one another with r2 > 0.90 for most comparisons. The
residual TP-3-Post was an exception to this with correlations ranging from
0.32 to 0.70 with the other residual spectra. The strongest contributions
across the residual spectra were at m/z 26, 27, 29, 31, 57, 58, 59, 71, and
83. Many of these are consistent with the most enhanced fragments described
earlier from the stress response spectra comparing the paired AG-1-Pre and
AG-1-Post spectra (m/z 26, 27, 31, and 58). The AG-1-Post experiment was
conducted the day following foliar MeJA treatment rather than on the same
day as MeJA, so the contribution of MeJA and its oxidation products to SOA
mass should have been minimal. This further supports the hypothesis that
enhanced m/z 31 and 58 are associated with a biogenic stress response. It is
also worth noting that m/z 29 was the largest fragment in each of the six
residual spectra, and specifically that the HR ion C2H5+ at
m/z 29 was increased more significantly than the other major HR ion at m/z
29, CHO+. Other larger HR ions found prominently in the residual
spectra were C3H5O+ (m/z 57), C2H3O2+ and
C3H7O+ (m/z 59), C3H3O2+ and
C4H7O+ (m/z 71), and C4H3O2+,
C5H7O+, and C6H11+ (m/z 83). At m/z 83, the
C5H7O+ was the most enhanced HR ion. The potential
enhancement of these ions due to biogenic stress response merits further
targeted investigation.
Residual stress spectra calculated by subtracting the optimized
linear addition of the paired baseline + MeJA standard spectra from the
post-treatment stress spectra. The x axis is the m/z value and the y axis is
the residual. Negative residuals have been removed to focus on the enhanced
m/z peaks.
A closer look at Abies grandis (grand fir) SOA
Three paired sets of aerosol growth experiments were performed with Abies grandis
emissions: two pre-treatment/foliar MeJA treatment experiments (AG-1 and
AG-2) and one pre-treatment/negative control experiment (AG-3). The negative
control results were presented in Sect. 3.1. BVOC measurements were
collected during aerosol growth chamber loading for AG-1, but not for the
other two sets of experiments due to a GC instrument malfunction. In the
companion paper, we hypothesized that the Abies grandis saplings used in experiment AG-1
had been exposed to an unidentified external stress outdoors where they were
being stored before being transported to the laboratory chamber (Faiola et
al., 2015). Consequently, this is one of the only experiments where
emissions actually decreased after MeJA treatment relative to the
pre-treatment value. This experiment provided an opportunity to investigate
the effects of a naturally elicited stressor on BVOC emissions and biogenic
SOA composition in contrast to stress elicited from plant hormone
application. Furthermore, despite the presence of an external stressor, the
MeJA treatment still induced emissions of 1,8-cineol and terpinolene
allowing us to investigate the impact of multiple stressors. This scenario
is representative of an environmentally relevant case because the presence
of multiple stressors in the natural environment is likely the rule rather
than the exception. The BVOC profiles during aerosol chamber loading for
experiment AG-1 are shown in Supplement (Fig. S3).
Organic mass spectra of SOA produced from the first Abies grandis pre-treatment
experiment (AG-1-Pre), the second Abies grandis pre-treatment experiment (AG-2-Pre), and
the second Abies grandis post-treatment experiment (AG-2-Post). The AG-1-Pre spectrum
represents a naturally elicited stress condition, the AG-2-Pre spectrum
represents a typical baseline condition, and the AG-2-Post represents a
typical post-treatment condition after MeJA plant hormone application.
The correlation between the AG-2-Pre SOA mass spectrum and the AG-3-Pre
spectrum was very strong, with an r2 of 0.97. The AG-1-Pre SOA spectrum
was less similar to the other two Abies grandis pre-treatment SOA spectra with r2
values of 0.66 (vs. AG-2-Pre) and 0.80 (vs. AG-3-Pre). The aerosol mass
spectra for AG-1-Pre, AG-2-Pre, and AG-2-Post are shown in Fig. 7 to
highlight some of the m/z contributing to the differences between the SOA
spectra. The AG-1-Pre SOA spectrum has a significant cluster of peaks
present around m/z 200 that were not observed in any other aerosol mass
spectra including the other SOA spectra produced from Abies grandis emissions. This
evidence further supports the hypothesis that the AG-1-Pre spectra was not
representative of a typical Abies grandis SOA baseline and that these plants had been
exposed to an unidentified stressor. The AG-2-Pre spectrum is more
representative of a typical baseline SOA spectrum. The mass spectrum of SOA
generated from alpha-pinene dark ozonolysis is shown in the Supplement (Fig. S4; Bahreini et al., 2005) for comparison with the
baseline biogenic SOA spectrum presented in this paper.
To investigate differences in the relative m/z enhancements and reductions
generated under the unidentified stress condition and the MeJA stress
condition, AG-2-Pre was selected to use as a typical Abies grandis baseline spectrum
for comparison. A stress response plot was generated for both the
unidentified stress effect and MeJA stress effect (Fig. 8). The
unidentified stress response was calculated by subtracting the normalized
spectrum of the AG-2-Pre experiment (baseline Abies grandis SOA) from the normalized
spectrum of the AG-1-Pre experiment (unidentified stress SOA). The MeJA
stress response was calculated by subtracting the normalized spectra of the
same AG-2-Pre experiment (baseline Abies grandis SOA) from its paired post-treatment MeJA
stress experiment, AG-2-Post (MeJA post-treatment SOA). The changes to the
m/z profile were substantially different between the two stress scenarios.
The MeJA SOA stress response spectrum demonstrated the most enhanced m/z
values at 15, 26, 27, 29, 31, 57, 58, 59, 71, and 97. The relative
contribution of m/z 43 was reduced. Recall that these spectra have been
normalized to the sum of total organics so a negative value in the stress
response spectra does not necessarily mean that the fragment was inhibited.
Rather, it demonstrates only that the relative contribution to the total has
been reduced. The fragment at m/z 43 is frequently the highest organic
fragment in chamber SOA (Chhabra et al., 2010); therefore, it is not unexpected that
any increases in other fragments will produce a decrease in the relative
contribution of m/z 43. The fragments most enhanced in the unidentified
stress response spectrum were different and included 41, 65–69, 77, 79, 81,
91, 93, 95, 105, 109, 117, 119, and 202.
Stress response spectra comparing the effects of two different
types of stress – an unidentified stress (red) and a MeJA treatment (blue).
The x axis shows the m/z values and the y axis denotes the difference
between the normalized stress spectrum and the normalized baseline spectrum.
The relative enhancement of most of these m/z values in the unidentified
stress response spectrum could be explained by the partitioning of less
oxidized compounds. For example, the two m/z series 77, 79, 81 and 91, 93,
95 are due to enhancements of the HR ions C6H5+,
C6H7+, C6H9+ and C7H7+,
C7H9+, C7H11+, respectively. Compare this to
the most enhanced HR ions in the MeJA stress spectrum, which included
CHO+, C2H5+, CH3O+, C2HO2+,
C3H5O+, C4H9+, C2H2O2+,
C3H6O+, C2H3O2+, C3H7O+.
C3H3O2+, C4H7O+,
C5H5O2+, C6H9O+, and
C7H13+. This list contains many more oxidized fragments than
the enhanced HR ions in the unidentified stress response spectrum.
The weaker presence of oxidized HR ions in the unidentified stress SOA
spectra could be the result of two possibilities or, possibly more likely, a
combination of the two explanations. One explanation is the
unidentified stress-induced emissions of large hydrocarbons, which produced
a higher proportion of larger, less oxidized fragments in the spectra. This
cause is suggested by the cluster of peaks greater than m/z 200,
particularly at m/z 202, in the AG-1-Pre spectrum that were not observed in
any of the other spectra. The HR ion identified here was
C16H10+, a large un-oxidized fragment that could have
originated from a large stress-induced hydrocarbon BVOC emission. The
compounds that contributed to these large m/z fragments were not detected by
the GC system so they cannot be positively identified here. However, large
16-carbon and 18-carbon compounds have been identified following herbivory
stress in other studies (De Boer et al., 2004; Mentel et al., 2013).
Another possibility is that the amount of ozone added to the chamber was not
sufficient to fully oxidize these particles to the same extent as other
experiments because the plant VOC emissions were so high. In all, 114 ppb of ozone
was added at the start of the experiment and it had fallen to 9 ppb by the
end (Table 2). With the high organic particle loadings generated in this
experiment (> 500 µg m-3) it is possible that some of
these larger emissions and their oxidation products were able to partition
to the particle phase in a less oxidized state than would normally occur
under lower mass loadings (Kroll and Seinfeld, 2008). Thus, the higher
emissions generated a large amount of overall organic particle mass, and the
combination of the presence of larger, less volatile emissions (and their
oxidation products) and an oxidant-limited system promoted the partitioning
of less oxidized components to the particle phase.
The correlations between the two paired pre-treatment/post-treatment Abies grandis SOA spectra were 0.86 and 0.77 for AG-1 and AG-2, respectively (Fig. 2).
Thus, despite the presence of an unidentified stressor under pre-treatment
conditions, the stress treatment still produced some small differences
between the pre-treatment and post-treatment SOA spectra in the AG-1
experiment. This is consistent with the BVOC emission profile where
emissions of 1,8-cineol were induced after treatment and the relative
contribution of beta-myrcene, limonene, and terpinolene increased
(Fig. S3, in the Supplement). Five of the top 10 most enhanced
fragments between the AG-1-Post and AG-1-Pre spectra were also observed in
the top 10 most enhanced fragments between the AG-2-Post and AG-2-Pre
spectra: m/z 15, 26, 27, 31, and 58. The dominant HR ions corresponding to
m/z 15, 26, and 27 were CH3+, C2H2+, and
C2H3+. These ions are not very specific and could be
generated from many organic compounds, so it is unlikely that they alone
will provide help in identification of an AMS mass spectral biotic stress
SOA “marker”. The dominant HR ions at m/z 31 and 58 were CH3O+,
C2H2O2+, C3H6O+. These ions could provide
a little more insight into precursors contributing to their presence in the
SOA spectra, and could possibly be the start to identifying AMS markers for
biogenic stress SOA. This will be discussed further in the following
sections while looking at more examples of the post-treatment SOA spectra in
detail.
(a) Summary of the elemental analysis results from all
pre-treatment SOA and negative control SOA. The pre-treatment experiment,
AG-1-Pre, is labeled as an unidentified stress (UNID stress) experiment.
NC is negative control. (b) Summary of elemental analysis results from all
experiments with paired pre-treatment/post-treatment SOA where a MeJA plant
stress response was observed. Green markers denote pre-treatment SOA. Red
markers denote post-treatment SOA. The black asterisk illustrates the
results from the MeJA single-component standard SOA. The dashed lines are
commonly included on Van Krevelen plots to indicate slopes of 0, -1, and -2,
and are included here to put results in context with previous work.
Elemental analysis
A summary of the elemental analysis results for all pre-treatment SOA and
negative control SOA is shown in Fig. 9a. This figure illustrates the
inter-plant variation in biogenic SOA composition. One clear outlier was the
SOA generated in experiment AG-1-Pre – the unidentified stress (UNID stress)
experiment that was discussed previously. All H : C ratios were similar
(∼ 1.5) throughout the pre-treatment experiments. This is
consistent with expected H : C ratios for SOA generated from biogenic
precursors (Chhabra et al., 2010). In contrast, the O : C ratios varied
between different tree types. In fact, the elemental analysis results
demonstrated a higher level of variability between pre-treatment SOA than
was expected from the UMR correlation coefficient analysis. This could
partially be caused by the exclusion of m/z 28 in the UMR analysis. The
CO+ ion was accounted for in the elemental analysis but not in the UMR
analysis. The contribution from organics at m/z 28 was a substantial
fraction of the total signal and is commonly estimated to be around the same
magnitude as m/z 44 – a significant peak for all of these spectra
contributing between 4 and 10 % of total organic signal.
Most pre-treatment SOA had an O : C within the range of 0.3–0.38. However,
there were some exceptions. Specifically, the Pinus aristata SOA had a higher O : C on
average than other pre-treatment biogenic SOA generated from emissions of
other tree types with O : C ranging from 0.39 to 0.47. Similarly, one
pre-treatment Picea pungens experiment and one pre-treatment mix experiment generated
biogenic SOA with higher O : C values than the average. The pre-treatment SOA
from the Picea pungens emissions could have been more representative of a stress
condition based on the BVOC emission profile – stress emissions of
1,8-cineol and beta-ocimene were measured (Faiola et al., 2015). A second
pre-treatment mix experiment was performed and produced SOA with a much
lower O : C than the first, so the high O : C results from the pre-treatment mix
emissions were not reproducible. Two of the three negative control SOA had
some of the lowest O : C ratios that were measured (excluding the UNID stress
experiment). The Thuja plicata negative control had substantially higher O : C than the
others, but it was very similar to the other pre-treatment Thuja plicata experiments.
A summary of the elemental analysis results from all paired pre- and
post-treatment experiments where a plant stress response was observed is
presented in Fig. 9b. The pre-treatment SOA that had a paired
post-treatment experiment where a stress response was observed had O : C that
ranged from 0.32 to 0.41 (excluding the unidentified stress experiment) or
0.32 to 0.37 if the possible mix SOA outlier is excluded as well. The paired
post-treatment SOA had O : C that ranged from 0.42 to 0.46. For all experiments,
the MeJA SOA shifted the O : C ratio to higher values relative to the paired
pre-treatment SOA. Each of these post-treatment experiments were performed
the same day as treatment except for the Abies grandis unidentified + MeJA stress
experiment (AG-1-Post). The unidentified stress post-treatment experiment
resulted in an increase of O : C from 0.19 in the pre-treatment SOA to 0.29 in
the post-treatment SOA. This effect could have been due to the stress
treatment or it could have been due to the unidentified stress waning after
the trees were transported to the laboratory – the post-treatment O : C was
still not as high as most pre-treatment SOA.
For all the same-day post-treatment experiments, the increased O : C could
be due to the oxidation products of the plant hormone, MeJA. The elemental
ratios from the SOA generated from the oxidation of the single-component
MeJA standard are also shown in Fig. 9b in black. Expected elemental
ratios calculated from the optimized linear addition of the baseline spectra
and the MeJA standard spectra yielded elemental ratios that were within
10 % of those measured for the paired post-treatment experiment (for same-day treatment/growth experiment only). This suggests that most of the
increase in the O : C may have been due to the MeJA and its oxidation products
rather than the influence of specific stress compounds in the SOA spectra.
However, the pre-treatment Picea pungens experiment where the plants appeared to be in a
stressed condition also had higher O : C in approximately the same Van
Krevelen space as these other post-treatment SOA. This suggests there are
compounds other than MeJA and its oxidation products that could also produce
SOA in this region of the Van Krevelen plot.
Results summary
The number of experiments and types of tree species examined in this study
has provided a rich, but complex, data set. When experiments are grouped
into categories by common characteristics, clear patterns emerge in the
data. First, we find that the SOA generation methods used in this study were
highly reproducible as evidenced by results from the three paired
pre-treatment/negative control experiments where all SOA spectra comparisons
produced correlations greater than 0.990. These results put all other
comparisons in context and suggest that any correlations less than 0.90 do
truly represent a difference between SOA mass spectra.
Most of the pre-treatment SOA generated from emissions of all tree species
had very similar UMR SOA spectra with nearly 80 % of all pre-treatment SOA
comparisons having an r2 greater than 0.90. This result, when combined
with the diversity in pre-treatment monoterpenoid emission profiles from
these trees presented in Faiola et al. (2015), suggests that aerosol mass
spectra of biogenic SOA formed from ozone-initiated chemistry under baseline
conditions all look very similar even with a different mix of monoterpenes
used to generate the SOA. These results are consistent with findings
presented by Kiendler-Scharr et al. (2009) who found similar AMS
characteristics between biogenic SOA generated from the emissions of
different types of plant species. In contrast, results from HR data analysis
showed a higher degree of variability between pre-treatment biogenic SOA
with O : C values ranging from 0.30 to 0.47 (excluding the UNID stress
experiment).
The presence of stress led to significant differences in the UMR SOA
spectra. For example, the SOA spectrum that was least similar to all other
SOA spectra was generated from the emissions of Abies grandis after the saplings had
apparently been exposed to an unidentified stressor before being transported
to the lab. Consequently, these results were not reproducible but did serve
as an opportunity to investigate a plant's response to a natural stressor.
The presence of substantial, discernible peaks in the UMR spectrum around
m/z 200 indicated the presence of higher molecular weight emissions that
were not identified with the GC system. Large 17-carbon compounds have been
observed as a plant's response to certain types of herbivores and, when
observed previously, resulted in substantially increased SOA yields (Mentel
et al., 2013). The AG-1-Pre results may have been due to a similar
phenomenon. The amount of SOA produced from these emissions was substantial
(> 500 µg m-3) and had a significantly lower O : C than
any other SOA reported here or reported elsewhere (Chhabra et al., 2010).
Other enhanced m/z in the UNID stress spectra were m/z 31 and m/z 58
corresponding to HR ions CH3O+, C2H2O2+, and
C3H6O+.
The other SOA spectra that had the lowest correlation coefficients when
compared to pre-treatment SOA were the 2013 post-treatment SOA. We attempted
to remove the influence of MeJA, and its oxidation products, on the
same-day post-treatment SOA spectra. The resulting residual spectra
highlighted differences to the SOA spectra that were due to the plants
response to the stress treatment. The same m/z that were enhanced in the
UNID spectra, m/z 31 and m/z 58 (HR ions CH3O+,
C2H2O2+, C3H6O+) were also enhanced in
each of the residual spectra. Other prevalent m/z in the residual spectra
were m/z 29 (primarily C2H5+ enhancement), m/z 57
(C3H5O+), m/z 59 (C2H3O2+ and
C3H7O+), m/z 71 (C3H3O2+ and
C4H7O+), and m/z 83 (primarily C5H7O+
enhancement). Ozone is the dominant atmospheric oxidant for many of the
terpenoid compounds emitted by vegetation (Atkinson and Arey, 2003).
Furthermore, no OH scavenger was used to suppress OH chemistry.
Consequently, these AMS results could be representative of what one would
expect in ambient conditions. The enhancement of these ions in ambient
data sets should be investigated to search for this possible biogenic stress
marker in aerosol spectra collected in a natural forest environment.
Additionally, our results demonstrate that plant hormones, such as MeJA, can
contribute to SOA formation and produce distinctive SOA mass spectra with
peaks at m/z 131 and m/z 157. The standard MeJA SOA was substantially more
oxidized than other biogenic SOA as was evidenced by its high relative
proportion of m/z 44 to the total organic mass and its high O : C ratio of
0.52. Plant emissions of stress hormones can equal emissions of monoterpenes
under stressed conditions, and others have even suggested using ambient
measurements of plant hormones to monitor for plant stress at an ecosystem
scale (Karl et al., 2008). It is possible that the mass spectral markers
associated with either the plants response to the stress treatment or the
markers associated with the MeJA plant hormone directly could also be used
to monitor for stress at an ecosystem scale.
Conclusions
The baseline aerosol mass spectra of biogenic SOA produced from real plant
emissions were similar across six different plant species when comparing UMR
results. However, the presence of stress appeared to change the composition
of the SOA to the extent that the UMR aerosol mass spectra looked
significantly different. This mass spectral biogenic stress marker could be
indicative of an herbivory stress aerosol signature in the natural forest
environment when stressed conditions produce stress-induced emissions
including, but not limited to, plant hormones such as MeJA.
Previous work has shown that environmental stresses can have significant and
widely varying impacts on BVOC emission rates and emission profiles.
Stressors may increase, or sometimes decrease, the amount of BVOCs emitted,
and often induce emissions of compounds not emitted under baseline
conditions. The work presented here builds on those previous efforts and
shows that herbivore-induced emissions not only affect the amount of SOA
subsequently formed as shown previously (Mentel et al., 2013), but also
affect SOA composition. Both of these herbivore effects will likely impact
the aerosol radiative properties. Changes to the amount of SOA produced
would have direct impacts on light extinction. The radiative impacts of
stress-induced changes to SOA composition, our primary focus in this work,
are less clear. For example, the involvement of larger hydrocarbon
precursors (> 15 carbons) would likely decrease SOA
hygroscopicity, whereas the involvement of more oxidized precursors (e.g.,
MeJA) would likely increase SOA hygroscopicity The net impact is
difficult to estimate without a more thorough quantitative understanding of
herbivore-induced BVOC emission rates. In addition to radiative effects, it
is also possible for new particle formation mechanisms to be enhanced by
herbivore-induced BVOCs. Recent findings have shown that biogenic emissions
play a critical role in particle nucleation (Riccobono et al., 2014), and
thus increases in herbivore-induced emissions could be expected to enhance
particle nucleation in forests. These potential effects need further study,
though controlled experiments will remain challenging due to the significant
variability in plant behavior that have limited efforts to parameterize
stress-induced emissions so far.
Future work on this topic should investigate SOA mass spectral fingerprints
for other stressors that could induce emissions of non-terpenoid compounds.
For example, any stressor that damages plant membranes produces bursts in
6-carbon oxygenated VOC products from the lipoxygenase pathway. This could be investigated in
the laboratory using real herbivores or pathogens that would damage plant
tissues. Additionally, tissue damage can occur under severe heat stress.
Future work should also generate SOA from the plant hormone methyl
salicylate, which is emitted at higher rates than MeJA and still could have oxidation products with low enough volatility to contribute to SOA formation. To our
knowledge, SOA has not been generated from this major plant hormone that has
been measured at significant levels in a forest environment. Other future
studies should focus on analyzing ambient AMS data sets collected in forest
environments to investigate whether or not the biogenic stress marker that
was identified here can be observed in field measurements. This could serve
as a monitoring tool to identify ecosystem-level plant stress.