Introduction
There is extensive evidence showing that boreal and temperate forests are
affected by anthropogenic activities, both industrial and agricultural. Such
activity results in unprecedented quantities of reactive nitrogen (N) being
released into the atmosphere, subsequently altering global nitrogen and
carbon (C) biogeochemical cycles (Bragazza et al., 2006; Doney et al., 2007;
Ollinger et al., 2002; Magnani et al., 2007; Neff et al., 2002a, b; Pregitzer
et al., 2008). Nitrogen enters natural ecosystems through atmospheric
deposition and biological fixation and is mainly lost through leaching and
gaseous fluxes back to the atmosphere (Hungate et al., 2003). The atmospheric
deposition of N to terrestrial ecosystems may lead to soil and aquatic
acidification, nutrient imbalance and enrichment, plant damage, and microbial
community changes, as well as loss of biodiversity (Bobbink et al., 1998;
Magnani et al., 2007; Lohse et al., 2008; Simkin et al., 2016).
In the United States, the deposition of atmospheric pollutants including N is
monitored by the National Atmospheric Deposition Program (NADP) and the EPA's
Clean Air Status and Trends Network (CASNET). However, these networks focus
only on inorganic N species (e.g., NH3 / NH4+ and
HNO3 / NO3-). Recent studies shed light on the importance of
organic N deposition, which is not routinely measured in national networks.
On a global basis, organic N may contribute ∼ 25 % of the total
N deposition (Gonzalez Benitez et al., 2009; Jickells et al., 2013;
Kanakidou et al., 2012; Keene et al., 2002; Neff et al., 2002a; Zhang et
al., 2012). Although ubiquitous, widespread routine monitoring of organic N
in the atmosphere is inhibited due to difficulties in sampling (Walker et
al., 2012) and inability to fully speciate the wide range of constituents
that make up this large pool of atmospheric N (Altieri et al., 2009, 2012;
Cape et al., 2011; Neff et al., 2002a; Samy et al., 2013). For these
reasons, understanding of the sources, atmospheric chemistry, and deposition
of organic nitrogen remains limited.
Atmospheric N from biogenic and anthropogenic emissions sources undergoes
complex transformation processes and photochemical reactions. Consequently,
the apportionment of atmospheric organic N to potential sources is challenging.
However, such information is required to advance atmospheric N models applied
to better understand the global N cycle. For example, Miyazaki et al. (2014)
examined aerosols collected in a deciduous forest and found in the summer
that water-soluble organic N (WSON) correlated positively with biogenic
hydrocarbon oxidation, and during fall WSON in the coarse particle fraction
was associated with primary biological emissions (e.g., emitted from soil,
vegetation, pollen, and bacteria). Such patterns underscore the fact that atmospheric
organic N measured in forested landscapes originates from a variety of
sources that contribute differently across seasons.
Recent advancements have been made in the speciation of organic N in aerosol for
some groups of compounds including amines, amino acids, and other
nitrogenated functional groups such as organonitrates (Day et al., 2010;
Place et al., 2017; Samy et al., 2013). Organic N in secondary aerosol and
aerosols associated with biomass burning sources are areas of increasing
interest, from both atmospheric chemistry and ecosystem exposure
perspectives for which more information is needed. Studies of secondary organic
aerosol (SOA) have identified a variety of nitrated organosulfate compounds
(e.g., nitrooxy-organosulfates) in both chamber and ambient aerosol samples
following isoprene and monoterpene oxidation. These compounds are either
produced under high NOx conditions or from nighttime NO3 radical
chemistry (Surratt et al., 2006, 2007, 2008, 2010; Darer et al., 2011; Lin
et al., 2013a; He et al., 2014; Worton et al., 2013). Potential SOA
precursors such as unsaturated green leaf volatiles (GLVs) released by
wounded plants (e.g., crop harvesting and insect attacks) may contribute
substantially to the budget of biogenic SOA formation, especially in remote
forests (Gomez-Gonzalez et al., 2008; Hamilton et al., 2013; Shalamzari et
al., 2016). The detection of reaction products such as organosulfates and
nitrooxy-organosulfates in ambient aerosols provides strong evidence of
influence from anthropogenic sources (e.g., SO2 and NOx) interacting
with biogenic precursors to form nitrogenated SOA (Chan et al., 2010; Lin et
al., 2013a; Meade et al., 2016).
In addition to being present in sulfur-containing SOA, organic nitrogen,
specifically nitro-aromatic compounds (e.g., nitrophenols and nitrocatechols), have been characterized as chemical tracers from biomass burning
(e.g.,
wildland and prescribed smoke, bushfires, residential wood burning). This is
in addition to levoglucosan, a widely used tracer of biomass burning (Iinuma
et al., 2010, 2016; Kahnt et al., 2013; Kitanovski et al., 2012; Gaston et
al., 2016). These nitrated compounds can form during the pyrolysis of plant
biopolymers such as cellulose. Furthermore, as combustion by-products, these
compounds are often defined as brown carbon (BrC) and are thus potentially light
absorbing (Mohr et al., 2013; Liu et al., 2015). Presumably, nitro-aromatics
could constitute a substantial portion of atmospheric organic N in aerosols
collected in regions affected by biomass burning.
This study investigates the composition of organic particulate matter in a
remote montane forest in the southeastern US, focusing on the role of
organic N in sulfur-containing SOA and aerosols associated with biomass
burning. The measurements target four groups of compounds: (1) nitro-aromatics
associated with biomass burning; (2) organosulfates and
nitrooxy-organosulfates produced from biogenic SOA precursors (i.e.,
isoprene, monoterpenes, and unsaturated aldehydes) interacting with
anthropogenic pollutants; (3) terpenoic acids formed from monoterpene
oxidation; and (4) organic molecular markers including methyltetrols, C-5
alkene triols, 2-methylglyceric acid, 3-hydroxyglutaric acid, and
levoglucosan. Terpenoic acids and organic markers are included to assist in
characterizing the extent of biogenic compound oxidation and atmospheric
processing (i.e., aerosol aging) as well as contributions from biomass
burning sources. Aerosol bulk chemical measurements are conducted to
estimate total water-soluble organic N and C concentrations.
The characterization of seasonal patterns in concentrations of organic N species
and the assessment of potential sources and formation processes are emphasized.
Experimental methods and materials
Sampling site and atmospheric aerosol collection
The study was conducted at the US Forest Service Coweeta Hydrologic
Laboratory, a 2185 ha experimental forest in southwestern North Carolina,
USA (35∘3′ N, 83∘25′ W) near the southern end of the
Appalachian Mountain chain. The climate is classified as maritime, humid
temperate, with mean monthly temperatures ranging from 3.3 ∘C in January
to 21.6 ∘C in July (Swift et al., 1988). Elevation ranges from 675 to
1592 m with a corresponding range in annual precipitation of 1800 to 2500 mm
(Swank and Crossley, 1988). The vegetation is characterized as mixed
coniferous–deciduous including oak, pines, and hardwoods (Bolstad et al.,
1998). Atmospheric measurements were conducted in the lowest part of the
basin (686 m), colocated with long-term measurements of air and
precipitation chemistry conducted by the CASTNET and NADP networks,
respectively.
The sampling site is 5 km west of Otto, NC (population 2500) and Highway 23
(Fig. S1 in the Supplement). Land to the north, west, and south of
Coweeta is undeveloped forest. Typical rural development is present to the
east of the site, consisting of houses and small-scale farming for hay and
crop production including some scattered cow and horse pastures, which are
small local ammonia (NH3) emission sources. The nearest metropolitan
areas include Atlanta, Georgia (175 km southwest), Chattanooga, Tennessee
(175 km west), Knoxville, Tennessee (110 km north–northwest), Asheville,
North Carolina (100 km northeast), and Greenville, South Carolina (100 km
southeast). The location of the sampling site within the context of NOx and
SO2 point sources in the eastern US is shown in the Supplement
(Fig. S2). Only minor point sources are present within ∼ 100 km of
the site.
The study period summarized here comprises three seasonal intensives
conducted during the spring, summer, and fall of 2015 as part of the Southern
Appalachia Nitrogen Deposition Study (SANDS). Each campaign was conducted
for approximately 3 weeks (21 May to 9 June, 6 to 25 August, 9
to 26 October). A high-volume Tisch TE-1000 (Tisch Environmental,
Cleves, OH) dual-cyclone PM2.5 sampler operated at a flow rate of 230 L min-1 was set up on the ground to collect 24 h (started at 7:00 local time)
integrated samples on pre-baked (550 ∘C for 12 h) quartz fiber (QF)
filters (90mm; Pall Corporation, Port Washington, NY). Under some
conditions, the 24 h integrated filter sampling technique may not fully
retain all semi-volatile organic nitrogen compounds (Gonzalez Benitez et
al., 2009). Field blanks were collected the same way except being loaded in
the sampler without the pump switched on. A total of 58 ambient samples and
10 field blanks were obtained. Collected filter samples were transferred
back to the laboratory in a cooler and stored in a freezer at -20 ∘C
before chemical analysis.
Trace gas and meteorological measurements
During the spring 2015 campaign, NOx concentrations were measured on a short
tower (7 m above the ground) colocated with the CASTNET and high-volume PM
samplers. NOx concentrations were measured using a commercial
NO-NO2-NOx analyzer (model 42S, Thermo Environmental Instruments,
Incorporated, Franklin, MA). Briefly, nitric oxide (NO) is measured directly
on one channel by chemiluminescence. On a second channel, NO2 is
converted to NO by a molybdenum catalyst heated to 325 ∘C,
yielding the concentration of NOx (NO + NO2). This approach may
overestimate NOx since other oxidized nitrogen gases such as HNO3,
PAN,
and HONO could also be reduced to NO on the heated molybdenum surface
(Fehsenfeld et al., 1987; Williams et al., 1998; Zellweger et al., 2000).
However, the use of an inlet filter and approximately 12 m of sample line
between the atmospheric inlet and converter likely minimized the potential
bias from HNO3. For subsequent campaigns, NOx concentrations were
estimated from a colocated NOy analyzer. Similar to the NOx instrument, NOy
and HNO3 were also measured using a modified model 42S NO-NO2-NOx
analyzer. The NOy technique is described in detail by Williams et al. (1998). Briefly, total oxidized reactive nitrogen (NOy) is converted to NO
using a molybdenum catalyst heated to 325 ∘C. On a second
channel, a metal denuder coated with potassium chloride (KCl) is used to
remove HNO3 before passing through a second molybdenum converter
heated to 325 ∘C. The difference between the total NOy
measurement and the HNO3-scrubbed NOy measurement is interpreted as
HNO3. NOx concentrations were estimated from the differences between
measured NOy and HNO3, which provided an upper-bound estimation as
gaseous N-containing species were not excluded (e.g., PAN and organic
nitrates). Hourly ozone concentrations were measured by CASTNET (US EPA,
2017) on a colocated 10 m tower. Hourly meteorological data were provided by
CASTNET (US EPA, 2017) and the Forest Service (Miniat et al., 2015; Oishi et
al., 2018), including temperature, relative humidity, solar radiation, and
precipitation.
Chemical analysis
Elemental and organic carbon analysis
A 1.5 cm2 QF punch was analyzed for elemental carbon (EC) and organic
carbon (OC) using a thermo-optical transmittance (TOT) method (Sunset
Laboratory Inc, Oregon, USA; Birch and Cary, 1996).
Water-soluble species by ion chromatography (IC) and total organic
carbon–total nitrogen (TOC-TN) analyzers
A second QF punch (1.5 cm2) from each sample was extracted with DI
water (18.2 M Ω⋅cm; Milli-Q reference system,
Millipore, Burlington, MA) in an ultrasonic bath for 45 min. The sample
extract was filtered through a 0.2 µm pore size PTFE membrane
syringe filter (Iso-disc, Sigma Aldrich, St. Louis, MO) before subsequent
analyses.
Water-soluble organic carbon (WSOC) and total N (WSTN) concentrations were
measured using a chemiluminescence method that included a total organic
carbon analyzer (TOC-Vcsh) combined with a total nitrogen module (TNM-1; Shimadzu Scientific Instruments, Columbia, MD). For WSOC measurements,
25 % phosphoric acid was mixed with sample extract (resulting in a 1.5 %
acid mixture) and sparged for 3 min to remove any existing
carbonate and bicarbonate.
Inorganic species (NH4+, NO3-, NO2-, and
SO42-) were analyzed using an ion chromatographer (IC; Dionex model
ICS-2100, Thermo Scientific, Waltham, MA). The IC was equipped with guard
(IonPac 2 mm AG23) and analytical columns (AS23) for anions. The samples were
analyzed using an isocratic eluent mix of carbonate / bicarbonate (4.5 / 0.8 mM) at
a flow rate of 0.25 mL min-1. Cations were analyzed by Dionex IonPac 2 mm CG12
guard and CS12 analytical columns; separations were conducted using 20 mM
methanesulfonic acid (MSA) as eluent at a flow rate of 0.25 mL min-1.
Multipoint (≥ 5) calibration was conducted using a mixture prepared
from individual inorganic standards (Inorganic Ventures, Christiansburg,
VA). A mid-level accuracy check standard was prepared from a certified
standards mix (AccuStandard, New Haven, CT) for quality assurance and quality
control purposes.
UV–Vis light absorption analysis
Several studies have shown that methanol can extract aerosol OC at higher
efficiencies than water and that a large fraction of light absorption in
the near-UV and visible ranges is ascribed to water-insoluble OC (Chen and
Bond, 2010; Liu et al., 2013; Cheng et al., 2016). In this study, a QF punch
(1.5 cm2) was extracted with 5 mL methanol (HPLC grade; Thermo Fisher
Scientific Inc.) in a tightly closed amber vial, sonicated for 15 min, and
then filtered through a 0.2 µm pore size PTFE filter (Iso-disc; Sigma
Aldrich, St. Louis, MO). The light absorption of filtered extracts was
measured with a UV–Vis spectrometer over λ= 200–900 nm at 0.2 nm
resolution (V660; Jasco Incorporated, Easton MD). The wavelength accuracy is
better than ±0.3 nm; the wavelength repeatability is less than
±0.05 nm. A reference cuvette containing methanol was used to account
for solvent absorption. The UV–Vis absorption of field blank samples was
negligible compared to ambient samples, but used for correction nonetheless.
For ease of analysis, the absorption at 365 nm referencing to absorption at
700 nm was used as a general measure of the absorption by all aerosol
chromophore components (Hecobian et al., 2010).
Analysis of isoprene and monoterpene SOA markers and anhydrosugars by
GC-MS
Aliquots of each filter (roughly 1/4) were extracted by 10 mL
of methanol and methylene chloride mixture (1 : 1, v/v) ultrasonically twice
(15 min each). The total extract was filtered and concentrated to a
final volume of ∼ 0.5 mL. Next, extracts were transferred to a
2 mL glass vial and concentrated to dryness under a gentle stream of
ultrapure N2 and reacted with 50 µL of N,
O-bis(trimethylsilyl) trifluoroacetamide (BSTFA) containing 1 %
trimethylchlorosilane (TMCS), and 10 µL of pyridine for 3 h at 70 ∘C. After cooling down to room temperature, internal standards
(mixture of 17.6 ng µL-1 acenaphthalene-d10 and
18.6 ng µ L-1 pyrene-d4 mixed in hexane) and pure hexane were added. The
resulting solution was analyzed by an Agilent 6890N gas chromatograph (GC)
coupled with an Agilent 5975 mass spectrometer (MS) operated in the electron
ionization mode (70 eV). An aliquot of 2 µL of each sample was injected
in splitless mode. The GC separation was carried out on a DB-5 ms capillary
column (30 m × 0.25 mm × 0.25 µm; Agilent
Technologies, Santa Clara, CA). The GC oven temperature was programmed from
50 ∘C (hold for 2 min) to 120 ∘C at 30 ∘C min-1 then ramped at 6 ∘C min-1 to a final
temperature of 300 ∘C (hold for 10 min). Linear calibration
curves were derived from six dilutions of quantification standards.
Anhydrosugars (levoglucosan) were quantified using authentic standard;
2-methyltetrols (2-methylthreitol and 2-methylerythritol) and C-5 alkene
triols were quantified using meso-erythritol; other SOA tracers (e.g.,
hydroxyl dicarboxylic acid) were quantified using cis-ketopinic acid (KPA; refer to Table S1 in the Supplement). The species not quantified
using authentic standards were identified by the comparison of mass spectra
to previously reported data (Claeys, et al., 2004, 2007; Surratt et al.,
2006; Kleindienst et al., 2007). Field blanks were collected and no
contamination was observed for identified species.
(a) Individual concentrations of nitrogen components to WSTN
(NH4+, NO3-, NO2-, and WSON). (b) Percent
contribution of WSON to WSTN. (c) Time series of OC, WSOC, temperature, and
precipitation. The start and end dates of each intensive sampling period are
shown.
Analysis of organosulfates, terpenoic acids, and nitro-aromatics by
high-performance liquid chromatography–electrospray ionization quadrupole
time-of-flight mass spectrometry (HPLC-ESI-QTOF-MS)
Approximately 3–5 mL of methanol was used to ultrasonically extract (twice
for 15 min) roughly half of each 90 mm QF sample. Internal standards (IS)
were spiked onto each filter sample prior to extraction (refer to Tables S2,
S3, and S4 for individual compounds and surrogate standards used for each
group of compounds). Extracts were filtered into a pear-shaped glass flask
(50 mL) and rotary evaporated to ∼ 0.1 mL. The concentrated
extracts were then transferred into a 2 mL amber vial that was rinsed with
methanol two to three times. The final sample extract volume was ∼ 500 µL prior to analysis. All the glassware used during the extraction
procedure was pre-baked at 550 ∘C overnight. Extracted samples
were stored at or below -20 ∘C prior to analysis and typically
analyzed within 7 days.
An HPLC coupled with a quadrupole time-of-flight mass spectrometer (1200
series LC and QTOF-MS, model 6520; Agilent Technologies, Palo Alto, CA) was
used for target compound identification and quantification. The QTOF-MS
instrument was equipped with a multimode ion source operated in electrospray
ionization (ESI) negative (-) mode. Optimal conditions were achieved under
parameters of 2000 V capillary voltage, 140 V fragmentor voltage, 65 V
skimmer voltage, 300 ∘C gas temperature, 5 L min-1 drying gas
flow rate, and 40 psig nebulizer. The ESI-QTOF-MS was operated over the m/z
range of 40 to 1000 at a 3 spectra s-1 acquisition rate. Target
compound separation was achieved by a C18 column (2.1×100 mm,
1.8 µm particle size; Zorbax Eclipse Plus, Agilent Technologies)
with an injection volume of 2 µL and flow rate of
0.2 mL min-1. The column temperature was kept at 40 ∘C, and
gradient separation was conducted with 0.2 % acetic acid (v : v) in
water (eluent A) and methanol (eluent B). The eluent B was maintained at
25 % for the first 3 min, increased to 100 % in 10 min, held at
100 % from 10 to 32 min, and then dropped back to 25 % from 32 to
37 min, with a 3 min post-run time. During each sample run, reference ions
were continuously monitored to provide accurate mass corrections (purine and
HP-0921 acetate adduct; Agilent G1969-85001). Typically, the instrument
exhibited 2 ppm mass accuracy. Tandem MS was conducted by targeting ions
under collision-induced dissociation (CID) to determine parent ion
structures. The Agilent software MassHunter was used for data acquisition
(version B05) and for further data analysis (Qualitative and Quantitative
Analysis, version B07). The mass accuracy for compound identification and
quantification was set at ±10 ppm. Calibration curves were generated
from diluted standard compound mixtures. Recoveries of the extraction and
quantification were performed by spiking known amounts of standards to blank
QF filters. Then the spiked blank filters were extracted and analyzed the
same way as ambient collected samples. The average recoveries of standard
compounds are listed in the Supplement Table S5 and ranged from
75.2 ± 5.6 to 129.4 ± 4.2 %. Isomers were identified for
several compounds; no further separation was conducted, and combined total
concentrations are reported in this study.
Summary of particulate and gaseous species measured at the Coweeta
sampling site in 2015.
Spring
Summer
Fall
(µg m-3)
Mean
Median
Min
Max
Mean
Median
Min
Max
Mean
Median
Min
Max
OM (OC ⋅ 2)
3.77
3.41
2.18
7.52
3.80
3.79
2.00
6.32
3.36
2.85
1.96
7.49
EC
0.05
0.05
0.03
0.10
0.05
0.05
0.02
0.08
0.07
0.07
0.03
3.75
WSOC
1.14
1.03
0.45
2.47
1.22
1.24
0.53
2.34
1.09
0.78
0.50
3.25
WSTN
0.33
0.29
0.14
0.86
0.34
0.32
0.11
0.59
0.21
0.20
0.08
0.52
WSON
0.06
0.07
ND
0.14
0.05
0.03
ND
0.11
0.03
0.02
ND
0.13
NH4+-N
0.27
0.24
0.08
0.74
0.29
0.28
0.09
0.48
0.18
0.17
0.08
0.38
NO3--N
0.00
0.00
ND
0.01
0.00
0.00
ND
0.01
0.00
0.00
ND
0.01
NO2--N
0.00
0.00
ND
0.00
0.00
0.00
ND
0.01
0.00
0.00
ND
0.00
SO42-
0.99
0.93
0.26
2.44
1.01
0.95
0.31
1.85
0.63
0.58
0.30
1.33
O3 (ppb)
25.1
21.6
13.9
46.1
15.8
15.8
9.0
22.8
19.4
20.5
11.1
26.9
NOx (ppb)
0.75
0.79
0.45
1.03
0.54
0.58
0.24
0.91
0.65
0.68
0.43
0.89
Temp (∘C)
18.4
18.6
14.8
22.3
20.7
20.6
18.1
22.8
11.6
11.7
5.2
17.1
RH %
81.7
84.9
61.0
94.8
82.1
83.1
71.9
88.5
77.7
74.9
65.1
92.0
Radiation (W m-2)
235
265
81
296
205
201
106
323
151
180
12
203
Source apportionment by positive matrix factorization
Positive matrix factorization (PMF) was used to identify potential sources
of compounds measured at Coweeta. Here we use the PMF2 model (Paatero,
1998a, b) coupled with a bootstrap technique (Hemann et al., 2009), which
has been applied in a number of previous studies (Xie et al., 2012, 2013,
2014). Briefly, PMF resolves factor profiles and contributions from a
series of PM compositional data with an uncertainty-weighted least-squares
fitting approach; the coupled stationary bootstrap technique generates 1000
replicated data sets from the original data set and each was analyzed with
PMF. Normalized factor profiles were compared between the base case solution
and bootstrapped solutions to generate a factor matching rate. The
determination of the factor number was based on the interpretability of
different PMF solutions (3–6 factors) and factor matching rate (> 50 %). Detailed
data selection criteria are presented in the Supplement.
Results and discussion
Meteorology, NOx, and O3
Statistics of atmospheric chemistry and meteorological measurements are
summarized by season in Table 1. In general, the sampling site was humid and
cool, even in the summer, with an average summer temperature of
∼ 21 ∘C and RH of 82 %. During the fall, much lower
temperatures (∼ 12 ∘C) and less humid conditions
(RH = 78 %) were observed. NOx concentrations were generally less than
1ppb, which is considered typical for such a remote forest site removed from
major emission sources.
[O3] (O3 concentration) was generally low (Table 1) with seasonal
averages of 15 to 25 ppb. Historical seasonal [O3] levels over the past 5
years (2011 to 2015) are shown in the Supplement Figure S3. A
spring maximum in [O3] is typically observed at this site, with lower
concentrations during summer. Seasonal clustered back trajectories (Fig. S4 in the Supplement) suggest that during spring the Coweeta
sampling site was under the influence of air masses transported from
Atlanta urban areas. In addition, a spring maximum [O3] may be due to
higher chemical consumption of O3 by reactive monoterpenes and
sesquiterpenes emitted in the forest during summer. With observed relatively
moderate summer temperatures and generally low [NOx], the site also
experiences frequent cloud cover in summer, lowering the intensity of solar
radiation, which may suppress ozone production relative to spring conditions.
Additionally, deposition of O3 to the forest would be expected to peak
during the summer when leaf area is greatest. O3 correlated positively
with NOx in summer and fall but not spring, indicating that O3 production
might be relatively more VOC limited in spring than the other seasons in
this region.
Time series of summed compound group concentrations of
nitro-aromatics, organosulfates, and terpenoic acids.
Bulk water-soluble organic nitrogen and carbon
Water-soluble bulk organic N (WSON) was estimated as the difference between
WSTN and the sum of the inorganic N species (NH4+,
NO3-, and NO2-). The measurement uncertainty of WSON
was estimated to be ∼ 30 % from the error propagation of WSTN
(2 %), NH4+ (1 %), NO3- (1 %), and
NO2- (1 %). Nitrogen component contributions to WSTN are
presented in Fig. 1a, which shows NH4+as the most abundant
component, contributing 85 ± 11 % w/w to total WSTN mass.
Typical NH4+ concentrations at the site were below
1.0 µg m-3(with an average of 0.32 µg m-3),
which is expected for such a remote site with no major local or regional
NH3 sources. The oxidized inorganic N components (NO3-
and NO2-) accounted for less than 2 % w/w of WSTN
measured. Such a small contribution of NO3- to inorganic N
(typically < 10 % of inorganic N
(NO3- + NH4+)) in PM2.5 is consistent with
long-term CASTNET measurements at Coweeta. The average contribution of WSON
to WSTN over the entire study period was 14 ± 11 % w/w. This
fraction reached a maximum of ∼ 18 % w/w in the spring
(average) and a minimum of ∼ 10 % in the fall (average), exhibiting
pronounced seasonal variability. Within individual samples (Fig. 1b), values
ranged from near zero to 45 %. Our study-wide average of 14 % falls
within the range of measurements at North American forest sites, including
Duke Forest, North Carolina (∼ 33 %; Lin et al., 2010) and Rocky
Mountain National Park (14–21 %; Benedict et al., 2012). Moreover, the
observed WSON contribution to WSTN in particles at Coweeta is consistent with
a global estimated range of 10–39 % (Cape et al., 2011).
WSOC accounted for roughly 62 ± 13 % of OC throughout the entire
study period with no significant seasonal variability. A time series of OC
and WSOC along with temperature and precipitation is presented in Fig. 1c. On
average, OC concentrations increased during warmer spring and summer seasons
and decreased when the temperature decreased in fall. Concentrations of OC
were positively correlated with temperature (r=0.30, p < 0.05),
presumably in response to emissions of biogenic precursors and the formation of
secondary organic aerosols by photooxidation. Spring and summer were
generally moist and warm with frequent precipitation (relative humidity
presented in Table 1). Precipitation events corresponded to decreasing OC and
WSOC concentrations, demonstrating the scavenging effect due to wet
deposition.
Spearman rank correlation coefficients among measured species and
meteorological variables as well as other gas-phase measurements are
presented in Table 2 for each season (p < 0.01 for values in
bold). As expected, NH4+ and SO42- tracked well
over each season (r > 0.9, p < 0.01).
NH4+ was mainly associated with SO42- given the
fact that NO3- and NO2- were generally negligible
compared to SO42-. WSOC is often used as an SOA surrogate and
accounts for a significant portion (62 % w/w) of OC during all
sampling periods. WSOC correlated strongly with OC over both summer and fall
(r > 0.95, p < 0.01), but less so during spring
(r=0.74, p < 0.01). WSOC also positively correlated with WSON
over spring and fall (r > 0.75, p < 0.01) but less
so during summer (r=0.5, p > 0.01). Note that both [WSOC]
and [OC] were highest in the summer, which likely indicates higher biogenic
emissions and SOA formation. However, the weak WSON–WSOC correlation suggests
a variety of source contributions to WSON and WSOC over the different
seasons. [EC] was negligible over the entire study except a modest spike at
the end of October when wood burning was most likely the source. Details on
this event are discussed in the subsequent sections. It is also noted that a
stronger correlation of WSON with NH4+ than with
NO3- was observed, which might suggest a key role of reduced
nitrogen in WSON formation (Cape et al., 2011; Jickells et al., 2013).
Spearman rank correlation coefficients among measured species and
meteorological variables by season. Nitro-aromatics (Nitro), Organosulfates
(OS), and terpenoic acids (Tacids) represent group summed concentrations.
Spring
OC
WSOC
NO3-
NH4+
SO42-
WSON
Abs365
Nitro
OS
Tacids
O3
NOx
Temp
RH
radiation
Precip
EC
0.853
0.474
0.177
0.690
0.705
0.129
0.875
0.583
0.645
0.579
0.430
0.263
0.364
-0.627
0.520
-0.458
OC
0.737
0.069
0.767
0.708
0.328
0.773
0.541
0.848
0.761
0.275
0.498
0.543
-0.408
0.441
-0.315
WSOC
0.105
0.523
0.429
0.768
0.424
0.241
0.805
0.723
0.185
0.543
0.472
-0.059
0.135
-0.145
NO3-
0.15
0.137
0.129
0.108
0.492
-0.104
-0.051
0.559
0.084
-0.203
-0.564
0.362
-0.169
NH4+
0.944
0.457
0.842
0.355
0.684
0.298
0.474
0.189
0.439
-0.510
0.441
-0.362
SO42-
0.400
0.827
0.277
0.642
0.229
0.457
0.051
0.540
-0.526
0.374
-0.306
WSON
0.215
-0.113
0.522
0.236
0.215
0.140
0.326
0.080
-0.105
0.055
Abs365
0.456
0.591
0.349
0.495
0.174
0.254
-0.612
0.507
-0.529
Nitro1
0.278
0.426
0.493
0.319
0.021
-0.537
0.307
-0.177
OS2
0.759
0.080
0.341
0.644
-0.084
0.162
-0.140
Tacids3
-0.066
0.571
0.442
0.000
0.141
-0.066
O3
0.068
0.026
-0.797
0.453
-0.219
NOx
0.227
-0.068
0.257
-0.165
Temp
-0.111
0.183
0.061
RH
-0.786
0.551
Radiation
-0.734
Summer
OC
WSOC
NO3-
NH4+
SO42-
WSON
Abs365
Nitro
OS
Tacids
O3
NOx
Temp
RH
radiation
Precip
EC
0.671
0.659
0.113
0.626
0.555
0.562
0.546
0.576
0.474
0.537
0.325
0.242
-0.402
-0.384
0.465
-0.356
OC
0.961
0.233
0.627
0.517
0.556
0.558
0.523
0.856
0.823
0.304
0.289
-0.379
-0.300
0.269
-0.189
WSOC
0.263
0.592
0.490
0.549
0.397
0.564
0.820
0.835
0.247
0.238
-0.302
-0.325
0.259
-0.269
NO3-
0.343
0.271
0.355
-0.143
0.165
0.325
0.469
0.642
0.665
0.120
-0.279
0.263
0.181
NH4+
0.977
0.550
0.405
0.535
0.609
0.585
0.320
0.415
-0.108
-0.388
0.421
-0.218
SO42-
0.465
0.343
0.477
0.487
0.474
0.241
0.350
-0.090
-0.426
0.447
-0.290
WSON
0.170
0.633
0.642
0.692
0.698
0.391
0.026
-0.637
0.555
-0.201
Abs365
0.086
0.423
0.278
0.149
0.140
-0.586
0.012
0.167
0.089
Nitro
0.573
0.614
0.367
0.418
-0.116
-0.346
0.247
-0.446
OS
0.905
0.338
0.472
-0.080
-0.175
0.098
0.087
Tacids
0.432
0.531
-0.150
-0.263
0.138
-0.035
O3
0.621
-0.045
-0.607
0.571
-0.046
NOx
-0.116
-0.049
0.018
0.214
Temp
-0.097
-0.012
0.172
RH
-0.919
0.607
Radiation
-0.583
Fall
OC
WSOC
NO3-
NH4+
SO42-
WSON
Abs365
Nitro
OS
Tacids
O3
NOx
Temp
RH
radiation
Precip
EC
0.719
0.695
0.449
0.216
0.127
0.707
0.897
0.779
0.154
0.472
0.042
0.106
-0.036
-0.044
-0.100
-0.380
OC
0.955
0.077
0.434
0.333
0.837
0.715
0.340
0.554
0.897
-0.282
-0.189
0.525
0.441
-0.441
0.047
WSOC
0.092
0.593
0.494
0.816
0.668
0.362
0.649
0.922
-0.222
-0.152
0.474
0.422
-0.470
0.146
NO3-
-0.044
-0.053
0.106
0.385
0.445
-0.300
-0.088
0.257
0.084
-0.375
-0.461
0.265
-0.385
NH4+
0.983
0.490
0.191
0.209
0.874
0.664
-0.158
-0.096
0.356
0.350
-0.410
0.265
SO42-
0.399
0.100
0.152
0.833
0.571
-0.110
-0.086
0.313
0.290
-0.342
0.244
WSON
0.789
0.486
0.546
0.746
-0.143
0.036
0.364
0.441
-0.538
0.224
Abs365
0.802
0.110
0.494
0.150
0.286
-0.096
0.011
-0.226
-0.273
Nitro
0.001
0.187
0.313
0.445
-0.455
-0.226
0.009
-0.378
OS
0.746
-0.350
-0.356
0.659
0.573
-0.581
0.466
Tacids
-0.401
-0.249
0.653
0.628
-0.587
0.241
O3
0.664
-0.746
-0.820
0.602
-0.340
NOx
-0.719
-0.418
0.389
-0.303
Temp
0.787
-0.639
0.490
RH
-0.847
0.638
Radiation
-0.640
Overall
OC
WSOC
NO3-
NH4+
SO42-
WSON
Abs365
Nitro
OS
Tacids
O3
NOx
Temp
RH
radiation
Precip
EC
0.545
0.422
0.361
0.214
0.216
0.175
0.753
0.642
-0.041
0.283
0.338
0.308
-0.396
-0.500
0.131
-0.449
OC
0.928
0.087
0.672
0.611
0.585
0.615
0.181
0.698
0.828
0.016
0.167
0.281
-0.017
0.207
-0.115
WSOC
0.110
0.643
0.564
0.726
0.444
0.120
0.729
0.848
-0.025
0.116
0.325
0.102
0.172
-0.054
NO3-
0.002
0.018
0.161
0.200
0.310
-0.190
0.097
0.536
0.384
-0.322
-0.433
0.189
-0.127
NH4+
0.976
0.543
0.358
-0.063
0.794
0.567
0.071
0.053
0.528
-0.061
0.313
-0.061
SO42-
0.493
0.348
-0.103
0.733
0.502
0.107
0.029
0.517
-0.107
0.339
-0.072
WSON
0.272
-0.061
0.575
0.590
0.233
0.185
0.356
0.103
0.283
0.044
Abs365
0.372
0.127
0.294
0.303
0.260
-0.218
-0.290
0.100
-0.255
Nitro
-0.302
0.004
0.245
0.153
-0.570
-0.467
-0.177
-0.332
OS
0.721
-0.138
0.004
0.742
0.234
0.244
0.188
Tacids
-0.031
0.230
0.352
0.295
0.086
0.131
O3
0.572
-0.283
-0.574
0.482
-0.152
NOx
-0.200
-0.086
0.287
-0.035
Temp
0.272
0.238
0.242
RH
-0.498
0.618
Radiation
-0.492
1Nitro-aromatics; 2organosulfates;3terpenoic acids; values
in bold indicate p < 0.01.
Nitro-aromatics
Concentrations of nitro-aromatics, organosulfate–nitrooxy-organosulfate, and
terpenoic acids are summarized in Tables 3, S2, S3, and S4. A time series of
compound class totals are presented in Fig. 2. Generally negligible
concentrations of nitro-aromatics were observed during spring and summer
except for occasional spikes. However, higher concentrations of
nitro-aromatics were observed in the fall when moderate correlations were
observed with levoglucosan (Fig. 3, r≥0.5, p < 0.01; see
Table S6 for correlation coefficients). A residential wood burning
contribution is likely given the lower temperatures observed during this
season. Similar positive correlations between nitro-aromatics and wood
burning are also reported during the winter season (Gaston et al., 2016;
Kahnt et al., 2013; Kitanovski et al., 2012; Iinuma et al., 2010, 2016).
Smoke at the sampling site on 19 and 21 October coincided with firewood
burning at the main office of the Coweeta Hydrologic Laboratory immediately
adjacent to the sampling location. Nitro-aromatics were relatively elevated,
but no significant increase in organosulfates or terpenoic acids was found
from these fresh smoke events. In contrast, an example of an aged biomass
burning signal is illustrated on 24 and 25 October. Pronounced spikes of
nitrocatechol (C6H5NO4),
methyl-nitrocatechol (C7H7NO4), and levoglucosan were observed
(Fig. 3), along with elevated concentrations of organosulfates, OC, and aged
biogenic aerosol tracers (terpenoic acids m/z 203 and 187 shown in Fig. 4a;
a
detailed discussion can be found in the subsequent section). However, EC was
only slightly higher. This event did not correspond to local burning at
Coweeta and was most likely associated with long-range transport. Clustering
of backward trajectories (120 h duration for individual trajectories; 48
total trajectories covering the 2-day event) suggests that northeast
Georgia (shown in Fig. S5 in the Supplement) is the most likely origin of the
biomass burning event observed on 24 and 25 October.
Time series of individual nitro-aromatic compounds and
levoglucosan.
Seasonal statistics of measured groups of compounds.
Spring
Summer
Fall
(ng m-3)
Mean
Median
Min
Max
Mean
Median
Min
Max
Mean
Median
Min
Max
Nitro-
0.07
0.00
ND
0.81
0.02
0.02
ND
0.04
0.28
0.17
0.04
1.78
aromatics
Organo-
96.77
83.05
33.07
255.17
153.36
125.41
38.93
306.66
34.69
15.27
0.17
118.68
sulfates1
Terpenoic
325.62
304.05
128.68
771.16
294.01
249.19
115.08
634.99
250.66
148.91
52.94
809.46
acids
% of OM2
% nitro-
0.00
0.00
ND
0.02
0.00
0.00
ND
0.00
0.01
0.01
0.00
0.02
aromatics
% organo-
2.47
2.42
1.19
3.64
3.87
3.80
1.95
5.56
0.98
0.63
0.31
2.21
sulfates
% terpenoic
8.65
8.29
4.62
12.88
7.50
7.77
3.80
11.64
6.48
5.21
2.70
12.00
acids
1Including nitrooxy-organosulfates; 2percent contribution of each group of
identified compounds (combined total) to organic matter.
Nitro-aromatics correlated with EC across the seasons; both were likely emitted
from biomass burning (Gaston et al., 2016; Iinuma et al., 2010; Kahnt et al.,
2013; Mohr et al., 2013). Interestingly, light absorption at λ=
365 nm was highly correlated (r=0.80, p < 0.01) with
nitro-aromatics in the fall when nitro-aromatic concentrations were elevated.
In addition, NOx correlated inversely (r=-0.72,
p < 0.01) with temperature in the fall. Lower fall temperatures
in the region may have resulted in frequent residential wood burning, which
emits NOx and light-absorbing BrC (e.g., nitro-aromatics; Liu et al.,
2015; Mohr et al., 2013). Although nitro-aromatics account for a minor
fraction of OM, they could potentially contribute to 4 % of light
absorption by BrC (Mohr et al., 2013). Overall, nitro-aromatics displayed
relatively week correlation with WSON (r < 0.65) across all
seasons; the extremely low concentrations observed suggest a generally small
contribution of nitro-aromatics to WSON at the sampling site; hence the lack
of strong correlation.
Organosulfates and nitrooxy-organosulfates
Organosulfate concentrations were highest in summer and lowest in fall
(Table 3), contributing 3.9 and 1.0 % w/w of organic matter (OM;
estimated by applying an OM / OC factor of 2) mass, respectively, during
these seasons. Organosulfate formation is an example of heterogeneous
chemistry involving the uptake of reactive precursors on acidified sulfate
aerosols requiring a mixture of biogenic and anthropogenic emissions. The air
masses at Coweeta are mainly from the southwest and westerly directions in
spring and summer, but during fall may become more stagnant and slow moving
during southwesterly conditions or shift to the northwest (see clustered back
trajectories shown in Fig. S4). Because Atlanta, GA is southwest of
Coweeta, southwesterly flow during spring and summer may be associated with
the
long-range transport of urban pollutants and precursors, including sulfate
and sulfuric acid, leading to elevated organosulfate formation compared to
fall when the prevailing wind direction changes.
Among all organosulfates identified, the isoprene-derived organosulfate
(m/z 215, 2-methyltetrol derived), which is formed from isoprene-derived
epoxydiols (IEPOX) under low NOx conditions, was the most abundant;
concentrations reached 167 ng m-3 in summer. Similar high
concentrations were also reported in ambient samples collected at other sites
in the southeastern US (Lin et al., 2013b; Worton et al., 2013). Of the six
nitrooxy-organosulfates identified, isoprene-derived m/z 260 was most
abundant, approximately 6-fold higher than monoterpene-derived m/z 294
nitrooxy-organosulfate.
A subset of possible organosulfates and nitrooxy-organosulfates produced from
isoprene and monoterpene oxidation exhibited strong correlations with
distinctive SOA tracers (e.g., markers 2-methylglyceric acid, C-5 alkene triols
and methyltetrols for isoprene oxidation products; tracer 3-hydroxyglutaric
acid for pinene oxidation products; see Table S7). Lack of correlation
between nitrooxy-organosulfate m/z 294 and 3-hydroxyglutaric acid may
indicate a favored nighttime nitrate radical formation pathway over
photochemical oxidation. Given that NOx levels at the rural Coweeta
sampling site were typically less than 1 ppb, photooxidation pathways
involving high [NOx] to form nitrooxy-organosulfates are less likely.
Though a contribution from photochemical oxidation cannot be ruled out (Lee
et al., 2016; Romer et al., 2016), nighttime nitrate radical chemistry is
most likely the dominating formation mechanism under such conditions. In
contrast to our observations, He et al. (2014) reported good correlations
(r > 0.5, p < 0.01) of m/z 294 with
3-hydroxyglutaric acid and higher daytime m/z 294 concentrations for summer
samples collected in Pearl River Delta, China, where a seasonal average
NOx level of 30 ppb was observed. The authors suggested that the
dominant m/z 294 formation pathway was through daytime photochemistry
rather than nighttime NO3 chemistry. The extremely low NOx
levels at our study site compared to that measured by He et
al. (2014) may explain the opposite behavior in terms of
m/z 294 formation mechanisms.
Organosulfates exhibited statistically significant correlations with WSON
only in the summer (r=0.64, p < 0.01), which reflected the
importance of N-containing organosulfates or their formation chemistry to
WSON during summer compared to the other seasons. During this season,
nitrooxy-organosulfates accounted for ∼ 2 % of bulk WSON on
average. A strong correlation may therefore not be expected.
Terpenoic acids
Terpenoic acids, which provide insight into the extent of biogenic compound
oxidation and atmospheric processing (i.e., aerosol aging), were the most
abundant group of compounds relative to nitro-aromatics and organosulfates.
On average, terpenoic acids accounted for 6.5 to 8.7 % w/w of OM in
PM2.5. The warmer spring and summer periods show higher production of
terpenoic acids compared to the cool and drier fall season. Higher emissions
of biogenic VOC precursors and higher solar radiation intensities
during warm seasons, which drive photochemistry, are factors contributing to
observed seasonal variability.
The terpenoic acids correlated well with WSOC and OC (Table 2). This is
expected as terpenoic acids accounted for a substantial portion of OM at the
site. Individual acids (except compounds C7H10O4 and
C9H14O4) exhibited strong correlations with the pinene-derived
SOA tracer 3-hydroxyglutaric acid (r > 0.75,
p < 0.01; correlation coefficients shown in Table S8), indicating
the presence of α- and β-pinene oxidation products. The poor
correlations between acids C7H10O4 (m/z 157) and
C9H14O4 (m/z 185) suggest the presence of biogenic VOC
precursors other than α- and β-pinene, such as limonene and
Δ3-carene (Gomez-Gonzalez et al., 2012).
(a) Time series of these four identified terpenoic
acids: 3-methyl-1,2,3-butanetricarboxylic acid (MBTCA, C8H12O6,
m/z 203), 2-hydroxyterpenylic acid (C8H12O5, m/z 187),
terpenylic acid (C8H12O4, m/z 171), and diaterpenylic acid
acetate (DTAA, C10H16O6,m/z 231); (b) correlation of
concentration ratios of higher-generation oxidation products (C8H12O6, m/z 203 and C8H12O5, m/z 187) to
early oxidation fresh SOA products (C8H12O4, m/z 171 and
C10H16O6, m/z 231) with temperature and (c) with solar
radiation.
Recent chamber studies identified several terpenoic acid structures that were
also observed in ambient aerosol samples, including
3-methyl-1,2,3-butanetricarboxylic acid (MBTCA, C8H12O6,
m/z 203), 2-hydroxyterpenylic acid (C8H12O5, m/z 187),
terpenylic acid (C8H12O4, m/z 171), and diaterpenylic acid
acetate (DTAA, C10H16O6, m/z 231; Claeys et al., 2009;
Kahnt et al., 2014). MBTCA and 2-hydroxyterpenylic acid have been identified
as highly oxygenated, higher-generation α-pinene SOA markers and
observed in high abundance in ambient aerosols (Gomez-Gonzalez et al., 2012;
Kahnt et al., 2014; Müller et al., 2012; Szmigielski et al., 2007).
Additionally, terpenylic acid and DTAA are characterized as early
photooxidation products from α-pinene ozonolysis. Claeys et al. (2009)
proposed further oxidation processes (aging) of terpenylic acid involving OH
radical chemistry to form 2-hydroxyterpenylic acid. Figure 4 provides a time
series of the terpenoic acids identified in this study. In general,
2-hydroxyterpenylic acid was the most abundant species across the seasons. To
assess the extent of aging, concentration ratios of higher-generation
oxidation products (C8H12O6, m/z 203 and
C8H12O5, m/z 187) to early oxidation fresh SOA products
(C8H12O4, m/z 171 and C10H16O6, m/z 231)
are calculated. Estimated seasonal averages of these ratios are 3.98, 4.37,
and 2.44 for spring, summer, and fall, respectively. Thus, during spring and
summer, aerosols observed at the site were more aged. Figure 4 shows the
correlation of these ratios with temperature (r=0.79,
p < 0.001) and solar radiation (r=0.23, p < 0.1). A
clear relationship between temperature and OH-radical-initiated oxidation
(aging) is evident. However, oxidation appears less dependent on solar
radiation at our sampling site. A similar higher contribution of these aged
biogenic SOA tracers was also reported under warm summer conditions
characterized by high temperature and high solar radiation (Claeys et al.,
2012; Gomez-Gonzalez et al., 2012; Hamilton et al., 2013; Kahnt et al.,
2014). Based on the typical chemical lifetime of biogenic SOA by OH oxidation
and the precipitation frequency at the Coweeta site, biogenic SOA collected
at Coweeta probably had an atmospheric lifetime of several days before
depletion by oxidation processes and/or scavenging by precipitation (Epstein
et al., 2014).
Terpenoic acids may also provide some insight into the formation mechanisms
of organosulfates. While organosulfate concentrations were highest during
summer, correlations with SO42- were strongest during spring and
fall and weakest during summer. Conversely, organosulfates and terpenoic
acids correlated strongly (r=0.91. p < 0.01) during summer.
Terpenoic acids are either first- or second-generation oxidation products from
gas-phase monoterpenes; particulate SO42- abundance should not
substantially influence the gas–particle partitioning of terpenoic acids. The
strong correlation between organosulfates and terpenoic acids in summer
suggests that organosulfate formation is limited by monoterpene emissions rather
than SO42- availability, while in the spring and fall (especially
fall), organosulfate production may be more limited by SO42-.
The degree of particle neutralization, calculated as the molar ratio of
NH4+ to the sum of SO42-and NO3-,
averaged 0.94, 0.98, and 0.94 for spring, summer, and fall, respectively.
Neutralization being close to but less than unity implies that aerosols are
slightly acidic at the site. Chamber studies have illustrated that acidified
SO42- could enhance heterogeneous reactions to form SOA from
isoprene and monoterpenes (Iinuma et al., 2009; Surratt et al., 2007, 2010).
Similar positive correlations observed at the Coweeta site were also found
between isoprene tracers, including isoprene-derived organosulfates and
SO42-, by Lin et al. (2013b) at a rural site in the southeastern
US. However, in contrast to chamber experiments, this study and other
ambient field measurements have not provided clear evidence of the acidity
enhancement of organosulfate formation (He et al., 2014; Lin et al., 2013b;
Worton et al., 2011), indicating possible differences in exact mechanisms and
processing to form these organosulfates under atmospheric conditions relative
to chamber studies. Recent mechanistic modeling simulations by
Budisulistiorini et al. (2017) suggest that the role of sulfate in
IEPOX–organosulfate formation might be through the surface area uptake of IEPOX
and the rate of particle-phase reaction.
Very good correlations between WSON and terpenoic acids were observed during
summer and fall (r≥0.7, p < 0.01). Given the secondary
nature of terpenoic acids, this finding may suggest that WSON during these
two seasons is associated with more aged air masses and perhaps dominated by
secondary organic components rather than primary emitted N-containing
constituents such as pollens, fungi, and bacteria (Elbert et al., 2007;
Miyazaki et al., 2014).
The contribution of identified N-containing species to WSTN and WSON
nitro-aromatics and nitrooxy-organosulfates combined were estimated to
account for as much as 28 % of WSON for samples impacted by local biomass
burning, which reflected the abundance and potential importance of these
groups of species to the atmospheric N-deposition budget. Seasonal average
ratios of identified WSON to WSTN ranged from 1.0 to 4.4 % with the
highest recorded for fall (Table 4). Nitrooxy-organosulfates dominated over
nitro-aromatics as a source of organic nitrogen, contributing
> 90 % to identified WSON across seasons. However, during
episodes of biomass burning, nitro-aromatics contributed as much as 32 %
of identified WSON compounds. The ratio of WSON to WSOC was estimated to be
0.05, 0.04, and 0.02 for spring, summer, and fall, which implies that organic N
is most enriched during spring, reflecting a spring maximum in seasonal
emissions of organic N from biological sources (e.g. pollens, spores, leaf
litter decomposition) combined with smaller contributions from secondary
atmospheric processes. The observed WSON / WSOC ratios in this study were
slightly lower than those reported for other forest sites (0.03–0.09; Lin
et al., 2010; Miyazaki et al., 2014), which are not as remote and pristine as
the forest site in this study. Anthropogenic influences at the study sites
described by Lin et al. (2010) and Miyazaki et al. (2014), such as
[SO42-] and [NOx], were ∼ 5 times higher than those
measured at the Coweeta site. Concentration-weighted average WSON / WSOC
ratios for identified compounds (nitro-aromatics,
organosulfates–nitrooxy-organosulfates, and terpenoic acids) in this study
were estimated to be 0.003. This value is 10 times less than the overall
WSON / WSOC ratio observed at the site, which indicates the existence of
other higher-N-content species in the aerosols. Moreover, the identified
ON / WSON percentage was estimated to be 1.0, 2.0, and 4.4 for spring,
summer, and fall, respectively. Such differences further suggest that much more
unidentified WSON compounds exist in spring when organic N is most enriched
from biological processes.
Ratios of identified nitrogen-containing compounds (nitro-aromatics
and nitrooxy-organosulfates) to WSON.
Spring
Summer
Fall
(ng N m-3)
Mean
Median
Min
Max
Mean
Median
Min
Max
Mean
Median
Min
Max
WSON
59
74
ND
140
46
33
ND
105
25
15
ND
133
Identified
0.48
0.36
0.1
1.75
0.65
0.53
0.12
1.83
0.46
0.26
0.07
1.70
ON
Identified
1.02
0.64
ND
3.09
2.04
1.71
ND
7.84
4.37
1.50
ND
27.90
ON / WSON %
PMF analysis
PMF analysis was conducted to identify individual source contributions to
total WSOC. Factor profiles and time series of factor contributions are
presented in Figs. 5 and 6. Listed in order of percent contribution to WSOC,
the five factors that were resolved include secondary sulfate processing
(35.3 %), isoprene SOA (24.3 %), WSON-containing OM (20.0 %),
biomass burning (15.1 %), and monoterpene SOA (5.2 %). Overall, these
factors could explain 89 ± 2 % of observed WSOC (r=0.88,
p < 0.0001). The secondary sulfate profile contained a signature
of high SO42-, which was most likely present as fine particulate
(NH4)2 SO4 and NH4HSO4. Secondary sulfate
was the most important factor during spring, though it was a significant
contributor in summer and fall as well. Isoprene SOA, which was identified
based on isoprene-derived organosulfates and isoprene SOA markers, was the
most important factor during summer. The biomass burning factor, which
exhibited a high portion of nitro-aromatic and levoglucosan markers,
dominated in the fall. This pattern agreed well with observed patterns of
nitro-aromatic compounds. Monoterpene SOA, which was resolved based on the
composition of monoterpene-derived organosulfates, was overall a minor
contributor with the exception of a few samples during the fall intensive.
Normalized factor profiles (error bar represents 1 standard
deviation).
WSON-containing OM contributed 20 % to WSOC overall, demonstrating a
significant association between organic N and C in PM2.5 at our study
site. The WSON-containing OM source profile exhibited weak correlation with
most measured species with the exception of modest correlations with
terpenoic acids. WSON-containing OM contributed more to WSOC in late spring
and early summer, which was consistent with the observed higher production of
nitrooxy-organosulfates during these sampling periods as well as terpenoic
acids. The relationship with terpenoic acids may reflect an association of
WSON with more aged air masses. Because nitro-aromatics and
nitrooxy-organosulfates contribute only a small portion of WSON on average,
the 20 % contribution of WSON-containing OM to WSOC primarily reflects the
contribution of organic N present in bulk WSON but unspeciated in this work.
Time series of factor contributions to WSOC (mean factor
contribution shown in brackets).
Conclusions
Ambient PM2.5 collected at a temperate mountainous forest site was
investigated on a bulk chemical and a molecular level during spring, summer,
and fall of 2015. Analyses focused on the speciation of nitro-aromatics
associated with biomass burning, organosulfates produced from biogenic SOA
precursors, and terpenoic acids formed from monoterpene oxidation. Among
these three groups, terpenoic acids were estimated to be most abundant,
contributing up to a seasonal average of 8.7 % of OM in PM2.5 during
spring. Warm periods in spring and summer exhibited the highest production of
terpenoic acids, when SOA correspondingly showed a higher degree of aging.
The relative abundance of aged biogenic SOA tracers (MBTCA and
2-hydroxyterpenylic acid), which reflect the degree of organic aerosol aging,
showed a strong correlation with temperature. Such a relationship might
indicate temperature dependence of OH-radical-initiated oxidation steps or
aging in the formation of higher-generation oxidation products.
Organosulfates showed a peak in summer and lowest concentrations during fall,
contributing averages of 3.9 and 1.0 % of OM mass, respectively, during
these seasons. Isoprene-derived organosulfate (m/z 215, 2-methyltetrol
derived), formed from isoprene-derived epoxydiols (IEPOX) under low
NOx conditions, was the most abundant identified organosulfate (up to
167 ng m-3 in summer). This observation is consistent with
observations of low NOx levels (< 1 ppb on average) at our
study site. Nighttime nitrate radical chemistry is most likely the dominant
formation mechanism for nitrooxy-organosulfates measured at this remote site
with background-level NOx.
Nitro-aromatics were most abundant at our study site during the fall (up to
0.01 % of OM mass). Moderate correlations were observed between
nitro-aromatics and the biomass burning marker levoglucosan, indicating a
common origin. Nitro-aromatics also correlated well with EC across seasons.
The highest concentrations of nitro-aromatics, specifically nitrocatechol and
methyl-nitrocatechol, were associated with aged biomass burning plumes as
indicated by correspondingly high concentrations of terpenoic acids.
Bulk measurements determined that WSOC accounted for 62 ± 13 % of
OC throughout the entire study period without significant seasonal
variability. PMF analysis indicated that a significant portion of this
organic carbon was associated with a resolved factor of WSON-containing OM.
As a component of total nitrogen in PM2.5, the largest contributions of WSON
to WSTN were observed in spring (∼ 18 % w/w) and the lowest in
the fall (∼ 10 % w/w). On average, identified nitro-aromatic
and nitrooxy-organosulfate compounds accounted for a small fraction of WSON,
ranging from ∼ 1 % in spring to ∼ 4 % in fall, though
they were observed to contribute as much as 28 % w/w of WSON in
individual samples that were impacted by local biomass burning. Of the
organic N compounds speciated in this study, nitrooxy-organosulfates
dominated over nitro-aromatics as a source of organic nitrogen, contributing
> 90 % to WSON across seasons. As a component of WSON,
nitro-aromatics were most important during episodes of biomass burning, when
their contribution to identified and total WSON was as much as 32 % and
3 %, respectively. Concentration-weighted average WSON / WSOC ratios
for compounds identified in this study were estimated to be 0.003. This
number is an order of magnitude lower than the overall WSON / WSOC ratio
observed, indicating a predominance of other uncharacterized N species. Other
N-containing substituents of WSON could include amino acids, amines, urea, and
N-heterocyclic compounds, as well as substances of biological origin such as
spores, pollens, and bacteria (Cape et al., 2011; Neff et al., 2002a). Ratios
of WSON to WSOC indicate that organic C is most enriched by organic N during
spring, perhaps reflecting a spring maximum in seasonal emissions of organic
N from biological sources combined with smaller contributions from secondary
atmospheric processes (e.g., nitrooxy-organosulfates).
Although nitro-aromatics and nitrooxy-organosulfates contribute a relatively
small fraction of organic N in PM2.5 at our study site, our observations
shed light on this complex but largely unknown portion of the atmospheric N
budget. Our results provide further understanding of the patterns and
composition of SOA in a remote mountain environment previously
uncharacterized. Similar to our results, other studies generally find that
individual groups of organic N compounds (e.g., amines, amino acids, urea)
cannot explain the majority of bulk WSON (Cape et al., 2011; Day et al.,
2010; Place et al., 2017; Samy et al., 2013), which globally accounts for
∼ 25 % of total N in rainfall (Cape et al., 2011; Jickells et al.,
2013). As methodological advances allow for greater speciation of this large
pool of atmospheric N, future work should emphasize the analysis of both primary
and secondary forms of organic N in individual samples, in addition to bulk
analyses, so that a more complete picture of organic N composition may be
developed for specific atmospheric chemical and meteorological conditions.
Additionally, as progress is made in better characterizing the composition
and sources of atmospheric organic N, the ecological and atmospheric science
communities must work together to develop a better understanding of the role
of atmospheric organic N in ecosystem N cycling.