Introduction
The high rate of Arctic warming (e.g., AMAP Assessment, 2015) has led to
considerable interest in the role of light-absorbing components of the
atmospheric aerosol (e.g., Sand et al., 2015). At the same time, the net
impact of the aerosol to Arctic climate has been suggested to be one of
cooling (Najafi et al., 2015). With continued Arctic warming, more open water
may lead to more precipitation (Kopeca et al., 2016) and industrialization of
the Arctic. Correspondingly, contributions from anthropogenic and natural
emissions to the Arctic aerosol are expected to change, underscoring the need
to strengthen our knowledge of the Arctic aerosol in order to offer more
constraints to models and enable better estimates of the impact of aerosol
particles on Arctic climate now and in the future.
Anthropogenic pollution in the Arctic, or Arctic haze, increases during
winter and remains elevated until about mid-spring due to shortened transport
times from southern pollution sources and reduced deposition by precipitation
(Rahn et al., 1977; Shaw, 1983; Barrie, 1986; Stohl, 2006; Law and Stohl,
2007; Quinn et al., 2007). Stohl (2006) showed that Arctic air near the
surface spends about 1 week north of 80∘ N in the winter and 2 weeks
north of 80∘ N in the summer. Below about 2 km, Europe and
northwestern Asia, or Eurasia, define
the dominant source region for pollution north of 80∘ N during
January and February, while contributions from south/central Asian sources
become dominant above 5 km (Stohl, 2006). Simulations by Fisher et
al. (2011) suggest sources of sulfate at the world's northernmost continuous
aerosol observatory situated on the shore of the Arctic Ocean (Alert,
Nunavut, Canada) are dominated by west Asia/Siberia, Europe, oxidation of
dimethyl sulfide (DMS) and volcanism during January and February, and by DMS
oxidation, Europe, east Asia, North America and west Asia/Siberia during
March and April. Simulations of black carbon (BC) by Stohl et al. (2013), Qi
et al. (2017) and Xu et al. (2017) attribute 25–40 % of the winter BC
and about 20 % of the spring BC at Alert to gas flaring in Russia, with
eastern and southern Asia making contributions to the BC of about 20 % in
January and about 40 % in April.
The summertime Arctic aerosol has a stronger association with transport from
ocean regions than continental regions (Stohl, 2006), and it was once
postulated as a representation of the background aerosol of the Northern
Hemisphere (Megaw and Flyger, 1973). Understanding the natural components of
the Arctic aerosol is as important for climate as understanding the
anthropogenic components (Carslaw et al., 2013). For example, the impact of
new particle formation from natural source emissions on summertime Arctic
clouds has been estimated to cool the Arctic atmosphere by 0.5 W m-2
(Croft et al., 2016).
Since aerosol composition measurements began in the Arctic about four decades
ago, when the average composition of the submicron aerosol during
winter–spring was estimated to be 2 µg m-3 of sulfate,
1 µg m-3 of organic compounds,
0.3–0.5 µg m-3 of BC and a few tenths of a
µg m-3 of other substances (Rahn and Heidam, 1981), sulfate
and equivalent BC (BC estimated from particle light absorption) during
winter–spring have declined at three of the four northernmost observatories:
Alert, Nunavut; Mount Zeppelin, Svalbard; Station Nord, Greenland (Heidam et
al., 1999; Hirdman et al., 2010). There have been no significant trends in
either sulfate or BC at the observatory in Barrow, Alaska (Hirdman et al.,
2010). Although measurements of methane sulfonic acid (MSA) from 1980 to 2009
show no net change in MSA at Alert (Sharma et al., 2012), MSA did increase
from 2000 to 2009 associated with the northward migration of the marginal ice
zone (Quinn et al., 2009; Sharma et al., 2012; Laing et al., 2013). Of the
four northernmost observatories, the highest MSA concentrations are measured
at Mount Zeppelin, likely due to its proximity to the waters between
Greenland and northern Europe, that
are a significant source of DMS from May to August (e.g., Lana et al., 2011).
Map showing Alert and identifying regions referenced in FLEXPART
analysis: (1) north-central Russia; (2) southeast Asia; (3) India; (4) western
Russia; (5) the Middle East; (6) Europe; (7) northwest Russia; (8) eastern North
America; (9) the Canadian Oil Sands; (10) Iceland and surrounding waters;
Canadian Northwest Territories (NWT).
Observations of organic components of the Arctic aerosol, aside from MSA, are
varied in detail, location and continuity. Shaw et al. (2010) found that the
total organic mass (OM) concentrations over 1 year at Barrow ranged from
0.07 µg m-3 in summer to 0.43 µg m-3 in
winter. Their organic functional group (OFG) analyses showed the
winter–spring OM consisted primarily of alkane and carboxylic acid groups
from combustion sources and carbohydrate-like substances hypothesized to be
from sea spray in spring and frost flower formation associated with new sea
ice formation in winter (Shaw et al., 2010; Russell et al., 2010). Marine
sources further contribute to OM in spring and summer through emissions of
biogenic volatile organic compounds (BVOCs; e.g., isoprene and terpenes) that
are oxidized in the atmosphere (Fu et al., 2009a), oxygenated VOCs (OVOCs;
Mungall et al., 2017) and trimethylamines (Köllner et al., 2017) as well
as from direct emissions of sea spray (Russell et al., 2010; Frossard et al.,
2011, 2014). During winter and early spring, much of the OM may come from
Eurasian fossil fuel sources (e.g., Behrenfeldt et al., 2008; Nguyen et al.,
2013; Barrett et al., 2015), and it is mixed with sulfates and nitrates
(Weinbruch et al., 2012). Organic acids and organosulfates measured in
samples from Station Nord suggest that OM during October to April is from
distant anthropogenic sources, whereas the year-round presence of organic
sulfates in samples collected at Mount Zeppelin indicates contributions from
local sources as well as long-range transport (Hansen et al., 2014). Tracers
of secondary organic aerosol (SOA) production from BVOC oxidation have been
found in the late spring and summer at Alert (Fu et al., 2009a, b) and in the
Arctic marine boundary layer (Hu et al., 2013). During May–September,
organic acids at Station Nord suggest evidence of a relatively high biogenic
influence (Hansen et al., 2014). Summer observations from ships in the
central Arctic Ocean (Chang et al., 2011) and the southeast Beaufort Sea
(Kawamura et al., 2012), as well as spring samples from Alert (Fu et al.,
2015), indicate OM from both marine and continental sources. Fu et al. (2013)
quantified 5 % of the sampled OC, and found the largest organic compound class was primary saccharides from
marine emissions (Russell et al., 2010) followed by secondary organic groups
likely formed from the oxidation of isoprene and terpenoids. The snow pack is
another potential source of organic precursors (e.g., Grannas et al., 2002;
Kos et al., 2014).
Reported here are the first multi-year measurements of organic aerosol
composition in combination with particle size distributions above
80∘ N. A total of 126 weekly-averaged observations of
submicron-particle chemistry and particle microphysics made at the
Dr. Neil Trivett Global Atmospheric Watch Observatory at Alert, Nunavut
(82.5∘ N, 62.5∘ W; elevation 210 m a.m.s.l.; Fig. 1) from
April 2012 to October 2014 are used to explore the relative contributions of
OM to the particle size distributions and the seasonal contributions to the
aerosol from OFG and offer new observations for model evaluation. The 10-day
back-trajectory analyses using FLEXPART, regression analyses and positive
matrix factorization (PMF) enable some associations of organic aerosol
components with source types and regions.
Methods
Instrumental methods
Routine outdoor high-volume samples of total suspended particles have been
collected at Alert for inorganic chemistry since 1980 (e.g., Barrie and
Hoff, 1985); those filters are not used here. In March 2011, two filters in
stainless steel holders were introduced inside the observatory, each set
behind a cyclone with a 1 µm cut diameter: a Teflon filter for
inorganic analysis and a quartz filter for organic carbon (OC) and
elemental carbon (EC). Those samples are also integrated over a week to
ensure detectable levels. A number of other measurements were also
introduced at Alert in March 2011, including particle number size
distributions and hourly averaged non-refractory particle composition. As a
special study, weekly collections of particles smaller than 1 µm on
Teflon filters were made for OFG analysis by Fourier transform infrared
(FTIR) spectroscopy. The OFG samples began in April 2012 and ended in
October 2014, setting the temporal boundaries for the present work.
The aerosol is drawn into the laboratory through an insulated sampling stack,
which is a vertically oriented 10 cm diameter stainless steel (SS) tube. The
intake is about 10 m above ground, and the flow rate is approximately
1000 L min-1. The aerosol is sampled out of that tube nearer the
center of the flow stream using one of six 0.95 cm SS tubes inserted about
30 cm up from the tube base. From there, ambient particles are delivered to
the sampling devices mostly via stainless steel tubing with some limited use
of other conductive tubing. The residence times of particles from outside to
their measurement point range is less than 3 s, leading to some warming of
the aerosol and reduction in relative humidity (RH).
The Teflon filters for inorganics and quartz filters for OC are sampled at a
flow rate of 27 L min-1. The Teflon filters are analyzed for major
inorganic ions as well as oxalate and MSA by ion chromatography (IC).
Details of the analytical methods and quality control remain the same as
described by Li and Barrie (1993). The quartz filters are analyzed for OC
and EC by thermal volatilization using three temperature steps, as discussed
by Huang et al. (2006), Chan et al. (2010) and Sharma et al. (2017). Here,
OC from this thermal method (hereafter TM-OC) is based on the sum of OC at
550 ∘C and pyrolyzed OC (POC) at 870 ∘C. Rationale for including POC
comes from isotopic analyses indicating little or no carbonate in the
samples. In addition, POC has been found to correlate with OC and water-soluble
OC (Chan et al., 2010).
The Teflon filters for OFG analyses were housed in a wooden box outside of
the observatory at ambient temperature to reduce the potential for
volatilization. For better compatibility with the other measurements, the
aerosol was still sampled out of the main tube with a 0.95 cm SS tube
leading from the bottom of the main tube back outside to the wooden box.
Flows through the OFG were approximately 8 L min-1, and it is assumed
that the 1–2 s the aerosol resided inside the tube within the
laboratory was insufficient to volatilize organic components. Also, for the
relatively low ambient temperatures, the OM may be present in solid form
(Zobrist et al., 2008), reducing volatilization potential. After exposure,
all filters were transferred to storage vessels in a cold area of the
observatory and then stored in a freezer at -19 ∘C until shipped to ECCC
in Toronto where the IC and OC / EC analyses were conducted. The OFG filters
were shipped to Scripps Institution of Oceanography for the OFG analysis.
All samples were shipped in insulated coolers with freezer packs to minimize
potential for volatilization and bacterial influence.
Prior to the OFG analysis by FTIR spectroscopy, the filters were
equilibrated in a temperature- and humidity-controlled cleanroom environment
for 24 h. The filters are stored in sealed petri dishes held below 0 ∘C
until they are moved from the freezer into the cleanroom for 24 h prior to
measurement in the FTIR spectrometer. The cleanroom is maintained at
20 ∘C and < 40 % relative humidity. A continuous N2 purge
is used in the spectrometer. There is no evidence that the composition
changes measurably during the freezer storage or cleanroom equilibration.
FTIR sample spectra were measured with a Tensor 27 spectrometer (Bruker,
Billerica, MA). The spectra were baselined and fitted with peaks to identify
OFG using the method described by Maria et al. (2003), Russell (2003),
Russell et al. (2009) and Takahama et al. (2013). Processed in that way, the
FTIR spectra provide OFG mass concentrations, including alkane, carboxylic
acid, organic hydroxyl, primary amine, carbonyl, alkene and aromatic
groups, through chemical bond-based measurements in atmospheric particles
collected on a substrate (Russell et al., 2009). Alkene, aromatic,
organosulfate and organonitrate groups were below detection limit for all
samples. Ketone and other non-acid carbonyl group contributions are
estimated from a comparison of moles of carboxylic C-OH groups and carbonyl
groups quantified; non-acid carbonyl groups (which can be present in esters,
aldehydes and ketones) are determined by the moles of carbonyl present in
excess of quantified moles of carboxylic C-OH groups. The moles of
carboxylic C-OH and carbonyl groups for which carbonyl was not determined to
be in excess had a correlation coefficient (r) of 0.84 and a regression
slope of 1.0. The non-acid carbonyl is determined to be ketonic rather than
aldehyde carbonyl, as absorption bands between 2700 and 2860 cm-1 indicative
of aldehydic hydrogen were not observed in the Alert
spectra. Further details regarding the interpretation of spectra for
apportioning absorbance to moles of bond or functional group, with
respective detection limits, are provided by Maria et al. (2003) and Russell
et al. (2009). Estimation of mass from these quantities is based on Russell (2003), where moles of measured bonds are converted to the moles of
comprising atoms, and values of OM are calculated from the sum of moles of
atoms multiplied by their respective molecular weights. The uncertainty in
OM has been calculated to be ±23 % (Russell, 2003).
Particle size distributions from 20 to 500 nm diameters at Alert are
measured with a TSI 3034 scanning mobility particle system (SMPS). Sizing
and concentrations of the SMPS are verified on site using monodisperse
particles of polystyrene latex and of ammonium sulfate size selected by
differential mobility using a TSI 3081 differential mobility analyzer (DMA).
Particle size distributions from 500 nm to 10 µm are measured with a
Grimm model 1.109 optical particle counter (OPC).
Measurements of the half-hour-averaged non-refractory chemical components of
particles smaller than 700 nm vacuum aerodynamic diameter (VAD) were made
with an Aerodyne Research Inc. aerosol chemical speciation monitor (ACSM)
(Ng et al., 2011). On-site calibrations of the ACSM are done with nearly
monodisperse particles of ammonium nitrate size selected using the DMA.
The 1 h averaged measurements of sulfate, nitrate and total organics are
available only for February to November 2013. The sulfate estimated from
the ACSM uses an updated relative ionization efficiency (e.g.,
Budisulistiorini et al., 2014).
Vehicles normally park about 700 m from the observatory. For construction at
the site, or if a vehicle must drive to it, the filter sampling is turned
off. Microphysical data are excluded when the wind direction is from 0
to 45∘ true north and for periods of short events (e.g., garbage
burning at Alert station, vehicles in the vicinity) to reduce potential
contamination from the Alert station approximately 8 km from the site. As
shown in the Supplement (Fig. S1), the impact of potential station influence
on concentrations of particles from 100 to 500 nm is negligible and quite
small for all sizes measured with the SMPS. There is no reason to expect
significant contamination of the filter samples.
Comparison of instrumental methods
In addition to TM-OC, OC is calculated from the OFG analysis following
Russell et al. (2009). Time series of both are shown in Fig. S2a. For
114 weeks across the study with coincident TM and OFG samples, the mean TM-OC
and OFG-OC are 113 ± 25 and 68 ± 16 ng m-3,
respectively. These means are slightly outside the uncertainties, suggesting
that TM-OC is significantly higher than OFG-OM. Higher TM-OC may occur due
to adsorption of VOCs by the quartz filters used in the TM analysis relative
to the Teflon filters used in the OFG sampling (e.g., Watson and Chow,
2002). Regression of OFG-OC with TM-OC (Fig. S2b) has a slope of 0.54 and a
coefficient of determination (hereafter CoD) of 0.30.
OM derived from the ACSM (ACSM-OM) is limited to 23 weeks during 15 February
2013 to 6 November 2013, and there are 21 weeks with corresponding ACSM
and OFG filter data. The collection efficiency of the ACSM can be a large
source of uncertainty. Unless strongly acidic, particles comprised of OM or
SO4= may tend to be more solid at the lower Arctic temperatures,
and that will increase the frequency of bounce off the oven before
volatilization (e.g., Middlebrook et al., 2012). The ACSM will underestimate
in comparison with filters cut at 1 µm diameter due to reduced particle
transmission efficiency above about 500 nm geometric diameter (Liu et al.,
2007), and refractory components (e.g., NaCl) go undetected. Also, whereas
the filter samples are integrated over 1 full week with relatively little
interruption, some ACSM data are interrupted over the course of a week due to
instrument problems, inlet zeroes and other disruptions of the instrument
sampling line. Crystalline (NH4)2SO4 measured with an ACSM in
the laboratory has a CE closer to 0.25. Here, two estimates of the CE are
considered. Following Quinn et al. (2006), the ACSM sulfate is compared
with the filter sulfate to derive a CE for each weekly average: the mean of
21 CE estimates is 0.21. The lower CE values may be due in part to the
presence of sulfate in particles larger than 500 nm, which could be
significant based on Fig. 3. Also, we consider a constant CE of 0.5, which
has been derived or assumed in many previous studies with AMSs and is
typical of organic-dominated particles (Middlebrook et al., 2012). A
comparison of time series of OFG-OM, TM-OC and ACSM-OM (sulfate-based CE
and 0.5 constant) for the period of ACSM data is shown in Fig. S2c. The
OFG-OM and the ACSM-OM compare within viable ACSM collection efficiencies.
Linear regressions of OFG-OM with ACSM-OM for the sulfate-based CE and the
constant CE of 0.5 have slopes of 0.52 (CoD = 0.64) and 1.16 (CoD = 0.46),
respectively.
Volume estimates from the filter measurements, the SMPS (< 500 nm)
and the SMPS plus OPC (500–1000 nm) are compared in Fig. S2c. The volume
estimates from the filter mass concentrations are calculated following Eq. (1):
Filtervolume=(OFG-OM/1.2)+NO3-+SO4=+NH4+/1.8+(Na+/2.16)+(EC/2.0),
where the denominators are the assumed component
densities in g cm-3. Linear regressions of the SMPS volumes and the
SMPS+OPC volumes versus the filter volumes through the origin have
respective slopes of 0.68 and 0.84, and CoDs of 0.87 and 0.83
(p < 0.01). The differences in slopes suggest that, on average,
approximately 25 % of the particle volume is found in the 500–1000 nm
particles. Ideally, the slope of SMPS + OPC versus filter volume should be
1. The lower value of 0.84 here may result from a number of issues,
including relatively more particles larger than 1 µm sampled
through the cyclones ahead of the filters versus the OPC 1 µm
definition, assumption of particle sphericity for the SMPS volume estimates
and the density assumptions in Eq. (1).
Overall, the OFG-OM compares with the other methods for measuring the organic
aerosol. The combined inorganic and OFG-OM particle volume concentration
estimates compare reasonably with the volume estimates from the
microphysical measurements. Hereafter, only the OFG-OM is used, and OM
refers to OFG-OM.
FLEXPART and PMF
FLEXPART
The Lagrangian particle dispersion model, FLEXPART 8.2 (Stohl, 2006 and
references therein), was used to construct 10-day back trajectories from the
Alert station during the period of investigation (April 2012 to October
2014). The meteorological fields were driven with ERA-Interim reanalysis
data (Dee et al., 2011) at a spatial resolution of 1∘ × 1∘
and 60 vertical levels. Parcels were released at noon each day
of the period from the Alert observatory location in a 4 m layer centered at 10 m
above the observatory, the approximate height of the sampling intake. The
switches were set so the response function (the trajectory output) was in
units of seconds and could therefore be summed over geographic regions. This
method provides information on how long a parcel of air spent over a region
with no consideration for loss processes or chemical transformation. The
residence times of the trajectories were aggregated by geographic region, as
shown in Fig. 1, for each month of the analysis period.
Positive matrix factorization
PMF has been used with FTIR measurements of atmospheric aerosols to separate
contributions from different sources at various locations from polar to
equatorial regions (Russell et al., 2009, 2011). PMF of the 126 PM1
mass-weighted FTIR spectra using scaling factor matrices calculated from
baselining errors with outliers downweighted during fitting processes (as
described in Russell et al., 2009) for “fpeak” rotational
values of ±1, ±0.8, ±0.6, ±0.4, ±0.2 and 0 resulted in
nearly identical factors. The fpeak value of 0 was used because
it had the minimum Q/Qexpected, a mathematical diagnostic that
describes the accuracy of the PMF fit (Paatero et al., 2002). Seed values of
0 to 100 (varied by 10) showed the consistency of the solutions. For
two-factor to six-factor solutions, Q/Qexpected decreased with
increasing number of factors, indicating that the measured spectra were a
better fit with more factors. However, solutions with 5 or more factors
included factors that had too small a fraction of the average mass to be well
represented by the 126 spectra data set (≤ 5 % OM), did not
correlate to any source markers and had degenerate or unrealistic spectra.
The four-factor solution was identified as the best solution because
solutions with fewer factors had higher Q/Qexpected and did not
sum to reproduce the original spectra as well.
Seasonal mass concentrations (ng m-3); statistics based on
126 weekly concentrations∗.
Period
OM
Alkane
Alcohol
Acid
Amine
Carbonyl
O / C
MSA
nss-SO4=
EC
NH4+
ss-Na+
nss-K+
groups
groups
groups
groups
groups
Averages
Winter (DJF)
132
46
35
24
22
6
0.85
0.9
510
43
51
74
6
Spring (MAM)
220
106
33
40
22
23
0.51
6.5
790
36
102
61
7
Summer (JJA)
65
31
12
14
6
2
0.70
5.4
60
12
17
3
1
Fall (SON)
104
44
20
14
15
12
0.59
2.2
260
15
13
28
1
All
129
57
24
23
16
11
0.65
4.0
380
25
44
37
3
Medians
Winter (DJF)
120
23
27
10
20
0
0.72
0.8
540
22
47
65
6
Spring (MAM)
240
107
22
23
19
0
0.46
4.4
730
34
87
39
7
Summer (JJA)
44
20
9
9
4
0
0.49
5.2
44
6
12
2
0
Fall (SON)
74
34
10
9
8
0
0.51
1.7
88
13
7
5
0
All
88
37
13
11
9
0
0.51
2.5
192
16
27
15
2
∗ Zero is used for samples below detection limit (DL),
including the 99 samples with carbonyl below DL. Median values of zero are
below DL.
Calculation of sea-salt and non-sea-salt quantities
The factors 0.037 × Na+ and 0.251 × Na+ are used
to remove the respective sea-salt components of SO4= and K+
(e.g., Keene et al., 1986), where Na+ is the total. Subsequently, a
factor of 1.15 × (nss-K+), based on the average mass ratio of
Na to K in the Earth's crust, is subtracted from the Na+ to estimate a
sea-salt Na+ (ss-Na+). No iteration is done because the average
mass concentration of nss-K+ was only 11 % of Na+.
Results and discussion
Filter-based chemistry and particle microphysics
Time series of weekly-average temperature, non-sea-salt sulphate
(nss-SO4=), sodium (Na+), EC and OM covering 10 April 2012 to
14 October 2014 are shown in Fig. 2, where increased levels of
nss-SO4= and OM during winter–spring coincide with the lower
temperatures. Seasonal averages and medians of the mass concentrations of OM,
functional groups, major inorganic ions and EC are given in Table 1. OM
ranges from 7 to 460 ng m-3 over the entire
sampling period, which corresponds closely with observations over 1 year at
Barrow, Alaska (Shaw et al., 2010). Average OM is 26, 28, 107 and 39 % of
the average nss-SO4= during winter (DJF), spring (MAM), summer (JJA)
and fall (SON), respectively. The springtime increase in nss-SO4=
relative to winter has been linked to an increase in photochemistry during
the light period (Barrie et al., 1994). A decrease in the mean EC and smaller
increase in median EC than OM from dark to light (i.e., winter to spring),
suggests that the roughly 70 % increase in OM results more from secondary processes following polar sunrise rather
than primary emissions.
Seasonally, OM and nss-SO4= were lowest during the summer, with the
lowest overall during the summer of 2013 (median of 31 and 33 ng m-3,
respectively). Median OM and nss-SO4= during 2012 were 64 and
63 ng m-3, respectively, and 50 and 42 ng m-3, respectively,
during 2014. The summer median and average OM vary according to the average
summer temperatures of +3.4 ∘C for 2012, -0.4 ∘C for
2013 and +1.9 ∘C for 2014. Median EC was also highest for the
warmer summer of 2012: 23 ng m-3 for 2012, 4 ng m-3 for 2013
and 5 ng m-3 for 2014. Such variation is consistent with greater
influence from southern latitudes that offers more temperature-dependent
emissions (e.g., vegetative sources or biomass burning). Similarities in MSA
and ss-Na+ among the three summers (respective MSA and ss-Na+
median ranges: 5–7 and 2–4 ng m-3) suggest a relative steady marine
influence with a lower sea spray component compared to the higher ss-Na+
during winter.
Time series of temperature and weekly-integrated mass concentrations
of OM, EC, nss-SO4= and Na+ for the study period: 10 April 2012
to 14 October 2014.
OM and nss-SO4= from 16 September 2014 to 13 October 2014 (220 and 1000 ng m-3, respectively) were much higher than
typically observed at Alert during that period: during the same periods in
2012 and 2013, respective OM was 77 and 55 ng m-3 and
respective nss-SO4= was 38 and 55 ng m-3. EC was
not higher during September and October of 2014 (10 ng m-3) compared
with 2012 (15 ng m-3) and 2013 (10 ng m-3).
Figure 3 shows weekly-averaged particle volume concentrations of sub-500 nm
particles (SMPS only) and sub-1000 nm particles (SMPS + OPC) versus the
sums of the mass concentrations of the major submicron filter constituents
(OM, NO3-, nss-SO4=, NH4+, Na+ and EC). Also
shown are the weekly-averaged volume-weighted mean diameters
(VMDs) for the distributions below 500 nm and
the OM fraction of the total submicron filter mass concentrations. The OM
fraction is shown as an average of 10-point intervals of successive mass
concentrations, due to relatively high scatter among the weekly points as
indicated by the error bars. The volume concentrations approach the
1.35 g cm-3 density curve (dashed line) at lower mass concentrations
as the OM fraction increases; 1.35 is the average of the densities calculated
as the average of the submicron filter mass concentrations less than
0.25 mg m-3 using Eq. (1). At higher mass concentrations, the
sub-500 nm volume deviates increasingly from the 1.35 g cm-3 curve
due in part to the presence of increasing amounts of material in particles
larger than 500 nm, confirmed by the sub-1000 nm volume points, and in part
due to an increase in density mostly as a result of the higher
nss-SO4= fractions. The OM fraction increases with lower volume
concentrations and for volume size distributions skewed towards smaller
particles: OM is a higher fraction of smaller particles in cleaner air. As
discussed in Sect. 3.4, about 40 % of the weekly variability in OM is
associated with nss-SO4=.
Scatter plot of volume
concentrations, ratio of OM to the sum of other measured chemical components
(NO3-, SO4=, NH4+, Na+ and EC) and particle
sizes (from SMPS) as a function of the “sum” of all major chemical
components (OM, NO3-, SO4=, NH4+, Na+ and EC).
Volume concentrations are shown for particles < 500 nm diameter
(SMPS only) and particles < 1000 nm diameter (SMPS + OPC).
Values of OM/sum are averaged over successive 10 values of the “sum” to
reduce scatter. The error bars represent the range of values of OM/sum for
each 10-point average. The dashed curve is the volume versus mass
concentration for a constant density of 1.35 g cm-3 calculated using
Eq. (1) as the average for the lower mass concentration points
(< 0.25 µg m-3).
Functional groups and oxygenation
Time series of the functional group relative contributions to OM are shown in
the bottom panel of Fig. 4; OM is the sum of the five functional groups.
Alkane, alcohol, acid, amine and non-acid carbonyl groups account for 42, 22,
18, 14 and 5 % of the overall mean OM (129 ng m-3), respectively.
Concentrations of the acid groups (Table 1) are consistent with Kawamura et
al. (2012), who found diacid concentrations generally less than
30 ng m-3 over the Arctic Ocean in summer. Time series of O / C
and OM are shown in the top panel of Fig. 4. The O / C is calculated from
the OFG as described by Russell et al. (2009). Variations in the
ACSM ratio of m/z 44 to m/z 43 (see
Fig. S3) are an expression of the degree of OM oxygenation (e.g., Ng et al.,
2010). Over the limited range of ACSM data, the 44/43 variations are
generally consistent with those of the O / C.
Monthly averages of OM, the functional groups, EC and nss-SO4= are
shown in Fig. 5a. Seasonal patterns of most pollutants in the Arctic are
generally well known (e.g., Quinn et al., 2007). Here, the most unique
observation is the maximum in OM in May, 1 month after that of
nss-SO4=, which is 1 month after the maximum in EC; the OM offset is
present in each of the three springs sampled and is not a result of
averaging. The peak in OM is largely due to increases in alkane and acid
groups. Figure 5b shows monthly-averaged OM plotted with the ratio of OM to
nss-SO4=, ss-Na+, MSA, the seasonal pattern of incident solar
irradiance (as a percentage of the yearly total) and O / C (multiplied by
10). The OM peak is coincident with the maximum in MSA and decreasing
ss-Na+, suggesting that the May peak in OM may be partly influenced by
secondary processes associated with marine sources. The maximum in
OM / nss-SO4= occurs in August, which is also when new particle
formation at Alert is a maximum (Leaitch et al., 2013; Freud et al., 2017).
The O / C August peak is entirely due to the summer of 2013: August
O / C is 0.47 in 2012, 1.67 in 2013 and 0.53 in 2014 (also, see
Sect. 3.4). Seasonally averaged O / C is highest during winter at 0.85,
which is at the upper end of values commonly observed in the atmosphere
(e.g., Aiken et al., 2008). The average spring O / C of 0.51 is
seasonally lowest. That would seem to contrast with the increase in
photo-oxidation potential following polar sunrise, but the differences among
the seasons suggests sources may be more important here.
Time series of weekly-averaged OM and O / C (top panel) and
percent contributions of the functional groups to OM (bottom panel).
Monthly averages showing the annual variations of (a) OM,
functional group concentrations, EC and nss-SO4=, and
(b) OM, OM / nss-SO4=, ss-Na+, O / C
(multiplied by 10) and MSA. Also shown in panel (b) is the annual
pattern of solar irradiance as a percentage of the total irradiance across a
year.
Potential source regions
Here, air parcel times over specific regions, derived from the particle
trajectory model FLEXPART, are associated with the chemical components of the
particles. Times are defined as percentages per month within 100 m of the
surface over a geographic region during the 10 days prior to reaching Alert.
The identified regions, shown in Fig. 1, were mostly selected to coincide
with higher anthropogenic emissions based on the NASA Ozone Monitoring Instrument (OMI) SO2 emissions
map (http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI; Krotkov et
al., 2016) with two exceptions: Region 10 (Iceland and surrounding waters)
was isolated because of emissions from volcanic fissure eruptions on Iceland,
specifically the Bárðarbunga volcano during late 2014 (e.g., Gauthier
et al., 2016; Schmidt et al., 2015; McCoy and Hartmann, 2015); the Canadian
Northwest Territories (NWT) region was included because it is a potential source of
biomass burning (BB) aerosol to Alert during summer and it includes the
Smoking Hills region of continuously burning lignite deposits at
approximately 69.5∘ N, 126.3∘ W (e.g., Macdonald et al.,
2017). Percentages of time spent over Regions 1–10 are shown in Fig. 6;
relative times over NWT are shown in Fig. S4.
Coefficients of determination (r2) for particle mass
concentration linear regressions with times spent over indicated regions
using monthly averages. Uncertainties in time over region are
< 75 %. Dark months are NDJF and spring months are MAM. Values
in italic font indicate p of slope is < 0.10. Values in bold font
indicate p of slope is < 0.05. A minus sign in parentheses
indicates a negative slope.
Time period
All: 25
All: 27
Dark months (8)
Spring months (7)
months
months
Region
1
1467
1
1467
1
1467
Particle species
nss-SO4=
0.14
0.14
0.54
0.61
0.60
0.59
EC
0.46
0.44
0.36
0.44
0.50
0.54
ss-Na+
0.25
0.28
0.13
0.16
0.17
0.13
NO3-
0.13
0.11
0.02
0.00
0.04
0.02
NH4+
0.05
0.04
0.54
0.59
0.02
0.03
nss-K+
0.30
0.25
0.87
0.77
0.50
0.54
MSA
0.16 (–)
0.22 (–)
0.01
0.00
0.10 (–)
0.12 (–)
OM
0.04
0.02
0.42
0.39
0.24 (–)
0.27 (–)
Alkane groups
0.00
0.00
0.03
0.04
0.14 (–)
0.16 (–)
Alcohol groups
0.11
0.11
0.44
0.32
0.26 (–)
0.28 (–)
Acid groups
0.02
0.00
0.19
0.20
0.42 (–)
0.45 (–)
Amine groups
0.15
0.15
0.25
0.37
0.00
0.00
Regressions of chemical concentrations with time over regions were done for
weekly- and monthly-averaged values. Figure 7a shows time series of
weekly-averaged concentrations of the dominant chemical component (nss-SO4=)
and percentage of time spent over the dominant region (Region 1; see Fig. 6).
Region 1 includes emissions from gas flaring that are believed to be a
significant source of BC to the high Arctic (e.g., Stohl et al., 2013; Sand
et al., 2013; Qi et al., 2017; Xu et al., 2017). Figure 7b shows the
monthly-averaged time series of the same two quantities shown in Fig. 7a.
More variations and offsets are apparent in the weekly averages leading to
lower CoD for linear regressions: CoDs for monthly and weekly averages are
given in Tables 2 and S1, respectively. The number of regressions with
significance at the 95 and 90 % levels is slightly higher using monthly
averages (29 and 40 %, respectively) compared with weekly averages (26
and 38 %), and the explanation of variance is higher using monthly
averages. Lower CoDs for the weekly-averaged results are likely due to
matching of 1-week samples with transport that may range from a few days to
more than 10 days (e.g., Qi et al., 2017). The broader patterns are better
represented by the monthly averages.
Monthly-averaged percentage of previous 10 days spent below 100 m
over Regions 1–10 identified in Fig. 1. Results are from the FLEXPART
trajectory analyses for daily air parcels arriving at Alert.
Correlations of chemical components at Alert with time spent over a region
are affected by many factors other than whether or not the region emits
those components or their precursors. For regions with primary emissions
enhanced by wind, correlations with time may be reduced because higher winds
will enhance aerosol but reduce time. For regions of significant particle
precursor emissions that lead to secondary particle formation, higher wind
speeds may dilute emissions potentially improving correlations. Variations
in deposition will alter the association of time over a region with particle
components. Also, in this case, sources with injection heights above 100 m
may be excluded. Still, time spent over a region offers a broad indication
of its potential importance for the aerosol at Alert.
(a) Time series of percentage time over Region 1 from
weekly averages of FLEXPART analysis with weekly-averaged nss-SO4=
mass concentrations. (b) Time series of time over Region 1 from
monthly averages of FLEXPART analysis with monthly-averaged nss-SO4=
and OM concentrations. The bars in panel (b) delineate the dark
months (NDJF) and spring months (MAM).
Region 1 dominates the times among the identified regions of anthropogenic
emissions (Regions 1–9). Collectively, Regions 1, 4, 6 and 7 (hereafter
Regions 1467), mostly covering Eurasia, comprise over 98 % of those
times, consistent with the analysis of Freud et al. (2017). The CoDs and
significance levels for linear regressions of the major chemical components
with relative time spent over Region 1 and Regions 1467 are given in Table 2.
Total months are reduced from 31 after constraining the monthly uncertainties
in the FLEXPART results to less than 75 %. Overall, the differences
between whether the species are regressed against Region 1 or Regions 1467
are small. For all months, the highest positive correlations are for EC,
nss-K+ and ss-Na+, and there are no correlations for OM or the
functional groups except the amine groups. When the points are confined to
only the periods of the dark months (NDJF) and sunlit spring months (MAM),
relatively high correlations of nss-SO4= and nss-K+ emerge for
both periods. Two potential sources of submicron nss-K+ are mining
activities in Regions 1 and 4, particularly potash mining (e.g., Orris et
al., 2014), and sweetening of gas flaring emissions by KCO3. Amine
solutions are also used to reduce acid gas emissions during gas flaring
(e.g., Rochelle, 2009; Wu et al., 2004), and that may be a factor in the
modest association of amine groups with Regions 1467. The strength of the
correlations of nss-K+ decreases from winter to spring as the relative
time over these regions decreases (Fig. 6). Ammonium
and nss-SO4= are associated with Regions 1 and 1467 during winter but
only nss-SO4= during spring. Combined with a greater relative
increase in spring compared with nss-SO4= (Table 1), it suggests that
sources of NH4+ are much different from winter to spring. The
negative correlations with MSA are not surprising considering Regions 1467
are mostly land surfaces, and subsequent transport to Alert is mostly over
ice; this transport pathway may be a net sink for MSA.
Regressions with time over Regions 1467 of dark- and spring-month
nss-SO4= and EC and dark-month OM (Fig. S5) are significant at the
90 % confidence level or higher. The regression results are used to
estimate percentages of those components associated with those regions by
applying the CoD to the average concentrations (Table 1) after subtraction
of the regression intercepts. Intercepts for the dark months are 271, 16 and 86 ng m-3 for nss-SO4=, EC and
OM, respectively; for spring, respective nss-SO4= and EC
intercepts are 641 and 29 ng m-3. As a result, it is
estimated here that 29 % of the nss-SO4=, 28 % of the EC and
14 % of the OM are associated with Region 1 during the dark months. During
spring, the estimates drop to 11 % for each of nss-SO4= and EC,
with no association for OM. These are minimum estimates since the
concentrations represented by the intercepts may include contributions from
Regions 1467. For comparison, simulations of BC with FLEXPART (Stohl et al.,
2013) and with GEOS-Chem (Xu et al., 2017) attribute 30–40 % of the winter
BC and about 20 % of the spring BC at Alert to gas flaring in Russia. In a
separate study using GEOS-Chem, Qi et al. (2017) estimated that 13 % of
the BC at Alert during March–April 2008 was from gas flaring in northern
Russia. The present results are in line with these modeled estimates.
The 11 % estimate of spring nss-SO4= associated with Regions 1467 indicates that most of nss-SO4= likely originated from other
areas, and the same can be said for OM. According to the simulations of Qi
et al. (2017) and Xu et al. (2017), emissions from eastern and southern Asia
(Regions 2, 3 and 5 here) contribute significantly to BC at Alert during
winter and spring. Times spent over Regions 2, 3 and 5 are indistinguishable
in Fig. 6. As suggested by Qi et al. (2017), the absence of a connection
with time spent over eastern and southern Asia is consistent with emissions
from this region taking longer to reach Alert than the 10 days used in the
present FLEXPART analysis. Longer transport times will tend to buffer
variations, and OM and nss-SO4= with origins in eastern and
southern Asia are likely represented by the regression intercepts, forming
part of the constant Arctic haze aerosol discussed by Brock et al. (2011).
Coefficients of determination for linear regressions based on
weekly-averaged samples. Values in italic font indicate p of slope is
< 0.10. Values in bold font indicate p of slope is
< 0.05. A minus sign in brackets indicates a negative slope.
Species
OM
Alkane
Alcohol
Acid
Amine
O / C
EC
ss-Na+
NH4+
nss-K+
MSA
groups
groups
groups
groups
All weeks (125, except for values with EC based on 119)
nss-SO4=
0.30
0.22
0.04
0.03
0.32
0.03 (–)
0.30
0.20
0.48
0.64
0.01
EC
0.23
0.22
0.00
0.18
0.11
0.02 (–)
0.01
0.30
0.54
0.00
ss-Na+
0.08
0.01
0.22
0.00
0.30
0.03
0.10
0.17
0.00
NH4+
0.25
0.26
0.02
0.10
0.09
0.04
0.56
0.03
nss-K+
0.23
0.17
0.05
0.08
0.14
0.01 (–)
0.00
MSA
0.08
0.14
0.00
0.09
0.01
0.05 (–)
34 dark weeks (NDJF)
nss-SO4=
0.31
0.27
0.01
0.12
0.22
0.08 (–)
0.56
0.02
0.72
0.63
0.36
EC
0.41
0.62
0.01 (–)
0.60
0.11
0.16 (–)
0.03
0.78
0.56
0.28
ss-Na+
0.09
0.02 (–)
0.57
0.03 (–)
0.39
0.18
0.00
0.00
0.12
NH4+
0.42
0.41
0.01
0.43
0.20
0.08 (–)
0.80
0.38
nss-K+
0.38
0.28
0.04
0.21
0.21
0.01 (–)
0.23
MSA
0.40
0.32
0.11
0.16
0.20
0.11 (–)
32 spring weeks (MAM)
nss-SO4=
0.03 (–)
0.06 (–)
0.01 (–)
0.13 (–)
0.01
0.00
0.24
0.12
0.16
0.62
0.03 (–)
EC
0.02
0.01
0.01 (–)
0.01 (–)
0.01
0.00
0.08 (–)
0.05
0.53
0.07 (–)
ss-Na+
0.03 (–)
0.04 (–)
0.01 (–)
0.02 (–)
0.17
0.01 (–)
0.00
0.01
0.02
NH4+
0.00
0.00
0.02 (–)
0.03 (–)
0.01 (–)
0.02 (–)
0.11
0.01 (–)
nss-K+
0.04 (–)
0.06 (–)
0.03 (–)
0.05 (–)
0.01 (–)
0.00
0.12 (–)
MSA
0.07
0.12
0.01 (–)
0.10
0.09
0.03 (–)
47 cleaner weeks (JJASO and nss-SO4= < 100 ng m-3)
nss-SO4=
0.10
0.16
0.06
0.10
0.00
0.11 (–)
0.11
0.02
0.43
0.25
0.09
EC
0.19
0.18
0.02
0.07
0.07
0.06 (–)
0.00
0.19
0.28
0.00
ss-Na+
0.02
0.00
0.45
0.00
0.02
0.01
0.02 (–)
0.00
0.00
NH4+
0.08
0.12
0.01
0.05
0.00
0.08 (–)
0.52
0.33
nss-K+
0.25
0.21
0.03
0.04
0.15
0.05 (–)
0.20
MSA
0.02
0.02
0.01
0.01
0.00
0.00
As mentioned in Sect. 3.1, OM and nss-SO4=, but not EC, were
unusually high from 16 September 2014 to 13 October 2014. Time spent over
Region 1 stands out for September 2014 (Fig. 6), but it is no different
than September 2012 and an increase in EC is expected if Region 1 were the
main source. The low EC and high nss-SO4= / OM rules out BB. One
possible source is the Smoking Hills (Sect. 3.3) of NWT, since time
spent over NWT was also anomalously high during August to October 2014
(Fig. S4). Another possible source is the fissure eruptions of the
Bárðarbunga volcano in Iceland that began in late August 2014 and
continued for some months. There is no significant time over the Iceland
region (Fig. 6), but the present FLEXPART analysis may not account for the
volcano emissions height of about 2 km and emissions transport could have
been longer than 10 days.
Chemical component correlations
Regressions among the OFGs, nss-SO4=, EC, ss-Na+,
NH4+ and nss-K+ are used to further connections with possible
sources. Carbonyl groups are excluded due to there being only 17 weekly-averaged samples above detection limit (DL). The regressions are done
for all weeks, dark weeks (NDJF), spring weeks (MAM) and cleaner weeks,
where the latter are defined as weeks with nss-SO4= less than 100 ng m-3; all cleaner weeks are during JJAS. CoDs and significance levels
based on linear regressions are given in Table 3. Figure 8a shows all OM
data plotted against nss-SO4=, and Fig. 8b shows OM versus
nss-SO4= for the dark and cleaner weeks; the associations are
positive and significant at better than 95 % confidence. However, there is
no association of OM and nss-SO4= for the spring weeks (not
shown), consistent with the discussion in Sect. 3.3 as well as the general
pattern in Table 3. Variations in OM during spring are not related to
variations in the inorganic components and EC, but connections may be hidden
within relatively large intercepts of the regressions.
Regressions of OM versus nss-SO4= for (a) all weeks
and (b) dark weeks (during NDJF) and cleaner weeks
(nss-SO4= < 100 ng m-3). Coefficients of
determination are indicated. Linear and power-law regressions are shown for
all points (p < 0.01), along with linear regressions for the dark period
(p < 0.01) and the cleaner period (p < 0.03).
The alkane and acid groups are associated with EC during the dark weeks
(Table 3), but not with Regions 1 and 1467 (Table 2), implying that the
variations in the alkane and acid groups and some EC derive from areas other
than Regions 1467 during the dark period. The modest correlation of alkane
groups with EC during the cleaner weeks is the result of correlations during
2012 (CoD = 0.18) and 2014 (CoD = 0.35). There is no correlation of alkane
groups and EC for the cleaner period of 2013, indicating low contributions
from combustion sources. Regressions of the alkane groups with
nss-SO4= separated by years (Fig. 9) offer another perspective for
the cleaner weeks. The slopes and intercepts for 2012 and 2014 are
relatively close, but the average concentration of alkane groups for the
summer of 2013 is approximately 3 times lower than the other years,
despite similar ranges of nss-SO4= (and EC). Higher ratios of
alkane groups to nss-SO4= during 2012 and 2014 suggest combustion
sources, including BB, but such sources are quite episodic and the general
covariance in alkane groups and nss-SO4= during the cleaner
weeks, particularly during 2013, is not combustion related. Recent
observations show SOA of marine origin and lower O / C to be a significant
factor in particle growth in the summer Arctic (Willis et al., 2017).
Combined alkane groups and nss-SO4= from marine precursor
emissions might be a factor in their covariance at Alert during the cleaner
months. Acid groups (not shown) exhibit similar associations with
nss-SO4= and the lowest concentrations during summer 2013.
Alcohol groups are correlated with ss-Na+ during the dark and cleaner
weeks but not during the spring weeks (Fig. 10). By considering the
variance in alcohol groups associated with ss-Na+ concentrations above
the regression intercept for the dark weeks, the fraction of alcohol groups
associated with ss-Na+ at Alert during the dark weeks is estimated at
54 %, rising to 69 % when O / C > 1.
Regressions of mass concentrations of alkane functional groups with
nss-SO4= for the cleaner weeks during each of the years 2012, 2013 and 2014
(2012: p < 0.1; 2013: p < 0.04; 2014:
p < 0.07).
Regressions of weekly-averaged mass concentrations of alcohol
functional groups with ss-Na+ for the spring weeks, dark weeks, cleaner
weeks and the 10 dark weeks with O / C > 1. Results of
linear regressions are indicated where significant (p < 0.05).
The lower plot expands the cleaner weeks and identifies the 9 weeks during
July and August of 2013 with O / C > 1.
O / C is above unity for 20 of the 126 weeks (Fig. 4), 10 of which were during
the dark period. A total of 9 of the other 10 weeks were from the summer of 2013. As
in Sect. 3.2, the average O / C for summer 2013 (1.16) is much higher than
for the summers of 2012 and 2014. Relative to OM, alcohol groups were
14, 40 and 14 % for the summers of 2012, 2013 and 2014,
respectively. In Fig. 10, the alcohol groups are correlated with ss-Na+
for the dark weeks with O / C > 1. Alcohol groups also correlate
with MSA during the 10 dark weeks (CoD = 0.85, slope = 88, p < 0.001)
and the 9 summer weeks (CoD = 0.60, slope = 2.5, p < 0.02). For
the summer of 2013, the association of alcohol groups with lower
concentrations of ss-Na+ and higher concentrations of MSA suggests a
stronger connection with secondary marine sources. That result may be
consistent with Mungall et al. (2017) who found evidence for summertime
secondary marine precursors in the form of oxygenated VOCs possibly due to
photochemical reactions at the surface of waters in the NARES Strait that
divides Ellesmere Island and Greenland.
Amine groups can have marine sources (Facchini et al., 2008; Köllner et
al., 2017) as well as anthropogenic sources. Here, the strongest
associations of the amine groups are with ss-Na+ for the dark and
spring weeks (Table 3). Marine emissions, either primary or secondary, may
contribute a significant fraction of the amine groups. Modest correlations
with EC and nss-K+ during the cleaner weeks suggest contributions to
the amine groups from combustion sources, possibly BB.
The limited presence of carbonyl groups (17 weeks above DL) is consistent
with the observations of Kawamura and Kasukabe (1996) and Kawamura et al. (2012).
A total of 9 of the 17 weeks were during spring when gas-phase carbonyls
(formaldehyde, acetaldehyde and acetone) can exhibit a diurnal cycle related
to snowpack chemistry during periods of ozone depletion (Grannas et al.,
2002). During these 9 weeks, the acid groups and alcohol groups were much
lower fractions of the OM (2 and 11 %, respectively), and 6 of the
weeks were during periods of depleted ozone (Fig. S7): hours per week with
ozone less than 50 % of the mean ranged from 9 to 57 %. Carbonyl
compounds moving to and from the snowpack during periods of low oxidant
concentrations may have contributed to carbonyl groups in particles without
increasing more oxidized groups.
Evidence for significant BB influence on the Alert aerosol was found during
5 of the 126 weeks or 4 % of the time. A higher fraction of carbonyl
groups and higher OM / nss-SO4= can be prominent features of BB
particles (e.g., Takahama et al., 2011). There were 4 weeks with
detectable carbonyl groups and with OM / nss-SO4= exceeding 0.5:
19 April 2012 (OM / nss-SO4= = 0.53), 4 May 2012 (0.57),
3 September 2012 (2.9) and 1 August 2014 (4.3). For those weeks, the carbonyl groups
correlate strongly with EC and nss-K+ (CoD > 0.96 and
tstat > 7), and the average functional groups' composition
(45 % alkane groups, 36 % carbonyl groups and 9 % amine groups)
corresponds closely with the OFG pattern of Takahama et al. (2011). The
4 weeks were during periods with significant forest fire activity in the
Northern Hemisphere and warmer temperatures at Alert. During April, August
and September of 2012, there were a number of forest fires in Siberia (e.g.,
Gorchakov et al., 2014). In July 2014, fires in Siberia (NASA Earth
Observatory) and the NWT were carried north, with the NWT fires reaching at
least as far as Resolute Bay, Nunavut (Köllner et al., 2017). One other
week, centered on 20 July 2012, had relatively high EC (65 ng m-3) and
OM (340 ng m-3; 2.3 times nss-SO4=). The functional group
pattern was different than the above with acid groups replacing the carbonyl
groups, which may have been a more processed BB aerosol.
Mean percentage contributions to OM from PMF factors (126 samples).
The difference from 100 % of the sum of factors is residual
(approximately 20 %). Values in parentheses are the factor mass concentrations
in ng m-3.
Seasonal period
% Factor 1 “FFC”
% Factor 2 “sea spray”
% Factor 3 “mixed”
% Factor 4 “secondary”
(ng m-3)
(ng m-3)
(ng m-3)
(ng m-3)
Winter (DJF)
23 (39)
17 (22)
18 (29)
18 (22)
Spring (MAM)
37 (68)
3 (8)
25 (63)
18 (31)
Summer (JJA)
47 (24)
6 (3)
13 (14)
12 (9)
Fall (SON)
35 (28)
7 (11)
18 (19)
18 (22)
All
37 (40)
8 (10)
18 (31)
17 (20)
Dark months (NDJF)
24 (38)
17 (25)
18 (31)
18 (21)
Cleaner months (JJASO)
42 (21)
5 (3)
17 (9)
16 (10)
Positive matrix factorization
PMF was applied to the OFG data to offer a further perspective on factors
contributing to OM. The OFG measured at Alert may have evolved during
transport, but it is unclear whether or how that may impact this PMF
analysis. The optimal PMF solution was four factors with an average residual
of 20 %. The fractional and absolute contributions by season and for the
dark and cleaner periods are given in Table 4. A time series of the factor
concentrations is shown in Fig. S6.
Factor 1 is labeled FFC for “fossil fuel combustion”, as it most strongly
correlates with EC (CoD = 0.29), NH4+ (CoD = 0.27) and
nss-SO4= (CoD = 0.19). Factor FFC makes the overall largest
contribution to OM (37 %), and it is dominated by alkane and alcohol
groups: 44 and 41 %, respectively. Its absolute contribution to OM is
largest in spring, and its relative contribution to OM is largest in summer.
As above, most of the spring OM may originate either from outside of Regions 1467 or beyond 10 days of travel time.
The high relative contribution of Factor 1 to summer OM may derive from residual spring aerosol and BB, the latter
being most significant during the summer of 2012. Considering the relatively
low CoD connecting this factor with EC, and the higher fraction of alkane
and alcohol groups, summer marine sources may also contribute to this
factor, in particular during 2013. However, there are no correlations of
this factor with Na+ or MSA.
Factor 2 is referred to as “sea spray”, because the highest correlations
are with ss-Na+ (CoD = 0.28), Mg++ (CoD = 0.26) and Cl-
(CoD = 0.46), and the slope of the correlation with MSA is negative. It is
dominated by alcohol groups (77 %) with acid groups comprising 12 %.
Overall, Factor 2 represents 8 % of the total OM. The largest seasonal
contribution from Factor 2 is during winter when wind speeds are on average
higher over the northern oceans and biological productivity is lower (e.g.,
Lana et al., 2011). Factor 2 is consistent with earlier discussion showing
alcohol groups closely correlated with ss-Na+ (Fig. 10). The domination
of this factor by alcohol groups agrees with the above estimate that 54 %
of the alcohol groups were associated with primary marine emissions.
The third factor is labeled “mixed”. The highest correlations of Factor 3
are with two of the lowest concentration components: NO3-
(CoD = 0.21) and MSA (CoD = 0.19). Overall, Factor 3 represents 18 % of
the OM, with the contribution reaching 25 % during spring and decreasing
to 13 % during summer. It is comprised mostly of alkane (66 %) and acid
groups (24 %). Alkane and acid groups are correlated with EC during the
dark period (Table 3), suggesting a significant contribution from combustion
sources. During the cleaner months, the alkane groups exhibit significant
but weak correlations with nss-SO4=, NH4+ and
nss-K+. The spring increase in alkane groups is not correlated with
major chemical components or with Regions 1 and 1467. As defined, Factor 3
appears to be a mix of combustion emissions and secondary oceanic sources,
transported over longer distances than those connected with Regions 1467.
The fourth factor is labeled “secondary”. It correlates predominantly
with nss-SO4= (CoD = 0.40) and EC (CoD = 0.17). The contribution
to OM from Factor 4 is 18 %, with lower variability across the seasons. At
61 %, acid groups are the largest contributor to Factor 4, followed by
alkane groups at 27 %. Factor 4 has the highest fractional contribution
from amine groups (12 %) of all factors. Many increases in Factor 4 are
coincident with increases in Factor 1 (Fig. S6), but there are significant
differences: Factor 4 has a lower CoD with EC, lower contributions from
alcohol and alkane groups, and higher contributions from acid groups and
amine groups. Amine groups show little association with Regions 1 and 1467
(Table 2), and the highest CoD for amine groups in Table 3 is with
ss-Na+: recent observations suggest a secondary Arctic marine source of
particulate amine coincident with but in smaller sizes than sea-salt
particles (Köllner et al., 2017). Also, Factor 4 is the dominant factor
associated with the anomalously high nss-SO4= during the last
2 months of the study, when EC was relatively low. The stronger associations
of Factor 4 with nss-SO4= and organic acid groups and weaker
associations with EC and alkane groups as well as the relatively high
contributions from amine groups suggest this factor is linked with secondary
processes and longer transport times.
Summary
A total of 2.5 years (April 2012 to October 2014) of weekly-averaged
observations of OFGs were combined with
observations of weekly-averaged inorganic components and aerosol particle
microphysics to explore the seasonal contributions from OFGs to the
submicron atmospheric aerosol and potential sources of the OFGs. These are
the first multi-year observations of organic aerosol functional groups above
80∘ N.
The study-average OM is 129 ng m-3 with a range of 7 to 460 ng m-3,
similar to OM sampled over 1 year at Barrow, Alaska (Shaw et
al., 2010). Seasonally, OM is highest during spring at 220 ng m-3 and
lowest during summer at 65 ng m-3. Relative to nss-SO4=, OM
is 26, 28, 107 and 39 % during winter (DJF), spring (MAM),
summer (JJA) and fall (SON), respectively. Overall, about 40 % of the
weekly variability in OM is associated with nss-SO4=. However,
during spring (MAM), there is no association between OM and
nss-SO4=, suggesting that the correlations during other seasons
have more to do with connections of sources than photochemistry. That said,
the maxima in both OM and nss-SO4= occur during spring in part at
least due to increased photochemical potential.
Study-averaged concentrations of alkane, alcohol, acid, amine and carbonyl
groups are 57, 24, 23, 15 and 11 ng m-3, respectively. The average percentages of the weekly
ratios of alkane, alcohol, acid, amine and carbonyl groups to OM are 42,
22, 18, 14 and 5 %, respectively. The average O / C is 0.65 with
winter O / C highest (0.85) and spring O / C lowest (0.51).
A combination of FLEXPART trajectories, linear regressions among the organic
and inorganic components and PMF were used to associate the organic aerosol
with potential origins with a focus on three time periods: the dark period,
comprising 34 weeks during November to February, inclusive; the sunlit
spring period, comprising 32 weeks during March to May, inclusive; and the
cleaner period, comprising 47 weeks during June to October, inclusive,
constrained to nss-SO4= less than 100 ng m-3. The main
findings follow:
At Alert, OM is a higher fraction of smaller particles in cleaner air.
Larger particles and a lower OM fraction are associated with higher mass
concentrations. On average, particle densities are close to 1.35 g cm-3
for smaller particles and lower mass concentrations, with higher values for
larger particles and higher mass concentrations.
The annual maximum in OM occurs in May, 1 month after that of
nss-SO4= and 2 months after the maximum in EC. The OM maximum is
mostly due to increases in alkane groups and to a lesser extent acid and
alcohol groups. It is coincident with the annual maximum in MSA and
decreasing ss-Na+. These features suggest that secondary OM from marine
sources overlaps with other sources contributing OM during the spring.
The maximum in OM / nss-SO4= is in August, coincident with new
particle formation at Alert.
Values of O / C exceeded unity for 20 of the 126 study weeks, 10 of which were
during the dark period when the fraction of alcohol groups was highest.
Approximately 54 % of the alcohol groups were associated with ss-Na+,
leading to the conclusion that higher O / C during the dark period is mainly
associated with sea spray, as at Barrow (Shaw et al., 2010) and over open
Arctic water in April (Frossard et al., 2011).
Values of O / C exceeded unity for 9 weeks in July–August of 2013 and were
highest and most persistent during this time: average of 1.4; median of 1.5.
The high O / C was again due to a relative increase in alcohol groups that
comprised 52 % of the OM, mostly at the expense of alkane groups, while
acid groups remained a similar fraction overall and relative to the other
summers. As the alcohol groups were strongly associated with MSA, a
secondary marine source is suggested.
Based on higher temperatures, higher fractions of alkane groups, higher OM
and EC as well as lower O / C (0.48–0.45), the summers of 2012 and 2014 had a
greater influence from combustion sources than 2013. While BB
was a factor during 2012 and 2014, there is evidence that alkane groups
and nss-SO4= from marine precursor emissions may be generally
present during the cleaner months.
During the dark period, 29, 28 and 14 % of nss-SO4=, EC
and OM, respectively, were associated with transport predominantly over the
gas flaring region in northern Russia and Eurasia in general.
During the spring period, 11 % each of nss-SO4= and EC were
associated with transport over the region of northern Russia and Eurasia,
with no association for OM. The difference between OM and nss-SO4=
may be due to differences from volatilization and SOA production during
transport as well as potentially more OM originating from outside of Eurasia
and from marine sources.
Large percentages of the Arctic haze characterized at Alert (> 60 %) likely have atmospheric residence times longer than 10 days from
their origin and/or are from outside of the Eurasian region.
In 4 % of the weeks, there was evidence for a significant contribution
from BB, coincident with forest fires in Siberia and the Canadian Northwest
Territories.
A total of 9 of 17 weeks with detectable carbonyl groups occurred in spring when
snowpack chemistry can be a significant source of gas-phase carbonyls
(Grannas et al., 2002).
Unusually high nss-SO4= and OM concentrations and relatively low
EC were observed from 16 September to 14 October 2014. Possible sources may
be the Smoking Hills in the Canadian NWT or volcanic emissions.