Introduction
The Arctic is warming rapidly compared to other locations globally, which has
implications for anomalous snow and ice melt (Jeffries et al., 2013).
Replacement of highly reflective surfaces by darker, more absorbing surfaces
(i.e., tundra and open ocean water) enhances Arctic warming, especially in
summer (Chapin et al., 2005). Such warming subsequently impacts the
ecological cycle, socioeconomics, and mid-latitude weather patterns (Screen
and Simmonds, 2010; Serreze and Barry, 2011). This warming feedback is one in
a complex system of interrelated processes resulting in “Arctic
amplification”, the observed enhanced warming seen in the Arctic to date,
and in part motivates the need to improve our understanding of atmospheric
processes that modulate energy reaching the Arctic surface (Serreze and
Francis, 2006).
In addition to the ice–albedo feedback described above, the principal
atmospheric constituents that perturb the surface energy budget are clouds
and aerosols (Tsay et al., 1989). Aerosols can directly scatter and absorb
solar radiation or indirectly impact atmospheric radiation through their
roles in the modulation of cloud microphysics by serving as cloud
condensation nuclei (CCN) or ice nucleating particles (INPs) (Boucher et al.,
2013). However, the ability of aerosols to serve as CCN or INPs depends on
their composition, size, and number concentration, each of which depends on
their source and extent of aging. Several previous studies have focused on
examining the sources of Arctic aerosols, including ground-based and airborne
research campaigns conducted in the Alaskan Arctic extending back to the
mid-20th century (e.g., Schnell and Raatz, 1984; Barrie, 1986; Delene and
Ogren, 2002; Quinn et al., 2002, 2009; Verlinde et al., 2007; Brock et al.,
2011; McFarquhar et al., 2011). To better understand aerosol properties in
this environment, two atmospheric research facilities have been established
on the North Slope of Alaska that encompass routine, aerosol measurements –
including, but not limited to, aerosol optical, physical, and chemical
properties, and CCN concentrations.
Utqiaġvik, Alaska (formally Barrow), features an observatory established
by the National Oceanic and Atmospheric Administration (NOAA) Earth System
Research Laboratory's (ESRL) Global Monitoring Division (GMD) in 1976.
Previous work at this facility involves different combinations of the
long-term, ground-based aerosol optical, physical, and chemical property
measurements to evaluate the annual cycle of aerosol sources at Utqiaġvik
(e.g., Polissar et al., 2001; Delene and Ogren, 2002; Quinn et al., 2002,
2009). For example, Quinn et al. (2002, 2009) used aerosol number
concentrations, optical properties, and chemistry measurements to conclude
that the winter and spring are impacted by aerosol transported from
mid-latitudes, while summer and fall aerosols contain contributions from
local biological activity, sea salt, and residual (i.e., unanalyzed) aerosol
mass that may represent mineral or organic species. More recently, Kolesar et
al. (2017) used a 6-year time series of particle size distributions to
conclude that particle growth events occurring at Utqiaġvik resulted from
gas-phase emissions originating from the oil fields of the Prudhoe Bay area,
approximately 300 km east of Utqiaġvik. Gunsch et al. (2017) found
submicron (i.e., < 1 µm in diameter) combustion-derived
particles were transported from the Prudhoe Bay oil field to Utqiaġvik
10 % of the time during August–September 2015. In addition to
Utqiaġvik, another northern Alaskan facility was recently established by
the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM;
since 2013) program at Oliktok Point
(https://dis.arm.gov/sites/amf/oli/). This site is located in the
northwest region of oil extraction activities in Prudhoe Bay, making it an
ideal location to determine the potential impacts of emissions from such
activities on the relatively pristine Arctic atmosphere. Aerosol optical,
physical, and chemical property measurements were implemented during the
summer of 2016. Overall, the North Slope provides a unique opportunity to
investigate aerosols and their impacts from the clean Arctic background,
long-range transport from lower latitudes, and regional oil extraction
activities.
While previous studies have provided significant insights into aerosol
properties in northern Alaska, one crucial deficiency is that most of them
comprise only ground-based observations of aerosol (e.g., Barrie,
1986; Polissar et al., 2001; Delene and Ogren, 2002; Quinn et al., 2002,
2009; Gunsch et al., 2017; Kolesar et al., 2017). The Arctic atmosphere can
be highly stratified (Persson et al., 2002; Graversen et al., 2008), thus
hindering vertical transport of aerosols from their surface sources.
Accordingly, numerous airborne campaigns have focused on evaluating sources
of mid- to upper-tropospheric aerosol and aerosol–cloud interactions. For
example, during the March 1983 NOAA Arctic Gas and Aerosol Sampling Program
(AGASP) flights over Alaska, aerosol number concentrations were found to vary
substantially over the vertical extent of the flight region (Schnell and
Raatz, 1984). Several airborne campaigns – including Aerosol, Radiation, and
Cloud Processes affecting Arctic Climate (ARCPAC), Arctic Research of the
Composition of the Troposphere from Aircraft and Satellites (ARCTAS-A), and
Indirect and Semi-direct Aerosol Campaign (ISDAC) – took place in the region
during April 2008 to characterize tropospheric pollution and its source
contributions to the Arctic haze season during the International Polar Year
(Brock et al., 2011; McFarquhar et al., 2011; Bian et al., 2013). These
studies presented valuable information on the vertical structure of Arctic
aerosol, and the relative contributions from Arctic background, fossil fuels,
and biomass burning sources but are limited to April or March only. Airborne
measurements available from the Mixed-Phase Arctic Cloud Experiment (M-PACE),
which took place from late September to late October 2008, were predominantly
focused on clouds and, with respect to aerosols, only encompassed aerosol
size distribution measurements and INP concentrations (Verlinde et al., 2007;
Prenni et al., 2009; Jackson et al., 2012). To our knowledge, only one study
reports airborne in situ aerosol measurements over the Alaskan Arctic during
summer: NASA's 1988 Arctic Boundary Layer Experiment (ABLE 3A) (Gregory et
al., 1992). However, this study was limited to flights between Fairbanks and
Utqiaġvik and to aerosol size distributions from 0.12 to 8 µm
in diameter – no other aerosol measurements were obtained.
In the context of warming temperatures, emissions from oil extraction, added
shipping routes due to a reduction in sea ice extent, and wildfires are
expected to increase in sub-Arctic boreal regions (Randerson et al., 2006;
Gautier et al., 2009; Harsem et al., 2011; Peters et al., 2011; de Groot et
al., 2013; Roiger et al., 2015). Thus, regional fossil fuel and biomass
burning combustion sources will further contribute to the aerosol population
may serve as an increasingly crucial source of aerosol in the future.
However, local pollution and other high-latitude Eurasian resource extraction
sources and their resulting impacts on clouds and radiation are poorly
quantified (Arnold et al., 2016). Hobbs and Rangno (1998) documented
increased cloud droplet number concentrations in air masses originating
around Prudhoe Bay through airborne measurements over the Beaufort Sea. In a
companion paper by Maahn et al. (2017), local emissions from Prudhoe Bay were
shown to impact cloud drop size in comparison with more pristine clouds over
Utqiaġvik. Such studies support the idea that emissions from oil
extraction activities in this region have air quality and climatic
implications and are important to assess. Additionally, Stohl et al. (2013)
reported that gas flaring emissions are underestimated in the Arctic, further
justifying the need to evaluate emissions from these sources.
In addition to industrial sources, it is recognized that Alaskan boreal fires
periodically influence the aerosol population over the North Slope. Eck et
al. (2009) reported high summertime (August) fire counts, impacting aerosol
optical depths (AODs) over Utqiaġvik. Stohl et al. (2006) reported
similar findings using ground-based absorption and CO (i.e., a tracer for
biomass burning) measurements at Utqiaġvik. Both studies concluded that
individual smoke transport events resulted in short episodes of higher AOD
and absorption values than typical springtime Arctic haze. Regardless of
their episodic behavior, summertime sub-Arctic boreal fires can produce
substantial quantities of aerosol that can reside in the troposphere for
1–2 weeks (Stohl et al., 2013). The Arctic summertime atmosphere is
historically less polluted as compared to the rest of the year (Quinn et al.,
2002; Leaitch et al., 2013; Heintzenberg et al., 2015); thus it is critical
to assess the impacts of potentially important local sources of summertime
aerosol on Arctic radiation and cloud microphysical processes. Here, we
present aerosol and trace gas observations from ARM's Fifth Airborne Carbon
Measurements (ACME-V) field campaign to evaluate local sources during the
summer of 2015 in the Alaskan Arctic.
Flight identification numbers, start and end times (mm/dd/yyyy) in
UTC, flight duration, and waypoints flown over of each G-1 research flight
during ACME-V. Waypoints O, U, A, I, and T represent Oliktok Point,
Utqiaġvik, Atqasuk, Ivotuk, and Toolik Lake, respectively. Dates and
times are provided as mm/dd hh:mm:ss.
Flight ID
Start (UTC)
End (UTC)
Duration (hh:mm:ss)
Waypoints
F01
06/04 22:31:21
06/05 01:59:40
03:28:19
O, U
F02
06/07 20:08:51
06/08 01:16:28
05:07:37
O, U, A, I, T
F03
06/08 19:56:37
06/09 00:52:47
04:56:10
O, U, A, I, T
F04
06/10 18:39:18
06/10 20:30:59
01:51:41
O
F05
06/12 21:58:20
06/13 00:11:26
02:13:06
O
F06
06/13 18:57:18
06/13 23:57:32
05:00:14
O, U, A, I, T
F07
06/15 21:57:26
06/16 00:35:49
02:38:23
O, U
F08
06/17 18:59:36
06/18 00:11:03
05:11:27
O, U, A, I, T
F09
06/20 19:00:37
06/21 00:04:12
05:03:35
O, U, A, I, T
F10
06/22 23:17:43
06/23 01:19:26
02:01:43
O
F11
06/23 19:14:17
06/24 00:19:21
05:05:04
O, U, A, I, T
F12
06/27 21:06:18
06/27 23:12:53
02:06:35
O, U
F13
06/30 18:59:46
06/30 22:30:34
03:30:48
O, I, T
F14
07/02 19:34:03
07/02 23:31:36
03:57:33
O, U, T
F15
07/05 18:57:14
07/06 00:10:57
05:13:43
O, U, A, I, T
F16
07/11 20:27:14
07/12 00:51:07
04:23:53
O, U, A, T
F17
07/14 19:01:15
07/14 21:18:25
02:17:10
O, T
F18
07/16 19:58:43
07/17 00:38:47
04:40:04
O, U, A, I, T
F19
07/18 19:54:33
07/19 00:19:00
04:24:27
O, U, A, I, T
F20
07/21 19:24:37
07/22 00:30:44
05:06:07
O, U, A, I, T
F21
07/22 19:29:54
07/23 00:14:20
04:44:26
O, U, A, I, T
F22
07/27 21:34:03
07/28 00:07:11
02:33:08
O, U
F23
07/30 21:18:11
07/31 01:03:48
03:45:37
O, U, A, T
F24
08/02 18:10:47
08/02 21:40:37
03:29:50
O, U, T
F25
08/06 19:01:12
08/06 23:34:33
04:33:21
O, U, A, I, T
F26
08/07 18:37:02
08/07 19:46:35
01:09:33
O
F27
08/08 19:42:22
08/08 21:52:35
02:10:13
O, U
F28
08/14 18:49:22
08/14 22:17:13
03:27:51
O, U
F29
08/16 19:54:12
08/16 23:53:40
03:59:28
O
F30
08/20 20:47:02
08/21 00:09:46
03:22:44
O, U, A, I, T
F31
08/25 19:18:13
08/25 23:52:06
04:33:53
O, U, A, I, T
F32
08/27 21:29:46
08/28 01:37:41
04:07:55
O, U, A, I, T
F33
08/28 22:30:14
08/29 00:51:35
02:21:21
O
F34
08/30 21:49:18
08/30 23:39:16
01:49:58
O
F35
09/02 19:00:45
09/02 23:27:41
04:26:56
O, A, I, T
F36
09/04 21:24:39
09/05 00:47:02
03:22:23
I, T
F37
09/07 18:28:10
09/07 21:23:57
02:55:47
O, A
F38
09/09 18:29:19
09/09 22:18:46
03:49:27
O, U, A, I
Map of the North Slope of Alaska including flight tracks from the
ACME-V field campaign colored by date. Sites where the G-1 aircraft
spiralled over are shown (profile waypoint), in addition to locations of
actively deployed oil wells (data obtained from
http://doa.alaska.gov/ogc/publicdb.html in March 2017), the location of
Deadhorse Airport, and approximate areas of the Brooks Mountain Range and
Prudhoe Bay.
Methods
Study location and dates
ACME-V flights were conducted over the North Slope of Alaska between five
waypoints, including Oliktok Point (70.51∘ N, 149.86∘ W),
Utqiaġvik (71.29∘ N, 156.79∘ W), Atqasuk
(70.48∘ N, 157.42∘ W), Ivotuk (68.49∘ N,
155.75∘ W), and Toolik Lake (68.63∘ N, 149.61∘ W)
(Fig. 1), all north of the Brooks Mountain Range. The campaign involved 38
research flights from 4 June to 9 September 2015, generally flying every
2–3 days (Table 1). The DOE ARM Gulfstream-1 (G-1; part of the ARM Aerial
Facility) aircraft typically flew below 1000 m above ground level
(m a.g.l.) between the waypoints, while spiralling up to 8000 m a.g.l.
above each waypoint. Data altitudes were converted to meters above mean sea
level (m a.m.s.l.) for a more direct comparison between measurement
locations. Flight tracks varied in the number and order of waypoints that
were overflown.
Aircraft aerosol and trace gas payload
The G-1 was equipped with a suite of atmospheric state, cloud, aerosol, and
trace gas instruments (see
https://www.arm.gov/research/campaigns/aaf2014armacmev for a complete
list of instrumentation and available data) (Biraud et al., 2016), though in
the current study we only focus on the aerosol, CO, and CO2
measurements. Total number concentrations (CN) of aerosol particles
3 nm–3 µm and 10 nm–3 µm in diameter (Dp)
were measured using two condensation particle counters (CPCs, TSI, Inc. models
3025 and 3010, respectively). The model 3025 and 3010 CPCs have a 50 % counting
efficiency of 3 and 10 nm particles, respectively. Aerosol size
distributions were measured using three different instruments, including an
ultra-high-sensitivity aerosol sizer (UHSAS, Droplet Measurement
Technologies, Inc.), a passive cavity aerosol spectrometer (PCASP, Droplet
Measurement Technologies, Inc. model SPP-200), and an optical particle
counter (OPC, Climet model C1-3100) in combination with a multi-channel
analyzer (Ortec model Easy-MCA-8k), which measured particle optical diameters
in the ranges of 0.06–1 µm, 0.1–3 µm, and
0.8–15 µm, respectively. The PCASP was operated with an anti-ice
heater, thus the particles measured are predominantly dry (Kassianov et al.,
2015). The UHSAS experienced instrumental complications during most of the
campaign; thus is not used for the current study to alleviate any limitations
and skewness from operation dates. Total aerosol light scattering and
absorption coefficients (Mm-1) were measured using a three-wavelength (450,
550, and 700 nm) nephelometer (TSI, Inc. model 3563) and three-wavelength (464,
528, and 648 nm) particle soot absorption photometer (PSAP, Radiance
Research, Inc.), respectively. Refractory black carbon (rBC) concentrations
were measured with the single-particle soot photometer (SP2, Droplet
Measurement Technologies, Inc.). The SP2 measures individual rBC particles
through laser-induced incandescence, making it selective for rBC (Sedlacek,
2016). Quality assurance/quality control (QA/QC) checks of the SP2 data
ensure that other potentially refractive particles such as mineral dust are
not counted as rBC particles. CO concentrations were measured with a
CO/N2O/H2O instrument (Los Gatos integrated cavity output
spectroscopy, model 907-0015-0001) and is used as a tracer for combustion
sources, including both biomass burning and fossil fuel combustion (Andreae
and Merlet, 2001; Brock et al., 2011; Liu et al., 2014). CO2
concentrations were measured by cavity ring-down spectroscopy (Picarro model
G2301) and together with the CO measurements were used to calculate modified
combustion efficiency (MCE) (Liu et al., 2014; Biraud and Reichl, 2016). MCE
is defined as ΔCO2/(ΔCO2 + ΔCO) where
ΔCO2 and ΔCO indicate the increase from background
CO2 and CO concentrations, respectively (Liu et al., 2014), and was
calculated for data in which fires impacted the measurements. Background
CO2 and CO concentrations of 383 and 0.054 ppmv, respectively, were
defined from the current measurements. These values were derived from
correlations of rBC mass versus CO2 and CO, and finding the minimum
value of CO2 and CO on the rBC axis.
All data were collected at 1 s intervals and are publicly available on the
ARM data archive (http://www.archive.arm.gov/armlogin/login.jsp).
Unless noted, all data presented are 1 s. Data quality was verified through
quality assurance and data quality checks by DOE ARM. CPC, PCASP, OPC, and
rBC data flagged for being in cloud were excluded from the current analysis,
since the isokinetic inlet used on the G-1 during the study does not discern
between interstitial aerosols and cloud particles, and cloud and aerosol size
ranges can potentially overlap. Data periods impacted by liquid and ice
clouds were defined as those having 1 × 107 m-3 droplets
and 100 m-3 ice particles larger than 400 µm, respectively
(Lance et al., 2011). When a cloud was found (defined as at least 10 s of
data where the cloud threshold is exceeded), aerosol observations 3 s before
and 3 s after the cloud were discarded as well to avoid contamination of the
aerosol probes with cloud particles (Maahn et al., 2017). CO data were used
in and out of cloud since there are no potential artifact issues. To
minimize the influence of localized contamination from take-off and landing
at the Deadhorse Airport (19.5 m a.m.s.l.), all data below 20 m a.m.s.l.
and within 3 km of the airport were removed. All data are presented in
Coordinated Universal Time (UTC).
Supporting satellite data
The source of aerosols from the central Alaskan fires was determined using
imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) on
board the Terra satellite. MODIS Aqua looked similar; thus only Terra
observations are discussed herein. AOD data from MODIS were acquired from the
Giovanni data server (http://giovanni.gsfc.nasa.gov/giovanni/) for
daily dark-target deep blue combined mean AOD at 550 nm and a 1∘
spatial resolution using a domain of 139 to 169∘ W and 57 to
72∘ N (MOD08_D3_6) (Ackerman et al., 1998). Fire and surface
thermal anomaly data were also acquired from MODIS using brightness
temperature measurements in the 4 and 11 µm channels (Giglio,
2010). The fire detection strategy is based on absolute detection of a fire
(when the fire strength is sufficient to detect) and on detection relative
to its background (to account for variability of the surface temperature and
reflection by sunlight) (Giglio et al., 2003). The algorithms include masking
of clouds, bright surfaces, glint, and other potential false alarms (Giglio
et al., 2003). Swaths from overpasses over the state of Alaska were used to
determine the daily locations of fires. The Alaskan fire season was
relatively active (i.e., had the highest density of fires) from mid-June to
mid-July 2015. Detected thermal anomalies or fires for the 4 June–31 August
period are used (thermal anomaly data were not available from
1 to 9 September).
Aerosol dispersion modeling
Aerosol dispersion simulations were executed to demonstrate aerosol transport
using version 4 of the Hybrid Single Particle Lagrangian Integrated
Trajectory (HYSPLIT4) model (Draxler, 1999; Stein et al., 2015) and
1∘ data from the NOAA/National Centers for Environmental Prediction
(NCEP) Global Data Assimilation System (GDAS) (Kalnay et al., 1996).
Simulation parameterization details are presented by Maahn et al. (2017), but
are reiterated briefly here. The HYSPLIT dispersion model simulates emission
and subsequent transport of aerosols in forward mode from a point source,
enabling qualitative assessment of the spatial extent of dispersion from a
source of interest. Aerosol mass concentrations were evaluated qualitatively
from one central Prudhoe Bay location and from five locations within the
active fire region in central Alaska at 100 m intervals from 0 to
5000 m a.g.l. for 72 h, a 6 h release of particles at a default emission
rate of one arbitrary mass unit for the study time period
(1 June–30 September 2015). The five locations were chosen based on equal
spacing within the highest density of fires determined from the satellite
analyses for the entire study time period. Other input parameters include
particle density (6 g cm-3); shape factor (1.0); particle diameter
(0.2 µm) (Eck et al., 1999; Rissler et al., 2006; Brock et al.,
2011; Sakamoto et al., 2015); dry deposition velocity
(1 × 10-4 m s-1) (Warneck, 1999); in-cloud scavenging,
defined as a ratio of the pollutant in rain (g L-1) measured at the
ground to that in air (g L-1 of air in the cloud layer)
(4 × 104); and below-cloud scavenging
(5 × 10-6 s-1). Radioactive decay and pollutant
resuspension were set to the default values of zero days and 0 m-1,
respectively. The results of the dispersion simulations provide arbitrary
mass concentrations of particles within the model grid after 72 h of release
from the five fire source locations.
Classification parameters and thresholds for characterization of
1 s data as being impacted by one (or more) of the sources shown in
Fig. 9b.
Source
Classification parameter and threshold
Prudhoe Bay
1. CPCdiff≥100 cm-3 2. Distance from Deadhorse < 50 km
Prudhoe Bay boundary layer (BL)
1. CPCdiff≥100 cm-3 2. Distance from Deadhorse < 50 km 3. Altitude ≤ 500 m a.m.s.l.
All fires
1. rBC ≥ 20 ng kg-1a 2. CO ≥ 0.1 ppmv
Fires south
1. rBC ≥ 20 ng kg-1 2. CO ≥ 0.1 ppmv 3. Latitude ≤ 69∘ N
Long-range transport
1. No overlapping data with Prudhoe Bay or fires 2. PCASP ≥ 400 cm-3b 3. Altitude ≥ 300 m a.m.s.l.b
Pristine
1. No overlapping data with Prudhoe Bay, fires, or LRT 2. PCASP < 400 cm-3 3. Distance from Deadhorse > 50 km
a Maahn et al. (2017). b Based on threshold
value selected from Fig. 8.
Based on a combination of the HYSPLIT results, thresholds of parameters from
previous work, and visual assessment of the proximity to potential sources
and vertical profiles, each 1 s data point was characterized as originating
from the Prudhoe Bay oil extraction activities (called Prudhoe Bay herein for
brevity), fires, or neither. Remaining data were classified as long-range
transport, pristine, or background (see Table 2 for classification parameters
and thresholds used). Visual assessments are discussed in more detail
throughout Sect. 3. Briefly, Prudhoe Bay emissions were characterized by
visual assessment of high concentrations of particles with diameters between
3 and 10 nm within 50 km of the Deadhorse Airport. HYSPLIT dispersion
results from Deadhorse were used to determine the spatial coverage of Prudhoe
Bay emissions. The boundary layer emissions from Prudhoe Bay were restricted
to 500 m a.m.s.l. (Maahn et al., 2017) and based on changes in vertical
profiles of the concentrations of 3–10 nm particles. Fire data were
characterized by using thresholds from Maahn et al. (2017). We additionally
constrained the data to fires south of 69∘ N to focus closer to the
source region (i.e., near the highest density of fires detected by MODIS that
overlapped with the flight region). It is important to note that 17 of the
1 s data points fell under both Prudhoe Bay and fires classifications, but
the remaining 496 430 data points were characterized under one source.
Long-range transport was determined by data that were not characterized as
Prudhoe Bay or fires, but had concentrations of 0.1–3 µm diameter
particles ≥ 400 cm-1 above 300 m a.m.s.l. based on visual
assessment of the vertical profiles. We note that a variety of sources could
contribute to this classification, but are likely long-range transported due
to the relative concentrations of particles, altitude, and dearth of the
other dominant regional sources of aerosol. Pristine conditions were
characterized by data that were not classified as from Prudhoe Bay, fires, or
long-range transport but had low 0.1–3 µm diameter particle
concentrations. Background conditions where characterized as any data
remaining after the aforementioned sources were determined.
Results and discussion
Prudhoe Bay is a persistent local source of small particles in the
boundary layer
Figures 2 and 3 show the spatial and vertical variability of select aerosol
quantities from ACME-V, respectively. A clear source of aerosol originated
from Prudhoe Bay as suggested by the HYSPLIT dispersion model (Fig. 2a) and
in situ CN measurements (Fig. 2b and c). The highest number concentrations of
3–10 nm particles (up to 104 particles cm-3; calculated from
subtracting the CPC 3010 from CPC 3025 number concentrations) and
10 nm–3 µm sized particles were observed within 50 km of the
Deadhorse Airport (i.e., used here as a proxy for Prudhoe Bay). Particles
within the 3–10 nm size range are associated with nucleated aerosol (i.e.,
spontaneous in situ aerosol formation from precursor gases) (Colbeck and
Lazaridid, 2014). These high number concentrations of small particles are
likely formed from gas-to-particle partitioning of reactive gases from
flaring and venting along the North Slope. Flaring and venting of gas, which
is prominent near the surface in the Arctic near oil and gas facilities
(Jaffe et al., 1995; Johnson and Coderre, 2011), could contribute the vapors
– such as secondary products from ozone, SOx and various aromatic
hydrocarbons – that induce nucleation of new particles (Wilson and McMurry,
1981; Parungo et al., 1992; Kulmala et al., 2004; Laaksonen et al., 2008;
Ismail and Umukoro, 2012; Emam, 2015). Additionally, a sharp decrease in the
concentrations of 3–10 nm particles and, to a lesser extent,
10 nm–3 µm particles was observed above 500 m a.m.s.l. (Fig. 3a
and b, respectively), indicating (1) transition of particles via growth into
the accumulation mode (0.1–2.5 µm) as the plume evolves and
disperses vertically (Colbeck and Lazaridid, 2014) or (2) dynamical
restriction of these particles in the atmospheric boundary layer. When
examining the ratio of the number of 3–10 nm particles to the number of
0.1–3 µm particles (i.e., accumulation mode) (Fig. 3c), the ratio
was > 1 (i.e., nucleation-mode particles were dominant) for 74 % of
the time and < 1 (i.e., accumulation-mode particles were dominant) for
26 % of the time below 500 m a.m.s.l. More specifically, the ratio was
> 1 for 86 % of the time when only considering data points classified
as Prudhoe Bay. This ratio decreased overall with altitude, indicating the
nucleation-mode particles were formed at the lowest altitudes closest to
their source, while accumulation-mode particles originated from growth of the
nucleation-mode aerosol or a different source (see Sect. 3.3).
Maps of column average values from 20 to 500 m a.m.s.l. for
(a) HYSPLIT aerosol mass concentrations from Prudhoe Bay,
(b) CN with Dp=3–10 nm, (c) CN with
Dp=10 nm–3 µm, and (d) rBC mass
concentrations. The size of the marker equates to the number of measurements
at each 0.25∘ latitude × 0.50∘ longitude grid point.
The five white markers show each of the sites where the G-1 spiralled over.
Vertical profiles of CN for (a) Dp=3–10 nm,
(b) Dp=10 nm–3 µm, (c) the ratio of
CN with Dp=3–10 nm to CN with Dp=0.1–3 µm, and (d) rBC. The dashed line in
(c) indicates a ratio of 1. Note that the x scale for
(d) is zoomed in to show the increase below 200 m a.m.s.l. for rBC
(i.e., does not show all rBC data). Data classified as originating from
Prudhoe Bay in the boundary layer are colored blue.
Relatively high mass concentrations of rBC (up to 464 ng kg-1) were
also observed in the Prudhoe Bay area below 500 m a.m.s.l. (Figs. 2d and
3d), which likely originated from local combustion sources such as on- and
off-road vehicles, facility heating, and to some extent, flaring (Bond et
al., 2013; Stohl et al., 2013). Particles measured immediately near oil
combustion sources – including rBC – normally have a size mode around
100–130 nm (Parungo et al., 1992; Chang et al., 2004). The smallest average
mass equivalent modal size of the rBC were 115 and 110 nm for all data and
those closest to Deadhorse, respectively, indicating: (1) particles were
“fresher” (i.e., less coated or “aged” from heterogeneous reactions)
closest to the Prudhoe Bay source (Maahn et al., 2017) and (2) modal sizes
are analogous to what might be expected from oil combustion sources after
slight aging due to farther proximity from sources (i.e., not direct
measurement from stacks). However, larger sizes and higher mass
concentrations (particularly above 500 m a.m.s.l.) of rBC were observed
further south (Fig. 2d) and were likely influenced by biomass burning
emissions as discussed in Sect. 3.2. The localized nature of the
smaller-sized CN and rBC to the Prudhoe Bay reflect the physical removal
processes of the dominant-sized particles occurring from this region. Both
nucleation- and Aitken-mode (i.e., 10–100 nm) aerosols have lifetimes on
the order of minutes to hours and thus have typical travel distances of
one to tens of kilometers (Wilson and Suh, 1997), corroborating our findings of such
aerosols within 50 km of Deadhorse Airport.
Our results demonstrate that Prudhoe Bay is a strong and persistent source of
nucleated aerosol and primary combustion aerosol; however, the high mass and
number concentrations of these aerosols are restricted to the boundary layer
and tend to remain localized to the Prudhoe Bay area. These aerosols may not
have strong direct effects on the regional atmospheric radiation budget due
to their inherently small size and low concentrations of larger
accumulation-mode particles (Friedlander, 2000). This is supported by the
fact that no noticeable spatial patterns in absorption and scattering were
observed as a function of distance from Deadhorse Airport (not shown).
However, as these particles age via atmospheric processing from co-emitted
gases such as SOx/NOx and grow larger into the accumulation mode,
it is possible they could have an impact after sufficient atmospheric
residence time downwind. We do not have the compositional data necessary to
determine the mixing state or extent of aging of these nucleation-mode
particles into the accumulation mode. Additionally, modeling studies have
suggested that BC aerosols from Prudhoe Bay oil extraction have a positive
net radiative forcing, particularly in summer due to greater absorption of
solar radiation (Ødemark et al., 2012). With regard to indirect effects,
Maahn et al. (2017) demonstrated the importance of Prudhoe Bay industrial
aerosol in modulation of cloud properties over the North Slope. Further,
Leaitch et al. (2016) and Burkart et al. (2017) recently published
observations of CCN diameters down to 20 nm in the Canadian Arctic; contrary
to the conventional wisdom that 100 nm is the threshold relevant for CCN.
However, these Canadian Arctic aerosols were likely compositionally different
due to their marine origin and thus could vary in hygroscopicity as compared
to oil extraction emissions.
Maps of AOD and fires (i.e., thermal anomalies; orange diamonds)
detected by MODIS for the 11 June–12 August time period, showing the
transition from few fires to the highest density of fires and back. Flight
tracks (green lines) and site locations (white circles) are also shown during
each corresponding time period. The blue markers in the top left panel
signify HYSPLIT fire start point locations. The white circle in the top right panel denotes
the fire closest to Toolik Lake on 30 June.
Regional fires impact air composition over much of central and
northern Alaska
Another dominant aerosol source observed during the ACME-V field campaign was
the central Alaskan wildfires. The 2015 summer fire season was particularly
active, leading to the second largest number of acres burned in Alaska since
records began in 1940 (Partain Jr. et al., 2016). The highest density of
fires detected from satellite lasted from mid-June to mid-July (Fig. 4).
These fires produced dense plumes of aerosol that propagated over much of the
North Slope as evidenced by the high values of AOD originating from the
central Alaska wildfires. Flights were impacted by the high-AOD regions from
late June until the end of July. The G-1 flew directly through the wildfire
plumes during 25 June–1 July near Toolik Lake (Fig. 4c), 9–15 July over
most of the flight track (Fig. 4e), and 16–22 July near Utqiaġvik and
Oliktok Point (Fig. 4f).
Same as Fig. 2, but column-averaged from 20 to 5000 m a.m.s.l. and
for (a) HYSPLIT aerosol mass concentrations with the five fire
locations as the simulation start points, (b) CN with Dp=0.1–3 µm, (c) rBC mass, and (d) CO.
The HYSPLIT dispersion simulations from the five fire source points (Fig. 4,
first row) indicate increased particle mass concentrations spread over the
flight region, particularly at the southern portion of the domain (Fig. 5a).
Analogously, in situ measurements show clear influence of Alaskan boreal
fires (Fig. 5b–d): the number of particles from 0.1 to 3 µm, rBC
mass, and CO were high in concentration, particularly at the southern portion
of the flight track close to the Brooks Range. Wildfires emit large
quantities of primary organic aerosol (POA) and can generate secondary
organic aerosol (SOA) that can develop coatings through aging while
transported over long distances (Andreae and Merlet, 2001; Collier et al.,
2016; Creamean et al., 2016). Therefore, we would expect to observe an
abundance of coarse and accumulation-mode aerosol and a dearth of nucleation-mode aerosol, due to the fact that nucleation of new particles is inhibited
by precursor vapors instead condensing onto pre-existing aerosol (discussed
in more detail in below). The largest impacts from the fires were observed
from 400 to 7000 m a.m.s.l. (Fig. 6a). MCE values during measurements
impacted by fires were close to 1 (Fig. 6b), indicating active flaming (i.e.,
“fresher” fires) instead of smouldering. Combined, the HYSPLIT and MCE data
suggest fires were recent, yet emissions from the fires were ejected high
into the troposphere. CO and rBC concentrations were strongly correlated
(r2=0.83) and reached 0.626 ppmv and 1490 ng kg-1,
respectively (Fig. 6c). CO is a poor tracer for oil extraction since it
originates from combustion; thus, aside from the operational vehicles in
Prudhoe Bay, we would expect the boreal fires to most strongly influence CO
during the campaign (Crutzen et al., 1979; Andreae and Merlet, 2001).
Background CO concentrations have been measured at 0.120 ppmv using
summertime surface measurements at Utqiaġvik and up to 0.4 ppmv during
ARCPAC airborne measurements of springtime long-range-transported biomass
burning plumes (Liang et al., 2004; Brock et al., 2011). Brock et al. (2011)
reported springtime rBC mass concentrations of up to 1000 ng m-3 using
the SP2 instrument also used in the current study. Although the fires were an
abundant source of absorbing rBC, highly scattering aerosol originated from
the fires (Fig. 6d), which could be explained by previous work indicating
fires produce larger quantities of organic carbon and sulfate (Penner et
al., 1992; Wiedinmyer et al., 2011). Our observations are parallel to
previous summertime observations from regional boreal fires in that such
fires produce substantial quantities of aerosol (Stohl et al., 2006; Eck et
al., 2009), which is likely due to the proximity of the measurements to the
source.
Vertical profiles of (a) CO and (b) calculated
modified combustion efficiency (MCE). MCE is only calculated for fire data.
Correlations between CO and (c) rBC mass and (d) scattering
coefficients at 550 nm for all data are also shown. Data classified as
originating from the fires are colored orange.
Notably, anomalously high rBC mass and CO concentrations were measured during
a few flights (F13, F17, and F18). Almost all measurements from F13 (30 June
flight) were considerably high – including CO (maximum of 0.626 ppmv), rBC
(1490 ng kg-1), aerosol number concentrations (20 596 cm-3 for
CPC 3010, 11 021 cm-3 for PCASP, and 30 cm-3 for OPC),
absorption (61.1 Mm-1), and scattering (978.1 Mm-1) – as
compared to other flights impacted by the fires (Fig. 7). AOD was relatively
high (> 0.1) and a fire was detected by MODIS close to Toolik Lake (see
Fig. 4c) on 30 June, which is likely why the measurements were highest when
spiralling over the waypoint and then decreased as the aircraft flew low
to/from adjacent waypoints. However, the plume on 30 June also reached higher
altitudes above Oliktok Point (as supported by MODIS), indicating the biomass
burning plume ascended as it propagated northward. The only exception to the
considerably high nature of the aerosol concentrations is nucleation-mode
aerosol, which was only slightly elevated in concentration (maximum of
2500 cm-3 as compared to a maximum of 101 940 cm-3 for the
same-sized particles from Prudhoe Bay) and was not elevated over Oliktok
Point, demonstrating the short lifetimes of these small-sized particles (via
growth into the accumulation mode) in densely populated biomass burning
plumes.
4-D profiles of (a) CO, (b) rBC mass,
(c) CN with Dp=0.1–3 µm, (d) CN with
Dp=0.8–15 µm, (e) absorption coefficient,
(f) scattering coefficient, (g) CN with Dp=3–10 nm, and (h) CN with Dp=10 nm–3 µm
from F13 on 30 June. The left, bottom, and right axes are altitude,
longitude, and latitude, respectively.
The large quantity of aerosol observed from the lowest to highest altitudes
flown by the aircraft closest to the Brooks Range indicate the thickness of
the biomass burning aerosol layer. These particles can have implications for both
the local energy budget and cloud formation. We observed how aerosols
from the 30 June fire event were highly absorbing and scattering relative to
the rest of the region (Fig. 7e and f, respectively). Both organic and
inorganic components of aerosols from wildfires can be highly hygroscopic and
serve as efficient CCN (Novakov and Corrigan, 1996; Petters et al., 2009a;
Engelhart et al., 2012), while mineral dust, carbonaceous, and biological
aerosols from wildfires have been shown to increase atmospheric INP
concentrations (Petters et al., 2009b; McCluskey et al., 2014). Additionally,
ejection of such a large quantity of aerosol and trace gases into the
atmosphere can affect air quality on the North Slope and to the Arctic beyond
over the course of a couple of weeks (Stohl et al., 2013).
Vertical profiles of (a) CN with Dp=0.1–3 µm and (b) CN with Dp=0.8–15 µm. Data classified as originating from Prudhoe Bay in the
boundary layer, fires, and long-range transported are colored blue, orange,
and red, respectively.
Relative contributions from regional and long-range-transported
sources of aerosol to North Slope
Weaker poleward advection and strong aerosol removal via wet deposition make
the Arctic less subject to transport from mid-latitude sources during summer
as compared to the spring Arctic haze season (Polissar et al., 2001; Eckhardt
et al., 2003; Garrett et al., 2010; Browse et al., 2012; Bian et al., 2013).
During summer, aerosol production from local natural sources – from
terrestrial and marine microbial processes and mechanical generation of sea
salt – is dominant at the ground and aloft (Gregory et al., 1992; Quinn et
al., 2002; Leaitch et al., 2013, 2016; Burkart et al., 2017). Although the
concentrations of pollutants from mid-latitudes is typically lower during
summer (Raatz and Shaw, 1984), we observed occasional episodic increases in
accumulation- and coarse-mode aerosol measured by the PCASP and OPC at higher
altitudes (Fig. 8), without the presence of Prudhoe Bay or Alaskan fire
tracers of CO and rBC. These layers were deficient in CO, rBC, and 3–10 nm
particles, and were present during flights where biomass burning was not
detected as a dominant source. Thus, we assume these events were not a result
of local or regional emissions that dominated the North Slope aerosol. These
diagnosed long-range transport events were only observed during flights 1, 9,
10, and 11, thus supporting the idea that poleward advection is less frequent
and wet removal processes are enhanced in summer as compared to the Arctic
haze season.
Recent studies have alluded to the possibility that the Arctic summer may not
be as pristine as previously thought (Stohl et al., 2013). Modeled emissions
from ARCTAS-A highlight summertime boreal fires and their impact on Arctic
pollution; however, these flights targeted local fire plumes and were limited
to the Canadian Arctic. Further, Bian et al. (2013) state that the ARCTAS-A
measurements “cannot provide a comprehensive and representative picture of
Arctic pollution in the summer”. We evaluated all ACME-V data and classified
each flight as impacted by fires, Prudhoe Bay, long-range transport, or some
combination of these (Fig. 9a). All flights contained at least a small
segment that was classified as background, but flights where only background
conditions were observed are labeled as such. Due to the aircraft being
based in Deadhorse, emissions from Prudhoe Bay impacted nearly every flight
(31 flights; remaining 7 flights were flagged for clouds near Prudhoe Bay;
thus those data were eliminated from the analysis), while regional fires
impacted 22 flights, and long-range transport impacted only 4 flights. For a
more statistical representation of the sources, we classified each 1 s data
point as influenced from fires at all flight locations, fires from the lowest
latitude flown to 69∘ N (i.e., a subset of all fires), Prudhoe Bay
emissions strictly near Prudhoe Bay, emissions near Prudhoe Bay in the
boundary layer, long-range transport, background, and pristine conditions
(Fig. 9b; see Table 2 for classification definitions). Background may include
aerosol from Prudhoe Bay or the fires after significant atmospheric residence
time, but we cannot distinguish these from local natural aerosol emission or
production that is traditionally observed with the measurements obtained.
This plot demonstrates the episodic behavior of the fires and the localized
behavior of Prudhoe Bay emissions. However, what we are calling “pristine”
conditions had the lowest occurrence overall (only 5 %), which contrasts
with previous North Slope summertime aerosol studies. It is important to note
that these data are dependent on the location and height of the aircraft,
thus may be biased. However, it provides a general overview of the sources of
aerosols in the context of the flight locations but may not be
representative of the entire North Slope at all times.
(a) The number of flights that were classified as impacted
by aerosols from the central Alaskan fires, Prudhoe Bay, long-range
transport, pristine conditions, and background (based on parameter thresholds
in Table 2). (b) Percentage of measurements that were impacted by fires, Prudhoe Bay, long-range transport, pristine conditions, and background. Fires south of 69∘ N are shown by the second bar from the left (percentage was calculated out of number of fire data points in b). The percentage of Prudhoe Bay data
points (i.e., from the blue portion in the first bar in b);
data in the boundary layer are shown by the last bar from the left.