Decreasing sea ice and increasing marine navigability in northern latitudes
have changed Arctic ship traffic patterns in recent years and are predicted
to increase annual ship traffic in the Arctic in the future. Development of
effective regulations to manage environmental impacts of shipping requires an
understanding of ship emissions and atmospheric processing in the Arctic
environment. As part of the summer 2014 NETCARE (Network on Climate and
Aerosols) campaign, the plume dispersion and gas and particle emission
factors of effluents originating from the Canadian Coast Guard icebreaker
Amundsen operating near Resolute Bay, NU, Canada, were investigated. The
Amundsen burned distillate fuel with 1.5 wt % sulfur. Emissions were
studied via plume intercepts using the Polar 6 aircraft measurements, an
analytical plume dispersion model, and using the FLEXPART-WRF Lagrangian
particle dispersion model. The first plume intercept by the research aircraft
was carried out on 19 July 2014 during the operation of the Amundsen in the
open water. The second and third plume intercepts were carried out on 20 and
21 July 2014 when the Amundsen had reached the ice edge and operated under
ice-breaking conditions. Typical of Arctic marine navigation, the engine load
was low compared to cruising conditions for all of the plume intercepts. The
measured species included mixing ratios of CO
Plume expansion rates (
International shipping is responsible for approximately 3.3 % of global
Ship emissions measurements from land-based, marine-based, and airborne
platforms have been reported in numerous studies
The sniffer method, including plume intercepts, is commonly used to study
ship emission factors, where the increase in concentration or mixing ratio of
pollutants compared to the background atmosphere can be observed
Many parameters change ship emission factors including engine load, fuel
type, and emissions abatement technologies. Ship speed reduction results in
better fuel economy and lower
Sulfur in ship fuels is primarily converted to
Slide valves, water-in-fuel emulsion, diesel particulate filters, emulsified
fuel, and sea water scrubbing are key abatement technologies to reduce
emission factors for various pollutants
In this study we use
measurements from airborne plume intercepts to estimate emission factors for
the Amundsen ship, while operating in the Arctic and burning low sulfur fuel,
for gaseous and particle pollutants. In addition, we study the geometrical
evolution of the Amundsen's plume in the Arctic marine boundary layer. We
compare these observations to other similar studies in mid-latitudes. The
first plume measurement was carried out on 19 July 2014 during the operation
of the Amundsen in the Lancaster Sound of the Northwest Passage
(74
The Amundsen (IMO: 7510846) (Fig.
Snapshot of Polar 6 aircraft while sampling Amundsen's plume during ice-breaking in Lancaster Sound (Photo credit: Maurice Levasseur).
The airborne instrument platform was the Polar 6 aircraft, a DC-3 converted
to a Basler BT-67, owned and operated by the German Alfred Wegener Institute
– Helmholtz Center for Polar and Marine Research (Fig.
State parameters and meteorological measurements were performed by an
AIMMS-20 instrument, manufactured by Aventech Research Inc., Barrie, Ontario,
Canada. The instrument consisted of three modules. The Air Data Probe (ADP)
measured the three-dimensional, aircraft-relative flow vector (true air
speed, angle-of-attack, and sideslip). The temperature and relative humidity
sensors were located in the aft section of the probe for protection. A
three-axis accelerometer pack facilitated direct turbulence measurement. The
inertial measurement unit (IMU) consisted of three gyros and three
accelerometers providing the aircraft angular rate and acceleration. A GPS
module provided the aircraft 3-D position and inertial velocity. Horizontal
and vertical wind speeds were measured with accuracies of 0.50 and
0.75
Trace gas
During the ship emission measurements, the
Trace gas NO
Particle number concentrations greater than 5
Aerosol particle size distributions from 70
Cloud condensation nuclei (CCN) concentrations were measured by a DMT CCN
Model 100 counter operating behind a DMT low pressure inlet at a reduced
pressure of approximately 650
Extensive calibrations and evaluations for CPC, UHSAS, and CCN measurements
were performed in the laboratory prior to integration of the instruments on
the aircraft and again with instrumentation in the aircraft at Resolute Bay.
Full discussions can be found in the study by
Particle size distribution for particle diameters greater than
0.25
The refractory black carbon (rBC) was measured using a single particle soot
photometer (SP2) from DMT Boulder. The SP2
Particle sampling is described in full detail by
The methodology of
A convenient and practical way to fit for plume expansion rates is to
intercept a portion of the plume and measure the mixing ratio of a chemically
inert species in the plume such as
Plume age could be estimated by the aircraft measurements. For this, plume
intercepts were first mapped on a latitude vs. longitude plot. This provided a
scatter plot to which a plume center line was fitted with a high order
polynomial. The wind measurements on board of the aircraft along the flight
track and closest to each point on the center line were then used to estimate
wind velocity along the plume center line. The plume age was estimated at
each intercept by calculating the time it took for a parcel of air from the
plume origin on the center line (
A common method to calculate emission factors (
For pollutant measurement in units of mass concentration (e.g.,
[
If a modal emission factor with units of [
In order to study the dispersion of ship emissions in the Polar boundary
layer, we used the FLEXPART-WRF model (
Regression was required in our analysis to relate one set
of measurements to another in order to estimate the plume growth rate and
various emission factors. However, since all measurements, including both
dependent and independent variables, had inherent uncertainties, the ordinary
least squares (OLS) approach could not be used. Instead, a multivariate least
squares method, called the orthogonal distance regression (ODR), was used
where the sum of squared orthogonal distances between each data point and a
linear model was minimized by fitting the model coefficients
Plume intercepts in the 3 consecutive days are referred to as plume 1
(19 July 2014), plume 2 (20 July 2014) and plume 3 (21 July 2014). The
flights were planned in advance using WRF and FLEXPART-WRF forecasts (not
shown) so that the aircraft could efficiently sample ship emissions downwind
of the stack. Following the campaign, WRF was run using ECMWF (European
Center for Medium-Range Weather Forecasts) analysis as initial and boundary
conditions,
Measurements of wind direction and wind speed during the plume sampling flights (black). The modeled wind speed and direction interpolated in space and time to the location of the aircraft are shown for the ECMWF analysis (blue) and WRF model (red). The flight altitude is shown in grey.
Snapshots of surface wind speed and direction predicted by WRF, with
run details provided in
Vertical profiles of potential temperature (
Snapshots of normalized FLEXPART-WRF predicted partial columns
(0–350 m) indicating the location of the ship (initial location of emitted
plume) and the predicted plume location. The flight track is shown in grey
and the ship track is shown in magenta. The vertical plume structure is
studied in Fig.
Plume location according to aircraft intercepts along the flight
track identified as enhancements above background NO
We also characterize boundary layer dynamics using balloon soundings launched
from the ship at the times of the flights for plumes 2 and 3
(Fig.
Vertical cross sections (normalized tracer concentrations) predicted
by FLEXPART-WRF along plumes (panels marked d – along plume) and across
plumes (panels marked e – across plume) for the same times as shown in
Fig.
It is known that both ship speed and engine load influence total fuel burnt
and emission factors. For the Amundsen, ship speed was not directly
correlated with engine load for two reasons. First, the Amundsen operated on
a diesel–electric system, which could provide propulsion power using
electricity while the engines were off or operating at partial load. Second,
because of the specifics of ships operating in the Arctic within sea ice,
even during stationary conditions, the engine could be running to power
ice-breaking operations. The average ship speeds during plumes 1, 2, and 3 were
3.23
In order to show the emissions distributions in the plume on different days
and the flight pattern, we used FLEXPART-WRF partial columns and vertical
cross sections. Given the low boundary layer heights, maps of the plume
distributions were calculated by summing the mass of particles in the lowest
350 m above the ocean and/or sea ice. Three example partial columns during plume
sampling are shown in Fig.
The predicted vertical distribution of emissions along and across the plumes
are shown in Fig.
Plume intercepts have been identified using the methodology of
An example time series plot for identified pollution peaks in
plume 3; sampling time for all instruments is 1
Calculated plume growth or expansion rate (
Using airborne meteorological and
Scatter plot for excess gas pollutant mixing ratio vs. excess
Figure
Scatter plot for excess particle concentration vs. excess carbon
dioxide for plumes 1
Linear regression analysis for excess gas pollutant mixing ratio vs.
excess carbon dioxide;
Linear regression analysis for excess particle concentration vs.
excess carbon dioxide;
Emission factors for NO
Table
Figure
Emission factors (
Figure
Emission factors for
Emission factors for NO
Figure
Emission factors for black carbon; numbers in brackets indicate
engine load (%), fuel type (HFO: heavy fuel oil with high sulfur content,
and MGO: marine gas oil with low sulfur content), or vessel class based on
gross metric tonnage (HSD: high speed diesel
Emission factors for
Figure
Emission factors for total particle count
Emission factors for black carbon; fuel type (HFO: heavy fuel oil
with high sulfur content, and MGO: marine gas oil with low sulfur content),
or vessel class based on gross metric tonnage (HSD: high speed diesel
Figure
Emission factors for cloud condensation nuclei
Emission factors for total particle count; fuel type (HFO: heavy
fuel oil with high sulfur content, and MGO: marine gas oil with low sulfur
content), or vessel class based on gross metric tonnage (HSD: high speed
diesel
Figure
Emission factors for cloud condensation nuclei; fuel type (HFO:
heavy fuel oil with high sulfur content, and MGO: marine gas oil with low
sulfur content), or vessel class based on gross metric tonnage (HSD: high
speed diesel
In an effort to understand ship emissions and processing in the Arctic
environment, the plume dispersion and emission factors from the Canadian
Coast Guard icebreaker Amundsen were quantified near Resolute Bay, NU,
Canada, during the summer 2014 NETCARE campaign. Three plumes (1, 2, and 3)
were studied on consecutive days from 19 to 21 July 2014 by airborne
interception using the Polar 6 aircraft, an analytical plume dispersion
model, and by the FLEXPART-WRF dispersion model. The first plume measurement
was carried out during the operation of Amundsen in the open water while
moving at an average speed of
The calculated analytical expansion rates were
The difference in plume expansion rate compared to mid-latitude observations was attributed to unique physics of the Arctic boundary layer, which was characterized by reduced turbulent mixing due to the thermally stable boundary layer. In addition, ship operation at partial engine load and ice-breaking mode contributed to different emission factors compared to cruising conditions.
One limitation of this study was that the Amundsen plume was not intercepted at higher engine loads near cruising conditions. Future studies should measure the emission factors and plume geometrical evolution under such conditions to provide a more complete understanding of plume chemistry and physics over the Arctic marine boundary layer.
Experimental data: NETCARE, which organized the aircraft flight campaign described in this
paper, is moving towards a publicly available, online data archive. In the meantime, the data
can be accessed by emailing the principal investigator of the network: Jon Abbatt at the
University of Toronto (jabbatt@chem.utoronto.ca)
Numerical data: The WRF modeling system can be downloaded from NCAR
(
The authors acknowledge a large number of people for their contributions to
this work. We appreciate expert internal review of the manuscript by
Pacal Bellavance and Paul Izdebski (Environment and Climate Change Canada –
ECCC). We thank Tim Papakyriakou and Greg Wentworth for providing ship track
and speed information. We thank Kenn Borek Air, in particular Kevin Elkes and
John Bayes for their skillful piloting that facilitated these plume
observations. We are grateful to John Ford and the University of Toronto
(UofT) machine shop, Jim Hodgson and Lake Central Air Services in Muskoka,
Jim Watson (Scale Modelbuilders, Inc.), Julia Binder and Martin Gehrman
(Alfred Wegener Institute – AWI – Helmholtz Center for Polar and Marine
Research), Mike Harwood and Andrew Elford (ECCC), for their support of the
integration of the instrumentation and aircraft, Yuan You (ECCC), for helping
with data analysis, and Jeremy Wentzell (ECCC), for sharing an