ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-16653-2018Assessing the impact of shipping emissions on air pollution in the Canadian
Arctic and northern regions: current and future modelled scenariosAir pollution in the Canadian Arctic and northern regionsGongWanminwanmin.gong@canada.caBeagleyStephen R.https://orcid.org/0000-0002-7403-1681CousineauSophieSassiMouradMunoz-AlpizarRodrigoMénardSylvainRacineJacintheZhangJunhuaChenJackhttps://orcid.org/0000-0002-3764-1149MorrisonHeatherSharmaSangeetaHuangLinhttps://orcid.org/0000-0002-8200-4632BellavancePascalLyJimIzdebskiPaulLyonsLynnHoltRichardScience and Technology Branch, Environment and Climate Change Canada,
Toronto, Ontario, M3H 5T4, CanadaMeteorological Service of Canada, Environment and Climate Change
Canada, Montreal, Quebec, H9P 1J3, CanadaScience and Technology Branch, Environment and Climate Change Canada,
Ottawa, Ontario, K1V 1C7, CanadaEnvironmental Protection Branch, Environment and Climate Change
Canada, Gatineau, Quebec, K1A 0H3, CanadaEnvironmental Protection Branch, Environment and Climate Change
Canada, Toronto, Ontario, M3H 5T4, CanadaEnvironmental Protection Branch, Environment and Climate Change
Canada, Vancouver, British Columbia, V6C 3S5, CanadaWanmin Gong (wanmin.gong@canada.ca)26November2018182216653166872February201817April201822October201823October2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/16653/2018/acp-18-16653-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/16653/2018/acp-18-16653-2018.pdf
A first regional assessment of the impact of shipping emissions on air
pollution in the Canadian Arctic and northern regions was conducted in this
study. Model simulations were carried out on a limited-area domain (at 15 km
horizontal resolution) centred over the Canadian Arctic, using the
Environment and Climate Change Canada's on-line air quality forecast model, GEM-MACH (Global
Environmental Multi-scale – Modelling
Air quality and CHemistry), to investigate the contribution from the marine shipping
emissions over the Canadian Arctic waters (at both present and projected
future levels) to ambient concentrations of criteria pollutants (O3,
PM2.5, NO2, and SO2), atmospheric deposition of sulfur (S) and
nitrogen (N), and atmospheric loading and deposition of black carbon (BC) in the Arctic.
Several model upgrades were introduced for this study, including the
treatment of sea ice in the dry deposition parameterization, chemical lateral
boundary conditions, and the inclusion of North American wildfire emissions.
The model is shown to have similar skills in predicting ambient O3 and
PM2.5 concentrations in the Canadian Arctic and northern regions, as the
current operational air quality forecast models in North America and Europe.
In particular, the model is able to simulate the observed O3 and PM
components well at the Canadian high Arctic site, Alert. The model assessment
shows that, at the current (2010) level, Arctic shipping emissions contribute
to less than 1 % of ambient O3 concentration over the eastern Canadian
Arctic and between 1 and 5 % of ambient PM2.5 concentration over the
shipping channels. Arctic shipping emissions make a much greater
contributions to the ambient NO2 and SO2 concentrations, at 10 %–50 % and 20 %–100 %, respectively. At the projected 2030
business-as-usual (BAU) level, the impact of Arctic shipping emissions is
predicted to increase to up to 5 % in ambient O3 concentration over a
broad region of the Canadian Arctic and to 5 %–20 % in ambient PM2.5
concentration over the shipping channels. In contrast, if emission controls
such as the ones implemented in the current North American Emission Control
Area (NA ECA) are to be put in place over the Canadian Arctic waters, the
impact of shipping to ambient criteria pollutants would be significantly
reduced. For example, with NA-ECA-like controls, the shipping contributions
to the population-weighted concentrations of SO2 and PM2.5 would be
brought down to below the current level. The contribution of Canadian Arctic
shipping to the atmospheric deposition of sulfur and nitrogen is small at
the current level, < 5 %, but is expected to increase to up to
20 % for sulfur and 50 % for nitrogen under the 2030 BAU scenario. At
the current level, Canadian Arctic shipping also makes only small
contributions to BC column loading and BC deposition, with < 0.1 % on
average and up to 2 % locally over the eastern Canadian Arctic for the former,
and between 0.1 % and 0.5 % over the shipping channels for the latter. The
impacts are again predicted to increase at the projected 2030 BAU level,
particularly over the Baffin Island and Baffin Bay area in response to the
projected increase in ship traffic there, e.g., up to 15 % on BC column
loading and locally exceeding 30 % on BC deposition. Overall, the study
indicates that shipping-induced changes in atmospheric composition and
deposition are at regional to local scales (particularly in the Arctic).
Climate feedbacks are thus likely to act at these scales, so climate impact
assessments will require modelling undertaken at much finer resolutions than
those used in the existing radiative forcing and climate impact assessments.
Introduction
Unprecedented rates of warming are increasing the navigability of the Arctic
Ocean and, subsequently, rendering this region accessible to increasing
resource exploitation and the development that goes along with it. Over
the past several decades, the extent of Arctic sea ice has declined. The
rate of decline of late summer sea-ice cover has been particularly rapid
since the beginning of this century (e.g., Serreze et al., 2007). The latest
climate model simulations predict that the retreat of Arctic sea ice will
continue throughout the 21st century, and that an ice-free Arctic Ocean
in late summertime may be realized by the middle to the end of this century (Boé et al., 2009; Wang and
Overland, 2009). The decline in Arctic sea ice has
raised the prospect of increased Arctic shipping activities and the
potential use of new transit routes, such as the Northern Sea Route, the
Northwest Passage (NWP), and the Transpolar Sea Route (e.g., Stephenson et al., 2013;
Melia et al., 2016). Pizzolato et al. (2016) conducted a coupled spatial
analysis between shipping activity and sea ice using observations in the
Canadian Arctic over the 1990–2015 period and found that there has been an
increase in shipping activities in Hudson Strait, Beaufort Sea, Baffin Bay,
and regions in the southern route of the Northwest Passage, and that the
increases in shipping activity are significantly correlated with the
reductions in sea-ice concentration in these regions.
Shipping is an important source of air pollutants. Emissions of exhaust
gases and particles from ocean-going ships contain carbon dioxide
(CO2), nitrogen oxides (NOx), carbon monoxide (CO), volatile
organic compounds (VOCs), sulfur dioxide (SO2), particulate sulfate
(SO4), black carbon (BC), and particulate organic matter (OM). These
pollutants lead to the production of ozone (O3) and fine particulate
matter (e.g., PM2.5), the latter primarily through oxidation of
SO2 and the formation and production of SO4 particles, which
degrade air quality. At the same time, O3 and SO4 resulting from
ship emissions, along with CO2 and BC directly emitted from shipping,
are also climate forcing agents which can impact the radiative balance
through either direct or indirect effects. Shipping emissions also contribute
to the deposition of nitrogen (N) and sulfur (S), which can impact
ecosystems through acidification and eutrophication. Recent studies have
suggested that around 15 % and 4 %–9 % of all global anthropogenic
emissions of NOx and SO2, respectively, are from ocean-going ships
(e.g., Corbett and Köhler, 2003; Eyring et al., 2005a). As most of the
ship emissions occur within 400 km of coastlines, they primarily contribute
to air pollution in coastal areas (e.g., Eyring et al., 2010; Viana et al.,
2014; Aksoyoglu et al., 2016; Aulinger et al., 2016). However, these
emissions can be transported hundreds of kilometres downwind and impact a
much broader region (e.g., Eyring et al., 2010; Aulinger et al., 2016).
Although Arctic marine shipping currently accounts for a small percentage of
global shipping emissions, it makes a proportionally bigger impact on the
environment than shipping does at lower latitudes due to the generally
pristine Arctic background, particularly in the Canadian Arctic Archipelago.
Furthermore, the lower troposphere in the Arctic is more isolated during
summer, which is also the Arctic shipping season, due to the retreating
Arctic dome, giving rise to much slower transport of pollutants from lower
latitudes and more efficient removal processes (Stohl, 2006; Law and Stohl, 2007). Local sources of air pollution, such as shipping, play a more
important role in determining air quality in this region during this time.
A number of studies assessing the impact of Arctic shipping emissions have
been conducted in recent years. Based on the high-growth scenario projection
of Eyring et al. (2005b) on future international shipping emissions (to year
2050) and assuming a fraction of the increase would occur in the Arctic,
Granier et al. (2006) predicted an increase in Arctic surface O3
concentration by a factor of 2 to 3 due to the increase in ship NOx
emissions. Ødemark et al. (2012) looked into short-lived climate forcers
from current shipping and petroleum activities in the Arctic based on
inventories developed by Peters et al. (2011) and found that radiative
forcing from shipping emissions is dominated by the direct and indirect
effects of sulfate from SO2 emissions during shipping season. The
overall effect from shipping on radiative forcing is negative. Dalsøren
et al. (2013) assessed the changes in surface concentrations of NO2,
O3, SO4, BC, and organic carbon (OC) between years 2004 and 2030,
based on the Arctic shipping inventories developed by Corbett et al. (2010),
which take into account Arctic shipping growth, possible emission control
measures, and the opening of diversion routes for shipping in the Arctic due
to the expected melting of sea ice. Based on the same inventories of Corbett
et al. (2010), Browse et al. (2013) investigated the impact of Arctic
shipping on BC deposition at high latitudes, and found that the overall
impact from Arctic shipping to total BC deposition remains low. Their
results show that Arctic shipping contributes a maximum of 1.9 % to the
total annual BC deposition north of 60∘ N at present levels
and a maximum of 5 % at 2050 levels under a high-growth scenario. Most of
these assessments were conducted using global models at coarse resolutions
(e.g., 2.8∘×2.8∘). In a recent study on
cross-polar transport and scavenging of Siberian aerosols, Raut et al. (2017) found that the model simulation at a coarser horizontal resolution
(i.e., 100 km instead of 40 km) was unable to resolve plume structures
transported across the polar region in summer. The model performed much
better at simulating cross-polar transport and processing using a finer
horizontal resolution (40 km). At a regional scale, Marelle et al. (2016)
used model simulations at 15 km resolution to estimate the regional impacts
of shipping pollution in northern Norway during a 15-day period in July 2012,
when an aircraft measurement campaign was conducted to characterize
pollution originating from shipping and other local sources. Their estimate
of the impact of shipping emissions on O3 production over the Norwegian
coast was considerably lower than the estimate of Ødemark et al. (2012),
which was based on a model simulation at a much coarser resolution
(2.8∘×2.8∘). The authors attributed the
difference in estimated impact, at least in part, to the non-linear effects
associated with the unrealistic instant dilution of ship NOx emissions
in global models run at coarse resolutions, particularly under pristine
background conditions as found in Vinken et al. (2011).
In this study, we assess the impact of emissions from marine shipping on the
Canadian Arctic using an on-line comprehensive air quality forecast model, Global
Environmental Multi-scale – Modelling
Air quality and CHemistry (GEM-MACH), configured for the Arctic at 15 km resolution. A detailed
baseline emission inventory for ships sailing in Canadian waters was
developed utilizing vessel movement data for 2010 supplied by the Canadian
Coast Guard (CCG) and activity-based emissions factors. Projections of
Canadian Arctic marine shipping emissions to a future year (2030) were made
based on two scenarios: business-as-usual (BAU) and emission controls (a.k.a.,
controlled). Model simulations for the Arctic shipping season were carried
out, with and without the marine shipping emissions over the Canadian Arctic
waters, at both the current (2010 baseline) and future (projected) levels.
The contributions from Canadian Arctic shipping emissions to ambient
concentrations of criteria pollutants (O3, PM2.5, NO2, and
SO2), total S and N deposition, and BC loading and deposition were
assessed in the context of their relevance to air quality, local ecosystems,
and climate. In the following sections, we will describe the Canadian
shipping emission inventories (Sect. 2) and the modelling system and
simulation setup (Sect. 3). An evaluation of the 2010 baseline simulation
against available observations is presented in Sect. 4, and the assessment
of the impact of the Arctic shipping emissions in Sect. 5. We will end
with conclusions in Sect. 6.
The 2010 Canadian national marine shipping inventory – Arctic shipping
activities (current and projections)
The 2010 Canadian national marine shipping emission inventory used for this
study was generated by using the Marine Emission Inventory Tool (MEIT)
developed for Environment and Climate Change Canada (ECCC) (SNC-Lavalin
Environment, 2012). The inventory includes all commercial marine vessel
classes tracked by the CCG within Canadian waters, as
well as small commercial craft such as ferries, tugboats, and fishing
vessels. All coastal area as well as inland rivers and lakes are included in
the inventory. The basis for the inventory is movement data as logged in the
Information System on Marine Navigation (INNAV) for eastern Canada and the
Arctic and the Vessel Traffic Operator Support System (VTOSS) through
CCG Vessel Traffic Services (VTS) for the west coast. INNAV data for 2010 are
representative of all ocean-going vessel (OGV) movements, whereas data gaps
exist in the 2010 VTOSS dataset. In addition, Pacific Pilotage Authority
movement data and port-level data are also used to supplement VTOSS data as
needed (SLE, 2012). The activity-based emission factors used in MEIT for
processing the 2010 national inventory were specific factors appropriate for
engine size (based on US EPA engine classification), speed, and fuel type
(Weir Marine Engineering, 2008; SLE, 2012). Emissions were calculated on a
voyage-by-voyage basis, and vessel speed and implied load on the main and
auxiliary engines were evaluated by each segment of a voyage. Temporal
resolution of the 2010 national marine inventory includes emissions by hour,
day, and month of the year, and spatial resolution includes emissions
allocated to regions of Canada (by province and many sub-regions defined in
previous marine emission inventory analysis work). The Arctic portion of the
2010 national marine emission inventory was further updated to include
revised main engine load factors (Innovation Maritime and SNC-Lavalin
Environment, 2013). The emission inventory covers criteria air contaminants
(CACs), such as NOx, sulfur oxides (SOx),
CO, VOCs (including VOCs from
combustion and fugitive VOCs from crude oil tankers but not fugitive VOC
emissions from oil barges and other petroleum tankers), PM (as total PM, PM10, and PM2.5 as well as elemental, organic,
and sulfate fractions), and ammonia (NH3), greenhouse gases (GHGs),
and air toxics.
2010 vessel movements in Canada's Arctic.
The Canadian Arctic waters defined in this study are the portion of Canadian
waters excluded from the North American Emission Control Area (NA ECA), which
include both coastal and inland waters north of 60∘ N,
the Hudson Bay, and James Bay (see Fig. 1). Canada's Arctic waters
(particularly in the high Arctic) are characterized by variable ice
conditions and extreme weather. The vastness and remoteness of the region
further contribute to the challenges that shippers are faced with when
sailing through these waters. Even during the summer months when ice levels
are at their lowest, ships must ensure that they have ice-strengthened hulls
or be escorted by a CCG icebreaker to ensure a safe and manageable transit.
Current marine traffic in Canada's Arctic is primarily comprised of vessels
heading to specific northern destinations. These vessels function as a vital
link between remote northern communities and the essential supplies they
need, typically from southern Canada. In addition to these vital community
resupply sealifts, ships transiting Canada's Arctic are also engaged in
hydrocarbon and mineral exploration (i.e., seismic exploration) and
extraction, ecotourism, and activities of the CCG, including ship escorts
and research missions.
Figure 1 shows 2010 vessel movements in Canada's Arctic waters from 120
active vessels and 978 total voyages (based on CCG data). The majority of
these trips were made by merchant vessels (348), followed by tug boats
engaged in community resupply (300), and tankers (169). Table 1 shows the
emission estimates from these activities. The majority of emissions come
from large commercial and merchant vessels such as general cargo vessels,
bulkers, and tankers, collectively. Table 2 compares the Arctic portion of
the marine shipping emission estimates to the other two Canadian regions by activities:
the west coast and eastern Canada (including the east coast, the Great Lakes,
and St. Lawrence Seaway). The Canadian Arctic marine shipping
emissions currently count for less than 2 % of the national marine
emission totals. Compared to existing pan-Arctic estimates, e.g., Corbett et
al. (2010) for 2004 and Winther et al. (2014) for 2012, the Canadian portion
of Arctic shipping emissions contributes to about 1 % of current
pan-Arctic shipping emissions.
Number of trips and estimated emissions (in tonnes) of CAC
pollutants from marine shipping activities over the Canadian Arctic waters
for the year 2010 base case and for the projected 2030 year scenarios (BAU
and ECA).
To project future shipping emissions in Canadian Arctic waters, a number of
factors were considered. Marine traffic is expected to increase in Canada's
Arctic as both current and planned resource development projects come
on-line. There are several operating and planned resource development
projects in Canada's north that will require regular servicing by ships,
including product transport, resupply vessels, drilling ships, and
platforms. In addition, it is expected that Arctic tourism, also known as
ecotourism, will increase in popularity as destinations become more
accessible with thinning levels of ice as a result of a changing climate,
and activities of the CCG will also likely increase.
An extensive review of ship traffic projections was conducted, utilizing
environmental assessment reports for resource development and other projects
in the Canadian Arctic that would be serviced by ships. In addition, expected
increases in other sectors, as noted above, were taken into account
(Innovation Maritime and SNC-Lavalin Environment, 2013). Based on this
information, a projection of the types and number of sailings of vessels and
their expected emissions in the future was developed (Environment and
Climate Change Canada, 2015). To validate the forecast, the growth rates
were compared with published data from companies and published studies
related to shipping forecasts in the Arctic (e.g., Corbett et al. 2010). In
predicting future shipping traffic, a limited number of transits via the
Northwest Passage were assumed, based on restricting the transit to bulk
carrier vessels only and by economic viability
The projection for the
2030 NWP transit is based on a gradual (linear) increase from 2020 to a 2050
high-growth (or business-as-usual) scenario assuming that bulk carriers
would carry the 2050 northern Europe–Asia bulk trade through the NWP. The
2050 bulk trade between northern Europe and Asia was projected at an annual
rate of increase based on historic trade data between 1975 and 2005 (see
Innovation Maritime and SNC-Lavalin Environment, 2013).
. Despite
predictions of an ice-free Arctic by the middle to the end of this century, sea-ice
variability, navigability, and dangerous weather remain constant challenges
for Arctic shipping (Haas and Howell, 2015). Combined, these factors present
an inherent degree of uncertainty in predicting future shipping levels in
the Canadian Arctic.
Also included in Table 1 are the projected trips and emissions in Canadian
Arctic water in 2030 by vessel classes. The largest anticipated increases in
marine activities are from merchant vessels, particularly merchant bulk and
passenger vessels. In estimating emissions related to the projected shipping
activities, the emission rates were adjusted to reflect the regulatory (both
domestic and international) and technological changes, such as fuel
standards and fleet turnover. The MARPOL Annex VI global cap on the sulfur
content of 0.5 % for fuel oil used on board ships is assumed to be in
place in the BAU scenario. For the controlled scenario,
it is assumed that the Canadian Arctic is designated as an emission control
area (ECA) for SOx, PM, and NOx, and therefore ships are subject to
comply with the 0.1 % sulfur in fuel limit, as well as the IMO Tier III
NOx standards for new vessels. Under the BAU scenario, a nearly
3-fold increase in total NOx shipping emissions is expected by
2030, mostly from merchant bulk vessel activities. The increases in SOx
and PM emissions (compared to the present levels) are moderate due to the
global cap on sulfur content in fuel. In comparison, under the ECA
scenario, the projected NOx emissions would be considerably reduced
(from BAU levels) to about 2-fold of the current (2010) level in total
amount, while the SOx emissions would be reduced to below the current (2010) level by the more stringent regulation in sulfur content (0.1 %).
Modelling system and simulation setup
The base model used for this study, GEM-MACH, is an on-line chemistry
transport model (CTM) embedded within the ECCC numerical weather forecast model GEM (Côté et
al., 1998a, b; Charron et al., 2012). A limited area version of GEM-MACH has been
in use as the ECCC's operational air quality prediction model since 2009 (Moran
et al., 2010). The representations of many atmospheric processes in GEM-MACH
are the same as in the ECCC's AURAMS (A Unified Regional Air-quality
Modelling System) off-line CTM (Gong et al., 2006), including gas-phase,
aqueous-phase, heterogeneous chemistry (inorganic gas-particle
partitioning); secondary organic aerosol formation; aerosol microphysics
(nucleation, condensation, coagulation, activation); sedimentation of
particles; and dry deposition and wet removal (in-cloud and below-cloud
scavenging) of gases and particles. Specifically, the gas-phase chemistry
mechanism in GEM-MACH is a modified version of the ADOM-II mechanism
(Stockwell and Lurmann, 1989), with 47 gas-phase species and 114 reactions;
aerosol chemical composition is represented by nine components: SO4, NO3, NH4, elemental carbon (EC),
primary organic matter (POA), secondary organic matter (SOA), crustal
material (CM), sea salt, and particle-bound water; aerosol particles are
assumed to be internally mixed. The operational version of GEM-MACH uses a
two-bin sectional representation of aerosol size distribution (Moran et al.,
2010), i.e., 0–2.5 and 2.5–10 µm. The two-bin configuration
was also used for this study.
In this study, model simulations were conducted over a domain with a rotated
latitudinal–longitudinal grid projection at a 15 km horizontal resolution.
The domain is centred over the Canadian Arctic with its southern boundary
extending south of the Canada–US border (see Fig. 3). Eighty vertical,
unevenly spaced, hybrid coordinate levels were used to cover between the
surface and 0.1 hPa, with the lowest terrain-following model layer of about
20 m (GEM-MACH version 1.5). Several model upgrades and special
considerations were made for this study:
Representation of sea ice and snow cover in dry deposition. Sea-ice cover from the Canadian Meteorological Centre's regional ice
analysis system (Buehner et al., 2012) and snow cover and depth based on
surface diagnostics were introduced to the dry deposition module to account
for ice–snow cover conditions. In contrast, the base model (GEM-MACH v1.5)
only takes into account permanent ice (glacier) cover in the dry deposition
module. In addition, a different (lower) dry deposition velocity for O3
over snow and ice was introduced following the recommendation of Helmig et al. (2007a).
Chemical lateral boundary conditions. Instead of using climatology-based lateral boundary conditions as is done
in the operational GEM-MACH (see Pavlovic et al., 2016), the MACC-IFS
(Monitoring Atmospheric Composition and Climate, Integrated Forecast System)
chemical reanalysis for 2010 (Inness et al., 2013), available every 3 h,
was used to build daily chemical boundary condition files for the GEM-MACH
Arctic domain. In addition, the southern boundary condition was enhanced by
using the operational GEM-MACH forecast archives for the simulation time
period in order to better represent the transport of pollutants from the North
American continent.
North American wildfire emissions. Wildfire emissions were included in this study as it has been shown that
northern boreal forest fires can be an important pollution source for the
Arctic in summertime (Law and Stohl, 2007). Retrospective daily wildfire
emissions per fire hotspot for the 2010 North American fire season were
generated using the same methodology as in the ECCC's FireWork system; an
air quality forecast system with representation of near-real-time biomass
burning emissions (Pavlovic et al., 2016). The fire emission processing
relies on the fire activity data from NASA's Moderate Resolution Imaging
Spectroradiometer (MODIS) and NOAA's Advanced Very High Resolution
Radiometer (NOAA-AVHRR), a fire behaviour prediction system – the Canadian
Wildland Fire Information System (CWFIS; Lee et al., 2002), and the Fire
Emission Production Simulator (FEPS) – a component of the BlueSky
modelling framework (Larkin et al., 2009) to determine the daily total emission per
fire hotspot. The per-fire-hotspot daily total emissions were then converted
to hourly, chemically speciated, and grid-cell-specific emissions using the
SMOKE emission processing system for use in GEM-MACH (see Pavlovic et al.,
2016, for details). The fire emissions are treated as major point-source
emissions in the model using the same Briggs plume rise algorithm (Briggs
1975) as anthropogenic point-source emissions, with assigned stack
parameters: 3 m, 773 K, and 1 m s-1 for stack height,
exit temperature, and velocity, respectively. Other fire plume injection
schemes were tested in this study, including one designed using satellite-derived plume statistics. In this scheme, the vegetation-type-based
(biome) statistics for plume height and depth derived from 5-year satellite
observations over North America (Val Martin et al., 2010) were used to
determine plume centre height and vertical spread for the flaming portion,
taking into consideration atmospheric stability, while the smoldering
portion of the emission is evenly spread within the modelled planetary
boundary layer (PBL). The test results showed that, while the different
plume injection schemes strongly impacted the modelled pollutant
concentrations over the fire source region, the differences were
considerably reduced at longer transport distances. As a result, the Briggs
plume-rise algorithm was used in the final simulations for this study, as was
used in the current FireWork system (Pavlovic et al., 2016), for
distributing fire emissions.
Canadian marine shipping emissions. The Canadian marine shipping emission inventories described earlier in Sect. 2
were further processed into model-ready point-source emissions. The
MEIT database provides ship route polygons, vessel activity information
associated with each route polygon, and link-based monthly emissions by
ship track, ship types, and fuel type. The database also includes stack
parameters by ship type, allowing plume-rise calculations in GEM-MACH. Table 3 shows the averaged stack parameters assigned to each fuel type. To reduce
data size and processing time, the more detailed ship types in the original
MEIT database were aggregated, based on vessel activities, into four
classes: merchant passenger, merchant commercial, fishing, and other (as
indicated in Table 1). The monthly emissions for the four classes were
mapped onto model grids along ship tracks, in a form of aggregated point
sources (by class) and then further allocated to hourly emissions by
applying uniform day-of-week and hour-of-day temporal profiles in the
SMOKE emission processing system (http://www.cmascenter.org/smoke/, last access: 13 November 2018). Figure 2 shows an example of the final
processed model-ready marine shipping emissions over Canadian waters used in
this study: NOx emissions from shipping for the month of August both at
the current 2010 and the projected 2030 (BAU) scenarios. The changes in
NOx shipping emissions between the projected 2030 and current 2010
level reflect the increased shipping activities over Baffin Bay and the
reduction over the Canadian east and west coast due to NA ECA regulations.
For assessing the impact of shipping emissions over the Canadian Arctic
waters, the shipping emissions outlined by the red line in Fig. 2 are
turned on or off in the model simulations as discussed in Sect. 5.
Stack parameters for different ship emission inventories
used in this study.
Average valuesHeavy dieselDieselGasolineStack height (m)41.8240.2324.52Stack diameter (m)111Stack velocity (m s-1)202020Stack gas exit275275275Temperature (C)
Processed model-ready NOx marine shipping emissions
for August (a) 2010 and (b) projected 2030 BAU over Canadian waters, the red
line outlining the Arctic region (including Hudson Bay) which is excluded
from the current North American ECA designation.
Other anthropogenic emissions included in the model simulations are based on
the 2010 Canadian Air Pollutant Emission Inventory (APEI) and the 2008 US
National Emission Inventory (NEI; https://www.epa.gov/air-emissions-inventories/2008-national-emissions-inventory-nei-data, last access: 13 November 2018),
processed to hourly area and major point-source emissions using SMOKE.
Supplementary anthropogenic emissions from the Emissions Database for Global
Atmospheric Research-Hemispheric Transport of Air Pollutants (EDGAR-HTAP) v2
(see http://edgar.jrc.ec.europa.eu/htap_v2/, last access: 13 November 2018;
Janssens-Maenhout et al., 2012) were used for areas outside the North
American continent. Biogenic emissions were estimated on-line using the BEIS
(Biogenic Emission Inventory System) v3.09 algorithms. Sea salt emissions were computed on-line within GEM-MACH
based on Gong et al. (2003).
GEM-MACH Arctic modelling domain overlaid with monitoring
sites: (a)O3 monitoring sites shown on top of the modelled average
ambient concentration over the July–September period; (b) the same as (a) but
for PM2.5; (c) the same as (a) but for NO2; (d) the same as (a) but for
SO2. (Subdivision of regions: crosses denote the sites in the western
region, filled triangles denote the sites in the eastern region, and
filled circles denote sites in the northern region).
The simulations were carried out for the time period of March to October
2010; the first month of the simulation is counted as spin-up and not
included in the analysis. The 8-month simulation was conducted by a
series of staggered 30 h runs with a 6 h (meteorology only) overlap,
starting at 00:00 UTC daily, to allow meteorological spin-up from
initialization; the meteorology is thus initialized at the beginning of
every 30 h run using the Canadian Meteorological Centre's regional
objective analyses while chemistry is continuous.
Regional evaluation for O3, PM2.5, NO2, and
SO2 (hourly statistics).
a Numbers in brackets are the scores calculated based on modelled and
observed hourly time series averaged over all sites within a given region as
in Im et al. (2015a, b).
Model evaluation – 2010 base case
The performance of GEM-MACH over the North American domain has been evaluated
in a number of existing studies (e.g., Moran et al.,
2011; Im et al., 2015a, b). As this is a first adaptation of the model for the Canadian Arctic
domain, evaluation of model performance against available observations was
carried out for criteria pollutants O3, PM2.5, NO2, and
SO2, focused on the July–September period (the peak Arctic shipping
season). The hourly observational data used for the evaluation were obtained
from the Canadian National Atmospheric Chemistry (NAtChem;
https://www.ec.gc.ca/natchem/, last access: 13 November 2018) database which contains monitoring data from
the National Air Pollution Surveillance (NAPS) network in Canada (http://www.ec.gc.ca/rnspa-naps/, last access: 13 November 2018) and the US Environment Protection
Agency's Air Quality System (AQS) database for US air quality data
(https://aqs.epa.gov/aqsweb/documents/data_mart_welcome.html, lsat access: 13 November 2018). For O3, additional data from the
World Data Centre for Greenhouse Gases (WDCGG;
https://ds.data.jma.go.jp/gmd/wdcgg/, last access: 13 November 2018) were also used. Data completeness
criteria of 75 % for daily data and 66 % for the full period were used
to screen the data. Figure 3 indicates the monitoring sites after the data
completeness screening process was completed for the 4 criteria pollutants.
Overall, most of the monitoring sites within the model domain are located
over southeastern Canada (Ontario, Quebec, and the Maritime provinces) and
southwestern Canada (British Columbia and Alberta). There are very few sites
in central Canada and north of 55∘ N. For this study, which
focuses on the Canadian Arctic and northern regions, a significant challenge
is the data sparsity over the region of interest: for the year 2010 (the
base year for the study), Alert, on the northern tip of Ellesmere Island
(82.45∘ N, 62.51∘ W), is the only air
monitoring site in the entire eastern Canadian Arctic. For comparing with
these ground-based monitoring observations, model results were extracted
from the lowest model level (∼20 m above local surface) at
given observational locations (nearest grid points). In contrast to surface
meteorological observations, there is no standard height for the air
chemistry measurements from the monitoring networks. However, the sampling
probes are generally located between 2 and 15 m above local surface based on
network guidelines. For the purpose of model evaluation, the model domain is
divided into three geographical sub-regions based on general climatological
and source characteristics: southwestern Canada (49–55∘ N, west of 100∘ W), southeastern Canada
(49–55∘ N, 75–100∘ W and 44–53∘ N,
50–75∘ W), and the northern region
(55–90∘ N, 75–160∘ W and 53–90∘ N,
50–75∘ W) covering both northern Canada and Alaska (US). The division of the sub-regions is indicated in
Fig. 3.
Statistical scores
Various statistical measures were computed to evaluate model performance
both at individual monitoring sites and as a group in the three geographical
sub-regions. Three sets of statistics were evaluated: based on hourly averaged, daily
averaged, and seasonally (July–September) averaged data. Table 4 presents
the results of the hourly based, regional (sector) statistical analysis
using a few selected evaluation metrics chosen to characterize overall model
performance for each of the criteria pollutants, while all three sets of the
statistical metrics (hourly, daily, and seasonal) are shown in the Supplement (Table S1). The statistical evaluation metrics are defined in
Appendix A.
O3
As shown in Table 4, for ambient O3 concentrations, the model performs
the best for the northern region in terms of model bias and error (e.g., MB,
NMB, RMSE, and NMSE). There is an overall over-prediction of ambient O3
concentrations by ∼3 ppbv on average for the northern region,
∼4 ppbv for the southwestern region, and ∼6 ppbv for the southeastern region. The model's predictive skill increases
with increased timescale as indicated by RMSE (or NMSE), with smallest
errors for seasonal averaged concentrations compared to daily and hourly
concentrations (Table S1). The Pearson correlation coefficient (r) for
hourly O3 is highest for the southeastern region (0.66) and lowest for
the southwestern region (0.54). Overall, the model showed similar skill for
modelling O3 in the northern domain as the operational regional air
quality models included in Im et al. (2015a) did for modelling the North America
domain in terms of NMSE, RMSE, and r. Note that the statistical scores in Im
et al. (2015a, b) were based on domain-mean hourly data. The equivalent
statistical scores were computed for this study and shown in Table 4 (in
brackets). The averaging essentially minimizes spatial variability amongst
the sites within the domain (or geographical sub-regions), and hence the
statistical scores on the regional averaged hourly data are much higher (in
terms of RMSE, NMSE, and r) than the regional statistical scores based on
hourly data at individual sites.
PM2.5
The regional statistical scores for PM2.5 show that the model
performed best over the southeastern region, with the lowest NMB and NMSE and
the highest correlation. The model under-predicted PM2.5 for the northern
region, with an overall negative bias of ∼-14 % and poor
correlation. It is worth noting, however, that there were very few
sites with data available for evaluating model prediction of PM2.5 in
the northern and southwestern regions, 9 in each, compared to 36 in the
southeastern region. In particular, of the nine northern sites, five are
located in Alaska – four in Anchorage and surrounding area, and one in Juneau,
with the other four in Northwest Territories (NT). There were no PM2.5
monitoring sites available over the entire eastern Canada North region. The
four sites in NT include one located in Yellowknife, the only city (and the
largest community) in NT, while the others are located in smaller
communities (Inuvik, Norman Wells, and Fort Liard). As PM2.5 contains
both primary and secondary components, the ambient concentration at these
northern sites is influenced by both long-range transport and local
emissions. There are large uncertainties in both emission estimates and the
spatial surrogates used for distributing estimated emissions in the northern
region (note that the Canadian Emission Inventory is at
provincial–territorial level). These uncertainties contribute to the poor
model performance at these northern sites. For example, as shown in
the Supplement, the model over-predicted PM2.5 at the
Yellowknife site while under-predicting at the other NT sites (see Table S2b). Furthermore, the modelled PM2.5 at Yellowknife site is dominated
by “crustal material” (see Fig. S1 in the Supplement), which is a major component of
primary PM emissions in NT. The spatial surrogates used for crustal material
are paved roads and mine locations. The paved road network in NT used in
processing the 2010 emission inventory was very limited, mainly concentrated
in Yellowknife and its surroundings. As for mine locations, the surrogate
was based on place-of-work data from the 2006 Canadian Census for the mining
industry (http://www12.statcan.ca/census-recensement/2006/rt-td/pow-ltd-eng.cfm, last access: 13 November 2018),
which can lead to allocating mining-related emissions to cities rather than
actual mining operation sites, as many mining company employees work at
headquarters which tend to be located in cities (e.g., Moran et al., 2015).
For the Inuvik site on the east channel of the Mackenzie Delta, the model
under-prediction may be partially attributable to an underestimation of
emissions from the oil fields in Prudhoe Bay on Alaska's North Slope in the
US 2008 Emission Inventory (https://www.epa.gov/air-emissions-modeling/20072008-version-5-air-emissions-modeling-platforms, last access: 13 November 2018).
NO2
For predicting NO2, the model performed the best, overall, for
the northern sites with the lowest NMB (8.3 %) and RMSE (5.6 ppb) and
highest r (0.56), based on hourly data (Table 4). However, the relatively
small overall bias may be misleading, as there are large positive and
negative model biases at the individual northern sites (Table S2c in the Supplement). This is
indicated by the large NMSE value (104 %). The 10 northern sites here
include 4 in NT, where the model generally under-predicted, and 6 in the
lower Athabasca oil sands region in Alberta, where the site-specific model
biases, in terms of NMB, varied between -64 % (at Fort Chipewyan) and
143 % (at Syncrude UE1), indicating significant heterogeneity. Again the
model performance at these sites is influenced by the uncertainties
(challenges) in estimating and representing emissions in these regions of
Canada (ECCC & AEP, 2016; Zhang, et al., 2018). Also note that the
NO2 observations from the NAPS network were reported in an increment of
1 ppb, which will have a considerable impact on the statistical scores,
particularly at more remote sites where NO2 concentrations are low and
of the order of < 1 ppbv. The high correlation between the modelled
and observed seasonal averaged concentrations (Table S1) indicates, however,
that the model captured the geographical distribution of the regional
NOx sources and plumes reasonably well.
SO2
The statistical scores for model prediction of SO2 are considerably
poorer than those for the other criteria pollutants discussed above, with
large biases (in terms of NMB) and errors (in terms of NMSE). Note that the
reference unit for SO2 in this comparison is µg m-3 at
standard atmosphere (0 ∘C) because the reported SO2
concentrations were converted to this unit in the NAtChem database. There
are several factors to be considered when interpreting these statistical
scores. Firstly, the group statistical scores for the northern sites are
largely influenced by the sites located in the lower Athabasca oil sands
region in Alberta and the Peace region of northeastern British Columbia (see
Table S2d), with considerable oil and gas industries there. The monitoring
sties in these regions are located at or near industrial facilities. The
modelled SO2 at these locations are primarily driven by the model
emission inputs. There are large model biases at these locations, again indicating potential deficiencies in emission estimates and processing in these
regions (e.g., spatial and temporal allocation of the annual emissions;
e.g., ECCC & AEP, 2016; Gordon et al., 2017; Zhang et al., 2018).
Secondly, similar to the case of NO2 discussed above, there is also a
precision issue with monitoring data reporting: SO2 concentrations are
reported at 1 ppb (or ∼2.86µg m-3) increments.
This is particularly problematic for model evaluation at more remote sites
(such as those in the Northwest Territories), where SO2 concentrations
are generally below 1 or 2 ppb, and the reported concentration values toggle
between 0, 1, and 2 ppb (or 0, 2.86, and 5.72 µg m-3 after
conversion in the NAtChem database). Again, despite the large mean bias
(∼10µg m-3) and RMSE (seasonal, ∼16µg m-3), the correlation between the modelled and observed
seasonal averaged SO2 concentrations in the northern region is high (r=0.90; see Table S1), indicating that the model was able to capture the
spatial distribution and structure of the observed concentrations.
Regional averaged O3 time series (24 h running
mean), modelled and observed: (a) northern, (b) southwestern, (c) southeastern; shades
indicate 1st–3rd quartile range.
Same as Fig. 4 but for PM2.5.
Time series
In addition to the statistical scores, the model's ability of simulating the
temporal variations in ambient concentrations of criteria pollutants during
the Arctic shipping season is examined here. Figures 4–7 show the
model–observation comparison of the regional averaged time series (shown as
24 h running means) of O3, PM2.5, NO2, and SO2 for
the three sub-regions. Given the monitoring site locations, the “northern”
regional average really represents only northwestern Canada (and Alaska in
the case of O3 and PM2.5).
Same as Fig. 4 but for NO2.
Same as Fig. 4 but for SO2. Dashed lines in (a) denote time series excluding sites in the Athabasca oil sands and
northeastern BC oil and gas industry areas (see text).
The regional O3 time series show that the overall temporal variability
is smallest at the northern sites and greatest at the southeastern sites
most strongly influenced by regional and synoptic events. The model generally
captured the temporal variations well. A positive bias in model prediction
is evident. For the southwestern region, the overall positive bias was
largely contributed by the over-prediction of the O3 nighttime minima
(not shown). The nighttime model bias can be a result of the model's
difficulty in simulating (or resolving) the stable nocturnal boundary layer,
where small differences in the actual O3 sources and/or sinks, like
O3 dry deposition, can have a large impact on O3 concentration
gradients, which might also be reflected in significant differences in
observed and simulated nocturnal O3 for different reference heights.
The more pronounced over-prediction events during the month of August at the
northern and southwestern sites are likely associated with large wild fire
events in British Columbia during that period. The model tends to
over-predict O3 in fire plumes (Gong et al., 2016; Pavlovic et al., 2016), particularly within a short transport time. A number of factors may be
contributing to the over-prediction, including uncertainties in emission
factors and the lack of representation of aerosol shading in the model, which
may lead to an overestimation of photolysis rates in fire plumes. The
possible causes are currently under investigation.
The northern regional averaged PM2.5 time series during the
July–September period is dominated by variations at small scales, implying
a strong influence of primary components from local sources at these
northern sites, while the southeastern regional PM2.5 time series is
more controlled by variations at larger scales, or regional events, implying
the dominance of secondary components and/or regional sources. The
southwestern time series contains the signature of both local and regional
influences with the main regional events in August, coinciding with the major
wild fire events in BC at that time. The model captured the general trends
well particularly for the regional events, while it had difficulty tracking
the local-scale variations, which is not unexpected given the model
resolution.
The regional averaged NO2 time series shows a nearly 7-day cycle,
particularly for the southwestern and northern sites. The model predictions
compare well for the northern and southwestern regions. For the southeastern
region the model captured the general trend well, but there is a tendency for
more significant over-prediction, particularly at the beginning of July.
Significant over-predictions of NO2 over eastern Canada during this
time period from the operational GEM-MACH forecast were also shown in the
evaluation of Moran et al. (2011). It should be noted that the southeastern
sites in this study are in close proximity to the southern boundary and are
more likely to be influenced by the model southern boundary condition, which
comes from the operational GEM-MACH forecast archives.
As a reflection of the SO2 regional statistical scores discussed above,
the comparison of regional averaged time series of the observed and modelled
SO2 for the northern region is strongly influenced by the sites located
near oil and gas facilities. Also shown in Fig. 7a are the regional
averaged time series, excluding the sites in the Athabasca oil sands and
northeastern BC oil and gas industry areas (in dashed lines). It is evident
that these sites are skewing the regional averages. The large discrepancies
between the model simulation and observations at these sites are indicative
of the possible deficiencies in the existing emission inventory and the
emission processing for these facilities. The model and observations are in
much better agreement at the northern sites, away from the oil and gas
facilities. The model simulation also compares well with the observations in
the southwestern region, closely tracking the observed general trend at the
regional scale. The comparison for the southeastern region shows a general
over-prediction by the model. In particular, the modelled group-averaged
time series shows a higher regional baseline level than indicated by the
observations. As shown in Fig. 3d, these southeastern sites are situated
under the influence of the model's southern boundary, and the modelled
average SO2 concentration over the July–August–September period shows a
regional plume originating from the southern boundary, reflecting the
influence of a major SO2 source area in the Ohio River valley. Note that
the emission inputs used by the operational GEM-MACH forecast in 2010, the
basis for the model southern chemical boundary condition for the current
study, were based on the 2006 Canadian, 2005 US, and 1999 Mexican national
emission inventories (Moran et al., 2011). Due to the various US EPA
emission control programs in recent years (e.g., Acid Rain Program,
NOx
Budget Trading Program, Clean Air Interstate Rule; see https://www.epa.gov/airmarkets, last access: 13 November 2018),
SO2 (and NOx) emissions over
eastern US have reduced considerably between 2005 and 2010. The model
over-prediction of ambient SO2 (and NO2, see above) in the
southeastern region in this study can therefore be, at least in part,
attributed to the possible over-prediction of SO2 (and NO2) from
the operational GEM-MACH over the US northeast.
Comparison with observations at the Alert site for
June–September 2010: (a)O3, (b) sulfate; nss denotes non-sea-salt, (c) EC, and (d) OC (OM).
Canadian high Arctic site, Alert
Several long-term monitoring measurements of atmospheric constituents have
been carried out at ECCC's Alert baseline observatory located at the
northern tip of the Ellesmere Island (82.45∘ N,
62.51∘ W) – one of the Global Atmosphere Watch global network
stations. For the year 2010 the measurements included, in addition to
O3 (continuous, hourly), inorganic aerosol components from weekly
high-volume samplers (Sirois and Barrie, 1999; Sharma et al., 2004), OC and EC using a thermal method from biweekly
quartz filter samples (Huang et al., 2006), and equivalent black carbon (EBC)
from aerosol light absorption measurements using an aethalometer (Shama
et al., 2017). These data are all used for evaluating model prediction at
this high Arctic location. The comparisons of the modelled and observed time
series of O3, sulfate, EC, and OC (OM – organic matter) over the
June–September period are shown in Fig. 8.
The model is seen to predict O3 very well at this high Arctic site; the
modelled O3 time series tracks closely with the observations, reaching a
minimum at the end of July and the beginning of August and then rising
steadily throughout late August and September. The model did not predict the
low ozone event observed at the beginning of June. The low ozone event may
be the result of ozone depletion involving bromine chemistry within the
Arctic marine boundary layer (Barrie and Platt, 1997), which is not
represented in this version of the model. The modelled sulfate also compared
well, particularly in terms of the general trend and magnitudes, with the
non-sea-salt sulfate measurements based on weekly samples.
The modelled EC is compared with both EBC derived from the continuous
aethalometer measurement and EC measurement using a thermal desorption
method from quartz filter sampling (biweekly in 2010). It can be seen that
while the modelled EC is overall biased low compared to the EBC from the
aethalometer measurement, and biased lower still compared to the biweekly
EC measurement, the model captured the general trends shown in both
observation sets. In particular, the event in early July was captured
by the model well, which is attributable to biomass burning emissions from
northern Canada. Sharma et al. (2017) discussed in depth the various
techniques for measuring black carbon mass at the Alert observatory and
showed that EC mass based on the thermal method is highest over summer
months, followed by the EBC mass estimate from the aethalometer measurement;
both are significantly greater than the refractory BC (rBC) mass measurement
using the Single Particle Soot Photometer (SP2). As a best estimate of BC mass
at Alert for comparison with chemical transport models, Sharma et al. (2017)
recommended using a combination of EC and rBC or EC with a scaling factor of 0.5(1+α)/α, where α is the EC / rBC ratio. The
scaled EC (using α of 3.5, based on Sharma et al., 2017) is
indicated in Fig. 8c with solid dots connected by the dashed line.
However, one needs to be careful in comparing the modelled aerosol EC
component with BC measurements, as they may not be strictly comparable
depending on the measurement techniques (e.g., Petzold et al., 2013; Sharma
et al., 2017) and how EC (or BC) is modelled (including emission input).
The modelled organic aerosol component (POA + SOA) is compared with the
biweekly measurement of OC from the thermal desorption method. For this
comparison the measured OC is converted to OM by applying an OM / OC ratio of
1.8. The total OC (TOC) from the OC / EC analysis includes OC released at
550 ∘C and pyrolyzed OC (POC) plus inorganic carbonate
carbon (CC) released at 850 ∘C. The estimate of CC fraction
of POC + CC is 40 % at Alert in summer time. The CC fraction was removed
from the TOC measurement for the comparison in Fig. 8d based on the
CC / (POC + CC) fraction. The measured OC component (at 550 ∘C) is also shown in Fig. 8d, indicating that this is the dominant
component of measured TOC at this site. Overall the model under-predicted
the organic aerosol component at this site compared to the measurement based on
the OC / EC analysis but again captured the event in the beginning of July (as
in the case of EC comparison above) associated with long-range transport of
biomass burning pollutants. Recent observations conducted in the Canadian
Arctic have suggested possible marine secondary organic aerosol production
over the Arctic Ocean during summer time from oceanic and biological sources
(e.g., Willis et al., 2016), which may explain at least in part the model
under-prediction of organic aerosols (Gong et al., 2017).
The evaluation results presented in this section demonstrate that GEM-MACH's
skill in predicting ambient O3 and PM2.5 in the Canadian
northern and Arctic regions is comparable to the skill level of the current
operational air quality forecast models in North America and Europe. The
model has reasonable skill in predicting NO2 and SO2 in the north
at a regional scale; at local scales the model prediction is strongly
influenced by emission inputs. The evaluation indicates a deficiency in
representing local emissions in the remote north and the need for improved
emission estimates and representation for the oil and gas facilities in
northeastern British Columbia and the Athabasca oil sand region in northern
Alberta. There is also a significant data gap in northern Canada,
particularly the eastern Arctic, for air quality monitoring and for model
evaluation. The model, however, is able to simulate the observed ambient
O3, and some of the PM components at Alert well, the only air quality
monitoring site in the eastern high Arctic.
While there has not been many regional modelling studies focused on the
Arctic and northern regions, there are some existing studies mostly using
global models with a focus on the Arctic. For example, Emmons et al. (2015)
reported a multi-model intercomparison project where model simulations using a number of models (nine global and two regional) were compared
with observations conducted during the 2008 International Polar Year in the
Arctic. In particular, comparisons were made with aircraft measurements
conducted in northern Canada and into the Arctic over a 12-day period
during late June to early July. They found that models generally
under-predicted O3 and SO2 in the mid-troposphere and over-predicted
NO2 in the boundary layer during this summer period. A direct
comparison in terms of model performance to the current study is difficult
to make, as the model evaluation in the current study is based on surface
observations over a longer time period. Shindell et al. (2008) also
compared global model simulations, conducted under the Task Force on
Hemispheric Transport of Air Pollution (TF HTAP), against long-term
observations at selected Arctic sites including Alert and Barrow. They found
that the models generally under-predict O3 at Barrow during summer by
as much as 10 ppb, and that models performed poorly in predicting sulfate
and BC at Alert. In comparison, the model evaluation from the current study
demonstrates much better model skills in predicting the ambient
concentrations of these pollutants in the Arctic (e.g., comparisons shown in
Fig. 8).
The model evaluation conducted in this study is mainly focused on the
atmospheric chemistry aspect. However, the model's ability to simulate the
vertical structure and stability of the coastal marine boundary layer has an
important influence on assessing the shipping emission impact on ambient
concentrations. Although the operational performance of the meteorological
model GEM (the hosting model for GEM-MACH) has continuously been evaluated
against surface and upper air observations and compared against other NWP
models of leading operational forecasting centres in the world, the Arctic
region alone had not been given significant attention in the past operational
evaluation exercises. To evaluate the GEM-MACH performance in simulating the
Arctic marine boundary layer, we compared the modelled vertical temperature
profiles with upper air soundings at a number of coastal sites in the Arctic
along the main shipping channels for the month of July in 2010. On average,
the modelled vertical temperature profiles compare with the observations well
(see Supplementary Materials, Fig. S2a). We also attempted to diagnose
boundary-layer (BL) heights based on the bulk Richardson number, following Mahrt
(1981) and Aliabadi et al. (2016a), from both modelled and observed profiles
at these selected Arctic sites. On average, the model and observation
diagnosed BL heights are within ±30 % of each other (see
Fig. S2b). Particularly, for the Resolute site, the model and observation
diagnosed BL heights for July, averaged at 315.4 m and 267.4 m,
respectively, are comparable to the estimated BL heights, 274±164 m,
over the same area during a recent field campaign in July 2014 (Aliabadi et
al., 2016b). It should be pointed out, however, that there is a large
ambiguity in the definition of BL height under stable conditions (such as the
case of the Arctic marine BL), and the diagnosed BL height can vary
considerably depending on the particular method (or parameterization) used
(e.g., Aliabadi et al., 2016a). A more detailed examination of GEM's forecast
capability in the Arctic is being pursued under the Year of Polar
Prediction (YOPP) initiative
(https://public.wmo.int/en/projects/polar-prediction, last access: 13 November 2018).
Impact of shipping emissions on Arctic air pollution
The impact of shipping emissions in the Canadian Arctic is assessed by
comparing pairs of model simulations, with and without the Canadian portion
of the Arctic shipping emissions, under three scenarios: current (2010),
projected 2030 BAU, and 2030 with ECA (see Sect. 2 above). To isolate the
impact of shipping emissions, only shipping emissions were changed between
the different scenarios, while meteorology, land use, and other emissions
(such as non-shipping anthropogenic emissions and wild fire emissions)
remained the same for all scenario simulations. The analysis is focused on
the July–August–September (JAS) peak Arctic shipping period. It should also
be stated that the impact is mostly assessed in relative terms in this study
for these considerations. (1) Since the modelled future scenarios do not
reflect changes in forcing factors other than shipping emissions, it is more
meaningful to assess the modelled relative response to the emission changes.
(2) There is robustness in using a model to assess relative changes: past studies
involving multi-models have shown that, despite the large difference in
performance amongst models, only relatively minor differences were found in
the relative response of concentrations to emission changes (Jones et al.,
2005; Hogrefe et al., 2008).
Modelled mean ambient O3 concentrations for
July–August–September (shipping season) of 2010 base year (a), and relative
contribution from Canadian Arctic shipping emissions for the 2010 base year
(b), 2030 BAU (c), and 2030 ECA (d). (The geographical subdivisions
indicated on (b) are referred to in the statistical assessment).
On ambient air concentration of criteria pollutants
The modelled JAS-averaged ambient concentrations of O3, PM2.5,
NO2, and SO2, and the corresponding contributions from Arctic
shipping are shown in Figs. 9–12, with a focus on the Canadian northern
and Arctic regions. The percentage ship contributions shown were computed as
conc(with arctic shipping)i,j-conc(without arctic
shipping)i,jconc(with arctic shipping)i,j×100(%),
where i and j denote pollutants (e.g., O3, PM2.5, NO2, and
SO2) and scenarios (i.e., 2010 base case, 2030 BAU, and 2030 ECA),
respectively.
Modelled mean ambient PM2.5 concentrations for
July–August–September (shipping season) of 2010 base year (a), and relative
contribution from Canadian Arctic shipping emissions for the 2010 base year
(b), 2030 BAU (c), and 2030 ECA (d).
The modelled ambient O3 concentrations averaged for the JAS period
range between 20 and 25 ppbv over most of the eastern Arctic (Fig. 9a).
The relatively high ambient concentrations over Greenland are due to the
high elevation. The Arctic shipping emissions contribute to less than 1 %
of the JAS-averaged O3 concentration at the present level (or 2010
base case); the impact is mostly felt between 50∘ and 100∘ W (Fig. 9b)
and Mackenzie Bay in the west. At the projected 2030 BAU level, the model
predicted considerably greater shipping contributions, showing up to 5 %
of the JAS-averaged ambient O3 concentration (Fig. 9c); the area
where shipping emissions contribute greater than 0.5 % extends to almost
all of the eastern Canadian Arctic (or Nunavut territories, NU). This is in
response to the projected increase in NOx emissions from Arctic
shipping in the 2030 BAU scenario. For the 2030 ECA scenario, the model
predicted shipping contributions to O3 concentrations are reduced
compared to the 2030 BAU scenario but are still greater than the present
2010 base-case level (Fig. 9d), particularly along Davis Strait and
Baffin Bay. This is consistent with the fact that projected NOx
emissions from Arctic shipping in 2030 under ECA are intermediate between
current 2010 and 2030 BAU levels (see Table 1).
Modelled mean ambient NO2 concentrations for
July–August–September (shipping season) of 2010 base year (a), and relative
contribution from Canadian Arctic shipping emissions for the 2010 base year
(b), 2030 BAU (c), and 2030 ECA (d).
The modelled JAS-averaged ambient PM2.5 concentrations show a general
south-to-north decreasing gradient, from a few micrograms per cubic metre
in the sub-Arctic regions to below 0.1 µg m-3 in the high Arctic
(Fig. 10a). As PM2.5 consists of both primary and secondary
components, the impact of shipping emissions accentuates the shipping
channels (Fig. 10b–d) more than in the case for O3. The
contributions from Arctic shipping emissions to the JAS-averaged PM2.5
concentrations are in the range of 1 %–5 % along the eastern Parry Channel, Pond
Inlet, and north of Baffin Island and generally < 0.5 % over land
at the present level (2010 base case; Fig. 10b). At the projected 2030
BAU level, the contributions from Arctic shipping emissions to ambient
PM2.5 concentrations are predicted to increase to 5 %–20 % over the
main shipping channels, particularly along the east coast of Baffin Island
and Lancaster Sound area (Fig. 10c). The greater contribution in this
case is due to the projected increase in both primary PM emissions and PM
precursor emissions (of SO2, NOx, and VOCs) from shipping; this is
evident from examining the shipping contributions to individual PM
components. The components contributing to the increase in total PM due to
shipping include primary PM, such as elemental carbon, primary organics, and
crustal material and secondary PM, such as sulfate, ammonium, and nitrate,
(see Figs. S3–S8 in the Supplement). Again, for the 2030 ECA
scenario, the model predicted a considerably reduced contribution from
shipping in comparison with the 2030 BAU scenario (Fig. 10d), primarily
resulting from the drastic reduction in sulfur emissions if ECA is in effect
over the Arctic waters.
Modelled mean ambient SO2 concentrations for
July–August–September (shipping season) of 2010 base year (a), and relative
contribution from Canadian Arctic shipping emissions for the 2010 base year
(a), 2030 BAU (c), and 2030 ECA (d).
For NO2 and SO2, both primary pollutants, the model shows that
Arctic shipping emissions make major contributions to ambient concentrations
over and near the Arctic waterways. The modelled JAS-averaged ambient
concentrations of NO2 and SO2 are 0.02–0.1 and 0.001–0.01 ppbv, respectively, over the eastern low Arctic and sub-Arctic, and generally
below 0.02 ppbv and 0.001 ppbv, respectively, over the high Arctic (Figs. 11a and 12a). The relatively elevated concentrations around the
lower east coast of Greenland primarily reflect shipping emissions based on
the 2010 HTAP inventory (used in this study for areas outside North America,
see section 3 above). At current (2010) levels, based on the model
simulations, the Arctic shipping emissions contribute to 10 %–50 %
(Fig. 11b) and 20 %–100 % (Fig. 12b) of the ambient NO2 and
SO2 concentrations, respectively, over the Arctic shipping channels.
The contributions are greatly increased at the projected 2030 BAU level, in
the case of NO2, to > 50 % over most of the shipping
channels (Fig. 11c) in response to a nearly 3-fold increase in
NOx emissions from Arctic shipping. In contrast, the contributions from
Arctic shipping to ambient SO2 concentrations are only moderately
higher at the projected 2030 BAU level compared to the present 2010 level
(Fig. 12c vs. 12b). This is in response to a more moderate
(∼32 %) increase in SO2 emissions over the 2010 level
(assuming the global cap of 0.5 % on sulfur content in fuels used onboard
ships is in effect, i.e., MARPOL Annex VI Regulation 14.8). Under the 2030
ECA scenario, there is a moderate decrease in the Arctic shipping
contribution to ambient NOx concentration (Fig. 11d vs. 11c), while there is a drastic decrease in the Arctic shipping
contribution to the ambient SO2 concentration (Fig. 12d vs. 12c). This is in accordance with the reductions of 35 % and 79 % in
NOx and SO2 emissions, respectively, from the 2030 BAU level when assuming the NA
ECA controls are in effect over the Canadian Arctic waters. In fact, the ECA
control on sulfur emissions would bring down the shipping contribution to
the ambient SO2 concentration to below the current 2010 base-case
level.
Division of geographical sectors over the Canadian Arctic
and northern regions for the assessment.
A more quantified (and area-specific) assessment of the impact of ship
emissions was carried out by dividing the area of interest into nine
geographical sectors (see Table 5; also indicated in Fig. 9b), and
shipping contribution statistics were computed for each of the geographical
sectors. Table 6 summarizes the mean, median, and maximum percentage
contributions from Arctic shipping emissions to the JAS-averaged ambient
concentrations of criteria pollutants for each of the nine sectors. The
percentage contributions (as defined in Eq. 1) were evaluated at individual
grid points, and statistics were then computed over all grid points within a
given geographical sector. Generally speaking, the shipping impact is
greater over the eastern Canadian Arctic than the western Canadian Arctic,
due to the proximity of the area to the Arctic shipping channels. In
addition, the western region of the Canadian Arctic is more strongly
impacted by North American boreal forest fire plumes during the summer
season, with relatively higher background concentrations of these criteria
pollutants than in the eastern region (e.g., Gong et al., 2016).
Percentage contribution from Arctic shipping to ambient
concentrations of criteria pollutants, by geographical sectors (see Table 1), for the July–August–September period.
At the current level (2010), the contribution statistics for O3 show
that both mean and median percentage contributions from Arctic shipping are
relatively uniform over the eastern sectors, with slightly higher
contributions over sectors E3 and E4 at around 0.3 % and the rest of the
eastern sectors at around 0.2 %. As for PM2.5, the shipping
contributions are higher over the northeastern sectors (north of 60∘ N) and
highest (> 0.5 % in mean value) over sectors E3 and E6, both of
which are in close proximity to the Arctic shipping routes (see Fig. 1).
Shipping contributions to ambient concentrations of NO2 and SO2
are much higher in comparison to O3 and PM2.5 and are again
highest over sectors E3 and E6 (with mean percentage contributions:
> 10 % for NO2 and ∼20 % and higher for
SO2). Shipping contributions over E4 (in close proximity to ship
traffic over Hudson Bay) and W6 (in close proximity to the Beaufort Sea) are
also pronounced in this case. Sector E6 has the highest relative
contribution from Arctic shipping emissions, which is attributed to its
proximity to northern Arctic shipping routes and it being the most
remote region with the lowest background concentrations, and hence, the most sensitive
area to local emissions. Note that the statistics shown in Table 6 imply
that the probability distribution functions (PDFs) of the percentage
shipping contributions for pollutants PM2.5, NO2, and SO2 are
highly skewed (i.e., large differences between means and medians and
confirmed by further statistical analyses undertaken but not shown here),
while the percentage contributions for O3 are relatively normally
distributed (i.e., small differences between mean and median values). This
is consistent with O3 being a secondary pollutant, and, with its
relatively long atmospheric lifetime, O3 has much higher background
ambient concentrations (and hence a smaller relative contribution from
shipping emissions) compared to the other pollutants assessed in this study.
At the projected 2030 BAU level, there is an overall increase in the
shipping contributions to ambient concentrations of the criteria pollutants
over all sectors (with the exception of sector W1, which is far away from
Arctic shipping routes). The average contribution from shipping to ambient
O3 concentrations increases to about 1 % or higher over the northeastern sectors (from < 0.4 % currently). The average shipping
contribution to the ambient PM2.5 concentration increases more
significantly over sectors E3, E5, and E6, e.g., 2 % over E3 compared to
0.6 % at the current level. The most significant contribution of ship
emissions to ambient levels of pollutants is for NO2, for which average
contributions are over 30 % in sector E6 and reaching 20 % in sector W3.
The increase in shipping contribution to ambient SO2 concentrations at
the projected 2030 BAU level is overall predicted to be more moderate
compared to the case of NO2 for most of the sectors, except for sector
W3, where the average shipping contributions increase to nearly 30 % from
just over 10 % at the current level. As mentioned above, for SO2, the
projected increase in shipping activity is partly offset by the global
sulfur cap coming into effect in 2020 (or by 2025 with a 5-year delay,
i.e., MARPOL Annex VI Regulation 14.8). If the same North American ECA
regulations were to be applied within the Arctic waters in 2030 (i.e., with
0.1 % sulfur cap and the IMO Tier III NOx standard for new vessels, the
2030 ECA scenario), the shipping contribution to ambient SO2
concentrations would be well below the current (2010) level, and the
shipping contribution to ambient PM2.5 would be brought roughly back to
the current level. There would be reductions in shipping contributions to
the ambient NO2 and O3 concentrations compared to the 2030 BAU
scenario, but the contributions would still be greater than the current
level. This is in line with the less stringent regulation (in comparison to
sulfur) on NOx under the NA ECA.
Population-weighted concentrations
Since criteria pollutants are closely related to health effects, it is
pertinent to look at the impact of Arctic shipping emissions in terms of
population-weighted concentrations. Population-weighted concentrations are
often used in population exposure and health effect analyses (e.g., Ivy et
al., 2008, Mahmud et al., 2012). It is calculated as
∑i=1npopi×conci∑i=1npopi,
where i designates each computational grid cell, and
popi and
conci denote population and concentration,
respectively, at grid cell i. Here population-weighted
concentrations of the criteria pollutants are calculated for Canada's
eastern and western Arctic, defined as north of 60∘ N,
60–100∘ W and 100–140∘ W, respectively.
Gridded population based on 2010 US and 2011 Canadian
census data.
Figure 13 shows the gridded population density over the model domain based
on the 2010 US and 2011 Canadian population data. As shown, over the eastern
Arctic, the populations are mostly distributed along coastlines in small
isolated communities and are thus more directly subjected to the impact from
shipping emissions than over the western Arctic. The time series of the
population-weighted concentrations of O3, PM2.5, NO2, and
SO2 and the corresponding shipping contributions over the
June–September period are plotted in Fig. 14a–d. Overall the
population-weighted concentrations are higher in the western Canadian Arctic
than in the east. The communities and population centres are larger in the
west and, in addition, the western Arctic is more affected by North American
boreal forest fire emissions in the summer months (e.g., Alaska, northern
British Columbia, and northern prairies; Gong et al., 2016). Conversely, the
relative contributions from ship emissions are higher in the east than in
the west, due to the proximity of the eastern communities to the shipping
channels and cleaner background air. The population-weighted O3
concentration over the eastern Arctic shows an overall summer minimum in
July and a slow recovery during late summer and early fall, which is
consistent with the general O3 seasonal trend observed at the Arctic
sites (Helmig et al., 2007b). In contrast, the time series for the western
Arctic shows higher values in mid-July and early August, likely due to
biomass burning impact in the region. The shipping contribution is
relatively uniform over the peak shipping season (JAS) over the eastern
Arctic, whereas over the western Arctic, the shipping contribution is greater
over the later part of the shipping season (September) than the early part
(i.e., July–August) when the region is impacted by biomass burning plumes
(Gong et al., 2016). Table 7 shows the statistics of ship contributions to
population-weighted concentrations over the eastern Arctic (i.e., mean,
median, maximum). When compared to the geographically based sectoral
statistics above, the ship impacts on population-weighted pollutant
concentrations are larger particularly over the eastern Arctic (in terms of
relevance to health impact). Similar to the sectoral statistical assessment
above, the application of ECA-like controls over Arctic waters (in the
projected 2030 emission scenario) would result in an important reduction in
shipping contributions to the ambient air pollution. In the case of
PM2.5 and SO2, the ECA-like controls would bring the projected 2030
shipping contributions down to, or well below, current (2010) levels,
respectively.
(a) Modelled population-weighted O3 concentrations
(8 h daily maximum) over the eastern and western Canadian Arctic (top
panels) and contributions from Canadian Arctic shipping emissions (bottom
panels). (b) Modelled population-weighted PM2.5 concentrations
over the eastern and western Canadian Arctic (top panels) and contributions
from Canadian Arctic shipping emissions (bottom panels). (c) Modelled population-weighted NO2 concentrations
over the eastern and western Canadian Arctic (top panels) and contributions
from Canadian Arctic shipping emissions (bottom panels).
(d) Modelled population-weighted SO2 concentration
over the eastern and western Canadian Arctic (top panels) and contributions
from Canadian Arctic shipping emissions (bottom panels).
Arctic shipping contributions to population-weighted
concentrations of criteria pollutants over eastern Canadian Arctic (north of
60∘ N, 60–100∘ W), for the July–August–September period.
It is interesting to compare the above model-based assessment of Arctic
shipping emissions on air quality with measurement-based analysis. Aliabadi
et al. (2015) conducted an analysis on the air quality measurements
collected during the 2013 shipping season from two monitoring stations in
the eastern Canadian Arctic: Cape Dorset (on Foxe Peninsula at the southern
end of Baffin Island) and Resolute, in Nunavut, both located near Arctic
shipping channels. Using back trajectories and high-resolution ship position
data, they estimated that ship emissions contributed to cumulated
concentrations (equivalent to dosage) of NOx, O3, SO2, and
PM2.5 of: 12.9 %–17.5 %, 16.2 %–18.1 %, 16.9 %–18.3 %, and
19.5 %–31.7 %, respectively, at Cape Dorset (southern site); and 1.0 %–7.2 %,
2.9 %–4.8 %, 5.5 %–10.0 %, and 6.5 %–7.2 %, respectively,
at Resolute (northern site). This may be loosely compared to the model
assessment based on population-weighted concentration above (Table 7),
bearing in mind the difference in metrics, as it is also weighted towards
small coastal communities. Ship contributions to O3 and PM2.5
concentrations were estimated to be higher based on the measurements than
from the model assessment. This may be due in part to the methodology used
in Aliabadi et al. (2015), where the concentrations exceeding the deemed
“background level” was attributed entirely to ship influence whenever a
back trajectory crossed a ship location. In the case of O3 and
PM2.5, which are either purely or partly secondary pollutants with
relative long lifetimes, this is likely to over attribute ship influence, as
the air parcel could well be influenced by other sources as well as ship
plumes. In contrast, the ship contributions to NO2 (or NOx in the
case of measurement-based analysis) and SO2 were estimated lower from
the measurements than from the model assessment. This can also be expected,
as the measurement sites were often influenced by local sources (e.g.,
garbage burning, off-road use of diesel, aeroplane landings and take-offs)
which are not represented well in the model simulations. Combined with
instrument lower detection limits (LDLs), the background levels in the
measurement analysis for NOx and SO2 are much greater than the
corresponding modelled background levels, which leads to greater ship
contribution (in relative sense) from the model assessment than from the
measurements.
(a) Total sulfur deposition over the
July–August–September period (accumulated) for the base case (2010); (b)
Arctic shipping contribution at current (2010) level; (c) the same as in (b) for the
2030 BAU scenario; (d) the same as in (b) for the 2030 ECA scenario.
(a) Total nitrogen deposition over the
July–August–September period (accumulated) for the base case (2010); (b) Arctic
shipping contribution at current (2010) level; (c) the same as in (b) for the
2030 BAU scenario; (d) the same as in (b) for the 2030 ECA scenario.
On deposition of S and N
The impacts of Arctic shipping on the deposition of sulfur and nitrogen at
current 2010 and projected 2030 levels were also examined in this study. The
model computes both dry and wet deposition fluxes of various sulfur- and
nitrogen-containing species. They include, for dry deposition, SO2,
pSO4, NO, NO2, HNO3, NH3, HONO, RNO3 (organic
nitrate), PAN (peroxyacetyl-nitrate), pNO3, pNH4, and for wet
deposition, HSO3-, SO4=, NO3-, and NH4+.
The modelled wet deposition includes both “rain-out”, i.e., tracer
transfer from cloud water to rain water due to precipitation production
(autoconversion, collision, coalescence), and “wash-out”, i.e., below-cloud
scavenging of aerosol particles and soluble gases by falling hydrometeors,
as described in Gong et al. (2006).
Shown in Figs. 15 and 16 are the modelled total sulfur and nitrogen
deposition fluxes accumulated over the JAS period and the contributions from
Arctic shipping emissions. The deposition fluxes are shown here for the 2010
base case only, due to the similarity in the geographical distribution
patterns between different scenarios, while the shipping contributions are
shown for all three scenarios. Overall the deposition fluxes are much lower
over the Arctic region compared to lower latitudes. The total sulfur
deposition (over the 3-month period) ranges from 0.2 to 0.5 kg S ha-1
over the Canadian sub-Arctic to 0.02–0.05 kg S ha-1
over the Canadian high Arctic; the corresponding ranges for total nitrogen
deposition are 0.1–0.5 and 0.01–0.05 kg N ha-1,
respectively. For the annual deposition estimate, the base case (2010)
simulation was extended to a full year. The annual total depositions of S
and N (based on the full-year model simulation) are 0.5–2 kg S ha-1 and
0.2–1 kg N ha-1, respectively, over the Canadian sub-Arctic, and
0.1–0.5 kg S ha-1 and 0.05–0.2 kg N ha-1, respectively, over the
Canadian high Arctic (see Fig. S9 in the Supplement). These levels
are in general accordance with previous model estimates (e.g., Hole et al.,
2009, Vet et al., 2014). The contribution to total sulfur deposition from
Arctic shipping is relatively small, below 5 %, at the 2010 base level;
however, the contribution from shipping increases to up to 20 % along the
coast of Baffin Bay in the 2030 BAU scenario. The 2030 ECA scenario brings
down the shipping contribution to generally below the current 2010 level
except for along the coast of Baffin Bay, where a major increase in shipping
activity from increased economic development is projected. The shipping
contribution to total N deposition is comparable to the case of S deposition
at the current 2010 level, but it increases substantially under the 2030 BAU
scenario, up to 50 %. With assumed ECA-like regulation, the shipping
contribution is slightly reduced but is still much greater than at the
current 2010 level.
Percentage contribution from Arctic shipping to surface
depositions of sulfur, nitrogen, and elemental carbon (BC) and column
loading of EC (BC), by geographical sectors (see Table 1), for the
July–August–September period.
Total S deposition (%) Total N deposition (%) Total BC deposition (%) BC column (%) sector no. meanmedmaxmeanmedmaxmeanmedmaxmeanmedmax2010E10.090.041.450.180.112.090.050.023.060.020.010.67E20.070.052.390.070.050.740.020.022.170.010.011.01E30.530.415.750.750.644.840.210.158.980.060.052.10E40.510.426.990.610.514.400.140.092.980.050.050.54E50.410.205.160.610.423.670.110.044.210.010.000.36E60.610.495.080.940.854.370.200.133.340.050.040.99W10.000.001.980.010.011.86-0.000.007.310.000.000.09W20.070.042.730.320.148.410.030.015.810.010.010.75W30.110.072.830.520.405.110.070.032.890.010.000.192030 BAUE10.120.062.690.710.425.820.850.625.890.040.020.45E20.070.051.720.230.171.650.600.269.010.020.010.55E31.540.5033.205.412.6057.500.700.4719.600.090.074.11E40.510.458.302.221.8720.000.540.294.810.090.080.71E51.610.5134.305.012.0759.000.610.359.150.04-0.012.15E61.610.9440.305.323.7960.901.461.1032.800.110.0415.90W10.010.003.520.040.032.100.320.137.360.000.000.19W20.120.061.820.980.4210.000.290.227.100.020.010.95W30.200.152.551.411.147.850.300.176.180.010.010.492030 ECAE10.040.031.120.530.324.080.090.052.930.040.030.33E20.020.021.050.170.131.330.030.032.240.020.010.35E30.360.1310.103.481.7444.600.310.1913.700.080.072.81E40.110.101.151.541.3311.300.170.102.960.070.060.67E50.370.1210.603.351.4148.500.210.065.590.030.001.36E60.360.2013.303.382.2958.300.320.1621.800.090.0410.20W10.000.002.110.030.023.130.000.009.060.000.000.19W20.030.032.460.670.308.380.050.026.310.020.010.95W30.050.041.030.890.735.250.090.043.680.010.010.31
The statistics of shipping contributions to the total depositions of S and N by
the nine geographical sectors are shown in Table 8. Similar to the cases of
ambient SO2 and NO2, the sectors most affected by Arctic shipping
emissions are the four northernmost sectors in the east (E3–E6).
However, in contrast to the cases of ambient SO2 and NO2, where
Arctic shipping contributions are much more important, the contributions to
total depositions of S and N from Arctic shipping are much less substantial.
This is in part due to the dominance of wet deposition in the total
depositions of S and N (as is discussed later) over the region of interest.
The dominance of wet deposition over dry deposition over northern Canada is
also found in a recent global assessment study of Vet et al. (2014), and it
is consistent with the fact that the area has relatively low emissions and
moderate precipitation amounts (particularly during the summer months).
While dry deposition is more associated with ambient (or near-surface)
concentrations, wet deposition is more associated with concentrations aloft
(i.e., at cloud levels and through the vertical column) and hence is more
affected by long-range transport and distant sources. Due to its moderate
solubility and fast oxidation pathways in the aqueous phase, SO2 can be
efficiently scavenged into cloud droplets, oxidized into sulfate, and be
transported and deposited (through rain-out) long distances from its
sources. Similarly, both NH3 and HNO3 can be readily scavenged by
cloud water and both contribute significantly to the wet deposition of N:
gaseous NH3 is highly soluble and, once absorbed by cloud water, will
mostly be in the form of ammonium ions (NH4+); HNO3 is
extremely soluble and will quickly dissociate into nitrate ions
(NO3-) once dissolved in cloud water (Seinfeld and Pandis, 1996).
Gridded land-cover fractions for lakes, tundra, and
barren–desert based on USGS v2.0 at 1 km resolution.
The deposition of S and N is of importance in considering ecosystem impacts,
e.g., acidification and eutrophication of terrestrial and aquatic systems
(Reuss and Johnson, 1986; Bouwman et al., 2002). To this end, land-cover-weighted deposition fluxes of S and N for three primary land-cover types
found in the Canadian Arctic, namely lakes, tundra, and barren–desert, were
computed and the contributions to the land-cover-weighted deposition from
Arctic shipping are examined. Figure 17 shows the gridded land-cover
fractions for the three land-cover types based on the US Geological
Survey's (USGS) Global Land Cover Characteristics (GLCC) database at 1 km
resolution (see https://lta.cr.usgs.gov/glcc/globdoc2_0, last access: 13 November 2018). Similar to the
population-weighted concentration, the land-cover-weighted deposition is
calculated as
∑i=1nfraci×Ai×depoi∑i=1nfraci×Ai,
where fraci,
Ai, and
depoi are gridded land-cover fraction (for a
given land-cover type), grid area, and deposition flux, respectively, at
grid cell i.
Land-cover-weighted deposition of S and N for the eastern
Canadian Arctic (60–90∘ N, 60–100∘ W) over the July–August–September
period and corresponding contributions from Arctic shipping.
Sulfur Nitrogen LC-weighted deposition Shipping contribution LC-weighted deposition Shipping contribution (kg S ha-1) (%) (kg N ha-1) (%) Land-cover type totaldrywettotaldrywettotaldrywettotaldrywet2010lakes0.1430.0110.1320.321.370.230.0840.0100.0730.411.670.23tundra0.1160.0080.1090.411.300.350.0680.0110.0580.601.970.35barren0.0730.0050.0670.531.190.470.0360.0050.0310.812.420.582030BAUlakes0.1430.0110.1320.341.350.250.0850.0110.0741.505.410.92tundra0.1160.0080.1090.521.800.430.0700.0110.0592.447.111.54barren0.0730.0050.0671.202.781.080.0370.0050.0324.8611.563.822030ECAlakes0.1420.0110.1320.080.260.060.0850.0110.0741.063.900.65tundra0.1160.0070.1080.120.390.100.0690.0110.0581.715.111.07barren0.0720.0050.0670.260.610.230.0370.0050.0323.017.362.35
Forsius et al. (2010) estimated critical loads of acidity (S and N) for
terrestrial ecosystems north of 60∘ latitude using a simple
mass balance (SMB) model and found that in northern North America, the
lowest critical loads (or most sensitive regions) occur in eastern Canada.
Table 9 shows the land-cover-weighted depositions of S and N (dry and wet, both total and separately) for the eastern Canadian Arctic
(60N–90∘ N, 60–100∘ W) over the JAS period and the respective contributions
from Arctic shipping. At the current level, land-cover-weighted total S
deposition over the eastern Canadian Arctic varies from 73 g ha-1 over
barren land to 143 g ha-1 over lakes for the 3-month period. The
corresponding numbers for the annual deposition of S over the eastern Canadian
Arctic, based on the extended annual simulation (2010 base case), are 288 g ha-1
over barren land, 652 g ha-1 over lakes (see Table S3 in
the Supplement), or a range of 18–40 eq ha-1 (assuming that
1 mole of S equals 2 acid equivalents; Bouwman et al., 2002),
which is well below the lowest critical load of acidity (based on 5th
percentile of the maximum critical load of S) estimated by Forsius et al. (2010)
for the area: 200 eq ha-1 a-1 (using an aluminum–base-cation
ratio criteria) or 100 eq ha-1 a-1 (using an acid neutralizing
capacity criteria, a more stringent measure). Note that caution needs to be
taken in interpreting the corresponding deposition values for the 2030
scenarios as there was no projection done for the anthropogenic emissions
other than for the marine shipping emissions over Canadian waters for these
model runs. The shipping contributions to the total deposition of S to the
three land-cover types are small (below 1 %), while the contributions to
dry deposition (which is more heavily tied to ambient concentrations) are
noticeably greater. As shown in Table 9, the total deposition of S (and N)
is dominated by wet deposition in this region. The land-cover-weighted N
deposition ranges between 36 g ha-1 (over barren land) and 84 g ha-1 (over lakes) over the JAS period at the present level. Again the
annual deposition of N, based on the full-year simulation (see Table S3),
ranges between 0.137 kg N ha-1 a-1 (over barren land) and 0.274 kg N ha-1 a-1 (over lakes), or 10–20 eq ha-1 a-1,
which is also below the critical load for acidification currently estimated
for the region in Forsius et al. (2010) as well as the empirical critical loads for
nutrient N of 1–3 kg N ha-1 a-1 for the North America ecoregion
of tundra (Pardo et al., 2011; Linder et al., 2013).
The contributions from Arctic shipping to total N deposition for the three
land-cover types are simulated as small at the current level, but are
predicted to increase significantly under the 2030 scenarios. It should be
noted that, although the current depositions of S and N over the Arctic
region are low and generally below the existing critical load estimates,
with the projected increase in global production of nitrogen needed to meet the growing demand for food and energy, atmospheric emissions
and depositions of nitrogen are expected to increase (Galloway et al., 2004;
Dentener et al., 2006); this situation combined with the expected increase
in shipping activities in Arctic waters could raise the level of deposition
to above the critical loads for the region. Furthermore, it is recognized
that the current estimates of critical loads for North American Arctic
ecosystems are highly uncertain due to a number of factors including
limitations in methodology and lack of data (Forsius et al., 2010; Pardo et
al., 2011; Linder et al., 2013). Given these considerations, a careful
assessment of potential ecosystem impacts from Arctic shipping emissions,
particularly in the future context, is warranted.
On black carbon
Black carbon, formally defined as an ideally light-absorbing substance
composed of carbon (Petzold et al., 2013), is a short-lived climate forcer (SLCF): it absorbs solar radiation, influences cloud processes, and alters
the melting of snow and ice and, hence, surface albedo (Bond et al., 2013;
Flanner et al., 2007). BC is emitted into the atmosphere from a variety of
combustion processes, including shipping activities. Although shipping
contributes only up to about 2 % of global BC emissions, it may constitute
a larger fraction of direct BC emissions in remote regions such as the
Arctic, an area with higher sensitivity to carbonaceous emissions due to
snow albedo effects (Bond et al., 2013). In our model, BC is represented by
the elemental carbon component of the internally mixed aerosols. By
its sources and chemical and physical properties represented in the model, the
modelled EC is equivalent to BC. In the context of the important radiative
effect of BC, the impact of Arctic shipping emissions on both column loading
and deposition of BC (or modelled EC) will be assessed here.
Modelled BC column loading (scaled up by 104, in kg m-2) averaged for over the 2010 July–August–September period (a), and
relative contributions from Canadian Arctic shipping emissions: (b) 2010
base year, (c) 2030 BAU, and (d) 2030 ECA.
Figure 18 shows the modelled EC (or modelled BC, hereafter) column loadings
averaged over the JAS period (2010 base case) and the percentage
contributions from Arctic shipping for the 2010 base case, 2030 BAU scenario, and 2030
ECA scenario. The contribution statistics by geographical sectors are
included in Table 8 (last column). The modelled, averaged BC column loading
over the Canadian Arctic (north of 60∘ N) ranges between 20 and 200 µg m-2 (Fig. 18a), higher over the western Canadian Arctic than the
east, where the region is strongly impacted by northern boreal forest fires
over western Canada and Alaska during the summer months. A similar range of
modelled BC loading over the Arctic is also reported by Eckhardt et al. (2015) in a recent multi-model assessment for simulating BC and sulfate in
the Arctic atmosphere. The contribution to BC loading from Canadian Arctic
shipping emissions at the 2010 baseline level is limited and localized,
generally below 0.1 % on average and up to 2 % over localized areas in
the eastern Canadian Arctic (Fig. 18b). In absolute terms, the shipping
contribution to BC loading is below 0.1 µg m-2 over most parts of
the Canadian Arctic. This is somewhat smaller than the estimate of
Ødemark et al. (2012), where the Arctic shipping contribution to the
tropospheric BC column is estimated at 0.38 µg m-2 averaged over
60–90∘ N. Noting that the present
assessment focuses on the impact of shipping over the Canadian Arctic waters
only, as opposed to shipping over the entire Arctic waters (as in the case of
Ødemark et al., 2012), the smaller contribution from this assessment is
expected, as shipping activities within Canadian Arctic waters constitute
only a small portion of overall Arctic shipping activities, e.g., compared
to the activities over the Barents Sea, Norwegian Sea, and along southwest
coast of Greenland (Arctic Council, 2009; Winther et al., 2014). There is a
considerable increase in the contribution to BC loadings from Canadian
Arctic shipping emissions in the 2030 BAU scenario of up to 15 % locally, as seen in Fig. 18c
and Table 8, particularly over Baffin Bay, in
response to projected increases in ship traffic there. Under the 2030 ECA
scenario, the modelled shipping contribution to BC loading is slightly
reduced from the 2030 BAU level, but it is still significantly greater than
that at the current 2010 level (Fig. 18d and Table 8).
The model simulated total (dry + wet) deposition of BC accumulated for the
JAS period at the current (2010) level is shown in Fig. 19 along with the
percentage contribution from shipping over Canadian Arctic waters under all
three scenarios. The contribution statistics by geographical sectors are
included in Table 8 (2nd last column). Modelled BC deposition over the
Canadian Arctic ranges from up to 50 mg m-2 in the southwest to around
0.5 mg m-2 in the northeast over the 3-month period. The modelled
area-averaged BC deposition flux for 60–90∘ N between 50∘ W and 140∘ W is 2.3 mg m-2 over the 3-month period, or 9.2 mg m-2 yr-1,
which is within the range of modelled BC deposition fluxes averaged over the
Arctic (60∘–90∘ N) from a multi-model
assessment of Jiao et al. (2014; see their Fig. 9). The contribution from
Canadian Arctic shipping at current levels is mostly between 0.1 % and
0.5 % over the shipping channels and locally up to 5 % (Fig. 19b).
Similar to the case of BC column loading discussed above, there is an
important increase in the shipping contribution to BC deposition in the 2030 BAU scenario over the east coast of Baffin Island (Fig. 19c). The
shipping contribution to BC deposition averaged over the northeast sector E6
increases to 1.5 %, exceeding 30 % locally, under the 2030 BAU scenario
(Table 8).
Modelled total BC deposition flux (scaled up by
104, in kg m-2) accumulated over the 2010 July–August–September period
(a), and relative contributions from Canadian Arctic shipping emissions: (b) 2010 base year, (c) 2030 BAU, and (d) 2030 ECA.
Since BC deposition to ice and snow is of most interest when considering the
potential albedo effect, averaged BC deposition fluxes to ice and snow,
defined as
∑i=1nFice/snow(i)×A(i)×depo(i)∑i=1nFice/snow(i)×A(i),
(where Fice/snow is the grid fraction of
ice and snow cover), have been computed, and the respective contributions from
shipping within Canadian Arctic waters are examined here. Table 10 shows
average monthly BC deposition fluxes to ice and snow (total, as well as dry and
wet, separately) over the Canadian Arctic region (60–90∘ N, 50–140∘ W) for
the three peak shipping months, July–September, and the corresponding
shipping contributions. Modelled monthly mean ice and snow cover fields
(shown in Fig. 20) are used for this calculation. As shown, the Arctic
ice and snow cover recedes progressively through the summer months. The monthly
BC deposition to ice and snow is highest in August due to higher
precipitation and wet deposition. There is a sharp reduction in September as
a result of the combination of a reduction in column BC loading (see
Fig. S10 in the Supplement) due to the reduced wildfire events in
western Canada in late summer and receding ice and snow cover further to the
north (Fig. 20). Again total deposition is largely dominated by the wet
component. In general, shipping over Canadian Arctic waters makes only a
small contribution to the total BC deposition on Arctic ice and snow; the
relative contribution is larger in September due to the reduced impact from
wildfire emissions. Proportionally, Arctic shipping makes a greater
contribution through dry deposition than through wet deposition over
northern regions as the emissions are more likely to be trapped within the
stable marine boundary layer and hence have a greater impact on the
near-surface atmospheric concentration. Table 10 also includes the shipping
contribution to BC deposition to ice and snow in absolute terms. It shows that
the shipping contributions are roughly double in the 2030 BAU scenario from
present levels. It is interesting to see that dry deposition is playing a
bigger role in this increase, particularly for the month of July, reflecting
a significant increase in near-surface atmospheric concentration of BC in
this scenario.
Monthly averaged ice and snow fraction for July, August, and
September 2010.
Averaged BC deposition on ice and snow over the Canadian
Arctic (60–90∘ N, 50–140∘ W), and contributions from shipping over the
Canadian Arctic waters.
BC deposition to ice and snow Arctic shipping Arctic shipping con- (mg m-2 mon-1) contribution (%) tribution (µg m-2 mon-1) Month totaldrywettotaldrywettotaldrywet201070.5600.0510.5090.030.090.020.160.040.1280.6150.0250.5910.040.340.030.270.080.1890.1630.0040.1590.140.770.120.220.030.192030BAU70.5610.0510.5100.060.270.040.340.140.2080.6170.0250.5930.090.670.060.540.170.3790.1630.0040.1590.271.320.240.440.060.39
It is seen from this assessment that current shipping emissions over
Canadian Arctic waters make relatively small contributions to both BC
loading and deposition in the Arctic. However, the contributions are expected
to increase in the 2030 scenarios. Assessing the radiative effect from BC
loading and deposition on snow attributable to the shipping emissions over
the Canadian Arctic waters is beyond the scope of this study. There are
existing efforts to assess radiative forcing from specific forcing agents
and/or emission sectors mostly using global models with relatively coarse
resolutions. For example, a global BC radiative forcing of ∼2 mW m-2 attributable to current international shipping (without the
consideration for BC snow albedo effect) was estimated by Eyring et al. (2010); Ødemark et al. (2012) estimated annual mean BC relative forcing
attributable to Arctic shipping activities at the present (2004) level to be
0.60 mW m-2 (due to BC in air) and 0.47 mW m-2 (due to BC in snow)
averaged over 60–90∘ N. The current
understanding is that overall net forcing from the present-day ship
emissions of SLCF pollutants is negative due to higher emission of sulfur
(Fuglestvedt et al., 2008; Eyring et al., 2010; Ødemark et al., 2012). As
seen from this assessment, shipping-induced changes in atmospheric
composition and deposition are occurring at regional to local scales
(particularly in the Arctic). Climate feedbacks are therefore likely to act
at these scales, and hence, climate forcing impact assessments will require
modelling undertaken at much finer resolutions than those used in the
existing relative forcing and climate impact assessments.
Summary and conclusions
In this study, an on-line air quality forecast model (GEM-MACH) was used for
a first regional assessment of the impact of Arctic shipping emissions on
air pollution in the Canadian Arctic and northern regions. First, the
model's ability to simulate ambient atmospheric compositions in the region
of interest was evaluated with available observations. The impacts of Arctic
shipping emissions at both present and projected future levels were then
assessed based on model sensitivity runs using a detailed marine emission
inventory for ships sailing in Canadian waters developed specially for this
study.
The adapted GEM-MACH for Arctic is shown to have similar skill in predicting
ambient O3 and PM2.5 in the Canadian northern and Arctic regions as
the current operational air quality forecast models in North America and
Europe. The model is able to simulate the observed ambient O3, and
some of the PM components well at the Canadian high Arctic site, Alert. The model
has reasonable skill in predicting NO2 and SO2 in the north at a
regional scale; at local scales the model prediction depends heavily on
emission inputs. The evaluation results indicate large uncertainties in the
representation of local emissions in the remote north and the need for
improved emission estimates and representation for the oil and gas
facilities in northeastern British Columbia and northern Alberta. There is a
significant data gap in northern Canada, particularly the eastern Arctic,
for air quality monitoring and model evaluation.
Key findings from the model assessment of the impact of Arctic shipping
emissions are the following.
At the current (2010) level, Arctic shipping emissions contribute to less
than 1 % of ambient O3 concentration over the eastern Arctic. This
contribution is expected to increase to up to 5 % in the 2030
business-as-usual (BAU) scenario with a broader region of impact.
In comparison, the impact of Arctic shipping emission on ambient PM2.5
concentration is more confined to areas close to the shipping channels.
Current (2010) levels of Arctic shipping contribute to 1 %–5 % of ambient
PM2.5 concentration over the shipping channels and < 0.5 %
over land. At the 2030 BAU level, the shipping contribution is expected to
increase to 5 %–20 % over the shipping channels.
For NO2 and SO2, both primary pollutants, Arctic shipping
emissions make significant contributions to ambient concentrations over the
eastern Arctic: 10 %–50 % for NO2 and 20 %–100 % for SO2,
over shipping channels and coastal regions with close proximity to shipping
routes at current (2010) level. The shipping contribution to NO2
concentrations is expected to increase to > 50 % under 2030
BAU, while the increase in the shipping contribution to SO2 concentrations
is more moderate due to the anticipated global cap on sulfur in ship fuel
that is due to come into effect.
Contrasting to the 2030 BAU, the 2030 ECA scenario, i.e., assuming the
Canadian Arctic will be designated as an emission control area (as is the
case for the east and west coasts of North America), will see a significant
reduction in the Arctic shipping contribution to ambient concentrations of
SO2 and PM2.5. Particularly, the Arctic shipping contributions to
the population-weighted concentration of SO2 and PM2.5 will be brought
down to below the current level.
Despite the significant contributions to the ambient concentrations of
SO2 and NO2, the Arctic shipping contribution to the deposition of
total S and N to the Arctic ecosystem is small, < 5 %, at present
(2010) level due to the dominance of wet deposition. However, the
contribution is expected to increase to up to 20 % for S and 50 % for N
under the 2030 BAU scenario.
Based on existing estimates of critical loads for northern terrestrial
ecosystems, the current S and N deposition to the three dominant land-cover
types (tundra, lakes, and barren–desert) in the Canadian Arctic and northern
region is well below the lowest critical loads for acidification and
eutrophication. However, given the large uncertainty in the current critical
load estimates for the Arctic ecosystem, the anticipated increase in
atmospheric emissions and deposition of nitrogen globally, and the expected
increase in Arctic shipping contribution to the deposition of N to the
north, more careful assessment of potential ecosystem impacts from Arctic
shipping emissions, particularly in the future context, is needed.
The contribution to BC loadings from Canadian Arctic shipping emissions at
the 2010 baseline level is limited and localized, generally below 0.1 % on
average and up to 2 % over localized areas in the eastern Canadian Arctic.
There is a considerable increase in the contribution to the BC loading from
the Canadian Arctic shipping emissions in the 2030 BAU scenario,
particularly over Baffin Bay with up to 15 % locally, in response to the
projected increase in ship traffic there.
The contribution to BC deposition from shipping in the Canadian Arctic at
current (2010) levels is mostly between 0.1 % and 0.5 % over the
shipping channels and locally up to 5 %. Similar to the case of BC column
loading, there is an important increase in the shipping contribution to BC
deposition in the 2030 BAU scenario over the east coast of Baffin Island.
The shipping contribution to BC deposition averaged over the eastern
Canadian high Arctic increases to 1.5 %, exceeding 30 % locally.
In general, shipping over the Canadian Arctic waters makes a small
contribution towards the total BC deposition on Arctic ice and snow (taking into
account of the sea-ice cover during the Arctic shipping season).
Proportionally, Arctic shipping makes a greater contribution to dry
deposition than to wet deposition over the northern regions as the emissions
are more likely to be trapped within the stable marine boundary layer and
hence have greater impact on the near-surface atmospheric concentration. The
analysis shows that shipping contributions to BC deposition fluxes to
ice and snow are roughly double in the 2030 BAU scenario from present levels, in
response to the projected increase in Arctic shipping activities.
It is indicative from this study that shipping-induced changes in
atmospheric composition and deposition are at regional to local scales
(particularly in the Arctic). Climate feedbacks are consequently likely to
act at these scales, thus climate impact assessments will require modelling
undertaken at much finer resolutions than those used in the existing
radiative forcing and climate impact assessments.
The following statistical measures are considered for the model evaluation
in this study, letting M be the vector of model output and O be the vector of
observation (both of N record length), with mean values M¯ and O¯,
respectively.Mean bias (MB)
MB=∑i=1NMi-OiN
Normalized mean bias (NMB)
NMB(%)=100×∑i=1NMi-Oi∑i=1NOi
Root mean square error (RMSE)
RMSE=∑i=1NMi-Oi2N
Normalized mean square error (NMSE)
NMSE%=100×N∑i=1NMi-Oi2∑i=1NMi∑i=1NOi
Pearson correlation coefficient (r)
r=∑i=1NMiOi-NM¯O¯∑i=1NMi2-NM¯∑i=1NOi2-NO¯
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-16653-2018-supplement.
WG led the model assessment study. WG and SC
designed the experiments with inputs from LL, HM, and RH. SRB was responsible
for code modifications and model configuration with assistance from WG. MS
was responsible for emission processing for the modelling study with help
from RMA, SC, and JZ. JC helped with the initial implementation of NA
wildfire emissions in the model. PB and JL were responsible for the 2010
Canadian marine shipping emission inventory and the 2030 projection. Model
simulations were carried out by SRB and RMA with assistance from SM. SRB
performed the majority of the analysis and was assisted by WG. SS and LH were
responsible for collecting and processing the air chemistry data from Alert
used for the model evaluation. JR helped with post-processing of the model
results. WG prepared the manuscript with contributions from all co-authors.
The authors declare that they have no conflict of
interest.
Acknowledgements
We would like to acknowledge ECCC's National Atmospheric Chemistry Database (NAtChem) and Analysis Facility for access to the North America air
monitoring data used for model evaluation in this study. We are thankful to
the agencies in Canada and US for maintaining the networks, in particular
the National Air Pollution Surveillance (NAPS) network in Canada and the US
EPA's Aerometric Information Retrieval System (AIRS) and Air Quality System (AQS) database. The World Data Centre for Greenhouse Gases (WDCGG) and
NOAA's Earth System Research Laboratory (Audra McClure-Begley and Irina Petropavlovskikh) are
gratefully acknowledged for providing the O3 monitoring data at Barrow,
Alaska. We would also like to acknowledge the MACC-II project and Xiaobo
Yang (ECMWF) for processing and providing the 2010 MACC-MOZART reanalysis
data. Monica Hilborn of the Environmental Protection Branch (EPB) at ECCC
diligently reviewed and verified the Canadian marine inventory numbers, and
Hui Peng of EPB at ECCC reviewed the manuscript. We are also grateful to
the GEM-MACH development team at ECCC for technical support.
Edited by: Laurens Ganzeveld
Reviewed by: two anonymous referees
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