Introduction
Aviation is the fastest growing form of transport (Eyring et al., 2010; Lee
et al., 2010; Uherek et al., 2010), with a projected growth in passenger air
traffic of 5 % yr-1 until 2030 (Barrett et al., 2012; ICAO, 2013),
and a projected near doubling of emissions by 2025, relative to 2005 (Eyers
et al., 2004). These emissions, and changes to them, have both climate and
air quality impacts (Lee et al., 2009; Barrett et al., 2010, 2012; Woody et
al., 2011).
Aviation emits a range of gas-phase and aerosol pollutants that can influence
climate. Emissions of carbon dioxide (CO2) from aviation warm the
climate (Lee et al., 2009, 2010). Emissions of nitrogen oxides (NOx)
warm the climate through tropospheric ozone (O3) formation, which acts
as a greenhouse gas, and cool climate via a decrease in the lifetime of the
well-mixed greenhouse gas methane (CH4) through increases in the OH
radical (Holmes et al., 2011; Myhre et al., 2011). Sulfate and nitrate
aerosols, formed from aviation sulfur dioxide (SO2) and NOx
emissions and through altered atmospheric oxidants, lead to a cooling (Unger,
2011; Righi et al., 2013; Dessens et al., 2014), and black carbon (BC)
emissions result in a warming (Balkanski et al., 2010). Additionally, the
formation of persistent linear contrails and contrail-cirrus from aircraft
leads to warming (Lee et al., 2010; Rap et al., 2010; Burkhardt and Karcher,
2011). Overall, aviation emissions are thought to have a warming impact on
climate, with net radiative forcing (RF) estimated as +55 mW m-2
(excluding cirrus cloud enhancement) (Lee et al., 2010).
Previous studies have separately assessed the impacts of aviation through
different atmospheric species. Short-term O3 has been estimated to have
a radiative effect ranging between 6 and 36.5 mW m-2 (Sausen et al.,
2005; Köhler et al., 2008; Hoor et al., 2009; Lee et al., 2009; Holmes et
al., 2011; Myhre et al., 2011; Unger, 2011; Frömming et al., 2012;
Skowron et al., 2013; Unger et al., 2013; Khodayari et al., 2014; Brasseur et
al., 2016). The aerosol direct effect is highly uncertain [-28 to
+20 mW m-2] (Righi et al., 2013), with the direct aerosol effects
for sulfate ranging between -0.9 and -7 mW m-2 (Sausen et al.,
2005; Fuglestvedt et al., 2008; Lee et al., 2009; Balkanski et al., 2010;
Unger, 2011; Gettelman and Chen, 2013; Brasseur et al., 2016), nitrate
ranging between -4 and -7 mW m-2 (Unger et al., 2013; Brasseur et
al., 2016), BC ranging between 0.1 and 0.3 mW m-2 (Sausen et al., 2005;
Fuglestvedt et al., 2008; Lee et al., 2009; Balkanski et al., 2010; Unger,
2011; Gettelman and Chen, 2013; Unger et al., 2013; Brasseur et al., 2016),
and for organic carbon (OC) ranging between -0.67 and -0.01 mW m-2
(Sausen et al., 2005; Fuglestvedt et al., 2008; Lee et al., 2009; Balkanski
et al., 2010; Unger, 2011; Gettelman and Chen, 2013; Unger et al., 2013). Few
studies estimate the aerosol cloud albedo effect (aCAE) from aviation: Righi
et al. (2013) assessed the aCAE to be -15.4 ± 10.6 mW m-2
while Gettelman and Chen (2013) estimate -21 ± 11 mW m-2.
Aviation emissions can increase atmospheric concentrations of fine
particulate matter with a dry diameter of < 2.5 µm (PM2.5).
Short-term exposure to PM2.5 can exacerbate existing respiratory and
cardiovascular ailments, while long-term exposure can result in chronic
respiratory and cardiovascular diseases, lung cancer, chronic changes in
physiological functions and mortality (Pope et al., 2002; World Health
Organisation, 2003; Ostro, 2004). In the US aviation emissions are estimated
to lead to adverse health effects in ∼ 11 000 people (ranging from
mortality, respiratory ailments and hospital admissions due to exacerbated
respiratory conditions) and ∼ 23 000 work loss days per annum (Ratliff
et al., 2009). Landing and take-off aviation emissions increase PM2.5
concentrations, particularly around airports (Woody et al., 2011), increasing
US mortality rates by ∼ 160 per annum.
Previous studies have estimated the number of premature mortalities due to
exposure to pollution resulting from aviation emissions. Barrett et al. (2012, 2010) used the methodology of Ostro (2004) to estimate that aviation
emissions are responsible for ∼ 10 000 premature mortalities a-1
due to increases in cases of cardiopulmonary disease and lung cancer. Yim et
al. (2015) using the same methodology but with the inclusion of the Rapid
Dispersion Code (RDC) to simulate the local air quality impacts of aircraft
ground level emissions estimated 13 920 (95 % CI: 7220–20 880)
mortalities a-1. Morita et al. (2014), using the integrated
exposure–response (IER) model from Burnett et al. (2014) to derive relative
risk (RR), estimate that aviation results in 405 (95 % CI: 182–648)
mortalities a-1 due to increases in cases of lung cancer, stroke,
ischemic heart disease, trachea, bronchus, and chronic obstructive pulmonary
disease. Jacobson et al. (2013) estimate 310 (95 % CI: -400 to 4300)
mortalities a-1 from aviation emissions due to cardiovascular effects.
Taking these studies in account, the different methodologies applied and
modes of mortality investigated aviation is estimated to be responsible for
between 310 and 13 920 mortalities a-1.
The introduction of cleaner fuels and pollution control technologies can
improve ambient air quality and reduce adverse health effects of fossil fuel
combustion (World Health Organisation, 2005). One proposed solution to reduce
the adverse health effects of aviation-induced PM2.5 is the use of
ultra-low sulfur jet fuel (ULSJ), reducing the formation of sulfate aerosol
(Barrett et al., 2012, 2010; Ratliff et al., 2009; Hileman
and Stratton, 2014). ULSJ fuels typically have a fuel sulfur content (FSC) of
15 ppm, compared with an FSC of between 550 and 750 ppm in standard aviation
fuels (Barrett et al., 2012). The current global regulatory standard for
aviation fuel is a maximum FSC of 3000 ppm (Ministry of Defence, 2011; ASTM
International, 2012).
Despite the potential for decreased emission of SO2, application of ULSJ
fuel will not completely remove the impacts of aviation on PM2.5. It is
estimated that over a half of aviation-attributable surface-level sulfate is
associated with oxidation of non-aviation SO2 by OH produced from
aviation NOx emissions, and not directly produced from aviation-emitted
SO2 (Barrett et al., 2010). Therefore, even a completely desulfurized
global aviation fleet would likely contribute a net source of sulfate
PM2.5. Nevertheless, previous work has shown that the use of ULSJ fuel
reduces global aviation-induced PM2.5 by ∼ 23 %, annually
avoiding ∼ 2300 (95 % CI: 890–4200) mortalities (Barrett et al.,
2012).
Altering the sulfur content of aviation fuel also modifies the net climate
impact of aviation emissions. A reduction in fuel sulfur content reduces the
formation of cooling sulfate aerosols (Unger, 2011; Barrett et al., 2012),
increasing the net warming effect of aviation emissions. The roles of sulfate
both in climate cooling and in increasing surface PM2.5 concentrations
mean that policy makers must consider both health and climate when
considering effects from potential reductions in sulfur emissions from a
given emissions sector (Fiore et al., 2012).
In this study, we investigate the impacts of changes in the sulfur content
of aviation fuel on climate and human health. A coupled tropospheric
chemistry-aerosol microphysics model is used to quantify global atmospheric
responses in aerosol and O3 to varying FSC scenarios. Radiative effects
due to changes in tropospheric O3 and aerosols are calculated using a
radiative transfer model, while the impacts of changes in surface PM2.5 on
human health are estimated using concentration response functions. Using a
coupled tropospheric chemistry-aerosol microphysics model that includes
nitrate aerosol allows us to assess the impacts of nitrate and aerosol
indirect effects in addition to the ozone and aerosol direct effects that
have been more routinely calculated.
Methods
Coupled chemistry-aerosol microphysics model
Model description
We use GLOMAP-mode (Mann et al., 2010), embedded within the 3-D off-line
Eulerian chemical transport model TOMCAT (Arnold et al., 2005; Chipperfield,
2006). Meteorology (wind, temperature and humidity) and large-scale transport
is specified from interpolation of 6-hourly European Centre for Medium Range
Weather Forecasts (ECMWF) reanalysis (ERA-40) fields (Chipperfield, 2006;
Mann et al., 2010). Cloud fraction and cloud top pressure fields are taken
from the International Satellite Cloud Climatology Project (ISCCP-D2) archive
for the year 2000 (Rossow and Schiffer, 1999).
GLOMAP-mode is a two-moment aerosol microphysics scheme representing
particles as an external mixture of seven size modes (four soluble and three insoluble)
(Mann et al., 2010). We use the nitrate-extended version of GLOMAP-mode
(Benduhn et al., 2016) which, as well as tracking size-resolved sulfate, BC,
OC, sea-salt and dust components, also includes a dissolution solver to
accurately characterise the size-resolved partitioning of ammonia and nitric
acid into ammonium and nitrate components in each soluble mode. Aerosol
components are assumed to be internally mixed within each mode. GLOMAP-mode
includes representations of nucleation, particle growth via coagulation,
condensation and cloud processing, wet and dry deposition, and in- and
below-cloud scavenging (Mann et al., 2010).
TOMCAT includes a tropospheric gas-phase chemistry scheme (inclusive of
Ox-NOy-HOx), treating the degradation of C1-C3
non-methane hydrocarbons (NMHCs) and isoprene, together with a sulfur
chemistry scheme (Spracklen et al., 2005; Breider et al., 2010; Mann et al.,
2010). The tropospheric chemistry is coupled to aerosol as described in
Breider et al. (2010).
The nitrate-extended version of the TOMCAT-GLOMAP-mode coupled model used in
this investigation employs a hybrid solver to simulate the dissolution of
semi-volatile inorganic gases (such as H2O, HNO3, HCl and NH3) into
the aerosol-liquid-phase.
Emissions of DMS are calculated using monthly mean sea-water concentrations
of DMS from (Kettle and Andreae, 2000), driven by ECMWF winds and sea-air
exchange parameterisations from Nightingale et al. (2000). Emissions of
SO2 are included from both continuous (Andres and Kasgnoc, 1998) and
explosive volcanoes (Halmer et al., 2002), and wildfires for year 2000 (Van
Der Werf et al., 2003; Dentener et al., 2006). Anthropogenic SO2
emissions (including industrial, power plant, road transport,
off-road transport and shipping sectors) are representative of the year 2000
(Cofala et al., 2005). Emissions of monoterpenes and isoprene are from
Guenther et al. (1995). NH3 emissions are from the EDGAR inventory
(Bouwman et al., 1997). NOx emissions are considered from anthropogenic
(Lamarque et al., 2010), natural (Lamarque et al., 2005) and biomass burning
(van der Werf et al., 2010) sources.
Annual mean emissions of BC and OC aerosol from fossil fuel and biofuel
combustion are from Bond et al. (2004). Monthly wildfire emissions are taken
from the GFED v1 (Global Fire Emissions Database) for the year 2000 (Van Der
Werf et al., 2003). For primary aerosol emissions we use geometric mean
diameters (Dg) with standard deviations as described by Mann et
al. (2010).
Here, we ran simulations at a horizontal resolution of
2.8∘ × 2.8∘ with 31 hybrid σ-p levels
extending from the surface to 10 hPa. All simulations were conducted for
16 months from September 1999 to December 2000 inclusive, with the first 4 months discarded as spin-up time.
Model evaluation
GLOMAP has been extensively evaluated against observations including
comparisons of speciated aerosol mass (Mann et al., 2010; Spracklen et al.,
2011b), aerosol number (Mann et al., 2010; Spracklen et al., 2010) and cloud
condensation nuclei (CCN) concentrations (Spracklen et al., 2011a). TOMCAT
simulated fields have been evaluated against observations, with CO and
O3 evaluated against aircraft observations (Arnold et al., 2005),
Mediterranean summertime ozone against satellite observations (Richards et
al., 2013), along with O3 evaluated against satellite observations
(Chipperfield et al., 2015). Benduhn et al. (2016) shows that simulated
surface concentrations of NO3 and NH4 are in reasonable agreement with
observations in Europe, the US and East Asia. Here we focus our evaluation on
the aerosol vertical profile as well as nitrate aerosol which has not
been evaluated previously.
Figure 1 presents simulated sulfate, nitrate, ammonium and organic aerosol
mass concentrations in comparison to airborne observations compiled by Heald
et al. (2011). The supplementary information presents the flight paths of
each of the aircraft field campaigns used in the study compiled by Heald et
al. (2011) (Fig. S1 in the Supplement), and details of each of the aircraft
field campaigns used (Table S1 in the Supplement). Observations were
predominantly made using an Aerodyne aerosol mass spectrometer (AMS).
Simulated profiles are for year 2000, while observational aerosol profiles
are from field campaigns conducted between 2001 and 2008.
Overall we find the model overestimates sulfates [NMB = +16.9 %],
while underestimating nitrates [NMB = -60.7 %], ammonium
[NMB = -47.1 %] and organic aerosols (OA)
[NMB = -56.2 %]. Model skill varies depending on the conditions
affecting each field campaign. To explore this, we use the broad
stratification of the field campaigns into anthropogenic pollution, biomass
burning and remote conditions as used by Heald et al. (2011) and shown in
Fig. 1. The model underestimates aerosol concentrations in biomass burning
regions [sulfate NMB = -14.9 %; nitrate NMB = -79.4 %;
ammonium NMB = -68.7 %, and; OA NMB = -74.5 %]. The model
performs better in polluted [sulfate NMB = +31.6 %; nitrate
NMB = -56.2 %; ammonium NMB = -28.6 %, and; OA
NMB = -40.9 %], and remote regions [sulfate
NMB = +25.4 %; nitrate NMB = -6.4 %; ammonium
NMB = -20.2 %, and; OA NMB = -41.5 %].
Comparison of observed (Obs) and simulated (Mod) (a) sulfate;
(b) nitrate; (c) ammonium, and (d) organic aerosol mass concentrations.
Observations are from airborne field campaigns compiled by Heald et al. (2011). Mean
values are represented by black dots, median values as shown by horizontal
lines, while boxes denote the 25th and 75th percentiles, and
whiskers denote the 5th and 95th percentile values.
The overestimation of sulfate aerosol is likely due to the decline in
anthropogenic SO2 emissions in Europe and the US between 2000 and 2008
(Vestreng et al., 2007; Hand et al., 2012). An underestimation of OA has been
reported previously (Heald et al., 2011; Spracklen et al., 2011b) and is
likely due to an underestimate in SOA formation in the model. Whitburn et
al. (2015) found biomass burning emissions of NH3 may be underestimated
which would affect a number of our comparisons.
Comparison of observed (solid lines) and simulated (dashed lines)
ozone profiles. Observations are taken from ozonesonde observations, and
arranged by launch location regions according to Tilmes et al. (2012).
The model underestimation of organic and inorganic aerosol components in
biomass burning influenced regions could partly be due to very concentrated
plumes in these regions affecting campaign mean concentrations. There is a
large uncertainty in biomass burning emissions and some evidence that
they may be underestimated (Kaiser et al., 2012), which may contribute to the
model bias. Biomass burning emissions also have large interannual variability
(van der Werf et al., 2010; Wiedinmyer et al., 2011), meaning that using
year-specific emissions might improve comparison against observations in these
regions. Underestimation in Arctic inorganic aerosol, which will affect the
ARCTAS comparisons, is a well-known problem in models, likely related to
problems with model wet deposition and emissions (Shindell et al., 2008;
Eckhardt et al., 2015). The model underestimate over West Africa (AMMA, DADEX
and DODO campaigns) is likely due to a combination of errors in biomass
burning emissions and poorly constrained emission sources from anthropogenic
activity (Knippertz et al., 2015).
Figure 2 presents simulated ozone concentration profiles in comparison to
ozonesonde observations compiled by Tilmes et al. (2012). Observations were
compiled from three networks, comprising 41 stations with continuous
sampling from 1995 to 2011: (i) The World Ozone and Ultraviolet Data Center
(WOUDC) (http://www.woudc.org/); (ii) the Global Monitoring Division
(GMD), and (iii) The Southern
Hemisphere ADditional OZonesondes (SHADOZ) (Tilmes et al., 2012).
Regional model-observation comparison profiles presented in Fig. 2
demonstrate good agreement between the model and ozonesonde profiles, while
demonstrating regional variations driven by variations in tropopause height,
showing no evidence of systematic model bias in the upper troposphere.
Notable differences are seen between simulated and observed ozone profiles
over the Praha launch site in Western Europe, with the model greatly
overestimating observed ozone.
Evaluation of ozone model bias is conducted for the troposphere, using a
chemical tropopause definition of 150 ppbv ozone, as previously used by
Stevenson et al. (2013), Young et al. (2013) and Rap et al. (2015). We find
the model overestimates global ozone concentrations
[NMB = + 7.0 %] with overestimates in Western Europe
[+18.9 %] and the Northern Hemisphere Polar West
[NMB = +14.4 %] regions and underestimates over the Atlantic/Africa
[NMB = -11.0 %] and Southern Hemisphere Polar
[NMB = -4.6 %] regions.
Differences between model and observational profiles can in part be explained
by the differences in years of simulation and observation, a poor
representation of deep convection resulting in model underestimations in the
tropics and overestimations downwind (Thompson et al., 1997), in tandem with
reductions in anthropogenic NOx emissions over this time period
(Konovalov et al., 2008).
Aviation emissions
Aircraft emit NOx, carbon monoxide (CO), SO2, BC, OC and
hydrocarbons (HCs). The historical emissions data set for the CMIP5 (5th
Coupled Model Intercomparison Project) model simulations used by the IPCC 5th
Assessment Report only included NOx and BC aviation emissions (Lamarque
et al., 2009). Recently there have been efforts to add HCs, CO and SO2
emissions to aviation emission inventories (Eyers et al., 2004; Quantify
Integrated Project, 2005–2012; Wilkerson et al., 2010).
Aviation emissions indices and total annual emissions for year
2000.
Species
Emissions index
Global emissions for year
Range of annual global emissions from
(g kg-1 of fuel)
2000 (Tg of species)
previous studies (Tg of species)
NOx
13.89a
2.786
1.98–3.286a,b,j,h,i,k,l
CO
3.61b
0.724
0.507–0.679b,h,i,j
HCHO
1.24c,d
0.249
0.01205b
C2H6
0.0394e
0.007899
0.00051b
C3H8
0.03e
0.006014
0.00444b
CH3OH
0.22d
0.044
0.00177b
CH3CHO
0.33d
0.066
0.00418b
(CH3)2CO
0.18d
0.036
0.00036b
SO2
1.1760b
0.236
0.182–0.221a,b,h,i,j
BC
0.0250a
0.005012
0.0039–0.0068a,b,h,i,j,k
OC
0.00625f,g
0.001253
0.003b,i
a Eyers et al. (2004), b Wilkerson et al. (2010),
c Spicer et al. (1994), d Knighton et al. (2007), e Anderson et al. (2006),
f Bond et al. (2004), g Hopke (1985), h Olsen et al. (2013), i Unger (2011), j Lee et al. (2010),
k Lamarque et al. (2010), l Quantify Integrated
Project (2005–2012).
Here we develop a new 3-D civil aviation emissions data set for the year 2000,
based on CMIP5 historical aviation emissions (Lamarque et al., 2009). The new
data set includes emissions of NOx, CO, SO2, BC, OC, and HCs. In
contrast to existing data sets which provide a general emissions index for HCs
(Eyers et al., 2004) we speciate HCs as formaldehyde (HCHO), ethane
(C2H6), propane (C3H8), methanol (CH3OH),
acetaldehyde (CH3CHO), and acetone ((CH3)2CO).
Table 1 describes our new emissions data set. NOx and BC emissions are
taken directly from Lamarque et al. (2009). We calculate fuel burn from BC
emissions data and the BC emissions index (Eyers et al., 2004) as used by
Lamarque et al. (2009). Following DuBois and Paynter (2006), we assume that
BC emissions scale linearly with fuel consumption. We estimate emissions for
other species using our calculated aviation fuel burn in combination with
published species-specific emissions indices (EI reported in g kg-1 of
fuel). Emission indices for CO and SO2 are from the FAA's aviation
environmental design tool (AEDT) (Wilkerson et al., 2010). OC emissions are
calculated using a BC : OC ratio of 4 (Bond et al., 2004);
resulting in an EI within the range determined by Wayson et al. (2009).
Speciated hydrocarbon emissions are calculated from experimental data
following the methodology of Wilkerson et al. (2010) using experimental data
from Knighton et al. (2007) and Anderson et al. (2006).
Our global aviation emissions typically lie within the range of previous
studies (Table 1). Our SO2 emissions are greater than those used by
Wilkerson et al. (2010) for 2006, despite the use of the same EI. This is due
to the greater global fuel burn considered by the base inventory used to
develop our emissions inventory (Eyers et al., 2004; Lamarque et al., 2010).
Our estimated OC emissions are lower than the emissions estimated in the AEDT
2006 inventory, due to the lower EI applied here. The lower EIOC
applied here (in comparison to Wilkerson et al., 2010) is a due to the phase
of flight considered when deriving the AEDT emissions inventory; where they
derive EIOC focusing on airport operations at ground-level
condition acknowledging the risk of overestimating aviation OC emissions,
while in comparison we consider aircraft operations after ground idle
conditions which risks underestimating aviation OC emissions.
We calculate the geometric mean diameter (Dg) for internally mixed
BC / OC particles as 50.5 nm from the mean particle mass derived using
the particle number emissions index (Eyers et al., 2004) and a constant
standard deviation set to σ = 1.59 nm.
Fuel sulfur content simulations
To explore the impact of aviation FSC on climate and air quality we performed
a series of 11 global model experiments (Table 2). In 7 of these model
experiments FSC values were varied globally between zero and 6000 ppm. Three
further simulations varied the vertical distribution of aviation emissions.
The first simulation collapses all aviation emissions to ground level
(GROUND), in order to compare an equivalent ground emission source and its
effects. Two simulations (SWITCH1 and SWITCH2) use a low FSC (15 ppm)
applied below the cruise phase of flight (< 8.54 km altitude) (Lee et
al., 2009; Köhler et al., 2013) combined with a high FSC at altitudes
above cruise level. The SWITCH1 scenario increases FSC in line with our HIGH scenario
above 8.54 km, while in the SWITCH2 scenario, emissions are scaled such that
total global sulfur emissions are the same as the standard simulation (NORM),
resulting in a FSC of 1420 ppm above 8.54 km. Results from all simulations
are compared against a simulation with aviation emissions excluded (NOAVI).
FSC and global SO2 emissions applied in each model experiment.
Scenario
Description
FSC
Total SO2
name
(ppm)
emitted (Tg)
NOAVI
No aviation emissions
n/a
0.0
NORM
Standard aviation emissions scenario
600
0.236
DESUL
Desulfurized case
0
0.0
ULSJ
Ultra low sulfur jet fuel
15
0.006
HALF
Half FSC of normal case
300
0.118
TWICE
Twice FSC of normal case
1200
0.472
HIGH
FSC at international specification limit
3000
1.179
OVER
Twice FSC specification limit
6000
2.358
GROUND
All emissions emitted at surface level (FSC as NORM)
600
0.236
SWITCH1
ULSJ FSC to 8.54 km, HIGH FSC content above
15/3000
0.491
SWITCH2
ULSJ FSC to 8.54 km, FSC = 1420 ppm above
15/1420
0.236
Radiative impacts
We calculate the aerosol direct radiative effect (aDRE), aerosol cloud albedo
effect (aCAE) and tropospheric O3 direct radiative effect (O3DRE) using
the offline Edwards and Slingo (1996) radiative transfer model. The radiative
transfer model considers six bands in the shortwave (SW) and nine bands in the
longwave (LW), adopting a delta-Eddington 2 stream scattering solver at all
wavelengths. The top-of-the-atmosphere (TOA) aerosol aDRE and aCAE are
calculated using the methodology described in Rap et al. (2013) and Spracklen
et al. (2011a), with the method for O3DRE as in Richards et al. (2013). To
determine the aCAE we calculated cloud droplet number concentrations (CDNCs)
using the monthly mean aerosol size distribution simulated by GLOMAP combined
with parameterisations from Nenes and Seinfeld (2003), updated by Fountoukis
and Nenes (2005) and Barahona et al. (2010). CDNC were calculated with a
prescribed updraft velocity of 0.15 m s-1 over ocean and
0.3 m s-1 over land. Changes to CDNC were then used to perturb the
effective radii of cloud droplets in low- and mid-level clouds (up to
600 hPa). The aDRE, aCAE and O3DREs for each aviation emissions scenario are
calculated as the difference in TOA net (SW + LW) radiative flux compared
to the NOAVI simulation.
Health effects
We calculate excess premature mortality from cardiopulmonary diseases and
increases in cases of lung cancer due to long-term exposure to
aviation-induced PM2.5 (Ostro, 2004). Using this function allows us to
compare our premature mortality estimates with those from previous studies (Barrett et al., 2012; Yim et al.,
2015) using the same concentration function; in future work estimates are required with updated methodologies
(Burnett et al., 2014). PM2.5 is used as a measure of likely health
impacts because chronic exposure is associated with adverse human health
impacts including morbidity and mortality (Dockery et al., 1993; Pope and
Dockery, 2006).
Impact of aviation emissions (FSC = 600 ppm) on surface annual mean
PM2.5 concentrations. (a) absolute
(NORM–NOAVI) and (b) percentage changes. Boxes show the European
(20–40∘ E, 35–66∘ N) and
North American (146–56∘ W, 29–72∘ N) regions.
Global aviation-induced aerosol mass burdens for
different emission scenarios. Values in parentheses show percentage change
relative to NORM case.
Scenario
All components (Gg)
Sulfates (Gg)
Nitrates (Gg)
NORM
16.9
12.9
5.7
ULSJ
12.4 (-26.8 %)
4.0 (-69.1 %)
5.9 (+4.5 %)
DESUL
12.1 (-28.4 %)
3.7 (-71.6 %)
6.0 (+5.1 %)
No NOx and SO2
2.0 (-88.3 %)
0.3 (-97.5 %)
0.1 (-97.9 %)
We relate annual excess mortality to annual mean surface PM2.5 via a
concentration response function (CRF) (Ostro, 2004). This response function
considers concentrations of PM2.5 for a perturbed case (X) (defined by
aviation emissions scenarios from Table 2) in relation to a baseline case
with no aviation emissions (X0) (NOAVI). To calculate excess mortality,
the relative risk (RR) for both cardiopulmonary disease and lung cancer is
calculated according to Ostro (2004) using a function of baseline (X0)
and perturbed (X) PM2.5 concentrations, and the disease-specific,
cause-specific coefficient (β):
RR=X+1X0+1β.
β coefficients for cardiopulmonary disease mortality of 0.15515
[95 % CI = 0.05624 - 0.2541] and lung cancer of 0.232 [95 %
CI = 0.086 - 0.379] are used (Pope et al., 2002; Ostro, 2004). The
95% confidence interval (CI) in β allow low-, mid- and high-range
mortality values to be calculated. The attribution factor (AF) from the
exposure to air pollution is calculated using Eq. (2):
AF=RR-1/RR.
Excess mortality (E) for both cardiopulmonary disease and lung cancer is
calculated using baseline mortality rates (B), the fraction of the
population over 30 years old (P30), along with the AF:
E=AF×B×P30.
Impact of aviation FSC on (a) global, (b) European
(20–40∘ E, 35–66∘ N), (c) North American (146–56∘ W, 29–72∘ N) surface annual mean
PM2.5 mass concentrations: FSC variations
(×), GROUND (⋄), SWITCH1
(-), and SWITCH2 (+) simulations. Solid
lines demonstrate the linear relationship between FSC and
PM2.5.
Global population data are taken from the Gridded World Population (GWP;
version3) project (Center for International Earth Science Information
Network, 2012) with country-specific data on the fraction of the population
under 30.
Results
Surface PM2.5
Figure 3 shows the simulated impact of aviation emissions with standard FSC
(FSC = 600 ppm; NORM) on surface PM2.5 concentrations. Aviation
increases annual mean PM2.5 concentrations by up to
∼ 80 ng m-3 (relative to the NOAVI simulation) over Central
Europe and Eastern China (Fig. 3a). Aviation emissions result in largest
fractional changes in annual mean PM2.5 concentrations (up to 0.8 %)
over North America and Europe (Fig. 3b).
Figure 4 shows the impact of aviation emissions on global and regional mean
PM2.5 concentrations as a function of FSC. With standard FSC
(FSC = 600 ppm), aviation increases global mean surface PM2.5
concentrations by 3.9 ng m-3; with increases in PM2.5 dominated
by sulfates [56.2 %], nitrates [26.0 %] and ammonium [16.0 %].
Aviation emissions increase European annual mean PM2.5 concentrations by
20.3 ng m-3 (Fig. 4b), substantially more than over North America
(Fig. 4c) where an annual mean increase of 6.3 ng m-3 is simulated.
Increased PM2.5 is dominated by nitrates, both over Europe [55.5 %]
and over North America [44.4 %]. Sulfates contribute up to 44.6 % of
increases in PM2.5 over North America, and 30.0 % over Europe.
Simulated differences in zonal annual mean sulfate (a) and
nitrate (b) concentrations from the use of ULSJ fuel relative to
standard fuel (ULSJ–NORM).
The use of ULSJ fuel (FSC = 15 ppm) reduces global annual mean surface
aviation-induced PM2.5 concentrations (in relation to the NORM case) by
35.7 % [1.4 ng m-3] (Fig. 4); predominantly due to changes in
sulfate [-1.4 ng m-3; -62.1 %] and ammonium
[-0.2 ng m-3; -37.9 %], which are marginally offset by very
small increases in nitrates [+3.2 × 10-3 ng m-3;
+0.3 %]. Aviation emissions also lead to small changes to other
aerosol components of +0.2 ng; which includes natural aerosols such as
dust [+0.3 ng m-3; +61.8 %], sodium [-19.5 %] and
chloride from sea salt [-19.5 %] with the changes due to changes in
aerosol lifetimes, along with changes in BC [-7.9 %] and OC
[-19.3 %].
In comparison to the global mean, switching to the use of ULSJ fuel in
aviation larger absolute reductions in PM2.5 of -4.2 ng m-3 are
simulated over Europe [Δsulfate = -3.4 ng m-3; Δnitrate = +0.1 ng m-3; Δammonium = -0.8 ng m-3; and Δothers = -0.1 ng m-3] and of -3.4 ng m-3 over North
America [Δsulfate = -2.9 ng m-3; Δnitrate = +0.02 ng m-3; Δammonium = -0.5 ng m-3; and Δothers = -0.01 ng m-3] (Fig. 4b, c). Over North America,
swapping to ULSJ fuel reduces aviation-induced PM2.5 by 53.4 %,
while a smaller reduction of 20.5 % is simulated over Europe. The smaller
fractional change in PM2.5 over Europe is caused by smaller reductions
in aviation-induced sulfate [-55.9 %] and ammonium [-18.4 %]
compared to over North America, which sees a reduction in ammonium of
41.6 % and a reduction in sulfates of 103 % indicating that over the
US the ULSJ fuel scenario sees a reduction in sulfates in relation to a NOAVI
scenario.
Complete desulfurization of jet fuel (FSC = 0 ppm; DESUL) reduces global
mean aviation-induced surface PM2.5 concentrations by 36.5 %
[-1.43 ng m-3], with changes in sulfates [-1.40 ng m-3;
-63.5 %] and ammonium [-0.24 ng m-3; -38.8 %]
dominating. Under this scenario the reductions in surface sulfate PM2.5
from aviation are 57.3 % over Europe and 105 % over North America.
ULSJ fuel therefore gives similar results to complete desulfurization, due to
the very small sulfur emission from ULSJ fuel (Table 2).
In summary, increases in FSC result in increased surface PM2.5, due to
increased sulfate outweighing the small reductions in nitrate. Simulated
changes in sulfate, nitrate, ammonium and total PM2.5 are linear
(R2 > 0.99, p value < 0.001 globally and for all individual
regions) with respect to FSC (Fig. 4). Larger emission perturbations would
likely lead to a non-linear response in atmospheric aerosol. The impact of
variations in FSC on PM2.5 are regionally variable; over Europe changes
in PM2.5 concentrations are observed to be more sensitive to changes in
FSC than over North America, and the global domain.
Estimated global aviation-induced mortality as a function of FSC,
and changes in vertical aviation emission distributions for year 2000
(Shaded region denotes the 95 % confidence through application of low-
and high-range cause-specific coefficients).
Impact of aviation emissions on low-cloud level (879 hPa) CCN
(Dp>50 nm) concentrations: (a) standard FSC
(NORM–NOAVI) and (b) FSC = 15 ppm (ULSJ–NOAVI). Blue boxes
define North American and European regions, and black boxes define Atlantic
(60–14∘ W, 1.4∘ S–60∘ N) and Pacific regions
(135∘ E–121∘ W, 15∘ S–60∘ N) referred
to in the text.
Figure 5 shows the impact of changing to ULSJ fuel on zonal mean sulfate and
nitrate concentrations relative to standard fuel (NORM). Table 3 reports the
global aerosol burden from aviation under different emission scenarios. With
standard FSC (FSC = 600 ppm), the global aviation-induced aerosol burden
is 16.9 Gg, dominated by sulfates (76.3 %) and nitrates (33.4 %).
The use of ULSJ (FSC = 15 ppm) reduces the global aerosol burden from
aviation by 26.8 %. Complete desulfurization of aviation fuel reduces the
global aerosol burden from aviation by 28.4 %, with the global sulfate
burden from aviation reduced by 71.6 % (Table 3). When aviation emissions
contain no sulfur, aviation-induced sulfate is formed through aviation
NOx-induced increases in OH concentrations, resulting in the oxidation
of SO2 from non-aviation sources (Unger et al., 2006; Barrett et al.,
2010).
In line with previous work, we find that a substantial fraction of aviation
sulfate can be attributed to aviation NOx emissions and not directly to
aviation SO2 emissions. We estimate that 36 % aviation-attributable
sulfates formed at the surface are associated with aviation NOx
emissions, compared to ∼ 63 % estimated by Barrett et al. (2010)
using the GEOS-Chem model (both estimates for FSC = 600 ppm).
Differences between model estimates can be attributed to differences in model
chemistry and microphysics, and different aviation NOx emissions. We
find desulfurization increases the aviation nitrate burden by 5.1 %
(Table 3); although much of this increase occurs at altitudes well above the
surface (Fig. 5) and so is not reflected in surface PM2.5
concentrations.
We explored the impacts of NOx emission reductions in combination with
fuel desulfurization. A scenario with desulfurized fuel and zero NOx
emissions reduces the global aviation-induced aerosol burden by 88.3 %
(Table 3), in comparison to a desulfurized only case (DESUL), where the
aviation-induced aerosol burden is reduced by 28.4 %. Removal of aviation
NOx and SO2 emissions results in a 95.0 % reduction in
aviation-induced global mean surface level aviation-induced PM2.5. These
results imply that only limited sulfate reductions can be achieved through
reducing FSC alone, with further reductions in aviation-induced PM2.5
sulfates requiring additional controls on aviation NOx emissions.
Premature mortality
Figure 6 shows estimated annual premature mortalities (from cardiopulmonary
disease and lung cancer) due to aviation-induced changes in PM2.5 as a
function of FSC. We estimate that aviation emissions with standard FSC
(FSC = 600 ppm) cause 3600 [95 % CI: 1310–5890] premature
mortalities each year, with 3210 [95 % CI: 1160–5250] mortalities
a-1 due to increases in cases of cardiopulmonary disease and 390
[95 % CI: 150–640] mortalities a-1 due to increases in cases of
lung cancer. Low-, mid- and high-range cause-specific coefficients (β)
are used to account for uncertainty in the health impacts caused by exposure
to PM2.5 (Sect. 2.5) (Ostro, 2004). Our estimated global mortality due
to aviation emissions is greatest in the Northern Hemisphere, which accounts
for 98.7 % of global mortalities. Europe and North America account for
42.3 and 8.4 % of mortality due to aviation emissions respectively.
Our estimate of the premature mortality due to aviation lies within the range
of previous estimates (310–13 920 mortalities a-1) (Barrett et al.,
2010, 2012; Jacobson et al., 2013; Morita et al., 2014; Yim et al., 2004).
Barrett et al. (2012) estimated ∼ 10 000 mortalities a-1 due to
aviation, almost a factor of 3 higher than our central estimate. The greater
aviation-induced mortality simulated by Barrett et al. (2012) can be
attributed to greater aviation-induced surface PM2.5 concentrations
simulated in their study, particulary over highly populated areas. Their
study simulated maximum aviation-induced PM2.5 concentrations over
Europe, eastern China and eastern North America greater than those in our
simulations by factors of 5 for Europe and eastern China and 2.5 over eastern
North America. Our aviation-induced sulfate concentrations compare well with
Barrett et al. (2012), indicating that the resulting differences in
aviation-induced surface PM2.5 concentrations are a result of other
aerosol components. Additionally, differences in mortality arise due to the
use of different cause-specific coefficients (β) within the same CRF,
as well as different population data sets. Morita et al. (2014) estimate that
aviation is responsible for 405 [95 % CI:
182–648] mortalities a-1. This lower estimate is primarily due to the
mortality functions used, with Morita et al. (2014) using the integrated
exposure response (IER) function as described by Burnett et al. (2014). The
IER function considers a PM2.5 concentration below which there is no
perceived risk, reducing estimated impacts of aviation in regions of low
PM2.5 concentrations.
Global and regional variations in low-cloud level (879 hPa) CCN
(Dp>50 nm): (a) changes in mean concentrations and
(b) percentage changes. See Fig. 5 for definitions of regions.
We estimate that aviation emissions with ULSJ fuel result in 2970 [95 %
CI: 1080–4870] premature mortalities globally per annum. Therefore, changing
from standard FSC to ULSJ would result in 620 [95 % CI: 230–1020] fewer
premature mortalities globally per annum; a reduction in aviation-induced
mortalities of 17.4 %. Regionally we find the implementation of an ULSJ
fuel reduces annual mortality by 180 over Europe and by 110 over North
America.
Barrett et al. (2012) estimated that swapping to ULSJ fuel could result in
∼ 2300 [95 % CI: 890–4200] fewer premature mortalities globally
per annum; a reduction of 23 %. In their work (using GEOS-Chem), the use
of ULSJ reduces global mean PM2.5 concentrations (sulfates, nitrates and
ammonium) by 0.89 ng m-3, less than the 1.61 ng m-3 reduction
in PM2.5 simulated here). Despite the greater reductions in global mean
surface layer PM2.5 concentrations simulated here, Barrett et al. (2012)
simulate greater reductions in PM2.5 over populated regions, resulting
in greater reductions of aviation-induced mortality under the ULSJ scenario.
Additionally, the GRUMPv1 population data set that Barrett et al. (2012) use
resolves population data on a finer scale compared to the resolution of GPWv3
population data set used here (Center for International Earth Science
Information Network, 2012); differences which could contribute to differences
in estimates of mortality.
We also estimate how aviation-induced mortality would change if FSC was
increased. We find that increasing FSC to 3000 ppm (HIGH) would increase
annual aviation-induced mortalities to 6030, an increase of 67.8 % in
relation to standard aviation (NORM; FSC = 600 ppm).
Sensitivity of cloud condensation nuclei to aviation FSC
Aviation emissions with standard FSC (NORM; FSC = 600 ppm) increase
global annual mean cloud condensation nuclei (CCN), here taken as the number
of soluble particles with a dry diameter greater than 50 nm, at low-cloud
level (879 hPa; 0.96 km) by 0.9 % (2.3 cm-3) (Fig. 7a). Increases
in CCN concentrations are greater in the Northern Hemisphere
[+3.9 cm-3; +1.4 %] compared to the Southern Hemisphere
[+0.7 cm-3; +0.5 %]. Maximum increases in low-level CCN are
simulated over the Pacific, central Atlantic and Arctic oceans.
The use of ULSJ (FSC = 15 ppm) reduces global mean low-level CCN
concentrations by 0.4 cm-3, [-18.2 %] relative to the NORM case
(Fig. 7). Northern Hemisphere CCN concentrations are reduced by
0.8 cm-3 [-19.4 %], while Southern Hemisphere concentrations are
reduced by 0.1 cm-3 [-11.5 %] (Fig. 7).
Figure 8 shows the sensitivity of low level CCN concentrations to FSC. As
with PM2.5, we find simulated changes in CCN are near linear with
respect to FSC (R2 > 0.99 and p value < 0.001 globally and for
all individual regions).
ULSJ fuel reduces global mean CCN by -0.42 cm-3 with largest
reductions over the Atlantic Ocean [-0.81 cm-3], North America
[-0.55 cm-3], and the Pacific Ocean [-0.51 cm-3], i.e. in
relation to standard aviation (ULSJ–NORM). The complete desulfurization of
aviation fuel results in reductions in CCN in relation to standard aviation
(DESUL–NORM), which follow the same regional trends (Fig. 8a).
Sensitivity of aerosol and ozone radiative effect to FSC
shows the calculated global mean net RE due to non-CO2 aviation
emissions. For standard FSC (FSC = 600 ppm) emissions the global mean
combined RE is -13.3 mW m-2. This combined radiative effect
(REcomb) results from a balance between a positive aDRE of
+1.4 mW m-2 and O3DRE +8.9 mW m-2, and a negative aCAE of
-23.6 mW m-2 (Fig. 9).
Our estimated aviation aerosol DRE [+1.4 mW m-2] lies in the middle
of the range given by previous work. The aviation aerosol DRE has been
previously assessed as highly uncertain, ranging between -28 and
+20 mW m-2 (Righi et al., 2013). Our estimated aviation-induced aCAE
[-23.6 mW m-2] lies within the range of uncertainty from previous
literature: Righi et al. (2013) estimated -15.4 ± 10.6 mW m-2
and Gettelman and Chen (2013) estimated -21 ± 11 mW m-2.
Our O3DRE estimate (+8.9 mW m-2), normalised by global aviation
NOx emission to +10.5 mW m-2 Tg(N)-1, is at the lower end
of current estimates [7.4–37.0 mW m-2 Tg(N)-1] (Sausen et al.,
2005; Köhler et al., 2008; Hoor et al., 2009; Lee et al., 2009; Holmes et
al., 2011; Myhre et al., 2011; Unger, 2011; Frömming et al., 2012;
Skowron et al., 2013; Unger et al., 2013; Khodayari et al., 2014). This can
be attributed to the lower net O3 chemical production efficiency (OPE)
within our model (1.33). Unger (2011) estimated an O3DRE of
7.4 mW m-2 Tg(N)-1 with a model OPE of ∼ 1, while the
ensemble of models considered by Myhre et al. (2011) have an OPE range of
1.5–2.4, resulting in an O3DRE range of
16.2–25.4 mW m-2 Tg(N)-1.
We calculate that an aviation fleet utilising ULSJ fuel would result in
a global annual mean REcomb of -6.3 mW m-2
[aDRE = +1.8 mW m-2; aCAE = –16.8 mW m-2; and
O3DRE = +8.7 mW m-2]. Thus, swapping from standard aviation fuel
to ULSJ fuel reduces the net cooling effect from aviation-induced aerosol and
O3 by 7.0 mW m-2, in comparison to the reduction of
3.3 mW m-2 estimated by Barrett et al. (2012). In our model, this
change is primarily due a reduction in cooling from the aCAE of
+6.7 mW m-2 combined with smaller contributions from an increased
aDRE of +0.4 mW m-2, and reduction in warming from the O3DRE of
-0.12 mW m-2 (Fig. 9).
Aviation-induced radiative effects due to variations in fuel sulfur
content (FSC), the ground release of aviation emissions (GROUND), and
variations in the vertical distribution of aviation SO2 emissions
(SWITCH1 and SWITCH2 simulations).
When we assume fully desulfurized aviation jet fuel (DESUL;
FSC = 0 ppm), the REcomb induced by aviation-induced aerosol
and O3 is very similar to that for ULSJ fuel and is estimated as
-6.1 mW m-2 [aDRE = +1.8 mW m-2;
aCAE = -16.6 mW m-2; and O3DRE = +8.7 mW m-2].
Increases in FSC result in reductions in the aerosol DRE (aDRE), changing
from a positive aerosol DRE for low FSC scenarios, to a negative aerosol DRE
for high FSC (FSC > 1200 ppm). As FSC is increased, we find the aCAE
exhibits a larger cooling effect, i.e. becoming more negative with increases
in FSC, increasing by a factor ∼ 5 as FSC is increased from 0 to
6000 ppm. The REcomb is dominated by these changes to the aCAE. Increases in FSC from 0 to 6000 ppm result in a greater negative
(cooling) aviation-induced REcomb; increasing in magnitude by a
factor of ∼ 5 (-16.6 mW m-2 for FSC = 0 ppm to
-82.1 mW m-2 for FSC = 6000 ppm) (Fig. 9). Therefore, we find
that increases in FSC provide a cooling effect due to the dominating effect
from aviation-induced aCAE.
Relationship between aviation-induced radiative effects and
mortality due to aviation non-CO2 emissions
Figure 10 shows the net RE and premature mortality for different aviation
emission scenarios. Increases in FSC lead to approximately linear increases
in both estimated mortality and the negative net RE. We quantify the impact
of FSC on mortality and REs in terms of d(mortalities)/d(FSC)
[mortalities ppm-1] and d(RE)/d(FSC) [mW m-2 ppm-1]. We
calculate the sensitivity of global premature mortality to be
1.0 mortalities ppm-1 [95 % CI = 0.4 to
1.6 mortalities ppm-1, where the range is due to uncertainty in β]. The global mean REcomb has a sensitivity of
-1.2 × 10-2 mW m-2 ppm-1, dominated by large
changes to the aCAE [-1.1 × 10-2 mW m-2 ppm-1],
and much smaller changes in the aDRE
[-6.9 × 10-4 mW m-2 ppm-1] and O3 RE
[+4.4 × 10-5 mW m-2 ppm-1].
Relationship between net radiative effect (sum of ozone direct
(O3DRE), aerosol direct radiative (aDRE) and aerosol cloud albedo (aCAE)
effects) and annual mortality rates: for low- mid- and high-range mortality
sensitivities.
The different slopes in the relationship between estimated RE and mortality
(Fig. 10) are driven by the range of coefficients used in the CRF. This
highlights the considerable uncertainty in the health impacts caused by
exposure to PM2.5. We note that uncertainty in the RE due to aerosol and
ozone exists, but is not included in Fig. 9.
To assess how the vertical distributions of aviation SO2 emissions
influence human health and climate effects, we performed three additional
simulations where we altered the vertical distribution of aviation SO2
emissions (GROUND, SWITCH1 and SWITCH2 simulations). In these simulations the
relationships between mortality and net RE deviate from the linear
relationship seen when varying FSC between 0 and 6000 ppm (Fig. 10).
In relation to the standard aviation emissions simulation
(FSC = 600 ppm; NORM), when we release all aviation emissions at the
surface (GROUND; FSC = 600 ppm) aviation-induced surface PM2.5
concentrations increase by +13.5 ng m-3 [+65.7 %] over Europe
and by +1.7 ng m-3 [+27.1 %] over North America, but decrease
by -1.4 ng m-3 [-36.7 %] globally (Fig. 4). Greater surface
layer PM2.5 perturbations (GROUND–NORM) over populated regions increase
aviation-induced annual mortality by +22.9 %
[+830 mortalities a-1] (Fig. 6).
Releasing aviation emissions at the surface (GROUND case) increases global
mean cloud level CCN by only 0.4 cm-3 relative to NOAVI; providing a
reduction in CCN of 82.1 % [-1.89 cm-3] relative to the NORM case
(i.e. GROUND–NORM). That is, injecting aviation emissions into the free
troposphere in the standard scenario is over 5 times more efficient at
increasing CCN concentrations compared to when the same emissions are
released at the surface [GROUND CCN = 0.4 cm-3; NORM
CCN = 2.3 cm-3]; both in relation to the NOAVI scenario. Similar
behaviour has been demonstrated previously for volcanic SO2 emissions by
Schmidt et al. (2012), where volcanic SO2 emissions injected into the
free troposphere (FT) were more than twice as effective at producing new CCN
compared to boundary layer emissions of DMS. Injection of aviation SO2
emissions at the surface will increase both deposition rates and aqueous
phase oxidation of SO2; the latter resulting in the growth of existing
CCN, but not the formation of new CCN. In contrast, when SO2 is emitted
into the FT the dominant oxidation mechanism is to H2SO4, leading
to the formation of new CCN through particle formation and the condensational
growth of particles to larger sizes. Subsequent entrainment of these new
particles into the lower atmosphere results in enhanced CCN concentrations in
low-level clouds. Reduced CCN formation when aviation emissions are injected
at the surface has implications for the aCAE. When aviation emissions are
released at the surface we calculate an aCAE of -2.3 mW m-2; a
factor of 10 smaller than the standard aviation scenario. This demonstrates
that low-level CCN concentrations and the aCAE are particularly sensitive to
aviation emissions, because of the efficient formation of CCN when SO2
emissions are injected into the FT. Injecting aviation emissions at the
surface also results in an increase in the aDRE of +5.9 mW m-2,
resulting in an REcomb of +5.0 mW m-2 (Fig. 9).
Surface O3 concentrations are also less sensitive to aviation when
emissions are located at the surface. Global mean aviation-induced surface
O3 concentrations are reduced from 0.15 ppbv (NORM) to 0.03 ppbv when
all emissions are in the surface layer. Releasing aviation emissions at the
surface also reduces the global O3 burden by 3.1 Tg. These
perturbations in O3 concentrations result in a reduction in the O3
radiative effect from +8.9 mW m-2 (NORM; FSC = 600 ppm) to
+1.5 mW m-2 (GROUND; FSC = 600 ppm) (Fig. 9). This is a
reflection of increases in the OPE of NOx with increases in altitude due
to lower background NOx and NMHC (non-methane hydrocarbon)
concentrations (Köhler et al., 2008; Stevenson and Derwent, 2009;
Snijders and Melkers, 2011; Skowron et al., 2013).
We investigated altering FSC between the take-off/landing and the cruise
phases of flight using two scenarios (SWITCH1 and SWITCH2) (Table 2). Our
SWITCH1 scenario increases global mean aviation-induced surface layer
PM2.5 concentrations by +2.1 ng m-3 [52.2 %], European mean
concentrations by +0.9 ng m-3 [+4.5 %], and North American
concentrations by +2.7 ng m-3 [+42.2 %] relative to NORM
(Fig. 4). These changes increase aviation-induced mortality by +17.4 %
[+630 mortalities a-1] (Fig. 6). This scenario results in greater
global mean increases in CCN (relative to NORM) of +1.2 cm-3
[+51.2 %], a larger cooling aCAE [-42.4 mW m-2], larger
warming aDRE [2.07 mW m-2], resulting in additional
-18.1 mW m-2 [136 %] of aviation-induced cooling [SWITCH1
REcomb of -31.4 mW m-2].
The SWITCH2 scenario was designed to have the same global total sulfur
emission as the normal aviation simulation. SWITCH2 increased global mean
surface aviation-induced PM2.5 concentrations by +0.3 ng m-3
[+6.6 %], but reduces mean surface PM2.5 concentrations over
Europe [-1.8 ng m-3; -8.7 %] and North America
[-0.8 ng m-3; -12.8 %] compared to NORM. Under this scenario
global aviation-induced mortality is decreased by 2.4 %
[-90 mortalities a-1] compared to the standard aviation simulation
(Fig. 6). The SWITCH2 scenario results in a REcomb of
-18.2 mW m-2, providing an additional -4.9 mW m-2
[36.6 %] cooling in relation to standard aviation emissions (NORM; FSC
= 600 ppm).
Discussion and conclusions
We have used a coupled chemistry-aerosol microphysics model to estimate the
impact of aviation emissions on aerosol and O3 concentrations,
premature mortality and radiative effect on climate.
We calculated the top-of-atmosphere (TOA) tropospheric O3 radiative
effect (O3DRE), aerosol direct RE (aDRE) and aerosol cloud albedo effect
(aCAE). We find that these non-CO2 REs result in a net cooling effect on
climate as has been found previously (Sausen et al., 2005; Lee et al., 2009;
Gettelman and Chen, 2013; Righi et al., 2013; Unger et al., 2013). For year
2000 aviation emissions with a standard fuel sulfur content
(FSC = 600 ppm), we calculate a global annual mean net TOA RE of
-13.3 mW m-2, due to a combination of O3DRE [+8.9 mW m-2],
aDRE [+1.4 mW m-2] and aCAE [-23.6 mW m-2].
Our O3DRE [+8.9 mW m-2] when normalised to represent the impact of
the emissions of 1 Tg(N) [+10.45 mW m-2 Tg(N)-1] is at the
lower end of the range provided by previous studies
[7.39–36.95 mW m-2 Tg(N)-1] (Sausen et al., 2005; Hoor et al.,
2009; Lee et al., 2009; Holmes et al., 2011; Myhre et al., 2011; Unger, 2011;
Frömming et al., 2012; Unger et al., 2013; Khodayari et al., 2014). This
can be attributed to our model's lower OPE of 1.33, in comparison to the
range of 1–2.4 from other models (Myhre et al., 2011; Unger, 2011).
Our estimate of aviation-induced aCAE [-23.6 mW m-2] lies just
outside the range provided by Gettelman and Chen (2013) and Righi et
al. (2013) [-15.4 to -21 mW m-2]. Our estimated aDRE
[+1.4 mW m-2] lies within the middle of the range given by previous
work (Sausen et al., 2005; Fuglestvedt et al., 2008; Lee et al., 2009;
Balkanski et al., 2010; Unger, 2011; Gettelman and Chen, 2013; Righi et al.,
2013; Unger et al., 2013).
We estimate that standard aviation (NORM; FSC = 600 ppm) is responsible
for approximately 3600 premature mortalities annually due to increased
surface layer PM2.5, in line with previous work (Barrett et al., 2012).
We find that aviation-induced mortalities are highest over Europe, eastern
North America and eastern China; reflecting larger regional perturbations in
surface layer PM2.5 concentrations. Comparing these estimates with total
global premature mortalities from ambient air pollution from all
anthropogenic sources (Lim et al., 2012), aviation is responsible for
0.1 % [0.04–0.18 %] of annual premature mortalities.
We investigated the impact of varying aviation FSC over the range
0–6000 ppm. Increases in FSC lead to increases in surface PM2.5
concentrations and subsequent increases in aviation-induced mortality.
Increases in FSC also lead to a more negative REcomb due to
enhanced aCAEs. We estimate that the use of ultra-low sulfur jet (ULSJ) fuel,
with a FSC of 15 ppm, could prevent 620 [230–1020] mortalities annually
compared to standard aviation emissions. Swapping to ULSJ fuel increases the
global mean net RE by +7.0 mW m-2 compared to standard aviation
emissions, largely due to a reduced aCAE. We calculate a larger warming
effect from switching to ULSJ fuel than that assessed by Barrett et
al. (2012), who did not evaluate changes in aCAE.
Absolute reductions in FSC result in limited reductions in aviation-induced
surface layer PM2.5. We estimate that aviation-NOx emissions are
responsible for 36.2 % of aviation-induced sulfate perturbations. Thus
further reductions in aviation-induced PM2.5 can potentially be achieved
if NOx emission reductions are implemented in tandem with reductions to
fuel sulfur content.
In line with previous work (Köhler et al., 2008; Stevenson and Derwent,
2009; Snijders and Melkers, 2011; Frömming et al., 2012; Skowron et al.,
2013), decreasing the altitude at which O3-forming species are emitted
results in a reduction in aviation-induced O3, and resulting O3DRE. This
is due to the relationship between altitude and OPE, and the inverse
relationship between altitude and background pollutant concentrations. We
also explored the sensitivity of emission injection altitude on aerosol,
mortality and aerosol RE. Injecting aviation emissions at the surface results
in a reduction in global mean concentrations of PM2.5 (relative to
NORM), but with higher regional concentrations over central Europe and
eastern America; resulting in higher annual mortalities due to aviation. We
find that aviation emissions are a factor of 5 less efficient at creating CCN
when released at the surface, resulting in an aCAE of -2.3 mW m-2, a
reduction of 90.1 % in relation to the standard aviation scenario. When
aviation SO2 emissions are injected into the free-troposphere, the
dominant oxidation pathway is to H2SO4 followed by particle
formation and condensational growth of new particles to larger sizes.
Subsequent entrainment of these new particles into the lower atmosphere leads
to increased CCN concentrations and impacts on cloud albedo. Aviation
SO2 emissions are therefore particularly efficient at forming CCN with
resulting impacts on cloud albedo.
We explored the impact of applying altitude-dependent variations in aviation
FSC. We tested a scenario with high FSC in the free troposphere and low FSC
near the surface, resulting in the same global aviation sulfur emission as
the standard aviation scenario. In this scenario, aviation-induced premature
mortalities were reduced by 2.4 % [-90 mortalities a-1] and the
magnitude of the negative REcomb was increased by 36.6 %,
providing an additional cooling impact of climate of -4.88 mW m-2.
Our simulations suggest that the climate and air quality impacts of aviation
are sensitive to FSC and the altitude of emissions. We explored a range of
scenarios to maximise climate cooling and reduce air quality impacts. Use of
ULSJ fuel (FSC = 15 ppm) at low altitude combined with high FSC in the
free troposphere results in increased climate cooling whilst reducing
aviation mortality. More complicated emission patterns, for example, use of
high FSC only whilst over oceans might further enhance this effect. However,
we note that the greatest reduction in aviation-induced mortality is
simulated for complete desulfurization of aviation fuel. Given the
uncertainty in both the climate and air quality impacts of aerosol and ozone,
additional simulations from a range of atmospheric models are required to
explore the robustness of our calculations. Finally, we note that our
calculations are limited to calculation of aviation-induced RE. Future work
needs to assess the complex climate impacts of altering aviation FSC. Future
work needs to estimate the health impacts of aviation using newly available
concentration response functions (Burnett et al., 2014).