Atmospheric Environment 45 (2011) 5415e5424
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Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv
Air quality and public health impacts of UK airports. Part I: Emissions
M.E.J. Stettler a, b, S. Eastham a, S.R.H. Barrett b, *
a
b
Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, United Kingdom
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 3 March 2011
Received in revised form
4 July 2011
Accepted 5 July 2011
The potential adverse human health and climate impacts of emissions from UK airports have become
a significant political issue, yet the emissions, air quality impacts and health impacts attributable to UK
airports remain largely unstudied. We produce an inventory of UK airport emissions e including aircraft
landing and takeoff (LTO) operations and airside support equipment e with uncertainties quantified. The
airports studied account for more than 95% of UK air passengers in 2005. We estimate that in 2005, UK
airports emitted 10.2 Gg [23 to þ29%] of NOx, 0.73 Gg [29 to þ32%] of SO2, 11.7 Gg [42 to þ77%] of CO,
1.8 Gg [59 to þ155%] of HC, 2.4 Tg [13 to þ12%] of CO2, and 0.31 Gg [36 to þ45%] of PM2.5. This
translates to 2.5 Tg [12 to þ12%] CO2-eq using Global Warming Potentials for a 100-year time horizon.
Uncertainty estimates were based on analysis of data from aircraft emissions measurement campaigns and
analyses of aircraft operations.
The First-Order Approximation (FOA3) e currently the standard approach used to estimate particulate
matter emissions from aircraft e is compared to measurements and it is shown that there are discrepancies greater than an order of magnitude for 40% of cases for both organic carbon and black carbon
emissions indices. Modified methods to approximate organic carbon emissions, arising from incomplete
combustion and lubrication oil, and black carbon are proposed. These alterations lead to factor 8 and a 44%
increase in the annual emissions estimates of black and organic carbon particulate matter, respectively,
leading to a factor 3.4 increase in total PM2.5 emissions compared to the current FOA3 methodology.
Our estimates of emissions are used in Part II to quantify the air quality and health impacts of UK airports,
to assess mitigation options, and to estimate the impacts of a potential London airport expansion.
Ó 2011 Elsevier Ltd. All rights reserved.
Keywords:
Aviation
Airport
Emissions
Air quality
Particulate matter
1. Introduction
1.1. Context
Aviation affects the environment via the emission of pollutants
from aircraft and supporting airport infrastructure, impacting on
human health and well-being, and on the climate (Lee et al., 2010).
Between 1960 and 2005 worldwide scheduled passenger air travel
grew from 109 billion to 3.7 trillion passenger-km travelled.
This represents an average growth rate of over 8% per year (IPCC,
1999; ICAO, 2006), while over the next two decades global air
travel is forecast to grow by 4.5e6% per year (Lee et al., 2009).
In the UK, a significant political issue has been the proposed
expansion of London Heathrow Airport, and potentially other
London airports. Heathrow expansion was the policy of the previous
administration, although the London Assembly (2010) criticised the
plans on air quality grounds and the current administration does
* Corresponding author.
E-mail address: [email protected] (S.R.H. Barrett).
1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.atmosenv.2011.07.012
not plan to increase capacity at Heathrow or Stansted airports
(HM Government, 2010). However, the debate on air quality
impacts of potential expansion has occurred without quantification
of those impacts on human health on a regional scale.
Emitted pollutants resulting from aviation include greenhouse
gases (GHGs) and particulate matter that contribute to forcing of
the climate (Lee et al., 2010) and gases and particulate matter that
are harmful to human health (Barrett et al., 2010). Aircraft engine
emissions include CO, CO2, H2O, SO2, NOx (NO þ NO2), a range of
hydrocarbons (HC), and volatile (sulphate and organic carbon) and
non-volatile (mostly soot) particulate matter (PM). Emitted PM
has an aerodynamic diameter much less than 2.5 mm (PM2.5), with
modal diameter less than 100 nm (Onasch et al., 2009; Petzold
et al., 2005). Non-volatile PM exists at the engine exit plane while
volatile PM nucleates as new particles or condenses on existing
particles in the cooling exhaust plume (Wayson et al., 2009; Onasch
et al., 2009; Petzold et al., 2005). PM2.5 is thought to have adverse
health impacts at concentrations down to pre-industrial levels and
there is epidemiological evidence to show that adverse effects are
associated with both short and long term exposure (WHO, 2006).
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M.E.J. Stettler et al. / Atmospheric Environment 45 (2011) 5415e5424
European Union air quality regulations state that there is no lower
threshold concentration at which PM2.5 is not harmful to human
health (EU, 2008).
Barrett et al. (2010) estimated that w10,000 premature
mortalities per year can be attributable to current aircraft operations at cruise (w80%) and the landing and takeoff cycle (LTO)
(w20%), which when compared to an estimated 0.8 million
premature mortalities globally due to anthropogenic air pollution
(Krzyzanowski and Cohen, 2008), represents less than 1% of this
figure. Ratliff et al. (2009) estimated that there are w160 annual
cases of premature mortality in the United States as a result of
airport operations (aircraft LTO and auxiliary power units (APUs))
attributable to PM2.5 exposure. Previous UK airport emissions
inventories, for example the Project for Sustainable Development
of Heathrow (DfT, 2006), pre-date the current method used to
estimate aircraft engine particulate matter emissions called FOA3.0
(Wayson et al., 2009). Underwood et al. (2001) assessed the risk of
exceedance of NO2 and PM10 limit values in the near vicinity of
regional UK airports (not including London airports), however
the scope of this study did not include a quantification of the level of
exceedance or the subsequent impact on public health. Airport
emissions inventory methodologies (Kim et al., 2009; EMEP/EEA,
2009; ICAO, 2007; IPCC, 2006) rely on reference emission indices
(EIs) e i.e. g of specified pollutant emitted per kg of fuel burnt e and
assumptions on the operational landing and takeoff (LTO) cycle.
However, empirical studies of operational aircraft jet engine emissions (Schäfer et al., 2003; Schürmann et al., 2007; Herndon et al.,
2008, 2009; Wood et al., 2008; Carslaw et al., 2008; Mazaheri
et al., 2009; Timko et al., 2010a) have revealed variability and
discrepancies between observed and certification EIs in the Engine
Emissions Databank (CAA, 2009). For example, Carslaw et al. (2008)
found that the NOx concentration in the engine exhaust plume at
takeoff could vary by up to 41% for aircraft with the same engines,
attributing the variation in emission indices to variation in engine
thrust setting. Timko et al. (2010a) observed that EI(CO) showed
variability of w25%.
constraints, the issue is likely to be raised again in future (Greater
London Authority, 2011). The purpose of this additional analysis is
that future debates or decisions on air quality and other impacts are
based on a more complete and quantitative understanding of
environmental impacts and possible mitigation options. Details
omitted from the main text of this paper are presented in the
Supporting Information (SI).
1.2. Purpose of paper
The 20 airports in the study accounted for 96% of UK air
passengers in 2005 and 87% of scheduled air traffic movements
(ATMs). We develop a 2005 emission inventory that is based
on schedule information from the Official Airline Guide (OAG)
(OAG aviation, 2005). The OAG covers 87% of all (scheduled and
chartered) flights at the 20 airports as a whole when compared to
statistics from the Civil Aviation Authority (CAA, 2005) (see SI); no
correction for un-scheduled flights is made.
This paper details the first step towards quantifying the
environmental impacts of UK airport operations. The objectives of
this paper are to: (i) develop an inventory of emissions from aircraft
LTO operations APUs, and airside support vehicles; (ii) quantify the
scientific and operational uncertainty in the emissions of different
species; and (iii) determine the most important sources of uncertainty. This analysis is limited to these three emissions sources,
where the LTO cycle is defined up to an elevation of 3000 ft AFE.
Although it is now thought non-LTO emissions may impact air
quality on a regional-to-global scale (Barrett et al., 2010), these are
outside of the scope of this study. Yim et al. (forthcoming) e hereafter referred to as Part II e will apply the inventories and uncertainty estimates developed in this paper to quantify the impacts of
UK airports on air quality and human health. Thus, the greatest
weighting in this paper is given to pollutants that impact upon air
quality, including primary PM. However, we also present the total
climate impact of airport operations through the CO2-eq metric
because: (i) this is an area of increasing concern amongst airport
operators (ACI, 2010); and (ii) while uncertainties in cruise emissions have previously been assessed, the likely larger uncertainties
in LTO emissions have not been studied (Barrett et al., 2010).
Additionally, Part II will examine potential mitigation strategies and
assess the impacts of a potential future expansion of London
Heathrow. While airport expansion in London has been ruled out
under the current UK administration, the previous administration
had supported a third runway at Heathrow and due to capacity
2. Methods
This section describes the methods used to estimate emissions at
airports arising from the aircraft LTO operations (2.3), use of APUs
(2.4), and airside support vehicles (2.5). The treatment of uncertainty
is discussed in the following subsection. While the methods are
similar to those used in the National Atmospheric Emissions
Inventory (NAEI) (Watterson et al., 2004), significant distinctions are
the revised PM2.5 methodology (and results), extended consideration of operational assumptions, and assessment of uncertainty in
estimates including attribution of sources of uncertainty.
2.1. Uncertainty
Uncertainties in model inputs are based on reviews of existing
literature and new empirical analysis. In most cases, probability
distributions have been assumed to be triangular with corresponding minimum, modal (nominal) and maximum values, unless
otherwise specified. Using Monte Carlo 2000-member ensembles,
the emissions model outputs (emissions totals for a number of
species) take the form of probability distributions. From these,
summary statistics are derived and reported as results in Section 3.
Variance-based global sensitivity analysis methods are implemented to estimate the contribution of each input parameter to the
total output variance. The sensitivity analysis approach is described
in the SI.
2.2. Schedules
2.3. Aircraft emissions
The LTO cycle is defined as all aircraft activity below a height of
3000 ft (z914 m) above field elevation (AFE). For departures, the
LTO cycle comprises taxiing out from the terminal to runway, hold on
the taxiway, the takeoff roll, initial climb (up to 450 m) and climb-out
to 3000 ft AFE. For arrivals this includes approach to runway from
3000 ft AFE, landing roll and taxi into the terminal (Watterson et al.,
2004; ICAO, 2007). The 3000 ft AFE boundary for the LTO cycle
is dictated by regulatory standards and is an approximation
to a representative atmospheric mixing height (ICAO, 2007). This
approximation is retained for the analysis given the regulatory
framework, however observations suggest the mixing height can
vary between 500 and 2000 m (Davies et al., 2007) with time of day
and meteorological conditions, and the influence of this on emissions estimates is explored in Section 3.2.
Engine fuel flow factors and emission indices for jet and turboprop engines have been obtained from the ICAO Engine Emissions
Databank (CAA, 2009) and the Emissions and Dispersion Modeling
M.E.J. Stettler et al. / Atmospheric Environment 45 (2011) 5415e5424
5417
Table 1
Comparison between ICAO defined LTO cycle and that used within the emissions model developed in this study for the example of an A320 at LHR.
Operation
ICAO default LTO cycle
Phase
Arrival
Approach
Taxi in
Departure
TIM (s)
This study
F00 (%)
240
420
30
7
Taxi out
1140
7
Takeoff
Climb
42
132
100
85
Phase
Mean TIM (s)
TIM uncertainty
F00 (%)
Approach
Landing roll
Reverse thrust
Taxi in
Taxiway acceleration
APU
Taxi out
Taxiway acceleration
Hold
Takeoff
Initial climb
Climb-out
286
46
15
371
10
900
780
10e20
341
29
38
61
10% for all
21e30
4e7
30
4e7
7e17%
e
4e7
7e17%
4e7
75e100
75e100
75e85
System (EDMS) (FAA, 2010), respectively. Engine assignments
from Kim et al. (2005) have been applied, where possible. In other
cases, manufacturer specifications were consulted. A complete list
of aircraft-engine assignments is shown in the SI. Methods for
each aircraft engine emission species are discussed in Sections
2.3.2e2.3.8 and summarised in Table 3.
2.3.1. Times-in-mode
Emissions during a particular phase of the LTO cycle are
proportional to the amount of time spent in that phase of operation
e the ‘time-in-mode’ (TIM). The standard ICAO certification LTO
cycle is generally not representative of operations at airports
(European Commission, 2001; Unique, 2004b; Patterson et al.,
2009). As such, estimates of operation times for different phases
of the LTO cycle are from the NAEI inventory (Watterson et al.,
2004), shown in the SI. These TIMs vary by aircraft size category.
Uncertainty in TIMs is estimated as 10% for airports for which
empirical data existed (LHR, LGW and STN) and 20% for those
where TIMs were estimated using the method described in
Watterson et al. (2004). These are of similar order to variations in
TIMs observed by Patterson et al. (2009) e deviations of 10e20% for
takeoff and climb-out and 15e20% for approach. Thus, the modelled
LTO cycle differs from the standard ICAO LTO as summarised in
Table 1.
2.3.2. Thrust settings and fuel flow
Fuel flow to the engine is approximately linearly proportional to
engine thrust setting (Wey et al., 2006, p. 7), which is defined here
as a percentage of maximum rated thrust (F00). It is likely that
deviations in thrust setting are the main cause of the discrepancies
between in situ measurements of emissions indices for a particular
LTO phase and those tabulated in the ICAO Engine Emissions
Databank corresponding to the default ICAO LTO cycle (Herndon
et al., 2009, 2008; Mazaheri et al., 2009; Carslaw et al., 2008;
Wood et al., 2008; Schürmann et al., 2007; Schäfer et al., 2003).
For instance, by comparing EI(NOx) observed during dedicated
engine tests and in advected plumes from in-use aircraft during taxi
operations, Wood et al. (2008) suggest that the thrust level used in
the real taxi operations can be described by two discrete modes:
Table 2
Reference parameters for the alternative EI(BC) model.
Parameter
EI(NOx)ref
SNMAx,ref
EI(CO)ref
‘ground idle’, with thrust settings resembling 4% instead of the 7%
defined by the default ICAO LTO cycle; and ‘taxiway acceleration’
with thrust settings up to 17%. British Airways operational
reports (British Airways, 2006) confirm that increases in thrust,
to approximately 10% and lasting approximately 10 s, do occur
routinely when an aircraft is required to cross an active runway or
make a sharp turn. Further evidence (King and Waitz, 2005;
Underwood et al., 2004; Patterson et al., 2009) has been consulted to represent operational variability in thrust settings at other
phases of the LTO model, which are summarised in Table 1.
Commercial aircraft routinely make use of engine bleed flow
to support air-conditioning and provide airframe power service,
requiring a proportion of the engine’s power output (Baughcum
et al., 1996; Herndon et al., 2009). Subsequently, fuel flow to the
engine is increased for a given thrust output and correction factors
suggested by Baughcum et al. (1996) have been applied. Engine
ageing can also affect engine fuel flow and emissions by reducing
the efficiency of the engine, with estimates of increased fuel use
ranging between 3 and 10% (Curran, 2006; Lukachko and Waitz,
1997). This, along with empirical evidence from Wey et al. (2006),
suggests a suitable uncertainty range of 10% for the ICAO fuel flow
indices.
We note that correlations in aircraft flight performance variables
(such as takeoff time and takeoff thrust) have not been accounted
for here. These would be dependent on operational decisions by
pilots as well as aircraft performance characteristics.
2.3.3. SOx emissions
Sulphur emissions are proportional to fuel burn and depend on
the fuel sulphur content (FSC), which varies by geographic region
and fluctuates over time (Hileman et al., 2010). In the UK, the mean
FSC has fluctuated between 360 ppm and 640 ppm over the past
Table 3
Summary of uncertainty estimates for aircraft engine emission species.
Species
Determining factor(s)
Parametric uncertainty
Fuel burn
Sulphur oxides
Fuel flow rate
FSC
10%
500 300 ppm
2% nominal [0.5%, 5%]
30% (mode ¼ 10%)
90%
60%
Uncertainty factors:
7% F00: [0.04, 37]
30% F00: [0.02, 2.89]
85, 100% F00: [0.27, 3.29]
[1, 40] mg kg1 fuel
(mode ¼ 20 mg kg1 fuel)
[3148, 3173] g kg1 fuel
3
NOx
HC
CO
Black carbon
EI(NOx)
EI(HC)
EI(CO)
EI(BC)
Organic carbon
EI(OCIC)
CO2
EI(CO2)complete
% F00
7
30
85
100
4
10
20
7
10
5
20
10
1
25
10
1
5418
M.E.J. Stettler et al. / Atmospheric Environment 45 (2011) 5415e5424
twenty years; in 2007, the mean was 483 ppm with a standard
deviation of 600 ppm with skew towards lower values (Hileman
et al., 2010; Rickard, 2008). The fuel sulphur content is estimated
as 500 300 ppm for the current study.
While the majority of elemental sulphur in jet fuel is emitted as
SIV (SO2), some is emitted as SVI (SO3) at the engine exhaust plane.
SO3 is rapidly converted to H2SO4, which is thermodynamically
favoured to exist in liquid phase thereby resulting in volatile
particulate matter (PM) emissions. Estimates for the SIV to SVI
conversion efficiency, 3, range between 0.08 0.01% (Timko et al.,
2010b) and >10% (Schumann et al., 2002). However, the higher
estimates are likely a result of measurement errors (Katragkou et al.,
2004), or the (now known to be erroneous) interpretation of volatile
organic material as SVI in earlier studies before organic PM emissions
from aircraft were discovered. The lower bound only includes
sulphate that coated soot particles and did not include nucleation or
growth mode particles (Timko et al., 2010b). Another recent value of
0.13% (Onasch et al., 2009) included only condensed phase SVI.
However, modelling the microphysical processes involved suggested
this value is equivalent to a conversion efficiency of 1e2% (Wong
et al., 2008). Other estimates suggest a nominal value of 2% is
suitable: 2 0.8% (Kiendler and Arnold, 2002); 2.3 1% (Sorokin
et al., 2004); 2.3 1.2% (Katragkou et al., 2004). Curtius et al.
(2002) estimate 3 to lie between 1.3 and 5.1% while Petzold et al.
(2005) estimate the value to be within the range 2.5e6% and
Kinsey et al. (2011) suggest a median value of 2.4%. Thus, a nominal
value of 2% with a range 0.5e5% has been used to account for the
range of estimates in the literature.
2.3.4. NOx emissions
The NOx emissions indices for all aircraft engines are positively
correlated to thrust setting, to which fuel flow is (approximately)
directly proportional. The Boeing Fuel Flow Method 2 (BFFM2)
(Baughcum et al. 1996) prescribes linear regression on a logelog
plot of EI(NOx) against fuel flow to obtain EI(NOx) for intermediate
thrust settings between certification data points. Comparing
observed EIs from Timko et al. (2010a) for six engines with the
corresponding EIs in the engine databank (shown in SI) suggests
that EI(NOx) is w10% lower than the ICAO Engine Emissions
Databank figure, as reported by Wood et al. (2008) and Timko et al.
(2010a), and that the uncertainty bounds are approximately 30%.
ICAO certification measures the total NOx emissions
(NOx ¼ NO þ NO2). However the proportions of each component
vary by engine and with thrust setting (Timko et al., 2010a). For most
engines, NO is the predominant species at high thrust conditions
(thrust settings of 65e100%) with NO2/NOx less than 10%. Analysis of
APEX data for the CFM56-2C1 (Wey et al., 2006) engine suggests
a suitable range to be 5e10%. At low power NO2 becomes dominant,
with NO2/NOx ranging between 75 and 98% at 4% thrust (Timko et al.,
2010a; Wood et al., 2008; Wormhoudt et al., 2007). Thus this is the
assumed range for the taxi mode. For approach, analysis of the
APEX data suggests that NO2/NOx z 12%, while charts in Timko et al.
(2010a) and Wood et al. (2008) indicate that NO2/NOx lies within the
range 12e20%.
Another important emission is nitrous acid (HONO), a precursor
to OH in the atmosphere and a component of total NOY emitted
(NOy ¼ NO þ NO2 þ HONO þ HNO3 þ organic nitrates þ .). Wood
et al. (2008) found that EI(NOx) and EI(NOY) agree within the
experimental uncertainty (at most 6% discrepancy) indicating that
HONO is likely included in the certification EI(NOx). Emissions of
HONO are variable with respect to thrust setting and engine model.
According to Wood et al. (2008), measured HONO/NOy ranges
between 2e7% and 0.5e1% for low and high (65e100%) thrust
settings respectively. Given that HONO/NOy is not known for the
majority of engines, the 2e7% range has been applied to taxi and
approach, and the 0.5e1% range to climb-out and takeoff. The
remaining NOx is assumed to be NO.
2.3.5. HC and CO emissions
EI(HC) and EI(CO) decrease with increasing thrust. Therefore,
the immediate effect of applying an idle thrust distribution below
the ICAO specified 7% is to increase HC and CO emissions compared
to previous approaches. BFFM2 (Baughcum et al., 1996) is employed
to find EI(HC) and EI(CO) at intermediate thrust settings. A bilinear
fit on a logelog plot of databank EI against fuel flow, consisting of
a line between the two lower power setting points and a horizontal
bisection of the two higher power setting points, has been implemented for each engine. Baughcum et al. (1996) recognised that
some emissions data sets did not fit the scheme described above;
these cases have been overcome by implementing solutions
described by Kim et al. (2005). Comparing observed EIs from Timko
et al. (2010a) with certification EIs (shown in SI) indicates that the
uncertainty in EI(CO) is 60%. EI(HC) (derived from EI(HCHO)) is
more uncertain and is represented by a uniform distribution with
bounds at 90%. HC emissions from aircraft engines are speciated
into the constituent hydrocarbons according to U.S. EPA (2009).
2.3.6. Non-volatile PM emissions
The primary form of non-volatile PM emitted by jet engines is
soot (Timko et al., 2010b), which is primarily black carbon (BC)
(Popovicheva et al., 2004; Petzold et al., 1999). Emissions indices
can be estimated using the First-Order Approximation 3.0 methodology (FAO3) (Wayson et al., 2009). This calculation is dependent
upon the mode-specific smoke number (SN) recorded in the engine
databank. The SN is the current regulatory measure of emissions
visibility and a dimensionless quantity related to the darkening of
particle-loaded filters due to deposited particulate matter, rated on
a scale from 0 to 100 (Sevcenco et al., 2009).
Published measurement data from the Aircraft Particle Emission
eXperiment (APEX1-3) (Wey et al., 2006; Timko et al., 2010b), the
Delta-Atlanta Hartsfield study (Lobo et al., 2007) and Agrawal et al.
(2008) enables the performance of FOA3 e intended to be a firstorder approximation e to be assessed in relation to the accuracy
of estimates of EI(BC) compared to observed values over a range of
different engine types and engine operating modes. These studies
have measured EI(BC) for a range of 12 separate engine models,
which account for 30% of scheduled ATMs at the twenty airports in
this study (see SI). Common thrust settings at which measurements
are taken amongst these studies are 7%, 30% and 85% F00. Fig. 1
shows the comparison between estimated EIs obtained by implementing FOA3 using mode-specific SNs and the measured EIs. Note,
while Wayson et al. (2009) recommend the use of mode-specific
SNs, some other implementations of FOA3 may use the maximum
SN where not all mode-specific SNs are reported. The method
outlined in Calvert (2006) has been used to account for the one
engine with incomplete SN data in the engine emissions databank.
Fig. 1 indicates that a significant number of data points lie on the
horizontal axis (i.e. have very small/zero EI(BC)FOA3 but non-zero
EI(BC)Measurement) and that there is an apparent systematic underestimation of EI(BC) by FOA3 at higher thrust e indeed 40% of
FOA3 estimated are greater than an order of magnitude smaller
than the mean measurement values. A possible reason for these
discrepancies is that the measurement of SN could be susceptible
to particle losses for diameters smaller than 20 nm (Sevcenco et al.,
2009), such that the assumed correlation between SN and exhaust
soot concentration index is inaccurate and not able to be applied
across different engine technologies. More discussion of the FOA3
discrepancy is in the SI.
Given the observed discrepancies, an alternative method of estimating EI(BC) is proposed using parameters available in the engine
M.E.J. Stettler et al. / Atmospheric Environment 45 (2011) 5415e5424
FOA3 7%
FOA3 30%
FOA3 85%
New 7%
New 30%
New 85%
2
10
1
10
0
10
−1
10
−1
10
0
10
1
10
2
10
3
10
Measured EI(BC) (mg/kg fuel)
Fig. 1. Performance of FOA3 (hollow) and the proposed method (filled) at estimating
EI(BC). Note that the zero estimated EIs for FOA3 are due to zero recorded SNs in the
ICAO Engine Emissions Databank.
emissions databank (CAA, 2009). Döpelheuer and Lecht (1998)
previously proposed a method of correcting engine exhaust soot
concentration given a reference value, equivalence ratio, combustor
inlet pressure and combustion flame temperature. Given that these
parameters are proprietary, an alternative model is proposed using
the EI(NOx), EI(CO), SNMAX and the engine Pressure Ratio (PR):
EIðNOx Þ
EIðBCÞ½mg=kg fuel ¼ 4:57$
EIðNOx Þref
!1:27
EIðCOÞ
$
EIðCOÞref
!0:4
where (F/F00) is the thrust setting as a fraction of full thrust, the
multiplicative constant and exponents have been obtained by
regression using the aforementioned measurement data and arbitrary reference values denoted by the subscript ‘ref’ are detailed in
Table 2.
The model is implemented for each of the four standard
thrust settings, EI(BC) for intermediate thrust settings are obtained
by linear interpolation. Fig. 1 also shows the performance of this
alternative model compared to FOA3. Uncertainties are estimated
from the minimum and maximum difference between the
estimated and measured EI(BC) and are shown in Table 3. The
uncertainty range for EI(BC) at 85% is smaller than at lower thrust
(a factor of 3), which is significant given that EI(BC) and fuel flow
are generally higher at higher thrust. This compares to maximum
discrepancies of greater than an order of magnitude using FOA3.
The R2 for FOA3 is 0.01 (meaning that the sum of residuals
between the model and the observations is larger than the
residuals between the mean of the observations and the observations) and for the proposed model the R2 is 0.58. This will be
examined further in future work (Stettler et al., forthcoming). For
the 12 engines used to derive this model, measured EIs were used
in the emissions inventory developed in this paper with the
proposed model filling-in for aircraft where measurements are not
available.
2.3.7. Volatile organic PM emissions
Organic carbon (OC) PM emission indices, EI(OC), are typically
estimated using FAO3 (Wayson et al., 2009; Ratliff et al., 2009),
which is based upon an empirical relationship between the EI(HC)
and EI(OC) as measured for the CFM56-2-C5 engine. However,
emissions measurement data from Timko et al. (2010b) (7 engines),
the Delta Atlanta Hartsfield (4 engines) and APEX 1 (1 engine)
measurement campaigns (supplied by Missouri S&T) suggest
that this relationship does not hold for all engine models: Fig. 2
demonstrates the difference between measured EI(OC) and those
calculated using FOA3. In 40% of cases, the discrepancy is greater
than an order of magnitude. Timko et al. (2010b) identified that OC
emissions comprise at least two primary exhaust gas components,
products of incomplete combustion (OCIC) and lubrication oil
(OCLO), and that EI(OCIC) lies with the range of 0.2e5 mg kg1 fuel
for all engines at 7, 30 and 85% F00 once OCLO is accounted for, with
no consistent trend with thrust setting. Lubrication oil emissions
are highly dependent on engine technology and Timko et al.
(2010b) estimate that the contribution of OCLO to the total mass
of OC generally lies within the range 10e20% for low thrust and 50%
for high thrust settings. Yu et al. (2010) suggest EI(OCLO) lies within
the range of 2e12 mg kg1 fuel at idle and increases with engine
thrust setting, suggesting a higher value at idle than that estimated
by Timko et al. (2010b). In other studies, Kinsey et al. (2011) suggest
that EI(OC) lies within the range 37e83 mg kg1 fuel and Agrawal
et al. (2008) measure EI(OC) within the range 4e62 mg kg1 fuel,
without discrimination between OCIC and OCLO. Thus, the EI(OCIC)
is estimated to lie in the range 1e40 mg kg1 fuel with EI(OCLO)
making up 10e20% and 40e60% of EI(OC) at low and high thrust
settings, respectively. These discrepancies highlight the large
uncertainty in OC emissions currently and the need for standardisation of measurement techniques, equipment and reporting
SNMAX
$
SNMAX;ref
Estimated EI(OC) (mg/kg fuel)
Estimated EI(BC) (mg/kg fuel)
3
10
5419
!0:2 F 1:25
$ PR$
F00
1
10
0
10
7%
30%
85%
−1
10
−1
10
0
10
1
10
Measured EI(OC) (mg/kg fuel)
Fig. 2. Performance of FOA3 at estimating organic PM emission indices at 7%, 30% and
85% F00.
5420
M.E.J. Stettler et al. / Atmospheric Environment 45 (2011) 5415e5424
before any large-scale measurement campaign of EI(OC) for regulatory purposes is embarked upon.
2.3.8. CO2 emissions
Under complete combustion EI(CO2)complete depends on the
ratio of carbon to hydrogen, for aviation fuel it lies within the
interval [3148, 3173] (Hileman et al., 2010). At low thrust, incomplete combustion leads to a decrease in EI(CO2) relative to assuming
complete combustion as EI(CO) and EI(HC) increase non-linearly
(Wey et al., 2006). Thus, the following implementation of a carbon
balance will be most significant at low thrust settings:
MWðCO2 Þ
EIðCO2 Þ ¼ EIðCO2 Þcomplete EIðCOÞ$
MWðCOÞ
MWðCO2 Þ$NHC
EIðHCÞ$
MWðHCÞ
MWðCO2 Þ$NC4 H7
EIðOCIC Þ$
MWðC4 H7 Þ
MWðCO2 Þ
EIðBCÞ$CBC $
MWðCÞ
where EI(X), MW(X), NX and CX are the emissions index, molecular
weight, number of carbon atoms and carbon content by weight of
species X. MW(HC) (¼82) and NHC (¼5) are weighted averages of the
component species given by U.S. EPA (2009). For OC, Timko et al.
(2010b) showed that a typical product of incomplete combustion
of fuel is C4H7. Thus, MW(OC) is taken as 55, and NOC as 2. Aircraft
soot, while mainly BC, is composed of two distinct fractions: a main
fraction and a fraction of impurities; however the ratio of these
fractions has not been quantified (Demirdjian et al., 2007). The
carbon content of the main fraction is w98 wt%, and w40e60 wt%
for the fraction of impurities (Popovicheva et al., 2004; Demirdjian
et al., 2007). Given the unknown ratio, the total carbon content of
BC is estimated to lie in the range 90e98% with a nominal value of
95%. Thus, the uncertainty of EI(CO2) is a function of the uncertainty
in the carbon to hydrogen ratio and the uncertainties associated
with all other carbon containing species.
2.4. APU emissions
APUs are gas turbines used to generate electricity while the
main engines are off and to provide bleed air to start the main
engines (ICAO, 2007). Only one mode of operation has been
assumed due to the available data. TIMs, fuel flows and emissions
indices for NOx, HC, CO have been taken from Watterson et al.
(2004) and are shown in the SI. Values for EI(NOx) and EI(CO)
were within the same order of magnitude with values derived from
measurement of APU exhaust plumes (Schäfer et al., 2003).
The NOx breakdown was assumed to be 90:9:1 for NO:NO2:HONO,
consistent with measurements (Gerstle et al., 1999) and similar to
an aircraft engine at high thrust setting. SOx, SVI and CO2 emissions
were derived using the same methods as described above for
aircraft engines. PM EIs measured by Gerstle et al. (1999) for two
APU models suggest the total PM mass index lies in the range
0.4e0.8 g kg1 fuel. ICAO (2007) proposes a model to estimate
EI(PM10) as a function of EI(NOx) applicable to a larger set of APU
models. However, when this model is applied to two APU models
tested by Gerstle et al. (1999), estimated EIs are a factor of 10 lower
than those observed, indicating an upper uncertainty bound. APU
PM is assumed to be similar in nature to aircraft engine PM, and
thus PM2.5. Furthermore, it is estimated that once SVI emissions are
subtracted, the PM is 95% soot by mass nominally (85e99%) given
observations by Timko et al. (2010b) of other gas turbine engines at
high thrust setting. The remaining mass is assigned as OC. All flights
are assumed to have made use of the APU, and airport stand
characteristics have not been accounted for. The parametric
uncertainties for APU emissions are summarised in Table 4.
2.5. Airside support equipment emissions
Emissions arising from airside support equipment (also called
ground support equipment (GSE)) have been derived using estimates of emissions factors (EFs) from Zürich Airport (ICAO, 2007;
Unique, 2004a) as nominal values. Technology-specific default GSE
emissions factors from EDMS combined with non-road emissions
factors (EEA, 2009) provide an upper bound. These values are shown
in the SI. Discrepancies between these two methods vary with
emissions species, in the maximum case PM emissions per LTO cycle
calculated using default assignments are greater by a factor of nine
compared to those estimated from operations at Zürich. Airside
operators have used ultra-low sulphur diesel at LHR since 2002
(Underwood et al., 2004) and this is assumed to be consistent across
all airports in the study. The SIV to SVI conversion efficiency for diesel
engines is reported to be approximately 2% (Truex et al., 1980).
Less than 1% of diesel engine measured NOy (as NO2) emissions are
HONO, while around 90% are NO and the remaining 9% are NO2
(Kurtenbach et al., 2001). PM emissions from heavy-duty diesel
engines are roughly half BC and half OC by mass (Miguel et al., 1998;
Rogge et al., 1993).
3. Results and discussion
3.1. Emissions
Emissions from UK airports in 2005 are detailed in Table 5.
The median is presented with uncertainty bounds given by the 5th
and 95th percentiles. In terms of primary PM, aircraft BC is the
largest component of emitted PM2.5 (BC þ OC þ SO4), accounting for
47%. Aircraft LTOs are also the dominant source for sulphates (86%),
however for OC the GSE contribution is over 66%. The GSE contribution to BC emissions is 20%, such that GSE are responsible for 28%
of the total mass of emitted particulate matter. Low FSC of fuel used
airside means that GSE emissions of SO2 and SO4 are negligible
compared to those from aircraft and APUs. APUs contribute 6% to
total PM2.5 emissions. The rationale for aggregating these distinct
PM species into a total PM2.5 category is that there is no distinction
in the concentrationeresponse functions used to derive health
impacts in Part II (U.S. EPA, 2011). Similarly, PM from different
airport sources is likely to have different size distributions and while
there is evidence to suggest that the toxicity of PM increases as its
size decreases, the epidemiological evidence base does not yet allow
discrimination of PM size fractions below 2.5 mm (U.S. EPA, 2011).
Emissions of CO2, SO2, NOx, CO and HC are also dominated by the
aircraft LTO. The majority of NOy (84%) is emitted as NO, while 14%
and 1.5% is emitted at NO2 and HONO respectively. Global Warming
Potentials (GWPs) for a 100-year time horizon with uncertainty
ranges from Fuglestvedt et al. (2010) are used to aggregate climate
Table 4
Summary of uncertainties applied to APU emissions.
Species
Determining factor(s)
Parametric uncertainty
Fuel flow
Sulphur
derivatives (SO2, SIV)
NOx
HC
CO
PM10
CO2
Fuel flow rate [kg s1]
FSC [ppm]
3 [%]
EI(NOx) [g kg1 fuel]
EI(HC) [g kg1 fuel]
EI(CO) [g kg1 fuel]
EI(PM10) [g kg1 fuel]
EI(CO2) [g kg1 fuel]
10%
500 300 ppm
[0.5%, 5%]
30%
60%
90%
Factor 10
[3148, 3173] g kg1 fuel
M.E.J. Stettler et al. / Atmospheric Environment 45 (2011) 5415e5424
5421
Table 5
UK airport emissions by source with (5the95th percentile) uncertainty ranges as percentage of the median.
Species
Units
Source
Total
Aircraft LTO
CO2
NOx
NO
NO2
HONO
HC
CO
SO2
SO4
OC
BC
Total PM2.5
CO2-eq
kg
kg
kg
kg
kg
kg
kg
kg
kg
kg
kg
kg
kg
NO2
NO2
NO2
NO2
CH4
2.02
8.19
6.74
1.28
1.38
1.57
1.04
6.26
2.29
2.00
1.45
1.91
2.08
109
106
106
106
105
106
107
105
104
104
105
105
109
[14
[26
[27
[33
[38
[66
[47
[29
[56
[66
[49
[41
[13
APU
to
to
to
to
to
to
to
to
to
to
to
to
to
þ14]
þ36]
þ36]
þ50]
þ55]
þ173]
þ87]
þ33]
þ89]
þ72]
þ75]
þ58]
þ13]
3.23
7.16
6.57
5.30
5.36
7.31
9.50
10.00
3.62
9.34
1.38
1.86
3.27
NAEI (2008)
GSE
108
105
105
104
103
104
105
104
103
102
104
104
108
[10
[22
[22
[28
[31
[64
[41
[29
[56
[85
[82
[64
[11
to
to
to
to
to
to
to
to
to
to
to
to
to
þ11]
þ24]
þ24]
þ37]
þ36]
þ62]
þ45]
þ31]
þ86]
þ240]
þ156]
þ123]
þ11]
6.42
1.17
1.05
1.05
1.17
1.24
3.11
9.86
3.74
4.26
4.26
8.51
8.14
107 [63 to þ113]
106 [59 to þ105]
106 [59 to þ105]
105 [59 to þ105]
104 [59 to þ105]
105 [61 to þ108]
105 [46 to þ91]
100 [44 to þ57]
101 [54 to þ102]
104 [63 to þ117]
104 [63 to þ117]
104 [63 to þ117]
107 [56 to þ89]
2.42
1.02
8.56
1.46
1.56
1.77
1.17
7.27
2.65
6.42
2.11
3.07
2.49
109
107
106
106
105
106
107
105
104
104
105
105
109
[13
[23
[24
[30
[35
[59
[42
[29
[56
[49
[41
[36
[12
to
to
to
to
to
to
to
to
to
to
to
to
to
þ12]
þ29]
þ30]
þ45]
þ49]
þ155]
þ77]
þ32]
þ89]
þ78]
þ54]
þ45]
þ12]
1.27 107
1.44 106a
3.72 107
8.33 105
7.38 104
Derived from VOC using a factor of (1.15)1.
a
forcing species into an estimate of CO2-eq emissions (see SI).
The largest contribution to total CO2-eq emissions is from CO2
emitted by aircraft, accounting for 81% and aircraft contribute to
84% of the CO2-eq emitted by UK airports.
These estimates can be compared with estimates from the NAEI
for the aviation sector in 2008. The NOx, HC and SO2 estimates agree
within the uncertainty bounds. However, the NAEI CO and PM2.5
estimates are over a factor of 3 greater and 4 less than our estimates,
respectively. The discrepancy in NOx estimates is likely due to
reduced thrust at takeoff being applied to all airports instead of just
LHR and LGW as in the NAEI methodology, while the largest
contributor to our higher PM2.5 estimates is the new BC emissions
methodology applied. CO emissions differ due to different thrust
setting assumptions and the emissions index interpolation/extrapolation scheme used.
Individual airports can be compared in terms of their emissions
per service unit. Fig. 3 shows estimated total PM2.5 and CO2-eq
and emissions per air traffic movement (ATM) and per passenger
(PAX) for the 20 airports in the study. Total PM2.5 emissions per ATM
are highest at LHR [157 g ATM1] however they are highest at LCY
[1.8 g PAX1] on a per passenger basis. CO2-eq emissions per ATM
and per passenger (PAX) are highest at LHR [2500 kg ATM1;
17 kg PAX1] and LGW [1700 kg ATM1; 15 kg PAX1], the two
busiest airports by ATMs and passengers in the UK.
3.2. LTO emissions
Fig. 4 shows the relative proportion of emissions attributable to
each phase of the LTO for various emissions species. It is clear that
emissions of NOx and PM2.5 arise primarily from high thrust modes,
50
a
40
30
20
10
0
50
b
40
30
per ATM
per PAX
2
Total PM
1
100
0
Total PM
2.5
200
per PAX (g)
3
300
2.5
per ATM (g)
a
0
Proportion of LTO emissions (%)
20
10
0
50
c
40
30
20
10
0
50
d
40
LH
R
LG
W
M
AN
ST
N
ED
BH I
X
G
LA
LT
N
LC
Y
AB
Z
BR
S
N
C
SO L
U
LP
L
BF
S
BH
D
LB
A
EM
A
PI
K
C
W
L
30
20
R
ol
ev
l
.T
hr
us
t
Ta
Ta
xi
In
xi
Ac
c.
Ar
r.
R
La
nd
in
g
ro
Ap
p
C
lim
b
O
ac
h
ut
b
ff
lim
C
al
iti
c.
D
xi
O
Ac
Ta
xi
Ta
LH
R
LG
W
M
AN
ST
N
ED
BH I
X
G
LA
LT
N
LC
Y
AB
Z
BR
S
N
C
SO L
U
LP
L
BF
S
BH
D
LB
A
EM
A
PI
K
C
W
L
Fig. 3. (a) Total PM2.5 and (b) CO2-eq per ATM and PAX for 20 UK airports in order of
descending number of ATMs in 2005.
In
0
0
d
0
10
−o
5
ke
1000
20
Ta
10
30
ut
2000
e
Sulfate
OC
BC
40
.
15
0
50
ol
per ATM
per PAX
ep
3000
10
20
H
4000
CO2−eq per PAX (kg)
2
CO −eq per ATM (kg)
b
Fig. 4. Relative proportion of emissions attributable to each phase of the LTO for (a)
CO2, (b) NOx, (c) CO, (d) HC and (e) PM2.5.
5422
M.E.J. Stettler et al. / Atmospheric Environment 45 (2011) 5415e5424
conversely CO and HC emissions arise from low thrust modes
reflecting the non-linear profile of EIs with thrust settings described
in Section 2.3. Mass of CO2 emitted is proportional to fuel flow which
is approximately proportional to thrust setting and consequently the
approach mode with greatest TIM is responsible for more CO2
emissions than any other LTO mode. These findings also have
implications for emissions mitigation potential at airports as some
strategies may favour some species but not others. For example,
introducing single-engine taxiing is not likely to significantly reduce
NOx or PM2.5 emissions, but would reduce CO, HC and CO2 emissions.
The efficacy of this and other mitigation strategies is evaluated in
Part II. As described above, the atmospheric mixing height is
observed to vary between 500 and 2000 m (Davies et al., 2007). For
these mixing heights [500 m, 2000 m] total emissions of CO2 and SOx
are changed by [28%, þ55%], NOx emissions by [36%, þ72%] and
total PM2.5 emissions are changed by [15%, þ99%]. The changes to
CO and HC emissions are less than 5%. This analysis suggests that the
uncertainty in CO2, SOx, NOx and PM2.5 emissions resulting from
mixing height variations are larger than the uncertainty resulting
from the other parameters combined.
3.3. Uncertainty analysis
Histograms depicting the probability density for UK airport
emissions of CO2, CO2-eq, PM2.5 and HC are shown in the SI.
3.3.1. Model sensitivity
The contribution from different model inputs to the total
variance of the output emissions estimates are estimated using the
Sobol’ method of global sensitivity analysis. Main-effect sensitivities, Si, capture the first-order contribution of each input factor to
the variance of the model output, such that the inputs can be ranked
in order of importance (Saltelli et al., 2008; Allaire and Willcox,
EF(GSE PM)
EI(BC) 85%
EI(BC) 7%
Thrust Take−off
EI(APU PM)
ε
A/C FF
a
EI(OC)
0
0.1
0.2
0.3
Total PM 2.5 Si
0.4
0.5
Thrust Taxi
A/C FF
Thrust Take−off
Thrust Approach
EF(GSE CO2)
TIM Taxi
TIM Approach
Table 6
Total UK aircraft particulate matter emissions estimated using the proposed alternative methodology and FOA3.
Species
New method
FOA3
Aircraft OC (kg)
Aircraft BC (kg)
Total PM2.5 (kg)
2.00 104 [66 to þ72]
1.45 105 [49 to þ75]
1.91 105 [41 to þ58]
1.39 104 [65 to þ127]
1.83 104 [16 to þ15]
5.67 104 [31 to þ40]
2010). These Si have been estimated for all model outputs, with
the most important factors for total PM2.5 and CO2-eq presented in
Fig. 5. In the case of total PM2.5, the most important model inputs are
the GSE EF(PM) (Si ¼ 0.39) and the uncertainty in aircraft EI(BC)
at 85% F00 (Si ¼ 0.38) reflecting the high degree of uncertainty in
emissions arising from airside support vehicles and aircraft BC and
their significant contribution to total PM2.5 emissions. For CO2-eq,
the most important factors are related to aircraft CO2 emissions
(aircraft fuel flow indices, thrust settings and LTO TIMs) reflecting
the dominant contribution of aircraft to total CO2-eq emissions
and in the case of the taxi thrust setting, the influence of the carbon
balance at low thrust due to EI(CO) and EI(HC). Indeed, aircraft HC
emissions have the largest uncertainty and the range is positively
skewed reflecting the non-linear increase in EI at low thrust e the
aircraft taxi thrust setting (Si ¼ 0.54) and EI(HC) (Si ¼ 0.36) are the
most important uncertain parameters. Aircraft CO emissions are also
positively skewed for the same reasons, with the similar influence of
aircraft taxi thrust setting (Si ¼ 0.53) and EI(CO) (Si ¼ 0.41). Another
effect of the large uncertainties in CO and HC is that on the relative
sizes of the LTO CO2 and CO2-eq uncertainty ranges: the CO2
uncertainty range is slightly larger (14%) compared to that of
EI(CO2-eq) (13%) even though uncertainty is introduced through
the GWPs. As EI(CO2) depends on other species through the carbon
balance, it is high when EI(CO) and EI(HC) are low, and vice versa.
However, CO2-eq is an aggregation of EI(CO2), EI(CO) and EI(HC) e
thus the uncertainty is reduced. The evidence for taxi thrust setting
<7% F00 and the importance of taxi thrust setting to CO and HC
emissions estimates, where EIs are modelled using BFFM2, suggests
that future measurement campaigns should explicitly measure and
report emissions at w4% thrust. The most important model inputs
with regards to aircraft NOx (largest source of airport NOx) are
EI(NOx) (Si ¼ 0.49) and takeoff thrust setting (Si ¼ 0.27). For aircraft
SO4, 3 is the most important model input (Si ¼ 0.75), however the
importance of this factor to PM2.5 is reduced. All GSE emissions
estimates are positively skewed as a result of the input EF uncertainties. Total-effect sensitivities for CO2-eq and PM2.5 are shown in
the SI.
3.3.2. Sensitivity to PM models
Estimates derived from FOA3 and the methodology outlined
above for BC and OC particulate matter are shown in Table 6. For BC,
the proposed alternative model leads to a factor 8 increase in
estimated emissions over FOA3. For OC the methodology applied in
this study leads to a 44% increase in the emissions estimate over
FOA3. Sulphate emission estimates are unchanged, leading to an
overall factor 3.4 increase in total PM2.5 emissions. The uncertainty
in the FOA3 estimates results from uncertainty the other model
parameters.
4. Conclusions
b
EI(BC) 85%
0
0.1
0.2
0.3
0.4
0.5
CO2−eq Si
Fig. 5. Main-effect sensitivity indices (Si) for (a) total PM2.5 and (b) CO2-eq emissions.
A methodology to calculate gaseous and particulate matter
emissions of air quality- and climate-concern from UK airports
(including airside equipment and aircraft in the LTO cycle) has been
developed and applied, with uncertainties explicitly estimated using
a Monte Carlo approach. Sources of uncertainty considered span
M.E.J. Stettler et al. / Atmospheric Environment 45 (2011) 5415e5424
operational (e.g. times-in-mode) and scientific (e.g. the SIV to SVI
conversion efficiency) factors, as well as structural modelling uncertainties (e.g. different PM emissions estimation methodologies).
We estimate that in 2005, UK airports emitted 10.2 Gg [23
to þ29%] of NOx, 0.73 Gg [29 to þ32%] of SO2, 11.7 Gg [42 to þ77%]
of CO, 1.8 Gg [59 to þ155%] of HC, 2.4 Tg [13 to þ12%] of CO2, and
0.31 Gg [36 to þ45%] of PM2.5, the latter estimated using revised
methods. This translates to 2.5 Tg [12 to þ12%] CO2-eq using Global
Warming Potentials for a 100-year time horizon. CO2-eq emissions
per ATM and per passenger are highest at LHR [2500 kg ATM1;
17 kg PAX1] and LGW [1700 kg ATM1; 15 kg PAX1]. Model
sensitivity analysis showed that the model inputs with greatest
contribution to the output variance of UK airport CO2-eq are related
to aircraft CO2 emissions (fuel flow indices, thrust settings and LTO
TIMs). For emissions of PM2.5 from UK airports, two most important
input uncertainties are the PM emissions factor for airside vehicles
and aircraft EI(BC) derived from the proposed alternative model
at 85% F00. Such findings may help prioritise future emissions
measurement work.
In Part II, emissions described here will be used in a regional
chemistry-transport model, with nested dispersion modelling, to
estimate the impact of UK airports on air quality and public health
(in terms of premature mortalities). In addition, a scenario in which
LHR is expanded will be assessed, along with possible mitigation
strategies.
Acknowledgements
Funding was from UK EPSRC as part of the Energy Efficient
Cities Initiative (www.eeci.cam.ac.uk) and the Airport Environmental Investment Toolkit. The authors are grateful to the Center of
Excellence for Aerospace Particulate Emissions Reduction Research,
Missouri University of Science and Technology (Missouri S&T), Rolla,
Missouri for supplying aircraft emissions data.
Appendix. Supporting information
Supporting information associated with this article can be
found, in the online version, at doi:10.1016/j.atmosenv.2011.07.012.
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