AERO2k Global Aviation Emissions Inventories for 2002 and 2025
AERO2k Global Aviation Emissions Inventories for 2002 and 2025
AERO2k Global Aviation Emissions Inventories for 2002 and 2025
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CONDITIONS OF SUPPLY<br />
This document is supplied by QinetiQ <strong>for</strong> Dr-Ing Dietrich Knoerzer, European<br />
Commission under Contract No. G4RD-CT-2000-00382<br />
<strong>AERO2k</strong> <strong>Global</strong> <strong>Aviation</strong><br />
<strong>Emissions</strong> <strong>Inventories</strong> <strong>for</strong> <strong>2002</strong><br />
<strong>and</strong> <strong>2025</strong><br />
C J Eyers, P Norman, J Middel, M Plohr, S Michot, K Atkinson,<br />
R A Christou<br />
QINETIQ/04/01113<br />
December 2004<br />
"Any person finding this document should h<strong>and</strong> it to a police station or post it to the<br />
Group Security Manager, QinetiQ Limited, Cody Technology Park, Farnborough,<br />
Hampshire GU14 0LX, UK with particulars of how <strong>and</strong> where found.<br />
Requests <strong>for</strong> wider use or release must be sought from:<br />
QinetiQ Ltd<br />
Cody Technology Park<br />
Farnborough<br />
Hampshire<br />
GU14 0LX<br />
UK<br />
Copyright © QinetiQ ltd 2004<br />
QINETIQ/04/01113
Administration page<br />
Customer In<strong>for</strong>mation<br />
Customer reference number GRD1-2000-25042<br />
Project title AERO2K<br />
Customer Organisation European Commission<br />
Customer contact Dr-Ing Dietrich Knoerzer<br />
Contract number G4RD-CT-2000-00382<br />
Milestone number Deliverable D14<br />
Date due 31 December 2004<br />
Principal author<br />
C J Eyers 0044 1252 392269<br />
QinetiQ, Cody Technology Park,<br />
Farnborough, Hampshire, GU14 0LX<br />
Additional authors<br />
P Norman<br />
QinetiQ, Cody Technology Park,<br />
Farnborough, Hampshire, GU14 0LX<br />
cjeyers@qinetiq.com<br />
J Middel 0031 20511 3559<br />
Nationaal Lucht- en<br />
Rulmtevaartlaboratium, Anthony<br />
Fokkerweg 2, 1059 CM Amsterdam<br />
middel@nlr.nl<br />
M Plohr 0049 2203 601 2115<br />
Deutsches Zentrum für Luft- und<br />
Raumfahrt (DLR), Linder Höhe, 51147 Köln<br />
Martin.Plohr@dlr.de<br />
S Michot 0033 1 69 88 75 00<br />
Eurocontrol Expérimental Centre, Centre<br />
de Bois de Bordes, B.P.15, 91222 Brétigny<br />
Sur Orge CEDEX, France<br />
K Atkinson 0044 1252 397283<br />
QinetiQ, Cody Technology Park,<br />
Farnborough, Hampshire, GU14 0LX<br />
katkinson@qinetiq.com<br />
R A Christou 0044 1252 397642<br />
QinetiQ, Cody Technology Park,<br />
Farnborough, Hampshire, GU14 0LX<br />
rachristou@qinetiq.com<br />
QINETIQ/04/01113 Page 2
Release Authority<br />
Name A W Stapleton<br />
Post Technical Manager Gas Turbine Technologies<br />
Date of issue December 2004<br />
Record of changes<br />
Issue Date Detail of Changes<br />
0.1 20 Sept 2004 First Draft (QinetiQ/FST/ENP/CR045302)<br />
1.0 December 2004 First Issue<br />
QINETIQ/04/01113 Page 3
Executive Summary<br />
This report describes the production of a global gridded aviation emissions inventory<br />
<strong>for</strong> <strong>2002</strong> <strong>and</strong> a <strong>for</strong>ecast of emissions <strong>for</strong> the year <strong>2025</strong>. The inventory covers both civil<br />
<strong>and</strong> military aviation <strong>and</strong> was developed under the EC FP5 <strong>AERO2k</strong> project.<br />
The civil aviation gridded data <strong>for</strong> <strong>2002</strong> <strong>and</strong> <strong>2025</strong> consist of fuel-used, NOx, H2O, CO2,<br />
CO, hydrocarbon, <strong>and</strong> particulate (mass <strong>and</strong> number) emissions plus distance flown<br />
in each grid cell. There are about 3 million cells on a 1 deg by 1 deg by 500ft altitude<br />
grid, with one grid <strong>for</strong> each month of <strong>2002</strong>. In addition, 4 6-hourly grids show the<br />
diurnal variation of flights through a 24-hour period.<br />
The military aviation gridded data consist of fuel-used, NOx, H2O, CO2, CO <strong>and</strong><br />
hydrocarbon plus distance flown in each grid cell on a 1 deg by 1 deg by 1000ft<br />
altitude grid.<br />
In addition, this report presents total fuel <strong>and</strong> emissions figures <strong>for</strong> global <strong>and</strong><br />
regional civil <strong>and</strong> <strong>for</strong> global military aviation fuel <strong>and</strong> emissions <strong>for</strong> the <strong>2002</strong><br />
inventory year <strong>and</strong> the <strong>2025</strong> <strong>for</strong>ecast year. These <strong>2025</strong> results are compared with<br />
results from other inventories <strong>and</strong> scenario projections.<br />
<strong>Global</strong> inventories of aircraft fuel usage <strong>and</strong> emissions are required <strong>for</strong> the<br />
quantification of environmental effects of aviation. For the key climate impact<br />
assessment models, the input data <strong>for</strong> individual emissions such as nitrogen oxides<br />
(NOx = NO2 + NO) need to be placed on a global grid. The fundamental requirements<br />
of an emission inventory are a description of the ‘activity’ <strong>and</strong> a parameterisation of<br />
the emissions. In the case of an aircraft emissions inventory, the routes <strong>and</strong> the<br />
frequency of the specific aircraft that fly those routes need to be known, in terms of<br />
the ‘activity’ statistics.<br />
In order to meet this requirement, this EC 5th Framework Programme project<br />
‘<strong>AERO2k</strong>’ has developed a new <strong>and</strong> improved global inventory of aviation fuel usage<br />
<strong>and</strong> emissions, updating <strong>and</strong> extending the work of previous inventories that are now<br />
approximately ten years out of date. Moreover, additional parameters (e.g. nonvolatile<br />
particle emissions <strong>and</strong> distance travelled/grid cell) are now needed <strong>for</strong> the<br />
climate modelling community in addition to the previously provided gas phase<br />
species of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HCs) <strong>and</strong> NOx.<br />
These new parameters have been added to the civil aviation inventory.<br />
To provide new aviation emissions data on a global gridded basis, <strong>AERO2k</strong> has taken<br />
the best available civil <strong>and</strong> military flight in<strong>for</strong>mation <strong>for</strong> the year <strong>2002</strong>. For civil<br />
aviation, this includes radar tracked flight data from the whole of North America <strong>and</strong><br />
Europe showing actual latitude, longitude <strong>and</strong> altitude along the flight path. Routing<br />
in<strong>for</strong>mation is used to place timetabled flights from the rest of the world onto a<br />
global grid. Using 40 representative aircraft, fuel used <strong>for</strong> each flight is calculated<br />
using per<strong>for</strong>mance data from the PIANO aircraft per<strong>for</strong>mance tool. Employing the<br />
latest publicly available in<strong>for</strong>mation on emissions factors, emissions are calculated<br />
based on aircraft height, weight <strong>and</strong> speed, throughout the flight. New in<strong>for</strong>mation<br />
on particulate emissions has been added to provide a first gridded estimate of<br />
particulate emissions from civil aviation. Calculated emissions from all flights are<br />
then allocated into one of over 3 million individual cells on a global 3D grid,<br />
representing the latitude, longitude <strong>and</strong> altitude of the flight segment. To assist with<br />
contrail <strong>and</strong> cirrus impact assessment, the distance flown in each cell is also recorded.<br />
Extensive validation activities have been per<strong>for</strong>med to ensure the integrity of the<br />
data processing <strong>and</strong> output. Key validation activities are reported in this report.<br />
QINETIQ/04/01113 Page 5
Civil<br />
<strong>Aviation</strong><br />
Military<br />
<strong>Aviation</strong><br />
Gridded results have been published on the World Wide Web at<br />
http://www.cate.mmu.ac.uk/aero2k.asp. Analysis of the data has also been carried<br />
out to produce the global <strong>and</strong> regional results contained in this report.<br />
Key headline results <strong>for</strong> <strong>2002</strong> are:<br />
Distance<br />
Flown<br />
Fuel<br />
Used<br />
CO 2<br />
Produced<br />
H 2O<br />
Produced<br />
CO<br />
Produced<br />
NO x<br />
Produced<br />
HC<br />
Produced<br />
(10 3 million<br />
n. miles) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg)<br />
Soot<br />
Produced<br />
QINETIQ/04/01113 Page 6<br />
Particles<br />
Produced<br />
17.9 156 492 193 0.507 2.06 0.063 0.0039 4.03 x 1025<br />
n/a 19.5 61 24.1 0.647 0.178 0.066 n/a n/a<br />
Total n/a 176 553 217 1.15 2.24 0.129 n/a n/a<br />
For civil aviation, the fuel-used <strong>and</strong> NOx results are in line with results from previous<br />
inventory work in that annual fuel-used <strong>and</strong> NOx continue to increase despite<br />
efficiency improvements in newer aircraft. The complex CO2/NOx trade-off related to<br />
engine pressure ratio is evidenced by the continuing increase in annual NOx<br />
emissions <strong>and</strong> substantially unchanging value of NOx emitted per kg of fuel-used (EI<br />
NOx). Conversely, the benefits of improving combustion efficiency in recent aircraft<br />
are now seen in a substantial reduction in annual CO <strong>and</strong> HC emissions <strong>and</strong> even<br />
greater reductions in fleet EI CO <strong>and</strong> HC <strong>for</strong> civil aviation.<br />
This reduction in CO <strong>and</strong> HC emissions from civil aviation can be contrasted with the<br />
estimated equivalent emissions from military aviation. Unlike previous inventories,<br />
the <strong>AERO2k</strong> military inventory has attempted to take full account of reheat operation<br />
with the outcome that estimates of CO, <strong>and</strong> to a lesser extent HC emissions from<br />
military aviation are increased compared with those earlier inventories. Whilst the<br />
<strong>AERO2k</strong> figures <strong>for</strong> military CO <strong>and</strong> HC should be regarded as maxima because of the<br />
difficulty in obtaining accurate reheat usage data, the implication is that military<br />
aviation is now a major source of these particular aviation emissions. This contrasts<br />
with earlier inventory results.<br />
To generate a <strong>for</strong>ecast <strong>for</strong> <strong>2025</strong>, a scenario has been developed within <strong>AERO2k</strong> in<br />
which dem<strong>and</strong> growth <strong>and</strong> technology improvements are based on estimates by<br />
Airbus <strong>and</strong> UK DTI. These estimates produce regional factors, globally averaging a<br />
multiplication of capacity by 2.6, Fuel used by 2.1 <strong>and</strong> NOx emitted by 1.6 times over<br />
the 23 years from <strong>2002</strong> to <strong>2025</strong>. This represents an average to high growth scenario<br />
with particularly successful embodiment of NOx reduction technology in new aircraft<br />
over the period.<br />
Using the regional dem<strong>and</strong> <strong>and</strong> technology factors generated by this scenario, the<br />
<strong>2002</strong> flight dataset was processed to <strong>for</strong>ecast gridded data <strong>for</strong> <strong>2025</strong>. This gridded<br />
data is published alongside the <strong>2002</strong> data at http://www.cate.mmu.ac.uk/aero2k.asp.
Key headline results <strong>for</strong> <strong>2025</strong> are:<br />
Distance<br />
Flown<br />
(10 3 million<br />
n. miles)<br />
Fuel Used CO2<br />
Produced<br />
H2O<br />
Produced<br />
CO<br />
Produced<br />
NOx<br />
Produced<br />
HC<br />
Produced<br />
(Tg) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg)<br />
Soot<br />
Produced<br />
Particles<br />
Produced<br />
<strong>2002</strong> 17.9 156 492 193 .507 2.06 .063 .0039 4.03 X 10 25<br />
<strong>2025</strong> 36.1 327 1029 404 1.15 3.308 .1447 .0087 8.54 x 10 25<br />
For <strong>2025</strong>, the scenario confirms the challenge faced by civil aviation in mitigating the mass<br />
of emissions resulting from meeting the increased passenger <strong>and</strong> freight dem<strong>and</strong>. Despite<br />
assumptions of increased average aircraft size <strong>and</strong> continuing success in introducing fuel<br />
saving <strong>and</strong> emissions reduction technology, fleet rollover timescales <strong>and</strong> the dem<strong>and</strong><br />
growth rate itself are still leading to significant increases in global emissions. Over the 23<br />
year period from <strong>2002</strong> to <strong>2025</strong>, satisfying a dem<strong>and</strong> increase of 2.6 times results in only an<br />
approximate doubling of distance flown as aircraft are assumed to be larger <strong>and</strong> to have<br />
higher load factors. Techncial improvements <strong>and</strong> the increased average aircraft size result<br />
in fuel consumed <strong>and</strong> hence of CO2 <strong>and</strong> H2O emissions being also approximately double the<br />
<strong>2002</strong> figure. With ongoing regulation <strong>and</strong> technical success, there is an increase in NOx by<br />
the lower factor of 1.6 times – but still an increase. The scenario also suggests an increase<br />
in CO, HC <strong>and</strong> particulate emissions as significant technology improvements run out in this<br />
area. The redistribution of these emissions around the globe as a result of the regionally<br />
differential growth rates is contained in the global gridded data.<br />
In conclusion, <strong>AERO2k</strong> provides a significant update of the quantity <strong>and</strong> location of<br />
emissions from global aviation. The use of radar tracked data <strong>for</strong> Europe <strong>and</strong> North America<br />
has considerably enhanced the knowledge of the actual global position (latitude, longitude<br />
<strong>and</strong> altitude) at which these emissions actually occur. Increased numbers of representative<br />
aircraft compared to previous inventories have improved the accuracy of the estimation, as<br />
has the updating of emissions parameters based on latest available research. The provision<br />
of particulate number estimates <strong>and</strong> distance flown per grid cell are firsts <strong>for</strong> global<br />
inventories. These new data give atmospheric scientists the opportunity to evaluate the<br />
likely impact of aviation on climate through aviation-induced contrails <strong>and</strong> cirrus clouds.<br />
Combined with the improved <strong>and</strong> updated gridded data <strong>for</strong> <strong>2002</strong> on the gaseous emissions,<br />
the overall <strong>AERO2k</strong> <strong>2002</strong> gridded data provide a major new data source <strong>for</strong> aviation climate<br />
impact assessment.<br />
Having carried out this work, the <strong>AERO2k</strong> data-integration tool represents a considerable<br />
investment <strong>and</strong>, at the same time a considerable opportunity. Beyond the current <strong>AERO2k</strong><br />
project, the tool has significant potential <strong>for</strong> further use to provide emissions data <strong>for</strong> a<br />
range of different years either as estimations based on actual flight data or as <strong>for</strong>ecasts <strong>for</strong><br />
a range of future years <strong>and</strong> scenarios. Data can be broken down into regional <strong>and</strong> national<br />
inventories <strong>and</strong> <strong>AERO2k</strong> itself can be integrated into economic or noise models to provide<br />
global insight into the sustainability of aviation. Hence, the <strong>AERO2k</strong> model has the<br />
potential to become a major policy analysis tool <strong>for</strong> evaluating global, regional national <strong>and</strong><br />
major airport emissions from actual flights <strong>and</strong> from future aviation scenarios. The vast<br />
majority of the work required to produce such a tool has already been completed within<br />
this <strong>AERO2k</strong> project.<br />
QINETIQ/04/01113 Page 7
List of contents<br />
1 Introduction 10<br />
2 Description of the Inventory 11<br />
2.1 <strong>2002</strong> Civil <strong>Aviation</strong> Inventory 11<br />
2.1.1 Civil Air Traffic Movements Database 11<br />
2.1.2 Aircraft Representation, Profiling <strong>and</strong> Fuel Prediction 21<br />
2.1.3 <strong>Emissions</strong> Parameterisation <strong>for</strong> Civil Aircraft 31<br />
2.1.4 Data Integration <strong>and</strong> Calculation <strong>for</strong> Civil <strong>Aviation</strong> 39<br />
2.2 <strong>2002</strong> <strong>Emissions</strong> from Military Flights 41<br />
2.2.1 Approach 41<br />
2.2.2 Air traffic <strong>and</strong> emissions <strong>for</strong>ecasting. 42<br />
2.2.3 Inventory of military aircraft 43<br />
2.2.4 Deployment <strong>and</strong> Utilisation of Military Aircraft 43<br />
2.2.5 Military aircraft types <strong>and</strong> reference aircraft 44<br />
2.2.6 Mission types <strong>and</strong> reference mission types <strong>for</strong> military flights 45<br />
2.2.7 Military fuel consumption <strong>and</strong> emissions profile allocation in airspace 46<br />
2.3 Forecast <strong>for</strong> <strong>2025</strong> 47<br />
2.3.1 Aircraft additions <strong>and</strong> retirements 48<br />
2.3.2 Forecasting: Capacity - fuel - emissions 49<br />
2.3.3 Regional growth factors 50<br />
2.3.4 Freighter capacity 50<br />
2.3.5 Fuel consumption <strong>and</strong> trends 51<br />
2.3.6 Stringency <strong>and</strong> EINOx 52<br />
2.3.7 Modelling stringency 52<br />
2.3.8 EINOx – effect of new aircraft on fleet 54<br />
2.3.9 Calculation of fuel used <strong>and</strong> emissions <strong>for</strong> <strong>2025</strong> 55<br />
2.4 Validation, Uncertainty <strong>and</strong> Sensitivity Analysis 57<br />
2.4.1 Validation of the <strong>2002</strong> Civil <strong>Aviation</strong> Inventory 57<br />
2.4.2 Validation of <strong>2002</strong> Military <strong>Aviation</strong> <strong>Emissions</strong> 77<br />
2.4.3 Validation of <strong>2025</strong> <strong>Aviation</strong> <strong>Emissions</strong> 77<br />
2.4.4 Uncertainties 78<br />
2.4.5 Sensitivities 85<br />
3 Results 89<br />
3.1 Results <strong>for</strong> <strong>2002</strong> Civil <strong>and</strong> Military <strong>Aviation</strong> 89<br />
3.1.1 Gridded data <strong>for</strong> <strong>2002</strong> 89<br />
3.1.2 Other Results - <strong>2002</strong> 90<br />
3.2 Results <strong>for</strong> <strong>2025</strong> Civil <strong>and</strong> Military <strong>Aviation</strong> 107<br />
3.2.1 Gridded Data <strong>for</strong> <strong>2025</strong> 107<br />
3.2.2 Other Results - <strong>2025</strong> 108<br />
QINETIQ/04/01113 Page 8
4 Conclusions 119<br />
5 Recommendations <strong>for</strong> Further Work 122<br />
6 References 124<br />
7 Acronyms <strong>and</strong> abbreviations 127<br />
A Appendix A 129<br />
A.1 Annual deviation of temperature from ISA st<strong>and</strong>ard (Ulbrich 2004) 129<br />
B Appendix B 130<br />
B.1 Definition of Geographic Regions 130<br />
B.1.1 Asia <strong>and</strong> Pacific 130<br />
B.1.2 Eastern <strong>and</strong> Southern Africa 130<br />
B.1.3 European <strong>and</strong> North Atlantic 131<br />
B.1.4 Middle East 131<br />
B.1.5 North American, Central American <strong>and</strong> Caribbean 131<br />
B.1.6 South American 132<br />
B.1.7 Western <strong>and</strong> Central Africa 132<br />
C Appendix C 133<br />
C.1 <strong>2002</strong> fuel consumed, emissions <strong>and</strong> distance flown by altitude 133<br />
D Appendix D 138<br />
D.1 Annual Values of Distance Flown, Fuel Consumed <strong>and</strong> <strong>Emissions</strong> <strong>for</strong><br />
Civil <strong>Aviation</strong> Flights between Regions 138<br />
Initial distribution list 142<br />
QINETIQ/04/01113 Page 9
1 Introduction<br />
<strong>Aviation</strong> has grown dramatically, both within the EU <strong>and</strong> globally. Worldwide air traffic is<br />
expected to continue to grow at rates of 3-5% per year [IPCC, 1999]. Within Europe, air<br />
traffic has grown by more than 50% over the last decade <strong>and</strong> Europe now has<br />
approximately 8.5 million flights per year <strong>and</strong> up to 28,000 flights on the busiest days.<br />
EUROCONTROL has predicted that today’s traffic will have doubled by 2020 [Eurocontrol,<br />
2004].<br />
These levels of growth will mean considerable increases in aviation traffic <strong>and</strong> emissions<br />
over the next 20 years. Whilst aviation brings considerable economic benefits, this growth<br />
is also associated with increased environmental pressures <strong>and</strong> consequently a growing<br />
need to improve the environmental per<strong>for</strong>mance of the industry.<br />
<strong>Aviation</strong> has considerable environmental impacts both at a local airport level <strong>and</strong> at a<br />
regional <strong>and</strong> global level. Local atmospheric issues are related to airport contributions to<br />
local air quality <strong>and</strong> the potential <strong>for</strong> health impacts of residential populations in<br />
surrounding areas. At a global level atmospheric impacts are entered on the potential <strong>for</strong><br />
aviation emissions to affect climate.<br />
<strong>Aviation</strong> emissions are increasingly recognised as a significant contributor to global climate<br />
impacts through radiative <strong>for</strong>cing or global warming. Models of radiative <strong>for</strong>cing associated<br />
with aviation emissions suggest that aviation currently contributes approximately 3.5% of<br />
the total anthropogenic <strong>for</strong>cing <strong>and</strong> this may increase to between 3-7% by 2050<br />
[IPCC1999]. Contributions to radiative <strong>for</strong>cing due to aviation could be considerably greater<br />
than this if emissions from other sources are reduced. For example, in the UK some<br />
<strong>for</strong>ecasts suggest that by 2030 CO2 emissions from aviation in the UK will amount to 16-18<br />
million tonnes of carbon. Depending on the levels of reductions in greenhouse emissions<br />
from other sectors, this could amount to about a quarter of the UK’s total contribution to<br />
global warming by that date [DFT, 2003]. However, the current state of knowledge of<br />
aviation impacts on radiative <strong>for</strong>cing is severely limited <strong>and</strong> further research tools, such as<br />
<strong>AERO2k</strong>, are required in order to reduce the considerable uncertainties in these calculations.<br />
The EC 5th Framework Programme <strong>AERO2k</strong> has developed a new <strong>and</strong> improved global<br />
inventory of aviation fuel usage <strong>and</strong> emissions, updating <strong>and</strong> extending the work of<br />
previous inventories, most of which are now approximately ten years out of date.<br />
Moreover, additional parameters (e.g. particle emissions <strong>and</strong> km travelled/grid cell) are<br />
now needed <strong>for</strong> the climate modelling community in addition to the previously provided<br />
gas phase species of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HCs) <strong>and</strong><br />
oxides of nitrogen (NOx).<br />
This report presents the methodology used to prepare the <strong>2002</strong> inventory, together with a<br />
commentary on key assumptions <strong>and</strong> validation. The report also describes the <strong>for</strong>ecasting<br />
assumptions made to generate the data <strong>for</strong> <strong>2025</strong>. Finally the report also contains selected<br />
output data in terms of global <strong>and</strong> regional figures <strong>for</strong> emissions from civil <strong>and</strong> military<br />
aviation in <strong>2002</strong> <strong>and</strong> <strong>2025</strong>.<br />
This report fulfils Deliverable D14 of the <strong>AERO2k</strong> project: <strong>AERO2k</strong> Final Report.<br />
QINETIQ/04/01113 Page 10
2 Description of the Inventory<br />
The <strong>AERO2k</strong> global inventory provides gridded output of aviation emissions. It covers both<br />
military <strong>and</strong> civil aviation, the grids <strong>for</strong> civil <strong>and</strong> military emissions being calculated <strong>and</strong><br />
presented separately. The gridded data can be presented in many ways. For the EC <strong>AERO2k</strong><br />
fifth framework project, the following gridded data have been generated <strong>and</strong> made<br />
available on the World-Wide Web at http://www.cate.mmu.ac.uk/aero2k.asp:<br />
• Civil aviation<br />
o Fuel consumed, emissions <strong>and</strong> distance flown <strong>for</strong> each of the 12 months in <strong>2002</strong><br />
o Fuel consumed, emissions <strong>and</strong> distance flown <strong>for</strong> each of the 12 months in <strong>2025</strong><br />
o Six-hourly tables showing global diurnal variation in emissions<br />
• Military aviation<br />
o Annual fuel consumed <strong>and</strong> emissions<br />
The remainder of this Section 2 describes the key features <strong>and</strong> methods that make up the<br />
<strong>2002</strong> <strong>and</strong> <strong>2025</strong> <strong>Inventories</strong> used to generate the gridded data.<br />
2.1 <strong>2002</strong> Civil <strong>Aviation</strong> Inventory<br />
This section describes the methods used to calculate emissions from global civil aviation in<br />
<strong>2002</strong>. The emissions calculated are:<br />
• Carbon dioxide (CO2)<br />
• Water (vapour) (H2O)<br />
• Oxides of nitrogen (NOx)<br />
• Unburnt hydrocarbons (HC)<br />
• Carbon Monoxide (CO)<br />
• Particulate (soot) mass<br />
• Particulate number<br />
In addition, data <strong>for</strong> fuel used <strong>and</strong> distance flown per grid cell are also calculated.<br />
2.1.1 Civil Air Traffic Movements Database<br />
The <strong>2002</strong> air traffic movements database consists of 4-D flight trajectories (latitude,<br />
longitude, altitude <strong>and</strong> time) enabling calculation of fuel consumption <strong>and</strong> emissions. The<br />
collection of data <strong>for</strong> <strong>2002</strong> was spread over six representative periods of one week each <strong>for</strong><br />
North America <strong>and</strong> Europe, the six weeks chosen to take account of diurnal, weekly <strong>and</strong><br />
seasonal variation in air traffic. Data <strong>for</strong> the rest of the world were extracted from the Back<br />
<strong>Aviation</strong> commercial database [Back, <strong>2002</strong>] <strong>and</strong> added to the chosen representative weeks.<br />
The collection of data required the collaboration of aviation authorities in order to gather as<br />
many measured flight trajectory data as possible. Schedule data from the Back <strong>Aviation</strong><br />
database does not include any flight trajectory in<strong>for</strong>mation <strong>and</strong> it was necessary to<br />
complete this data with flight route <strong>and</strong> aircraft per<strong>for</strong>mance in<strong>for</strong>mation when available.<br />
The various <strong>for</strong>mats, origin <strong>and</strong> sheer-size of data involved necessitated the development<br />
of a toolset allowing the automatic importation of the data, their filtering, their<br />
st<strong>and</strong>ardization, their analysis, their merging <strong>and</strong> the storage of the final flight movement<br />
inventory. Analytical studies <strong>for</strong> improving data quality <strong>and</strong> creating trajectories <strong>for</strong><br />
schedule data were carried out. For the days of the year <strong>for</strong> which no data were collected,<br />
flight data were derived from inventories realized <strong>for</strong> the data collection periods while<br />
trends were obtained from Back <strong>Aviation</strong> (Back <strong>2002</strong>) scheduled flights database.<br />
An <strong>AERO2k</strong> prototype flight movement tool was developed, initially using a sample data set<br />
in order to develop a procedure that would import AMOC <strong>and</strong> ETMS flight data in a<br />
QINETIQ/04/01113 Page 11
st<strong>and</strong>ardised Access database. This procedure developed, it was then possible to import the<br />
delivered <strong>for</strong>mat, to extract the portion of data needed, to per<strong>for</strong>m the required conversion<br />
or process <strong>and</strong> to import the data in the desired <strong>for</strong>mat. The importation of air traffic data<br />
from various sources required the harmonisation of the in<strong>for</strong>mation. For this, databases<br />
linking the data between sources were developed, namely two databases on airports <strong>and</strong><br />
airlines <strong>and</strong> an application <strong>for</strong> converting local time to GMT. The relationship between the<br />
different applications required <strong>for</strong> the import procedure is shown in Figure 1<br />
Imported flight tables<br />
Source<br />
application<br />
output files<br />
CallSign<br />
EventTime<br />
DepartureAirport<br />
Airlines Airports<br />
AirlineCode<br />
Aero2k:<br />
Generate unique keys <strong>for</strong> traffic flights<br />
Convert all event times to st<strong>and</strong>ard time<br />
(GMT)<br />
Compute FLUID field<br />
Split flights as midnight<br />
LocationID<br />
<strong>Global</strong> Time<br />
Zone Converter<br />
QINETIQ/04/01113 Page 12<br />
AirportCode<br />
GMTEventTim<br />
Figure 1: Relationships between the different applications required <strong>for</strong> importing data to<br />
<strong>AERO2k</strong> flight movement tool.<br />
Based on the overall observations, a prototype flight movement tool was developed <strong>and</strong> to<br />
facilitate the use <strong>and</strong> underst<strong>and</strong>ing of <strong>AERO2k</strong> flight movement tool, a graphical user<br />
interface (GUI) was created. An overview of the tool is shown in Figure 2. The tool should<br />
allow the import/conversion of the flight movement data i.e. AMOC <strong>and</strong> ETMS into a<br />
st<strong>and</strong>ard <strong>for</strong>mat. Flight movement data from the different sources would then be merged<br />
into a single table. This table of flights would be completed with schedule data from other<br />
parts of the world (obtained from Back <strong>Aviation</strong>). Schedule data would require time<br />
conversion into GMT <strong>and</strong> the generation of a trajectory. Once all data are merged, an<br />
inventory <strong>for</strong> the selected day where data had been collected (representative day) would be<br />
created.
AMOC ETMS<br />
Data converter<br />
St<strong>and</strong>ard <strong>for</strong>mat<br />
Merge table<br />
Representative day<br />
generator<br />
Schedule Database<br />
Time converter<br />
Text file converter <strong>for</strong><br />
exportation<br />
Figure 2: <strong>AERO2k</strong> flight movement tool overview<br />
Great Circle<br />
Generator<br />
Virtual day<br />
generator<br />
Airport table<br />
Airline table<br />
Fleet table<br />
Flight trajectory data either in AMOC or ETMS are provided as latitude <strong>and</strong> longitude values.<br />
In order to visualize these values, a GIS application was developed in VBA (Visual Basic <strong>for</strong><br />
applications). This application can directly read data from an Access database. It converts<br />
the flight legs latitude <strong>and</strong> longitude into trajectories. This application was used <strong>for</strong>:<br />
GIS<br />
• Assessing the coverage of AMOC <strong>and</strong> ETMS data<br />
• Radar tracks are dispersed while flight plan tracks correspond exactly to defined<br />
flights routes<br />
• Selecting flights <strong>and</strong> checking the data quality<br />
• Checking the merge of flight trajectories.<br />
Output from this tool is illustrated in Figure 3 <strong>and</strong> Figure 4.<br />
QINETIQ/04/01113 Page 13
Figure 3: <strong>Global</strong> view illustrating the difference between US radar data (ETMS in blue) <strong>and</strong><br />
European flight plan data (AMOC in red) (4-5-6/10/2000).<br />
Figure 4: Detailed view <strong>for</strong> Europe illustrating the difference between US radar data (ETMS in<br />
blue) <strong>and</strong> European flight plan data (AMOC in red) (4-5-6/10/2000).<br />
The realisation of a world flight movement inventory implies the use of flight data from<br />
various sources. This means that flights may be duplicated <strong>and</strong> trajectories may be partially<br />
present in a source. Figure 5 illustrates the same flight present in ETMS <strong>and</strong> AMOC showing<br />
an incomplete European part <strong>for</strong> the ETMS flight <strong>and</strong> an incomplete American part <strong>for</strong> the<br />
AMOC flight. For this reason, a method was developed <strong>for</strong> identifying such flights <strong>and</strong><br />
selecting a single trajectory or merge trajectories in order to cover the longest trajectory<br />
possible. After merging both flights, a complete trajectory is obtained Figure 6. The<br />
procedure <strong>for</strong> merging trajectories is based on the assessment of the trajectories <strong>and</strong> the<br />
attribution of a zone indicator from which the selection of the trajectory is derived.<br />
QINETIQ/04/01113 Page 14
ETMS AMOC<br />
Figure 5: Same flight identified in both ETMS <strong>and</strong> AMOC sources.<br />
Figure 6: Previous flight (Figure 5) after merging of both sources.<br />
ETMS<br />
Following initial development in MS Access, the tool was migrated to an Oracle 9i database.<br />
Within the database, the mix of airport codes in ETMS <strong>and</strong> AMOC data required further<br />
work on development of the airport table. AMOC data mainly used a 4-letter code; ETMS<br />
data used mainly three-letter code (IATA or FAA codes) <strong>for</strong> American airports <strong>and</strong> four-letter<br />
code <strong>for</strong> Europe <strong>and</strong> the rest of the world. It appears that some three-letter codes referred<br />
to two different airports. For example, BUY can refer to Burlington airport in North Carolina<br />
in the USA (identified as KBUY by ICAO) but also to Bunbury airport in Western Australia<br />
(identified as YBUN by ICAO). This type of problem did not allow the production of a one-toone<br />
static table listing IATA <strong>and</strong> ICAO airport codes. Two airport tables were then created,<br />
one used <strong>for</strong> AMOC <strong>and</strong> the rest of the world, another one <strong>for</strong> ETMS data. The table created<br />
<strong>for</strong> ETMS data considered predominantly codes of airports situated in the USA.<br />
Both airport tables were completed with data obtained from the Volpe Center <strong>and</strong> other<br />
trusted sources. The coordinates of the airports were checked using DAFIF (Digital<br />
Aeronautical Flight In<strong>for</strong>mation File) data (Figure 7).<br />
QINETIQ/04/01113 Page 15<br />
AMOC
Figure 7: Illustration of the location of all airports listed in the table (colours attributed<br />
according to the time zone).<br />
Part of the objective of <strong>AERO2k</strong> was to construct a global aircraft movement database that<br />
represented as closely as possible the actual civil movements <strong>for</strong> <strong>2002</strong>. In order to be<br />
representative of yearly global emission variations, it had to account <strong>for</strong> the seasonal,<br />
weekly <strong>and</strong> diurnal variation in air traffic. This objective was achieved by selecting periods<br />
of data collection <strong>for</strong> the world <strong>and</strong> by extending these data in creating aircraft movement<br />
inventory <strong>for</strong> days where no data were collected.<br />
The choice of the data to collect was influenced by the availability of ATC data while the<br />
choice of the length of collection periods was influenced by the logistics of merging such<br />
data into a comprehensive annual data set. It was decided that the basis year <strong>for</strong> collecting<br />
the data would be <strong>2002</strong> (rather than 2001) in order to reduce the risk of not getting data<br />
not kept by ATC authorities. This risk did not concern North America <strong>and</strong> Europe but could<br />
have been true in Africa, Asia <strong>and</strong> South America.<br />
<strong>2002</strong> was also affected by the application of RVSM (Reduced Vertical Separation Minimum)<br />
in Europe i.e. the decrease of the vertical separation between aircrafts in cruise from 2000 ft<br />
to 1000 ft. <strong>2002</strong> was thus considered as more representative of the new rules in aviation in<br />
Europe than 2001.<br />
The choice of the data collection period was based on data available <strong>for</strong> the ECAC area <strong>and</strong><br />
confirmed by data found <strong>for</strong> the US air traffic. An analysis of air traffic showed that these<br />
two areas represented over 70% of the air traffic in the world (Figure 8). For both areas, the<br />
monthly traffic trends were examined in order to identify the key months <strong>and</strong> the suitable<br />
weeks.<br />
QINETIQ/04/01113 Page 16
Latin America<br />
& Caribbean<br />
9%<br />
North America<br />
43%<br />
Europe<br />
29%<br />
Asia <strong>and</strong><br />
Pacific<br />
15%<br />
Africa<br />
2%<br />
Middle East<br />
2%<br />
Figure 8: Total scheduled aircraft departures estimated <strong>for</strong> the year 2000 by ICAO region<br />
Key months <strong>for</strong> collecting data were selected based on data published by the CFMU <strong>for</strong> the<br />
ECAC area <strong>and</strong> available within Eurocontrol. A qualitative assessment was done based on<br />
the daily traffic recorded by the CFMU <strong>and</strong> shown in Figure 9.<br />
Figure 9: Average monthly traffic in the CFMU area from 1997 to 2001. [ CFMU, <strong>2002</strong>].<br />
The analysis of Figure 9 shows that the year can be divided into four parts according to the<br />
seasons. During the winter period, a constant increase of the traffic can be noticed from<br />
January to March. At spring, the increase started in winter carries on from April to June with<br />
a slightly steeper slope. In summer, only small variations are observed between July <strong>and</strong><br />
August while September appears to be the busiest month of the year. In autumn a steady<br />
decrease is observed from October to December. This analysis led to the selection of<br />
February, April, June, September, October <strong>and</strong> December <strong>for</strong> collecting data. February <strong>and</strong><br />
April were chosen as intervals of time representing the increase observed during winter <strong>and</strong><br />
spring. June <strong>and</strong> September announce the beginning <strong>and</strong> end of the summer season with a<br />
higher number of flights than the annual average <strong>for</strong> 2001 (Table 1). The events of the 11th<br />
QINETIQ/04/01113 Page 17
of September modified the market <strong>for</strong> the end of the year. However, based on the five-year<br />
trend, October initiates the decrease of the number of flights <strong>and</strong> December the end with<br />
the least flights in the year.<br />
Daily traffic in the<br />
ECAC area (number of<br />
flights)<br />
1997 1998 1999 2000 2001 Average 1997-2001<br />
19662 20685 22062 23068 22995 21694<br />
Table 1: Yearly traffic averages <strong>for</strong> the ECAC area from 1997 to 2001 [CFMU, <strong>2002</strong>]<br />
To ensure the six months retained were properly chosen, the proportion of annual traffic<br />
represented by these six months was calculated. The analysis showed that these six<br />
months accounted <strong>for</strong> roughly 49% of 2000 <strong>and</strong> 2001 traffic in the ECAC area (Table 2).<br />
Month Monthly traffic in 2000 Monthly traffic in 2001<br />
Number of<br />
flights %<br />
Number of<br />
flights %<br />
January 623804 7.39 654635 7.8<br />
February 623275 7.38 612330 7.3<br />
March 690681 8.18 699459 8.33<br />
April 676452 8.01 691183 8.24<br />
May 751662 8.9 751421 8.95<br />
June 743599 8.81 753254 8.97<br />
July 761903 9.02 772110 9.2<br />
August 766299 9.08 776145 9.25<br />
September 761251 9.02 753600 8.98<br />
October 746574 8.84 732617 8.73<br />
November 672274 7.96 627108 7.47<br />
December 625007 7.4 569356 6.78<br />
Total 8442781 100 8393218 100<br />
6 months<br />
selected 49 49<br />
Table 2: Monthly traffic in 2000 <strong>and</strong> 2001 <strong>for</strong> the ECAC area [CFMU, <strong>2002</strong>].<br />
The evolution of the traffic <strong>for</strong> all weeks between 1999 <strong>and</strong> 2001 is shown in Figure 10.<br />
From this data, no trend could be drawn from one year to the other. A strong variation can<br />
occur from week to week within a month. There<strong>for</strong>e, the selection of a period within the<br />
selected month was done arbitrarily. The second week of the month was chosen <strong>for</strong><br />
February (week 6), April (week 15), June (week 24), September (week 37) <strong>and</strong> October (week<br />
41). The first week of December (week 49) was retained <strong>for</strong> project deadline reasons as later<br />
data would not be available until after the Christmas period.<br />
QINETIQ/04/01113 Page 18
Figure 10: Evolution of the traffic in the CFMU area. [CFMU, <strong>2002</strong>].<br />
Finding equivalent sources to European data in the USA revealed difficulties. Data<br />
expressed in terms of number of flights were published by the US Department of<br />
Transportation but mentioned only traffic between the US <strong>and</strong> the rest of the world. Other<br />
sources, such as the Air Transport Association, publishes data expressed in Revenue<br />
Passenger Miles, Revenue Passenger Enplanements, Available Seat Miles or Passenger Load<br />
Factor but not in terms of number of flights. The analysis of monthly traffic was then based<br />
on data published by the US Department of Transportation <strong>for</strong> 1999 <strong>and</strong> 2000. These data<br />
include all traffic arriving at U.S. airports <strong>and</strong> departing from U.S airports on non-stop<br />
commercial international flights. Figure 11 illustrates this traffic data.<br />
120000<br />
100000<br />
80000<br />
60000<br />
40000<br />
20000<br />
0<br />
Jan<br />
Fev<br />
March<br />
April<br />
May<br />
June<br />
July<br />
QINETIQ/04/01113 Page 19<br />
Aug<br />
Sept<br />
Oct<br />
Nov<br />
Dec<br />
2000<br />
1999<br />
Figure 11: Non-stop travel between the US <strong>and</strong> the Rest of the World (number of departures)<br />
[US Department of Transportation, 2001]
It can be seen that both years show the same trend. February is the lowest season <strong>and</strong><br />
December traffic is busier than in January. Traffic in March, April, May <strong>and</strong> June is quite<br />
constant. July <strong>and</strong> August are the months showing the highest traffic. A decrease is<br />
observed in September after which the traffic remains fairly constant until December.<br />
Combining this in<strong>for</strong>mation with the selection of the months <strong>for</strong> the ECAC area (i.e.<br />
February, April, June, September, October <strong>and</strong> December) led to confirmation of the months<br />
already selected on the basis of European ECAC data.<br />
Because obtaining data from parts of the world may be difficult, it was initially decided not<br />
to overload data suppliers with overwhelming requests, but to limit our dem<strong>and</strong> to a day of<br />
data collected six times a year. In contrast, Europe <strong>and</strong> the USA, from whom it was easier to<br />
get data, would supply data <strong>for</strong> a week (Monday to Sunday). Despite various requests to<br />
aviation authorities around the world, little useful data were <strong>for</strong>thcoming. It was there<strong>for</strong>e<br />
decided to complete the data with the Back <strong>Aviation</strong> database. Table 3 summarises the<br />
days of collection. The dates are defined according to UTC time.<br />
<strong>2002</strong> February April June September October December<br />
Monday 4 8 10 9 7 2<br />
Tuesday 5 9 11 10 8 3<br />
Wednesday 6 10 12 11 9 4<br />
Thursday 7 11 13 12 10 5<br />
Friday 8 12 14 13 11 6<br />
Saturday 9 13 15 14 12 7<br />
Sunday 10 14 16 15 13 8<br />
Table 3: Timetable <strong>for</strong> collecting data<br />
The importation of the flight data <strong>and</strong> the merge of the trajectories produce flight<br />
movement inventories that partially represents the annual air traffic in the world. To have a<br />
better underst<strong>and</strong>ing of the world air traffic <strong>for</strong> the entire base year requires the estimation<br />
of air traffic <strong>for</strong> all the other days of the year. To achieve this, the annual flight movement<br />
inventory was developed from the data collected six times a year at different seasons.<br />
These six periods of one week correspond to what was called representative weeks made of<br />
seven representative days. Any day <strong>for</strong> which no data were collected is called a virtual day.<br />
To extend the data collected to a full year, a method was developed allowing the creation<br />
of an inventory <strong>for</strong> any virtual day. Virtual days inventories are generated from<br />
representative days inventories on which flight movement trends are applied. Trends are<br />
derived from the Back <strong>Aviation</strong> database, which includes in<strong>for</strong>mation on the distribution of<br />
flight frequencies <strong>for</strong> scheduled commercial operations. Back <strong>Aviation</strong> database includes<br />
only flights scheduled by airlines <strong>and</strong> so counts less flight than the inventory made <strong>for</strong><br />
<strong>AERO2k</strong>, which accounts <strong>for</strong> unscheduled flights. In a year, the number of flights varies<br />
according to the season, the day in the week <strong>and</strong> the country. These three criteria should be<br />
taken into consideration when creating the virtual days.<br />
From the worldwide collection of data, 42 merged inventories (6 weeks, 7 days per week)<br />
are created. From these inventories, the number of flights per region <strong>and</strong> <strong>for</strong> each<br />
representative day is calculated.<br />
For this, the representative days were classified into seasons as shown in Table 4. A virtual<br />
day chosen as Wednesday 9 th of January <strong>2002</strong> is related to the ratio matching the season<br />
<strong>and</strong> the day i.e. Wednesday 6 th of February <strong>2002</strong>. The day selected is the closest day with<br />
the same day of the week.<br />
QINETIQ/04/01113 Page 20
<strong>2002</strong> Feb<br />
No.<br />
days April<br />
No.<br />
days June<br />
No.<br />
days Sept<br />
No.<br />
days Oct<br />
No.<br />
days Dec<br />
Mon 4 13 8 6 10 7 9 13 7 10 2 3<br />
Tues 5 13 9 6 11 7 10 13 8 9 3 4<br />
Wed 6 13 10 6 12 7 11 13 9 9 4 4<br />
Thurs 7 12 11 7 13 7 12 13 10 9 5 4<br />
Fri 8 13 12 7 14 6 13 14 11 9 6 4<br />
Sat 9 13 13 7 15 6 14 13 12 10 7 3<br />
Sun 10 13 14 7 16 6 15 13 13 10 8 3<br />
Season Winter 90 Spring 46 Spring 46 Summer 92 Autumn 66 Autumn 25<br />
From<br />
/<br />
to<br />
21/12<br />
to<br />
20/03<br />
21/03<br />
to<br />
5/05<br />
6/05<br />
to<br />
20/06<br />
21/06<br />
to<br />
20/09<br />
21/09<br />
to<br />
25/11.<br />
Table 4: Listing of the representative days in <strong>2002</strong> <strong>and</strong> classified in seasons.<br />
26/11<br />
To<br />
20/12.<br />
The objective of the work described in this section was to deliver a world civil flight<br />
movement inventory. This was done by generating six inventories corresponding to the<br />
weeks of data collection. As an example Figure 12 shows a representation in GIS of all<br />
flights in the world departing on the 8/02/<strong>2002</strong>.<br />
Figure 12: Map of all flights over the world departing on the 8/02/<strong>2002</strong><br />
2.1.2 Aircraft Representation, Profiling <strong>and</strong> Fuel Prediction<br />
Having developed a database of aircraft movements, it was necessary to represent each<br />
movement with a representative aircraft (<strong>and</strong> engine) <strong>and</strong> to predict the fuel used during<br />
the flight. This process is described here.<br />
Civil Aircraft Representation<br />
The world aircraft fleet consists of numerous aircraft types, variants <strong>and</strong> sub variants. Each<br />
airline specifies its own aircraft as well as selecting engine type. It was there<strong>for</strong>e necessary<br />
to make a simplification of the global fleet of aircraft in order that the modelling was kept<br />
within manageable proportions whilst retaining the accuracy required <strong>for</strong> the global<br />
inventory. This was achieved through selecting a small number of aircraft that, combined,<br />
QINETIQ/04/01113 Page 21<br />
No.<br />
days
are broadly representative of the entire world fleet in terms of per<strong>for</strong>mance, fuel use <strong>and</strong><br />
emissions production.<br />
Forty representative aircraft were selected by this process. These aircraft include turboprop,<br />
bizjet, regional jet <strong>and</strong> large civil jet transport categories of aircraft. These 40 types are<br />
representative of over 300 different aircraft types or variants within the world’s fleet.<br />
The aircraft types considered within the <strong>AERO2k</strong> project are as follows:<br />
• Large jets (LJ), comprising the bulk of the civil subsonic jet transport fleet, with<br />
greater than 100 seats;<br />
• Regional jets (RJ), turbofan or turbojet powered short-range aircraft with up to 100<br />
seats;<br />
• Turboprops (TP), aircraft powered by two or more turboprop engines;<br />
• Bizjets (BJ), small turbofan or turbojet aircraft with less than about 20 seats.<br />
The <strong>AERO2k</strong> movement data are based on instrument flight rules (IFR 1 ) rated aircraft, which<br />
are required to file flight plans be<strong>for</strong>e departure. Visual flight rules (VFR 2 ) movements are<br />
excluded, as data are not available.<br />
Visual flight rules movements can largely be classed as general aviation (GA) or helicopter<br />
movements, <strong>for</strong> which emissions data are not readily available. Thus, all piston-engined<br />
aircraft, single-engined turboprops (i.e. the two predominant categories which comprise GA<br />
aircraft types), <strong>and</strong> helicopters are excluded from the study. The proportion of VFR flights of<br />
the total global number of movements is relatively small <strong>and</strong> there<strong>for</strong>e the percentage of<br />
total fuel burn is also small: estimates by Boeing <strong>for</strong> 1992 were that approximately 2% of<br />
total global aviation fuel burn was attributable to GA 3 [Mortlock & Van Alstyne, 1998].<br />
Certain aircraft types are operated by both civil <strong>and</strong> military sectors so that it was necessary<br />
to include such aircraft, e.g. civil operations of Lockheed C130 Hercules military<br />
transport/L100 civil transport.<br />
In order to provide guidance <strong>for</strong> the level of detail <strong>and</strong> ef<strong>for</strong>t that should be applied to the<br />
principal aircraft types to be covered in the inventory (i.e. LJ, RJ, TP <strong>and</strong> BJ), a simple analysis<br />
was per<strong>for</strong>med on a sample of flight movement data. The relative proportions of the<br />
numbers of flights by each of the four classes are given in Table 5.<br />
1 IFR: Instrument Flight Rules. There are two different categories of flight rules under which a<br />
pilot or aircraft can operate. These rules are determined by the weather <strong>and</strong> the way the<br />
pilots navigate the aircraft. Under instrument flight rules, pilots rely on in<strong>for</strong>mation displayed<br />
on instruments or equipment within the aircraft <strong>for</strong> navigation. IFR flights are required to file<br />
flight plans with air traffic control be<strong>for</strong>e departure.<br />
2 VFR: Visual Flight Rules. Under VFR, pilots navigate using visual in<strong>for</strong>mation from looking<br />
outside the aircraft, following roads, rivers, or other l<strong>and</strong>marks. VFR flights are not required to<br />
file flight plans.<br />
3 Boeing‘s estimate of GA fuel use as a percentage of total aviation fuel use is approximately<br />
3% [Mortlock & Van Alstyne, 1998], but their definition of GA included business jet aircraft.<br />
Bizjets accounted <strong>for</strong> one third of GA fuel use in the study, there<strong>for</strong>e 2% of total fuel use was<br />
attributable to non-bizjet GA.<br />
QINETIQ/04/01113 Page 22
Aircraft type % of movements % of distance travelled<br />
LJ 68.8 87.8<br />
RJ 10.6 5.4<br />
TP 19 5.6<br />
BJ 1.7 1.2<br />
total movements =<br />
48,988<br />
total distance =<br />
34,711,952 n.miles<br />
Table 5: Proportions of movements <strong>and</strong> distances in nautical miles (n.miles) travelled by<br />
aircraft type from EUROCONTROL sample dataset<br />
The percentage of distance travelled can also be used as a very rough guide to the relative<br />
proportions of total fuel used <strong>and</strong> pollutant emissions produced by each of the four classes.<br />
There<strong>for</strong>e, the large jet transport (LJ) category was the primary focus <strong>and</strong> resulted in a<br />
larger number of representative types than the other categories.<br />
The selection of representative aircraft types was made in three main steps:<br />
• Grouping aircraft by seat capacity, engine technology, maximum take-off weight<br />
(MTOW) <strong>and</strong> configuration<br />
• Determination of the numbers of aircraft within a particular seat<br />
capacity/technology category<br />
• Availability of suitable per<strong>for</strong>mance model.<br />
It was important to ensure that the selection of representative aircraft types was<br />
compatible with the <strong>for</strong>ecast methodology <strong>for</strong> <strong>2025</strong>, which was to be based upon seat<br />
capacity ‘b<strong>and</strong>s’ <strong>and</strong> technology levels. The classification of ‘technology level’ can be used<br />
to identify older generation aircraft types that are still in operation today but which will be<br />
removed from service in developed countries by the <strong>for</strong>ecast year (<strong>2025</strong>).<br />
Some examples of the major aircraft types <strong>and</strong> groupings are presented in Table 6 to Table<br />
9 <strong>for</strong> LJ, RJ, TP <strong>and</strong> BJ classes, respectively.<br />
QINETIQ/04/01113 Page 23
No. of seats<br />
100–124<br />
125–159<br />
160–199<br />
200–249<br />
250–314<br />
315–399<br />
Old technology<br />
(Chapter 2 noise)<br />
BAC 1-11<br />
B737- 100<br />
B737- 200<br />
DC9<br />
Tu134<br />
B707<br />
DC8<br />
IL62<br />
IL76<br />
B727<br />
Tu154<br />
B747-100<br />
An124 Ruslan<br />
Chapter 3 noise,<br />
CAEP 2 NOx<br />
MD87<br />
B737-300<br />
B737-400<br />
MD81<br />
MD82<br />
MD83 (MD80<br />
series)<br />
MD88<br />
MD90<br />
A310<br />
L1011<br />
DC10<br />
A300<br />
L1011<br />
MD11<br />
B747-200<br />
B747-300<br />
Chapter 4 noise,<br />
CAEP 4 NOx capable<br />
B717<br />
A318<br />
A319<br />
B737-500<br />
B737-600<br />
A320<br />
B737-700<br />
A321<br />
B757-200<br />
Tu204<br />
B737-800<br />
B737-900<br />
B757-300<br />
B767-200<br />
A330-200<br />
A340-300<br />
IL86<br />
IL96-300<br />
B767-300<br />
B777-200<br />
A330-300<br />
A340-600<br />
B747-400<br />
B767-400<br />
B777-300<br />
400–499 B747-400<br />
500–624<br />
A380-800 4<br />
B747-400<br />
625–799 A380-900 5<br />
Table 6: Large jet (LJ) aircraft by seat capacity b<strong>and</strong> <strong>and</strong> noise/NOx emissions technology<br />
4 Future type<br />
5 Future type<br />
QINETIQ/04/01113 Page 24
No. of seats<br />
Old technology<br />
(Chapter 2 noise)<br />
30 to 50 Yak40 (YK40)<br />
70 F28 F70<br />
90 Yak42 (YK42) F100<br />
Chapter 3 noise,<br />
CAEP 2 NOx<br />
Chapter 4 noise,<br />
CAEP 4 NOx capable<br />
Embraer 135 (E135)<br />
Embraer 145 (E145)<br />
CRJ 200 (CRJ2)<br />
Avro RJ/RJX/BAe146<br />
(BA46)<br />
Table 7: Regional jet (RJ) aircraft by seat capacity b<strong>and</strong> <strong>and</strong> noise/NOx emissions technology<br />
(aircraft ICAO type codes are shown in brackets)<br />
Seat b<strong>and</strong> category<br />
<strong>and</strong> configuration<br />
30 seat twin<br />
50 seat twin<br />
70 seat twin<br />
mid-size quad<br />
large quad<br />
Aircraft type (ICAO code)<br />
Saab 340 (SF34)<br />
Dash 8-100 (DH8A)<br />
Dornier 328 (D328)<br />
ATR 42 (AT43/AT45)<br />
Saab 2000 (SB20)<br />
Fokker 50 (F50)<br />
Dash 8-300 (DH8C)<br />
ATR 72 (AT72)<br />
Dash 8-400 (DH8D)<br />
ATP (ATP)<br />
Antonov AN-12 (AN12)<br />
Lockheed L188 (L188)<br />
Lockheed C-130 Hercules (C130)<br />
Shorts Belfast (BELF)<br />
Table 8: Turboprop (TP) aircraft by seat capacity b<strong>and</strong> (Aircraft ICAO type codes are shown in<br />
brackets)<br />
Category by size Aircraft type (ICAO code)<br />
Small<br />
Learjet 35 (LJ35)<br />
Citation C550/C551 (C550)<br />
Medium Falcon 2000 (F2TH)<br />
Large Gulfstream IV (GLF4)<br />
Table 9: Bizjet (BJ) aircraft by seat capacity b<strong>and</strong> (aircraft ICAO type codes are shown in<br />
brackets)<br />
In the second step, aircraft representative type selection was based on the numbers of each<br />
type in service from a global fleet database [JP Fleets, 2000]. A representative type was<br />
selected as the most numerous occurrence within a seat-b<strong>and</strong>/technology category.<br />
Configuration was also considered, especially <strong>for</strong> smaller aircraft types, i.e. TP, BJ <strong>and</strong> RJ <strong>for</strong><br />
which most variation in configuration occurs (e.g. engines on fuselage, high tail etc).<br />
The per<strong>for</strong>mance package selected <strong>for</strong> fuel usage predictions was PIANO [PIANO, <strong>2002</strong>].<br />
Availability of a st<strong>and</strong>ard model in the PIANO package was there<strong>for</strong>e also a consideration in<br />
selection. If, <strong>for</strong> example, the first choice of aircraft was not available by the above<br />
selection criteria, the next available within the PIANO suite of models was selected. Where<br />
there was no suitable selection available, new aircraft models were added to PIANO. The<br />
representative aircraft types selected are presented in Table 11.<br />
QINETIQ/04/01113 Page 25
Selection of representative engines<br />
Aircraft types are seldom fitted with a single type of engine, as a choice is usually available<br />
either from within one manufacturer’s ‘family’ of engines or from other engine<br />
manufacturers.<br />
Market pressures ensure that engines from different manufacturers intended <strong>for</strong> the same<br />
airframe have similar fuel consumption rates. Table 10 presents data <strong>for</strong> a high take-off<br />
gross weight (TOGW) Boeing 777-200, which are available with engines of similar<br />
technology <strong>and</strong> thrust rating from three manufacturers [Jane’s, <strong>2002</strong>]. The maximum<br />
range of this aircraft with each engine is within one percent of the mean value, indicating<br />
the similarity of fuel consumption rates of all three engines. There<strong>for</strong>e, fuel burn data<br />
produced <strong>for</strong> one engine variant using an aircraft per<strong>for</strong>mance prediction tool can be<br />
applied to the same aircraft with a different engine with some degree of confidence.<br />
Design<br />
range(n.miles) a<br />
Deviation<br />
from mean<br />
range<br />
Proportion of 777-200<br />
fleet fitted with engines<br />
from this manufacturer b<br />
GE 7625 + 0.66 % 29.9 %<br />
Pratt & Whitney 7505 - 0.92 % 31.9 %<br />
Rolls-Royce 7595 + 0.26 % 38.2 %<br />
mean range (n.miles) 7575<br />
Table 10: Comparison of maximum range <strong>for</strong> high TOGW B777-200 aircraft fitted with<br />
different engines (Data sources: a Jane’s, <strong>2002</strong>; b JP Fleets, 2000)<br />
However, emissions can vary considerably depending on the engine type. <strong>Emissions</strong><br />
characteristics there<strong>for</strong>e need to be carefully compared. The only publicly available data<br />
source to allow such a comparison is L<strong>and</strong>ing <strong>and</strong> Take-off (LTO) cycle certification data<br />
contained in the <strong>Emissions</strong> Databank [ICAO, 1995]. Two approaches may be taken to<br />
represent emissions of engines equipped on airframes: a ‘generic’ engine may be modelled<br />
from a variety of engines fitted to a particular airframe; or an engine that is representative<br />
of one fitted to an airframe may be selected by objective criteria. For the ANCAT/EC2 global<br />
inventory [Gardner 1998] a representative aircraft type was fitted with a single generic<br />
engine type, <strong>for</strong> which fuel <strong>and</strong> emissions characteristics were weighted by the known<br />
engine population fitted to those aircraft types.<br />
Here, an alternative approach was devised in which both the generic <strong>and</strong> representative<br />
engine approaches were combined. Firstly, generic emissions characteristics were<br />
developed from data from the <strong>Emissions</strong> Databank <strong>and</strong> these were used as the basis of<br />
selecting a representative engine. The simple generic approach was not compatible with<br />
the methods used in <strong>AERO2k</strong> <strong>for</strong> predicting emissions, as this was based on detailed engine<br />
aero-thermodynamic models. Thus, a representative (real) engine type must be selected <strong>for</strong><br />
which emissions data are available.<br />
Generic emissions characteristics were developed by weighting the emission index <strong>for</strong> NOx<br />
(EINOx 6 ) <strong>for</strong> the number of engines in the fleet’s population, <strong>for</strong> each of the four thrust<br />
settings of the LTO cycle. In the next step, the generic emission characteristics were<br />
compared with real engine data <strong>and</strong> the closest fit determined by a polynomial leastsquares<br />
regression. The final choice of engine was not determined solely using this method;<br />
one additional criterion was used in the selection process. Once all the representative<br />
aircraft had been assigned an engine using the above method, the selected engines were<br />
compared <strong>for</strong> similarity of emissions characteristics. Where close similarities were found,<br />
<strong>for</strong> instance within families of engines from one manufacturer, the most representative<br />
engine was chosen. In this way the number of engines was consolidated.<br />
6 EINOx: emissions of NOx normalised by fuel flow, in units of grammes of NOx per kg of fuel.<br />
QINETIQ/04/01113 Page 26
Representative Aircraft <strong>and</strong> Engine Listing<br />
The representative aircraft <strong>and</strong> engine types are presented in Table 11 below.<br />
Large jets<br />
ICAO code Representative aircraft<br />
No. of<br />
engines<br />
Representative<br />
engine<br />
Databank<br />
unique ID<br />
A306 Airbus A300 600R 2 PW4x62 1PW058<br />
A310 Airbus A310-300 2 PW4x62 1PW058<br />
A319 Airbus A319 2 CFM56-5C4 2CM015<br />
A320 Airbus A320-200 2 CFM56-5C4 2CM015<br />
A321 Airbus A321-100 2 CFM56-5C4 2CM015<br />
A330 Airbus A330-300 2 CF6-80E1A3 5GE085<br />
A340 Airbus A340-300 4 CFM56-5C4 2CM015<br />
A340 (R) 7<br />
Airbus A340-300 4 D-30KP-2 1AA002<br />
B703 Boeing B707-320C 4 D-30KP-2 1AA002<br />
B712 Boeing B717-200 2 BR700-715C1-30 4BR007<br />
B722 Boeing B727-200A 3 JT8D-15 1PW010<br />
B732 Boeing B737-200 2 JT8D-15 1PW010<br />
B734 Boeing B737-400 2 CFM56-3C-1 1CM007<br />
B737 Boeing B737-600 2 CFM56-7B26 3CM033<br />
B738 Boeing B737-800 2 CFM56-7B26 3CM033<br />
B742 Boeing B747-200B 4 JT9D-7R4G2 1PW029<br />
B744 Boeing B747-400 4 PW4x62 1PW058<br />
B752 Boeing B757-200 2 PW2040 4PW073<br />
B763 Boeing B767-300ER 2 PW4x62 1PW058<br />
B772 Boeing B777-200 2 PW4090 3PW066<br />
BA11 Rombac 1-11 2 SPEY Mk511 1RR016<br />
L101 Lockheed L1011 3 JT9D-7J 1PW024<br />
DC9 Douglas DC 9-34 4 JT8D-15 1PW010<br />
MD11 McDonnell Douglas MD-11 3 PW4x62 1PW058<br />
MD80 McDonnell Douglas MD-82/88 2 JT8D-15 1PW010<br />
MD90 McDonnell Douglas MD-90-30 2 CFM56-5C4 2CM015<br />
7 Representative type <strong>for</strong> large four-engine Russian (CIS) types similar to A340, uses PIANO<br />
A340 model but emissions data <strong>for</strong> CIS-made engines prevalent on these types.<br />
QINETIQ/04/01113 Page 27
Regional jets<br />
ICAO code Representative aircraft<br />
No. of<br />
engines<br />
Representative<br />
engine<br />
Databank<br />
unique ID<br />
BA46 Avro RJ 85 4 ALF 502L-2 1TL001<br />
E145 Embraer EMB-145 2 ALF502L2 1TL001<br />
F100 Fokker F100 2 Tay 650-15 1RR021<br />
F70 Fokker F70 2 Tay 611-8 1RR019<br />
YK42 Yakovlev Yak-42M 3 D-36 1ZM001<br />
Turboprops<br />
Bizjets<br />
ICAO code Representative aircraft<br />
No. of<br />
engines<br />
Representative<br />
engine<br />
Databank<br />
unique ID 8<br />
AT72 ATR72 2 PW127 -<br />
C130 Lockheed Martin L100/C130 4 T56-A-15 -<br />
F50 Fokker F50 2 PW127 -<br />
L188 Lockheed L188 Electra 4 501-D13 -<br />
SF34 Saab 340B 2 GE CT7-9B -<br />
ICAO code Representative aircraft<br />
No. of<br />
engines<br />
Representative<br />
engine<br />
Databank<br />
unique ID 9<br />
C550 Cessna Citation III 2 PW530A -<br />
F2TH Dassault Falcon 2000 2 CFE738 1AS002<br />
F900 Dassault Falcon 900 C 3 TFE731 -<br />
GLF4 Gulfstream G IV-SP 2 Tay 611-8 1RR019<br />
Table 11: List of <strong>AERO2k</strong> representative aircraft <strong>and</strong> engine types<br />
The choice of representative aircraft is, by necessity, not an entirely objective process<br />
requiring some element of ‘expert judgement’, although the methodology set out <strong>and</strong> used<br />
defines some of the criteria that are objective in nature. Additionally, there is an element of<br />
pragmatism dictated by available data/models <strong>and</strong> necessity <strong>for</strong> compatibility with other<br />
exercises requiring data-reduction (e.g. <strong>for</strong>ecasting). This compatibility was the primary<br />
driver, the seat b<strong>and</strong> <strong>and</strong> technology categories <strong>for</strong>ming the basis of the initial division of<br />
aircraft groups. In some cases there is more than one representative type within a<br />
category, where <strong>for</strong> instance there are aircraft within the same category with different<br />
numbers of engines.<br />
Likewise, the choice of representative engines is a mixture of objective <strong>and</strong> subjective<br />
criteria. Included in this process is the development of generic engines, in order to define<br />
the closest real engine. This process is based upon the ICAO LTO Certification Data in the<br />
<strong>Emissions</strong> Databank, which are generated in closely-specified sea-level tests, so do not<br />
necessarily reflect per<strong>for</strong>mance at altitude, where the bulk of emission takes place.<br />
Accepting the limitations of this 10 , comparisons have been made based on NOx data from<br />
this source.<br />
8<br />
Turboprop engine data <strong>for</strong> these types are available from FOI Aeronautics Division, FFA,<br />
Sweden.<br />
9<br />
DLR have data available <strong>for</strong> the engine types not represented in the <strong>Emissions</strong> Databank.<br />
10<br />
Limitations of the LTO data are that it provides emissions data <strong>for</strong> NOx, CO <strong>and</strong> HC only, at<br />
ground level conditions. CO <strong>and</strong> HC are primarily of concern to local air quality, <strong>and</strong> emissions<br />
of these at altitude flight conditions are at very low levels. The smoke data provided in the<br />
emissions databank are very limited, as smoke (as a smoke number) has to be reported only at<br />
the worst condition <strong>and</strong> there is no requirement to report that condition (power setting).<br />
Thus data <strong>for</strong> NOx (which has been the pollutant of primary concern <strong>for</strong> some time at typical<br />
flight altitudes) will be used to make comparisons of engine emissions characteristics.<br />
QINETIQ/04/01113 Page 28
Fuel Profiling <strong>and</strong> Prediction<br />
Fuel profiling <strong>and</strong> prediction takes place within the <strong>AERO2k</strong> Data Integration Tool (Figure<br />
19).<br />
The method <strong>for</strong> assigning fuel data to the flight profiles in the flights relies on a series of<br />
data-tables as follows:<br />
1. Take-off. Using 60.9% of maximum payload, estimate the take-off weight <strong>for</strong> the<br />
mission range to be flown. Taxi, take-off <strong>and</strong> climb out (to 3000ft) data from emissions<br />
databank <strong>and</strong> airport-specific departure times-in-mode look-up table.<br />
2. Climb (>3000ft). Determine initial cruise altitude from the profile data, calculate fuel<br />
used in climb from climb data tables, re-calculate aircraft mass at top of climb, <strong>and</strong><br />
calculate distance flown.<br />
3. Cruise. Select appropriate cruise fuel flow data from the cruise data tables, <strong>for</strong> the<br />
altitude, Mach number <strong>and</strong> aircraft mass. Continue to re-calculate distance flown <strong>and</strong><br />
aircraft mass through-out the cruise segment.<br />
4. Step-climb or mid-cruise descent if appropriate, then repeat Cruise step.<br />
5. Descent (to 3000ft). Descent fuel from final cruise altitude to 3000ft calculated from<br />
descent data tables.<br />
6. L<strong>and</strong>ing. Data from emissions databank <strong>and</strong> airport-specific arrival times-in-mode lookup<br />
table.<br />
The method chosen necessitated the adoption of a number of assumptions <strong>for</strong> assigning<br />
fuel data to flight profiles. These assumptions have been split into two categories.<br />
Background assumptions are described in Section 2.4.4. These are a priori assumptions that<br />
have been made out of necessity to simplify the modelling process or due to a lack of<br />
available data or the variability of some features, e.g. the extent to which fuel tankering<br />
takes place. Many of these assumptions have been made due to unavoidable limitations of<br />
the flight movement data, <strong>for</strong> example, the flight movement data details, <strong>for</strong> each flight,<br />
the aircraft type, location in space (longitude, latitude, altitude) <strong>and</strong> time at a number of<br />
intervals throughout the flight. The limitation of this data is that it does not include the<br />
aircraft weight (mass) or flight speed, <strong>and</strong> <strong>for</strong> some flights the intervals can be spaced too<br />
far apart to capture precise details about the time or location of direction or altitude<br />
changes.<br />
The specific aircraft per<strong>for</strong>mance assumptions used are described below. These are<br />
quantifiable assumptions that must be made as accurately as practicably possible within<br />
the constraints of computing capability <strong>and</strong> available data.<br />
It is noted that each of these sets of assumptions will have an impact on the quantity of<br />
fuel <strong>and</strong> emissions recorded in the inventory. Where it has been possible, the effect of these<br />
assumptions on fuel burn has been investigated by parametric study or from available<br />
sources of in<strong>for</strong>mation in the public domain. A decision was taken early in the <strong>AERO2k</strong><br />
project that arbitrary increases to the fuel burn or emissions would not be made to account<br />
<strong>for</strong> effects that cannot be accurately modelled.<br />
QINETIQ/04/01113 Page 29
Aircraft per<strong>for</strong>mance assumptions<br />
The flight movement data <strong>for</strong> each flight detail the aircraft type, location in space<br />
(longitude, latitude, altitude) <strong>and</strong> time at a number of intervals throughout the flight. The<br />
variable resolution of this data has an impact on the modelling of the aircraft per<strong>for</strong>mance<br />
<strong>and</strong> there<strong>for</strong>e fuel burn during flight. The assumptions required to deal with this are<br />
described below.<br />
The L<strong>and</strong>ing <strong>and</strong> Take-Off (LTO) cycle<br />
There are several assumptions made which concern the L<strong>and</strong>ing <strong>and</strong> Take-Off (LTO) cycle,<br />
which encompasses all operation below 3000ft altitude. Time in mode data are provided<br />
from research carried out within the <strong>AERO2k</strong> project, which is used in conjunction with<br />
emissions data from the Aircraft Engine Exhaust <strong>Emissions</strong> Databank [QinetiQ, 2004]. The<br />
LTO data provide average times in mode on an airport-by-airport basis <strong>for</strong> all airports. An<br />
implicit assumption is that the fuel <strong>and</strong> emissions data from the emissions databank,<br />
which are recorded <strong>for</strong> use in emissions certification, are sufficiently relevant <strong>and</strong><br />
acceptable <strong>for</strong> use <strong>for</strong> inventory work.<br />
When the fuel <strong>and</strong> emissions data are allocated onto the global grid, all the data from the<br />
LTO cycle will be allocated to the grid cell (longitude, latitude) in which the airport sits. No<br />
account will be made <strong>for</strong> horizontal distance travelled, so that the first flight point <strong>and</strong> final<br />
flight point which cross the 3000ft boundary will be assumed to be immediately above the<br />
airport. This approach has been adopted <strong>for</strong> computational reasons <strong>and</strong> is not expected to<br />
have any effect on the results.<br />
The final assumption <strong>for</strong> LTO operations in <strong>AERO2k</strong> is that all airports are at sea level. In<br />
reality this would impact time to climb to cruise altitude <strong>and</strong> the overall aircraft<br />
per<strong>for</strong>mance. This is not expected to have a significant impact on the overall inventory<br />
result as the total fuel consumed during all LTO operations is small relative to total aviation<br />
fuel (
mass of payload carried, on a global average basis the error due to payload variation will be<br />
small when using the global average figure of 60.9% of maximum payload <strong>for</strong> all aircraft.<br />
Reserves<br />
All commercial aircraft flights must carry a certain quantity of fuel in addition to the fuel<br />
required to per<strong>for</strong>m the mission, as a contingency in case of diversion to another airport or<br />
other problems. The fuel required <strong>for</strong> a mission is calculated based on the distance <strong>and</strong><br />
aircraft mass plus payload, <strong>and</strong> taking into account other factors such as the weather,<br />
including the likelihood of having to divert off-course or to another airport due to sudden<br />
change of weather conditions. On top of this, contingency reserve fuel will be added.<br />
Carrying reserve fuel is a m<strong>and</strong>atory requirement, but the exact quantity required to be<br />
carried is variable country to country, <strong>and</strong> may vary according to airline policy. The reserve<br />
quantity is usually a function of mission range or the quantity of mission fuel, <strong>and</strong> depends<br />
on whether the flight is international or domestic, or long <strong>and</strong> short haul.<br />
In <strong>AERO2k</strong>, reserves allowances <strong>for</strong> long haul flights includes an additional 5% of the fuel<br />
required to complete the mission, plus fuel <strong>for</strong> a 200nm diversion <strong>and</strong> a 30 minute low<br />
altitude hold; whereas <strong>for</strong> short haul flights the reserves allowances will typically be 5% of<br />
the fuel required to complete the mission plus fuel <strong>for</strong> a 100nm diversion <strong>and</strong> a 45 minute<br />
low altitude hold.<br />
Flight speeds<br />
The flight speed of aircraft can vary depending on altitude, headwinds <strong>and</strong> airline practice.<br />
The flight movement contains data <strong>for</strong> longitude, latitude <strong>and</strong> time, but low resolution on<br />
the time data <strong>and</strong> no data on winds make calculation of flight speed inaccurate. There<strong>for</strong>e a<br />
st<strong>and</strong>ard flight speed has been used in the modelling. Airlines typically use a number of<br />
operating speeds depending on route, type of operation, etc. As it happens, fuel<br />
consumption rate is relatively insensitive to small changes of speed either side of the speed<br />
<strong>for</strong> minimum fuel consumption. This speed minimises fuel burn per mile (or maximises<br />
specific air range, or SAR) <strong>for</strong> a given weight <strong>and</strong> altitude, <strong>and</strong> is commonly referred to as<br />
the maximum range cruise (MRC) speed. The MRC speed is not necessarily the optimum<br />
when other operating variables (e.g. time) are taken into account. It is not possible to model<br />
ECON cruising speed which does take operating costs into account, as the compromise<br />
between time <strong>and</strong> cost is airline-specific. A common compromise which is used in airline<br />
practice is to fly faster than the MRC speed at a point which gives 99% of the maximum<br />
SAR. This is commonly also called the long-range cruise (LRC) speed. The speed which gives<br />
99% of the maximum SAR is used in <strong>AERO2k</strong>.<br />
2.1.3 <strong>Emissions</strong> Parameterisation <strong>for</strong> Civil Aircraft<br />
Having allocated representative aircraft <strong>and</strong> engines <strong>and</strong> calculated fuel usage <strong>for</strong> each<br />
flight segment, emissions are calculated using emission indices. These indices are based on<br />
publicly available engine data <strong>and</strong> are described in the following paragraphs. Of particular<br />
note is the inclusion of an estimation of particulate number <strong>and</strong> mass within <strong>AERO2k</strong>. Data<br />
on gas turbine particulate emissions are not widely available <strong>and</strong>, in comparison with other<br />
emissions, there are greater uncertainties surrounding the actual values in flight.<br />
Correlation algorithms have been generated from recent research in Europe <strong>and</strong> it is<br />
believed that this is the first time that such particulate data have been included in a global<br />
inventory. Together with the new “distance flown” parameter, this in<strong>for</strong>mation should<br />
provide a foundation <strong>for</strong> estimation of climate impact of cirrus <strong>and</strong> contrails from aircraft.<br />
CO 2 <strong>and</strong> H 2O<br />
Carbon dioxide <strong>and</strong> water are the main products of the combustion of hydrocarbon fuel<br />
<strong>and</strong> are there<strong>for</strong>e directly coupled to fuel mass flow. 1kg of Jet A-1 with the mean totals<br />
<strong>for</strong>mula C12H23 produces, completely oxidised, 3156g CO2 <strong>and</strong> 1237g H2O [Rachner, 1998].<br />
In the strict sense, partially or unburned species (CO <strong>and</strong> HC) have to be subtracted from<br />
those. The amount of hydrocarbons emitted is less than 1% of the CO2 <strong>and</strong> H2O emissions<br />
QINETIQ/04/01113 Page 31
<strong>and</strong> their hydrogen/carbon ratio is unknown, there<strong>for</strong>e they will be neglected. To subtract<br />
the CO from the CO2 emissions, their different molar mass has to be taken into account:<br />
NO x<br />
EICO2 = EICO2,ideal - 44/28 x EICO<br />
The production of thermal NOX in the combustor of an aircraft engine is coupled with the<br />
chemical reaction process within the hot flame region of the primary combustion zone <strong>and</strong><br />
the residence time of the reacting species. The dominating reaction with respect to this<br />
time is the <strong>for</strong>mation of nitric oxide (NO) <strong>and</strong> atomic nitrogen (N) out of atomic oxygen (O)<br />
<strong>and</strong> gaseous nitrogen (N2) as prescribed by the first equation of the extended Zeldovich<br />
mechanism [Heywood, 1973]. However, such theoretical approaches require knowledge of<br />
actual engine pressures <strong>and</strong> temperatures, data which is, <strong>for</strong> most engines, proprietary: i.e.<br />
they are not publicly available,<br />
To avoid this problem, a method can be used based on correlating the EI NOX with fuel flow.<br />
For this, a reference function of the emission index versus engine fuel flow has to be<br />
established by measurements under reference inlet conditions. This could <strong>for</strong> instance be<br />
done by choosing the four data points from certification measurements <strong>for</strong> an engine,<br />
which represent different thrust settings at sea level static (SLS) <strong>and</strong> international st<strong>and</strong>ard<br />
atmospheric (ISA) conditions. These four data points are published in the ICAO engine<br />
emissions data bank [ICAO, 1995].<br />
Hence if an engine is running at other than ISA SLS conditions all pressures (p3) <strong>and</strong><br />
temperatures (T3) at the combustor inlet plotted against fuel flow (wfuel) will line up to one<br />
function when pressures, temperatures <strong>and</strong> fuel flow are corrected to reference engine<br />
inlet conditions (Tt,1 <strong>and</strong> Pt,1) by the following equations:<br />
Equation A<br />
Equation B<br />
Equation C<br />
T<br />
T<br />
3<br />
3 , corr = with<br />
θt<br />
p3<br />
P3<br />
= with<br />
, corr<br />
δ<br />
t<br />
θ<br />
t<br />
Tt , 1<br />
=<br />
288.<br />
15K<br />
pt, 1<br />
δ t =<br />
101.<br />
3kPa<br />
Some idealisation has been made here in assuming, that <strong>for</strong> a real similarity the<br />
compressor polytropic efficiency <strong>and</strong> the combustion efficiency is constant. Another<br />
approximation using an EI NOX correlation <strong>for</strong>mula has to be made in avoiding T3 to appear<br />
in the exponent.<br />
There<strong>for</strong>e the following simplification has been used:<br />
In combination with Equation A <strong>and</strong> Equation B the following relationship can be<br />
established:<br />
QINETIQ/04/01113 Page 32
Since at ISA <strong>and</strong> SLS engine inlet conditions δt = θt = 1 is given, the EI NOX then represents<br />
the corrected i.e. the reference value. To gain the exponents a <strong>and</strong> b it has been considered<br />
that from theory a = 0.5 would be the value of choice, but in reality observed from<br />
measurements <strong>for</strong> the CF6 engine family show that an exponent of a = 0.4 is more<br />
appropriate [Bahr, 1991].<br />
For a pressure exponent in the range of 0.4 to 0.5, a temperature exponent of about b = 3<br />
would be well in line with theoretical methods. Thus the exponents of a first choice were: a<br />
= 0.4 <strong>and</strong> b = 3.<br />
An application of this simplified method of EI NOX versus fuel flow correlation is shown in<br />
Figure 13. The analysis of different engine inlet operating conditions reveals the collapse of<br />
values by correcting with respect to the ISA SLS reference condition. Although the actual EI<br />
NOX values during flight operation were calculated from a more sophisticated prediction<br />
method [Deidewig, 1996], the application of the fuel flow method shows the expected<br />
effect.<br />
Figure 13: EI NOX versus fuel flow <strong>for</strong> CF6-80C2B1F showing “corrected” values<br />
It should be noted that the data provided in <strong>AERO2k</strong> represents all oxides of nitrogen<br />
calculated to be emitted from the engine jetpipe. At that point, only around 5 to 10% of the<br />
total NOx is in the <strong>for</strong>m of nitrogen dioxide (NO2) [Wilson 2001]. Further conversion takes<br />
place downstream, although the rate <strong>and</strong> extend is dependent upon the properties of the<br />
jet plume <strong>and</strong> speed <strong>and</strong> altitude of the aircraft.<br />
CO <strong>and</strong> HC<br />
Carbon monoxide <strong>and</strong> hydrocarbons are products of an incomplete combustion of a fossil<br />
fuel. There<strong>for</strong>e they are directly coupled with the combustion efficiency ηC. If one sets the<br />
lower heat value of the unburned hydrocarbons equal to the lower heat value of the<br />
kerosene, one can derive the following equation [Dodds, 1990]:<br />
Equation D<br />
This equation has been applied to the ICAO SLS data of engines developed in different<br />
decades, see Figure 14.<br />
QINETIQ/04/01113 Page 33
Combustion Efficiency [%]<br />
100<br />
98<br />
96<br />
94<br />
92<br />
90<br />
88<br />
86<br />
JT3D-3B, ca. 1962<br />
RB211-524B, ca. 1975<br />
CF6-80C2B1F, ca. 1982<br />
GE90-85B, ca. 1994<br />
0 10 20 30 40 50 60 70 80 90 100<br />
Thrust [%]<br />
Figure 14: Calculated combustion efficiencies versus thrust at SLS following [ICAO, 1995] <strong>and</strong><br />
Equation C.<br />
The combustion efficiency of an aircraft engine can be correlated with a parameter called<br />
Ω, which is the reciprocal value of the simplified combustor loading parameter Θ (see <strong>for</strong><br />
example [Münzberg, 1977] <strong>and</strong> [Lefebvre, 1983]).<br />
Equation E<br />
The idea of the emission correlation explained here is to use Ω as the reference function <strong>for</strong><br />
CO <strong>and</strong> HC. Due to the fact, that the volume of the combustor VC is unknown in most cases,<br />
but a constant value, the parameter (Ω ⋅ VC) has been introduced. This leads to emission<br />
correlations of the following <strong>for</strong>m:<br />
Equation F<br />
Figure 15 shows the CO <strong>and</strong> HC SLS emission indices <strong>for</strong> the CF6-50C2 published in [ICAO,<br />
1995] versus (Ω ⋅ VC) used as reference function.<br />
QINETIQ/04/01113 Page 34
EI CO, EI HC [g/kg]<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
Take Off<br />
EI CO=369,6(Ω∗V C) 2 -14,96(Ω∗V C)+0,647<br />
EI HC=158,6(Ω∗V C) 2 -19,12(Ω∗V C)+0,985<br />
0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45<br />
Ω ∗ VC [kg/s/bar 1.8 ]<br />
Figure 15: Measured EI CO <strong>and</strong> EI HC versus (Ω.VC) <strong>for</strong> CF6-50C2<br />
At very high altitudes the evaporation properties of aircraft engines may change due to<br />
lower temperatures <strong>and</strong> pressures in the combustion chamber <strong>and</strong> lower mass flows of fuel<br />
<strong>and</strong> air through the injection system. There<strong>for</strong>e a correction concerning the evaporation<br />
time tE based on the changing Sauter mean diameter (SMD) in comparison to a reference<br />
value at SLS(ref) has been developed [Döpelheuer, 1997]. This finally leads to correlations of<br />
the <strong>for</strong>m:<br />
Equation G<br />
The correlations have been validated with P&W 305 data. The results are shown in Figure<br />
16. Open symbols represent uncorrected data (Equation F), filled symbols represent data<br />
correlated with the correction term concerning the evaporation time (Equation G). For the<br />
P&W 305 the value <strong>for</strong> the constant, c, resulting from the experiments is 0.4.<br />
QINETIQ/04/01113 Page 35<br />
Idle
Calculated EI CO [g/kg]<br />
50<br />
45<br />
40<br />
35<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
Take Off<br />
without correction<br />
0 0,01 0,02 0,03 0,04 0,05 0,06 0,07 0,08<br />
Ω ∗ V C [kg/s/bar 1,8 ]<br />
Figure 16: Calculated EI CO versus ( VC) <strong>for</strong> P&W 305<br />
with correction<br />
ICAO SLS data<br />
26000 ft altitude<br />
35000 ft "<br />
40000 ft "<br />
43000 ft "<br />
50000 ft "<br />
Up to an altitude of 35000 ft the data correlated with Equation F (uncorrected, open<br />
symbols) lie on a line directly determined from the SLS data <strong>and</strong> used as reference function.<br />
But <strong>for</strong> altitudes above 35000 ft the values are higher than the expected values due to<br />
evaporation properties getting worse.<br />
The values correlated with Equation G (corrected, filled symbols) all lie on the SLS reference<br />
function. The altitude emission index of CO <strong>for</strong> the P&W 305 can thereby be determined by<br />
evaluating the altitude value of (Ω ⋅ VC) with Equation F <strong>and</strong> finding the corresponding SLS<br />
reference value of EI CO (see arrows in Figure 16). Hence this method determines altitude<br />
emission indices out of altitude combustor inlet conditions in combination with ground<br />
measurements.<br />
As <strong>for</strong> NOx, the HC emissions calculated represent the full range of unburnt hydrocarbons<br />
emitted from the engine jetpipe. The proportion of methane to other longer chain<br />
hydrocarbons varies significantly with engine type <strong>and</strong> engine conditions.<br />
Soot<br />
The soot production <strong>and</strong> oxidation mechanism is very complex <strong>and</strong> not well known. In<br />
particular, the non-homogeneous flow <strong>and</strong> temperature fields in the combustion chamber,<br />
the different influences of the injection systems <strong>and</strong> combustor technologies, the influence<br />
of the type of fuel burned <strong>and</strong> the rare measurements make it difficult to calculate the<br />
amount of soot emitted by aircraft engines. Furthermore neither a soot emission index nor<br />
a soot concentration, but only the Smoke Number SN is measured <strong>for</strong> the ICAO certification<br />
process, <strong>and</strong> only then at the worst of the four measured conditions, see Figure 17.<br />
QINETIQ/04/01113 Page 36<br />
Idle
SN in % from value at 100 % SLS-Thrust<br />
120<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
0 10 20 30 40 50 60 70 80 90 100<br />
SLS-Thrust in %<br />
Figure 17: SN versus SLS-thrust in percentage terms <strong>for</strong> several engines [ICAO, 1995]<br />
CFM56-3C<br />
(Rerated)<br />
CFM56-5C2<br />
CF6-50C1, -C2<br />
CF6-80C2B2F<br />
RB211-22B<br />
RB211-524B<br />
series<br />
ALF 502R-5<br />
Figure 17 shows the extremely variable behaviour between engines regarding the quantity<br />
of soot emitted. Furthermore the absolute values of the SN not shown in this figure differ<br />
widely. A semi-empirical soot correlation with variable reference values has been developed<br />
to cope with these problems [Döpelheuer, 1997]. The different variable reference functions<br />
<strong>for</strong> every investigated engine taken from ground measurements consider the special<br />
properties of the combustor <strong>and</strong> injection system, the actual operating condition is taken<br />
into account by thermodynamic combustor data.<br />
To derive a suitable soot correlation function a two step process has been employed. As a<br />
first step, the soot concentration values CSoot at SLS have to be determined out of the ICAO<br />
SN measurements. The connection between CSoot <strong>and</strong> the SN is dependant on the soot<br />
properties (especially the particle size distribution) of the engine looked at. A combination<br />
of the results of [Champagne, 1971] <strong>and</strong> [Hurley, 1993] has been used <strong>for</strong> concentrations<br />
up to 6 mg/m 3 , <strong>for</strong> higher concentrations the function of [Whyte, 1982] has been applied.<br />
The necessary reference function <strong>for</strong> the soot correlation introduced here is of the <strong>for</strong>m CSoot<br />
versus T3 <strong>for</strong> SLS conditions. The amount of soot produced varies significantly with the<br />
different injection systems <strong>and</strong> combustor technologies used. There<strong>for</strong>e a soot correlation<br />
on the basis of a variable reference function will be used.<br />
As a second step, the emission index of soot <strong>for</strong> other than sea level static conditions is<br />
calculated with the help of these reference functions <strong>and</strong> actual thermodynamic data<br />
effecting the soot production. These are the combustor inlet pressure p3, the flame<br />
temperature Tfl <strong>and</strong> the equivalence ratio Φ as parameter considering the actual ratio of<br />
atomic carbon to atomic oxygen C/O. Measured results of model flames <strong>and</strong> combustor<br />
tests have been combined to determine a soot correlation of the following <strong>for</strong>m:<br />
QINETIQ/04/01113 Page 37
Equation H<br />
The reference variables (ref) are the SLS values at the same combustor inlet temperature T3<br />
as the operating condition looked at. The other variables are the actual values obtained<br />
from the investigated operating condition.<br />
It should be noted that the particulate mass algorithms seek to quantify non-volatile<br />
particles only. No reliable data is available to quantify volatile particles in terms of either<br />
mass or number.<br />
Particulate Numbers<br />
More than the mass of soot emitted by aircraft engines, the number (<strong>and</strong> size) of particles is<br />
important in order to estimate the effects of soot emissions on climate <strong>and</strong> health. Since<br />
the emitted soot particles are not of uni<strong>for</strong>m diameter, a model is needed that describes<br />
the size distribution of the particles as accurately as possible. It is generally accepted <strong>and</strong><br />
supported by measurements <strong>and</strong> electron-microscopic observation, that the log-normal<br />
distribution is a good representation of the real size distribution of aircraft engine soot<br />
particles. Using the log-normal distribution, the soot aerosol properties are determined by<br />
three parameters:<br />
• The particle number concentration N<br />
• The geometric mean diameter μ<br />
• The geometric st<strong>and</strong>ard deviation σ<br />
Some in<strong>for</strong>mation on these soot parameters is available from measurements of soot<br />
emissions from different aircraft engine types. In [Döpelheuer, <strong>2002</strong>] these measurements<br />
are summarised <strong>and</strong> a model is created that reflects the general dependency of μ <strong>and</strong> σ on<br />
engine parameters. The data show that the geometric mean diameter is growing with<br />
increasing combustor inlet pressure <strong>and</strong> temperature, while the st<strong>and</strong>ard deviation is not<br />
dependant on engine parameters but only on the soot mass concentration, which is<br />
available from the above soot correlation. Based on this in<strong>for</strong>mation a correlation of μ <strong>and</strong><br />
σ with engine parameters has been developed.<br />
With the particle size distribution parameters available, the particle number concentration<br />
can be determined by equating the soot mass concentration determined by the soot<br />
correlation with that resulting from the particle size distribution, if a model of the<br />
diameter-dependant material density of the particles is available. This model has also been<br />
developed in [Döpelheuer, <strong>2002</strong>] <strong>and</strong> integrated into a method to determine the number<br />
concentration of aircraft engine soot aerosol.<br />
Since these methods need individually measured parameters of the particle size<br />
distribution (which are not routinely measured during engine certification like e.g. the<br />
smoke number), only a small number of engine measurements was available <strong>for</strong> validation<br />
<strong>and</strong> application (<strong>for</strong> a list of the respective publications see [Döpelheuer, <strong>2002</strong>]). Although<br />
the number of available measurements is small, many different engine types are covered<br />
(Low BPR, high BPR, civil <strong>and</strong> military engines). A combined analysis of these measurements<br />
yields to a general characteristic of particle number per gram of aircraft engine generated<br />
soot versus flight altitude as shown in Figure 18:<br />
QINETIQ/04/01113 Page 38
Particle Number per Gram of Soot<br />
1,8E+16<br />
1,6E+16<br />
1,4E+16<br />
1,2E+16<br />
1E+16<br />
8E+15<br />
6E+15<br />
4E+15<br />
2E+15<br />
0<br />
0 2000 4000 6000 8000 10000 12000 14000 16000 18000<br />
Altitude [m]<br />
Figure 18: Particle number per gram of soot dependant on the flight altitude (global fleet<br />
average)<br />
Due to the lack of measured data of the individual engine types, this function has been<br />
used to determine the (non-volatile) particle number EIs of the representative engines. It is<br />
obvious that this procedure is not suited to deliver accurate results <strong>for</strong> individual flights,<br />
but instead delivers the best estimate possible with the available data <strong>for</strong> a number of<br />
aircraft movements.<br />
2.1.4 Data Integration <strong>and</strong> Calculation <strong>for</strong> Civil <strong>Aviation</strong><br />
The sections above have described the methods used to calculate fuel <strong>and</strong> emissions <strong>for</strong><br />
each flight. In this section, the description covers the data integration <strong>and</strong> calculation<br />
software which per<strong>for</strong>ms the actual calculation <strong>and</strong> generates the gridded data. In outline,<br />
this process requires that flight data <strong>for</strong> each individual flight are taken from the Air Traffic<br />
Movements Database (Section 2.1.1). Representative aircraft <strong>and</strong> engines are allocated to<br />
each flight in accordance with the table generated from the output of the Aircraft<br />
Representation, Profiling <strong>and</strong> Fuel Prediction Module (Section 2.1.2). Take off weight is<br />
calculated from the great circle mission length plus allowances <strong>for</strong> diversion <strong>and</strong> delay.<br />
<strong>Emissions</strong> are calculated <strong>for</strong> each flight using emissions indices, i.e. emission per kg of fuel<br />
used, <strong>for</strong> each representative aircraft. These emission indices (EIs) were generated in the<br />
<strong>Emissions</strong> Parameterisation Module <strong>and</strong> they vary with Mach number, throttle setting <strong>and</strong><br />
altitude (Section 2.1.3). The resultant data provide emissions <strong>and</strong> fuel used <strong>for</strong> each leg of<br />
each flight. A leg represents a portion of a flight <strong>for</strong> which identifiable data are available.<br />
There are about 30 legs per flight on average <strong>and</strong> the geographical location of each leg is<br />
known through its 4-D start <strong>and</strong> finish coordinates (latitude, longitude, altitude <strong>and</strong> time).<br />
This process is shown diagrammatically in Figure 19:<br />
QINETIQ/04/01113 Page 39
Figure 19: Data integration process<br />
Plotting these data on a grid of longitude, latitude <strong>and</strong> altitude is carried out on a flight-byflight<br />
basis, allocating emissions to each of the geographical grid cells through which the<br />
flight passes. Cell size is selectable within <strong>AERO2k</strong>. The chosen output cell size of 1 deg by 1<br />
deg by 500ft represents a manageable data size whilst giving the resolution required <strong>for</strong><br />
climatologists. If required, the <strong>AERO2k</strong> tool can provide higher resolution grids until<br />
limitations are reached due to resolution of the input data. At this stage, distance flown in<br />
each grid cell is also calculated.<br />
Dependant upon the <strong>for</strong>m of output data required, the 42 tables of daily flight-by-flight<br />
emissions data may be queried <strong>and</strong> manipulated. No general automation of this process<br />
has been implemented within <strong>AERO2k</strong>.<br />
For the <strong>AERO2k</strong> <strong>2002</strong> emissions inventory, the 42 data tables are geographically gridded<br />
<strong>and</strong> then annualised to provide the main gridded output. Additional MS Access queries are<br />
also run on the data to provide aircraft-type, regional <strong>and</strong> global totals <strong>for</strong> distance-flown,<br />
fuel-used <strong>and</strong> emissions.<br />
<strong>Emissions</strong> gridding is carried out by analysing each flight segment in turn from the chosen<br />
database of flights. For analysis of the <strong>2002</strong> data, this database would consist of one of the<br />
42 representative days. For each flight segment, the crossing-points into <strong>and</strong> out of each<br />
grid cell are computed using navigational <strong>for</strong>mulae. The emissions <strong>for</strong> each flight segment<br />
are than allocated into each cell accordingly. The process is repeated <strong>for</strong> every flight to<br />
produce the total (daily) gridded data.<br />
To produce the 6-hourly data <strong>for</strong> the diurnal variation analysis, representative days are<br />
divided into 4 six-hourly periods be<strong>for</strong>e gridding.<br />
To annualise the gridded data from each of the 42 representative days, each actual day of<br />
the year <strong>2002</strong> is allocated to a representative day in accordance with Table 4.<br />
Each of the 42 representative days there<strong>for</strong>e represents a whole number of actual days <strong>and</strong><br />
it is a simple but data-intensive process to multiply the data <strong>for</strong> each of the representative<br />
days by the appropriate whole number. The sum of these 42 days multiplied by the number<br />
of representative days that they represent then <strong>for</strong>ms the total distance-flown, fuel-used<br />
<strong>and</strong> emissions <strong>for</strong> civil aviation <strong>for</strong> <strong>2002</strong>.<br />
Output from the gridding process is a table, approximately 0.5Gb in size, containing the<br />
fuel-used, emissions <strong>and</strong> distance flown in each of the 3.24 million cells (<strong>for</strong> the 1deg x<br />
1deg x 500ft grid). Many of these cells are of course empty. For <strong>AERO2k</strong>, the <strong>2002</strong> data are<br />
presented in the <strong>for</strong>m of 12 monthly tables of gridded emissions together with four tables<br />
QINETIQ/04/01113 Page 40
each representing a week-averaged figure <strong>for</strong> the 4 six-hourly period of a day. The data are<br />
presented on the <strong>AERO2k</strong> website at http://www.cate.mmu.ac.uk/aero2k.asp.<br />
The gridded emissions described above represent the main output of the <strong>AERO2k</strong> project.<br />
However the annualised data provide a rich source of data to provide further insight into<br />
civil aviation emissions.<br />
To assist policymakers <strong>and</strong> other stakeholders, a small number of data queries have been<br />
undertaken on the 42 representative days to provide totalled data <strong>for</strong> each fuel used <strong>and</strong><br />
emission type, set against representative aircraft-type plus a range of global <strong>and</strong> regional<br />
breakdowns.<br />
As an example, regional data are available within the airports table, each airport being<br />
assigned to one of seven global regions defined by Eurocontrol. Hence flights in each<br />
representative day can be assigned, along with their emissions, to any region. According to<br />
whether the flight terminates in the same or a different region, the emissions can be<br />
assigned to intra- or inter-regional flights <strong>and</strong> then annualised <strong>for</strong> <strong>2002</strong>. As a future<br />
application <strong>for</strong> <strong>AERO2k</strong>, similar analysis could be carried out <strong>for</strong> individual countries on a<br />
domestic/international flight basis.<br />
Results from these data queries are reported in Section 3.<br />
2.2 <strong>2002</strong> <strong>Emissions</strong> from Military Flights<br />
The <strong>2002</strong> military emissions inventory has been compiled entirely separately from the civil<br />
inventory. Whilst it had initially been hoped to assemble military flight inventories, fuel <strong>and</strong><br />
emissions parameters as part of a single flight inventory, national security prevented the<br />
same quality of data being made available to <strong>AERO2k</strong>. Rather than dilute the civil aviation<br />
data with approximations <strong>for</strong> military aviation, the two gridded inventories have been<br />
compiled <strong>and</strong> presented separately. Despite these reservations, it is believed that the<br />
military data represent a significant step <strong>for</strong>ward in knowledge of global emissions from<br />
military aviation. A 3-dimensional military aviation gridded inventory has been constructed<br />
to include fuel, carbon monoxide (CO), unburnt hydrocarbons (HC), nitrogen-oxides (NOx),<br />
carbon dioxide CO2 <strong>and</strong> water vapour H2O. No attempt has been made to estimate<br />
particulate data due the shortage of reliable in<strong>for</strong>mation.<br />
2.2.1 Approach<br />
The paucity of the military movements data, in particular radar data, the lack of emissions<br />
certification data <strong>for</strong> military engines, the wide diversity in missions <strong>and</strong> their<br />
characteristics, the wide variety of aircraft types <strong>and</strong> confidentiality of specific<br />
per<strong>for</strong>mance, lead to a general lack of availability of sufficient data. This finding <strong>for</strong>ces the<br />
adoption of an approach <strong>for</strong> military aviation emissions <strong>and</strong> movement inventories that is<br />
quite different to the civil aviation approach, while maintaining a similar overall work<br />
breakdown. This overall work breakdown (<strong>for</strong> both civil <strong>and</strong> military inventories) comprises<br />
the following steps:<br />
• Air traffic movements database construction, (<strong>for</strong> the military inventory on a per<br />
country basis, <strong>for</strong> the civil inventory on radar track records).<br />
• Aircraft representation profiling <strong>and</strong> fuel prediction yielding typical flight profiles of<br />
typical military missions. This includes the development of conversion factors to<br />
convert representative aircraft <strong>and</strong> representative missions into any aircraft-enginemission<br />
combinations.<br />
• Emission parameterisation, <strong>for</strong> military aircraft including addressing specific military<br />
issues such as afterburning. These parameterisation methods are then used to<br />
generate the emissions along flight profiles.<br />
QINETIQ/04/01113 Page 41
2.2.2 Air traffic <strong>and</strong> emissions <strong>for</strong>ecasting.<br />
Given the general lack of radar data on military movements a comparable approach to the<br />
civil inventory compilation cannot be directly followed. As an alternative, the foundation<br />
<strong>for</strong> the military movement database is:<br />
• The aircraft type, engine type <strong>and</strong> number in fleet per aircraft type per country.<br />
• The mission(s) to be per<strong>for</strong>med by aircraft type.<br />
• The utilisation of aircraft expressed in flying hours per year.<br />
• Selection of a limited set of representative aircraft <strong>and</strong> missions.<br />
• Selection of suitable scaling factors to convert specific aircraft <strong>and</strong> mission into any<br />
aircraft-mission.<br />
• Simulation of fuel consumption <strong>and</strong> emissions along the flight path of representative<br />
aircraft-missions.<br />
For a limited number of countries having a high number of aircraft <strong>and</strong> flights, there is<br />
additional in<strong>for</strong>mation compiled on:<br />
• The location of air <strong>for</strong>ce bases<br />
• The type <strong>and</strong> number of aircraft allocated to each of these air <strong>for</strong>ce bases.<br />
• Military airspace lateral <strong>and</strong> altitude limits.<br />
The above-described in<strong>for</strong>mation allows the building of a movements database. The data<br />
h<strong>and</strong>ling process is depicted in Figure 20. The green boxes denote data. The grey ellipses<br />
denote data processing or linking.<br />
The large centre grey boxes denote specific software tools that are embedded in the data<br />
processing. The E-MISSION (military (e-) mission analysis) program is effectively a coupling<br />
of the NLR Gas turbine Simulation Program GSP <strong>and</strong> a military mission per<strong>for</strong>mance<br />
program to calculate time, fuel <strong>and</strong> emission profiles from military aircraft per<strong>for</strong>mance,<br />
engine characteristics <strong>and</strong> mission profiles. The Flight Profile tool is a MATLAB tool to<br />
combine all relevant military in<strong>for</strong>mation (fleet inventory, airspace, bases, utilisation, fuel<br />
<strong>and</strong> emission profiles) <strong>and</strong> processes the in<strong>for</strong>mation into a 3-dimensional grid <strong>for</strong><br />
emissions <strong>and</strong> fuel distribution. This grid is then the interface to the emissions grid building<br />
<strong>and</strong> <strong>for</strong>ecasting parts.<br />
Fleet per Country<br />
4<br />
Aircraft - Engine<br />
types & Missions<br />
13a<br />
13b<br />
13c<br />
7<br />
Reference<br />
AC types<br />
Reference<br />
Engines<br />
Reference<br />
Missions<br />
2<br />
Utilisation 8<br />
Gasturbine<br />
Simulation<br />
Program<br />
Figure 20: Overview of database processing<br />
E-Mission tool<br />
11<br />
10<br />
Air Force Bases<br />
5<br />
Number<br />
of Flights<br />
Aircraft-Engine<br />
Conversion<br />
Factors<br />
Military Airspace<br />
14<br />
Reference Flight<br />
& <strong>Emissions</strong><br />
Profiles<br />
15<br />
WP1:<br />
(Military)<br />
Mov. DB<br />
WP1:<br />
Fuel & Em.<br />
Grids &<br />
Profiles DB<br />
Legend: Outputs<br />
QINETIQ/04/01113 Page 42<br />
1<br />
3<br />
6<br />
9<br />
12<br />
Military Movements DB<br />
Mission DB<br />
Flight-Profile tool<br />
16<br />
Inputs<br />
Process
2.2.3 Inventory of military aircraft<br />
The military component inventories include all aircraft types as listed in [NLR, 2004]. This<br />
inventory includes not only the front-line aircraft types such as fighters, bombers <strong>and</strong><br />
attack aircraft, but also includes helicopters, support, tanker intelligence, liaison, special<br />
operations as well as executive <strong>and</strong> transport aircraft. The inventory is based on owning<br />
country <strong>and</strong> includes aircraft assets from all branches of the military as well as guard,<br />
reserve <strong>and</strong> paramilitary <strong>for</strong>ces where applicable. The inventory is categorised by country,<br />
aircraft type, mission(s) <strong>and</strong> region <strong>and</strong> distinguishes about 1500+ aircraft derivative types<br />
<strong>and</strong> 380 compound missions.<br />
Many military aircraft are designed to per<strong>for</strong>m multiple missions, either true multi-role, or<br />
in swing-role. Typically 2 or 3 missions can be distinguished <strong>for</strong> such aircraft. Typical split<br />
between the roles, <strong>and</strong> reference missions are assumed.<br />
2.2.4 Deployment <strong>and</strong> Utilisation of Military Aircraft<br />
For the allocation of fuel use <strong>and</strong> emissions into the three dimensional space, it is assumed<br />
that aircraft typically fly within their home territory. One exception to this is the NATO area<br />
in Western Europe where it is assumed that NATO aircraft could be operated (if range<br />
permits) throughout the NATO airspace. Although NATO member states, Greece <strong>and</strong> Turkey<br />
are exempt from this assumption.<br />
For the NATO countries having the largest military aircraft fleet, i.e. Spain, Germany, United<br />
Kingdom, France, Italy, <strong>and</strong> the United States, the aircraft have been allocated to their<br />
airbases. A total of 422 air <strong>for</strong>ce bases <strong>and</strong> 192 airspace bases have been included in the<br />
movement database, <strong>and</strong> are depicted in Figure 21 <strong>and</strong> Figure 22. Some countries have<br />
airbases located in a <strong>for</strong>eign country (only shown if within Europe).<br />
40° N<br />
50° N<br />
10 ° W<br />
60° N<br />
0 °<br />
QINETIQ/04/01113 Page 43<br />
10 ° E<br />
Figure 21: (NATO) airbases <strong>for</strong> specific countries in Europe<br />
20 ° E
30 ° N<br />
150 °<br />
W<br />
45 ° N<br />
60 ° N<br />
135 ° W<br />
Figure 22: Airbases in the USA<br />
120 ° W<br />
Conversion from aircraft count in an inventory into number of flights or flying hours<br />
requires an estimate of the aircraft utilisation (i.e. the number of flights or flying hours per<br />
aircraft). For the purpose of this study, the USAF planning methods have been applied. In<br />
this method, there is a distinction between the Total Active Inventory (TAI) <strong>and</strong> the Primary<br />
Aircraft Inventory (PAI). The Total Active Inventory comprises aircraft assigned to operating<br />
<strong>for</strong>ces <strong>for</strong> mission, training, test, or maintenance. Not all of these aircraft have operational<br />
status: maintenance requirements, backup <strong>and</strong> spare aircraft reduce the effective<br />
operational number of aircraft. This TAI inventory includes primary, backup, <strong>and</strong> attrition<br />
aircraft.<br />
The Primary Aircraft Inventory (PAI) is the number of aircraft assigned to meet Primary<br />
Aircraft Authorisation (PAA), i.e. authorised number of aircraft to per<strong>for</strong>m the unit’s<br />
operation mission. PAA is generally some fraction of the total aircraft inventory. Aircraft not<br />
part of the PAI, are usually undergoing (scheduled or unscheduled) maintenance,<br />
modifications, inspection, or repair. The ratio of operational aircraft to the total possessed<br />
aircraft depends on the type of aircraft. Bombers, large transport <strong>and</strong> electronic surveillance<br />
<strong>and</strong>/or reconnaissance plat<strong>for</strong>ms tend to have a higher ratio of operation aircraft to total<br />
possessed aircraft.<br />
Not all states necessarily use their aircraft at the same rate as the US. Some unclassified<br />
data exist to substantiate non-US military aircraft utilisation. Hence, estimates were made<br />
from the US utilisation rates according to [L<strong>and</strong>au, 1994].<br />
105 ° W<br />
2.2.5 Military aircraft types <strong>and</strong> reference aircraft<br />
Military aircraft <strong>and</strong> engines are not subjected to international emissions st<strong>and</strong>ards <strong>and</strong><br />
certification. As a consequence there is hardly any data publicly available on fuel <strong>and</strong><br />
emissions characteristics on military engines. However, <strong>for</strong> a limited number of (military)<br />
aircraft, the per<strong>for</strong>mance <strong>and</strong> (engine) emissions characteristics, sometimes even including<br />
the transient behaviour, are available in sufficient detail to warrant modelling with NLR’s<br />
mission analysis model <strong>and</strong> gas turbine simulation tool GSP.<br />
Apart from the availability of sufficient aircraft, engine <strong>and</strong> mission data, other aspects<br />
relevant to the selection of the reference aircraft are the volume of aircraft in the fleet, <strong>and</strong><br />
the mission to be per<strong>for</strong>med. The mission analysis model (E-mission) is used to determine<br />
the required engine per<strong>for</strong>mance along a mission profile <strong>and</strong> the latter is to generate the<br />
specific emissions <strong>and</strong> fuel per<strong>for</strong>mance. An example of the resulting missions, fuel<br />
consumption <strong>and</strong> emissions profiles are shown in Figure 23.<br />
QINETIQ/04/01113 Page 44<br />
°<br />
90<br />
W<br />
° W<br />
75<br />
° W<br />
60
altitude [m]<br />
15000<br />
10000<br />
Fuelflow [kg/s]<br />
HC [g/s]<br />
5000<br />
Escort using F-15 Eagle<br />
0 0.5 1 1.5 2<br />
x 10 4<br />
0<br />
20<br />
time [s]<br />
15<br />
10<br />
5<br />
0 0.5 1 1.5 2<br />
x 10 4<br />
0<br />
200<br />
time [s]<br />
150<br />
100<br />
50<br />
0 0.5 1 1.5 2<br />
x 10 4<br />
0<br />
time [s]<br />
altitude [m]<br />
NO x [g/s]<br />
CO [g/s]<br />
15000<br />
10000<br />
0 0.5 1 1.5 2<br />
x 10 6<br />
0<br />
400<br />
distance from base [s]<br />
QINETIQ/04/01113 Page 45<br />
5000<br />
300<br />
200<br />
100<br />
0 0.5 1 1.5 2<br />
x 10 4<br />
0<br />
400<br />
time [s]<br />
300<br />
200<br />
100<br />
0 0.5 1 1.5 2<br />
x 10 4<br />
0<br />
time [s]<br />
Figure 23: Escort mission profiles, altitude, speed, fuel consumption <strong>and</strong> emissions as a<br />
function of time<br />
2.2.6 Mission types <strong>and</strong> reference mission types <strong>for</strong> military flights<br />
Military aircraft are designed to fulfil one dedicated or more (multi-role or swing-role)<br />
missions. For the purpose of this inventory, the multi-role or swing-role missions are split<br />
into individual missions with a distribution in number of flights per individual mission.<br />
Different aircraft types can usually per<strong>for</strong>m the same kind of mission, with the main<br />
differences limited to range, speeds, operating altitude, ordnance delivered or weaponry<br />
<strong>and</strong> effectiveness. In st<strong>and</strong>ard operations during peacetime, effectiveness, ordnance <strong>and</strong><br />
weaponry do not affect the fuel <strong>and</strong> emission characteristics to a significant extent. For the<br />
purpose of this study, differences in aircraft range, speed <strong>and</strong> operating altitudes are taken<br />
into account wherever data are available.<br />
Because of a general lack of data on non-NATO countries, a limited set of missions is<br />
derived from st<strong>and</strong>ardised NATO/US-Navy missions [Gallaher, USAF, 1977], [USAF, 1989].<br />
These missions are typically defined by generic values <strong>for</strong> power setting (e.g. mil thrust,<br />
afterburning thrust), speeds (e.g. cruise at mil power, dash speed) or time (e.g. 20 minutes<br />
on station). For the selected reference aircraft types, the generic characteristics are<br />
converted into quantifiable numbers. A total of 11 missions <strong>for</strong> fixed wing aircraft <strong>and</strong> a<br />
total of 6 <strong>for</strong> helicopters have been selected <strong>for</strong> this inventory.<br />
All missions have a basic scheme shown in Figure 24.
Takeoff<br />
Climb<br />
Cruise<br />
Loiter<br />
Figure 24: Mission design template<br />
In-Flight<br />
Refuelling<br />
Mission Specific<br />
Requirements<br />
Air to Air combat<br />
Ground Attack<br />
Weapon Delivery<br />
Reconnaissance<br />
Terrain Following<br />
Anti-Submarine search<br />
etc.<br />
Descent<br />
Approach<br />
L<strong>and</strong>ing<br />
Once the speed, altitude <strong>and</strong> throttle settings are defined along a mission, the next step is<br />
to evaluate the fuel burn <strong>and</strong> emissions (NOx, CO <strong>and</strong> HC) along the missions over time <strong>for</strong><br />
this specific aircraft-engine-mission combination, with the known reference aircraft<br />
per<strong>for</strong>mance <strong>and</strong> engine characteristics.<br />
For this purpose, the NLR Gas turbine Simulation Program (GSP) is coupled to a mission<br />
profile program to convert <strong>and</strong> combine mission profiles into a fuel (<strong>and</strong> optionally<br />
emissions) profiles. The calculation of the fuel burn <strong>and</strong> emissions is described in <strong>AERO2k</strong><br />
deliverable Report D13b [Broomhead, 2004]. Finally, the resulting fuel burn <strong>and</strong> emissions<br />
profiles are converted from the specific aircraft-engine-mission into some other aircraftengine-missions<br />
using the per<strong>for</strong>mance indicators.<br />
2.2.7 Military fuel consumption <strong>and</strong> emissions profile allocation in airspace<br />
Once aircraft <strong>and</strong> missions are analysed <strong>and</strong> the fuel consumption <strong>and</strong> emissions along a<br />
flight profile are known as a function of altitude <strong>and</strong> time, these emissions <strong>and</strong> fuel<br />
consumption are allocated in a three dimensional space representing the airspace<br />
surrounding the earth globe. For the vertical distribution of fuel <strong>and</strong> emissions, the mission<br />
profiles provide sufficient in<strong>for</strong>mation (although a correction <strong>for</strong> the air base ground level<br />
still needs to be included). These mission profiles do not supply any in<strong>for</strong>mation on the<br />
distribution in the horizontal plane i.e. the geographical location. A three-way approach has<br />
been defined to allocate the emissions in 3-dimensional space.<br />
Which of the three geographic aggregation levels is applied is dependent on the availability<br />
of data on the location of air <strong>for</strong>ce bases <strong>and</strong> aircraft allocated <strong>and</strong> military airspace<br />
locations as well as the country area relative to aircraft range. Each level dem<strong>and</strong>s a<br />
different approach of allocating the fuel <strong>and</strong> emissions into space:<br />
Country profile: The flights, fuel consumption <strong>and</strong> emissions are spread out in longitude<br />
<strong>and</strong> latitude over the home-country using the scaled fuel <strong>and</strong> (e-) mission profiles <strong>for</strong> each<br />
aircraft <strong>and</strong> mission type in the fleet. Such profile is the single option if no in<strong>for</strong>mation is<br />
available on the location of air <strong>for</strong>ce bases <strong>and</strong> the numbers <strong>and</strong> types of aircraft. This<br />
profile is also the preferred approach in case the area of the country is small compared to<br />
the range of the aircraft. Even in the case of detailed data on air <strong>for</strong>ce bases etc., this profile<br />
serves to account <strong>for</strong> ferry flights, base rotations etc. This type of movements requires the<br />
aircraft numbers, missions <strong>and</strong> utilisation to be known or estimated. Integration over all<br />
QINETIQ/04/01113 Page 46
aircraft types, missions <strong>and</strong> utilisation yields a fuel <strong>and</strong> emissions altitude distribution on a<br />
per country basis.<br />
Airfield profile: The flights, fuel consumption <strong>and</strong> emissions can be allocated/linked to air<br />
<strong>for</strong>ce bases, aircraft types <strong>and</strong> mission. In this case the flying time, fuel <strong>and</strong> emissions<br />
distribution are confined to areas within the concentric circles around air <strong>for</strong>ce bases. The<br />
area in which the emissions are confined is based on the typical mission range of the<br />
aircraft type under consideration. Furthermore, the missions are confined again to the<br />
countries’ borders (or ‘friendly’ airspace e.g. NATO). For some major countries, the locations<br />
of the air bases are known as well as the number <strong>and</strong> type of aircraft stationed, <strong>and</strong> their<br />
associated typical missions. This type of approach allows a regionalised or local fuel <strong>and</strong><br />
emission altitude distribution.<br />
Airspace profile: The flights, fuel consumption <strong>and</strong> emissions distributions are based on<br />
flights between air <strong>for</strong>ce bases <strong>and</strong> dedicated airspace that are well within reach of the<br />
aircraft type <strong>and</strong> mission located at that air <strong>for</strong>ce base. In this case it is assumed that a<br />
significant part of the flight is spent within this airspace with a ferry to <strong>and</strong> from the air<br />
<strong>for</strong>ce base. This type is effectively a point to point flight <strong>and</strong> allows a spot or local fuel <strong>and</strong><br />
emission altitude distribution.<br />
In cases where all relevant data are available, <strong>and</strong> the airspace profile can be used, usually<br />
there is a mix of all three of the cases above to cover variations in operations, with<br />
emphasis on the airspace <strong>and</strong> airfield profiles.<br />
Each of the three different movement types (country, airfield <strong>and</strong> airspace profiles) require<br />
a different approach to (global) grid aggregation of the fuel, time <strong>and</strong> emissions<br />
distribution. For all three cases, the vertical distribution is according to the aircraft type <strong>and</strong><br />
mission type specific fuel <strong>and</strong> emissions profiles.<br />
All of these profiles are finally converted from data produced at the level of country, airfield<br />
or airspace profiles into a 3-dimensional grid that spans the earth <strong>and</strong> is comparable to the<br />
civil aviation inventory.<br />
Results from the military aviation inventory are described in Section 3.<br />
2.3 Forecast <strong>for</strong> <strong>2025</strong><br />
In this section, the method used to develop the <strong>2025</strong> <strong>for</strong>ecast <strong>for</strong> civil aviation is described.<br />
The starting point <strong>for</strong> calculation of fuel usage <strong>and</strong> emissions in <strong>2025</strong> is capacity. Data was<br />
obtained from <strong>AERO2k</strong> modelling of the <strong>2002</strong> missions <strong>for</strong> the base year <strong>and</strong> in<strong>for</strong>mation<br />
provided by Airbus <strong>for</strong> the <strong>for</strong>ecast year. The quantity of fuel, <strong>for</strong> the <strong>for</strong>ecast year, was<br />
calculated from capacity <strong>and</strong> traffic efficiency that was assumed to improve year-on-year to<br />
reflect current <strong>and</strong> predicted trends. The emissions per<strong>for</strong>mance of newly introduced<br />
aircraft is assumed to improve in line with progressively more stringent levels relative to<br />
CAEP4 – the fuel efficiency effect of increases in pressure ratio are accommodated.<br />
Changes in other emissions are assumed to be in line with changes in fuel consumption.<br />
For each representative aircraft, in the base year fleet, the engine emissions certification<br />
parameter is described by the ICAO certification specifier Dp/Foo 11 . Dp/Foo is plotted against<br />
overall engine pressure ratio (PR) <strong>and</strong> improved technology is demonstrated by increased<br />
margin against limit values. The position of the various stringencies - CAEP2 (31 December<br />
2003 <strong>and</strong> earlier), CAEP4 (post 1 January 2004), <strong>and</strong> stringencies relative to CAEP4, are<br />
shown in Figure 25. Note that CAEP4-64% is equivalent to CAEP2-80%, an aspirational<br />
11 where Dp is the total mass of emissions produced during a l<strong>and</strong>ing / take off cycle (LTO) <strong>and</strong><br />
Foo is maximum sea level thrust<br />
QINETIQ/04/01113 Page 47
emissions per<strong>for</strong>mance target set out by ACARE in their Vision 2020 12 to direct research to<br />
achieve an 80% reduction in NOx. By inference this suggests an equivalent reduction in<br />
Dp/Foo. The ‘characteristic’ values of Dp/Foo <strong>for</strong> all of the engines in the emissions databank<br />
are shown in Figure 25.<br />
NOx Dp/F oog/kN<br />
120<br />
100<br />
80<br />
60<br />
40<br />
20<br />
For compliance engine<br />
Dp/Foo must not exceed<br />
regulatory line<br />
CAEP2<br />
CAEP4<br />
CAEP2-70%<br />
CAEP4-64%<br />
At PR30 stringency is a<br />
% of CAEP4 at PR30<br />
then parallel to CAEP4<br />
Figure 25: Regulatory Dp/Foo to CAEP2, CAEP4 <strong>and</strong> stringencies to CAEP4<br />
2.3.1 Aircraft additions <strong>and</strong> retirements<br />
QINETIQ/04/01113 Page 48<br />
CAEP2<br />
CAEP4<br />
CAEP4-5%<br />
CAEP4-10%<br />
CAEP-15%<br />
CAEP4-20%<br />
CAEP4-30%<br />
CAEP4-40%<br />
CAEP4-50%<br />
CAEP4-64%<br />
Characteristic<br />
Dp/Foo all engines<br />
Using the world’s <strong>2002</strong> fleet of “representative” aircraft types, there are four groups of<br />
aircraft; those that are already out of production <strong>and</strong> that will retire be<strong>for</strong>e <strong>2025</strong>, those that<br />
are going to be out of production by <strong>2025</strong> but still flying at that date, those that will be in<br />
production in <strong>2025</strong>, <strong>and</strong> new aircraft such as the A380 that are currently under construction<br />
at the beginning of the <strong>for</strong>ecast period.<br />
The rules governing retirement age were taken from the retirement policy given in a report<br />
showing that at least half of the narrow-bodied aircraft would be retired by age 27 <strong>and</strong><br />
wide-bodied by 35. Some aircraft were flying to age 45; however these were not taken into<br />
account because of the difficulty in identifying these aircraft from the <strong>2002</strong> data. For this<br />
reason where aircraft are retired, the date of retirement was calculated at these set times<br />
after introduction into the fleet: the movement of passenger types into the freighter fleet<br />
or their extended lives as a result were not explicitly modelled. The model “replaces”<br />
retired aircraft with new representative types in essentially the same seat b<strong>and</strong>, dividing<br />
the outgoing aircraft’s capacity in ASK equally amongst the incoming replacements.<br />
Aircraft in the other three groups are modelled to comply with all of the emissions<br />
stringencies from their effective date, <strong>and</strong> are introduced into the fleet as if they were ‘new’<br />
aircraft.<br />
These concepts of replacing retiring aircraft <strong>and</strong> assuming others would always comply<br />
with stringencies allows the establishment of a history of capacity, fuel <strong>and</strong> emissions <strong>for</strong><br />
the fleet as a whole so that it can predict the quantity of fuel used <strong>and</strong> NOx emitted <strong>for</strong><br />
<strong>for</strong>ecast years. The relative proportion of aircraft retiring <strong>and</strong> new entrants affects the rate<br />
at which the fleet turns over, <strong>and</strong> hence the speed with which fleet NOx per<strong>for</strong>mance is<br />
improved.<br />
12 EUROPA: ACARE Vision 2020:<br />
http://europa.eu.int/comm/research/growth/aeronautics2020/pdf/aeronautics2020_en.pdf
2.3.2 Forecasting: Capacity - fuel - emissions<br />
Capacity is the product of the actual distance travelled between departure <strong>and</strong> arrival<br />
airports, frequency, <strong>and</strong> the seating capacity of the individual aircraft. This may be reduced,<br />
mathematically, to the sum <strong>for</strong> all representative aircraft of the product of total distance<br />
per representative aircraft type <strong>and</strong> that aircraft’s seating capacity. The <strong>2002</strong> database<br />
yielded the actual distance each representative aircraft flew <strong>and</strong> the number of missions,<br />
which was supplemented by a list of the seating capacity of each representative aircraft.<br />
The base year capacity was there<strong>for</strong>e derived yielding a total global capacity <strong>for</strong> <strong>2002</strong> of<br />
4.787 10 12 ASK, equivalent to 2.585 10 12 ASNM.<br />
The capacity <strong>for</strong>ecast <strong>for</strong> <strong>2025</strong> was based on data provided by Airbus <strong>and</strong> included the<br />
predicted growth of traffic (revenue passenger km) <strong>and</strong> the number of aircraft required to<br />
satisfy it. The predicted traffic growth was converted into a predicted capacity growth,<br />
based on a load factor increase from 0.7 in <strong>2002</strong> to 0.74 in <strong>2025</strong>. This represents an<br />
industry consensus on the improvement in aircraft load <strong>and</strong> passenger management over<br />
the <strong>for</strong>ecast period. This there<strong>for</strong>e results in the capacity growth being slightly lower than<br />
the traffic growth. This in<strong>for</strong>mation <strong>for</strong> <strong>2025</strong> gives a snapshot of the fleet <strong>and</strong> the capacity<br />
it will per<strong>for</strong>m in ASK, but does not contain the age profile (<strong>and</strong> there<strong>for</strong>e retirement<br />
predictions) needed to underpin the fuel <strong>and</strong> NOx calculations. The growth values,<br />
regionally, based on the Airbus in<strong>for</strong>mation were applied to the base year data, <strong>and</strong><br />
summed to provide a global value, this suggested total capacity was 1.243 10 13 ASK,<br />
equivalent to 6.712 10 12 ASNM.<br />
These values are not identical to those used in the DTI <strong>for</strong>ecast that supported IPCC,<br />
there<strong>for</strong>e other data sources were investigated, <strong>and</strong> the results are shown in Figure 26. The<br />
Airbus <strong>Global</strong> Market Forecast (GMF) data 13 <strong>for</strong> 2000 <strong>and</strong> 2020 are almost the same as the<br />
DTI <strong>for</strong>ecast that supported the IPCC. Although the growth rates are derived from the same<br />
source, the base year starting point is different, being lower than either the DTI or the<br />
Airbus GMF <strong>for</strong>ecasts <strong>and</strong> reflects, possibly, the reaction following the terrorist events of<br />
September 2001 after which air traffic declined more severely than at the Gulf War of<br />
1990/1991. Although traffic is recovering, in some regions airlines have increased yield by<br />
running fewer missions at higher load factors – thus reducing capacity, but traffic growth<br />
may be similar.<br />
Traffic ASK 10 8<br />
140000<br />
120000<br />
Capacity<br />
100000<br />
80000<br />
AERO2K Capacity <strong>2025</strong><br />
AIRBUS GMF 2020<br />
AIRBUS GMF 2000<br />
60000<br />
ICAO 2001<br />
ORACLE 1998<br />
40000<br />
ORACLE 2000<br />
OAG MAX <strong>2002</strong><br />
20000<br />
OAG MAX <strong>2002</strong> + 10% <strong>for</strong> charter traffic<br />
AERO2K Capacity <strong>2002</strong><br />
0<br />
DTI Log Historic<br />
1995 2000 2005 2010 2015 2020 <strong>2025</strong> 2030<br />
Figure 26: <strong>Global</strong> capacity <strong>and</strong> trends<br />
Year<br />
The model that UK DTI used to support IPCC is shown in Figure 26. However, <strong>for</strong> <strong>AERO2k</strong><br />
the trend shown in Figure 26 had to be normalised to align the base year (<strong>2002</strong>) data that<br />
13 Airbus: <strong>Global</strong> Market Forecast 2001-2020,<br />
http://www.airbus.com/pdf/media/gmf2001.pdf<br />
QINETIQ/04/01113 Page 49
originated from EUROCONTROL <strong>and</strong> <strong>for</strong>ecast year (<strong>2025</strong>) data. Here the <strong>AERO2k</strong> data<br />
included schedule passenger, charter, <strong>and</strong> freight flights. To generate the normalised curve,<br />
the UK DTI model builds up a year-by-year change in fleet capacity, based on the base year<br />
data, the aircraft delivered, retired, <strong>and</strong> fleet growth. From this an annual growth rate can<br />
be determined, this was 4.234 percent. At a global level this is justifiable although capacity<br />
in <strong>and</strong> between individual regions may follow different patterns. The rate at which aircraft<br />
are replaced also affects the speed with which compliant aircraft are introduced when<br />
stringency changes. Typically the rate of introduction of new aircraft is approximately 5%<br />
to 7% of capacity, <strong>and</strong> retirement <strong>and</strong> attrition (crashes) is approximately 1% to 2%.<br />
Capacity has to be converted into a quantity of fuel <strong>and</strong> emissions <strong>for</strong> <strong>2025</strong>, <strong>and</strong> this is<br />
accomplished using the “Traffic Efficiency” method developed previously by UK DTI to<br />
support the IPCC [IPCC, 1999]. Further, more detailed, alternatives have been considered as<br />
a result of improved data <strong>and</strong> modelling capability. Using projected delivery <strong>and</strong><br />
retirement data <strong>for</strong> aircraft, the DTI model generates a year-by-year change in capacity, fuel<br />
used <strong>and</strong> NOx emitted. The link between capacity <strong>and</strong> fuel is ‘traffic efficiency’ – defined<br />
here as the quantity of ASK per<strong>for</strong>med per kg of fuel used, <strong>and</strong> the link between NOx<br />
emitted <strong>and</strong> fuel is the fleet emissions index <strong>for</strong> NOx (EINOx), , grams of NOx produced per<br />
kg of fuel used. The fleet EINOx trend is influenced by introduction into the fleet of ‘new’<br />
aircraft that comply with the latest emissions certification st<strong>and</strong>ards (stringency): this<br />
drives the year-on-year trend in EINOx of the new aircraft fleet.<br />
2.3.3 Regional growth factors<br />
The <strong>2002</strong> <strong>and</strong> Airbus <strong>for</strong>ecast data are based on capacity between individual city pairs <strong>and</strong><br />
there<strong>for</strong>e the quantity of fuel used <strong>and</strong> NOx emitted within a region <strong>and</strong> between regions<br />
can be defined.<br />
The difference in capacity between the base <strong>and</strong> <strong>for</strong>ecast years enabled the DTI to prepare<br />
matrices of regional growth factors <strong>for</strong> multiplying capacity, fuel <strong>and</strong> NOx. This method<br />
does not account <strong>for</strong> the possible introduction of new airports <strong>and</strong> assumes all future<br />
traffic grows only along existing routes. In the base year the <strong>2002</strong> passenger <strong>and</strong> freight<br />
data were combined whilst in the <strong>for</strong>ecast year the AIRBUS data identified these market<br />
sectors individually. There was, there<strong>for</strong>e, a need to identify freight movements <strong>and</strong><br />
convert the freight capacity into an equivalent passenger capacity given in ASK.<br />
2.3.4 Freighter capacity<br />
The freight capacity is currently growing faster than passenger capacity, <strong>and</strong> there<strong>for</strong>e<br />
there was a need to identify it <strong>and</strong> apply the appropriate growth rates. The <strong>2002</strong> mission<br />
database included freighter movements but did not identify them directly. There<strong>for</strong>e a<br />
method based on timetable data from OAG was used. For each movement the OAG data<br />
includes aircraft type <strong>and</strong> capacity. Capacity <strong>for</strong> freighters is given in Available Tonne Miles<br />
(or km) but this had to be converted into an equivalent passenger capacity because the<br />
base year data included freighter movements that were not individually identified. The<br />
conversion into seating capacity of the representative passenger aircraft, took into account<br />
the difference in seating offered on over <strong>and</strong> under 2900nm missions. DTI prepared these<br />
data at the city pair level, <strong>and</strong> there<strong>for</strong>e could identify flows within <strong>and</strong> between regions.<br />
The percentage of freight from the OAG data was applied to the AERO2K data, at a regional<br />
level. The global total capacity offered by freight aircraft in the base year converted into<br />
ASK as described above was 3647 10 8 ASK. Total capacity calculated from the <strong>AERO2k</strong> <strong>2002</strong><br />
data was 47873 10 8 ASK <strong>and</strong> there<strong>for</strong>e freight <strong>for</strong>med 7.6 percent of the total, which is<br />
approximately the same percentage as the OAG data.<br />
Airbus <strong>for</strong>ecast regional freighter growth in RTK <strong>and</strong> freighter movements <strong>for</strong> the <strong>for</strong>ecast<br />
year <strong>and</strong> converted this into an equivalent ASK, this capacity identified movements<br />
between region city pairs. As with the scheduled passenger data, to ensure consistency<br />
between base <strong>and</strong> <strong>for</strong>ecast years the freighter growth values were applied to the base year<br />
QINETIQ/04/01113 Page 50
data to generate new <strong>for</strong>ecast data. The freight capacity <strong>for</strong> the <strong>for</strong>ecast year was 12717<br />
10 8 ASK, compared against the total capacity of 124262 10 8 ASK, <strong>and</strong> there<strong>for</strong>e freight<br />
<strong>for</strong>med 10.2 percent of the total. This demonstrates the fact that the growth rate of freight<br />
is greater than passenger capacity.<br />
2.3.5 Fuel consumption <strong>and</strong> trends<br />
Fuel consumption <strong>for</strong> the fleet is calculated from total annual capacity (in ASK) using the<br />
concept of ‘traffic efficiency’, defined above. The fleet traffic efficiency in the base year was<br />
calculated as 31.91 ASK/kg. This may be compared with the previous DTI <strong>for</strong>ecast <strong>for</strong> IPCC<br />
<strong>for</strong> <strong>2002</strong> of 34.09 ASK/kg (equivalent to 18.4 ASM/kg). Over time aircraft <strong>and</strong> engine design<br />
has improved <strong>and</strong> aircraft are using less fuel, i.e. traffic efficiency has improved. Flying<br />
shorter distances between airports through improvements in air traffic management (ATM)<br />
will also improve TE by reducing the quantity of fuel used. A literature search suggests an<br />
improvement in TE of between 1.0 <strong>and</strong> 2.9% per year [Lee Joosung, 2001], <strong>and</strong> the rates<br />
used previously by DTI <strong>for</strong> the IPCC fall in this b<strong>and</strong> [Greene, 1996]. For that work the DTI<br />
used an increase in ASM per kg of fuel of 1.3% pa to 2009, 1.0% pa from 2010 to 2020 <strong>and</strong><br />
0.5% pa thereafter, <strong>and</strong> these values have been modelled again here.<br />
An alternative approach to TE has also been investigated to provide regional variation. This<br />
approach considers the aspects of TE improvement in the following areas:<br />
• Improvements in Airframe/Engine technology of replacement aircraft;<br />
• Increased efficiency of new aircraft (growth);<br />
• Improvements on existing aircraft (upgrades etc);<br />
• Improvements to ATM.<br />
By considering the <strong>2002</strong> world fleet at a regional level (<strong>2002</strong> regional fleet make-up), <strong>and</strong><br />
using the replacement in<strong>for</strong>mation stated previously, the effect of this on TE per region<br />
may be estimated, over the <strong>for</strong>ecast period.<br />
A simplification is used to assess the effect that additional new aircraft have on regional TE.<br />
Historically, improvements <strong>for</strong> new production aircraft have averaged 1 – 2% per year in<br />
fuel efficiency. Using a conservative value of 1% pa in this case as a scenario <strong>for</strong> the 23 year<br />
<strong>for</strong>ecast period, <strong>and</strong> the changes in capacity at a regional level, an estimation <strong>for</strong> changes to<br />
TE due to new aircraft may be obtained. This is clearly a very simplified approach, <strong>and</strong> other<br />
factors may be required in the future to provide more accurate data. However, this will<br />
produce an indication of variations at a regional level that are not shown through a<br />
generalised global approach to TE. This approach assumes that all growth is accommodated<br />
by new aircraft entering the fleet (as distinct from, <strong>for</strong> example, returning from “desert<br />
storage”) has been used to validate <strong>and</strong> in<strong>for</strong>m the global TE values adopted <strong>for</strong> this work.<br />
Improvements to the fleet’s fuel efficiency <strong>for</strong> existing aircraft (through modifications or inlife<br />
upgrades) are difficult to predict, but a constant global improvement value of around<br />
0.4% pa, based on suggested improvements considered feasible during the <strong>for</strong>ecast<br />
timeframe, is incorporated.<br />
Finally, by comparing the base year GC distance <strong>and</strong> actual distance travelled at a regional<br />
level, an estimation of the potential improvement that may be achieved through ATM<br />
developments over the <strong>for</strong>ecast period may be introduced to the model.<br />
Thus, from a starting point of a trend in capacity, a trend in fuel used between the base <strong>and</strong><br />
<strong>for</strong>ecast years was established to enable global fuel consumption to be predicted <strong>for</strong> the<br />
<strong>for</strong>ecast year.<br />
Where aircraft were retired <strong>and</strong> replaced a means had to be devised to assess the change to<br />
the quantity of fuel that would be used so that the new quantity of NOx could be<br />
determined. The modelling approach assumed the capacity in ASK of retiring aircraft was<br />
shared equally by the replacements. The fuel used by the replacements was calculated by<br />
applying individual aircraft traffic efficiency to each replacement aircraft’s share of the<br />
QINETIQ/04/01113 Page 51
total capacity. In this way, the ASK remained the same, but the quantity of fuel changed to<br />
reflect the change in aircraft. The quantity of fuel used was used only to trend NOx<br />
produced by the replacement aircraft that per<strong>for</strong>med the same ASK as be<strong>for</strong>e.<br />
2.3.6 Stringency <strong>and</strong> EINOx<br />
The CAEP4 limit m<strong>and</strong>ated from 1 January 2004 is 16.25% lower than CAEP2 at an engine<br />
pressure ratio of 30 (see Figure 25) <strong>and</strong> CAEP are considering further regulatory<br />
stringencies. Where currently the CAEP4 regulatory line has a kink point at PR30, reflecting<br />
an increase in the regulatory requirement from this point, potential c<strong>and</strong>idate stringencies<br />
also have a kink point at PR30 <strong>and</strong> the certification requirement may depend on whether<br />
engine pressure ratios is above or below PR30. For example, below PR30 the proposals<br />
being considered are a straight percentage of the CAEP4 limit value, whilst above PR30, the<br />
percentage at PR30 is taken, converted into an absolute reduction in Dp/Foo, <strong>and</strong> then that<br />
value runs parallel to the CAEP4 line.<br />
The conceptual work described in [Addleton, 2004] established a mathematical relationship<br />
between EINOx of ‘new’ aircraft on entry into service <strong>and</strong> stringency relative to CAEP2<br />
levels. The analysis investigated the effect of changes in Dp/Foo on individual aircraft/engine<br />
combinations to predict changes in individual aircraft EINOx based on the quantity of fuel<br />
consumed remaining the same. That is, in the event the NOx regulatory limit is tightened a<br />
manufacturer would make his engine comply with the new rule. The NOx per<strong>for</strong>mance<br />
would be improved, but it is assumed at this stage that the fuel consumption remains<br />
unchanged. NOx emitted was summed <strong>for</strong> all aircraft, <strong>and</strong> when based on the original<br />
quantity of fuel consumed, yielded EINOx <strong>for</strong> a ‘new’ fleet. This EINOx was applied as a<br />
global figure to all new aircraft introduced in a given year to satisfy the growth in capacity<br />
(<strong>and</strong> retirements), <strong>and</strong> aircraft introduced in a given year retained that EINOx value until<br />
retirement.<br />
This analysis investigated, mathematically, the effect of changes in stringency <strong>and</strong> changes<br />
in pressure ratio. A rise in pressure ratio is likely to result in reduced fuel consumption,<br />
although the analysis was unable to determine any definitive effect of changes in pressure<br />
ratio on changes in fuel economy at this time – fuel efficiency improvements are there<strong>for</strong>e<br />
addressed through traffic efficiency trends. The best indicator <strong>for</strong> changes in EINOx proved<br />
to be Dp/Foo on an engine-by-engine basis, but there are benefits <strong>and</strong> caveats as follows:<br />
• Previous work carried out by UK DTI suggests that, <strong>for</strong> individual engines, a reduction<br />
in Dp/Foo might lead to a similar or marginally larger reduction in whole mission <strong>and</strong><br />
cruise EINOx. The inference is that improvements may be larger than implied by the<br />
reduction in Dp/Foo.<br />
• <strong>Global</strong>ly, differences in Dp/Foo without reference to a specific engine are not a good<br />
indicator of fleet EINOx trends. For example, if an engine from one manufacturer<br />
replaces an engine made by another, the change in NOx per<strong>for</strong>mance may not be the<br />
same as the ratio of the two Dp/Foo values – the engine with the higher Dp/Foo may<br />
yield lower overall NOx values due to the shape of its NOx characteristic curve<br />
(relating engine fuel flow to EINOx at the ICAO certification points).<br />
• Reductions in EINOx only apply to engines newly introduced into the fleet <strong>and</strong> not the<br />
fleet as a whole.<br />
2.3.7 Modelling stringency<br />
Although the initial conceptual work included reference to the EC LOW-NOx programme 14 ,<br />
an engine manufacturer participating in EC-sponsored CYPRESS project 15 contributed views<br />
14 CORDIS RTD-PROJECTS: LOWNOX I - LOW EMISSION COMBUSTER TECHNOLOGY, Project Reference:<br />
AERO0016, 1990-1992.<br />
QINETIQ/04/01113 Page 52
egarding the range of stringencies to be modelled, based on what might be feasible over<br />
the <strong>for</strong>ecast period.<br />
Note should be taken:<br />
• that when a stringency is introduced into the model, it is assumed to apply only to<br />
those aircraft entering service after the date of applicability <strong>and</strong> not the whole fleet.<br />
• the levels of stringencies modelled do to not imply a commitment on ICAO CAEP to<br />
introduce them – they are simply used as one regulatory scenario.<br />
• That the dates do not necessarily mean that a regulatory stringency has already been,<br />
or even will be, recommended by IACO CAEP; a stringency is a modelling convenience<br />
to establish a trend in EINOx based on possible scenarios.<br />
The effect of stringencies relative to CAEP4 were modelled <strong>for</strong> each certificated engine in<br />
the representative fleet <strong>and</strong> assigned dates <strong>for</strong> introducing stringency into the model.<br />
Where an engine did not meet a regulatory stringency because its Dp/Foo was above a level<br />
in the scenario, it was assumed to be improved by its manufacturer to just comply with the<br />
regulatory rule as shown in Figure 27. This figure also demonstrates the effect of increasing<br />
pressure ratio. In practice, when an engine model fails a stringency, the manufacturer<br />
designs it to meet a st<strong>and</strong>ard that is more severe (ie, lower emissions) than the stringency it<br />
failed so that it may remain in production <strong>and</strong> comply with possible future (even more<br />
stringent) legislation that may dem<strong>and</strong> even lower Dp/Foo limits.<br />
Figure 27: Effect of increased pressure ratio on possible reduction in Dp/Foo<br />
This is the rationale <strong>for</strong> using a CAEP4-20% step in 2004 instead of CAEP4, as many engines<br />
that are likely to be brought into service from that date are already at that level. Further,<br />
aircraft were assumed to meet whatever stringency was modelled until they retire.<br />
Not all of the fleet of representative aircraft were selected. To determine the effect of<br />
stringency only those aircraft whose engines have ICAO certificated data were selected.<br />
Thus jets <strong>and</strong> turboprops whose engines are uncertificated were excluded from the analysis<br />
because their emissions source data was not from certification sources. However, trends<br />
15 CYPRESS: EC Contract No: G4RD-CT-2000-00383. Future Engine Cycle Prediction <strong>and</strong> <strong>Emissions</strong><br />
Study, publication pending<br />
QINETIQ/04/01113 Page 53
<strong>for</strong> these unregulated jets <strong>and</strong> turboprops were assumed to be the same as the trends<br />
modelled <strong>for</strong> the regulated jets.<br />
Using concepts developed in Annex A, stringencies were modelled to predict fleet EINOx<br />
based on in<strong>for</strong>mation contained in the CYPRESS report <strong>and</strong> advice from an engine<br />
manufacturer in a scenario as follows:<br />
• <strong>2002</strong> – no alteration in current emission per<strong>for</strong>mance of engines<br />
• 2004 – CAEP4 - 20% based on many aircraft are already compliant to CAEP4<br />
• 2010 – CAEP4 - 40%, engine PR rises by 5.0%, based on the ANTLE work<br />
undertaken by RR 16<br />
• 2012 – CAEP4 - 40%, engine PR rises by 10%<br />
• 2016 – CAEP4 - 50%, engine PR rises by 15%<br />
• 2020 – CAEP4 - 64%, engine PR rises by 20% - working towards an ACARE 17 goal<br />
of CAEP2 – 80% (CAEP4 – 76%)<br />
• Data from individual aircraft/engine combinations aggregated to fleet level <strong>for</strong><br />
aircraft entering service<br />
Changes in EINOx are assumed to be directly proportional to changes in Dp/Foo on an<br />
engine-by-engine basis. The effect of introducing these stringencies is shown in<br />
Table 12.<br />
Year <strong>2002</strong> 2004 2010 2012 2016 2020<br />
Stringency relative to CAEP4 % 0 20 40 40 50 64<br />
Dp/Foo relative to CAEP4 % 0 80 60 60 50 36<br />
Rise in PR relative to base year % 0 0 5 10.0 15.0 20.0<br />
EINOx of a new fleet 13.171 10.887 9.013 9.639 8.84 7.457<br />
Table 12: Dp/Foo relative to CAEP <strong>and</strong> possible dates effective in the model<br />
2.3.8 EINOx – effect of new aircraft on fleet<br />
Each change in stringency is assumed to precipitate a change in the certificated NOx<br />
per<strong>for</strong>mance of new aircraft introduced into the fleet from the date the stringency<br />
becomes effective, <strong>and</strong> hence their EINOx values. Fuel consumption improvements<br />
are accommodated by ongoing improvements in traffic efficiency, (the effect of<br />
changes to PR are also incorporated in this way). There<strong>for</strong>e, on an engine-by-engine<br />
basis, the change in margin between an engine’s characteristic Dp/Foo <strong>and</strong> a level of<br />
stringency can be used to determine the change in EINOx <strong>for</strong> that engine. By<br />
applying these individual changes in EINOx to the (unchanged) quantity of fuel used<br />
by each new aircraft, a new total quantity of NOx <strong>for</strong> the new fleet can be calculated.<br />
Since the quantity of NOx from each new aircraft entering the fleet is now different<br />
(lower), a new EINOx can be established <strong>for</strong> representative aircraft entering the fleet<br />
by summing their total NOx <strong>and</strong> fuel <strong>and</strong> calculating EINOx from their total NOx <strong>and</strong><br />
total fuel. This is done on an individual aircraft type basis - a straight average of the<br />
EINOx values <strong>for</strong> the representative aircraft does not give the EINOx <strong>for</strong> newly<br />
introduced aircraft.<br />
Such calculations were per<strong>for</strong>med <strong>for</strong> all of the stringencies <strong>and</strong> their associated<br />
changes in pressure ratio. At each change in stringency, aircraft are introduced at the<br />
new level of stringency, <strong>and</strong> continue to be introduced at that level until the model<br />
16<br />
ROLLS-ROYCE: http://www.rolls-royce.de/News/2001/E_01-06-16_DRAFT_Parisantle.PDF<br />
17<br />
European Aeronautics: A Vision <strong>for</strong> 2020:<br />
http://europa.eu.int/comm/research/growth/aeronautics2020/en/index.html<br />
QINETIQ/04/01113 Page 54
changes the stringency level again. Additional continuous reductions in NOx<br />
emissions occur as a result from improved traffic efficiency (reflecting the industry<br />
trend of reduced fuel consumption <strong>and</strong> hence reduced NOx) on a year-on-year basis.<br />
The analysis suggests that <strong>for</strong> newly introduced aircraft, EINOx would reduce from<br />
13.17g/kg fuel to 7.457g/kg by <strong>2025</strong>, based on estimated data. By incrementally<br />
introducing aircraft into the fleet, whole fleet EINOx values were calculated to be<br />
13.17 <strong>and</strong> 10.12 g/kg fuel <strong>for</strong> <strong>2002</strong> <strong>and</strong> <strong>2025</strong> respectively. The difference shows how<br />
fleet EINOx lags behind the introduction of stringency because of the long time<br />
aircraft are retained in service.<br />
2.3.9 Calculation of fuel used <strong>and</strong> emissions <strong>for</strong> <strong>2025</strong><br />
For civil aviation, the distance flown, fuel <strong>and</strong> NOx traffic efficiency factors described<br />
above are applied to the <strong>2002</strong> data to produce <strong>for</strong>ecast data <strong>for</strong> <strong>2025</strong>. A summary of<br />
the growth factors is provided in Table 13 to Table 15 .<br />
Asia &<br />
Pacific<br />
Eastern &<br />
Southern<br />
Africa<br />
Europe &<br />
North<br />
Atlantic<br />
Middle<br />
East<br />
North,<br />
Central<br />
America &<br />
Caribbean<br />
South<br />
America<br />
Western &<br />
Central<br />
Africa<br />
<strong>Global</strong><br />
Total<br />
Asia &<br />
Pacific<br />
Eastern<br />
&<br />
Southern<br />
Africa<br />
Europe<br />
& North<br />
Atlantic<br />
Middle<br />
East<br />
North,<br />
Central<br />
America &<br />
Caribbean<br />
South<br />
America<br />
3.7283 2.2887 4.1458 2.4616 3.3865 2.9955<br />
Western<br />
&<br />
Central<br />
Africa<br />
2.2807 2.4532 2.8373 2.6219 2.5304 2.8726 2.7222<br />
4.1356 2.9268 3.4719 2.0839 2.6004 3.2508 2.8788<br />
2.5901 2.6771 1.9725 2.7625 2.7466 2.7415<br />
3.1119 2.4758 2.5305 2.8342 1.6934 2.7030 2.5065<br />
2.7500 2.8331 3.0860 2.8108 2.7670 2.8547<br />
2.4678 2.8929 2.7553 2.4995 2.8537 2.4269<br />
Table 13: Capacity multiplier <strong>2002</strong> to <strong>2025</strong> (scheduled passenger, charter, <strong>and</strong> freight<br />
flights)<br />
QINETIQ/04/01113 Page 55<br />
<strong>Global</strong><br />
Total<br />
2.5956
Asia &<br />
Pacific<br />
Eastern &<br />
Southern<br />
Africa<br />
Europe &<br />
North<br />
Atlantic<br />
Middle<br />
East<br />
North,<br />
Central<br />
America &<br />
Caribbean<br />
South<br />
America<br />
Western &<br />
Central<br />
Africa<br />
<strong>Global</strong><br />
Total<br />
Asia &<br />
Pacific<br />
Eastern<br />
&<br />
Southern<br />
Africa<br />
Europe<br />
& North<br />
Atlantic<br />
Middle<br />
East<br />
North,<br />
Central<br />
America &<br />
Caribbean<br />
South<br />
America<br />
2.4876 0.9430 1.6408 1.3549 1.3934 1.2267<br />
Western<br />
&<br />
Central<br />
Africa<br />
0.9017 3.1178 1.2249 1.7812 1.2198 0.9444 1.5496<br />
1.5933 1.2400 3.7435 1.3987 1.2949 1.5791 1.7079<br />
1.4332 1.8539 1.3369 1.8310 1.1827 1.4844<br />
1.2620 0.8255 1.2805 1.2555 1.8521 1.7722 1.1302<br />
1.1632 0.9162 1.5355 1.9149 2.7523 1.9490<br />
1.9662 1.7644 1.6844 1.0398 1.9374 2.5210<br />
Table 14: Fuel multiplier <strong>2002</strong> to <strong>2025</strong> (scheduled passenger, charter, <strong>and</strong> freight flights)<br />
Asia &<br />
Pacific<br />
Eastern &<br />
Southern<br />
Africa<br />
Europe &<br />
North<br />
Atlantic<br />
Middle<br />
East<br />
North,<br />
Central<br />
America &<br />
Caribbean<br />
South<br />
America<br />
Western &<br />
Central<br />
Africa<br />
<strong>Global</strong><br />
Total<br />
Asia &<br />
Pacific<br />
Eastern<br />
&<br />
Southern<br />
Africa<br />
Europe<br />
& North<br />
Atlantic<br />
Middle<br />
East<br />
North,<br />
Central<br />
America &<br />
Caribbean<br />
South<br />
America<br />
1.6695 0.6167 1.2306 0.8586 0.9995 0.8251<br />
Western<br />
&<br />
Central<br />
Africa<br />
0.5854 2.5348 0.8910 1.2250 1.0514 0.6179 1.2541<br />
1.1524 0.8733 3.2598 0.9922 0.8731 1.0443 1.2601<br />
0.9089 1.2887 0.9858 1.1787 0.7412 1.0460<br />
0.8957 0.6480 0.8654 0.7865 1.6266 1.2739 0.7003<br />
0.8113 0.5860 1.0211 1.4311 2.3752 1.4749<br />
1.5864 1.3316 1.3479 0.7688 1.4689 2.1111<br />
Table 15: NOx multiplier <strong>2002</strong> to <strong>2025</strong> (scheduled passenger, charter, <strong>and</strong> freight flights)<br />
QINETIQ/04/01113 Page 56<br />
<strong>Global</strong><br />
Total<br />
2.0834<br />
<strong>Global</strong><br />
Total<br />
1.6008
Each <strong>2002</strong> flight is queried to identify its start <strong>and</strong> finish region. The appropriate<br />
regional fuel or NOx factor is then applied to the fuel <strong>and</strong> emissions on each flight leg<br />
to produce a <strong>2025</strong> data file containing <strong>2002</strong> flights with revised data appropriate to<br />
<strong>2025</strong>. Distance flown is not recalculated at this detail level. These flights are then<br />
aggregated, either using the gridding software to produce <strong>2025</strong> gridded data or by<br />
data queries to produce global <strong>and</strong> regional data. These results are described in<br />
Section 3.<br />
For military aviation, no separate <strong>for</strong>ecast has been produced <strong>for</strong> <strong>2025</strong>. It is<br />
recommended that the <strong>2002</strong> data are used <strong>for</strong> <strong>2025</strong> (See Section 2.4.3).<br />
2.4 Validation, Uncertainty <strong>and</strong> Sensitivity Analysis<br />
Validation of <strong>AERO2k</strong> has been carried out, in stages, throughout the development<br />
<strong>and</strong> integration of the <strong>AERO2k</strong> inventory <strong>and</strong> <strong>for</strong>ecast. This section describes the key<br />
conclusions from the validation, uncertainty <strong>and</strong> sensitivity work.<br />
2.4.1 Validation of the <strong>2002</strong> Civil <strong>Aviation</strong> Inventory<br />
Air Traffic Movements<br />
The air traffic movement database contains a comprehensive coverage of IFR<br />
movements <strong>for</strong> the ECAC area <strong>and</strong> North America using ATC data. Significant inputs<br />
were made to avoid double-counting flights. However, the complexities of flight<br />
identification <strong>and</strong> real world data errors mean that some uncertainties due to the<br />
absence or double counting of flights are unavoidable. For the remainder of global<br />
aviation, schedule data was used, thereby omitting non-schedule (e.g. charter) flights<br />
<strong>and</strong> including cancelled flights. These uncertainties are difficult to estimate as no<br />
source was found to estimate the total number of flights in the world. As mentioned<br />
by Attilio Costaguta, Chief of ICAO statistics section: “While in terms of revenuetonne<br />
kilometres, ICAO has a fairly good coverage, this is not the case <strong>for</strong> aircraft<br />
movements. Data <strong>for</strong> smaller regional or domestic airlines are generally not<br />
submitted to ICAO. Thus the number of departures shown may be significantly<br />
smaller than those which actually took place.” [Costaguta, 2001].<br />
In order to assess the uncertainties present in the inventory, a qualitative assessment<br />
is summarised in Table 16. It shows the dependencies, relationships, limitations <strong>and</strong><br />
assumptions made <strong>for</strong> each of the modelling steps.<br />
Modelling<br />
steps<br />
Import of<br />
AMOC<br />
headers<br />
Import of<br />
ETMS<br />
headers<br />
Update of<br />
maximum<br />
stop time<br />
Import of<br />
AMOC legs<br />
Dependencies Relationships Limitations Assumptions<br />
AMOC traffic.txt<br />
file<br />
ETMS flight.txt file<br />
AMOC headers<br />
<strong>and</strong> AMOC<br />
- Airport table<br />
- Aircraft table<br />
- ETMS airport<br />
table<br />
- Aircraft table<br />
OID that makes<br />
the link between<br />
Capacity of the simulator<br />
that generates AMOC<br />
traffic.txt file<br />
Lack of arrival time <strong>for</strong><br />
AMOC data<br />
Capacity of the simulator<br />
that generates AMOC<br />
Data generated<br />
are<br />
representative<br />
of all flight<br />
plans registered<br />
in the ECAC<br />
QINETIQ/04/01113 Page 57<br />
area<br />
Data generated<br />
are<br />
representative<br />
of all flights <strong>for</strong><br />
North America<br />
Use of last<br />
event time
Import of<br />
ETMS legs<br />
Merge of<br />
AMOC <strong>and</strong><br />
ETMS<br />
Import of<br />
schedule data<br />
headers<br />
Import of<br />
schedule data<br />
legs<br />
Existing<br />
trajectory<br />
Profiles <strong>and</strong><br />
waypoints<br />
flight.txt file AMOC headers<br />
<strong>and</strong> legs<br />
ETMS headers <strong>and</strong> OID that makes<br />
ETMS<br />
the link between<br />
flight_chord.txt ETMS headers<br />
file<br />
<strong>and</strong> legs<br />
Consistency check<br />
Assessment<br />
Great Circle Profiles<br />
Merge of<br />
scheduled<br />
data with<br />
AMOC <strong>and</strong><br />
ETMS<br />
Schedule data<br />
recorded in Back<br />
<strong>Aviation</strong> database<br />
Merge of AMOC<br />
<strong>and</strong> ETMS data<br />
- ETMS headers <strong>and</strong><br />
legs<br />
- CARAT (NIMA<br />
data)<br />
- Airport table<br />
- Aircraft table<br />
- Airline table<br />
- Aircraft table<br />
- List of city-pairs<br />
(AMOC, ETMS,<br />
Back <strong>Aviation</strong>)<br />
- Airport table<br />
flight.txt file<br />
- Data missing in fields<br />
- Rounding of the eventtime<br />
to the minute<br />
- Trajectory kinks due to<br />
erroneous speed <strong>and</strong><br />
flight level<br />
- Variation of the<br />
number <strong>and</strong> distribution<br />
of the aircraft position<br />
along the trajectory<br />
according to the sources<br />
- Criteria defined <strong>for</strong><br />
assessing the duplication<br />
of flights<br />
- Quality of the existing<br />
trajectory<br />
- Representatives of a<br />
week of traffic (February<br />
<strong>2002</strong>)<br />
- Shortest trajectory<br />
created by CARAT, not<br />
necessary the trajectory<br />
used<br />
- Aircraft grouping system<br />
Defined number of<br />
positions calculated<br />
Criteria defined <strong>for</strong><br />
assessing the duplication<br />
of flights<br />
Data generated<br />
are<br />
representative of<br />
all scheduled <strong>and</strong><br />
commercial<br />
flights declared<br />
by airlines in the<br />
world<br />
Air traffic<br />
management<br />
conditions<br />
applied to air<br />
traffic in the rest<br />
of the world.<br />
Great Circle<br />
represent a<br />
trajectory<br />
Table 16: Dependencies, relationships, limitations <strong>and</strong> assumptions made <strong>for</strong> each<br />
modelling steps<br />
QINETIQ/04/01113 Page 58
Although the number of flights contained within the inventory may be less than the<br />
actual number flown, the capture of IFR flights from US <strong>and</strong> European radar data <strong>and</strong><br />
the use of timetable data <strong>for</strong> the rest of the world make it extremely likely that<br />
almost all flights by larger aircraft will have been captured.<br />
However, previous inventory work had shown that avoiding duplication of flights<br />
within the flight database is not a simple task. Code sharing, alternative airline <strong>and</strong><br />
airport codes, merging datasets from flight movement <strong>and</strong> timetable in<strong>for</strong>mation<br />
plus errors in the source data all contribute to the possible duplication of flights. As<br />
described in section 2.1.1, considerable work was carried out to blend the in<strong>for</strong>mation<br />
from the various sources without the introduction of duplicates. To supplement this,<br />
further checks on the flights database were carried using additional criteria using the<br />
<strong>AERO2k</strong> data integration software.<br />
These additional checks of the flights database verified two features. Firstly that the<br />
flights in the database are all unique <strong>and</strong> have not been duplicated, <strong>and</strong> secondly that<br />
the number of flights in the database <strong>for</strong> each country broadly matches estimates<br />
made by airport operators <strong>and</strong> air traffic control. By looking at samples of data <strong>for</strong> a<br />
particular area, four main types of possible error in the data were examined. These<br />
were:<br />
• Airport location identifiers varying between IATA codes <strong>and</strong> ICAO codes,<br />
<strong>for</strong> example HNL <strong>and</strong> PHNL <strong>for</strong> Honolulu airport,<br />
• Timetable <strong>and</strong> air-traffic data recording slightly different take-off <strong>and</strong><br />
arrival times,<br />
• Call sign errors<br />
− Where code-sharing airlines swap the airline identifying part of the<br />
call sign (the alphabetical part), <strong>for</strong> example, AAL307 to EGF307.<br />
− Where one of the source databases had recorded the airline <strong>and</strong><br />
flight components of the call sign in different fields <strong>and</strong> these had<br />
been merged badly, <strong>for</strong> example, ELY002 became ELY2.<br />
• Where the aircraft had been changed, possibly somewhere in the<br />
intervening period between a schedule being submitted <strong>and</strong> take-off. So<br />
often, an Embraer E135 in the schedule data was replaced by an Embraer<br />
E145 <strong>for</strong> the actual flight.<br />
Sample data were checked <strong>and</strong> where any type of error was identified, improvements<br />
were made to the flight database generation software <strong>and</strong> a complete new flights<br />
database was supplied which contained none of these duplications.<br />
Aircraft representation <strong>and</strong> Fuel Profiling<br />
Piano validation<br />
The aircraft per<strong>for</strong>mance package selected to provide fuel usage predictions <strong>for</strong><br />
<strong>AERO2k</strong> is PIANO [PIANO, 2004]. PIANO (Project Interactive Analysis <strong>and</strong><br />
Optimisation) is a professional software tool <strong>for</strong> commercial aircraft analysis. PIANO<br />
has a history of prior use in inventory projects, i.e. ANCAT/EC2 [Gardner, 1998], <strong>and</strong><br />
<strong>for</strong> projects such as the EC 5 th Framework project NEPAIR [Norman, 2003]. PIANO is<br />
open source rather than an inaccessible in-house code, <strong>and</strong> has the flexibility to<br />
predict per<strong>for</strong>mance at any flight altitude, speed <strong>and</strong> aircraft mass. Detailed flight<br />
per<strong>for</strong>mance evaluations in PIANO are derived from first principles, based on<br />
aerodynamics, aircraft mass, <strong>and</strong> engine characteristics. PIANO calculates the mass of<br />
mission fuel plus reserves that are required <strong>for</strong> a given mission distance <strong>and</strong> payload<br />
mass. The climb, cruise <strong>and</strong> descent segments of a mission are all analysed using<br />
detailed step-by-step techniques. The aircraft models have generic engines typical of<br />
the technology <strong>and</strong> thrust requirements of the class <strong>and</strong> there<strong>for</strong>e provide a typical<br />
QINETIQ/04/01113 Page 59
Aircraft<br />
representation of the fuel burn of an aircraft type. PIANO generates fuel profiles<br />
covering the entire flight cycle, though in <strong>AERO2k</strong> it will only be used to provide data<br />
<strong>for</strong> operation above 3000ft.<br />
The PIANO software tool has been favourably compared to external sources such as<br />
BADA data from Eurocontrol <strong>and</strong> to real airline operational data. It should be noted<br />
that all validation work is based on statistically small samples of data. These provide<br />
“snapshots” of actual operation against which to compare the PIANO tool. It should<br />
be appreciated that in the world’s fleet of aircraft there are innumerable different<br />
combinations of aircraft type <strong>and</strong> variant, weights, configurations, engines <strong>and</strong><br />
operational variables. There<strong>for</strong>e the aim of this validation was to provide reasonable<br />
confidence but noting that exceptions <strong>and</strong> differences in per<strong>for</strong>mance will always<br />
occur.<br />
PIANO comparison with ANCAT/EC2 data<br />
Some validation of PIANO was carried out in the ANCAT/EC2 project [Gardner, 1998].<br />
Predicted mission fuel consumption data from flight planning tools from British<br />
Airways <strong>and</strong> Virgin are quoted in the ANCAT/EC2 report. In Table 17 <strong>and</strong> Table 18,<br />
these data are compared to some of the PIANO models to be used in <strong>AERO2k</strong>.<br />
Mission<br />
distanc<br />
e<br />
Cruise<br />
altitude<br />
(nm) FL<br />
(ft / 100)<br />
TOW<br />
(kg) Mission<br />
fuel (kg)<br />
BA<br />
CAPRA 18 <strong>AERO2k</strong> PIANO ANCAT/EC2 PIANO<br />
Mission<br />
fuel (kg)<br />
<strong>AERO2k</strong> /<br />
CAPRA %<br />
Mission<br />
fuel (kg)<br />
ANCAT /<br />
CAPRA %<br />
A320-200 1000 350 69939 6510 6445 99.0 6127 94.1<br />
A320-200 1000 370 64029 6038 5995 99.3 6029 99.9<br />
B737-200 1000 310 51861 6558 6471 98.7 6034 92.0<br />
B737-400 500 350 55842 3510 3574 101.8 3510 100.0<br />
B737-400 1000 350 53988 6078 6051 99.6 5788 95.2<br />
B737-400 1500 350 58222 9238 9046 97.9 8947 96.8<br />
B747-200B 3000 310/350 305083 70474 64955 92.2 67083 95.2<br />
B747-400 3000 350/390 291925 60968 58361 95.7 56582 92.8<br />
B757-200 500 350 92595 4980 4559 91.5 4644 93.3<br />
B757-200 2500 350 98468 19853 18184 91.6 19190 96.7<br />
B767-300ER 500 350/370 126618 5969 6225 104.3 5679 95.1<br />
B767-300ER 4000 350/390 167382 44577 43550 97.7 41635 93.4<br />
DC-10-30 19<br />
2000 350 199767 34456 33321 96.7 32690 94.9<br />
F100 500 350 41956 3031 3085 101.8 2969 98.0<br />
Mean = 97.7 Mean = 95.5<br />
Table 17: Comparison of PIANO to British Airways data from ANCAT/EC2<br />
The data in Table 17 show an overall improvement <strong>for</strong> <strong>AERO2k</strong> compared to<br />
ANCAT/EC2. This is due mainly to the PIANO aircraft models used <strong>for</strong> <strong>AERO2k</strong>. It<br />
should also be noted that PIANO Version 2.5 was used <strong>for</strong> ANCAT/EC2 whereas PIANO<br />
Version 3.9 will be used <strong>for</strong> <strong>AERO2k</strong>, although using different PIANO versions should<br />
not make an appreciable difference to the results.<br />
In Table 17 it is noticed that there is a poor match <strong>for</strong> the two flights <strong>for</strong> the B757,<br />
with PIANO underestimating by approximately 8.5%. The reason <strong>for</strong> this could be due<br />
to the use of a different aircraft variant or different engines compared to that used in<br />
CAPRA, or perhaps some operational reason that leads to a substantially increased<br />
18 Data from the British Airways CAPRA model is <strong>for</strong> 15 different missions using 10 aircraftengine<br />
combinations. CAPRA is used in per<strong>for</strong>mance evaluation <strong>and</strong> is linked to BA’s fuel<br />
monitoring system.<br />
19 Lockheed L-1011 PIANO model used <strong>for</strong> this aircraft <strong>for</strong> <strong>AERO2k</strong>.<br />
QINETIQ/04/01113 Page 60
fuel burn. One point of interest in Table 17 is the comparison made <strong>for</strong> the DC-10. In<br />
<strong>AERO2k</strong> the representative type <strong>for</strong> this aircraft is the Lockheed L-1011, <strong>and</strong> it was<br />
this model that was used to generate the PIANO fuel data in the above table. It can be<br />
seen that the result is apparently better than that of the DC10 model used in<br />
ANCAT/EC2.<br />
Aircraft Mission<br />
distance<br />
B747-<br />
200<br />
B747-<br />
400<br />
(nm)<br />
Cruise<br />
altitude VIRGIN<br />
FL Trip Fuel<br />
(ft/100) (kg)<br />
<strong>AERO2k</strong> PIANO<br />
60.9% payload<br />
Trip Fuel <strong>AERO2k</strong>/V<br />
(kg) IRGIN %<br />
<strong>AERO2k</strong> PIANO 70%<br />
payload<br />
Trip Fuel <strong>AERO2k</strong>/V<br />
(kg) IRGIN %<br />
ANCAT/EC2 PIANO<br />
Trip Fuel<br />
(kg)<br />
ANCAT/<br />
VIRGIN %<br />
1000 370 23000 21974 95.54 22348 97.17 22692 98.66<br />
2000 330/370 44000 40711 92.53 41541 94.41 42860 97.41<br />
3000 350 68000 62511 91.93 63714 93.70 65145 95.80<br />
4000 330/370 92000 83636 90.91 85690 93.14 88976 96.71<br />
5000 330/370 118000 107885 91.43 110585 93.72 115294 97.71<br />
mean = 92.47 mean = 94.43 mean = 97.26<br />
1000 370 20000 21255 106.28 21606 108.03 20332 101.66<br />
2000 370 39000 39710 101.82 40421 103.64 38621 99.03<br />
3000 350/390 59000 58619 99.35 60110 101.88 58157 98.57<br />
4000 330/370 81000 80246 99.07 81946 101.17 79458 98.10<br />
5000 330/370 104000 103207 99.24 105563 101.50 102339 98.40<br />
mean = 101.15 mean = 103.25 mean = 99.15<br />
Table 18: Comparison of PIANO to Virgin data from ANCAT/EC2<br />
The data in Table 18 show a good correlation <strong>for</strong> the B747-400 <strong>for</strong> flight distances<br />
greater than 1000nm. The difference <strong>for</strong> the shorter flight is probably due to a<br />
difference in the climb fuel predicted by PIANO <strong>and</strong> Virgin. The comparison <strong>for</strong> the<br />
B747-200 is less good, <strong>and</strong> again could be due to the use of a different aircraft<br />
variant, different engines or some operational reason that leads to a substantially<br />
increased fuel burn. In this case an alternative reason might be that the aircraft is<br />
typically operated with a much higher payload mass than that used in PIANO <strong>for</strong> this<br />
comparison.<br />
It is not known to what extent the flight planning models incorporate “real world”<br />
effects such as deterioration or winds. Regardless, the comparison of PIANO to within<br />
3% on average to both the BA data <strong>and</strong> the Virgin B747-400 is good.<br />
PIANO Comparison with BADA data<br />
BADA (Base of Aircraft Data) is an aircraft per<strong>for</strong>mance database produced <strong>and</strong> used<br />
by Eurocontrol [BADA, 2003]. BADA consists of sets of ASCII files containing<br />
per<strong>for</strong>mance <strong>and</strong> operating procedure data <strong>for</strong> 267 different aircraft types. Of these,<br />
87 datasets were developed using reference sources such as flight manuals <strong>and</strong><br />
operating manuals. The remaining 180 types are linked to <strong>and</strong> are represented by one<br />
of the other 87 aircraft. The main application <strong>for</strong> BADA is trajectory simulation <strong>and</strong><br />
prediction within the domain of Air Traffic Management. The Operations<br />
Per<strong>for</strong>mance Model of BADA defines the aircraft type, mass, flight envelope,<br />
aerodynamics, engine thrust <strong>and</strong> fuel consumption. Data from BADA Version 3.3 have<br />
been compared to PIANO data <strong>for</strong> a number of different aircraft types. An example is<br />
given in Figure 28 <strong>for</strong> a Boeing B747-400 aircraft. Fuel consumption rates in terms of<br />
nautical miles (nm) travelled per kg of fuel consumed are compared at a number of<br />
different altitudes (flight levels) at different aircraft masses.<br />
QINETIQ/04/01113 Page 61
Fuel consumption rate (nm per kg fuel)<br />
0.07<br />
0.065<br />
0.06<br />
0.055<br />
0.05<br />
0.045<br />
0.04<br />
0.035<br />
0.03<br />
190000 240000 290000 340000 390000<br />
Aircraft mass (kg)<br />
BADA410<br />
BADA390<br />
BADA370<br />
BADA350<br />
BADA330<br />
BADA310<br />
BADA290<br />
BADA280<br />
BADA260<br />
BADA240<br />
PIANO410<br />
PIANO390<br />
PIANO370<br />
PIANO350<br />
PIANO330<br />
PIANO310<br />
PIANO290<br />
PIANO280<br />
PIANO260<br />
PIANO240<br />
Figure 28: Comparison of PIANO to BADA data <strong>for</strong> a Boeing B747-400 at a number of<br />
different altitudes (legend shows flight levels) <strong>for</strong> different aircraft masses<br />
The B747-400 in Figure 28 shows a very good match between PIANO <strong>and</strong> BADA, with<br />
the arithmetic mean difference of all data points of +0.37% with a st<strong>and</strong>ard deviation<br />
of 3.10. PIANO <strong>and</strong> BADA produce similar results <strong>for</strong> most aircraft. However, it is<br />
typical that there are some differences somewhere within the cruise flight regime.<br />
When comparisons are made <strong>for</strong> other aircraft, these differences can become<br />
apparent at different flight conditions <strong>for</strong> different aircraft, some are at lower flight<br />
altitudes with low aircraft mass, others at higher altitudes. The mean percentage<br />
difference values were calculated <strong>for</strong> all aircraft where there were data available in<br />
BADA to match the <strong>AERO2k</strong> representative aircraft type PIANO models. These are<br />
shown in Table 19.<br />
QINETIQ/04/01113 Page 62
Aircraft type Representative type code Piano as % of BADA St<strong>and</strong>ard deviation<br />
A300 600R A306 101.71 2.20<br />
A310-300 A310 104.77 4.38<br />
A319 A319 100.88 2.77<br />
A320-200 A320 102.34 2.30<br />
A321-100 A321 101.31 2.79<br />
A330-300 A330 110.62 3.13<br />
A340-300 A340 102.17 3.71<br />
ATR72 AT72 112.79 4.25<br />
B727-200 B722 89.10 0.85<br />
B737-200 B732 102.98 13.09<br />
B737-800 B738 126.95 8.81<br />
B747-200B B742 106.56 3.61<br />
B747-400 B744 100.37 3.10<br />
B757-200 A752 110.51 4.34<br />
B767-300ER B763 94.99 2.75<br />
B777-200 B772 115.79 5.39<br />
BAC1-11 BA11 83.74 1.48<br />
BAe 146 BA46 101.99 7.45<br />
Dassault Falcon 900 F900 91.41 4.30<br />
DC9 DC9 112.17 13.41<br />
Fokker F100 F100 156.06 14.73<br />
Fokker F50 F50 97.53 4.27<br />
Fokker F70 F70 107.22 2.38<br />
Lockheed L1011-500 L101 91.23 7.68<br />
MD11 MD11 105.70 2.62<br />
MD80 series MD80 91.64 3.11<br />
Mean = 104.71<br />
Table 19: Comparison of PIANO data to BADA<br />
The arithmetic mean of the per<strong>for</strong>mance data <strong>for</strong> each aircraft incorporates up to ten<br />
different cruise altitudes <strong>and</strong> three aircraft weights, similar to those shown in Figure<br />
28. The arithmetic mean of all types analysed in this exercise shows PIANO fuel burn<br />
data are less than 5% higher than that of BADA. The st<strong>and</strong>ard deviation is 13.98.<br />
Approximately 35% of the aircraft models agree to within 3%, approximately 50%<br />
agree to within 7%, <strong>and</strong> 85% to within 13%. It should be noted that whilst the brief<br />
description of these aircraft types <strong>for</strong> BADA <strong>and</strong> PIANO are the same, e.g. B737-800,<br />
the actual aircraft variant upon which the models are based may have significant<br />
differences, such as different maximum take-off weights, payload capabilities (due to<br />
interior fitments) <strong>and</strong> different engines.<br />
In Table 19 PIANO estimates fuel burn <strong>for</strong> the B757 on average10.5% higher than<br />
BADA, while in Figure 17, PIANO underestimates the fuel burn <strong>for</strong> this aircraft by<br />
approximately 8.5%. The exact reason <strong>for</strong> this is not known but it is anticipated that it<br />
is due to differences in the variant modelled as described in the paragraph above.<br />
Airline operational data<br />
PIANO was compared to airline operational data. Data <strong>for</strong> two aircraft types were<br />
available <strong>and</strong> were analysed. The results <strong>for</strong> each aircraft type were comparable but<br />
data <strong>for</strong> one of the types only covered two routes (city pairs). There<strong>for</strong>e the results<br />
presented are <strong>for</strong> the one aircraft type <strong>for</strong> which data <strong>for</strong> a large number of routes<br />
were available. The aircraft type used <strong>for</strong> comparison in this section is a medium size<br />
airliner which is typically used on all route lengths, i.e. short haul through to long-<br />
QINETIQ/04/01113 Page 63
haul routes. The aircraft type <strong>and</strong> the operator are subject to commercial sensitivity,<br />
so they cannot be disclosed. The flights encompassed mission ranges from 75 to over<br />
4000 n.miles <strong>and</strong> cruise flight altitudes from 17000 to 41000 feet.<br />
The operational flight data were recorded by on-board flight data recorders <strong>and</strong><br />
provided data on longitude, latitude, altitude, aircraft weight, engine fuel<br />
consumption rates, flight speed (Mach), outside temperature <strong>and</strong> wind direction <strong>and</strong><br />
speed. All these data were recorded at intervals of approximately four seconds<br />
throughout each flight. From these data the fuel burn <strong>and</strong> other in<strong>for</strong>mation <strong>for</strong><br />
particular flight segments could be calculated. The flights considered included<br />
destinations to <strong>and</strong> from all points of the compass from the airline’s home base.<br />
There was there<strong>for</strong>e no particular bias on direction or on prevailing winds. The<br />
analysis of the data included corrections <strong>for</strong> the effect of winds by calculating an<br />
‘equivalent headwind’ from the recorded wind direction <strong>and</strong> speed. This gave a better<br />
indication of the equivalent still air distance that was flown, <strong>and</strong> allowed comparison<br />
to the great circle distance between origin <strong>and</strong> destination airports. There was no<br />
correction made <strong>for</strong> ambient temperatures that were different from ISA st<strong>and</strong>ard<br />
temperatures <strong>for</strong> the given flight altitude.<br />
PIANO was used to replicate as closely as possible the flight segments from the<br />
operational data. For this exercise the aircraft mass was matched as closely as<br />
possible, as was the distance travelled (compared to the distance corrected <strong>for</strong> wind<br />
effects from the operational data) <strong>and</strong> the flight speed. It should be noted that the<br />
long-range cruise (LRC) speed is used in the inventory (99% SAR).<br />
Comparisons were made <strong>for</strong> aircraft take off weight (showing the 60.9% payload,<br />
used in Aero2k, gave the best fit with airline data), climb <strong>and</strong> descent (showing good<br />
agreement with “normal” climb <strong>and</strong> descent data, excepting outlying data probably<br />
due to exceptional items such as air traffic delays) <strong>and</strong> cruise. An example of cruise<br />
data is shown in Figure 29.<br />
Fuel mass <strong>for</strong> flight segment (kg), PIANO<br />
40000<br />
35000<br />
30000<br />
25000<br />
20000<br />
15000<br />
10000<br />
5000<br />
0<br />
0 10000 20000 30000 40000<br />
Fuel mass <strong>for</strong> flight segment (kg) operational data<br />
PIA NO <strong>and</strong><br />
operational data<br />
1-1 line<br />
Figure 29: Operational fuel burn <strong>for</strong> individual flight segments compared to equivalent<br />
calculated PIANO fuel burn data<br />
In Figure 29, cruise data <strong>for</strong> 162 flight segments from the 77 operational flights were<br />
compared to calculations made with PIANO <strong>for</strong> equivalent flight segments. The flight<br />
segments were each portions of flights that were carried out at a constant altitude,<br />
<strong>and</strong> encompassed distances from 15 to 2500 nm at cruise flight altitudes from 17000<br />
QINETIQ/04/01113 Page 64
to 41000 feet. The mean of calculated PIANO fuel burn data is 97.77% of the<br />
operational data. The goodness of fit (R 2 ) 20 is 0.997.<br />
In summary, PIANO has been shown to produce fuel burn data that agrees with fuel<br />
burn data from reference sources (other prediction programmes <strong>and</strong> operational<br />
flight data), without any systematic errors. Comparison to operational flight data has<br />
shown an especially good match at cruise where most fuel is consumed. Small<br />
discrepancies between the PIANO predictions <strong>and</strong> the reference sources can be<br />
accounted <strong>for</strong> by a number of reasons:<br />
• manoeuvres in flight (such as turns),<br />
• engine <strong>and</strong> airframe deterioration,<br />
• sensor error on the operational flight data,<br />
• modelling inaccuracies of the other prediction programmes (the airline route<br />
planning systems <strong>and</strong> BADA),<br />
• the use of different aircraft variants <strong>and</strong>/or aircraft weights by PIANO <strong>and</strong> the<br />
reference sources.<br />
Validation of Representative aircraft types<br />
The selection of representative aircraft <strong>and</strong> engines was described in [Norman, <strong>2002</strong>]<br />
<strong>and</strong> was based on consideration of number of seats, engine technology, maximum<br />
take-off weight (MTOW) <strong>and</strong> configuration, <strong>and</strong> number of aircraft in the fleet.<br />
Analysis of one day’s flight movements (4 th February <strong>2002</strong>) has been used to confirm<br />
the selection of aircraft types by scrutiny of the numbers of aircraft movements by<br />
each type, indicating the relative importance of accuracy of models (Table 20). Some<br />
aircraft types are responsible <strong>for</strong> only a very small proportion of movements. These<br />
tend to be older aircraft types such as the Boeing 707 (B703) <strong>and</strong> Lockheed Tristar<br />
(L101).<br />
20 Goodness of Fit (R 2 ) is the coefficient of determination, defined as 1 – (sum of the squares about<br />
the mean)/(sum of the squared errors). As a fit becomes more ideal, the R² values approach 1.0.<br />
QINETIQ/04/01113 Page 65
Representative Total Percentage of Representative Total Percentage of<br />
type movements total<br />
type movements total<br />
A306 919 0.96 B772 877 0.92<br />
A310 386 0.40 BA11 26 0.03<br />
A319 2380 2.50 BA46 3<br />
2455 2.57<br />
A320 4758 4.99 C130 2<br />
84 0.09<br />
A321 1029 1.08 C550 1<br />
3805 3.99<br />
A330 465 0.49 DC9 2094 2.20<br />
A340 396 0.42 E145 3<br />
10383 10.89<br />
A34R 20 0.02 F100 3<br />
1529 1.60<br />
AT72 2<br />
3503 3.67 F2TH 1<br />
1098 1.15<br />
B703 100 0.10 F50 2<br />
8722 9.14<br />
B712 639 0.67 F70 3<br />
888 0.93<br />
B722 1349 1.41 F900 1<br />
497 0.52<br />
B732 2061 2.16 GLF4 1<br />
603 0.63<br />
B734 10357 10.86 L101 276 0.29<br />
B736 3638 3.81 L188 2<br />
47 0.05<br />
B738 2204 2.31 MD11 259 0.27<br />
B742 438 0.46 MD80 5206 5.46<br />
B744 957 1.00 MD90 460 0.48<br />
B752 3167 3.32 SF34 2<br />
15094 15.82<br />
B763 2141 2.24 YK42 3<br />
76 0.08<br />
Table 20: Numbers of movements by each aircraft type <strong>for</strong> 4th February <strong>2002</strong> ( 1 bizjets;<br />
2 turboprops; 3 regional jets; everything else - ‘large jets’)<br />
Maximum take-off weight<br />
The mean maximum take-off weight (MTOW) <strong>for</strong> all aircraft represented by each<br />
representative aircraft type was calculated from JP fleets data [BUCHair, <strong>2002</strong>]. These<br />
mean MTOW values are there<strong>for</strong>e based on the number of aircraft of each type in the<br />
world fleet. This data were compared to the MTOW data <strong>for</strong> the PIANO models being<br />
used <strong>for</strong> the representative aircraft types (Figure 30).<br />
PIANO model MTOW (kg)<br />
450000<br />
400000<br />
350000<br />
300000<br />
250000<br />
200000<br />
150000<br />
100000<br />
50000<br />
+10%<br />
0<br />
0 50000 100000 150000 200000 250000 300000 350000 400000 450000<br />
Average MTOW (kg) from fleet data<br />
-10%<br />
Figure 30: PIANO model MTOW against mean MTOW, all aircraft<br />
QINETIQ/04/01113 Page 66
There is a good comparison in Figure 30 between the mean take-off weight data of<br />
actual aircraft <strong>and</strong> the PIANO models being used, as indicated in the statistics in Table<br />
21.<br />
Data values<br />
Mean MTOW (PIANO as percentage of mean value) 102.48%<br />
St<strong>and</strong>ard deviation 7.12<br />
R 2 value 0.996<br />
Table 21: Data statistics <strong>for</strong> Figure 30<br />
Comparison to other aircraft<br />
The 40 representative aircraft types used in <strong>AERO2k</strong> represent several hundred<br />
different aircraft types <strong>and</strong> variants. It is possible within PIANO to compare the<br />
per<strong>for</strong>mance of the representative types to a large number of other aircraft types <strong>and</strong><br />
variants to give an impression of their ‘representativeness’.<br />
The other PIANO models used in the comparisons in this section are those provided,<br />
without guarantee, with the st<strong>and</strong>ard version of PIANO. They have not been validated<br />
in the way the representative types have in <strong>AERO2k</strong>. The models provided with PIANO<br />
do not include all possible aircraft types <strong>and</strong> variants, only a selection. Finally, there is<br />
no weighting to account <strong>for</strong> the numbers of movements by each type of aircraft.<br />
There<strong>for</strong>e this section serves as an indicator of basic trends only.<br />
This exercise derives from the premise that aircraft weight <strong>and</strong> distance flown are<br />
dominating factors to the mass of fuel consumed in flight. Specific groups of aircraft<br />
are plotted together to show how good the chosen representative aircraft models are,<br />
on an MTOW basis. Definite trends can be seen <strong>for</strong> different mission distances of<br />
total mission fuel burn against MTOW. Aircraft have been separated by ‘generation’<br />
into newer <strong>and</strong> older aircraft types: older technology aircraft tend to consume<br />
significantly more fuel. The trend lines shown on the plots are <strong>for</strong> the representative<br />
aircraft. Figure 31 shows the fuel burn <strong>for</strong> all aircraft <strong>for</strong> a mission distance of 500nm.<br />
Figure 32 <strong>and</strong> Figure 33 show similar data but are <strong>for</strong> mission distances of 2000nm<br />
<strong>and</strong> 3500nm respectively.<br />
Fuel burn (kg)<br />
16000<br />
14000<br />
12000<br />
10000<br />
8000<br />
6000<br />
4000<br />
2000<br />
0<br />
0 100000 200000 300000 400000<br />
MTOW (kg)<br />
500nm 'current' aircraft type<br />
500nm 'old' aircraft type<br />
500nm current representative type<br />
500nm old representative type<br />
Figure 31: Fuel burn against MTOW <strong>for</strong> 500nm mission, comparing representative types<br />
<strong>and</strong> represented types (using PIANO models)<br />
QINETIQ/04/01113 Page 67
Fuel burn (kg)<br />
50000<br />
45000<br />
40000<br />
35000<br />
30000<br />
25000<br />
20000<br />
15000<br />
10000<br />
5000<br />
0<br />
0 100000 200000 300000 400000<br />
MTOW (kg)<br />
2000nm 'current' aircraft type<br />
2000nm 'old' aircraft type<br />
2000nm current representative type<br />
2000nm old representative type<br />
Figure 32: Fuel burn against MTOW <strong>for</strong> 2000nm mission, comparing representative<br />
types <strong>and</strong> represented types (using PIANO models)<br />
Fuel burn (kg)<br />
90000<br />
80000<br />
70000<br />
60000<br />
50000<br />
40000<br />
30000<br />
20000<br />
10000<br />
0<br />
0 100000 200000 300000 400000<br />
MTOW (kg)<br />
3500nm 'current' aircraft type<br />
3500nm 'old' aircraft type<br />
3500nm current representative type<br />
3500nm old representative type<br />
Figure 33: Fuel burn against MTOW <strong>for</strong> 3500nm mission, comparing representative<br />
types <strong>and</strong> represented types (using PIANO models)<br />
It can be seen in Figure 31 to Figure 33 that there is a definite correlation between the<br />
trendline <strong>for</strong> the representative aircraft types <strong>and</strong> the aircraft data at all mission<br />
distances, <strong>for</strong> both older <strong>and</strong> newer generation aircraft types. Goodness of fit (R 2 )<br />
values <strong>for</strong> Figure 31 to Figure 33 are shown in Table 22.<br />
500nm<br />
2000nm<br />
3500nm<br />
(Figure 31)<br />
(Figure 32)<br />
(Figure 33)<br />
‘Current’ aircraft 0.97024 0.96799 0.94484<br />
‘Old’ aircraft 0.96037 0.92445 0.94342<br />
Table 22: Goodness of fit (R2) <strong>for</strong> Figure 31 to Figure 33<br />
QINETIQ/04/01113 Page 68
Figure 34 shows the fuel burn <strong>for</strong> selected short range <strong>and</strong> selected medium range<br />
aircraft <strong>for</strong> a mission distance of 500nm.<br />
Fuel burn (kg)<br />
7000<br />
6000<br />
5000<br />
4000<br />
3000<br />
2000<br />
1000<br />
0<br />
0 20000 40000 60000 80000 100000 120000<br />
MTOW (kg)<br />
500nm current representative type<br />
500nm old representative type<br />
Airbus A319<br />
Airbus A320-200<br />
Airbus A321-100<br />
Avro RJ 85<br />
B737-200<br />
B737-400<br />
B737-700<br />
B737-800<br />
B757-200<br />
Douglas MD-82/88<br />
Embraer EMB-145<br />
Fokker F100<br />
FokkerF70<br />
Figure 34: Fuel burn against MTOW <strong>for</strong> 500nm mission <strong>for</strong> selected short range aircraft<br />
types<br />
Figure 34 demonstrates a reasonable correlation between the representative types<br />
<strong>and</strong> the other aircraft, with R 2 <strong>for</strong> the ‘current’ aircraft types of 0.91 <strong>and</strong> <strong>for</strong> the older<br />
aircraft types of 0.74. It should be noted that there are a very wide variety of design<br />
<strong>and</strong> size of aircraft in the short-range aircraft group shown in Figure 34, leading to<br />
more variation in per<strong>for</strong>mance <strong>and</strong> there<strong>for</strong>e a relatively lower R 2 value.<br />
Figure 35 <strong>and</strong> Figure 36 show the fuel burn <strong>for</strong> selected medium to long-range<br />
aircraft types <strong>for</strong> mission distances of 2000nm <strong>and</strong> 3500nm respectively.<br />
Fuel burn (kg)<br />
50000<br />
45000<br />
40000<br />
35000<br />
30000<br />
25000<br />
20000<br />
15000<br />
10000<br />
5000<br />
0<br />
50000 150000 250000 350000 450000<br />
MTOW (kg)<br />
2000nm current representative type<br />
2000nm old representative type<br />
Airbus A300 600R<br />
Airbus A310-300<br />
Airbus A330-300<br />
Airbus A340-300<br />
B707-320C<br />
B747-200B<br />
B747-400<br />
B757-200<br />
B767-300ER<br />
B777-200<br />
Douglas DC 10-30<br />
MD-11<br />
Figure 35: Fuel burn against MTOW <strong>for</strong> 2000nm mission <strong>for</strong> selected medium-long<br />
range aircraft types<br />
QINETIQ/04/01113 Page 69
Fuel burn (kg)<br />
90000<br />
80000<br />
70000<br />
60000<br />
50000<br />
40000<br />
30000<br />
20000<br />
10000<br />
0<br />
150000 200000 250000 300000 350000 400000 450000<br />
MTOW (kg)<br />
3500nm current representative type<br />
3500nm old representative type<br />
Airbus A330-300<br />
Airbus A340-300<br />
B707-320C<br />
B747-200B<br />
B747-400<br />
B767-300ER<br />
B777-200<br />
Douglas DC 10-30<br />
MD-11<br />
Figure 36: Fuel burn against MTOW <strong>for</strong> 3500nm mission <strong>for</strong> selected long range aircraft<br />
types<br />
Figure 35 <strong>and</strong> Figure 36 again demonstrate reasonable levels of correlation between<br />
the representative types <strong>and</strong> the other aircraft, as shown in Table 23.<br />
2000nm (Figure 35) 3500nm (Figure 36)<br />
‘Current’ aircraft 0.94 0.85<br />
‘Old’ aircraft 0.90 -<br />
Table 23: Goodness of fit (R2) <strong>for</strong> Figure 35 <strong>and</strong> Figure 36<br />
The good correlations in this section show that range <strong>and</strong> take-off weight (or aircraft<br />
mass) are dominant factors in fuel usage <strong>and</strong> that the representative aircraft types<br />
that are being used are representative of a much broader range of aircraft types <strong>and</strong><br />
variants.<br />
Russian/Former Soviet Union aircraft<br />
The selection of representative aircraft used sample flight movement data <strong>and</strong> JP<br />
Fleets data [BUCHair, <strong>2002</strong>]. The sample flight movement data only contained data<br />
<strong>for</strong> flights originating or terminating in Europe <strong>and</strong> North America so very few flights<br />
by aircraft produced in Russia/Former Soviet Union (FSU) were recorded. Analysis of<br />
JP Fleets showed there to be very large numbers of Russian/FSU-produced aircraft in<br />
the world fleet although the number of these aircraft still in service could not be<br />
determined. Thus there was some uncertainty over the selection of representative<br />
types <strong>for</strong> Russian/FSU aircraft. As PIANO contains very few models of aircraft<br />
produced in Russia/FSU, it was decided that these aircraft would be represented by<br />
similar western aircraft, the selections being made based on technology age,<br />
configuration <strong>and</strong> MTOW. Only limited validation could be carried out, although<br />
validation using one of the few aircraft to have a model in PIANO (Ilyushin IL96)<br />
showed fuel burn agreement within 10%. The numbers of movements by<br />
Russian/FSU aircraft are relatively low in comparison to the total number of<br />
movements (approximately 1% of the total number of all movements). This lessens<br />
the impact on the end inventory of the reduced ability to validate the models <strong>for</strong><br />
these aircraft.<br />
QINETIQ/04/01113 Page 70
<strong>Emissions</strong> Parameterisation<br />
The correlation methods described <strong>for</strong> emissions of CO, hydrocarbons <strong>and</strong> soot are<br />
the only known algorithms that combine a physical background with available<br />
measurement data to an individual <strong>for</strong>mula <strong>for</strong> each engine. These methods have<br />
revealed good agreement with the few available measurement data. In contrast, the<br />
data <strong>for</strong> non-volatile particulate numbers are based only on a general correlation <strong>and</strong><br />
can only be regarded as a preliminary estimate. Further research is needed in this<br />
area to characterise particulate emissions, including volatile particles, both on the<br />
ground <strong>and</strong> at altitude.<br />
There are several NOx correlations available which are based on different approaches.<br />
Since there will be no internal combustor data available in <strong>AERO2k</strong>, only the more<br />
simple methods come into consideration of which the p3-T3 method <strong>and</strong> the DLR fuel<br />
flow method are widely known <strong>and</strong> accepted. The p3-T3 method delivers highly<br />
accurate results, if the pressure exponent <strong>and</strong> the p3-T3 relation of each engine are<br />
known. Un<strong>for</strong>tunately these data are not available <strong>for</strong> <strong>AERO2k</strong>. There<strong>for</strong>e the DLR fuel<br />
flow method, which is based purely on published data from the ICAO database, <strong>and</strong><br />
has demonstrated good accuracy, has been used in <strong>AERO2k</strong>.<br />
Examples of validation evidence <strong>for</strong> these models have been presented in Section<br />
2.1.3 <strong>and</strong> are more fully examined in [Plohr, 2004].<br />
Data integration <strong>and</strong> calculation<br />
This section describes the additional checks carried out to validate the data<br />
integration software that <strong>for</strong>ms the heart of <strong>AERO2k</strong>. These checks are in addition to<br />
the validation carried on the individual modules described above.<br />
Comparison of individual flights, flight legs <strong>and</strong> overall results with FAA SAGE Inventory<br />
Separately from the contracted <strong>AERO2k</strong> work, a comparison is being undertaken<br />
under the stewardship of ICAO between <strong>AERO2k</strong> <strong>and</strong> the US FAA inventory SAGE. The<br />
SAGE inventory is still under development <strong>and</strong> this comparison is scheduled to<br />
continue beyond the current <strong>AERO2k</strong> project. At the time of writing, the comparison<br />
work is not available <strong>for</strong> publication. However, much work has been undertaken as<br />
part of this comparison <strong>and</strong>, to date, the comparison of these two major inventory<br />
projects has shown substantially similar results despite a number of detail<br />
differences in approach.<br />
Fuel Used Validation by r<strong>and</strong>om sampling of flights<br />
Validation checks have been carried out on the <strong>AERO2k</strong> output on an individual flight<br />
basis. The following paragraphs describe a validation check that the fuel-used output<br />
from <strong>AERO2k</strong> agrees with the PIANO modelling data on which it was based.<br />
Flight data <strong>for</strong> the week beginning 5 th February <strong>2002</strong> were processed using the<br />
<strong>AERO2k</strong> program, to produce three different ratios namely:<br />
− The fuel burn per nautical mile travelled,<br />
− The take-off mass as a function of the distance travelled,<br />
− The speed as minutes per nautical mile.<br />
The GC distance between the departure <strong>and</strong> arrival airports was added allowing any<br />
major detours to be spotted. Of the thous<strong>and</strong>s of flights examined, only one flight<br />
showed any detectable error in the distance flown, in that it had flown 189% of the<br />
GC distance to get to its destination. The ratios discussed below were all examined to<br />
see if there was anything else unusual about this flight, but there were no other<br />
anomalies. It is possible that since this was a one-hour flight, the GC route <strong>and</strong> the<br />
route taken by the aircraft to fly along designated flight corridors were vastly<br />
different – perhaps an ATC routing or diversion issue.<br />
QINETIQ/04/01113 Page 71
In order to have a frame of reference when examining the <strong>AERO2k</strong> results, mission<br />
tables were prepared in PIANO <strong>for</strong> each representative aircraft type that was<br />
examined. The assumption was made that the aircraft were flying at a speed that<br />
gave 99% of their specific air range, 60.9% of the max payload, <strong>and</strong> altitudes that<br />
were typical of the selected flights were chosen, as well as a range of distances that<br />
would allow a good comparison with the selected flights. The results from the<br />
mission tables <strong>and</strong> <strong>AERO2k</strong> were then plotted to compare the two <strong>and</strong> to find any<br />
<strong>AERO2k</strong> results that deviated from the “norm”. Three graphs were plotted <strong>for</strong> each<br />
aircraft type: fuel burn per nautical mile (n.mile), the take-off weight per n.mile, <strong>and</strong><br />
the speed, measured as n.mile per minute. An example of each of the graphs is<br />
pictured below in Figure 37 to Figure 39.<br />
Fuel used (kg)<br />
8000<br />
7000<br />
6000<br />
5000<br />
4000<br />
3000<br />
2000<br />
1000<br />
0<br />
Fuel used v Distance<br />
0 200 400 600 800 1000 1200<br />
GC distance (n.miles)<br />
Figure 37: Fuel used vs. distance results <strong>for</strong> the MD80<br />
T/O mass (kg)<br />
70000<br />
60000<br />
50000<br />
40000<br />
30000<br />
20000<br />
10000<br />
0<br />
Take off weight v Distance<br />
Piano<br />
QINETIQ/04/01113 Page 72<br />
A2K<br />
0 200 400 600 800 1000 1200<br />
Distance (n.miles)<br />
Figure 38: Take off weight vs distance to be travelled <strong>for</strong> the MD80.<br />
Piano<br />
A2K
Journey time (min)<br />
180<br />
160<br />
140<br />
120<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
Distance v Time<br />
Piano<br />
0 200 400 600 800 1000 1200<br />
Distance (n.miles)<br />
Figure 39: Time taken to complete the journey compared to the distance of the flight<br />
<strong>for</strong> the MD80.<br />
In order to find out if there were any systematic problems with the size of the aircraft,<br />
the <strong>AERO2k</strong> representative aircraft examined were grouped by size into small,<br />
medium <strong>and</strong> large categories.<br />
In the small category, the flights that had been r<strong>and</strong>omly selected were all relatively<br />
short, low flights, so they were difficult to simulate in PIANO. This meant that in<br />
general there was quite a large deviation from the PIANO trends <strong>for</strong> each of the<br />
aircraft. Closer examination, <strong>for</strong> example by checking just the cruise sections of a<br />
flight, or by inputting the exact journey distance <strong>and</strong> altitudes revealed no problems<br />
<strong>and</strong> that the <strong>AERO2k</strong> results were in good agreement with PIANO. It should of course<br />
be remembered that the flights will never precisely match the PIANO data, as in<br />
<strong>AERO2k</strong> the aircraft fly far more complex missions than can be simply simulated in<br />
PIANO. The fuel burn, flight time <strong>and</strong> take off weight are far more likely to be affected<br />
by small in-flight changes in smaller aircraft than in larger aircraft as they have a<br />
proportionally greater effect.<br />
The medium <strong>and</strong> large aircraft both showed a closer correlation to the PIANO data<br />
than the small aircraft. The only real deviation from the expected results was <strong>for</strong><br />
some A340 <strong>and</strong> 737-400 flights. In both cases the root cause of the problem was<br />
traced to the point times in the flights <strong>and</strong> these were corrected using further data<br />
traps within the <strong>AERO2k</strong> data integration software. This data check confirmed the<br />
decision made early in the project not to rely on the known unreliability of flight<br />
point times as a fundamental source of aircraft speed data but to use the timing data<br />
only <strong>for</strong> temporal ordering of the flight leg data.<br />
Fuel-used checks on a flight leg basis<br />
The majority of fuel usage <strong>and</strong> emissions from civil aviation occurs during the cruise<br />
phase. Constant-altitude cruise data from selected flights have been compared to<br />
PIANO data <strong>for</strong> similar aircraft weights <strong>and</strong> speeds at the same altitude to confirm<br />
the correct implementation of the PIANO-derived data within the <strong>AERO2k</strong> dataintegration<br />
module. As an additional check, fuel-used data have also been calculated<br />
using the BADA per<strong>for</strong>mance tool [BADA, 2003]. The results from each of the three<br />
calculations (<strong>AERO2k</strong>, PIANO, <strong>and</strong> BADA) are shown in Figure 40 to Figure 43 <strong>for</strong> the<br />
A320, A340, B737 <strong>and</strong> B747 aircraft.<br />
QINETIQ/04/01113 Page 73<br />
A2K
Fuel consumption (nm/kg)<br />
0.250<br />
0.230<br />
0.210<br />
0.190<br />
0.170<br />
0.150<br />
0.130<br />
A320-200<br />
45000 50000 55000 60000 65000 70000 75000<br />
Aircraft Weight (kg)<br />
AERO2K FL330 99%SAR<br />
PIANO FL330 M0.78<br />
BADA FL330 M0.78<br />
AERO2K FL370 99%SAR<br />
PIANO FL370 M0.78<br />
BADA FL370 M0.78<br />
Figure 40: Fuel consumption comparisons between <strong>AERO2k</strong>, PIANO <strong>and</strong> BADA <strong>for</strong> A320-<br />
200<br />
Fuel consumption (nm/kg)<br />
0.100<br />
0.095<br />
0.090<br />
0.085<br />
0.080<br />
0.075<br />
A340-300<br />
0.070<br />
150000 160000 170000 180000 190000 200000 210000<br />
Aircraft Weight (kg)<br />
<strong>AERO2k</strong> FL370 99%SAR<br />
PIANO FL370 M0.81<br />
BADA FL370 M0.81<br />
<strong>AERO2k</strong> FL390 99% SAR<br />
PIANO FL390 M0.81<br />
BADA FL390 M0.81<br />
Figure 41: Fuel consumption comparisons between <strong>AERO2k</strong>, PIANO <strong>and</strong> BADA <strong>for</strong> A340-<br />
300<br />
QINETIQ/04/01113 Page 74
Fuel consumption (nm/kg)<br />
0.21<br />
0.19<br />
0.17<br />
0.15<br />
0.13<br />
0.11<br />
0.09<br />
0.07<br />
0.05<br />
B737-800<br />
47000 57000 67000 77000<br />
Aircraft Weight (kg)<br />
<strong>AERO2k</strong> FL320 99%SAR<br />
PIANO FL310 M0.79<br />
BADA FL310 M0.79<br />
PIANO FL330 M0.79<br />
BADA FL330 M0.79<br />
Figure 42: Fuel consumption comparisons between <strong>AERO2k</strong>, PIANO <strong>and</strong> BADA <strong>for</strong> B737-<br />
800<br />
Fuel consumption (nm/kg)<br />
0.065<br />
0.06<br />
0.055<br />
0.05<br />
0.045<br />
0.04<br />
0.035<br />
0.03<br />
B747-400<br />
190000 240000 290000 340000 390000<br />
Aircraft mass (kg)<br />
<strong>AERO2k</strong> FL350 99%SAR<br />
PIANO FL350 M0.85<br />
BADA FL350 M0.85<br />
Figure 43: Fuel consumption comparisons between <strong>AERO2k</strong>, PIANO <strong>and</strong> BADA <strong>for</strong> B747-<br />
400<br />
The figures demonstrate the operation of the <strong>AERO2k</strong> data integration software<br />
which uses look up tables derived from PIANO. The <strong>AERO2k</strong> fuel-used data are<br />
calculated at 99% of the Specific Air Range (SAR) speed, with recalculation of aircraft<br />
QINETIQ/04/01113 Page 75
weight <strong>for</strong> every flight leg, <strong>and</strong> every 300km if the flight leg is longer than 300km.<br />
This is compared with PIANO <strong>and</strong> BADA data calculated at a constant Mach Number<br />
(close to but not equal to 99% SAR). The fuel consumption from PIANO <strong>and</strong> <strong>AERO2k</strong><br />
data are close, with steps in the <strong>AERO2k</strong> data where weight is not recalculated <strong>for</strong><br />
300km. This demonstrates the correct implementation of the PIANO per<strong>for</strong>mance<br />
lookup tables within <strong>AERO2k</strong>.<br />
The difference between PIANO <strong>and</strong> BADA data is simply the difference in the<br />
assumptions used to derive the respective PIANO <strong>and</strong> BADA per<strong>for</strong>mance models.<br />
These differences were examined in <strong>AERO2k</strong> Deliverable D8 [Norman, 2004],<br />
concluding that across the range of representative aircraft, PIANO compared<br />
favourably with BADA <strong>and</strong> other available operational data.<br />
Checks on a <strong>Global</strong>, Regional <strong>and</strong> National Basis<br />
Previous inventories have published verified results <strong>for</strong> fuel used, NOx <strong>and</strong> in some<br />
cases other emissions, <strong>for</strong> both civil <strong>and</strong> military emissions. Whilst <strong>AERO2k</strong> includes<br />
considerably more detail calculation <strong>and</strong> also updated technical in<strong>for</strong>mation, it would<br />
be expected that the <strong>AERO2k</strong> results would be comparable with previous results<br />
unless there has been some technical development either in the aircraft fleet or in the<br />
emissions indices.<br />
A comparison of results <strong>for</strong> annual fuel-used <strong>and</strong> emissions with previous inventories<br />
is shown in Table 24. <strong>Emissions</strong> indices <strong>for</strong> these results are shown in Table 25.<br />
Civil Total Mass (Tg)<br />
Aero2k NASA1999 NASA1992 ANCAT 1992 DLR 1992<br />
Fuel Used 156 128 113.85 114.2 112.24<br />
NOx 2.06 1.69 1.44 1.6 1.6<br />
CO 0.507 0.685 1.29 n/a n/a<br />
HC 0.063 0.189 0.26 n/a n/a<br />
Particulate Mass 0.0039 n/a n/a n/a n/a<br />
Particulate Number 4.03E+25 n/a n/a n/a n/a<br />
Table 24: Comparison of <strong>AERO2k</strong> fuel-used <strong>and</strong> emissions global totals with previous<br />
inventories<br />
Civil EIs (g/kg)<br />
Aero2k NASA1999 NASA1992 ANCAT 1992 DLR 1992<br />
Fuel Used - - - -<br />
NOx 13.2 13.2 12.6 14 14.2<br />
CO 3.25 5.4 11.3 n/a n/a<br />
HC 0.4 1.5 2.3 n/a n/a<br />
Particulate Mass 0.025 n/a n/a n/a n/a<br />
Particulate Number<br />
(/kg)<br />
2.58E+14 n/a n/a n/a n/a<br />
Table 25: Comparison of <strong>AERO2k</strong> fuel-used <strong>and</strong> emissions EI with previous inventories<br />
The fuel used figure reflects the doubling in air traffic since 1992, offset by the fuel<br />
efficiency improvements, suggested as being in the region of 1% per year. Increasing<br />
pressure ratio, plus other technology improvements have allowed significant<br />
improvements in combustion efficiency in the last 25 years (Figure 44) <strong>and</strong> these<br />
engines are now dominating the civil aircraft fleet. Hence the significant reduction in<br />
CO <strong>and</strong> HC EIs. Despite improvements in NOx control technology, the effect of<br />
increasing pressure ratios has offset these, resulting in a slight increase in NOx EI over<br />
the decade. Examination of certification data [ICAO 1995] from newly certificated<br />
QINETIQ/04/01113 Page 76
engines over the period confirms this trend. From a validation point of view, whilst<br />
not an exhaustive check, these results fall within the expected b<strong>and</strong>.<br />
% of ICAO St<strong>and</strong>ards<br />
450<br />
400<br />
150<br />
100<br />
50<br />
0<br />
Ref: The Boeing Company<br />
Hydrocarbons<br />
Carbon monoxide<br />
Pre81 81-91 91-present<br />
Year of Engine Certification<br />
Figure 44: Decrease in certificated HC <strong>and</strong> CO emissions by period of engine certification<br />
2.4.2 Validation of <strong>2002</strong> Military <strong>Aviation</strong> <strong>Emissions</strong><br />
The military aviation database contains comprehensive coverage of fleet inventory. In<br />
many cases the actual state of airworthiness needs to be estimated. In addition, the<br />
number of movements <strong>and</strong> flying hours per aircraft (type) are fairly well known in<br />
case of NATO countries (making up the majority of aircraft movements), but <strong>for</strong> other<br />
countries, in particular the <strong>for</strong>mer Soviet states <strong>and</strong> China, these data are not known<br />
<strong>and</strong> can only be estimated.<br />
In addition, there is considerable uncertainty on where the emissions are actually<br />
released into the earth's atmosphere. Missions, even if they have the same purpose<br />
(<strong>for</strong> training) may have considerable differences in actual profiles. The calculated CO<br />
<strong>and</strong> HC emissions depend <strong>for</strong> a great deal on the accuracy of the estimated use of<br />
afterburning. There is little <strong>and</strong> conflicting in<strong>for</strong>mation on the amount of<br />
afterburning used across the range of operations at world scale. Considering that this<br />
inventory assumes afterburning whenever opportunity is there, the current values of<br />
CO <strong>and</strong> EI-CO will be relatively close to the upper limit of the feasible range.<br />
2.4.3 Validation of <strong>2025</strong> <strong>Aviation</strong> <strong>Emissions</strong><br />
The civil aviation emissions modelled <strong>for</strong> <strong>2025</strong> are, obviously, not based on actual<br />
data but are the result of <strong>for</strong>ecast dem<strong>and</strong> growth <strong>and</strong> technology assumptions.<br />
These assumptions are based on DTI’s work <strong>for</strong> CAEP <strong>and</strong> on Airbus’ expectation of<br />
aviation market growth. Whether they actually occur will depend on a number of<br />
factors both external <strong>and</strong> internal to aviation. The Airbus work is based on that<br />
manufacturer’s view of the future aviation dem<strong>and</strong> <strong>and</strong> the aircraft size categories<br />
needed to meet that dem<strong>and</strong>. As such the <strong>2025</strong> dem<strong>and</strong> tends toward a European<br />
QINETIQ/04/01113 Page 77
view, which is appropriate <strong>for</strong> this EC project. However it is recognised that these are<br />
other views of regional <strong>and</strong> global aviation dem<strong>and</strong> growth <strong>and</strong> these views could, in<br />
future work, be used as the input to a further <strong>AERO2k</strong> emissions <strong>for</strong>ecast. In fact, the<br />
global averaged dem<strong>and</strong> growth of 4.2% per year is close to the 4.3% used in the<br />
CAEP/6 FESG <strong>for</strong>ecast to 2020 [FESG, 2003]. Coincidentally, this FESG growth figure<br />
has recently been amended to 4.2% to 2020 [CAEP, 2004]. Whilst regional emphasis<br />
may vary within these <strong>for</strong>ecasts, the <strong>AERO2k</strong> growth to <strong>2025</strong> is in line with other<br />
global <strong>for</strong>ecasts. It does, however, remain a <strong>for</strong>ecast based on assumptions, not a<br />
certainty.<br />
Similarly, the models used by DTI to generate the fleet rollover <strong>and</strong> resultant fleet<br />
technology improvements are the models accepted by CAEP FESG <strong>for</strong> their globally<br />
accepted <strong>for</strong>ecast work <strong>for</strong> the CAEP process. The input data <strong>for</strong> these models, in<br />
terms of technology improvements, are based on the data referenced in Section 2.3<br />
have been checked with a major European engine manufacturer <strong>and</strong> accepted as<br />
being a reasonable <strong>for</strong>ecast assuming continued pressure on NOx <strong>and</strong> fuel<br />
consumption. Once again, whether these assumptions represent what actually occurs<br />
is subject to events both inside <strong>and</strong> outside aviation.<br />
For military aviation, by its very nature, <strong>for</strong>ecasting global distribution <strong>and</strong> quantity<br />
of emissions from military aviation over a 20+ year timescale is less predictable than<br />
<strong>for</strong>ecasting emissions from civil aviation. Military aviation is not subject to growth in<br />
line with economic prosperity but is more prone to variation from one or more global<br />
<strong>and</strong> regional political scenarios. Given that military aviation generally contributes<br />
only around 10% of total aviation emissions, it was considered that providing globally<br />
distributed data <strong>for</strong> military emissions <strong>for</strong> <strong>2025</strong> was so subject to these scenario<br />
variations that any particular <strong>for</strong>ecast could not be validated within the overall scope<br />
of <strong>AERO2k</strong>. Hence a generalised assumption was made that the calculated <strong>2002</strong><br />
values should be used <strong>for</strong> <strong>2025</strong> military emissions.<br />
2.4.4 Uncertainties<br />
As with any inventory, there are a significant number of assumptions made to allow<br />
quantification of the data <strong>and</strong> to keep the data to manageable proportions. This<br />
section highlights some of the more significant uncertainties that arise from those<br />
assumptions.<br />
Key Background Assumptions<br />
Improved routing<br />
Previous inventories assumed a great circle 21 route was flown between origin <strong>and</strong><br />
destination airports <strong>and</strong> also assumed a typical cruise altitude. The flight movement<br />
data <strong>for</strong> <strong>AERO2k</strong> provide longitude, latitude <strong>and</strong> altitude data at a number of<br />
intervals throughout each flight. This allows appropriate fuel <strong>and</strong> emissions data <strong>for</strong><br />
the actual flight route <strong>and</strong> altitude to be selected <strong>for</strong> the inventory. It will still be<br />
assumed that a great circle will be flown between each waypoint through the flight.<br />
This approach is a significant improvement on previous inventories as there can be a<br />
large difference between the actual route flown <strong>and</strong> the great circle route. The<br />
reasons <strong>for</strong> these differences can be due to air traffic control (congestion, flight<br />
corridors etc), restrictions on airspace (military areas or overfly rights <strong>for</strong> some<br />
countries) or weather (turbulence, storms) or avoiding/taking advantage of prevailing<br />
winds.<br />
The <strong>AERO2k</strong> flight movement data cannot cover every part of every flight in great<br />
detail, however. There are two aspects to this, a large-scale <strong>and</strong> a small-scale effect:<br />
21 The great circle distance is the shortest distance between two points on a sphere.<br />
QINETIQ/04/01113 Page 78
The large-scale effect occurs when there are large distances between the waypoints.<br />
The intervals between waypoints can be spaced far apart in some regions of the<br />
world. The fine detail of the flight route in this region is not available but <strong>AERO2k</strong> still<br />
offers a significant improvement compared to using a great circle between origin <strong>and</strong><br />
destination airports.<br />
The small-scale effect concerns parts of the flight where the intervals between<br />
waypoints may be small but the aircraft is per<strong>for</strong>ming rapid or frequent changes in<br />
course or altitude. These primarily affect climb or descent segments of the flight <strong>and</strong><br />
it is not possible to capture precise details about the time or location of direction or<br />
altitude changes. The effects of these will have the largest impact on short flights<br />
where the climb phase <strong>and</strong> holding during descent contribute to a larger percentage<br />
of the total flight fuel burn. Analysis of the operational flight data used <strong>for</strong> validation<br />
in Section 2.4 shows a mean true distance travelled of 4.5% farther than great circle<br />
routes.<br />
Wind<br />
The effect of wind on aircraft during flight is, <strong>for</strong> a given flight speed, to alter the<br />
speed relative to the ground. Flying into a headwind means aircraft effectively fly<br />
slower relative to the ground, the actual distance flown (equivalent still air distance)<br />
increases, taking more time to reach the destination <strong>and</strong> consequently consuming<br />
more fuel. Flying with a tailwind has the opposite effect. A side-wind will push the<br />
aircraft off its intended course so a corrected course will be necessary to compensate.<br />
Most of the time the wind will act at an angle relative to the direction of flight so the<br />
effect will be a composite of the components of head/tail <strong>and</strong> sidewinds. In practice<br />
flight speeds may be altered to try to mitigate the effects to some extent.<br />
The <strong>AERO2k</strong> flight movement data consist of radar track data <strong>for</strong> flights over North<br />
America <strong>and</strong> the north Atlantic, <strong>and</strong> modelled flight trajectories <strong>for</strong> the rest of the<br />
world. The radar track data intrinsically incorporate the effects of winds as they<br />
impact directly on the flight times. For the rest of the world the trajectory model<br />
assumes still air.<br />
Computational methods exist to calculate the effect of winds using ‘equivalent<br />
headwinds’ [Sawyer, 1950] <strong>and</strong> meteorological statistics. However the computational<br />
ef<strong>for</strong>t required to implement such methods into the inventory would be prohibitive.<br />
Instead, an assumption is made <strong>for</strong> the flight speeds to be used (see Section 2.1.2).<br />
Whilst the effect of winds is only partially included in the movement database<br />
through the flight track data, the predicted flight trajectories do try to account <strong>for</strong> the<br />
presence of prevailing winds on some routes <strong>and</strong> minimise their effects where<br />
possible by predicting flight routes <strong>and</strong> altitudes which take advantage of tailwinds<br />
or minimise headwinds.<br />
Analysis of the operational flight data used <strong>for</strong> validation shows that winds at cruise<br />
altitudes can affect the equivalent still air flight distance by as much as ±20%. The<br />
mean difference from this data is an increase in equivalent still air distance of 1.5%.<br />
Atmosphere<br />
The use of ISA (International St<strong>and</strong>ard Atmosphere) st<strong>and</strong>ard day temperatures <strong>for</strong><br />
the <strong>AERO2k</strong> inventory was agreed as an a priori assumption early in the project. There<br />
is an effect on overall aircraft per<strong>for</strong>mance with changes in ambient temperature,<br />
<strong>and</strong> this affects the fuel consumption <strong>and</strong> emissions produced. This is primarily due<br />
to the effect of inlet air temperature on engine per<strong>for</strong>mance. The complications of<br />
modelling the changes of ambient temperature on a seasonal basis <strong>and</strong> with latitude<br />
were judged to be too great within the scope of the project as fuel burn <strong>and</strong><br />
emissions datasets would have to be replicated <strong>for</strong> each different temperature b<strong>and</strong>.<br />
<strong>Global</strong> mean variation from ISA st<strong>and</strong>ard day temperatures with height <strong>for</strong> the<br />
period 1958 to 2001 are shown in Appendix A [Ulbrich, 2004]. Even on this annually-<br />
QINETIQ/04/01113 Page 79
averaged basis, there is considerable deviation from st<strong>and</strong>ard ISA temperatures with<br />
aircraft flying in areas of ISA ± 20deg. However, the predominance of flights, between<br />
30°N <strong>and</strong> 60°N, lie within the ISA ± 5deg b<strong>and</strong> , suggesting that the overall deviation<br />
of fuel burn from ISA-derived per<strong>for</strong>mance levels will be small (
During flight a number of manoeuvres are per<strong>for</strong>med which cannot be accurately<br />
modelled based on the flight movement data or the PIANO aircraft per<strong>for</strong>mance tool.<br />
Between <strong>and</strong> during the climb, cruise <strong>and</strong> descent segments the aircraft will make<br />
turns <strong>and</strong> accelerate (or decelerate), which will require more fuel than <strong>for</strong> straight<br />
<strong>and</strong> level flight. The effect of these manoeuvres on total fuel burn is, however,<br />
considered to be small over a flight distance.<br />
An additional manoeuvre to be considered is step-climbs made during cruise. These<br />
predominantly take place on longer routes where an aircraft burns a considerable<br />
amount of fuel. As the aircraft gets lighter the optimum altitude <strong>for</strong> minimum fuel<br />
consumption increases, <strong>and</strong> the pilot will seek permission from air traffic control to<br />
increase the flight level to match the aircraft’s optimum altitude as closely as<br />
possible. The increased fuel consumption during the step climb is usually far offset by<br />
the reduction in fuel consumption at the higher altitude.<br />
The flight movement data do not provide the resolution to give the exact time <strong>and</strong><br />
location where step climbs take place. The fuel allocation method there<strong>for</strong>e selects<br />
cruise fuel burn data appropriate to the altitudes at each waypoint throughout each<br />
flight rather than making independent assumptions about when a step-climb took<br />
place <strong>and</strong> allocating additional fuel to account <strong>for</strong> the manoeuvre. Any error<br />
introduced by not including a specific climb fuel allowance will be smaller than the<br />
error from not changing the cruise flight altitude at the appropriate point in the<br />
cruise. Both of these errors will still be considerably lower than those <strong>for</strong> using<br />
average cruise altitudes, as used in previous inventory approaches.<br />
Deterioration<br />
Deterioration of engines <strong>and</strong> the airframe leads to increased fuel consumption. Over<br />
time, a gas turbine engine’s per<strong>for</strong>mance will be reduced due to deterioration of its<br />
component parts, due to tip clearances losses <strong>and</strong> accumulation of deposits on blade<br />
surfaces. The engine then has to be worked harder to produce the required thrust.<br />
The result is increased fuel consumption rates, which can show marked increases over<br />
the service life of an engine. On the airframe, surface irregularities, badly fitting seals,<br />
control surface alignment <strong>and</strong> instrumentation errors can lead to increased drag,<br />
which again results in increased fuel consumption rates.<br />
The EC 4 th Framework project AEROCERT [Norman, 2001] found deterioration of<br />
engine per<strong>for</strong>mance encountered in in-service engines would result in typical<br />
maximum fuel consumption increases of 3 to 4%. The rate of deterioration of the<br />
per<strong>for</strong>mance of an airframe is slow compared to that of an engine. Cumulative<br />
deteriorations on an airframe may, over many years, represent an overall efficiency<br />
loss that equates to up to 2% of cruise fuel burn. These results are comparable to<br />
typical increases in fuel consumption due to deterioration of between 2 <strong>and</strong> 6%<br />
stated by Eurocontrol [Mykoniatis <strong>and</strong> Martin, 1998]. This report also states that, in<br />
practice, engine per<strong>for</strong>mance degradation has only a small overall impact on total<br />
fuel burn, even on long distance flights. This is because an aircraft will not typically<br />
have all of its engines in a highly deteriorated state <strong>and</strong> because airframe<br />
deterioration builds up only slowly over many years.<br />
From consideration of the in<strong>for</strong>mation above, it is estimated that the total increase in<br />
fuel consumption across the fleet due to deterioration will not be more than about<br />
3% compared to a br<strong>and</strong> new aircraft. This increased fuel consumption is implicitly<br />
included in the data from airlines <strong>and</strong> in the published per<strong>for</strong>mance data that are<br />
used <strong>for</strong> validation in Section 2.4.1. There<strong>for</strong>e it is assumed that the effects of<br />
deterioration are adequately represented in the <strong>AERO2k</strong> inventory.<br />
Flight Uncertainties<br />
<strong>AERO2k</strong> has sought to include all global flights under IFR rules per<strong>for</strong>med in <strong>2002</strong>. The<br />
decision to omit non-IFR flights (primarily general aviation) immediately introduces<br />
an element of underestimate; of the order of 2% of the total civil aviation fuel used<br />
QINETIQ/04/01113 Page 81
data. The capture of data <strong>for</strong> non-IFR flights in detail is a major <strong>and</strong> disproportionate<br />
task. It was there<strong>for</strong>e excluded from <strong>AERO2k</strong> in the project specification.<br />
Capture of data on IFR flights was based on the complex task of merging three flight<br />
databases – Europe (ECAC actual flight tracked data), North America (AMOC actual<br />
flight tracked data) <strong>and</strong> Back <strong>Aviation</strong> global timetable data. Whilst attempts were<br />
made to gain actual flight data <strong>for</strong> the rest of the word, this was not available within<br />
the project timescales <strong>and</strong> budget. This remaining timetabled fraction represents less<br />
than 30% of global aviation. The <strong>AERO2k</strong> data there<strong>for</strong>e do not include nontimetabled<br />
(e.g. charter) flights outside Europe <strong>and</strong> North America. The effect of such<br />
omission will vary around the globe, dependant upon the proportion of nontimetabled<br />
IFR flights in a particular region. The major charter flight concentration in<br />
Europe has however been included <strong>and</strong> the overall impact is estimated to be a small<br />
(
Representative Aircraft <strong>and</strong> Fuel Profiling Uncertainties<br />
<strong>AERO2k</strong> uses 40 representative aircraft to represent a world fleet of over 20000<br />
aircraft of over 200 different types (<strong>and</strong> many variations within these types). This<br />
methodology there<strong>for</strong>e represents an approximation, <strong>for</strong> which the uncertainties <strong>for</strong><br />
individual aircraft were dealt with in <strong>AERO2k</strong> Deliverable Reports D7 [Norman <strong>2002</strong>]<br />
<strong>and</strong> D8 [Norman, 2004].<br />
For the overall emissions inventory, all major types of civil aircraft have been<br />
included, omitting only small aircraft (which are unlikely to appear in IFR flights) <strong>and</strong><br />
rare larger types (which by their small numbers <strong>and</strong>/or low utilisation do not cause<br />
significant global emissions e.g. Antonov AN124).<br />
For these major types, representative engines have been selected on the basis of their<br />
NOx emissions. This methodology assumes that competitive engine types on the<br />
same aircraft will have similar fuel consumption (within 1% or 2%); as a key issue in<br />
global aviation, NOx is then the next distinguishing parameter <strong>for</strong> which data are<br />
available. The process <strong>for</strong> selection using average NOx is covered in D7 [Norman,<br />
<strong>2002</strong>] <strong>and</strong> D8 [Norman, 2004] <strong>and</strong> in Section 2.1.2 above, concluding that <strong>for</strong> the<br />
global inventory, the overall NOx emissions will represent a best estimate (subject of<br />
course to the engine modelling <strong>and</strong> NOx parameterisation used to calculate NOx at<br />
altitude). Other non-fuel related emissions will have a higher degree of uncertainty<br />
(primarily HC <strong>and</strong> CO) as these will depend upon the per<strong>for</strong>mance of the particular<br />
chosen representative engine. The emission levels of both these parameters are<br />
primarily dependant upon engine generation (i.e. entry into service date). The<br />
representative engines chosen are also dependant upon entry into service; hence this<br />
HC <strong>and</strong> CO improvement trend will be captured in the emissions data. It is recognised<br />
that this process does not provide the same degree of precision as it does <strong>for</strong> NOx.<br />
However, given the current relative importance of these emissions, the NOx-based<br />
approach is considered to be most appropriate <strong>for</strong> the representative engines.<br />
Additionally, it should be recognised that uncertainties over operational practice<br />
(particularly actual idle settings) are likely to be greater than any averaging effect<br />
from the choice of representative engine. In terms of impact, most HC <strong>and</strong> CO<br />
emissions from aviation are emitted during the l<strong>and</strong>ing <strong>and</strong> take-off cycle <strong>and</strong>, whilst<br />
important in terms of local air quality, the remaining emissions at altitude are<br />
currently considered by climatologists to be of lesser importance compared to other<br />
aviation effects at altitude.<br />
For the representative engines, fuel flows <strong>for</strong> the LTO cycle have been taken from the<br />
certification engine test data <strong>and</strong> thus represent a close approximation to actual fuel<br />
flows (subject to background assumptions in Section 2.4.4). However, fuel flows at<br />
altitude are a closely guarded commercial secret <strong>and</strong> thus must be calculated from<br />
available aircraft <strong>and</strong> engine per<strong>for</strong>mance models. Uncertainties in this process have<br />
been covered extensively in <strong>AERO2k</strong> Deliverable Report D8 [Norman, 2004]. It is<br />
apparent from the evidence presented in D8 that there is considerable variation in<br />
the fuel flows modelled by different methods. The report concludes that the PIANObased<br />
methods produce fuel burn data agreeing with reference sources without any<br />
systematic errors, with especially good match at the important cruise conditions.<br />
The method used in the <strong>AERO2k</strong> emissions inventory model <strong>for</strong> assigning fuel data to<br />
the flight profiles was described in Section 2.1.2. A number of a priori background<br />
assumptions were made in this part of the process (Section 2.4.4). Although the<br />
impact of a number of these background assumptions cannot be accurately<br />
modelled, <strong>and</strong> they have there<strong>for</strong>e deliberately not been accounted <strong>for</strong> in <strong>AERO2k</strong>, it<br />
is apparent that, taken together, they are likely to represent a bias effect on the<br />
<strong>AERO2k</strong> results. Most of the impacts are thought to increase fuel <strong>and</strong> emissions at<br />
altitude ie winds ~+1.5%, holding/stacking
altitude may be a small underestimate <strong>and</strong> data users may wish to make their own<br />
assessment of these effects. Conversely, the implicit assumptions in the CAEP-derived<br />
LTO cycle are to overestimate emissions. Although much of this overestimate has<br />
been eliminated from <strong>AERO2k</strong> by use of alternative airport- <strong>and</strong> aircraft-specific<br />
times-in-mode, there are still specific operational issues such as reduced thrust takeoff<br />
<strong>and</strong> idle setting (often 5g/kg) 15%<br />
CO <strong>and</strong> HC (EI < 5g/kg) 0,3g/kg (1g/kg exceptionally)<br />
DLR Soot Soot mass 10% (30% exceptionally)<br />
DLR fuel flow NOx 5%<br />
The correlation methods used <strong>for</strong> emissions of CO, hydrocarbons <strong>and</strong> soot are the<br />
only known algorithms that combine a physical background with available<br />
measurement data to an individual <strong>for</strong>mula <strong>for</strong> each engine. Since these methods<br />
have revealed good agreement with the few available measurement data, they have<br />
been used <strong>for</strong> <strong>AERO2k</strong>.<br />
Aero2k does not hold any internal combustion data, so although several NOx<br />
correlations, based on different approaches exist, only the more simple methods are<br />
available to the model. Of the simpler methods, the DLR fuel flow method <strong>and</strong> the p3-<br />
T3 method are the most widely known <strong>and</strong> accepted. However, the p3-T3 method<br />
requires the pressure exponent <strong>and</strong> the p3-T3 relation of each engine to be known,<br />
<strong>and</strong> this in<strong>for</strong>mation is not available in Aero2k. The DLR fuel flow method, on the<br />
other h<strong>and</strong>, produces accurate results based purely on published data from the ICAO<br />
database, so it was chosen as the method <strong>for</strong> calculating NOx in Aero2k.<br />
For particulate numbers, this work in <strong>AERO2k</strong> is a first attempt to make a global<br />
quantification in the <strong>for</strong>m of an inventory. Due to the lack of measured data of the<br />
individual engine types, this function based on correlating non-volatile particle<br />
numbers to particle mass has been used to determine the particle number emission<br />
indices of the representative engines. It is obvious that this procedure is not suited to<br />
deliver accurate results <strong>for</strong> individual flights, but delivers the best estimate possible<br />
with the few available data <strong>for</strong> a number of aircraft movements. The estimate of<br />
particle numbers must there<strong>for</strong>e be regarded as an “order-of-magnitude” estimate<br />
that can be significantly improved if further research is carried out on the<br />
characteristics of particle emissions from gas turbines.<br />
Data Integration Uncertainty<br />
Data integration is generally a precise process – taking flight data, matching<br />
representative aircraft <strong>and</strong> engines, calculating fuel flow <strong>and</strong> emissions <strong>and</strong><br />
allocating these to flights, regions <strong>and</strong> geographical grids etc. There are, however, a<br />
small number of assumptions in the data integration, which are included to reduce<br />
computing time or to increase accuracy. The main assumptions are:<br />
LTO emissions are assumed to occur at the airport coordinates<br />
QINETIQ/04/01113 Page 84
LTO times (to <strong>and</strong> from 3000ft) are assumed to be in accordance with the typical LTO<br />
times contained in the look-up table <strong>for</strong> the particular airport. Where actual flight<br />
data are available <strong>for</strong> the flight phases less than 300ft, these are not used.<br />
Airports are assumed to be at sea-level 22<br />
Aircraft weight is recalculated only at each data point or every 300nm, whichever is<br />
smaller (see Figure 41 <strong>and</strong> Figure 42, which indicate the impact of this assumption)<br />
Aircraft speed is not calculated from distance flown divided by time. Instead, the<br />
aircraft is assumed to fly at 99% of Specific Air Range (SAR) [Norman, 2004]. This<br />
assumption is in line with airline practise <strong>and</strong> avoids erroneous calculation of speed<br />
due to errors in the time data, deviation from great circle distance <strong>and</strong> wind effects.<br />
Climb <strong>and</strong> descent fuel does not depend upon the geographical distance covered but<br />
only on the change of altitude of the climb/descent <strong>and</strong> the weight of the aircraft.<br />
Where precise values are not available in the look-up tables, values <strong>for</strong> fuel <strong>and</strong><br />
emissions are calculated by interpolation.<br />
The impact of these assumptions on data will become apparent only if detailed<br />
queries are made on the data. However, <strong>for</strong> the overall inventory results, the impact<br />
of these assumptions is small <strong>and</strong> normally negligible.<br />
2.4.5 Sensitivities<br />
Representative Days, Weeks <strong>and</strong> Year<br />
<strong>AERO2k</strong> global gridded emissions have been calculated from the best available flight<br />
data from 6 representative weeks in <strong>2002</strong>. In using representative weeks to reduce<br />
data collection <strong>and</strong> processing time (<strong>and</strong> even in using a single year), there is a risk<br />
that data may be affected by individual events that are not “typical” of aviation over<br />
the longer term. Whilst there is a small increase in uncertainty introduced by use of<br />
the representative weeks method, there is a significant improvement in the utility of<br />
the tool thorough reduced data processing times.<br />
For <strong>2002</strong> as a whole, the after effects of the events of 9/11 were being felt in the<br />
downturn in aviation. At the end of <strong>2002</strong>, the SARS outbreak was appearing on the<br />
world scene <strong>and</strong> again just beginning to affect traffic in terms of passenger numbers.<br />
A football (soccer) World Cup was held in Japan <strong>and</strong> Korea in June <strong>2002</strong>. All these<br />
factors are reflected in the air traffic represented in <strong>AERO2k</strong> but they do demonstrate<br />
that the traffic <strong>for</strong> any year is unique to that year. Different effects were apparent in<br />
2001 eg the immediate affects of 11 Sept, <strong>and</strong> in 2003 e.g. the Iraq War <strong>and</strong> the full<br />
SARS outbreak.<br />
A similar effect is present in the representative weeks, which is more difficult to<br />
quantify. The representative weeks have been annualised using flight number data<br />
<strong>for</strong> every day of the year. Seasons were found to be the strongest effect <strong>and</strong> the<br />
annualisation process has been carried out on this basis. However, short-term effects<br />
in <strong>2002</strong> can cause data <strong>for</strong> individual days within the “representative days” to be<br />
affected. The 6 representative weeks (42 days) within the <strong>AERO2k</strong> <strong>2002</strong> data have<br />
been examined <strong>for</strong> consistency <strong>and</strong> two events were identified. On Wednesday 11<br />
22 In reality this would impact time to climb to cruise altitude <strong>and</strong> the overall aircraft<br />
per<strong>for</strong>mance. This is not expected to have a significant impact on the overall inventory result,<br />
as the total fuel consumed during all LTO operations is small relative to total aviation fuel<br />
(
Sept <strong>2002</strong> the flight data showed a significant fall in the number of flights compared<br />
to the weekly patterns <strong>for</strong> the other representative weeks. Examination of the data<br />
showed the main fall was in flights to or from the USA with almost full recovery by<br />
the next day. A similar effect was seen toward the end of the December week (2 to 9<br />
Dec) with a considerable dip in the number of US originating flights. Weather records<br />
show a major snowstorm along the US eastern seaboard from 5 December onwards<br />
with over 150mm of snow falling in one hour at Washington Dulles airport.<br />
Consideration was given to taking account of these events. However, they are not<br />
totally unique events <strong>and</strong> are representative of many weather, sport <strong>and</strong> cultural<br />
events occurring during the year that change the overall volume of actual air traffic.<br />
There is a remaining concern that the Sept 11 anniversary would be one of the larger<br />
effects <strong>and</strong> its effect may be exaggerated by its use in the representative week.<br />
Operational Practice <strong>and</strong> Other Unquantifiable effects<br />
The various uncertainties involved in aircraft operation have been mentioned in a<br />
number of earlier sections in this report. Together, the varying operational procedures<br />
<strong>for</strong> take off settings, cruise speeds, fuel tankering etc, bring uncertainty to the<br />
calculation. It has been the policy in <strong>AERO2k</strong> not to try <strong>and</strong> make approximate<br />
allowances <strong>for</strong> factors that are not fully understood <strong>and</strong> it is left to the user to<br />
account <strong>for</strong> these if required. In summarising the various effects, the vast majority of<br />
these suggest that <strong>AERO2k</strong> represents a small underestimate in terms of fuel, CO2<br />
<strong>and</strong> H20 emissions. For NOx, CO <strong>and</strong> HC emissions, the underestimate in terms of fuel<br />
may be offset by an overestimate of LTO emissions due to use of the ICAO published<br />
values which represent maximum rather than average emissions.<br />
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Further analysis using the detail data contained within <strong>AERO2k</strong><br />
The current design of the <strong>AERO2k</strong> software is, in accordance with the aims of the<br />
project, focussed on providing global gridded data <strong>for</strong> use by climatologists. Although<br />
featuring many enhancements compared to previous inventories, like them, it<br />
includes a range of assumptions that are designed to provide this climate data within<br />
the limitations of af<strong>for</strong>dable computing power. As a consequence, there are<br />
limitations in the extent to which the unpublished data within the <strong>2002</strong> flight<br />
database should be used <strong>for</strong> analysis at a more microscopic level. For example,<br />
subdivision of the data to country <strong>and</strong> even major airport level should retain<br />
adequate level of accuracy. However, the global omission of non-IFR flights<br />
(representing around 2% of global fuel burn) would result in unrepresentative data if<br />
analysis were taken <strong>for</strong> small domestic airports where non-IFR flights dominate.<br />
Because of the use of representative aircraft, detailed analysis by individual aircraft<br />
type can only be carried out with care However good representation by aircraft<br />
weight, seat-class range can be carried out successfully.<br />
In a similar way, the focus on global climate effects does not require a high degree of<br />
accuracy in the LTO region. <strong>AERO2k</strong> contains a number of enhancements such as<br />
airport specific LTO times-in-mode compared to previous inventories. However,<br />
airport specific studies using the data would need to be undertaken with care in order<br />
to include specific operational practices such as reduced thrust take-off <strong>and</strong><br />
controlled descent approach.<br />
Regional Variation Sensitivity<br />
Flight data used to construct the <strong>2002</strong> inventory consists of two styles of data. The<br />
first is actual flight tracked data from Europe <strong>and</strong> North America, which represents an<br />
accurate representation of actual latitude, longitude <strong>and</strong> altitudes flown. The second,<br />
<strong>for</strong> the remainder of the world, is based on simulated trajectories <strong>for</strong>med from<br />
timetable data enhanced with knowledge of aircraft routings <strong>and</strong> flight profiles. As a<br />
consequence of this difference, there is a difference in the degree of accuracy of<br />
gridded data between these two regions. Perhaps of particular relevance to<br />
climatologists is the absence of actual (as opposed to simulated) flight trajectories in<br />
the southern hemisphere. Whilst estimates, particularly of altitude, have been made<br />
based on typical airline practice, there is a small degree of uncertainty over the<br />
vertical distribution of flights, <strong>and</strong> there<strong>for</strong>e emissions in the southern hemisphere.<br />
Whether this uncertainty is significant will depend upon the altitude sensitivity of<br />
climate impacts in these regions.<br />
There is also a small difference in the percentage of flights covered between the same<br />
regions. For Europe <strong>and</strong> North America, flight tracking picks up both timetabled <strong>and</strong><br />
non timetabled flights. Non-timetabled IFR flights <strong>for</strong> the remainder of the world,<br />
such as charter, business or VIP flights are calculated to represent a small proportion<br />
of fuel-used <strong>and</strong> emissions [Norman, 2004]. Nevertheless, if making inter-regional<br />
comparisons, this small but difficult to quantify difference in source data will become<br />
larger.<br />
Sensitivity Summary<br />
In summary it is concluded that with the additional representative aircraft, flight<br />
inventory <strong>and</strong> updated emissions data, <strong>AERO2k</strong> represents a significant <strong>and</strong><br />
important step <strong>for</strong>ward over previous inventories. The core figures of fuel-used, CO2,<br />
H2O <strong>and</strong> NOx have confirmed <strong>and</strong> refined data from previous inventories,<br />
demonstrating that continued growth in aviation since the mid-1990s is still<br />
outstripping the considerable advances in fuel-saving <strong>and</strong> emissions reduction<br />
technology.<br />
QINETIQ/04/01113 Page 87
Overall, the predicted fuel-used, CO2, H2O <strong>and</strong> NOx figures are considered highly likely<br />
to represent a small underestimate of the likely actual figures, resulting from the<br />
deliberate decision not to try to include effects which could not be quantified within<br />
the current work programme of the <strong>AERO2k</strong> project (e.g. winds, tankering).<br />
Additionally, the lack of complete flight data <strong>for</strong> parts of the world <strong>and</strong> the deliberate<br />
omission of non-IFR flights are also likely to represent a small underestimation.<br />
For CO <strong>and</strong> HC emissions, uncertainties in source data show a greater variation on an<br />
engine-to-engine basis as well as between engine types [ICAO 1995]. Together with<br />
the approximation of using representative engines selected on a NOx-basis <strong>and</strong> the<br />
significant reductions possible from operational measures at airports, the<br />
uncertainties in CO <strong>and</strong> HC from individual flights is significant. However, most HC<br />
<strong>and</strong> CO emissions from aviation are emitted during the l<strong>and</strong>ing <strong>and</strong> take-off cycle<br />
<strong>and</strong>, whilst important in terms of local air quality, the remaining emissions at altitude<br />
are currently considered by climatologists to be of lesser importance compared to<br />
other aviation effects at altitude. Nevertheless, when compared to previous<br />
inventories, the <strong>AERO2k</strong> results clearly demonstrate the continued improvement in<br />
HC <strong>and</strong> CO emissions achieved with recent aviation combustion technology.<br />
The new in<strong>for</strong>mation on particulates is subject to a greater degree of uncertainty,<br />
primarily due to the lack of measured data <strong>and</strong> scientific underst<strong>and</strong>ing of the<br />
<strong>for</strong>mation of particulates in gas turbines. Only a small number of engine<br />
measurements are available <strong>for</strong> validation <strong>and</strong> application <strong>and</strong> a generic method has<br />
been used to cover all engines. It is obvious that this procedure is not suited to deliver<br />
accurate results <strong>for</strong> individual flights, but delivers the best global estimate possible<br />
given the paucity of available data on this subject.<br />
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3 Results<br />
<strong>AERO2k</strong> results fall into three categories: inventory results <strong>for</strong> <strong>2002</strong> civil aviation,<br />
inventory results <strong>for</strong> <strong>2002</strong> military aviation <strong>and</strong> a civil aviation <strong>for</strong>ecast <strong>for</strong> <strong>2025</strong>.<br />
Within each of these categories, the results are presented as global gridded data, as<br />
global totals, regional totals <strong>and</strong> totals by representative aircraft. Distribution of<br />
emissions by altitude is also shown.<br />
3.1 Results <strong>for</strong> <strong>2002</strong> Civil <strong>and</strong> Military <strong>Aviation</strong><br />
3.1.1 Gridded data <strong>for</strong> <strong>2002</strong><br />
The main results from <strong>AERO2k</strong> <strong>for</strong> <strong>2002</strong> are the global gridded data <strong>for</strong> fuel-used,<br />
emissions <strong>and</strong> distance flown. These data are described in outline here <strong>and</strong> are<br />
published on the <strong>AERO2k</strong> WebPages at http://www.cate.mmu.ac.uk/aero2k.asp.<br />
The gridded data consist of:<br />
• 12 tables, one <strong>for</strong> each month of <strong>2002</strong>, each containing the civil aviation<br />
emissions, fuel used <strong>and</strong> distance flown in a global grid of resolution 1 deg by 1<br />
deg by 500ft<br />
• tables showing the civil aviation emissions, fuel used <strong>and</strong> distance flown over 4<br />
successive 6-hour periods<br />
• 1 table showing annual military aviation emissions, fuel used <strong>and</strong> distance<br />
flown in a global grid of resolution 1 deg by 1 deg by 500ft.<br />
These data are intended <strong>for</strong> use by atmospheric scientists <strong>for</strong> the estimation of<br />
climate impact of current <strong>and</strong> future aviation.<br />
For the civil aviation gridded tables, although the data are given at a vertical<br />
resolution of 500 feet, these data need to be treated with care. The majority of the<br />
flight data provide altitude in<strong>for</strong>mation to 3 significant figures (e.g. FL302<br />
representing 30200ft). As air traffic convention provides <strong>for</strong> vertical separation by<br />
constraining actual cruise flight close to the round thous<strong>and</strong>s of feet altitude, much<br />
of the cruise data are of the <strong>for</strong>m of round thous<strong>and</strong>s of feet e.g. “FL300”<br />
representing 30000ft. With a grid vertical resolution of 500ft, this results, <strong>for</strong><br />
example around 30000ft, in a large emissions value falling into the 30000ft to<br />
30499ft cell with very few in the 29500ft to 29999ft cell.<br />
In some senses, this is a real effect, with emissions concentrated in horizontal b<strong>and</strong>s<br />
1000ft apart. These b<strong>and</strong>s are blurred by the effects of slight variations from the<br />
target flight altitude, by errors <strong>and</strong> uncertainties in radar in<strong>for</strong>mation <strong>and</strong> even in the<br />
position of the engines on the aircraft. However, to which of the cells (<strong>for</strong> example<br />
just above or just below 30000ft) the emissions should be allocated is somewhat<br />
arbitrary. Further blurring will occur with dispersion of the emissions but this is a<br />
matter <strong>for</strong> atmospheric scientists.<br />
For users that require it, a much smoother altitude profile can be obtained by<br />
decreasing the gridding resolution to 1000ft, using an initial 500ft cell at ground<br />
level. Subsequent cells will cover 500 to 1500ft etc, thereby enveloping flight around<br />
each round-thous<strong>and</strong>-feet flight level. The 500ft resolution has been retained in the<br />
data presented to assist those seeking greater resolution to assist in quantification of<br />
cirrus <strong>and</strong> contrail effects.<br />
For military data, the effect is very different. Here actual flight data are not available<br />
<strong>and</strong> all data are based on typical flight plans, <strong>for</strong> latitude, longitude <strong>and</strong> <strong>for</strong> altitude.<br />
Hence, differences in emissions values at 500ft resolution do not represent an actual<br />
QINETIQ/04/01113 Page 89
difference from actual military flights. This is one of a number of reasons <strong>for</strong><br />
publishing the civil <strong>and</strong> military gridded data separately.<br />
3.1.2 Other Results - <strong>2002</strong><br />
To complement the global gridded results, further analysis has been carried out to<br />
provide “headline” <strong>and</strong> summary data <strong>for</strong> <strong>2002</strong> fuel <strong>and</strong> emissions 23 .<br />
<strong>Global</strong> <strong>Emissions</strong> – Civil <strong>and</strong> Military <strong>Aviation</strong><br />
The global consumption of fuel, emissions of CO2, H20, NOx, CO, HC, particulate mass,<br />
particulate numbers <strong>and</strong> distance flown <strong>for</strong> <strong>2002</strong> is given in Table 26.<br />
Distance<br />
CO2 H2O CO NOx HC Soot Particles<br />
Fuel Used<br />
Flown Produced Produced Produced Produced Produced Produced Produced<br />
(10 3 million<br />
n. miles)<br />
(Tg) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg)<br />
Civil<br />
24<br />
<strong>Aviation</strong><br />
17.9 156 492 193 .507 2.06 .063 .0039<br />
4.03 X<br />
10 25<br />
Military<br />
<strong>Aviation</strong><br />
n/a 19.5 61.0 24.1 .647 .178 .066 n/a n/a<br />
Total n/a 176 553 217 1.15 2.24 .129 n/a n/a<br />
Table 26: <strong>2002</strong> annual fuel <strong>and</strong> emissions - civil <strong>and</strong> military aviation<br />
Fuel used by civil aviation 25 in <strong>2002</strong> is shown to have risen above 150Tg. Military<br />
aviation uses a further 19.5Tg, representing just over 11% of the total fuel used. CO2<br />
emissions from aviation exceeded half a billion tons in <strong>2002</strong>, representing about 2% 26<br />
of global anthropomorphic CO2 emissions. NOx emissions rose above 2Tg <strong>for</strong> civil<br />
aviation with military aviation contributing a further 8% of the total. In contrast, the<br />
continued effect of significant improvements in combustion efficiency to better than<br />
99% in more recent aircraft has stemmed any increase in CO <strong>and</strong> HC emissions. The<br />
new calculation of military emissions to include reheat operation, which is relatively<br />
inefficient in combustion terms, shows values of CO <strong>and</strong> HC emissions <strong>for</strong> military<br />
aviation to be on a similar level to those of the civil fleet. These military figures <strong>for</strong> CO<br />
<strong>and</strong> HC should be regarded as maxima as there is some uncertainty over the actual<br />
afterburner use in service throughout the globe. Finally, the figure <strong>for</strong> the number of<br />
particles produced should be regarded as a first estimate <strong>for</strong> the purposes of climate<br />
effect assessment.<br />
<strong>Emissions</strong> indices <strong>for</strong> the civil <strong>and</strong> military fleets are shown in Table 27, emphasising<br />
the combustion efficiency <strong>and</strong> afterburning effects on CO <strong>and</strong> HC emissions<br />
described above. A figure of 8.71 kg of fuel used per nautical mile flown <strong>for</strong> the global<br />
civil aviation fleet is also given.<br />
23<br />
Fuel, NOx, CO2, H20, CO, HC, Particulate mass (soot), Particulate number, distance flown<br />
24<br />
Civil aviation includes IFR flights only<br />
25<br />
Civil aviation includes IFR flights only<br />
26<br />
http://www.eia.doe.gov/emeu/cabs/carbonemiss/chapter1.html<br />
QINETIQ/04/01113 Page 90
Civil<br />
<strong>Aviation</strong><br />
Military<br />
<strong>Aviation</strong><br />
Fuel Used<br />
per n.mile<br />
EI CO2 EI H2O EI CO EI NOx EI HC EI Soot EI Particles<br />
(kg/n.mile) (g/kg) (g/kg) (g/kg) (g/kg) (g/kg) (g/kg)<br />
(number/<br />
kg)<br />
27 8.71 3150 1238 3.25 13.2 0.4 0.025 2.6 x 1014<br />
n/a 3150 1238 33.1 9.1 3.4 n/a n/a<br />
Table 27: <strong>2002</strong> annual emissions indices- civil <strong>and</strong> military aviation<br />
Putting the civil aviation results into some <strong>for</strong>m of transport productivity measure,<br />
ICAO Circular 299-AT/129 [ICAO, 2003] quotes data <strong>for</strong> passengers, freight <strong>and</strong> mail<br />
carried in <strong>2002</strong> (scheduled traffic only). Assuming <strong>AERO2k</strong> has captured 75% of global<br />
non-scheduled traffic from the radar tracking data <strong>and</strong> that scheduled traffic<br />
represents 88% of all traffic [ICAO, 2003], <strong>AERO2k</strong> suggests global average data <strong>for</strong><br />
IFR flights as shown in Table 28.<br />
Average<br />
payload<br />
(tonnes)<br />
CO2 H<br />
Fuel Used<br />
2O CO NOx HC Soot Particles<br />
Produced Produced Produced Produced Produced Produced Produced<br />
per<br />
per per per per per per per<br />
Payload<br />
Payload Payload Payload Payload Payload Payload Payload<br />
Tonne.km<br />
Tonne.km Tonne.km Tonne.km Tonne.km Tonne.km Tonne.km Tonne.km<br />
(kg<br />
fuel/tonne.<br />
km)<br />
(kg<br />
CO2/tonne.<br />
km)<br />
(kg<br />
H2O/tonne.<br />
km)<br />
(kg<br />
CO2/tonne.<br />
km)<br />
(kg<br />
NOx/tonne.<br />
km)<br />
(kg<br />
HC/tonne.<br />
km)<br />
(kg<br />
soot/tonne.<br />
km)<br />
QINETIQ/04/01113 Page 91<br />
(number of<br />
particles/<br />
tonne.km)<br />
Civil<br />
<strong>Aviation</strong> 28 13.03 0.36 1.14 0.45 0.00117 0.00477 0.00015 9.03E-06 9.33E+13<br />
Table 28: <strong>2002</strong> annual emissions indices per payload tonne - civil aviation<br />
A comparison of the fuel used <strong>and</strong> emissions results with those from previous<br />
inventories is contained in Section 3.1.2.<br />
<strong>Global</strong> Spatial <strong>and</strong> Temporal Distribution of Civil <strong>Aviation</strong> <strong>Emissions</strong> <strong>for</strong> <strong>2002</strong><br />
The <strong>AERO2k</strong> gridded data provides the detailed global spatial <strong>and</strong> temporal<br />
distribution of fuel used, emissions <strong>and</strong> distance flown. Figure 45 to Figure 47 show<br />
three visualisations of the gridded data.<br />
27 Civil aviation includes IFR flights only<br />
28 Civil aviation includes IFR flights only
Figure 45: Visualisation of fuel used <strong>for</strong> one day<br />
Figure 45 shows a 3-D global representation of fuel used in one day. The vertical scale<br />
has been exaggerated to assist in the visualisation. Figure 46 is the same plot with<br />
the areas of lower concentration removed. Areas of high aviation fuel usage over the<br />
three traditional areas of North America, Europe <strong>and</strong> the Far East are revealed.<br />
Figure 46: Visualisation of fuel used <strong>for</strong> one day - Isosurfaces <strong>for</strong> higher concentrations<br />
Figure 47 focuses on Europe, in this case visualising NOx levels in each grid cell. NOx<br />
from westbound flights has been removed to give a 3D view of the centre of Western<br />
Europe. Climb out <strong>and</strong> cruise paths area clearly shown.<br />
QINETIQ/04/01113 Page 92
Figure 47: Visualisation of NOx emissions - Isosurfaces <strong>for</strong> higher concentrations<br />
Figure 45 to Figure 47 visualise fuel used <strong>and</strong> emissions <strong>for</strong> flights over a single day.<br />
However, global distribution of aviation emissions varies by season as well as by<br />
altitude <strong>and</strong> by the emission itself. Whilst seasonal variations are primarily due to<br />
dem<strong>and</strong> <strong>and</strong> meteorological variations (such as the position of the jet stream), much<br />
larger variations are due to the production rate of each emission at various power,<br />
altitude <strong>and</strong> speed conditions. As a result, most CO <strong>and</strong> HC emissions occur below<br />
3000ft whilst NOx emissions are biased toward cruise altitudes. These results are<br />
shown graphically in Figure 48 <strong>and</strong> Figure 49. Seasonal variation <strong>for</strong> fuel used is<br />
shown in Figure 50. Further tabulation of <strong>2002</strong> fuel consumed, emissions <strong>and</strong><br />
distance flown by altitude is contained in Appendix C.<br />
QINETIQ/04/01113 Page 93
Altitude (ft)<br />
altitude (ft)<br />
60,000<br />
50,000<br />
40,000<br />
30,000<br />
20,000<br />
10,000<br />
0<br />
60,000<br />
50,000<br />
40,000<br />
30,000<br />
20,000<br />
10,000<br />
Comparison of fuel used <strong>and</strong> emissions at altitude - 8th-13thApril <strong>2002</strong><br />
0 200 400 600 800 1000 1200 1400 1600<br />
Fuel Used or <strong>Emissions</strong> (Mg)<br />
Fuel CO2 H2O<br />
Figure 48: <strong>2002</strong> civil aviation emissions of fuel, CO2 <strong>and</strong> H2O by altitude<br />
0<br />
<strong>Emissions</strong> at altitude - 8th-13th April <strong>2002</strong><br />
0 1 2 3 4 5 6<br />
<strong>Emissions</strong> (Mg)<br />
CO NOx HC Soot<br />
Figure 49: <strong>2002</strong> civil aviation emissions of NOx, CO, HC <strong>and</strong> soot by altitude<br />
QINETIQ/04/01113 Page 94
Altitude (ft)<br />
60000<br />
50000<br />
40000<br />
30000<br />
20000<br />
10000<br />
Seasonal fuel used at altitude<br />
0<br />
0 100 200 300 400 500 600 700<br />
Fuel Used (Mg)<br />
April Sept October February<br />
Figure 50: Seasonal variation of <strong>2002</strong> civil aviation fuel usage by altitude<br />
For military aviation in <strong>2002</strong>, the lack of detailed data on military flight schedules<br />
results in considerably increased uncertainty within these data compared to the civil<br />
database. For that reason, no seasonal breakdown of military emissions is presented.<br />
However, the globally accumulated vertical distribution of fuel consumption, NOx,<br />
HC, CO as well as time specified by altitude b<strong>and</strong> is given in Figure 51.<br />
QINETIQ/04/01113 Page 95
HeightB<strong>and</strong> [*1000 m]<br />
25<br />
20<br />
15<br />
10<br />
5<br />
World results: Fuel <strong>and</strong> Time<br />
Fuel profile [kg]<br />
Time profile [s]<br />
0 0.5 1 1.5 2<br />
x 10 10<br />
Fuel [kg], Time [s]<br />
0 20 40 60<br />
Emission Index [g/kg]<br />
QINETIQ/04/01113 Page 96<br />
HeightB<strong>and</strong> [* 914.4 m]<br />
25<br />
20<br />
15<br />
10<br />
5<br />
World results: Emission Index profile<br />
EI-HC profile [g]<br />
EI-CO profile [g]<br />
EI-NO x profile [g]<br />
Figure 51: <strong>2002</strong> military fuel burn, flight time <strong>and</strong> emissions indices by altitude<br />
Figure 51 shows that peak fuel burn occurs in the 9-14 km altitude b<strong>and</strong>. In terms of<br />
emission indices, NOx emissions peak in the 0-1 km altitude b<strong>and</strong>) <strong>and</strong> approximately<br />
20-25 km b<strong>and</strong>s. At low altitudes, takeoff <strong>and</strong> climb out are mainly contributing. At<br />
very high altitude, engine operating conditions also increase NOx production. It<br />
should be noticed that at low altitude, military aircraft NOx production is relatively<br />
low because of the use of afterburning that ‘kills’ quite some NOx. Hence the peak is<br />
less pronounced compared to the civil inventory.<br />
The considerable CO <strong>and</strong> HC emissions indices throughout the altitude b<strong>and</strong>s<br />
correspond with the use of afterburning during manoeuvres <strong>and</strong>/or climb.<br />
It should be noted that the number of aircraft capable of operating beyond 14-km<br />
altitude is rare in terms of aircraft types <strong>and</strong> numbers as well as flights. If operating in<br />
these altitude b<strong>and</strong>s, engine-operating conditions are near or at the edges of the<br />
operating envelope. In many cases, this results in relatively high emissions per kg fuel<br />
used.<br />
Based on the movement database aircraft inventory, location, the reference aircraft<br />
<strong>and</strong> aircraft-reference aircraft conversion factors, missions <strong>and</strong> utilisation data, the<br />
fuel use, flight time <strong>and</strong> emissions are allocated into a world spanning, threedimensional<br />
grid, with a cell size of 1deg x 1 deg x 1000 ft. Figure 52 shows the<br />
world-gridded fuel distribution, aggregated over altitude. For enhanced view, fuel<br />
consumption levels below a threshold value are not included, <strong>and</strong> appear as white<br />
(uncoloured) areas. Figure 53 shows a focus of Europe. The underlying grid measures<br />
1 deg. by 1deg.
Figure 52: Military global fuel consumption distribution, aggregated over altitude.<br />
Figure 53: Military fuel consumption distribution, aggregated over altitude, focussed on<br />
Europe<br />
QINETIQ/04/01113 Page 97<br />
©<br />
©<br />
10<br />
8<br />
6<br />
4<br />
2<br />
10 7<br />
0<br />
2003<br />
.<br />
5 107<br />
4.5<br />
4<br />
3.5<br />
3<br />
2.5<br />
2<br />
1.5<br />
1<br />
0.5<br />
0<br />
2003<br />
.
0%<br />
3%<br />
10%<br />
Regional Distribution of fuel consumed, emissions <strong>and</strong> distance flown <strong>for</strong> civil<br />
aviation<br />
Whilst emissions occur along the entire path of each flight, emissions can be<br />
attributed to the origin <strong>and</strong> destination of each flight. The following seven figures<br />
(Figure 54 to Figure 60) show the annual distribution of civil aviation distance flown,<br />
fuel consumed <strong>and</strong> NOx emissions between seven regions used in <strong>AERO2k</strong>. These<br />
regions are:<br />
• Asia <strong>and</strong> Pacific<br />
• Eastern <strong>and</strong> Southern Africa<br />
• European <strong>and</strong> North Atlantic<br />
• Middle East<br />
• North American, Central American <strong>and</strong> Caribbean<br />
• South American<br />
• Western <strong>and</strong> Central Africa<br />
Differences between the proportions of distance flown <strong>and</strong> fuel used shown in the<br />
charts can be primarily attributed to differences in average aircraft size <strong>and</strong> route<br />
lengths. NOx emissions tend to closely follow fuel consumption at this level of<br />
analysis. A detailed listing of the countries allocated to each region is contained in<br />
Appendix B. Numerical values <strong>for</strong> distance flown, fuel used <strong>and</strong> all emissions are<br />
tabulated in Appendix D.<br />
Distance Flown<br />
11%<br />
0%<br />
ASIA AND PACIFIC To ASIA AND PACIFIC<br />
76%<br />
ASIA AND PACIFIC To EASTERN AND SOUTHERN AFRICA<br />
ASIA AND PACIFIC To EUROPEAN AND NORTH ATLANTIC<br />
ASIA AND PACIFIC To MIDDLE EAST<br />
ASIA AND PACIFIC To NORTHAMERICAN CENTRAL AMERICAN<br />
AND CARIBBEAN<br />
ASIA AND PACIFIC To SOUTHAMERICAN<br />
3%<br />
14%<br />
1%<br />
16%<br />
Fuel Used<br />
0%<br />
ASIA AND PACIFIC To ASIA AND PACIFIC<br />
QINETIQ/04/01113 Page 98<br />
66%<br />
ASIA AND PACIFIC To EASTERN AND SOUTHERN AFRICA<br />
ASIA AND PACIFIC To EUROPEAN AND NORTH ATLANTIC<br />
ASIA AND PACIFIC To MIDDLE EAST<br />
ASIA AND PACIFIC To NORTHAMERICAN CENTRAL<br />
AMERICAN AND CARIBBEAN<br />
ASIA AND PACIFIC To SOUTH AMERICAN<br />
3%<br />
13%<br />
1%<br />
16%<br />
NOx<br />
0%<br />
ASIA AND PACIFIC To ASIA AND PACIFIC<br />
67%<br />
ASIA AND PACIFIC To EASTERN AND SOUTHERN AFRICA<br />
ASIA AND PACIFIC To EUROPEAN AND NORTHATLANTIC<br />
ASIA AND PACIFIC To MIDDLE EAST<br />
ASIA AND PACIFIC To NORTHAMERICAN CENTRAL<br />
AMERICAN AND CARIBBEAN<br />
ASIA AND PACIFIC To SOUTHAMERICAN<br />
Figure 54: Regional distance flown, fuel consumption <strong>and</strong> NOx emissions <strong>for</strong> flights<br />
departing from the Asia <strong>and</strong> Pacific region
Distance Flown<br />
51%<br />
36%<br />
4%<br />
0%<br />
1%<br />
5%<br />
3%<br />
EASTERN AND SOUTHERN AFRICA To ASIA AND PACIFIC<br />
EASTERN AND SOUTHERN AFRICA To EASTERN AND SOUTHERN<br />
AFRICA<br />
EASTERN AND SOUTHERN AFRICA To EUROPEAN AND NORTH<br />
ATLANTIC<br />
EASTERN AND SOUTHERN AFRICA To MIDDLE EAST<br />
EASTERN AND SOUTHERN AFRICA To NORTHAMERICAN<br />
CENTRAL AMERICAN AND CARIBBEAN<br />
EASTERN AND SOUTHERN AFRICA To SOUTH AM ERICAN<br />
EASTERN AND SOUTHERN AFRICA To WESTERN AND CENTRAL<br />
AFRICA<br />
72%<br />
55%<br />
Fuel Used<br />
QINETIQ/04/01113 Page 99<br />
27%<br />
4%<br />
0%<br />
1%<br />
9%<br />
4%<br />
EASTERN AND SOUTHERN AFRICA To ASIA AND PACIFIC<br />
EASTERN AND SOUTHERN AFRICA To EASTERN AND<br />
SOUTHERN AFRICA<br />
EASTERN AND SOUTHERN AFRICA To EUROPEAN AND<br />
NORTH ATLANTIC<br />
EASTERN AND SOUTHERN AFRICA To MIDDLE EAST<br />
EASTERN AND SOUTHERN AFRICA To NORTHAMERICAN<br />
CENTRAL AMERICAN AND CARIBBEAN<br />
EASTERN AND SOUTHERN AFRICA To SOUTH AM ERICAN<br />
EASTERN AND SOUTHERN AFRICA To WESTERN AND<br />
CENTRAL AFRICA<br />
56%<br />
NOx<br />
24%<br />
5%<br />
0%<br />
1%<br />
4%<br />
10%<br />
EASTERN AND SOUTHERN AFRICA To ASIA AND PACIFIC<br />
EASTERN AND SOUTHERN AFRICA To EASTERN AND<br />
SOUTHERN AFRICA<br />
EASTERN AND SOUTHERN AFRICA To EUROPEAN AND<br />
NORTHATLANTIC<br />
EASTERN AND SOUTHERN AFRICA To M IDDLE EAST<br />
EASTERN AND SOUTHERN AFRICA To NORTHAMERICAN<br />
CENTRAL AMERICAN AND CARIBBEAN<br />
EASTERN AND SOUTHERN AFRICA To SOUTHAMERICAN<br />
EASTERN AND SOUTHERN AFRICA To WESTERN AND<br />
CENTRAL AFRICA<br />
Figure 55: Regional distance flown, fuel consumption <strong>and</strong> NOx emissions <strong>for</strong> flights<br />
departing from the Eastern <strong>and</strong> Southern Africa region<br />
Distance Flown<br />
5%<br />
13%<br />
6%<br />
1%<br />
2%<br />
1%<br />
EUROPEAN AND NORTHATLANTIC To ASIA AND PACIFIC<br />
EUROPEAN AND NORTHATLANTIC To EASTERN AND SOUTHERN<br />
AFRICA<br />
EUROPEAN AND NORTHATLANTIC To EUROPEAN AND NORTH<br />
ATLANTIC<br />
EUROPEAN AND NORTHATLANTIC To MIDDLE EAST<br />
EUROPEAN AND NORTHATLANTIC To NORTH AMERICAN<br />
CENTRAL AMERICAN AND CARIBBEAN<br />
EUROPEAN AND NORTHATLANTIC To SOUTHAMERICAN<br />
EUROPEAN AND NORTHATLANTIC To WESTERN AND CENTRAL<br />
AFRICA<br />
54%<br />
Fuel Used<br />
6%<br />
21%<br />
3%<br />
3%<br />
1%<br />
12%<br />
EUROPEAN AND NORTHATLANTIC To ASIA AND PACIFIC<br />
EUROPEAN AND NORTHATLANTIC To EASTERN AND<br />
SOUTHERN AFRICA<br />
EUROPEAN AND NORTHATLANTIC To EUROPEAN AND<br />
NORTHATLANTIC<br />
EUROPEAN AND NORTHATLANTIC To MIDDLE EAST<br />
EUROPEAN AND NORTHATLANTIC To NORTH AMERICAN<br />
CENTRAL AMERICAN AND CARIBBEAN<br />
EUROPEAN AND NORTHATLANTIC To SOUTHAMERICAN<br />
EUROPEAN AND NORTHATLANTIC To WESTERN AND<br />
CENTRAL AFRICA<br />
49%<br />
NOx<br />
7% 24%<br />
3%<br />
3%<br />
1%<br />
13%<br />
EUROPEAN AND NORTH ATLANTIC To ASIA AND PACIFIC<br />
EUROPEAN AND NORTH ATLANTIC To EASTERN AND<br />
SOUTHERN AFRICA<br />
EUROPEAN AND NORTH ATLANTIC To EUROPEAN AND<br />
NORTH ATLANTIC<br />
EUROPEAN AND NORTH ATLANTIC To MIDDLE EAST<br />
EUROPEAN AND NORTH ATLANTIC To NORTHAMERICAN<br />
CENTRALAMERICAN AND CARIBBEAN<br />
EUROPEAN AND NORTH ATLANTIC To SOUTH AMERICAN<br />
EUROPEAN AND NORTH ATLANTIC To WESTERN AND<br />
CENTRALAFRICA<br />
Figure 56: Regional distance flown, fuel consumption <strong>and</strong> NOx emissions <strong>for</strong> flights<br />
departing from the European <strong>and</strong> North Atlantic region
50%<br />
Distance Flown<br />
MIDDLE EAST To ASIA AND PACIFIC<br />
32%<br />
2%<br />
2%<br />
0%<br />
14%<br />
MIDDLE EAST To EASTERN AND SOUTHERN AFRICA<br />
MIDDLE EAST To EUROPEAN AND NORTH ATLANTIC<br />
MIDDLE EAST To MIDDLE EAST<br />
MIDDLE EAST To NORTHAMERICAN CENTRAL AMERICAN AND<br />
CARIBBEAN<br />
MIDDLE EAST To WESTERN AND CENTRAL AFRICA<br />
47%<br />
Fuel Used<br />
MIDDLE EAST To ASIA AND PACIFIC<br />
QINETIQ/04/01113 Page 100<br />
31%<br />
2%<br />
16%<br />
4%<br />
0%<br />
MIDDLE EAST To EASTERN AND SOUTHERN AFRICA<br />
MIDDLE EAST To EUROPEAN AND NORTHATLANTIC<br />
MIDDLE EAST To MIDDLE EAST<br />
MIDDLE EAST To NORTHAMERICAN CENTRAL AMERICAN<br />
AND CARIBBEAN<br />
MIDDLE EAST To WESTERN AND CENTRAL AFRICA<br />
44%<br />
NOx<br />
33%<br />
MIDDLE EAST To ASIA AND PACIFIC<br />
2%<br />
17%<br />
MIDDLE EAST To EASTERN AND SOUTHERN AFRICA<br />
MIDDLE EAST To EUROPEAN AND NORTHATLANTIC<br />
MIDDLE EAST To MIDDLE EAST<br />
4%<br />
0%<br />
MIDDLE EAST To NORTHAMERICAN CENTRAL AMERICAN<br />
AND CARIBBEAN<br />
MIDDLE EAST To WESTERN AND CENTRAL AFRICA<br />
Figure 57: Regional distance flown, fuel consumption <strong>and</strong> NOx emissions <strong>for</strong> flights<br />
departing from the Middle East region<br />
Distance Flown<br />
87% 1%<br />
0%<br />
0%<br />
4%<br />
0%<br />
8%<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
ASIA AND PACIFIC<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
EASTERN AND SOUTHERN AFRICA<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
EUROPEAN AND NORTHATLANTIC<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
MIDDLE EAST<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
SOUTH AMERICAN<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
WESTERN AND CENTRALAFRICA<br />
74%<br />
Fuel Used<br />
0%<br />
15%<br />
0%<br />
2%<br />
0%<br />
9%<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
ASIA AND PACIFIC<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
EASTERN AND SOUTHERN AFRICA<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
EUROPEAN AND NORTH ATLANTIC<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
MIDDLE EAST<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
SOUTH AMERICAN<br />
NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
WESTERN AND CENTRAL AFRICA<br />
70%<br />
0%<br />
NOx<br />
18%<br />
0%<br />
2%<br />
0%<br />
10%<br />
NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN To<br />
ASIA AND PACIFIC<br />
NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN To<br />
EASTERN AND SOUTHERN AFRICA<br />
NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN To<br />
EUROPEAN AND NORTH ATLANTIC<br />
NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN To<br />
MIDDLE EAST<br />
NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN To<br />
NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN<br />
NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN To<br />
SOUTH AMERICAN<br />
NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN To<br />
WESTERN AND CENTRAL AFRICA<br />
Figure 58: Regional distance flown, fuel consumption <strong>and</strong> NOx emissions <strong>for</strong> flights<br />
departing from the North <strong>and</strong> Central American <strong>and</strong> Caribbean region
68%<br />
Distance Flown<br />
18%<br />
SOUTH AMERICAN To ASIA AND PACIFIC<br />
0%<br />
0%<br />
0%<br />
14%<br />
SOUTH AMERICAN To EASTERN AND SOUTHERN AFRICA<br />
SOUTH AMERICAN To EUROPEAN AND NORTHATLANTIC<br />
SOUTH AMERICAN To NORTHAMERICAN CENTRAL AMERICAN<br />
AND CARIBBEAN<br />
SOUTH AMERICAN To SOUTHAMERICAN<br />
SOUTH AMERICAN To WESTERN AND CENTRALAFRICA<br />
3%<br />
6%<br />
56%<br />
22%<br />
Fuel Used<br />
56%<br />
SOUTHAMERICAN To ASIA AND PACIFIC<br />
QINETIQ/04/01113 Page 101<br />
22%<br />
0%<br />
0%<br />
0%<br />
SOUTHAMERICAN To EASTERN AND SOUTHERN AFRICA<br />
SOUTHAMERICAN To EUROPEAN AND NORTHATLANTIC<br />
SOUTHAMERICAN To NORTHAMERICAN CENTRAL AMERICAN<br />
AND CARIBBEAN<br />
SOUTHAMERICAN To SOUTHAMERICAN<br />
SOUTHAMERICAN To WESTERN AND CENTRAL AFRICA<br />
23%<br />
NOx<br />
49%<br />
SOUTHAMERICAN To ASIA AND PACIFIC<br />
26%<br />
0%<br />
1%<br />
1%<br />
SOUTHAMERICAN To EASTERN AND SOUTHERN AFRICA<br />
SOUTHAMERICAN To EUROPEAN AND NORTHATLANTIC<br />
SOUTHAMERICAN To NORTHAMERICAN CENTRAL AMERICAN<br />
AND CARIBBEAN<br />
SOUTHAMERICAN To SOUTHAMERICAN<br />
SOUTHAMERICAN To WESTERN AND CENTRAL AFRICA<br />
Figure 59: Regional distance flown, fuel consumption <strong>and</strong> NOx emissions <strong>for</strong> flights<br />
departing from the South American region<br />
Distance Flown<br />
0%<br />
29%<br />
6%<br />
WESTERN AND CENTRAL AFRICA To EASTERN AND SOUTHERN<br />
AFRICA<br />
WESTERN AND CENTRAL AFRICA To EUROPEAN AND NORTH<br />
ATLANTIC<br />
WESTERN AND CENTRAL AFRICA To MIDDLE EAST<br />
WESTERN AND CENTRAL AFRICA To NORTHAMERICAN CENTRAL<br />
AMERICAN AND CARIBBEAN<br />
WESTERN AND CENTRAL AFRICA To SOUTHAMERICAN<br />
WESTERN AND CENTRAL AFRICA To WESTERN AND CENTRAL<br />
AFRICA<br />
62%<br />
3%<br />
Fuel Used<br />
11% 0%<br />
19%<br />
5%<br />
WESTERN AND CENTRAL AFRICA To EASTERN AND SOUTHERN<br />
AFRICA<br />
WESTERN AND CENTRAL AFRICA To EUROPEAN AND NORTH<br />
ATLANTIC<br />
WESTERN AND CENTRAL AFRICA To MIDDLE EAST<br />
WESTERN AND CENTRAL AFRICA To NORTHAMERICAN<br />
CENTRALAMERICAN AND CARIBBEAN<br />
WESTERN AND CENTRAL AFRICA To SOUTHAMERICAN<br />
WESTERN AND CENTRAL AFRICA To WESTERN AND CENTRAL<br />
AFRICA<br />
63%<br />
3%<br />
NOx<br />
12% 0%<br />
17%<br />
5%<br />
WESTERN AND CENTRAL AFRICA To EASTERN AND SOUTHERN<br />
AFRICA<br />
WESTERN AND CENTRAL AFRICA To EUROPEAN AND NORTH<br />
ATLANTIC<br />
WESTERN AND CENTRAL AFRICA To MIDDLE EAST<br />
WESTERN AND CENTRAL AFRICA To NORTHAMERICAN<br />
CENTRAL AMERICAN AND CARIBBEAN<br />
WESTERN AND CENTRAL AFRICA To SOUTHAMERICAN<br />
WESTERN AND CENTRAL AFRICA To WESTERN AND CENTRAL<br />
AFRICA<br />
Figure 60: Regional distance flown, fuel consumption <strong>and</strong> NOx emissions <strong>for</strong> flights<br />
departing from the Western <strong>and</strong> Central Africa region<br />
It should be noticed that in order to produce the <strong>for</strong>ecast <strong>for</strong> <strong>2025</strong>, factors from the<br />
DTI/Airbus growth <strong>and</strong> technology development <strong>for</strong>ecast (Section 2.3) have been<br />
applied to regional data <strong>for</strong> <strong>2002</strong>.
Distance Flown, Fuel Consumed, <strong>and</strong> <strong>Emissions</strong> <strong>for</strong> Civil <strong>Aviation</strong>, distributed by<br />
representative aircraft type<br />
Table 29 shows the distribution of the calculated fuel consumed, emissions <strong>and</strong><br />
distance flown <strong>for</strong> each of the 40 representative aircraft types used within <strong>AERO2k</strong>.<br />
All major aircraft types within the global civil aviation fleet have been allocated to<br />
one of these representative types.<br />
Representative Distance Flown Fuel Used<br />
Aircraft<br />
CO2<br />
Produced<br />
H2O<br />
Produced<br />
CO<br />
Produced<br />
NOx<br />
Produced<br />
HC<br />
Produced<br />
Soot<br />
Produced<br />
QINETIQ/04/01113 Page 102<br />
Particles<br />
Produced<br />
Nautical miles<br />
-9 (Gg) (Gg) (Gg) (Gg) (Gg) (Gg) (Gg) x10-23<br />
x10<br />
A306 0.27 3447 10862 4267 10.13 48.76 0.78 0.084 8.18<br />
A310 0.20 2091 6588 2588 6.04 26.77 0.43 0.045 5.21<br />
A319 0.48 3128 9850 3873 14.60 39.16 2.32 0.032 2.77<br />
A320 1.14 7455 23481 9229 30.38 100.68 4.50 0.085 7.48<br />
A321 0.24 1797 5660 2224 6.53 28.27 0.79 0.025 2.25<br />
A330 0.34 4185 13197 5181 7.26 72.88 1.89 0.049 5.67<br />
A340 0.43 5931 18703 7342 9.34 95.42 0.76 0.066 8.37<br />
A34R 0.03 456 1426 564 7.67 3.14 1.46 0.029 3.49<br />
AT72 0.23 621 1957 769 3.15 6.75 0.02 0.087 5.51<br />
B703 0.10 1419 4433 1757 28.72 8.86 5.62 0.094 10.87<br />
B712 0.07 441 1389 546 1.97 4.90 0.01 0.005 0.39<br />
B722 0.37 4164 13125 5155 10.27 39.70 1.99 0.175 16.43<br />
B732 0.31 2211 6966 2737 6.78 21.26 1.20 0.100 8.43<br />
B734 1.72 11817 37196 14629 62.08 124.09 3.33 0.148 15.29<br />
B736 0.73 4547 14330 5629 13.14 58.57 1.23 0.084 7.33<br />
B738 0.73 4813 15172 5958 10.84 66.74 1.02 0.096 9.37<br />
B742 0.42 9152 28869 11330 9.17 150.44 1.31 0.182 24.26<br />
B744 1.11 23206 73161 28729 48.85 292.75 3.54 0.428 58.87<br />
B752 1.15 9094 28657 11258 27.16 123.84 2.11 0.240 26.65<br />
B763 1.24 13979 44066 17306 33.69 187.13 2.55 0.268 33.01<br />
B772 0.68 10312 32527 12766 10.67 224.00 1.06 0.148 19.07<br />
BA11 0.00 12 37 14 0.09 0.12 0.01 0.001 0.05<br />
BA46 0.21 1300 4088 1609 9.22 9.58 1.07 0.046 3.99<br />
C130 0.03 204 643 253 0.88 1.37 0.27 0.013 1.12<br />
C550 0.61 987 3109 1222 4.15 7.39 0.53 0.034 3.00<br />
DC9 0.34 3308 10412 4095 16.85 21.94 2.58 0.162 12.52<br />
E145 1.10 3180 9999 3937 23.45 23.30 2.82 0.111 10.04<br />
F100 0.16 1028 3231 1273 8.57 8.34 1.05 0.037 3.01<br />
F2TH 0.23 464 1459 575 3.87 6.02 0.35 0.016 1.65<br />
F50 0.40 1236 3891 1530 5.93 14.48 0.02 0.183 11.47<br />
F70 0.11 588 1849 727 3.38 5.36 0.62 0.030 2.54<br />
F900 0.11 275 865 341 2.92 2.98 0.25 0.009 0.96<br />
GLF4 0.16 581 1827 719 3.53 4.88 0.64 0.022 2.22<br />
L101 0.22 3617 11379 4478 22.93 65.93 4.49 0.084 10.09<br />
L188 0.01 78 245 96 0.24 0.40 0.02 0.004 0.31<br />
MD11 0.30 4480 14124 5547 10.68 54.26 0.75 0.086 11.63<br />
MD80 0.98 8187 25806 10136 20.54 85.03 3.89 0.338 31.15<br />
MD90 0.07 574 1807 710 2.46 7.77 0.37 0.007 0.54<br />
SF34 0.85 1476 4645 1827 8.07 12.02 5.42 0.262 17.23<br />
YK42 0.02 204 643 252 0.43 2.99 0.05 0.008 0.82<br />
Table 29: <strong>2002</strong> Fuel, emissions <strong>and</strong> distance flown by representative aircraft type – civil<br />
aviation
The 40 representative types can be divided into four categories – large jet, regional<br />
jet, turboprop <strong>and</strong> business jet. Allocating the distance flown, fuel <strong>and</strong> emissions into<br />
each category gives the following category totals, plus fuel consumption <strong>and</strong><br />
emission indices (Figure 61 <strong>and</strong> Table 30 <strong>and</strong> Table 31). It is left to the reader to<br />
allocate efficiency factors representing the amount of freight <strong>and</strong> passengers carried<br />
by each representative type <strong>and</strong> aircraft category, with the caveat that attempts to<br />
substitute different categories of aircraft on any route can only be done with full<br />
knowledge of route length, dem<strong>and</strong> <strong>and</strong> other detailed operational factors.<br />
Business Jet<br />
Turboprop<br />
Regional Jet<br />
Large Jet<br />
Totals<br />
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%<br />
Distance Flown Fuel Used NOx Produced Soot Produced<br />
Figure 61: Distribution of distance flown, fuel used, NOx <strong>and</strong> soot production between<br />
civil aircraft categories in <strong>2002</strong><br />
Distance<br />
Flown<br />
(n.miles<br />
x10 -9 )<br />
Fuel<br />
Used<br />
CO2 H2O CO NOx HC Soot Particles<br />
(Gg) (Gg) (Gg) (Gg) (Gg) (Gg) (Gg) (x10 -23 )<br />
Large Jet 13.68 143823 453596 177995 428.81 1952.59 50 3.061 339.43<br />
Regional Jet 1.6 6302 19874 7799 45 49.57 5.61 0.232 20.36<br />
Turboprop 1.52 3623 11427 4478 18.27 35.02 5.75 0.549 35.64<br />
Business Jet 1.11 2307 7275 2855 14.47 21.27 1.77 0.081 7.83<br />
Table 30: <strong>2002</strong> Fuel, emissions <strong>and</strong> distance flown by representative aircraft category –<br />
civil aviation<br />
QINETIQ/04/01113 Page 103
<strong>Emissions</strong><br />
Indices<br />
Fuel<br />
consumption<br />
(kg/nm)<br />
CO2 H2O CO NOx HC Soot Particles<br />
(g/kg<br />
fuel)<br />
(g/kg<br />
fuel)<br />
(g/kg<br />
fuel)<br />
(g/kg<br />
fuel)<br />
(g/kg<br />
fuel)<br />
(g/kg<br />
fuel)<br />
QINETIQ/04/01113 Page 104<br />
(number/kg<br />
fuel x 10 -20 )<br />
Large Jet 10.51 3154 1238 2.98 13.58 0.35 0.02 2.36<br />
Regional Jet 3.94 3154 1238 7.14 7.87 0.89 0.04 3.23<br />
Turboprop 2.38 3154 1238 5.04 9.67 1.59 0.15 9.84<br />
Business Jet 2.08 3153 1238 6.27 9.22 0.77 0.04 3.39<br />
Table 31: <strong>2002</strong> Fuel consumption <strong>and</strong> emissions indices by representative aircraft<br />
category – civil aviation<br />
Comparison of <strong>2002</strong> results with other Inventory results<br />
Previous global emissions inventories have estimated global aviation emissions <strong>for</strong><br />
different years using different detailed in<strong>for</strong>mation sources, assumptions <strong>and</strong><br />
methodologies – different both to each other <strong>and</strong> to <strong>AERO2k</strong>. Because of these<br />
differences, detailed analysis comparing the results raises a number of uncertainties.<br />
Nevertheless, each inventory is attempting to quantify global emissions from<br />
aviation <strong>for</strong> a particular year. <strong>Global</strong> totals <strong>for</strong> civil <strong>and</strong> military aviation from four of<br />
these recent inventories (DLR 1992 [Schmitt, 1997], ANCAT/EC2 1992 [Gardner,<br />
1998], NASA 1992 [Baughcum, 1996] <strong>and</strong> NASA 1999 [Sutkis, 2001]) are compared<br />
with the <strong>AERO2k</strong> results in Table 32 <strong>and</strong> displayed graphically in Figure 62 to Figure<br />
65.<br />
<strong>Emissions</strong> - Total<br />
Mass<br />
(Tg)<br />
Aero2k NASA1999 29 NASA1992 ANCAT 1992 DLR 1992<br />
Fuel Used 156 128 113.85 114.2 112.24<br />
NOx 2.06 1.69 1.44 1.6 1.6<br />
CO 0.507 0.685 1.29 n/a n/a<br />
HC 0.063 0.189 0.26 n/a n/a<br />
Particulate Mass 0.0039 n/a n/a n/a n/a<br />
Particulate Number 4.03E+25 n/a n/a n/a n/a<br />
EIs (g/kg)<br />
Aero2k NASA1999 NASA1992 ANCAT 1992 DLR 1992<br />
Fuel Used - - - -<br />
NOx 13.2 13.2 12.6 14 14.2<br />
CO 3.25 5.4 11.3 n/a n/a<br />
HC 0.4 1.5 2.3 n/a n/a<br />
Particulate Mass 0.025 n/a n/a n/a n/a<br />
Particulate Number<br />
(/kg)<br />
2.58E+14 n/a n/a n/a n/a<br />
Table 32: <strong>2002</strong> <strong>Global</strong> fuel, emissions <strong>and</strong> emissions indices <strong>for</strong> civil aviation<br />
The civil aviation fuel-used figure reflects the increase in air traffic since 1992, offset<br />
by the fuel efficiency improvements, suggested as being in the region of 1% per year.<br />
29 The NASA 1999 figure shown includes only scheduled flights
Increasing pressure ratio, plus other technology improvements have allowed<br />
significant improvements in combustion efficiency in the last 25 years <strong>and</strong> these<br />
engines are now dominating the civil aircraft fleet (Figure 44). Hence the significant<br />
reduction in CO <strong>and</strong> HC EIs. Conversely, <strong>for</strong> NOx, the effect of increasing pressure<br />
ratios has largely offset significant improvements in NOx control technology,<br />
resulting in only a slight decrease in NOx EI over the decade (compared to the<br />
ANCAT/DLR results).<br />
<strong>Emissions</strong> from military aviation have a greater degree of uncertainty when<br />
compared to the civil aviation data. Underst<strong>and</strong>able secrecy surrounding the<br />
per<strong>for</strong>mance <strong>and</strong> flight profiles requires a degree of approximation. Military fuelused,<br />
emissions <strong>and</strong> EIs are shown in Table 33.<br />
<strong>Emissions</strong> – Total<br />
Mass<br />
(Tg)<br />
Aero2k NASA1999 NASA1992 ANCAT 1992 DLR 1992<br />
Fuel Used 19.5 n/a 25.55 17.08 17.1<br />
NOx 0.18 n/a 0.23 0.2 0.2<br />
CO 0.647 n/a 0.29 n/a n/a<br />
HC 0.066 n/a 0.06 n/a n/a<br />
EIs (g/kg)<br />
Aero2k NASA1999 NASA1992 ANCAT 1992 DLR 1992<br />
Fuel Used - - - - -<br />
NOx 9.1 n/a 8.9 11.9 11.8<br />
CO 33.1 n/a 11.2 n/a n/a<br />
HC 3.4 n/a 2.4 n/a n/a<br />
Table 33: <strong>2002</strong> <strong>Global</strong> fuel, emissions <strong>and</strong> emissions indices <strong>for</strong> military aviation<br />
The data show military fuel-used at around 12% of the civil aviation fuel consumed<br />
whilst lower average pressure ratios lead to lower NOx EIs <strong>and</strong> hence total military<br />
NOx at around 9% of the civil aviation emission. In contrast, military emissions of CO<br />
<strong>and</strong> HC are calculated to be on a par with civil aviation emissions. Whilst these results<br />
are highly sensitive to assumptions on afterburner usage <strong>and</strong> per<strong>for</strong>mance, they do<br />
highlight the significant improvements made within civil aviation in this area.<br />
Figure 62 to Figure 65 present this tabulated data in graphical <strong>for</strong>m.<br />
QINETIQ/04/01113 Page 105
Fuel Used (Tg)<br />
180<br />
160<br />
140<br />
120<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
1970 1975 1980 1985 1990 1995 2000 2005<br />
Figure 62: <strong>2002</strong> <strong>Global</strong> fuel consumed 30<br />
NOx <strong>Emissions</strong> (Tg)<br />
3<br />
2<br />
1<br />
0<br />
1970 1975 1980 1985 1990 1995 2000 2005<br />
Figure 63: <strong>2002</strong> <strong>Global</strong> NOx emissions 31<br />
30 The NASA 1999 figure shown includes only scheduled flights<br />
31 The NASA 1999 figure shown includes only scheduled flights<br />
NASA<br />
DLR<br />
ANCAT<br />
AERO2K<br />
Military<br />
NASA<br />
Military<br />
DLR<br />
ANCAT 1992<br />
AERO2K<br />
QINETIQ/04/01113 Page 106
CO <strong>Emissions</strong> (Tg)<br />
1<br />
0.5<br />
0<br />
1970 1975 1980 1985 1990 1995 2000 2005<br />
Figure 64: <strong>2002</strong> <strong>Global</strong> CO emissions 32<br />
HC <strong>Emissions</strong> (Tg)<br />
0.35<br />
0.3<br />
0.25<br />
0.2<br />
0.15<br />
0.1<br />
0.05<br />
0<br />
1970 1975 1980 1985 1990 1995 2000 2005<br />
Figure 65: <strong>2002</strong> <strong>Global</strong> HC emissions 33<br />
3.2 Results <strong>for</strong> <strong>2025</strong> Civil <strong>and</strong> Military <strong>Aviation</strong><br />
3.2.1 Gridded Data <strong>for</strong> <strong>2025</strong><br />
NASA<br />
NASA<br />
Military<br />
AERO2K<br />
Military<br />
AERO2K<br />
As <strong>for</strong> <strong>2002</strong>, the main results from <strong>AERO2k</strong> <strong>for</strong> <strong>2025</strong> are the global gridded data <strong>for</strong><br />
fuel-used, emissions <strong>and</strong> distance flown, published on the <strong>AERO2k</strong> WebPages at<br />
http://www.cate.mmu.ac.uk/aero2k.asp. The data consist of 12 tables, one <strong>for</strong> each<br />
month of <strong>2025</strong>, each containing the civil aviation emissions, fuel used <strong>and</strong> distance<br />
flown in a global grid of resolution 1 deg by 1 deg by 500ft. Comments in Section<br />
3.1.1 regarding the vertical resolution also apply to this data.<br />
There are no separate results <strong>for</strong> military aviation in <strong>2025</strong>. In any analysis of total<br />
<strong>2025</strong> emissions, is recommended that the results <strong>for</strong> <strong>2002</strong> are used. This conclusion is<br />
explained further in Section 2.4.3.<br />
32 The NASA 1999 figure shown includes only scheduled flights<br />
33 The NASA 1999 figure shown includes only scheduled flights<br />
QINETIQ/04/01113 Page 107
3.2.2 Other Results - <strong>2025</strong><br />
As described in Section 2.3, results <strong>for</strong> <strong>2025</strong> are based on factored <strong>2002</strong> results <strong>and</strong><br />
represent just one possible scenario <strong>for</strong> global aviation. In addition to the global <strong>and</strong><br />
regional results from <strong>AERO2k</strong>, results from other scenario work are presented <strong>for</strong><br />
comparison.<br />
<strong>2025</strong> <strong>Global</strong> <strong>Emissions</strong> from Civil <strong>Aviation</strong><br />
The global consumption of fuel, emissions of CO2, H20, NOx, CO, HC, particulate mass<br />
(soot), particulate numbers <strong>and</strong> distance flown <strong>for</strong> <strong>2025</strong> is given in Table 34.<br />
Distance<br />
Flown<br />
(10 3 million n.<br />
miles)<br />
Fuel Used<br />
CO2<br />
Produced<br />
H2O<br />
Produced<br />
CO<br />
Produced<br />
NOx<br />
Produced<br />
HC<br />
Produced<br />
Soot<br />
Produced<br />
(Tg) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg)<br />
QINETIQ/04/01113 Page 108<br />
Particles<br />
Produced<br />
<strong>2002</strong> 17.9 156 492 193 .507 2.06 .063 .0039 4.03 X 10 25<br />
<strong>2025</strong> 36.1 327 1029 404 1.15 3.308 0.1447 0.0087 8.54 x 10 25<br />
Table 34: <strong>2025</strong> Annual fuel <strong>and</strong> emissions - civil aviation<br />
The assumptions used to model the <strong>2025</strong> emissions provide <strong>for</strong> aviation in <strong>2025</strong><br />
satisfying a dem<strong>and</strong> increase averaging 2.6 times the <strong>2002</strong> figure over the globe, with<br />
a move toward larger, more fuel efficient aircraft. As a result of this 2.6 times increase<br />
in the number of available-seat-kilometres, fuel used by civil aviation 34 in <strong>2025</strong> has<br />
risen above 300Tg, doubling the figure from <strong>2002</strong>. CO2 emissions from civil aviation<br />
have also doubled, exceeded a billion tons in <strong>2025</strong>. In contrast, NOx emissions rose<br />
only 1.6 times, resulting from improvement in fleet NOx technology. The effect of<br />
significant improvements in combustion efficiency seen in the <strong>2002</strong> figures in the<br />
<strong>for</strong>m of reducing CO <strong>and</strong> HC emissions is not seen in the <strong>2025</strong> figures, as any further<br />
improvements are small <strong>and</strong> are not seen to keep pace with the traffic dem<strong>and</strong><br />
increase. Again, the figure <strong>for</strong> the number of particles produced should be regarded<br />
as a first estimate <strong>for</strong> the purposes of climate effect assessment.<br />
<strong>AERO2k</strong> emissions indices <strong>for</strong> the <strong>2002</strong> <strong>and</strong> <strong>2025</strong> civil aviation fleets are shown in<br />
Table 35.<br />
Fuel Used<br />
per n.mile<br />
EI CO2 EI H2O EI CO EI NOx EI HC EI Soot<br />
EI<br />
Particles<br />
(kg/n.mile) (g/kg) (g/kg) (g/kg) (g/kg) (g/kg) (g/kg) (/kg)<br />
<strong>2002</strong> 8.72 3150 1238 3.25 13.2 0.40 0.03 2.6 x 10 14<br />
<strong>2025</strong> 9.06 3150 1238 3.52 10.1 0.44 0.03 2.6 x 10 14<br />
Table 35: <strong>2002</strong> Annual emissions indices- civil <strong>and</strong> military aviation<br />
A comparison of these <strong>for</strong>ecasts with those from previous inventories is described<br />
later in this section.<br />
<strong>Global</strong> Spatial <strong>and</strong> Temporal Distribution of Civil <strong>Aviation</strong> <strong>Emissions</strong> <strong>for</strong> <strong>2025</strong><br />
For the <strong>2025</strong> <strong>for</strong>ecast, <strong>AERO2k</strong> has not attempted to model changes in air traffic<br />
management. Excepting the changes in frequency of each flight, dependent upon the<br />
regional growth <strong>and</strong> technology change factors, the profile of emissions by altitude is<br />
not significantly changed on a global scale. The greater effect is the change in global<br />
distribution of the emissions toward the high growth regions. This effect is seen in<br />
the gridded data <strong>and</strong> also in the traffic flows in the following Figure 66, which should<br />
be compared with Figure 54 to Figure 60 in Section 3.1.2.<br />
34 Civil aviation includes IFR flights only
11%<br />
0%<br />
2%<br />
Distance Flown<br />
11% 0%<br />
ASIA AND PACIFIC To ASIA AND PACIFIC<br />
76%<br />
ASIA AND PACIFIC To EASTERN AND SOUTHERN AFRICA<br />
ASIA AND PACIFIC To EUROPEAN AND NORTH ATLANTIC<br />
ASIA AND PACIFIC To MIDDLE EAST<br />
ASIA AND PACIFIC To NORTHAMERICAN CENTRAL AMERICAN<br />
AND CARIBBEAN<br />
ASIA AND PACIFIC To SOUTHAMERICAN<br />
42%<br />
Distance Flown<br />
38%<br />
MIDDLE EAST To ASIA AND PACIFIC<br />
2%<br />
15%<br />
MIDDLE EAST To EASTERN AND SOUTHERN AFRICA<br />
MIDDLE EAST To EUROPEAN AND NORTHATLANTIC<br />
MIDDLE EAST To MIDDLE EAST<br />
3%<br />
0%<br />
MIDDLE EAST To NORTHAM ERICAN CENTRAL AMERICAN AND<br />
CARIBBEAN<br />
MIDDLE EAST To WESTERN AND CENTRAL AFRICA<br />
Distance Flown<br />
39%<br />
48%<br />
QINETIQ/04/01113 Page 109<br />
4%<br />
0%<br />
1%<br />
3%<br />
5%<br />
EA STERN AND SOUTHERN AFRICA To ASIA AND PACIFIC<br />
EA STERN AND SOUTHERN AFRICA To EASTERN AND SOUTHERN<br />
AFRICA<br />
EA STERN AND SOUTHERN AFRICA To EUROPEAN AND NORTH<br />
ATLANTIC<br />
EA STERN AND SOUTHERN AFRICA To MIDDLE EAST<br />
EA STERN AND SOUTHERN AFRICA To NORTHAMERICAN<br />
CENTRAL AMERICAN AND CARIBBEAN<br />
EA STERN AND SOUTHERN AFRICA To SOUTHAMERICAN<br />
EA STERN AND SOUTHERN AFRICA To WESTERN AND CENTRAL<br />
AFRICA<br />
80%<br />
Distance Flown<br />
0%<br />
7%<br />
0%<br />
11%<br />
2%<br />
0%<br />
NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN To ASIA<br />
AND PACIFIC<br />
NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
EA STERN AND SOUTHERN AFRICA<br />
NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
EUROPEAN AND NORTHATLA NTIC<br />
NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
MIDDLE EAST<br />
NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN<br />
NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
SOUTH AMERICAN<br />
NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN To<br />
WESTERN AND CENTRAL AFRICA<br />
3%<br />
61%<br />
Distance Flown<br />
5%<br />
0%<br />
26%<br />
5%<br />
WESTERN AND CENTRAL AFRICA To EASTERN AND SOUTHERN<br />
AFRICA<br />
WESTERN AND CENTRAL AFRICA To EUROPEAN AND NORTH<br />
ATLANTIC<br />
WESTERN AND CENTRAL AFRICA To MIDDLE EAST<br />
Figure 66: Regional distance flown, <strong>for</strong> <strong>2025</strong><br />
WESTERN AND CENTRAL AFRICA To NORTH AMERICAN CENTRAL<br />
AM ERICAN AND CARIBBEAN<br />
WESTERN AND CENTRAL AFRICA To SOUTH AMERICAN<br />
WESTERN AND CENTRAL AFRICA To WESTERN AND CENTRAL<br />
AFRICA<br />
76%<br />
Distance Flown<br />
3% 10%<br />
7%<br />
1%<br />
2%<br />
1%<br />
EUROPEAN AND NORTHATLA NTIC To ASIA AND PACIFIC<br />
EUROPEAN AND NORTHATLA NTIC To EASTERN AND SOUTHERN<br />
AFRICA<br />
EUROPEAN AND NORTHATLA NTIC To EUROPEAN AND NORTH<br />
ATLANTIC<br />
EUROPEAN AND NORTHATLA NTIC To MIDDLE EAST<br />
EUROPEAN AND NORTHATLA NTIC To NORTH AMERICAN<br />
CENTRAL AMERICAN AND CARIBBEAN<br />
EUROPEAN AND NORTHATLA NTIC To SOUTH AMERICAN<br />
EUROPEAN AND NORTHATLA NTIC To WESTERN AND CENTRAL<br />
AFRICA<br />
67%<br />
Distance Flown<br />
18%<br />
SOUTH AMERICAN To ASIA AND PACIFIC<br />
0%<br />
0%<br />
0%<br />
15%<br />
SOUTH AMERICAN To EASTERN AND SOUTHERN AFRICA<br />
SOUTH AMERICAN To EUROPEAN AND NORTHATLANTIC<br />
SOUTH AMERICAN To NORTHAMERICAN CENTRAL AMERICAN<br />
AND CARIBBEAN<br />
SOUTH AMERICAN To SOUTHAM ERICAN<br />
SOUTH AMERICAN To WESTERN AND CENTRAL AFRICA
Distance Flown, Fuel Consumed <strong>and</strong> <strong>Emissions</strong> <strong>for</strong> Civil <strong>Aviation</strong> in <strong>2025</strong>, by<br />
representative aircraft category<br />
Figure 67 shows the distribution of the calculated fuel consumed, emissions <strong>and</strong><br />
distance flown <strong>for</strong> the 4 categories of civil aircraft types used within <strong>AERO2k</strong>. These<br />
data compare with the <strong>2002</strong> data in Figure 61 showing the proportion of distance<br />
flown by large jets increasing to over 90% although technical improvements have<br />
maintained their share of NOX <strong>and</strong> soot emissions at the <strong>2002</strong> proportions.<br />
Business Jet<br />
Turboprop<br />
Regional Jet<br />
Large Jet<br />
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%<br />
Fuel Used NOx Produced Soot Produced<br />
Figure 67: Distribution of distance flown, fuel used, NOx <strong>and</strong> soot production between<br />
civil aircraft categories in <strong>2025</strong><br />
Comparison of <strong>2025</strong> Results with other Inventory Results <strong>and</strong> Forecasts<br />
Previous global emissions inventories have also <strong>for</strong>ecast global aviation emissions <strong>for</strong><br />
future years. Each <strong>for</strong>ecast uses different detail in<strong>for</strong>mation sources, assumptions,<br />
methodologies <strong>and</strong> also different assumptions to underpin the <strong>for</strong>ecast itself. As <strong>for</strong><br />
the <strong>2002</strong> data, because of these differences, detail comparison between the <strong>for</strong>ecasts<br />
contains numerous uncertainties as to the source of those differences. Nevertheless,<br />
each inventory is attempting to <strong>for</strong>ecast global emissions from aviation. <strong>Global</strong><br />
<strong>for</strong>ecasts from three of these recent inventories were produced <strong>for</strong> the year 2015 (DLR<br />
1992 [Schmitt 1997], ANCAT/EC2 1992 [Gardner 1998], NASA 1992 [Baughcum<br />
1996]. These <strong>for</strong>ecasts are presented together with the inventory data <strong>and</strong> the<br />
<strong>AERO2k</strong> <strong>2025</strong> <strong>for</strong>ecast in Figure 68 to Figure 71.<br />
QINETIQ/04/01113 Page 110
Fuel Used (Tg)<br />
350<br />
300<br />
250<br />
200<br />
150<br />
100<br />
50<br />
0<br />
1970 1980 1990 2000 2010 2020 2030<br />
Figure 68: <strong>2025</strong> <strong>Global</strong> fuel consumed<br />
NASA<br />
QINETIQ/04/01113 Page 111<br />
DLR<br />
ANCAT<br />
AERO2K<br />
Military<br />
For total civil aviation fuel used, the <strong>AERO2k</strong> <strong>2025</strong> <strong>for</strong>ecast shows the expected<br />
increase in the total quantity of fuel used described in Section 2.3 above. The increase<br />
is less severe than that <strong>for</strong>ecast in the NASA <strong>and</strong> ANCAT/EC2 studies, the reasons <strong>for</strong><br />
which require further study outside the scope of the <strong>AERO2k</strong> project.<br />
NOx <strong>Emissions</strong> (Tg)<br />
5.0<br />
4.0<br />
3.0<br />
2.0<br />
1.0<br />
0.0<br />
1970 1980 1990 2000 2010 2020 2030<br />
Figure 69: <strong>2025</strong> <strong>Global</strong> NOx emissions<br />
NASA<br />
DLR<br />
ANCAT<br />
AERO2K<br />
Military<br />
As with fuel used, NOx emissions are <strong>for</strong>ecast to continue to rise despite the<br />
introduction of lower NOx technologies as new aircraft enter the fleet. In a fairly<br />
aggressive NOx-reduction scenario, the assumptions in <strong>AERO2k</strong> include a<br />
combination of this improved technology, plus continued regulation to moderate the<br />
increase in NOx emissions. This contrasts with the much less aggressive NOxreduction<br />
scenario used in the 2015 <strong>for</strong>ecasts made by NASA <strong>and</strong> ANCAT in the mid-<br />
1990s.
CO <strong>Emissions</strong> (Tg)<br />
1.5<br />
1.0<br />
0.5<br />
0.0<br />
1970 1980 1990 2000 2010 2020 2030<br />
Figure 70: <strong>2025</strong> <strong>Global</strong> CO emissions<br />
HC <strong>Emissions</strong>(Tg)<br />
0.4<br />
0.3<br />
0.3<br />
0.2<br />
0.2<br />
0.1<br />
0.1<br />
0.0<br />
1970 1980 1990 2000 2010 2020 2030<br />
Figure 71: <strong>2025</strong> <strong>Global</strong> HC emissions<br />
NASA<br />
AERO2K<br />
Military<br />
NASA<br />
AERO2K<br />
Military<br />
For CO <strong>and</strong> HC, <strong>AERO2k</strong> has simply assumed that there is little or no further<br />
improvement in the respective emission indices. Hence the reductions seen up to<br />
<strong>2002</strong> as 1960/70s combustion technology aircraft are retired are overtaken by<br />
increases in traffic volume.<br />
Comparison of <strong>2025</strong> results with other Scenarios<br />
In addition to the <strong>for</strong>ecasts from previous inventory work described in the preceding<br />
section, there have been a number of other attempts to quantify future emissions<br />
from aviation using scenario tools to generate estimations of <strong>2002</strong> traffic dem<strong>and</strong><br />
<strong>and</strong> technology improvement, each constrained by factors such as airport capacity.<br />
Predictions beyond <strong>2025</strong> are increasingly uncertain <strong>and</strong> the use of scenarios permits a<br />
range of assumptions <strong>and</strong> possible outcomes to be accommodated. In this section,<br />
the <strong>AERO2k</strong> <strong>2025</strong> <strong>for</strong>ecast is compared with three such scenario studies – ICAO FESG 35<br />
35 Forecasting <strong>and</strong> Econocmics Sub-group<br />
QINETIQ/04/01113 Page 112
[ICAO, 1999], EDF 36 (Vendantham <strong>and</strong> Oppenheimer, 1994, 1998) <strong>and</strong> CONSAVE 37<br />
[Berghof, 2004].<br />
In the ICAO FESG <strong>for</strong>ecast, three dem<strong>and</strong> scenarios are generated (high, medium <strong>and</strong><br />
low) together with high <strong>and</strong> low technology assumptions <strong>for</strong> each dem<strong>and</strong> scenario,<br />
all based on market maturity concepts. In the EDF <strong>for</strong>ecast, a logistic model is used<br />
focusing particularly on dem<strong>and</strong> growth in developing countries. Finally, the<br />
preliminary results from CONSAVE use four varied socio-political scenarios to describe<br />
four extremes of aviation growth. The <strong>AERO2k</strong> results are plotted against each of<br />
these scenarios in turn.<br />
Comparison with FESG Scenarios<br />
Based on data from historical growth trends, the ICAO FESG Scenarios use a single<br />
global growth model of traffic dem<strong>and</strong> based on a logistics growth curve function,<br />
without constraints. Hence the FESG scenarios to a large extent reflect assumptions<br />
of no fundamental change in the trends within society or within aviation itself. GDP<br />
<strong>and</strong> aviation dem<strong>and</strong> growth rates are based on Boeing <strong>for</strong>ecasts to 2015 <strong>and</strong> the<br />
IPCC IS92 scenarios thereafter. Three growth scenarios representing high, medium<br />
<strong>and</strong> low growth are illustrated. Within each of the 3 dem<strong>and</strong> growth scenarios, there<br />
are 2 technology scenarios representing higher <strong>and</strong> lower achievement in terms of<br />
embodiment of fuel consumption <strong>and</strong> emissions reduction technology through the<br />
fleet.<br />
Against these FESG scenarios, the <strong>AERO2k</strong> <strong>for</strong>ecasts <strong>for</strong> global fuel consumption, <strong>and</strong><br />
hence CO2, lie slightly above the medium growth scenario. In contrast, the <strong>AERO2k</strong><br />
NOx <strong>for</strong>ecast lies below the medium growth scenario, perhaps reflecting the <strong>AERO2k</strong><br />
assumption of ongoing technical success in meeting a series of increased NOx<br />
stringency measures. Further analysis requires detailed examination of the scenarios<br />
beyond the scope of this report.<br />
Fuel Used (Tg)<br />
800<br />
700<br />
600<br />
500<br />
400<br />
300<br />
200<br />
100<br />
0<br />
High<br />
Dem<strong>and</strong><br />
Medium<br />
Dem<strong>and</strong><br />
Low<br />
Dem<strong>and</strong><br />
1960 1980 2000 2020 2040 2060<br />
Figure 72: Comparison of global aviation fuel used with FESG scenarios<br />
36 Environmental Defense Fund<br />
37 Constrained Scenarios on <strong>Aviation</strong> <strong>and</strong> <strong>Emissions</strong><br />
NASA<br />
DLR<br />
ANCAT<br />
AERO2K<br />
Military<br />
FESG<br />
QINETIQ/04/01113 Page 113
NOx <strong>Emissions</strong> (Tg)<br />
12.0<br />
10.0<br />
8.0<br />
6.0<br />
4.0<br />
2.0<br />
High Dem<strong>and</strong><br />
Med Dem<strong>and</strong><br />
Low Dem<strong>and</strong><br />
0.0<br />
1960 1980 2000 2020 2040 2060<br />
Figure 73: Comparison of global aviation NOx emissions with FESG scenarios<br />
Comparison with EDF Scenarios<br />
NASA<br />
DLR<br />
ANCAT<br />
AERO2K<br />
Military<br />
FESG<br />
The US-based Environmental Defence Fund has produced <strong>for</strong>ecasts <strong>for</strong> 2020 <strong>and</strong> 2050<br />
(<strong>and</strong> 2100) [Vendantham <strong>and</strong> Oppenheimer 1994, 1998] based on the six IPCC<br />
scenarios (IS92a-f) combined with two dem<strong>and</strong> sets – base <strong>and</strong> high. In terms of<br />
general level of activity, the base cases are similar to the FESG scenarios. The high<br />
growth scenarios model substantially higher developing-world-driven aviation<br />
growth up to market saturation around 2040. Five of these cases (2 base cases, 3 high<br />
cases) are plotted in Figure 74 <strong>and</strong> Figure 75 <strong>for</strong> CO2 (in terms of Tg of carbon 38 ) <strong>and</strong><br />
NOx together with the equivalent results <strong>for</strong> <strong>AERO2k</strong>.<br />
AERO2K<br />
Figure 74: Comparison of global aviation CO2 emissions with EDF projections (from IPCC<br />
1999)<br />
38 Multiply Tg carbon by 44/12 to obtain Tg CO2<br />
QINETIQ/04/01113 Page 114
AERO2K<br />
Figure 75: Comparison of global aviation NOx emissions with EDF projections (from<br />
IPCC 1999)<br />
<strong>AERO2k</strong> results <strong>for</strong> both <strong>2002</strong> <strong>and</strong> <strong>2025</strong> appear to fall below the EDF IS92C base<br />
prediction <strong>for</strong> CO2 <strong>and</strong> close to it <strong>for</strong> NOx. However care is needed in making such<br />
comparisons. <strong>AERO2k</strong> includes only civil IFR <strong>and</strong> military flights whilst the EDF<br />
predictions have also attempted to account <strong>for</strong> general aviation, fully global charter 39<br />
business <strong>and</strong> other operational effects. Nevertheless, comparison between the FESG,<br />
EDF <strong>and</strong> <strong>AERO2k</strong> <strong>for</strong>ecasts <strong>for</strong> NOx demonstrates the scenario-dependent variability<br />
even in these business-as-usual based scenarios.<br />
Comparison with CONSAVE<br />
At time of writing early results from the EC Framework 5 research project CONSAVE<br />
are becoming available. Using the Dutch AERO model (which has no link other than<br />
name to <strong>AERO2k</strong>), CONSAVE has taken four global economic <strong>and</strong> socio-political<br />
scenarios <strong>and</strong> modelled aviation in each of these scenarios [Berghof, 2004]. The four<br />
scenarios modelled are:<br />
− Unlimited Skies (ULS) in which aviation has thrived through economic growth <strong>and</strong><br />
through having no unacceptable environmental impact (i.e. effects are resolved<br />
elsewhere).<br />
− Regulatory Push & Pull (RPP) where the regulatory bodies actively control, push<br />
<strong>and</strong> guide the aviation developments towards the future.<br />
− Fractured World (FW) where regional protectionism <strong>and</strong> antagonism has a<br />
significant adverse impact on the dem<strong>and</strong> <strong>and</strong> supply <strong>for</strong> aviation.<br />
− Down to Earth (DtE) in which society becomes environmentally protective <strong>and</strong> at<br />
the same time loses interest in (long distance) (air) travel.<br />
All four CONSAVE scenarios share a common development up to the year 2005,<br />
harmonised with the ICAO/FESG <strong>for</strong>ecasts. The scenarios start to diverge significantly<br />
from 2010 onwards. These scenarios include constrained conditions where the<br />
aviation system has responded to pressures of limited infrastructure, high fuel prices,<br />
<strong>and</strong>, in one case, kerosene-to-hydrogen (LH2) transition pushes the aviation system to<br />
39 Aero2k has charter from Europe <strong>and</strong> North America.<br />
QINETIQ/04/01113 Page 115
its limits. Built on the newest "background" material in fields external to transport,<br />
they are intended to set a framework <strong>for</strong> assessment of the long-term development<br />
in aviation.<br />
CONSAVE models the aviation growth under each of these four scenarios, predicting<br />
CO2 <strong>and</strong> NOx emissions <strong>for</strong> the years 2000, 2020 <strong>and</strong> 2050. These results are<br />
compared with <strong>AERO2k</strong> results <strong>for</strong> <strong>2002</strong> <strong>and</strong> <strong>2025</strong> in Figure 76 to Figure 80.<br />
Dem<strong>and</strong> (billion RTK)<br />
4500<br />
4000<br />
3500<br />
3000<br />
2500<br />
2000<br />
1500<br />
1000<br />
500<br />
0<br />
1990 2000 2010 2020 2030 2040 2050 2060<br />
Unlimited Skies Regulatory Push & Pull (LH2)<br />
Regulatory Push & Pull (All Kero) Fractured World<br />
Downto earth <strong>AERO2k</strong><br />
Figure 76: Comparison of global civil aviation dem<strong>and</strong> with CONSAVE scenarios<br />
Fuel used (Tg)<br />
900<br />
800<br />
700<br />
600<br />
500<br />
400<br />
300<br />
200<br />
100<br />
0<br />
1990 2000 2010 2020 2030 2040 2050 2060<br />
Unlimited Skies Regulatory Push & Pull (LH2)<br />
Regulatory Push & Pull (All Kero) Fractured World<br />
Downto earth <strong>AERO2k</strong><br />
Figure 77: Comparison of global civil aviation fuel used with CONSAVE scenarios<br />
QINETIQ/04/01113 Page 116
CO2 emissions(Tg)<br />
3000<br />
2500<br />
2000<br />
1500<br />
1000<br />
500<br />
0<br />
1990 2000 2010 2020 2030 2040 2050 2060<br />
Unlimited Skies Regulatory Push & Pull (LH2)<br />
Regulatory Push & Pull (All Kero) Fractured World<br />
Downto earth <strong>AERO2k</strong><br />
Figure 78: Comparison of global civil aviation CO2 emissions with CONSAVE scenarios 40<br />
H2O emissions(Tg)<br />
2000.0<br />
1800.0<br />
1600.0<br />
1400.0<br />
1200.0<br />
1000.0<br />
800.0<br />
600.0<br />
400.0<br />
200.0<br />
0.0<br />
1990 2000 2010 2020 2030 2040 2050 2060<br />
Unlimited Skies Regulatory Push & Pull (LH2)<br />
Regulatory Push & Pull (All Kero) Fractured World<br />
Dow n to earth <strong>AERO2k</strong><br />
Figure 79: Comparison of global civil aviation H2O emissions with CONSAVE scenarios<br />
40 Note: In the RP&P(LH2) scenario, no allowance is shown <strong>for</strong> CO2 emitted during hydrogen<br />
production. Dependent upon production technology, such emissions could be significant.<br />
Similarly water vapour emissions the RP7P(LH2) scenario would be higher than in all the other<br />
scenarios.<br />
QINETIQ/04/01113 Page 117
NOx emissions(Gg)<br />
8000<br />
7000<br />
6000<br />
5000<br />
4000<br />
3000<br />
2000<br />
1000<br />
0<br />
1990 2000 2010 2020 2030 2040 2050 2060<br />
Unlimited Skies Regulatory Push & Pull (LH2)<br />
Regulatory Push & Pull (All Kero) Fractured World<br />
Dow n to earth <strong>AERO2k</strong><br />
Figure 80: Comparison of global civil aviation NOx emissions with CONSAVE scenarios<br />
A full analysis of the CONSAVE results will be published in the CONSAVE project<br />
report [Berghof, 2004]. However, an initial comparison with <strong>AERO2k</strong> shows<br />
passenger/freight dem<strong>and</strong> <strong>for</strong> the <strong>AERO2k</strong> <strong>2025</strong> scenario to lie between the<br />
technology driven, relatively unregulated “Unlimited Skies” scenario <strong>and</strong> the more<br />
regulated “Regulatory Push & Pull” scenario. This matches the scenario modelled in<br />
<strong>AERO2k</strong> which assumes relatively high technical success in an environmentally<br />
regulated scenario.<br />
In terms of fuel <strong>and</strong> CO2, the <strong>AERO2k</strong> scenario models proportionally higher usage<br />
<strong>and</strong> emissions, but results still lie between the two CONSAVE scenarios. For NOx, the<br />
assumptions in <strong>AERO2k</strong> are more aggressive with emissions lying close to those in the<br />
CONSAVE “Regulatory Push & Pull” scenario. This is consistent with the assumptions<br />
in Section 2.3. Further analysis would require a detailed comparison of the aircraft<br />
fleet <strong>and</strong> emissions assumptions in the two projects, which is outside the scope of<br />
this report. However, the comparison of <strong>AERO2k</strong>, which focuses on emissions<br />
calculation, <strong>and</strong> CONSAVE, which focuses on scenarios, shows the results to be<br />
compatible.<br />
QINETIQ/04/01113 Page 118
4 Conclusions<br />
This report has described the production of a global gridded aviation emissions<br />
inventory <strong>for</strong> <strong>2002</strong> <strong>and</strong> a <strong>for</strong>ecast of emissions <strong>for</strong> the year <strong>2025</strong>, <strong>for</strong> both civil <strong>and</strong><br />
military aviation. In addition, the report presented total “headline” figures <strong>for</strong> global<br />
<strong>and</strong> regional aviation fuel used <strong>and</strong> emissions <strong>for</strong> the <strong>2002</strong> inventory year <strong>and</strong> the<br />
<strong>2025</strong> <strong>for</strong>ecast year.<br />
<strong>Global</strong> inventories of aircraft fuel usage <strong>and</strong> emissions are required <strong>for</strong> the<br />
quantification of environmental effects of aviation. For the key climate impact<br />
assessment models, the input data <strong>for</strong> individual emissions need to be placed on a<br />
global grid. In order to meet this requirement, this EC 5th Framework Programme<br />
project ‘<strong>AERO2k</strong>’ has developed a new <strong>and</strong> improved global inventory of aviation fuel<br />
usage <strong>and</strong> emissions. Additional parameters (e.g. particle emissions <strong>and</strong> distance<br />
travelled/grid cell) are now needed <strong>for</strong> the climate modelling community in addition<br />
to the previously provided gas phase species of carbon dioxide (CO2), carbon<br />
monoxide (CO), hydrocarbons (HCs) <strong>and</strong> NOx. These new parameters have been<br />
added to the civil aviation inventory in <strong>AERO2k</strong>.<br />
To provide new aviation emissions data on a global gridded basis, <strong>AERO2k</strong> has taken<br />
the best available civil <strong>and</strong> military flight in<strong>for</strong>mation <strong>for</strong> the year <strong>2002</strong>. For civil<br />
aviation, this included radar tracked flight data from North America <strong>and</strong> Europe<br />
showing actual latitude, longitude <strong>and</strong> altitude along the flight path. Routing<br />
in<strong>for</strong>mation was used to place timetabled flights from the rest of the world onto a<br />
global grid. Using 40 representative aircraft, fuel used <strong>for</strong> each flight was calculated<br />
using per<strong>for</strong>mance data from the PIANO aircraft per<strong>for</strong>mance tool. Employing the<br />
latest publicly available in<strong>for</strong>mation on emissions factors, emissions were calculated<br />
based on aircraft height, weight <strong>and</strong> speed, throughout the flight. New in<strong>for</strong>mation<br />
on non-volatile particulate emissions has been added to provide a first gridded<br />
estimate of particulate emissions from civil aviation. Extensive validation activities<br />
have been per<strong>for</strong>med to ensure the integrity of the data processing <strong>and</strong> output.<br />
The production of these gridded data included a number of challenges, principally in<br />
generating a robust <strong>2002</strong> flights database from a range of input sources, in grouping<br />
<strong>and</strong> modelling the representative aircraft <strong>and</strong> engines, in parameterising the<br />
emissions <strong>and</strong> in combining the data to generate the global gridded data. Within<br />
each stage, the assumptions made were balanced between the requirements <strong>for</strong><br />
accuracy <strong>and</strong> the need to keep the data <strong>and</strong> computing requirements within<br />
manageable proportions. Wide ranging validation activities have been per<strong>for</strong>med to<br />
ensure the integrity of the assumptions, data processing <strong>and</strong> output. Key validation<br />
activities have been reported in this <strong>and</strong> other <strong>AERO2k</strong> reports. Based on this<br />
validation work, the results <strong>for</strong> <strong>AERO2k</strong> are believed to represent an important step<br />
<strong>for</strong>ward over previous inventories, although the absolute accuracy of the data is<br />
difficult to determine without considerable further work beyond the scope of the<br />
project. Estimates of uncertainties have been provided <strong>for</strong> individual elements of the<br />
work where available. Because of the decision not to include effects which could not<br />
be quantified (eg winds, tankering) <strong>and</strong> the omissions of non-timetable flights<br />
outside Europe <strong>and</strong> North America, it is probable that the total figures given<br />
represent a slight underestimate of the actual total emissions from civil aviation.<br />
QINETIQ/04/01113 Page 119
Gridded results have been published on the World Wide Web at<br />
http://www.cate.mmu.ac.uk/aero2k.asp. Analysis of the data has also been carried<br />
out to produce the global <strong>and</strong> regional results contained in this report. Key headline<br />
results <strong>for</strong> <strong>2002</strong> were:<br />
Distance<br />
CO2 H<br />
Fuel Used<br />
2O CO NOx HC Soot Particles<br />
Flown Produced Produced Produced Produced Produced Produced Produced<br />
(10 3 million<br />
n. miles)<br />
(Tg) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg)<br />
Civil<br />
<strong>Aviation</strong> 41 17.9 156 492 193 .507 2.06 .063 .0039<br />
4.03 X<br />
10 25<br />
Military<br />
<strong>Aviation</strong><br />
n/a 19.5 61.0 24.1 .647 .178 .066 n/a n/a<br />
Total n/a 176 553 217 1.15 2.24 .129 n/a n/a<br />
For civil aviation in <strong>2002</strong>, the fuel-used <strong>and</strong> NOx results are in line with results from<br />
previous inventory work. Annual fuel-used continues to increase despite efficiency<br />
improvements in newer aircraft. The complex CO2/NOx trade-off related to engine<br />
pressure ratio is evidenced by the continuing increase in annual NOx emissions <strong>and</strong><br />
substantially unchanging value of NOx emitted per kg of fuel-used (EI NOx).<br />
Conversely, the benefits of improving combustion efficiency in recent aircraft are now<br />
seen in a substantial reduction in annual CO <strong>and</strong> HC emissions <strong>and</strong> even greater<br />
reductions in fleet EI CO <strong>and</strong> HC <strong>for</strong> civil aviation.<br />
This reduction in CO <strong>and</strong> HC emissions from civil aviation can be contrasted with the<br />
estimated equivalent emissions from military aviation. Unlike previous inventories,<br />
the <strong>AERO2k</strong> military inventory has attempted to take full account of reheat operation<br />
with the outcome that estimates of CO, <strong>and</strong> to a lesser extent HC emissions from<br />
military aviation are increased compared with those earlier inventories. Whilst the<br />
<strong>AERO2k</strong> figures <strong>for</strong> military CO <strong>and</strong> HC should be regarded as maxima because of the<br />
difficulty in obtaining accurate reheat usage data, the implication is that military<br />
aviation is now a major source of these particular aviation emissions. This contrasts<br />
with earlier inventory results.<br />
As previously mentioned, new data, not available from previous inventories, are<br />
presented in the <strong>for</strong>m of particulate mass <strong>and</strong> number <strong>and</strong> in distance flown in each<br />
grid cell. Both these parameters have increasing importance in the assessment of<br />
aviation climate impacts from contrail <strong>and</strong> cirrus <strong>for</strong>mation. For particulates, it is<br />
recognised that the scientific underst<strong>and</strong>ing of particulate emissions from aircraft<br />
engines at altitude is in its infancy. Whilst using the best available in<strong>for</strong>mation from<br />
DLR, leaders in measurement of these emissions, the particle number data presented<br />
must be regarded as a first estimate.<br />
To generate a <strong>for</strong>ecast <strong>for</strong> <strong>2025</strong>, a scenario has been developed within <strong>AERO2k</strong> in<br />
which dem<strong>and</strong> growth <strong>and</strong> technology improvements are based on estimates by<br />
Airbus <strong>and</strong> UK DTI. These estimates produce regional factors, globally averaging a<br />
multiplication of capacity by 2.6, fuel used by 2.1 <strong>and</strong> NOx emitted by 1.6 times over<br />
the 23 years from <strong>2002</strong> to <strong>2025</strong>. This represents a continuous dem<strong>and</strong> growth<br />
scenario at around 4.2% per annum with particularly successful embodiment of NOx<br />
reduction technology in new aircraft over the period.<br />
Using the regional dem<strong>and</strong> <strong>and</strong> technology factors generated by this scenario, the<br />
<strong>2002</strong> flight dataset was processed to <strong>for</strong> <strong>for</strong>ecast gridded data <strong>for</strong> <strong>2025</strong>. This gridded<br />
41 Civil aviation includes IFR flights only<br />
QINETIQ/04/01113 Page 120
data is published alongside the <strong>2002</strong> data at http://www.cate.mmu.ac.uk/aero2k.asp.<br />
Key headline results <strong>for</strong> <strong>2025</strong> are:<br />
Distance<br />
Flown<br />
(10 3 million n.<br />
miles)<br />
Fuel Used<br />
CO 2<br />
Produced<br />
H 2O<br />
Produced<br />
CO<br />
Produced<br />
NO x<br />
Produced<br />
HC<br />
Produced<br />
Soot<br />
Produced<br />
(Tg) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg)<br />
QINETIQ/04/01113 Page 121<br />
Particles<br />
Produced<br />
<strong>2002</strong> 17.9 156 492 193 .507 2.06 .063 .0039 4.03 X 10 25<br />
<strong>2025</strong> 36.1 327 1029 404 1.15 3.308 0.1447 0.0087 8.54 x 10 25<br />
For <strong>2025</strong>, the scenario confirms the challenge faced by civil aviation in mitigating the<br />
mass of emissions resulting from meeting the increased passenger <strong>and</strong> freight<br />
dem<strong>and</strong>. Despite assumptions of increased average aircraft size <strong>and</strong> continuing<br />
success in introducing fuel saving <strong>and</strong> emissions reduction technology, fleet rollover<br />
timescales <strong>and</strong> the dem<strong>and</strong> growth rate itself are still leading to significant increases<br />
in global emissions. Over the 23 year period from <strong>2002</strong> to <strong>2025</strong>, satisfying a dem<strong>and</strong><br />
increase of 2.6 times results in a doubling of fuel consumed <strong>and</strong> hence of CO2 <strong>and</strong><br />
H2O emissions. With ongoing regulation <strong>and</strong> technical success, there is an increase in<br />
NOx by the lower factor of 1.6 times – but still an increase. The scenario also suggests<br />
an increase in CO, HC <strong>and</strong> particulate emissions as significant technology<br />
improvements run out in this area. The redistribution of these emissions around the<br />
globe as a result of the regionally differential growth rates is contained in the global<br />
gridded data.<br />
In conclusion, <strong>AERO2k</strong> has provided a significant update of the quantity <strong>and</strong> location<br />
of emissions from global aviation. The use of radar tracked data <strong>for</strong> Europe <strong>and</strong> North<br />
America has considerably enhanced the knowledge of the actual global position<br />
(latitude, longitude <strong>and</strong> altitude) at which these emissions occur. Increased numbers<br />
of representative aircraft compared to previous inventories have improved the<br />
accuracy of the estimation, as has the updating of emissions parameters based on<br />
latest available research. The provision of particulate number estimates <strong>and</strong> distance<br />
flown per grid cell are firsts <strong>for</strong> global inventories. These new data give atmospheric<br />
scientists the opportunity to evaluate the likely impact of aviation on climate through<br />
aviation-induced contrails <strong>and</strong> cirrus clouds. Combined with the improved <strong>and</strong><br />
updated gridded data <strong>for</strong> the gaseous emissions, the overall <strong>AERO2k</strong> <strong>2002</strong> <strong>and</strong> <strong>2025</strong><br />
gridded data provide a major new data source <strong>for</strong> aviation climate impact<br />
assessment.
5 Recommendations <strong>for</strong> Further Work<br />
There is a significant opportunity to capitalise on the FP5 investment in <strong>AERO2k</strong> in<br />
order to meet the increasing need <strong>for</strong> substantiated aviation emissions data <strong>and</strong> its<br />
climate effect. Within the terms of the <strong>AERO2k</strong> project, the software has been<br />
developed to run data <strong>for</strong> <strong>2002</strong> on a global basis as a “one-shot” exercise to provide<br />
global data. However, development <strong>and</strong> further exploitation of the <strong>AERO2k</strong> model<br />
would provide authoritative global, regional <strong>and</strong> national emissions data essential to<br />
in<strong>for</strong>m debate on the environmental effects of aviation in Europe <strong>and</strong> globally <strong>and</strong><br />
evaluate the means to regulate <strong>and</strong> control those emissions. <strong>AERO2k</strong> model has the<br />
potential to become a major policy analysis tool <strong>for</strong> evaluating global, regional<br />
national <strong>and</strong> major airport emissions from actual flights <strong>and</strong> from future aviation<br />
scenarios. The vast majority of the work required to produce such a tool has already<br />
been completed within the initial <strong>AERO2k</strong> project described in this report.<br />
Future work falls into two main categories, namely the exploitation <strong>and</strong> where<br />
appropriate, development of <strong>AERO2k</strong> to provide global/regional emissions data <strong>and</strong><br />
secondly, the integration of <strong>AERO2k</strong> into a suite of compatible modules modelling the<br />
interrelationships between climate effect, air quality, noise <strong>and</strong> economics within<br />
aviation.<br />
To develop the <strong>AERO2k</strong> software requires a short programme to improve the data<br />
search <strong>and</strong> analysis capability. Designed as a one off-activity, <strong>AERO2k</strong> is currently a<br />
loosely connected group of utilities implemented in MS Access 2000 requiring<br />
considerable programming ef<strong>for</strong>t <strong>and</strong> time to retrieve new in<strong>for</strong>mation. To allow cost<br />
effective use of the wealth of data incorporated in <strong>AERO2k</strong>, it is required to bring<br />
together these utilities into a client/server database application. In addition,<br />
effectiveness needs to be enhanced by:<br />
− Addition of a user interface to improve cost effectiveness of query <strong>and</strong><br />
scenario input<br />
− Implementation of a module to allow user-defined scenario changes by<br />
adjustment of the flight database <strong>and</strong> application of further traffic efficiency<br />
factors to those flights.<br />
− Consideration of the requirement <strong>for</strong> addition of future aircraft types <strong>and</strong><br />
new destination pairs.<br />
− Automation of changes to search criteria including variable regions, group of<br />
countries (eg EC25) <strong>and</strong> individual States.<br />
With this additional functionality, <strong>AERO2k</strong> can be rerun with different scenario <strong>and</strong><br />
propulsion technology data to provide revised <strong>2002</strong> <strong>and</strong> <strong>2025</strong> data <strong>for</strong> different<br />
scenarios – these could be different technology assumptions, ATC improvements,<br />
cruise height changes <strong>and</strong> so on. If appropriate, latest in<strong>for</strong>mation could be used to<br />
update current <strong>AERO2k</strong> assumptions <strong>and</strong> source data (e.g. engine models, emissions<br />
parameters)<br />
Additionally, <strong>AERO2k</strong> has been built to provide global data, but has immense<br />
potential to provide detail data (airport, country, air quality etc). The overall<br />
programme is in place but to allow detail queries to provide valid in<strong>for</strong>mation, it<br />
would be necessary to:<br />
QINETIQ/04/01113 Page 122
− Increase the number of weeks data<br />
− Increase the number of aircraft models <strong>and</strong> engine combinations<br />
− Improve detail of the LTO assumptions such as reduced throttle settings, idle<br />
settings<br />
− Incorporate additional tail number <strong>and</strong> fleet data to provide improved granularity of<br />
fleet make up (to complement addition of aircraft models)<br />
Similarly, the current coverage of <strong>AERO2k</strong> includes only “quantifiable” effects <strong>and</strong><br />
only IFR flights. To complete the overall aviation emissions picture, an estimation of<br />
these effects (e.g. winds, tankering) <strong>and</strong> the emissions contribution of non-IFR flights<br />
are required.<br />
<strong>AERO2k</strong> is based on a flights database supplied from the Eurocontrol infrastructure,<br />
in collaboration with the FAA. In cooperation with Eurocontrol, further research in<br />
improving the method of flight data collection <strong>and</strong> analysis should be encouraged. In<br />
particular, further work linked to the intrinsic quality of the data would be required to<br />
gain in data quality, quality which would read through to the emissions estimates<br />
themselves. There is a need <strong>for</strong> an international ef<strong>for</strong>t to harmonize airports, aircraft<br />
<strong>and</strong> airline coding <strong>and</strong> <strong>for</strong> all parties to apply a unique code system. The increasing<br />
number of code-sharing flights leads to their possible duplication as the same flight<br />
appears under different callsigns. A recognised system <strong>for</strong> identifying code-sharing<br />
callsigns would need to be put in place by the airlines. The use of a unique identifier<br />
linked to the tail number <strong>for</strong> example would also facilitate greatly the identification<br />
of flights <strong>and</strong> the merging of trajectories. The automatic report of flight in<strong>for</strong>mation<br />
would avoid data typing errors such as an “O” typed instead of a “0” <strong>and</strong> so would<br />
reduce the number of flights listed as inconsistent.<br />
In order to gain full international acceptance <strong>for</strong> the results from <strong>AERO2k</strong>, it is<br />
recommended that <strong>AERO2k</strong> is subjected to scrutiny alongside the FAA SAGE (System<br />
<strong>for</strong> Assessing <strong>Global</strong> <strong>Emissions</strong>) model <strong>for</strong> approval by CAEP, with the objective of<br />
providing an authoritative European assessment tool <strong>for</strong> global aviation emissions.<br />
Maintaining such approval would require maintenance of the <strong>AERO2k</strong> model to<br />
include additional base years, new aircraft <strong>and</strong> new emissions data as they became<br />
available.<br />
Such an approval would strengthen the use of <strong>AERO2k</strong> to support the development of<br />
UNFCCC fuel used <strong>and</strong> emissions reporting by States, by provision of <strong>AERO2k</strong> data to<br />
improve lower tier methodologies <strong>and</strong> as a higher tier methodology in itself.<br />
Provision of data <strong>for</strong> additional years would allow short term comparison of aviation<br />
trends, assist in UNFCCC methodology improvement <strong>and</strong> actual national reporting.<br />
On this last point, use of <strong>AERO2k</strong> as a higher tier methodology <strong>for</strong> calculation of<br />
national emissions offers EC states considerable cost savings <strong>and</strong> improvements in<br />
accuracy compared to the manual methods currently used<br />
As a separate issue, an evaluation of the actual climate impact is the natural followon<br />
from <strong>AERO2k</strong> emissions data. Integration of the <strong>AERO2k</strong> model with one or more<br />
climate impact models to allow cost effective production of the relative climate<br />
impacts of modelled scenarios. Similarly the integration with AQ, ATC, noise <strong>and</strong><br />
economics models is an important issue in the sustainable aviation debate <strong>and</strong> an<br />
investigation of c<strong>and</strong>idates <strong>and</strong> approach to providing a European inter-relationship<br />
modelling capability is required, based on use of loosely connected but compatible<br />
models.<br />
QINETIQ/04/01113 Page 123
6 References<br />
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7 Acronyms <strong>and</strong> abbreviations<br />
ACARE Advisory Council <strong>for</strong> Aeronautics Research in Europe<br />
AERO NLR’s <strong>Aviation</strong> <strong>Emissions</strong> <strong>and</strong> Evaluation of Reduction Options Model<br />
<strong>AERO2k</strong><br />
The EC 5th Framework Programme project ‘<strong>AERO2k</strong>’ developing a new <strong>and</strong><br />
improved global inventory of aviation fuel usage <strong>and</strong> emissions <strong>for</strong> <strong>2002</strong><br />
<strong>and</strong> a <strong>for</strong>ecast of emissions <strong>for</strong> <strong>2025</strong><br />
AMOC ATFM (Air Traffic Flow Management) Modelling Capability<br />
The ANCAT/EC2 global aircraft emissions inventories produced by the<br />
ANCAT/EC2<br />
ECAC/ANCAT/EC <strong>Emissions</strong> Inventory Database Group; ANCAT: Group of<br />
experts on the Abatement of Nuisances Caused by Air Transport; ECAC:<br />
European Civil <strong>Aviation</strong> Conference<br />
AQ Air Quality<br />
ASK Available seat kilometres<br />
ASNM Available seat nautical miles<br />
ATC Air Traffic Control<br />
BADA<br />
Base of Aircraft Data: aircraft per<strong>for</strong>mance database produced <strong>and</strong> used by<br />
Eurocontrol<br />
BPR Bypass Ratio<br />
CAEP Committee on <strong>Aviation</strong> Environmental Protection<br />
CFMU Central Flow Management Unit<br />
CO Carbon monoxide<br />
CO2<br />
Carbon dioxide<br />
DLR Deutsche Forschungsanstalt für Luft- und Raumfahrt e.V, Germany<br />
DTI UK department of Trade <strong>and</strong> Industry<br />
EC European Commission<br />
ECAC European Civil <strong>Aviation</strong> Conference<br />
EDF Environmental Defence Fund<br />
EI<br />
<strong>Emissions</strong> Index (normally expressed as weight of emission per weight of<br />
fuel used<br />
ETMS Enhanced Traffic Management System<br />
FAA Federal <strong>Aviation</strong> Administration<br />
FESG Forecasting <strong>and</strong> Economics Sub Group<br />
FL Flight level<br />
FSU Former Soviet Union<br />
Gb Gigabyte (= 10 9 bytes)<br />
Gg Gigagramme (= 10 9 grammes = 1000 tonnes)<br />
GC Great circle distance, the shortest distance between two points on a sphere<br />
GIS Geographic In<strong>for</strong>mation System<br />
GMT Greenwich Mean Time<br />
GSP NLR’s Gas Turbine Per<strong>for</strong>mance Programme<br />
HC Hydrocarbons<br />
H2O Water (normally in vapour <strong>for</strong>m)<br />
IATA International Air Transport Association<br />
ICAO International Civil <strong>Aviation</strong> Organisation<br />
IFR Instrument flight rules<br />
WMO/UNEP Intergovernmental Panel on Climate Change (jointly<br />
IPCC<br />
established by the World Meteorological Organisation <strong>and</strong> the United<br />
Nations Environment Programme)<br />
ISA International St<strong>and</strong>ard Atmosphere<br />
Jet A-1 A specification <strong>for</strong> aviation kerosene<br />
LTO The L<strong>and</strong>ing <strong>and</strong> Take-Off cycle<br />
QINETIQ/04/01113 Page 127
Mg Mega grammes (=10 6 grammes = 1 tonne)<br />
MS Microsoft<br />
n/a Not applicable<br />
NLR<br />
Nationaal Lucht -en Ruimtevaartlaboratorium<br />
Laboratory), Netherl<strong>and</strong>s<br />
(National Aerospace<br />
n.mile Nautical mile<br />
NOx<br />
oxides of nitrogen, NO2 + NO<br />
OAG OAG Worldwide Ltd – a provider of airline schedule data<br />
PIANO<br />
PIANO (Project Interactive Analysis <strong>and</strong> Optimisation) is a professional<br />
software tool <strong>for</strong> commercial aircraft analysis<br />
PR Pressure ratio (overall engine)<br />
SAGE<br />
The US FAA’s SAGE (System <strong>for</strong> Assessing <strong>Aviation</strong>’s <strong>Global</strong> <strong>Emissions</strong>)<br />
project<br />
SAR Specific air range<br />
SARS Severe Acute Respiratory Syndrome<br />
SLS Sea Level Static<br />
R 2<br />
Goodness of Fit (or coefficient of determination) defined as 1 – (sum of the<br />
squares about the mean)/(sum of the squared errors)<br />
Tg Teragrammes (=10 12 grammes = 1 Megatonne)<br />
UNFCCC United Nations Framework Convention on Climate Change<br />
USAF United States Air Force<br />
UTC Coordinated Universal Time<br />
VBA Visual Basic <strong>for</strong> Applications<br />
WP Work Package<br />
QINETIQ/04/01113 Page 128
A Appendix A<br />
A.1 Annual deviation of temperature from ISA st<strong>and</strong>ard (Ulbrich 2004)<br />
Height in<br />
hPa<br />
Temperature<br />
QINETIQ/04/01113 Page 129
B Appendix B<br />
B.1 Definition of Geographic Regions<br />
The following tables show the allocation of individual countries to regions <strong>for</strong> the<br />
purposes of presentation of <strong>AERO2k</strong> results.<br />
B.1.1 Asia <strong>and</strong> Pacific<br />
American Samoa Kiribati Papua New Guinea<br />
Australia Laos Philippines<br />
Bangladesh Macau Polynesia (French)<br />
Bhutan Malaysia Samoa<br />
British Indian Ocean Territory Maldives Singapore<br />
Brunei Darussalam Marshall Isl<strong>and</strong>s Solomon Isl<strong>and</strong>s<br />
Cambodia; Kingdom of Micronesia South Korea<br />
China Midway Atoll (USA) Sri Lanka<br />
Christmas Isl<strong>and</strong> Mongolia Taiwan<br />
Cocos (Keeling) Isl<strong>and</strong>s Myanmar Thail<strong>and</strong><br />
Cook Isl<strong>and</strong>s Nauru Tonga<br />
East Timor Nepal Tuvalu<br />
Fiji New Caledonia (French) USA<br />
Guam New Zeal<strong>and</strong> USA Minor Outlying Isl<strong>and</strong>s<br />
Guam (USA) Niue Vanuatu<br />
Hong Kong Norfolk Isl<strong>and</strong> Vietnam<br />
India North Korea Wake Isl<strong>and</strong> (USA)<br />
Indonesia Northern Mariana Isl<strong>and</strong>s Wallis And Futuna Isl<strong>and</strong>s<br />
Japan Palau<br />
B.1.2 Eastern <strong>and</strong> Southern Africa<br />
Angola Madagascar Somalia<br />
Botswana Malawi South Africa<br />
Burundi Mauritius Swazil<strong>and</strong><br />
Comoros Mayotte Tanzania<br />
Djibouti Mozambique Tanzania United Republic of<br />
Eritrea Namibia Ug<strong>and</strong>a<br />
Ethiopia Reunion (French) Zambia<br />
Kenya Rw<strong>and</strong>a Zimbabwe<br />
Lesotho Seychelles<br />
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B.1.3 European <strong>and</strong> North Atlantic<br />
Albania Great Britain Pol<strong>and</strong><br />
Algeria Greece Portugal<br />
Andorra; Principality of Greenl<strong>and</strong> Romania<br />
Armenia Hungary Russian Federation<br />
Austria Icel<strong>and</strong> Slovak Republic<br />
Azerbaidjan Irel<strong>and</strong> Slovenia<br />
Belarus Italy Spain<br />
Belgium Kazakhstan<br />
Svalbard <strong>and</strong> Jan Mayen<br />
Isl<strong>and</strong>s<br />
Bosnia-Herzegovina Kyrgyz Republic (Kyrgyzstan) Sweden<br />
Bulgaria Latvia Switzerl<strong>and</strong><br />
Croatia Lithuania Tadjikistan<br />
Czech Republic Luxembourg Tunisia<br />
Denmark Macedonia Turkey<br />
Estonia Malta Turkmenistan<br />
Faroe Isl<strong>and</strong>s Moldavia Ukraine<br />
Finl<strong>and</strong> Monaco United Kingdom<br />
France Morocco Uzbekistan<br />
Georgia Netherl<strong>and</strong>s Yugoslavia<br />
Germany Netherl<strong>and</strong>s Antilles<br />
Gibraltar Norway<br />
B.1.4 Middle East<br />
Afghanistan; Islamic State of Jordan Qatar<br />
Bahrain Kuwait Saudi Arabia<br />
Cyprus Lebanon Sudan<br />
Egypt Libya Syria<br />
Iran Oman United Arab Emirates<br />
Iraq Pakistan Yemen<br />
Israel Palestinian Territory<br />
B.1.5 North American, Central American <strong>and</strong> Caribbean<br />
Anguilla Dominican Republic Puerto Rico<br />
Antigua And Barbuda El Salvador Saint Kitts & Nevis Anguilla<br />
Aruba Grenada Saint Lucia<br />
Bahamas Guadeloupe (French) Saint Pierre <strong>and</strong> Miquelon<br />
Barbados Guatemala Saint Vincent & Grenadines<br />
Belize Haiti Trinidad <strong>and</strong> Tobago<br />
Bermuda Honduras Turks <strong>and</strong> Caicos Isl<strong>and</strong>s<br />
Canada Jamaica USA<br />
Cayman Isl<strong>and</strong>s Martinique (French) Virgin Isl<strong>and</strong>s (British)<br />
Costa Rica Mexico Virgin Isl<strong>and</strong>s (USA)<br />
Cuba Montserrat<br />
Dominica Nicaragua<br />
QINETIQ/04/01113 Page 131
B.1.6 South American<br />
Argentina Ecuador Paraguay<br />
Bolivia Falkl<strong>and</strong> Isl<strong>and</strong>s Peru<br />
Brazil French Guyana Suriname<br />
Chile Guyana Uruguay<br />
Colombia Panama Venezuela<br />
B.1.7 Western <strong>and</strong> Central Africa<br />
Benin Equatorial Guinea Mali<br />
Burkina Faso Gabon Mauritania<br />
Cameroon Gambia Niger<br />
Cape Verde Ghana Nigeria<br />
Central African Republic Guinea<br />
Saint Tome (Sao Tome) And<br />
Principe<br />
Chad Guinea Bissau Senegal<br />
Congo Ivory Coast (Cote D'Ivoire) Sierra Leone<br />
Congo; The Democratic<br />
Republic Of The<br />
Liberia Togo<br />
QINETIQ/04/01113 Page 132
C Appendix C<br />
C.1 <strong>2002</strong> fuel consumed, emissions <strong>and</strong> distance flown by altitude<br />
Tabulation of <strong>2002</strong> fuel consumed, emissions <strong>and</strong> distance flown by altitude covers<br />
one week in each of four months, spread though the year. These months broadly<br />
cover the 4 seasons. Altitudes are given <strong>for</strong> the top of each altitude b<strong>and</strong> (ie “3000ft”<br />
covers emissions from 0 to 3000ft).<br />
Weekly Fuel Used (Mg)<br />
Altitude (ft) April Sept October February<br />
3000 347 374 363 341<br />
6000 114 121 123 113<br />
9000 127 134 134 132<br />
12000 141 141 142 161<br />
15000 122 129 130 131<br />
18000 117 125 125 121<br />
21000 107 117 116 108<br />
24000 107 115 115 111<br />
27000 120 130 130 124<br />
30000 183 182 184 189<br />
33000 350 360 378 343<br />
36000 537 586 566 496<br />
39000 434 510 484 410<br />
42000 69 99 82 73<br />
45000 3.042 2.968 2.840 2.791<br />
48000 0.646 0.649 0.613 0.468<br />
50000 0.015 0.021 0.011 0.009<br />
Table C1 – <strong>2002</strong> weekly fuel consumed – civil aviation – distribution by altitude <strong>and</strong><br />
time of year<br />
Weekly CO2 Produced (Mg)<br />
Altitude (ft) April Sept October February<br />
3000 1090 1170 1140 1070<br />
6000 359 381 386 358<br />
9000 400 421 423 417<br />
12000 444 446 447 508<br />
15000 385 408 409 414<br />
18000 368 394 395 380<br />
21000 337 368 367 340<br />
24000 338 363 363 351<br />
27000 379 411 410 391<br />
30000 577 575 579 594<br />
33000 1100 1140 1190 1080<br />
36000 1690 1850 1780 1560<br />
39000 1370 1610 1530 1290<br />
42000 219 311 259 231<br />
45000 9.580 9.350 8.950 8.790<br />
48000 2.030 2.040 1.930 1.470<br />
50000 0.047 0.066 0.035 0.029<br />
Table C2 – <strong>2002</strong> weekly CO2 emissions– civil aviation – distribution by altitude <strong>and</strong> time<br />
of year<br />
QINETIQ/04/01113 Page 133
Weekly H2O Produced (Mg)<br />
Altitude (ft) April Sept October February<br />
3000 429 463 450 422<br />
6000 141 150 152 140<br />
9000 157 165 166 164<br />
12000 174 175 176 200<br />
15000 151 160 161 163<br />
18000 145 155 155 149<br />
21000 133 145 144 134<br />
24000 133 143 143 138<br />
27000 149 161 161 153<br />
30000 227 226 228 233<br />
33000 433 446 468 424<br />
36000 664 725 701 614<br />
39000 537 631 599 507<br />
42000 86 122 102 91<br />
45000 3.770 3.670 3.520 3.450<br />
48000 0.800 0.803 0.759 0.579<br />
50000 0.018 0.026 0.014 0.011<br />
Table C3 – <strong>2002</strong> weekly H20 emissions – civil aviation – distribution by altitude <strong>and</strong><br />
time of year<br />
Weekly CO Produced (Mg)<br />
Altitude (ft) April Sept October February<br />
3000 3.730 4.040 3.920 3.680<br />
6000 0.290 0.314 0.311 0.291<br />
9000 0.285 0.306 0.300 0.292<br />
12000 0.307 0.319 0.311 0.337<br />
15000 0.269 0.294 0.288 0.277<br />
18000 0.260 0.287 0.282 0.259<br />
21000 0.250 0.281 0.273 0.245<br />
24000 0.259 0.287 0.281 0.264<br />
27000 0.303 0.350 0.331 0.314<br />
30000 0.465 0.477 0.473 0.492<br />
33000 0.819 0.892 0.896 0.813<br />
36000 1.170 1.310 1.240 1.100<br />
39000 0.783 0.926 0.865 0.745<br />
42000 0.117 0.162 0.131 0.127<br />
45000 0.013 0.013 0.012 0.011<br />
48000 0.00310 0.00300 0.00279 0.00224<br />
50000 0.00008 0.00011 0.00004 0.00005<br />
Table C4 – <strong>2002</strong> weekly CO emissions – civil aviation – distribution by altitude <strong>and</strong> time<br />
of year<br />
QINETIQ/04/01113 Page 134
Weekly NOx Produced (Mg)<br />
Altitude (ft) April Sept October February<br />
3000 4.620 5.010 4.900 4.540<br />
6000 1.830 1.950 1.960 1.810<br />
9000 2.000 2.120 2.110 2.060<br />
12000 2.170 2.240 2.240 2.400<br />
15000 1.890 2.020 2.020 1.990<br />
18000 1.760 1.900 1.910 1.790<br />
21000 1.590 1.740 1.740 1.590<br />
24000 1.510 1.630 1.630 1.530<br />
27000 1.510 1.640 1.650 1.540<br />
30000 2.120 2.130 2.150 2.180<br />
33000 4.000 4.090 4.340 3.940<br />
36000 6.450 7.000 6.770 5.980<br />
39000 5.540 6.490 6.210 5.230<br />
42000 0.899 1.290 1.090 0.945<br />
45000 0.029 0.028 0.027 0.028<br />
48000 0.00569 0.00551 0.00557 0.00398<br />
50000 0.00012 0.00016 0.00009 0.00007<br />
Table C5 – <strong>2002</strong> weekly NOx emissions – civil aviation – distribution by altitude <strong>and</strong><br />
time of year<br />
Weekly HC Produced (Mg)<br />
Altitude (ft) April Sept October February<br />
3000 0.5010 0.5420 0.5240 0.4960<br />
6000 0.0405 0.0440 0.0427 0.0409<br />
9000 0.0405 0.0436 0.0417 0.0420<br />
12000 0.0440 0.0458 0.0438 0.0484<br />
15000 0.0385 0.0423 0.0407 0.0395<br />
18000 0.0376 0.0417 0.0399 0.0372<br />
21000 0.0346 0.0387 0.0369 0.0338<br />
24000 0.0342 0.0379 0.0362 0.0347<br />
27000 0.0378 0.0446 0.0408 0.0394<br />
30000 0.0563 0.0585 0.0574 0.0612<br />
33000 0.0877 0.0994 0.0977 0.0877<br />
36000 0.1230 0.1390 0.1290 0.1180<br />
39000 0.0713 0.0874 0.0810 0.0667<br />
42000 0.0111 0.0158 0.0126 0.0120<br />
45000 0.0017 0.0018 0.0016 0.0016<br />
48000 0.00046 0.00050 0.00041 0.00037<br />
50000 0.00001 0.00002 0.00001 0.00001<br />
Table C6 – <strong>2002</strong> weekly HC emissions – civil aviation – distribution by altitude <strong>and</strong> time<br />
of year<br />
QINETIQ/04/01113 Page 135
Weekly Soot Produced (Mg)<br />
Altitude (ft) April Sept October February<br />
3000 0.01585 0.01708 0.01638 0.01573<br />
6000 0.00468 0.00499 0.00489 0.00469<br />
9000 0.00500 0.00527 0.00512 0.00528<br />
12000 0.00539 0.00541 0.00526 0.00618<br />
15000 0.00428 0.00455 0.00443 0.00454<br />
18000 0.00386 0.00417 0.00404 0.00390<br />
21000 0.00319 0.00352 0.00342 0.00317<br />
24000 0.00276 0.00298 0.00291 0.00286<br />
27000 0.00282 0.00312 0.00303 0.00294<br />
30000 0.00393 0.00394 0.00394 0.00414<br />
33000 0.00638 0.00664 0.00692 0.00628<br />
36000 0.00824 0.00899 0.00861 0.00770<br />
39000 0.00555 0.00647 0.00612 0.00525<br />
42000 0.00079 0.00112 0.00092 0.00086<br />
45000 0.0000485 0.0000486 0.0000455 0.0000444<br />
48000 0.0000104 0.0000107 0.0000095 0.0000077<br />
50000 0.0000002 0.0000003 0.0000001 0.0000002<br />
Table C7 – <strong>2002</strong> weekly particulate mass emissions – civil aviation – distribution by<br />
altitude <strong>and</strong> time of year<br />
Weekly Particles Produced x10 -22<br />
Altitude (ft) April Sept October February<br />
3000 7.69 8.28 7.94 7.63<br />
6000 3.45 3.66 3.66 3.47<br />
9000 3.78 3.99 3.94 3.99<br />
12000 4.19 4.23 4.18 4.76<br />
15000 3.63 3.87 3.83 3.87<br />
18000 3.56 3.85 3.78 3.63<br />
21000 3.20 3.52 3.46 3.20<br />
24000 3.06 3.29 3.25 3.18<br />
27000 3.44 3.79 3.70 3.58<br />
30000 5.22 5.24 5.24 5.48<br />
33000 9.46 9.86 10.30 9.29<br />
36000 13.20 14.50 13.80 12.30<br />
39000 9.19 10.70 10.10 8.68<br />
42000 1.310 1.840 1.510 1.410<br />
45000 0.080 0.080 0.075 0.073<br />
48000 0.0169 0.0173 0.0154 0.0126<br />
50000 0.0004 0.0006 0.0002 0.0003<br />
Table C8 – <strong>2002</strong> weekly particulate number – civil aviation – distribution by altitude<br />
<strong>and</strong> time of year<br />
QINETIQ/04/01113 Page 136
Weekly Distance Flown (n.miles x10 -6 )<br />
Altitude (ft) April Sept October February<br />
3000 2.25 2.35 2.44 2.07<br />
6000 87.20 90.64 95.38 76.65<br />
9000 62.89 64.23 67.79 59.86<br />
12000 70.75 68.46 72.17 74.53<br />
15000 68.50 70.74 74.50 65.88<br />
18000 72.30 75.84 79.78 66.72<br />
21000 69.76 74.94 78.56 63.77<br />
24000 72.66 76.47 79.89 68.51<br />
27000 82.40 87.73 91.59 77.29<br />
30000 114.04 114.22 119.89 105.53<br />
33000 186.82 195.01 207.44 167.80<br />
36000 252.32 276.08 280.09 215.86<br />
39000 207.72 245.90 247.34 184.49<br />
42000 48.01 60.39 56.48 47.24<br />
45000 5.98 5.49 5.92 4.64<br />
48000 1.41 1.30 1.39 0.86<br />
50000 0.032 0.036 0.034 0.016<br />
Table C9 – <strong>2002</strong> weekly distance flown – civil aviation – distribution by altitude <strong>and</strong><br />
time of year<br />
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D Appendix D<br />
D.1 Annual Values of Distance Flown, Fuel Consumed <strong>and</strong> <strong>Emissions</strong> <strong>for</strong> Civil<br />
<strong>Aviation</strong> Flights between Regions<br />
Asia <strong>and</strong> Pacific Departures<br />
Distance<br />
Flown<br />
Fuel Used CO2 H2O CO NOx HC Soot Particles<br />
Arrival Region<br />
n.miles<br />
-9<br />
x10<br />
(Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10-23<br />
Asia <strong>and</strong> Pacific 2.160 22500 70910 27860 65.120 338.100 7.081 0.5157 48.85<br />
Eastern <strong>and</strong> Southern<br />
Africa<br />
0.013 214 676 266 0.298 3.214 0.026 0.0036 0.52<br />
European <strong>and</strong> North<br />
Atlantic<br />
0.279 4933 15560 6107 9.202 66.950 0.795 0.0826 12.00<br />
Middle East<br />
North American,<br />
0.075 938 2959 1162 1.732 15.050 0.205 0.0154 1.95<br />
Central American <strong>and</strong><br />
Caribbean<br />
0.328 5550 17500 6871 10.040 78.980 0.877 0.0930 13.62<br />
South American 0.002 34 106 42 0.045 0.553 0.003 0.0005 0.07<br />
Table D1 – <strong>2002</strong> Regional fuel, emissions <strong>and</strong> distance flown <strong>for</strong> departures from Asia &<br />
Pacific region<br />
Eastern <strong>and</strong> Southern Africa Departures<br />
Arrival Region<br />
Distance<br />
Fuel Used<br />
Flown<br />
n.miles x10<br />
CO2 H2O CO NOx HC Soot Particles<br />
-<br />
9 (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10 -23<br />
Asia <strong>and</strong> Pacific 0.012 209 658 258 0.286 3.151 0.025 0.0035 0.50<br />
Eastern <strong>and</strong> Southern<br />
Africa<br />
0.113 617 1944 764 1.919 7.686 0.279 0.0258 1.96<br />
European <strong>and</strong> North<br />
Atlantic<br />
0.079 1254 3956 1553 2.081 17.620 0.186 0.0205 2.99<br />
Middle East 0.009 98 310 122 0.205 1.490 0.029 0.0018 0.21<br />
North American, Central<br />
American <strong>and</strong> Caribbean<br />
0.001 8 27 11 0.027 0.101 0.003 0.0002 0.02<br />
South American 0.001 25 79 31 0.027 0.390 0.003 0.0005 0.07<br />
Western <strong>and</strong> Central<br />
Africa<br />
0.007 92 291 114 0.203 1.156 0.017 0.0017 0.23<br />
Table D2 – <strong>2002</strong> Regional fuel, emissions <strong>and</strong> distance flown <strong>for</strong> departures from<br />
Eastern <strong>and</strong> Southern Africa region<br />
QINETIQ/04/01113 Page 138
European <strong>and</strong> North Atlantic Departures<br />
Distance<br />
Flown<br />
Fuel<br />
Used<br />
CO2 H2O CO NOx HC Soot Particles<br />
Arrival Region<br />
n.miles<br />
-9<br />
x10<br />
(Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10-23<br />
Asia <strong>and</strong> Pacific 0.290 5259 16580 6510 9.281 73.21 0.790 0.0923 12.91<br />
Eastern <strong>and</strong> Southern<br />
Africa<br />
0.075 1227 3869 1519 2.006 17.55 0.184 0.0215 2.99<br />
European <strong>and</strong> North<br />
Atlantic<br />
3.679 24020 75630 29740 110.900 278.90 14.240 0.7584 68.33<br />
Middle East<br />
North American,<br />
0.255 2665 8399 3299 7.380 37.69 0.978 0.0517 6.46<br />
Central American <strong>and</strong><br />
Caribbean<br />
0.653 9123 28760 11290 18.060 137.10 1.887 0.1586 21.62<br />
South American 0.082 1186 3741 1468 1.695 18.12 0.133 0.0182 2.54<br />
Western <strong>and</strong> Central<br />
Africa<br />
0.044 532 1677 659 1.538 7.331 0.193 0.0106 1.37<br />
Table D3 – <strong>2002</strong> Regional fuel, emissions <strong>and</strong> distance flown <strong>for</strong> departures from<br />
European <strong>and</strong> North Atlantic region<br />
Middle East Departures<br />
Distance<br />
Flown<br />
n.miles<br />
Arrival Region<br />
x10<br />
Fuel<br />
Used<br />
CO2 H2O CO NOx HC Soot Particles<br />
-9 (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10-23<br />
Asia <strong>and</strong> Pacific 0.073 915 2884 1132 1.647 14.670 0.192 0.0149 1.89<br />
Eastern <strong>and</strong> Southern<br />
Africa<br />
0.010 99 311 122 0.208 1.483 0.030 0.0018 0.21<br />
European <strong>and</strong> North<br />
Atlantic<br />
0.265 2771 8734 3431 7.963 37.900 1.056 0.0531 6.75<br />
Middle East<br />
North American,<br />
0.170 1811 5705 2242 5.826 28.530 0.736 0.0480 3.95<br />
Central American <strong>and</strong><br />
Caribbean<br />
0.013 210 662 260 0.280 3.458 0.023 0.0035 0.49<br />
Western <strong>and</strong> Central<br />
Africa<br />
0.002 27 84 33 0.066 0.366 0.008 0.0006 0.08<br />
Table D4 – <strong>2002</strong> Regional fuel, emissions <strong>and</strong> distance flown <strong>for</strong> departures from<br />
Middle East region<br />
QINETIQ/04/01113 Page 139
North American, Central American <strong>and</strong><br />
Caribbean Departures<br />
Distance<br />
Flown<br />
Fuel<br />
Used<br />
CO2 H2O CO NOx HC Soot Particles<br />
Arrival Region<br />
n.miles<br />
-9<br />
x10<br />
(Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10-23<br />
Asia <strong>and</strong> Pacific 0.329 5681 17910 7033 10.130 81.420 0.866 0.1031 14.36<br />
Eastern <strong>and</strong> Southern<br />
Africa<br />
0.005 96 302 119 0.171 1.233 0.012 0.0018 0.26<br />
European <strong>and</strong> North<br />
Atlantic<br />
0.674 9260 29200 11460 18.340 139.300 1.946 0.1563 21.77<br />
Middle East<br />
North American,<br />
0.014 216 681 267 0.366 3.540 0.040 0.0035 0.51<br />
Central American <strong>and</strong><br />
Caribbean<br />
7.370 46850 147600 58000 196.200 541.100 27.450 1.4780 135.00<br />
South American 0.119 1284 4047 1590 3.376 18.200 0.420 0.0259 3.37<br />
Western <strong>and</strong> Central<br />
Africa<br />
0.003 48 150 59 0.096 0.810 0.010 0.0009 0.13<br />
Table D5 – <strong>2002</strong> Regional fuel, emissions <strong>and</strong> distance flown <strong>for</strong> departures from North<br />
American, Central American <strong>and</strong> Caribbean region<br />
South American Departures<br />
Distance<br />
Flown<br />
n. miles<br />
Arrival Region<br />
x10<br />
Fuel<br />
Used<br />
CO2 H2O CO NOx HC Soot Particles<br />
-9 (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10-23<br />
Asia <strong>and</strong> Pacific 0.002 25 79 31 0.036 0.400 0.002 0.0003 0.05<br />
Eastern <strong>and</strong> Southern<br />
Africa<br />
0.001 27 85 33 0.027 0.431 0.003 0.0005 0.07<br />
European <strong>and</strong> North<br />
Atlantic<br />
North American,<br />
0.086 1202 3790 1488 1.843 18.240 0.156 0.0174 2.53<br />
Central American <strong>and</strong><br />
Caribbean<br />
0.114 1187 3740 1469 3.454 16.040 0.449 0.0230 3.11<br />
South American 0.417 2978 9378 3686 12.210 35.170 1.478 0.0971 7.25<br />
Western <strong>and</strong> Central<br />
Africa<br />
0.0001 0.70170 2.21200 0.86870 0.00169 0.00938 0.00013 0.00001 0.00160<br />
Table D6 – <strong>2002</strong> Regional fuel, emissions <strong>and</strong> distance flown <strong>for</strong> departures from South<br />
American region<br />
QINETIQ/04/01113 Page 140
Western <strong>and</strong> Central Africa<br />
Arrival Region<br />
Distance<br />
Flown<br />
Fuel<br />
Used<br />
CO2 H2O CO NOx HC Soot Particles<br />
Produced Produced Produced Produced Produced Produced Produced<br />
nautical miles<br />
-9<br />
x10<br />
(Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10-23<br />
Eastern <strong>and</strong><br />
Southern Africa<br />
0.005 46.46 147 57.51 0.107 0.590 0.010 0.0009 0.11<br />
European <strong>and</strong><br />
North Atlantic<br />
0.047 537.3 1693 665.10 1.360 7.239 0.161 0.0094 1.28<br />
Middle East<br />
North American,<br />
0.002 26.28 83 32.53 0.066 0.351 0.009 0.0006 0.08<br />
Central American<br />
<strong>and</strong> Caribbean<br />
0.005 91.64 289 113.40 0.201 1.370 0.020 0.0017 0.24<br />
South American 0.0001 1.032 3.26 1.28 0.002 0.014 0.00017 0.000018 0.0024<br />
Western <strong>and</strong><br />
Central Africa<br />
0.025 166.3 524 205.90 0.711 2.017 0.095 0.007537 0.55<br />
Table D7 – <strong>2002</strong> Regional fuel, emissions <strong>and</strong> distance flown <strong>for</strong> departures from<br />
Western <strong>and</strong> Central Africa region<br />
QINETIQ/04/01113 Page 141
Initial distribution list<br />
External<br />
QinetiQ<br />
Dr Ing. D Koertzer DG Research-H.3 Aeronautics, EC<br />
Mr T Elliff, EUROCONTROL<br />
Mr R M Gardner, DfT<br />
Prof. D S Lee, Manchester Metropolitan University<br />
Dr C Marizy, Airbus France<br />
Dr J Middel, NLR<br />
Mr M Plohr, DLR Köln<br />
Mr P Newton, DTI<br />
Dr D Raper, Manchester Metropolitan University<br />
Prof. Dr R Sausen, DLR Oberpfaffenhofen<br />
In<strong>for</strong>mation Resources<br />
Mr C J Eyers, QinetiQ<br />
Mr D A Addleton, QinetiQ/DTI<br />
Mr K D Brundish, QinetiQ<br />
Mr A Stapleton, QinetiQ<br />
Mr M Savill, QinetiQ<br />
QINETIQ/04/01113 Page 142
Report documentation page<br />
Originator’s Report Number QinetiQ/04/001113<br />
Originator’s Name <strong>and</strong> Location<br />
Customer Contract Number <strong>and</strong> Period<br />
Covered<br />
Chris Eyers<br />
QinetiQ, Cody Technology Park, Farnborough,<br />
Hampshire, GU14 0LX<br />
G4RD-CT-2000-00382<br />
Customer Sponsor’s Post/Name <strong>and</strong> Location Dr.-Ing Dietrich Knoerzer, EC DG Res<br />
Report Protective Marking <strong>and</strong><br />
any other markings<br />
Date of issue Pagination No. of<br />
references<br />
Unlimited 31 Dec 2004 Cover + 144 60<br />
Report Title<br />
<strong>AERO2k</strong> <strong>Global</strong> <strong>Aviation</strong> <strong>Emissions</strong> <strong>Inventories</strong> <strong>for</strong> <strong>2002</strong> <strong>and</strong> <strong>2025</strong><br />
Translation / Conference details (if translation give <strong>for</strong>eign title / if part of conference then<br />
give conference particulars)<br />
-<br />
Title Protective Marking Unlimited<br />
Authors<br />
Downgrading Statement -<br />
Secondary Release Limitations -<br />
Announcement Limitations -<br />
C Eyers, P Norman, J Middel, M Plohr, S Michot, K<br />
Atkinson, R Christou.<br />
Keywords / Descriptors <strong>Aviation</strong> fuel usage <strong>and</strong> emissions<br />
Abstract<br />
This report describes the work undertaken within the EC 5 th Framework Programme <strong>AERO2k</strong><br />
WP1 to produce an inventory of aviation emissions <strong>for</strong> the year <strong>2002</strong> <strong>and</strong> a <strong>for</strong>ecast <strong>for</strong><br />
<strong>2025</strong>. The report summarises development of the inventory <strong>and</strong> <strong>for</strong>ecast <strong>and</strong> includes<br />
validation of the results. The results from this work are presented as global <strong>and</strong> regional<br />
results in this report whilst gridded emissions data are available on the World Wide Web at<br />
http://www.cate.mmu.ac.uk/aero2k.asp. The gridded data is thereby available <strong>for</strong> use by<br />
atmospheric scientists to assess aviation effects on climate; the global <strong>and</strong> regional results<br />
provide a firm foundation <strong>for</strong> policy <strong>and</strong> scenario work.<br />
Abstract Protective Marking: Unlimited<br />
This <strong>for</strong>m meets DRIC-SPEC 1000 issue 7<br />
QINETIQ/04/01113 Page 143
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