AERO2k Global Aviation Emissions Inventories for 2002 and 2025

CONDITIONS OF SUPPLY 

This document is supplied by QinetiQ for Dr-Ing Dietrich Knoerzer, European 

Commission under Contract No. G4RD-CT-2000-00382 

AERO2k Global Aviation 

Emissions Inventories for 2002 

and 2025 

C J Eyers, P Norman, J Middel, M Plohr, S Michot, K Atkinson, 

R A Christou 

QINETIQ/04/01113 

December 2004 

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Hampshire GU14 0LX, UK with particulars of how and where found. 

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QinetiQ Ltd 

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Copyright © QinetiQ ltd 2004 

QINETIQ/04/01113

Administration page 

Customer Information 

Customer reference number GRD1-2000-25042 

Project title AERO2K 

Customer Organisation European Commission 

Customer contact Dr-Ing Dietrich Knoerzer 

Contract number G4RD-CT-2000-00382 

Milestone number Deliverable D14 

Date due 31 December 2004 

Principal author 

C J Eyers 0044 1252 392269 

QinetiQ, Cody Technology Park, 

Farnborough, Hampshire, GU14 0LX 

Additional authors 

P Norman 



cjeyers@qinetiq.com 

J Middel 0031 20511 3559 

Nationaal Lucht- en 

Rulmtevaartlaboratium, Anthony 

Fokkerweg 2, 1059 CM Amsterdam 

middel@nlr.nl 

M Plohr 0049 2203 601 2115 

Deutsches Zentrum für Luft- und 

Raumfahrt (DLR), Linder Höhe, 51147 Köln 

Martin.Plohr@dlr.de 

S Michot 0033 1 69 88 75 00 

Eurocontrol Expérimental Centre, Centre 

de Bois de Bordes, B.P.15, 91222 Brétigny 

Sur Orge CEDEX, France 

K Atkinson 0044 1252 397283 



katkinson@qinetiq.com 

R A Christou 0044 1252 397642 



rachristou@qinetiq.com 

QINETIQ/04/01113 Page 2

Release Authority 

Name A W Stapleton 

Post Technical Manager Gas Turbine Technologies 

Date of issue December 2004 

Record of changes 

Issue Date Detail of Changes 

0.1 20 Sept 2004 First Draft (QinetiQ/FST/ENP/CR045302) 

1.0 December 2004 First Issue 


Executive Summary 

This report describes the production of a global gridded aviation emissions inventory 

for 2002 and a forecast of emissions for the year 2025. The inventory covers both civil 

and military aviation and was developed under the EC FP5 AERO2k project. 

The civil aviation gridded data for 2002 and 2025 consist of fuel-used, NOx, H2O, CO2, 

CO, hydrocarbon, and particulate (mass and number) emissions plus distance flown 

in each grid cell. There are about 3 million cells on a 1 deg by 1 deg by 500ft altitude 

grid, with one grid for each month of 2002. In addition, 4 6-hourly grids show the 

diurnal variation of flights through a 24-hour period. 

The military aviation gridded data consist of fuel-used, NOx, H2O, CO2, CO and 

hydrocarbon plus distance flown in each grid cell on a 1 deg by 1 deg by 1000ft 

altitude grid. 

In addition, this report presents total fuel and emissions figures for global and 

regional civil and for global military aviation fuel and emissions for the 2002 

inventory year and the 2025 forecast year. These 2025 results are compared with 

results from other inventories and scenario projections. 

Global inventories of aircraft fuel usage and emissions are required for the 

quantification of environmental effects of aviation. For the key climate impact 

assessment models, the input data for individual emissions such as nitrogen oxides 

(NOx = NO2 + NO) need to be placed on a global grid. The fundamental requirements 

of an emission inventory are a description of the ‘activity’ and a parameterisation of 

the emissions. In the case of an aircraft emissions inventory, the routes and the 

frequency of the specific aircraft that fly those routes need to be known, in terms of 

the ‘activity’ statistics. 

In order to meet this requirement, this EC 5th Framework Programme project 

‘AERO2k’ has developed a new and improved global inventory of aviation fuel usage 

and emissions, updating and extending the work of previous inventories that are now 

approximately ten years out of date. Moreover, additional parameters (e.g. nonvolatile 

particle emissions and distance travelled/grid cell) are now needed for the 

climate modelling community in addition to the previously provided gas phase 

species of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HCs) and NOx. 

These new parameters have been added to the civil aviation inventory. 

To provide new aviation emissions data on a global gridded basis, AERO2k has taken 

the best available civil and military flight information for the year 2002. For civil 

aviation, this includes radar tracked flight data from the whole of North America and 

Europe showing actual latitude, longitude and altitude along the flight path. Routing 

information is used to place timetabled flights from the rest of the world onto a 

global grid. Using 40 representative aircraft, fuel used for each flight is calculated 

using performance data from the PIANO aircraft performance tool. Employing the 

latest publicly available information on emissions factors, emissions are calculated 

based on aircraft height, weight and speed, throughout the flight. New information 

on particulate emissions has been added to provide a first gridded estimate of 

particulate emissions from civil aviation. Calculated emissions from all flights are 

then allocated into one of over 3 million individual cells on a global 3D grid, 

representing the latitude, longitude and altitude of the flight segment. To assist with 

contrail and cirrus impact assessment, the distance flown in each cell is also recorded. 

Extensive validation activities have been performed to ensure the integrity of the 

data processing and output. Key validation activities are reported in this report. 


Civil 

Aviation 

Military 


Gridded results have been published on the World Wide Web at 

http://www.cate.mmu.ac.uk/aero2k.asp. Analysis of the data has also been carried 

out to produce the global and regional results contained in this report. 

Key headline results for 2002 are: 

Distance 

Flown 

Fuel 

Used 

CO 2 

Produced 

H 2O 

Produced 

CO 

Produced 

NO x 

Produced 

HC 

Produced 

(10 3 million 

n. miles) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg) 

Soot 

Produced 

QINETIQ/04/01113 Page 6 

Particles 

Produced 

17.9 156 492 193 0.507 2.06 0.063 0.0039 4.03 x 1025 

n/a 19.5 61 24.1 0.647 0.178 0.066 n/a n/a 

Total n/a 176 553 217 1.15 2.24 0.129 n/a n/a 

For civil aviation, the fuel-used and NOx results are in line with results from previous 

inventory work in that annual fuel-used and NOx continue to increase despite 

efficiency improvements in newer aircraft. The complex CO2/NOx trade-off related to 

engine pressure ratio is evidenced by the continuing increase in annual NOx 

emissions and substantially unchanging value of NOx emitted per kg of fuel-used (EI 

NOx). Conversely, the benefits of improving combustion efficiency in recent aircraft 

are now seen in a substantial reduction in annual CO and HC emissions and even 

greater reductions in fleet EI CO and HC for civil aviation. 

This reduction in CO and HC emissions from civil aviation can be contrasted with the 

estimated equivalent emissions from military aviation. Unlike previous inventories, 

the AERO2k military inventory has attempted to take full account of reheat operation 

with the outcome that estimates of CO, and to a lesser extent HC emissions from 

military aviation are increased compared with those earlier inventories. Whilst the 

AERO2k figures for military CO and HC should be regarded as maxima because of the 

difficulty in obtaining accurate reheat usage data, the implication is that military 

aviation is now a major source of these particular aviation emissions. This contrasts 

with earlier inventory results. 

To generate a forecast for 2025, a scenario has been developed within AERO2k in 

which demand growth and technology improvements are based on estimates by 

Airbus and UK DTI. These estimates produce regional factors, globally averaging a 

multiplication of capacity by 2.6, Fuel used by 2.1 and NOx emitted by 1.6 times over 

the 23 years from 2002 to 2025. This represents an average to high growth scenario 

with particularly successful embodiment of NOx reduction technology in new aircraft 

over the period. 

Using the regional demand and technology factors generated by this scenario, the 

2002 flight dataset was processed to forecast gridded data for 2025. This gridded 

data is published alongside the 2002 data at http://www.cate.mmu.ac.uk/aero2k.asp.


Distance 

Flown 

(10 3 million 

n. miles) 

Fuel Used CO2 

Produced 

H2O 

Produced 

CO 

Produced 

NOx 

Produced 

HC 

Produced 

(Tg) (Tg) (Tg) (Tg) (Tg) (Tg) (Tg) 

Soot 

Produced 

Particles 

Produced 

2002 17.9 156 492 193 .507 2.06 .063 .0039 4.03 X 10 25 

2025 36.1 327 1029 404 1.15 3.308 .1447 .0087 8.54 x 10 25 

For 2025, the scenario confirms the challenge faced by civil aviation in mitigating the mass 

of emissions resulting from meeting the increased passenger and freight demand. Despite 

assumptions of increased average aircraft size and continuing success in introducing fuel 

saving and emissions reduction technology, fleet rollover timescales and the demand 

growth rate itself are still leading to significant increases in global emissions. Over the 23 

year period from 2002 to 2025, satisfying a demand increase of 2.6 times results in only an 

approximate doubling of distance flown as aircraft are assumed to be larger and to have 

higher load factors. Techncial improvements and the increased average aircraft size result 

in fuel consumed and hence of CO2 and H2O emissions being also approximately double the 

2002 figure. With ongoing regulation and technical success, there is an increase in NOx by 

the lower factor of 1.6 times – but still an increase. The scenario also suggests an increase 

in CO, HC and particulate emissions as significant technology improvements run out in this 

area. The redistribution of these emissions around the globe as a result of the regionally 

differential growth rates is contained in the global gridded data. 

In conclusion, AERO2k provides a significant update of the quantity and location of 

emissions from global aviation. The use of radar tracked data for Europe and North America 

has considerably enhanced the knowledge of the actual global position (latitude, longitude 

and altitude) at which these emissions actually occur. Increased numbers of representative 

aircraft compared to previous inventories have improved the accuracy of the estimation, as 

has the updating of emissions parameters based on latest available research. The provision 

of particulate number estimates and distance flown per grid cell are firsts for global 

inventories. These new data give atmospheric scientists the opportunity to evaluate the 

likely impact of aviation on climate through aviation-induced contrails and cirrus clouds. 

Combined with the improved and updated gridded data for 2002 on the gaseous emissions, 

the overall AERO2k 2002 gridded data provide a major new data source for aviation climate 

impact assessment. 

Having carried out this work, the AERO2k data-integration tool represents a considerable 

investment and, at the same time a considerable opportunity. Beyond the current AERO2k 

project, the tool has significant potential for further use to provide emissions data for a 

range of different years either as estimations based on actual flight data or as forecasts for 

a range of future years and scenarios. Data can be broken down into regional and national 

inventories and AERO2k itself can be integrated into economic or noise models to provide 

global insight into the sustainability of aviation. Hence, the AERO2k model has the 

potential to become a major policy analysis tool for evaluating global, regional national and 

major airport emissions from actual flights and from future aviation scenarios. The vast 

majority of the work required to produce such a tool has already been completed within 

this AERO2k project. 


List of contents 

1 Introduction 10 

2 Description of the Inventory 11 

2.1 2002 Civil Aviation Inventory 11 

2.1.1 Civil Air Traffic Movements Database 11 

2.1.2 Aircraft Representation, Profiling and Fuel Prediction 21 

2.1.3 Emissions Parameterisation for Civil Aircraft 31 

2.1.4 Data Integration and Calculation for Civil Aviation 39 

2.2 2002 Emissions from Military Flights 41 

2.2.1 Approach 41 

2.2.2 Air traffic and emissions forecasting. 42 

2.2.3 Inventory of military aircraft 43 

2.2.4 Deployment and Utilisation of Military Aircraft 43 

2.2.5 Military aircraft types and reference aircraft 44 

2.2.6 Mission types and reference mission types for military flights 45 

2.2.7 Military fuel consumption and emissions profile allocation in airspace 46 

2.3 Forecast for 2025 47 

2.3.1 Aircraft additions and retirements 48 

2.3.2 Forecasting: Capacity - fuel - emissions 49 

2.3.3 Regional growth factors 50 

2.3.4 Freighter capacity 50 

2.3.5 Fuel consumption and trends 51 

2.3.6 Stringency and EINOx 52 

2.3.7 Modelling stringency 52 

2.3.8 EINOx – effect of new aircraft on fleet 54 

2.3.9 Calculation of fuel used and emissions for 2025 55 

2.4 Validation, Uncertainty and Sensitivity Analysis 57 

2.4.1 Validation of the 2002 Civil Aviation Inventory 57 

2.4.2 Validation of 2002 Military Aviation Emissions 77 

2.4.3 Validation of 2025 Aviation Emissions 77 

2.4.4 Uncertainties 78 

2.4.5 Sensitivities 85 

3 Results 89 

3.1 Results for 2002 Civil and Military Aviation 89 

3.1.1 Gridded data for 2002 89 

3.1.2 Other Results - 2002 90 

3.2 Results for 2025 Civil and Military Aviation 107 

3.2.1 Gridded Data for 2025 107 

3.2.2 Other Results - 2025 108 


4 Conclusions 119 

5 Recommendations for Further Work 122 

6 References 124 

7 Acronyms and abbreviations 127 

A Appendix A 129 

A.1 Annual deviation of temperature from ISA standard (Ulbrich 2004) 129 

B Appendix B 130 

B.1 Definition of Geographic Regions 130 

B.1.1 Asia and Pacific 130 

B.1.2 Eastern and Southern Africa 130 

B.1.3 European and North Atlantic 131 

B.1.4 Middle East 131 

B.1.5 North American, Central American and Caribbean 131 

B.1.6 South American 132 

B.1.7 Western and Central Africa 132 

C Appendix C 133 

C.1 2002 fuel consumed, emissions and distance flown by altitude 133 

D Appendix D 138 

D.1 Annual Values of Distance Flown, Fuel Consumed and Emissions for 

Civil Aviation Flights between Regions 138 

Initial distribution list 142 


1 Introduction 

Aviation has grown dramatically, both within the EU and globally. Worldwide air traffic is 

expected to continue to grow at rates of 3-5% per year [IPCC, 1999]. Within Europe, air 

traffic has grown by more than 50% over the last decade and Europe now has 

approximately 8.5 million flights per year and up to 28,000 flights on the busiest days. 

EUROCONTROL has predicted that today’s traffic will have doubled by 2020 [Eurocontrol, 

2004]. 

These levels of growth will mean considerable increases in aviation traffic and emissions 

over the next 20 years. Whilst aviation brings considerable economic benefits, this growth 

is also associated with increased environmental pressures and consequently a growing 

need to improve the environmental performance of the industry. 

Aviation has considerable environmental impacts both at a local airport level and at a 

regional and global level. Local atmospheric issues are related to airport contributions to 

local air quality and the potential for health impacts of residential populations in 

surrounding areas. At a global level atmospheric impacts are entered on the potential for 

aviation emissions to affect climate. 

Aviation emissions are increasingly recognised as a significant contributor to global climate 

impacts through radiative forcing or global warming. Models of radiative forcing associated 

with aviation emissions suggest that aviation currently contributes approximately 3.5% of 

the total anthropogenic forcing and this may increase to between 3-7% by 2050 

[IPCC1999]. Contributions to radiative forcing due to aviation could be considerably greater 

than this if emissions from other sources are reduced. For example, in the UK some 

forecasts suggest that by 2030 CO2 emissions from aviation in the UK will amount to 16-18 

million tonnes of carbon. Depending on the levels of reductions in greenhouse emissions 

from other sectors, this could amount to about a quarter of the UK’s total contribution to 

global warming by that date [DFT, 2003]. However, the current state of knowledge of 

aviation impacts on radiative forcing is severely limited and further research tools, such as 

AERO2k, are required in order to reduce the considerable uncertainties in these calculations. 

The EC 5th Framework Programme AERO2k has developed a new and improved global 

inventory of aviation fuel usage and emissions, updating and extending the work of 

previous inventories, most of which are now approximately ten years out of date. 

Moreover, additional parameters (e.g. particle emissions and km travelled/grid cell) are 

now needed for the climate modelling community in addition to the previously provided 

gas phase species of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HCs) and 

oxides of nitrogen (NOx). 

This report presents the methodology used to prepare the 2002 inventory, together with a 

commentary on key assumptions and validation. The report also describes the forecasting 

assumptions made to generate the data for 2025. Finally the report also contains selected 

output data in terms of global and regional figures for emissions from civil and military 

aviation in 2002 and 2025. 

This report fulfils Deliverable D14 of the AERO2k project: AERO2k Final Report. 


2 Description of the Inventory 

The AERO2k global inventory provides gridded output of aviation emissions. It covers both 

military and civil aviation, the grids for civil and military emissions being calculated and 

presented separately. The gridded data can be presented in many ways. For the EC AERO2k 

fifth framework project, the following gridded data have been generated and made 

available on the World-Wide Web at http://www.cate.mmu.ac.uk/aero2k.asp: 

• Civil aviation 

o Fuel consumed, emissions and distance flown for each of the 12 months in 2002 

o Fuel consumed, emissions and distance flown for each of the 12 months in 2025 

o Six-hourly tables showing global diurnal variation in emissions 

• Military aviation 

o Annual fuel consumed and emissions 

The remainder of this Section 2 describes the key features and methods that make up the 

2002 and 2025 Inventories used to generate the gridded data. 

2.1 2002 Civil Aviation Inventory 

This section describes the methods used to calculate emissions from global civil aviation in 

2002. The emissions calculated are: 

• Carbon dioxide (CO2) 

• Water (vapour) (H2O) 

• Oxides of nitrogen (NOx) 

• Unburnt hydrocarbons (HC) 

• Carbon Monoxide (CO) 

• Particulate (soot) mass 

• Particulate number 

In addition, data for fuel used and distance flown per grid cell are also calculated. 

2.1.1 Civil Air Traffic Movements Database 

The 2002 air traffic movements database consists of 4-D flight trajectories (latitude, 

longitude, altitude and time) enabling calculation of fuel consumption and emissions. The 

collection of data for 2002 was spread over six representative periods of one week each for 

North America and Europe, the six weeks chosen to take account of diurnal, weekly and 

seasonal variation in air traffic. Data for the rest of the world were extracted from the Back 

Aviation commercial database [Back, 2002] and added to the chosen representative weeks. 

The collection of data required the collaboration of aviation authorities in order to gather as 

many measured flight trajectory data as possible. Schedule data from the Back Aviation 

database does not include any flight trajectory information and it was necessary to 

complete this data with flight route and aircraft performance information when available. 

The various formats, origin and sheer-size of data involved necessitated the development 

of a toolset allowing the automatic importation of the data, their filtering, their 

standardization, their analysis, their merging and the storage of the final flight movement 

inventory. Analytical studies for improving data quality and creating trajectories for 

schedule data were carried out. For the days of the year for which no data were collected, 

flight data were derived from inventories realized for the data collection periods while 

trends were obtained from Back Aviation (Back 2002) scheduled flights database. 

An AERO2k prototype flight movement tool was developed, initially using a sample data set 

in order to develop a procedure that would import AMOC and ETMS flight data in a 


standardised Access database. This procedure developed, it was then possible to import the 

delivered format, to extract the portion of data needed, to perform the required conversion 

or process and to import the data in the desired format. The importation of air traffic data 

from various sources required the harmonisation of the information. For this, databases 

linking the data between sources were developed, namely two databases on airports and 

airlines and an application for converting local time to GMT. The relationship between the 

different applications required for the import procedure is shown in Figure 1 

Imported flight tables 

Source 

application 

output files 

CallSign 

EventTime 

DepartureAirport 

Airlines Airports 

AirlineCode 

Aero2k: 

Generate unique keys for traffic flights 

Convert all event times to standard time 

(GMT) 

Compute FLUID field 

Split flights as midnight 

LocationID 

Global Time 

Zone Converter 


AirportCode 

GMTEventTim 

Figure 1: Relationships between the different applications required for importing data to 

AERO2k flight movement tool. 

Based on the overall observations, a prototype flight movement tool was developed and to 

facilitate the use and understanding of AERO2k flight movement tool, a graphical user 

interface (GUI) was created. An overview of the tool is shown in Figure 2. The tool should 

allow the import/conversion of the flight movement data i.e. AMOC and ETMS into a 

standard format. Flight movement data from the different sources would then be merged 

into a single table. This table of flights would be completed with schedule data from other 

parts of the world (obtained from Back Aviation). Schedule data would require time 

conversion into GMT and the generation of a trajectory. Once all data are merged, an 

inventory for the selected day where data had been collected (representative day) would be 

created.

AMOC ETMS 

Data converter 

Standard format 

Merge table 

Representative day 

generator 

Schedule Database 

Time converter 

Text file converter for 

exportation 

Figure 2: AERO2k flight movement tool overview 

Great Circle 

Generator 

Virtual day 

generator 

Airport table 

Airline table 

Fleet table 

Flight trajectory data either in AMOC or ETMS are provided as latitude and longitude values. 

In order to visualize these values, a GIS application was developed in VBA (Visual Basic for 

applications). This application can directly read data from an Access database. It converts 

the flight legs latitude and longitude into trajectories. This application was used for: 

GIS 

• Assessing the coverage of AMOC and ETMS data 

• Radar tracks are dispersed while flight plan tracks correspond exactly to defined 

flights routes 

• Selecting flights and checking the data quality 

• Checking the merge of flight trajectories. 

Output from this tool is illustrated in Figure 3 and Figure 4. 


Figure 3: Global view illustrating the difference between US radar data (ETMS in blue) and 

European flight plan data (AMOC in red) (4-5-6/10/2000). 

Figure 4: Detailed view for Europe illustrating the difference between US radar data (ETMS in 

blue) and European flight plan data (AMOC in red) (4-5-6/10/2000). 

The realisation of a world flight movement inventory implies the use of flight data from 

various sources. This means that flights may be duplicated and trajectories may be partially 

present in a source. Figure 5 illustrates the same flight present in ETMS and AMOC showing 

an incomplete European part for the ETMS flight and an incomplete American part for the 

AMOC flight. For this reason, a method was developed for identifying such flights and 

selecting a single trajectory or merge trajectories in order to cover the longest trajectory 

possible. After merging both flights, a complete trajectory is obtained Figure 6. The 

procedure for merging trajectories is based on the assessment of the trajectories and the 

attribution of a zone indicator from which the selection of the trajectory is derived. 


ETMS AMOC 

Figure 5: Same flight identified in both ETMS and AMOC sources. 

Figure 6: Previous flight (Figure 5) after merging of both sources. 

ETMS 

Following initial development in MS Access, the tool was migrated to an Oracle 9i database. 

Within the database, the mix of airport codes in ETMS and AMOC data required further 

work on development of the airport table. AMOC data mainly used a 4-letter code; ETMS 

data used mainly three-letter code (IATA or FAA codes) for American airports and four-letter 

code for Europe and the rest of the world. It appears that some three-letter codes referred 

to two different airports. For example, BUY can refer to Burlington airport in North Carolina 

in the USA (identified as KBUY by ICAO) but also to Bunbury airport in Western Australia 

(identified as YBUN by ICAO). This type of problem did not allow the production of a one-toone 

static table listing IATA and ICAO airport codes. Two airport tables were then created, 

one used for AMOC and the rest of the world, another one for ETMS data. The table created 

for ETMS data considered predominantly codes of airports situated in the USA. 

Both airport tables were completed with data obtained from the Volpe Center and other 

trusted sources. The coordinates of the airports were checked using DAFIF (Digital 

Aeronautical Flight Information File) data (Figure 7). 


AMOC

Figure 7: Illustration of the location of all airports listed in the table (colours attributed 

according to the time zone). 

Part of the objective of AERO2k was to construct a global aircraft movement database that 

represented as closely as possible the actual civil movements for 2002. In order to be 

representative of yearly global emission variations, it had to account for the seasonal, 

weekly and diurnal variation in air traffic. This objective was achieved by selecting periods 

of data collection for the world and by extending these data in creating aircraft movement 

inventory for days where no data were collected. 

The choice of the data to collect was influenced by the availability of ATC data while the 

choice of the length of collection periods was influenced by the logistics of merging such 

data into a comprehensive annual data set. It was decided that the basis year for collecting 

the data would be 2002 (rather than 2001) in order to reduce the risk of not getting data 

not kept by ATC authorities. This risk did not concern North America and Europe but could 

have been true in Africa, Asia and South America. 

2002 was also affected by the application of RVSM (Reduced Vertical Separation Minimum) 

in Europe i.e. the decrease of the vertical separation between aircrafts in cruise from 2000 ft 

to 1000 ft. 2002 was thus considered as more representative of the new rules in aviation in 

Europe than 2001. 

The choice of the data collection period was based on data available for the ECAC area and 

confirmed by data found for the US air traffic. An analysis of air traffic showed that these 

two areas represented over 70% of the air traffic in the world (Figure 8). For both areas, the 

monthly traffic trends were examined in order to identify the key months and the suitable 

weeks. 


Latin America 

& Caribbean 

9% 

North America 

43% 

Europe 

29% 

Asia and 

Pacific 

15% 

Africa 

2% 

Middle East 

2% 

Figure 8: Total scheduled aircraft departures estimated for the year 2000 by ICAO region 

Key months for collecting data were selected based on data published by the CFMU for the 

ECAC area and available within Eurocontrol. A qualitative assessment was done based on 

the daily traffic recorded by the CFMU and shown in Figure 9. 

Figure 9: Average monthly traffic in the CFMU area from 1997 to 2001. [ CFMU, 2002]. 

The analysis of Figure 9 shows that the year can be divided into four parts according to the 

seasons. During the winter period, a constant increase of the traffic can be noticed from 

January to March. At spring, the increase started in winter carries on from April to June with 

a slightly steeper slope. In summer, only small variations are observed between July and 

August while September appears to be the busiest month of the year. In autumn a steady 

decrease is observed from October to December. This analysis led to the selection of 

February, April, June, September, October and December for collecting data. February and 

April were chosen as intervals of time representing the increase observed during winter and 

spring. June and September announce the beginning and end of the summer season with a 

higher number of flights than the annual average for 2001 (Table 1). The events of the 11th 


of September modified the market for the end of the year. However, based on the five-year 

trend, October initiates the decrease of the number of flights and December the end with 

the least flights in the year. 

Daily traffic in the 

ECAC area (number of 

flights) 

1997 1998 1999 2000 2001 Average 1997-2001 

19662 20685 22062 23068 22995 21694 

Table 1: Yearly traffic averages for the ECAC area from 1997 to 2001 [CFMU, 2002] 

To ensure the six months retained were properly chosen, the proportion of annual traffic 

represented by these six months was calculated. The analysis showed that these six 

months accounted for roughly 49% of 2000 and 2001 traffic in the ECAC area (Table 2). 

Month Monthly traffic in 2000 Monthly traffic in 2001 

Number of 

flights % 

Number of 

flights % 

January 623804 7.39 654635 7.8 

February 623275 7.38 612330 7.3 

March 690681 8.18 699459 8.33 

April 676452 8.01 691183 8.24 

May 751662 8.9 751421 8.95 

June 743599 8.81 753254 8.97 

July 761903 9.02 772110 9.2 

August 766299 9.08 776145 9.25 

September 761251 9.02 753600 8.98 

October 746574 8.84 732617 8.73 

November 672274 7.96 627108 7.47 

December 625007 7.4 569356 6.78 

Total 8442781 100 8393218 100 

6 months 

selected 49 49 

Table 2: Monthly traffic in 2000 and 2001 for the ECAC area [CFMU, 2002]. 

The evolution of the traffic for all weeks between 1999 and 2001 is shown in Figure 10. 

From this data, no trend could be drawn from one year to the other. A strong variation can 

occur from week to week within a month. Therefore, the selection of a period within the 

selected month was done arbitrarily. The second week of the month was chosen for 

February (week 6), April (week 15), June (week 24), September (week 37) and October (week 

41). The first week of December (week 49) was retained for project deadline reasons as later 

data would not be available until after the Christmas period. 


Figure 10: Evolution of the traffic in the CFMU area. [CFMU, 2002]. 

Finding equivalent sources to European data in the USA revealed difficulties. Data 

expressed in terms of number of flights were published by the US Department of 

Transportation but mentioned only traffic between the US and the rest of the world. Other 

sources, such as the Air Transport Association, publishes data expressed in Revenue 

Passenger Miles, Revenue Passenger Enplanements, Available Seat Miles or Passenger Load 

Factor but not in terms of number of flights. The analysis of monthly traffic was then based 

on data published by the US Department of Transportation for 1999 and 2000. These data 

include all traffic arriving at U.S. airports and departing from U.S airports on non-stop 

commercial international flights. Figure 11 illustrates this traffic data. 

120000 

100000 

80000 

60000 

40000 

20000 

0 

Jan 

Fev 

March 

April 

May 

June 

July 


Aug 

Sept 

Oct 

Nov 

Dec 

2000 

1999 

Figure 11: Non-stop travel between the US and the Rest of the World (number of departures) 

[US Department of Transportation, 2001]

It can be seen that both years show the same trend. February is the lowest season and 

December traffic is busier than in January. Traffic in March, April, May and June is quite 

constant. July and August are the months showing the highest traffic. A decrease is 

observed in September after which the traffic remains fairly constant until December. 

Combining this information with the selection of the months for the ECAC area (i.e. 

February, April, June, September, October and December) led to confirmation of the months 

already selected on the basis of European ECAC data. 

Because obtaining data from parts of the world may be difficult, it was initially decided not 

to overload data suppliers with overwhelming requests, but to limit our demand to a day of 

data collected six times a year. In contrast, Europe and the USA, from whom it was easier to 

get data, would supply data for a week (Monday to Sunday). Despite various requests to 

aviation authorities around the world, little useful data were forthcoming. It was therefore 

decided to complete the data with the Back Aviation database. Table 3 summarises the 

days of collection. The dates are defined according to UTC time. 

2002 February April June September October December 

Monday 4 8 10 9 7 2 

Tuesday 5 9 11 10 8 3 

Wednesday 6 10 12 11 9 4 

Thursday 7 11 13 12 10 5 

Friday 8 12 14 13 11 6 

Saturday 9 13 15 14 12 7 

Sunday 10 14 16 15 13 8 

Table 3: Timetable for collecting data 

The importation of the flight data and the merge of the trajectories produce flight 

movement inventories that partially represents the annual air traffic in the world. To have a 

better understanding of the world air traffic for the entire base year requires the estimation 

of air traffic for all the other days of the year. To achieve this, the annual flight movement 

inventory was developed from the data collected six times a year at different seasons. 

These six periods of one week correspond to what was called representative weeks made of 

seven representative days. Any day for which no data were collected is called a virtual day. 

To extend the data collected to a full year, a method was developed allowing the creation 

of an inventory for any virtual day. Virtual days inventories are generated from 

representative days inventories on which flight movement trends are applied. Trends are 

derived from the Back Aviation database, which includes information on the distribution of 

flight frequencies for scheduled commercial operations. Back Aviation database includes 

only flights scheduled by airlines and so counts less flight than the inventory made for 

AERO2k, which accounts for unscheduled flights. In a year, the number of flights varies 

according to the season, the day in the week and the country. These three criteria should be 

taken into consideration when creating the virtual days. 

From the worldwide collection of data, 42 merged inventories (6 weeks, 7 days per week) 

are created. From these inventories, the number of flights per region and for each 

representative day is calculated. 

For this, the representative days were classified into seasons as shown in Table 4. A virtual 

day chosen as Wednesday 9 th of January 2002 is related to the ratio matching the season 

and the day i.e. Wednesday 6 th of February 2002. The day selected is the closest day with 

the same day of the week. 


2002 Feb 

No. 

days April 

No. 

days June 

No. 

days Sept 

No. 

days Oct 

No. 

days Dec 

Mon 4 13 8 6 10 7 9 13 7 10 2 3 

Tues 5 13 9 6 11 7 10 13 8 9 3 4 

Wed 6 13 10 6 12 7 11 13 9 9 4 4 

Thurs 7 12 11 7 13 7 12 13 10 9 5 4 

Fri 8 13 12 7 14 6 13 14 11 9 6 4 

Sat 9 13 13 7 15 6 14 13 12 10 7 3 

Sun 10 13 14 7 16 6 15 13 13 10 8 3 

Season Winter 90 Spring 46 Spring 46 Summer 92 Autumn 66 Autumn 25 

From 

/ 

to 

21/12 

to 

20/03 

21/03 

to 

5/05 

6/05 

to 

20/06 

21/06 

to 

20/09 

21/09 

to 

25/11. 

Table 4: Listing of the representative days in 2002 and classified in seasons. 

26/11 

To 

20/12. 

The objective of the work described in this section was to deliver a world civil flight 

movement inventory. This was done by generating six inventories corresponding to the 

weeks of data collection. As an example Figure 12 shows a representation in GIS of all 

flights in the world departing on the 8/02/2002. 

Figure 12: Map of all flights over the world departing on the 8/02/2002 

2.1.2 Aircraft Representation, Profiling and Fuel Prediction 

Having developed a database of aircraft movements, it was necessary to represent each 

movement with a representative aircraft (and engine) and to predict the fuel used during 

the flight. This process is described here. 

Civil Aircraft Representation 

The world aircraft fleet consists of numerous aircraft types, variants and sub variants. Each 

airline specifies its own aircraft as well as selecting engine type. It was therefore necessary 

to make a simplification of the global fleet of aircraft in order that the modelling was kept 

within manageable proportions whilst retaining the accuracy required for the global 

inventory. This was achieved through selecting a small number of aircraft that, combined, 


No. 

days

are broadly representative of the entire world fleet in terms of performance, fuel use and 

emissions production. 

Forty representative aircraft were selected by this process. These aircraft include turboprop, 

bizjet, regional jet and large civil jet transport categories of aircraft. These 40 types are 

representative of over 300 different aircraft types or variants within the world’s fleet. 

The aircraft types considered within the AERO2k project are as follows: 

• Large jets (LJ), comprising the bulk of the civil subsonic jet transport fleet, with 

greater than 100 seats; 

• Regional jets (RJ), turbofan or turbojet powered short-range aircraft with up to 100 

seats; 

• Turboprops (TP), aircraft powered by two or more turboprop engines; 

• Bizjets (BJ), small turbofan or turbojet aircraft with less than about 20 seats. 

The AERO2k movement data are based on instrument flight rules (IFR 1 ) rated aircraft, which 

are required to file flight plans before departure. Visual flight rules (VFR 2 ) movements are 

excluded, as data are not available. 

Visual flight rules movements can largely be classed as general aviation (GA) or helicopter 

movements, for which emissions data are not readily available. Thus, all piston-engined 

aircraft, single-engined turboprops (i.e. the two predominant categories which comprise GA 

aircraft types), and helicopters are excluded from the study. The proportion of VFR flights of 

the total global number of movements is relatively small and therefore the percentage of 

total fuel burn is also small: estimates by Boeing for 1992 were that approximately 2% of 

total global aviation fuel burn was attributable to GA 3 [Mortlock & Van Alstyne, 1998]. 

Certain aircraft types are operated by both civil and military sectors so that it was necessary 

to include such aircraft, e.g. civil operations of Lockheed C130 Hercules military 

transport/L100 civil transport. 

In order to provide guidance for the level of detail and effort that should be applied to the 

principal aircraft types to be covered in the inventory (i.e. LJ, RJ, TP and BJ), a simple analysis 

was performed on a sample of flight movement data. The relative proportions of the 

numbers of flights by each of the four classes are given in Table 5. 

1 IFR: Instrument Flight Rules. There are two different categories of flight rules under which a 

pilot or aircraft can operate. These rules are determined by the weather and the way the 

pilots navigate the aircraft. Under instrument flight rules, pilots rely on information displayed 

on instruments or equipment within the aircraft for navigation. IFR flights are required to file 

flight plans with air traffic control before departure. 

2 VFR: Visual Flight Rules. Under VFR, pilots navigate using visual information from looking 

outside the aircraft, following roads, rivers, or other landmarks. VFR flights are not required to 

file flight plans. 

3 Boeing‘s estimate of GA fuel use as a percentage of total aviation fuel use is approximately 

3% [Mortlock & Van Alstyne, 1998], but their definition of GA included business jet aircraft. 

Bizjets accounted for one third of GA fuel use in the study, therefore 2% of total fuel use was 

attributable to non-bizjet GA. 


Aircraft type % of movements % of distance travelled 

LJ 68.8 87.8 

RJ 10.6 5.4 

TP 19 5.6 

BJ 1.7 1.2 

total movements = 

48,988 

total distance = 

34,711,952 n.miles 

Table 5: Proportions of movements and distances in nautical miles (n.miles) travelled by 

aircraft type from EUROCONTROL sample dataset 

The percentage of distance travelled can also be used as a very rough guide to the relative 

proportions of total fuel used and pollutant emissions produced by each of the four classes. 

Therefore, the large jet transport (LJ) category was the primary focus and resulted in a 

larger number of representative types than the other categories. 

The selection of representative aircraft types was made in three main steps: 

• Grouping aircraft by seat capacity, engine technology, maximum take-off weight 

(MTOW) and configuration 

• Determination of the numbers of aircraft within a particular seat 

capacity/technology category 

• Availability of suitable performance model. 

It was important to ensure that the selection of representative aircraft types was 

compatible with the forecast methodology for 2025, which was to be based upon seat 

capacity ‘bands’ and technology levels. The classification of ‘technology level’ can be used 

to identify older generation aircraft types that are still in operation today but which will be 

removed from service in developed countries by the forecast year (2025). 

Some examples of the major aircraft types and groupings are presented in Table 6 to Table 

9 for LJ, RJ, TP and BJ classes, respectively. 


No. of seats 

100–124 

125–159 

160–199 

200–249 

250–314 

315–399 

Old technology 

(Chapter 2 noise) 

BAC 1-11 

B737- 100 

B737- 200 

DC9 

Tu134 

B707 

DC8 

IL62 

IL76 

B727 

Tu154 

B747-100 

An124 Ruslan 

Chapter 3 noise, 

CAEP 2 NOx 

MD87 

B737-300 

B737-400 

MD81 

MD82 

MD83 (MD80 

series) 

MD88 

MD90 

A310 

L1011 

DC10 

A300 

L1011 

MD11 

B747-200 

B747-300 


CAEP 4 NOx capable 

B717 

A318 

A319 

B737-500 

B737-600 

A320 

B737-700 

A321 

B757-200 

Tu204 

B737-800 

B737-900 

B757-300 

B767-200 

A330-200 

A340-300 

IL86 

IL96-300 

B767-300 

B777-200 

A330-300 

A340-600 

B747-400 

B767-400 

B777-300 

400–499 B747-400 

500–624 

A380-800 4 

B747-400 

625–799 A380-900 5 

Table 6: Large jet (LJ) aircraft by seat capacity band and noise/NOx emissions technology 

4 Future type 

5 Future type 


No. of seats 

Old technology 

(Chapter 2 noise) 

30 to 50 Yak40 (YK40) 

70 F28 F70 

90 Yak42 (YK42) F100 


CAEP 2 NOx 


CAEP 4 NOx capable 

Embraer 135 (E135) 

Embraer 145 (E145) 

CRJ 200 (CRJ2) 

Avro RJ/RJX/BAe146 

(BA46) 

Table 7: Regional jet (RJ) aircraft by seat capacity band and noise/NOx emissions technology 

(aircraft ICAO type codes are shown in brackets) 

Seat band category 

and configuration 

30 seat twin 

50 seat twin 

70 seat twin 

mid-size quad 

large quad 

Aircraft type (ICAO code) 

Saab 340 (SF34) 

Dash 8-100 (DH8A) 

Dornier 328 (D328) 

ATR 42 (AT43/AT45) 

Saab 2000 (SB20) 

Fokker 50 (F50) 

Dash 8-300 (DH8C) 

ATR 72 (AT72) 

Dash 8-400 (DH8D) 

ATP (ATP) 

Antonov AN-12 (AN12) 

Lockheed L188 (L188) 

Lockheed C-130 Hercules (C130) 

Shorts Belfast (BELF) 

Table 8: Turboprop (TP) aircraft by seat capacity band (Aircraft ICAO type codes are shown in 

brackets) 

Category by size Aircraft type (ICAO code) 

Small 

Learjet 35 (LJ35) 

Citation C550/C551 (C550) 

Medium Falcon 2000 (F2TH) 

Large Gulfstream IV (GLF4) 

Table 9: Bizjet (BJ) aircraft by seat capacity band (aircraft ICAO type codes are shown in 

brackets) 

In the second step, aircraft representative type selection was based on the numbers of each 

type in service from a global fleet database [JP Fleets, 2000]. A representative type was 

selected as the most numerous occurrence within a seat-band/technology category. 

Configuration was also considered, especially for smaller aircraft types, i.e. TP, BJ and RJ for 

which most variation in configuration occurs (e.g. engines on fuselage, high tail etc). 

The performance package selected for fuel usage predictions was PIANO [PIANO, 2002]. 

Availability of a standard model in the PIANO package was therefore also a consideration in 

selection. If, for example, the first choice of aircraft was not available by the above 

selection criteria, the next available within the PIANO suite of models was selected. Where 

there was no suitable selection available, new aircraft models were added to PIANO. The 

representative aircraft types selected are presented in Table 11. 


Selection of representative engines 

Aircraft types are seldom fitted with a single type of engine, as a choice is usually available 

either from within one manufacturer’s ‘family’ of engines or from other engine 

manufacturers. 

Market pressures ensure that engines from different manufacturers intended for the same 

airframe have similar fuel consumption rates. Table 10 presents data for a high take-off 

gross weight (TOGW) Boeing 777-200, which are available with engines of similar 

technology and thrust rating from three manufacturers [Jane’s, 2002]. The maximum 

range of this aircraft with each engine is within one percent of the mean value, indicating 

the similarity of fuel consumption rates of all three engines. Therefore, fuel burn data 

produced for one engine variant using an aircraft performance prediction tool can be 

applied to the same aircraft with a different engine with some degree of confidence. 

Design 

range(n.miles) a 

Deviation 

from mean 

range 

Proportion of 777-200 

fleet fitted with engines 

from this manufacturer b 

GE 7625 + 0.66 % 29.9 % 

Pratt & Whitney 7505 - 0.92 % 31.9 % 

Rolls-Royce 7595 + 0.26 % 38.2 % 

mean range (n.miles) 7575 

Table 10: Comparison of maximum range for high TOGW B777-200 aircraft fitted with 

different engines (Data sources: a Jane’s, 2002; b JP Fleets, 2000) 

However, emissions can vary considerably depending on the engine type. Emissions 

characteristics therefore need to be carefully compared. The only publicly available data 

source to allow such a comparison is Landing and Take-off (LTO) cycle certification data 

contained in the Emissions Databank [ICAO, 1995]. Two approaches may be taken to 

represent emissions of engines equipped on airframes: a ‘generic’ engine may be modelled 

from a variety of engines fitted to a particular airframe; or an engine that is representative 

of one fitted to an airframe may be selected by objective criteria. For the ANCAT/EC2 global 

inventory [Gardner 1998] a representative aircraft type was fitted with a single generic 

engine type, for which fuel and emissions characteristics were weighted by the known 

engine population fitted to those aircraft types. 

Here, an alternative approach was devised in which both the generic and representative 

engine approaches were combined. Firstly, generic emissions characteristics were 

developed from data from the Emissions Databank and these were used as the basis of 

selecting a representative engine. The simple generic approach was not compatible with 

the methods used in AERO2k for predicting emissions, as this was based on detailed engine 

aero-thermodynamic models. Thus, a representative (real) engine type must be selected for 

which emissions data are available. 

Generic emissions characteristics were developed by weighting the emission index for NOx 

(EINOx 6 ) for the number of engines in the fleet’s population, for each of the four thrust 

settings of the LTO cycle. In the next step, the generic emission characteristics were 

compared with real engine data and the closest fit determined by a polynomial leastsquares 

regression. The final choice of engine was not determined solely using this method; 

one additional criterion was used in the selection process. Once all the representative 

aircraft had been assigned an engine using the above method, the selected engines were 

compared for similarity of emissions characteristics. Where close similarities were found, 

for instance within families of engines from one manufacturer, the most representative 

engine was chosen. In this way the number of engines was consolidated. 

6 EINOx: emissions of NOx normalised by fuel flow, in units of grammes of NOx per kg of fuel. 


Representative Aircraft and Engine Listing 

The representative aircraft and engine types are presented in Table 11 below. 

Large jets 

ICAO code Representative aircraft 

No. of 

engines 

Representative 

engine 

Databank 

unique ID 

A306 Airbus A300 600R 2 PW4x62 1PW058 

A310 Airbus A310-300 2 PW4x62 1PW058 

A319 Airbus A319 2 CFM56-5C4 2CM015 

A320 Airbus A320-200 2 CFM56-5C4 2CM015 


A330 Airbus A330-300 2 CF6-80E1A3 5GE085 


A340 (R) 7 

Airbus A340-300 4 D-30KP-2 1AA002 

B703 Boeing B707-320C 4 D-30KP-2 1AA002 

B712 Boeing B717-200 2 BR700-715C1-30 4BR007 

B722 Boeing B727-200A 3 JT8D-15 1PW010 

B732 Boeing B737-200 2 JT8D-15 1PW010 

B734 Boeing B737-400 2 CFM56-3C-1 1CM007 

B737 Boeing B737-600 2 CFM56-7B26 3CM033 

B738 Boeing B737-800 2 CFM56-7B26 3CM033 

B742 Boeing B747-200B 4 JT9D-7R4G2 1PW029 

B744 Boeing B747-400 4 PW4x62 1PW058 

B752 Boeing B757-200 2 PW2040 4PW073 

B763 Boeing B767-300ER 2 PW4x62 1PW058 

B772 Boeing B777-200 2 PW4090 3PW066 

BA11 Rombac 1-11 2 SPEY Mk511 1RR016 

L101 Lockheed L1011 3 JT9D-7J 1PW024 

DC9 Douglas DC 9-34 4 JT8D-15 1PW010 

MD11 McDonnell Douglas MD-11 3 PW4x62 1PW058 

MD80 McDonnell Douglas MD-82/88 2 JT8D-15 1PW010 

MD90 McDonnell Douglas MD-90-30 2 CFM56-5C4 2CM015 

7 Representative type for large four-engine Russian (CIS) types similar to A340, uses PIANO 

A340 model but emissions data for CIS-made engines prevalent on these types. 


Regional jets 


No. of 

engines 


engine 

Databank 

unique ID 

BA46 Avro RJ 85 4 ALF 502L-2 1TL001 

E145 Embraer EMB-145 2 ALF502L2 1TL001 

F100 Fokker F100 2 Tay 650-15 1RR021 

F70 Fokker F70 2 Tay 611-8 1RR019 

YK42 Yakovlev Yak-42M 3 D-36 1ZM001 

Turboprops 

Bizjets 


No. of 

engines 


engine 

Databank 

unique ID 8 

AT72 ATR72 2 PW127 - 

C130 Lockheed Martin L100/C130 4 T56-A-15 - 

F50 Fokker F50 2 PW127 - 

L188 Lockheed L188 Electra 4 501-D13 - 

SF34 Saab 340B 2 GE CT7-9B - 


No. of 

engines 


engine 

Databank 

unique ID 9 

C550 Cessna Citation III 2 PW530A - 

F2TH Dassault Falcon 2000 2 CFE738 1AS002 

F900 Dassault Falcon 900 C 3 TFE731 - 

GLF4 Gulfstream G IV-SP 2 Tay 611-8 1RR019 

Table 11: List of AERO2k representative aircraft and engine types 

The choice of representative aircraft is, by necessity, not an entirely objective process 

requiring some element of ‘expert judgement’, although the methodology set out and used 

defines some of the criteria that are objective in nature. Additionally, there is an element of 

pragmatism dictated by available data/models and necessity for compatibility with other 

exercises requiring data-reduction (e.g. forecasting). This compatibility was the primary 

driver, the seat band and technology categories forming the basis of the initial division of 

aircraft groups. In some cases there is more than one representative type within a 

category, where for instance there are aircraft within the same category with different 

numbers of engines. 

Likewise, the choice of representative engines is a mixture of objective and subjective 

criteria. Included in this process is the development of generic engines, in order to define 

the closest real engine. This process is based upon the ICAO LTO Certification Data in the 

Emissions Databank, which are generated in closely-specified sea-level tests, so do not 

necessarily reflect performance at altitude, where the bulk of emission takes place. 

Accepting the limitations of this 10 , comparisons have been made based on NOx data from 

this source. 

8 

Turboprop engine data for these types are available from FOI Aeronautics Division, FFA, 

Sweden. 

9 

DLR have data available for the engine types not represented in the Emissions Databank. 

10 

Limitations of the LTO data are that it provides emissions data for NOx, CO and HC only, at 

ground level conditions. CO and HC are primarily of concern to local air quality, and emissions 

of these at altitude flight conditions are at very low levels. The smoke data provided in the 

emissions databank are very limited, as smoke (as a smoke number) has to be reported only at 

the worst condition and there is no requirement to report that condition (power setting). 

Thus data for NOx (which has been the pollutant of primary concern for some time at typical 

flight altitudes) will be used to make comparisons of engine emissions characteristics. 


Fuel Profiling and Prediction 

Fuel profiling and prediction takes place within the AERO2k Data Integration Tool (Figure 

19). 

The method for assigning fuel data to the flight profiles in the flights relies on a series of 

data-tables as follows: 

1. Take-off. Using 60.9% of maximum payload, estimate the take-off weight for the 

mission range to be flown. Taxi, take-off and climb out (to 3000ft) data from emissions 

databank and airport-specific departure times-in-mode look-up table. 

2. Climb (>3000ft). Determine initial cruise altitude from the profile data, calculate fuel 

used in climb from climb data tables, re-calculate aircraft mass at top of climb, and 

calculate distance flown. 

3. Cruise. Select appropriate cruise fuel flow data from the cruise data tables, for the 

altitude, Mach number and aircraft mass. Continue to re-calculate distance flown and 

aircraft mass through-out the cruise segment. 

4. Step-climb or mid-cruise descent if appropriate, then repeat Cruise step. 

5. Descent (to 3000ft). Descent fuel from final cruise altitude to 3000ft calculated from 

descent data tables. 

6. Landing. Data from emissions databank and airport-specific arrival times-in-mode lookup 

table. 

The method chosen necessitated the adoption of a number of assumptions for assigning 

fuel data to flight profiles. These assumptions have been split into two categories. 

Background assumptions are described in Section 2.4.4. These are a priori assumptions that 

have been made out of necessity to simplify the modelling process or due to a lack of 

available data or the variability of some features, e.g. the extent to which fuel tankering 

takes place. Many of these assumptions have been made due to unavoidable limitations of 

the flight movement data, for example, the flight movement data details, for each flight, 

the aircraft type, location in space (longitude, latitude, altitude) and time at a number of 

intervals throughout the flight. The limitation of this data is that it does not include the 

aircraft weight (mass) or flight speed, and for some flights the intervals can be spaced too 

far apart to capture precise details about the time or location of direction or altitude 

changes. 

The specific aircraft performance assumptions used are described below. These are 

quantifiable assumptions that must be made as accurately as practicably possible within 

the constraints of computing capability and available data. 

It is noted that each of these sets of assumptions will have an impact on the quantity of 

fuel and emissions recorded in the inventory. Where it has been possible, the effect of these 

assumptions on fuel burn has been investigated by parametric study or from available 

sources of information in the public domain. A decision was taken early in the AERO2k 

project that arbitrary increases to the fuel burn or emissions would not be made to account 

for effects that cannot be accurately modelled. 


Aircraft performance assumptions 

The flight movement data for each flight detail the aircraft type, location in space 

(longitude, latitude, altitude) and time at a number of intervals throughout the flight. The 

variable resolution of this data has an impact on the modelling of the aircraft performance 

and therefore fuel burn during flight. The assumptions required to deal with this are 

described below. 

The Landing and Take-Off (LTO) cycle 

There are several assumptions made which concern the Landing and Take-Off (LTO) cycle, 

which encompasses all operation below 3000ft altitude. Time in mode data are provided 

from research carried out within the AERO2k project, which is used in conjunction with 

emissions data from the Aircraft Engine Exhaust Emissions Databank [QinetiQ, 2004]. The 

LTO data provide average times in mode on an airport-by-airport basis for all airports. An 

implicit assumption is that the fuel and emissions data from the emissions databank, 

which are recorded for use in emissions certification, are sufficiently relevant and 

acceptable for use for inventory work. 

When the fuel and emissions data are allocated onto the global grid, all the data from the 

LTO cycle will be allocated to the grid cell (longitude, latitude) in which the airport sits. No 

account will be made for horizontal distance travelled, so that the first flight point and final 

flight point which cross the 3000ft boundary will be assumed to be immediately above the 

airport. This approach has been adopted for computational reasons and is not expected to 

have any effect on the results. 

The final assumption for LTO operations in AERO2k is that all airports are at sea level. In 

reality this would impact time to climb to cruise altitude and the overall aircraft 

performance. This is not expected to have a significant impact on the overall inventory 

result as the total fuel consumed during all LTO operations is small relative to total aviation 

fuel (

mass of payload carried, on a global average basis the error due to payload variation will be 

small when using the global average figure of 60.9% of maximum payload for all aircraft. 

Reserves 

All commercial aircraft flights must carry a certain quantity of fuel in addition to the fuel 

required to perform the mission, as a contingency in case of diversion to another airport or 

other problems. The fuel required for a mission is calculated based on the distance and 

aircraft mass plus payload, and taking into account other factors such as the weather, 

including the likelihood of having to divert off-course or to another airport due to sudden 

change of weather conditions. On top of this, contingency reserve fuel will be added. 

Carrying reserve fuel is a mandatory requirement, but the exact quantity required to be 

carried is variable country to country, and may vary according to airline policy. The reserve 

quantity is usually a function of mission range or the quantity of mission fuel, and depends 

on whether the flight is international or domestic, or long and short haul. 

In AERO2k, reserves allowances for long haul flights includes an additional 5% of the fuel 

required to complete the mission, plus fuel for a 200nm diversion and a 30 minute low 

altitude hold; whereas for short haul flights the reserves allowances will typically be 5% of 

the fuel required to complete the mission plus fuel for a 100nm diversion and a 45 minute 

low altitude hold. 

Flight speeds 

The flight speed of aircraft can vary depending on altitude, headwinds and airline practice. 

The flight movement contains data for longitude, latitude and time, but low resolution on 

the time data and no data on winds make calculation of flight speed inaccurate. Therefore a 

standard flight speed has been used in the modelling. Airlines typically use a number of 

operating speeds depending on route, type of operation, etc. As it happens, fuel 

consumption rate is relatively insensitive to small changes of speed either side of the speed 

for minimum fuel consumption. This speed minimises fuel burn per mile (or maximises 

specific air range, or SAR) for a given weight and altitude, and is commonly referred to as 

the maximum range cruise (MRC) speed. The MRC speed is not necessarily the optimum 

when other operating variables (e.g. time) are taken into account. It is not possible to model 

ECON cruising speed which does take operating costs into account, as the compromise 

between time and cost is airline-specific. A common compromise which is used in airline 

practice is to fly faster than the MRC speed at a point which gives 99% of the maximum 

SAR. This is commonly also called the long-range cruise (LRC) speed. The speed which gives 

99% of the maximum SAR is used in AERO2k. 

2.1.3 Emissions Parameterisation for Civil Aircraft 

Having allocated representative aircraft and engines and calculated fuel usage for each 

flight segment, emissions are calculated using emission indices. These indices are based on 

publicly available engine data and are described in the following paragraphs. Of particular 

note is the inclusion of an estimation of particulate number and mass within AERO2k. Data 

on gas turbine particulate emissions are not widely available and, in comparison with other 

emissions, there are greater uncertainties surrounding the actual values in flight. 

Correlation algorithms have been generated from recent research in Europe and it is 

believed that this is the first time that such particulate data have been included in a global 

inventory. Together with the new “distance flown” parameter, this information should 

provide a foundation for estimation of climate impact of cirrus and contrails from aircraft. 

CO 2 and H 2O 

Carbon dioxide and water are the main products of the combustion of hydrocarbon fuel 

and are therefore directly coupled to fuel mass flow. 1kg of Jet A-1 with the mean totals 

formula C12H23 produces, completely oxidised, 3156g CO2 and 1237g H2O [Rachner, 1998]. 

In the strict sense, partially or unburned species (CO and HC) have to be subtracted from 

those. The amount of hydrocarbons emitted is less than 1% of the CO2 and H2O emissions 


and their hydrogen/carbon ratio is unknown, therefore they will be neglected. To subtract 

the CO from the CO2 emissions, their different molar mass has to be taken into account: 

NO x 

EICO2 = EICO2,ideal - 44/28 x EICO 

The production of thermal NOX in the combustor of an aircraft engine is coupled with the 

chemical reaction process within the hot flame region of the primary combustion zone and 

the residence time of the reacting species. The dominating reaction with respect to this 

time is the formation of nitric oxide (NO) and atomic nitrogen (N) out of atomic oxygen (O) 

and gaseous nitrogen (N2) as prescribed by the first equation of the extended Zeldovich 

mechanism [Heywood, 1973]. However, such theoretical approaches require knowledge of 

actual engine pressures and temperatures, data which is, for most engines, proprietary: i.e. 

they are not publicly available, 

To avoid this problem, a method can be used based on correlating the EI NOX with fuel flow. 

For this, a reference function of the emission index versus engine fuel flow has to be 

established by measurements under reference inlet conditions. This could for instance be 

done by choosing the four data points from certification measurements for an engine, 

which represent different thrust settings at sea level static (SLS) and international standard 

atmospheric (ISA) conditions. These four data points are published in the ICAO engine 

emissions data bank [ICAO, 1995]. 

Hence if an engine is running at other than ISA SLS conditions all pressures (p3) and 

temperatures (T3) at the combustor inlet plotted against fuel flow (wfuel) will line up to one 

function when pressures, temperatures and fuel flow are corrected to reference engine 

inlet conditions (Tt,1 and Pt,1) by the following equations: 

Equation A 

Equation B 

Equation C 

T 

T 

3 

3 , corr = with 

θt 

p3 

P3 

= with 

, corr 

δ 

t 

θ 

t 

Tt , 1 

= 

288. 

15K 

pt, 1 

δ t = 

101. 

3kPa 

Some idealisation has been made here in assuming, that for a real similarity the 

compressor polytropic efficiency and the combustion efficiency is constant. Another 

approximation using an EI NOX correlation formula has to be made in avoiding T3 to appear 

in the exponent. 

Therefore the following simplification has been used: 

In combination with Equation A and Equation B the following relationship can be 

established: 


Since at ISA and SLS engine inlet conditions δt = θt = 1 is given, the EI NOX then represents 

the corrected i.e. the reference value. To gain the exponents a and b it has been considered 

that from theory a = 0.5 would be the value of choice, but in reality observed from 

measurements for the CF6 engine family show that an exponent of a = 0.4 is more 

appropriate [Bahr, 1991]. 

For a pressure exponent in the range of 0.4 to 0.5, a temperature exponent of about b = 3 

would be well in line with theoretical methods. Thus the exponents of a first choice were: a 

= 0.4 and b = 3. 

An application of this simplified method of EI NOX versus fuel flow correlation is shown in 

Figure 13. The analysis of different engine inlet operating conditions reveals the collapse of 

values by correcting with respect to the ISA SLS reference condition. Although the actual EI 

NOX values during flight operation were calculated from a more sophisticated prediction 

method [Deidewig, 1996], the application of the fuel flow method shows the expected 

effect. 

Figure 13: EI NOX versus fuel flow for CF6-80C2B1F showing “corrected” values 

It should be noted that the data provided in AERO2k represents all oxides of nitrogen 

calculated to be emitted from the engine jetpipe. At that point, only around 5 to 10% of the 

total NOx is in the form of nitrogen dioxide (NO2) [Wilson 2001]. Further conversion takes 

place downstream, although the rate and extend is dependent upon the properties of the 

jet plume and speed and altitude of the aircraft. 

CO and HC 

Carbon monoxide and hydrocarbons are products of an incomplete combustion of a fossil 

fuel. Therefore they are directly coupled with the combustion efficiency ηC. If one sets the 

lower heat value of the unburned hydrocarbons equal to the lower heat value of the 

kerosene, one can derive the following equation [Dodds, 1990]: 

Equation D 

This equation has been applied to the ICAO SLS data of engines developed in different 

decades, see Figure 14. 


Combustion Efficiency [%] 

100 

98 

96 

94 

92 

90 

88 

86 

JT3D-3B, ca. 1962 

RB211-524B, ca. 1975 

CF6-80C2B1F, ca. 1982 

GE90-85B, ca. 1994 

0 10 20 30 40 50 60 70 80 90 100 

Thrust [%] 

Figure 14: Calculated combustion efficiencies versus thrust at SLS following [ICAO, 1995] and 

Equation C. 

The combustion efficiency of an aircraft engine can be correlated with a parameter called 

Ω, which is the reciprocal value of the simplified combustor loading parameter Θ (see for 

example [Münzberg, 1977] and [Lefebvre, 1983]). 

Equation E 

The idea of the emission correlation explained here is to use Ω as the reference function for 

CO and HC. Due to the fact, that the volume of the combustor VC is unknown in most cases, 

but a constant value, the parameter (Ω ⋅ VC) has been introduced. This leads to emission 

correlations of the following form: 

Equation F 

Figure 15 shows the CO and HC SLS emission indices for the CF6-50C2 published in [ICAO, 

1995] versus (Ω ⋅ VC) used as reference function. 


EI CO, EI HC [g/kg] 

70 

60 

50 

40 

30 

20 

10 

0 

Take Off 

EI CO=369,6(Ω∗V C) 2 -14,96(Ω∗V C)+0,647 

EI HC=158,6(Ω∗V C) 2 -19,12(Ω∗V C)+0,985 

0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45 

Ω ∗ VC [kg/s/bar 1.8 ] 

Figure 15: Measured EI CO and EI HC versus (Ω.VC) for CF6-50C2 

At very high altitudes the evaporation properties of aircraft engines may change due to 

lower temperatures and pressures in the combustion chamber and lower mass flows of fuel 

and air through the injection system. Therefore a correction concerning the evaporation 

time tE based on the changing Sauter mean diameter (SMD) in comparison to a reference 

value at SLS(ref) has been developed [Döpelheuer, 1997]. This finally leads to correlations of 

the form: 

Equation G 

The correlations have been validated with P&W 305 data. The results are shown in Figure 

16. Open symbols represent uncorrected data (Equation F), filled symbols represent data 

correlated with the correction term concerning the evaporation time (Equation G). For the 

P&W 305 the value for the constant, c, resulting from the experiments is 0.4. 


Idle

Calculated EI CO [g/kg] 

50 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 

Take Off 

without correction 

0 0,01 0,02 0,03 0,04 0,05 0,06 0,07 0,08 

Ω ∗ V C [kg/s/bar 1,8 ] 

Figure 16: Calculated EI CO versus ( VC) for P&W 305 

with correction 

ICAO SLS data 

26000 ft altitude 

35000 ft " 

40000 ft " 

43000 ft " 

50000 ft " 

Up to an altitude of 35000 ft the data correlated with Equation F (uncorrected, open 

symbols) lie on a line directly determined from the SLS data and used as reference function. 

But for altitudes above 35000 ft the values are higher than the expected values due to 

evaporation properties getting worse. 

The values correlated with Equation G (corrected, filled symbols) all lie on the SLS reference 

function. The altitude emission index of CO for the P&W 305 can thereby be determined by 

evaluating the altitude value of (Ω ⋅ VC) with Equation F and finding the corresponding SLS 

reference value of EI CO (see arrows in Figure 16). Hence this method determines altitude 

emission indices out of altitude combustor inlet conditions in combination with ground 

measurements. 

As for NOx, the HC emissions calculated represent the full range of unburnt hydrocarbons 

emitted from the engine jetpipe. The proportion of methane to other longer chain 

hydrocarbons varies significantly with engine type and engine conditions. 

Soot 

The soot production and oxidation mechanism is very complex and not well known. In 

particular, the non-homogeneous flow and temperature fields in the combustion chamber, 

the different influences of the injection systems and combustor technologies, the influence 

of the type of fuel burned and the rare measurements make it difficult to calculate the 

amount of soot emitted by aircraft engines. Furthermore neither a soot emission index nor 

a soot concentration, but only the Smoke Number SN is measured for the ICAO certification 

process, and only then at the worst of the four measured conditions, see Figure 17. 


Idle

SN in % from value at 100 % SLS-Thrust 

120 

100 

80 

60 

40 

20 

0 

0 10 20 30 40 50 60 70 80 90 100 

SLS-Thrust in % 

Figure 17: SN versus SLS-thrust in percentage terms for several engines [ICAO, 1995] 

CFM56-3C 

(Rerated) 

CFM56-5C2 

CF6-50C1, -C2 

CF6-80C2B2F 

RB211-22B 

RB211-524B 

series 

ALF 502R-5 

Figure 17 shows the extremely variable behaviour between engines regarding the quantity 

of soot emitted. Furthermore the absolute values of the SN not shown in this figure differ 

widely. A semi-empirical soot correlation with variable reference values has been developed 

to cope with these problems [Döpelheuer, 1997]. The different variable reference functions 

for every investigated engine taken from ground measurements consider the special 

properties of the combustor and injection system, the actual operating condition is taken 

into account by thermodynamic combustor data. 

To derive a suitable soot correlation function a two step process has been employed. As a 

first step, the soot concentration values CSoot at SLS have to be determined out of the ICAO 

SN measurements. The connection between CSoot and the SN is dependant on the soot 

properties (especially the particle size distribution) of the engine looked at. A combination 

of the results of [Champagne, 1971] and [Hurley, 1993] has been used for concentrations 

up to 6 mg/m 3 , for higher concentrations the function of [Whyte, 1982] has been applied. 

The necessary reference function for the soot correlation introduced here is of the form CSoot 

versus T3 for SLS conditions. The amount of soot produced varies significantly with the 

different injection systems and combustor technologies used. Therefore a soot correlation 

on the basis of a variable reference function will be used. 

As a second step, the emission index of soot for other than sea level static conditions is 

calculated with the help of these reference functions and actual thermodynamic data 

effecting the soot production. These are the combustor inlet pressure p3, the flame 

temperature Tfl and the equivalence ratio Φ as parameter considering the actual ratio of 

atomic carbon to atomic oxygen C/O. Measured results of model flames and combustor 

tests have been combined to determine a soot correlation of the following form: 


Equation H 

The reference variables (ref) are the SLS values at the same combustor inlet temperature T3 

as the operating condition looked at. The other variables are the actual values obtained 

from the investigated operating condition. 

It should be noted that the particulate mass algorithms seek to quantify non-volatile 

particles only. No reliable data is available to quantify volatile particles in terms of either 

mass or number. 

Particulate Numbers 

More than the mass of soot emitted by aircraft engines, the number (and size) of particles is 

important in order to estimate the effects of soot emissions on climate and health. Since 

the emitted soot particles are not of uniform diameter, a model is needed that describes 

the size distribution of the particles as accurately as possible. It is generally accepted and 

supported by measurements and electron-microscopic observation, that the log-normal 

distribution is a good representation of the real size distribution of aircraft engine soot 

particles. Using the log-normal distribution, the soot aerosol properties are determined by 

three parameters: 

• The particle number concentration N 

• The geometric mean diameter μ 

• The geometric standard deviation σ 

Some information on these soot parameters is available from measurements of soot 

emissions from different aircraft engine types. In [Döpelheuer, 2002] these measurements 

are summarised and a model is created that reflects the general dependency of μ and σ on 

engine parameters. The data show that the geometric mean diameter is growing with 

increasing combustor inlet pressure and temperature, while the standard deviation is not 

dependant on engine parameters but only on the soot mass concentration, which is 

available from the above soot correlation. Based on this information a correlation of μ and 

σ with engine parameters has been developed. 

With the particle size distribution parameters available, the particle number concentration 

can be determined by equating the soot mass concentration determined by the soot 

correlation with that resulting from the particle size distribution, if a model of the 

diameter-dependant material density of the particles is available. This model has also been 

developed in [Döpelheuer, 2002] and integrated into a method to determine the number 

concentration of aircraft engine soot aerosol. 

Since these methods need individually measured parameters of the particle size 

distribution (which are not routinely measured during engine certification like e.g. the 

smoke number), only a small number of engine measurements was available for validation 

and application (for a list of the respective publications see [Döpelheuer, 2002]). Although 

the number of available measurements is small, many different engine types are covered 

(Low BPR, high BPR, civil and military engines). A combined analysis of these measurements 

yields to a general characteristic of particle number per gram of aircraft engine generated 

soot versus flight altitude as shown in Figure 18: 


Particle Number per Gram of Soot 

1,8E+16 

1,6E+16 

1,4E+16 

1,2E+16 

1E+16 

8E+15 

6E+15 

4E+15 

2E+15 

0 

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 

Altitude [m] 

Figure 18: Particle number per gram of soot dependant on the flight altitude (global fleet 

average) 

Due to the lack of measured data of the individual engine types, this function has been 

used to determine the (non-volatile) particle number EIs of the representative engines. It is 

obvious that this procedure is not suited to deliver accurate results for individual flights, 

but instead delivers the best estimate possible with the available data for a number of 

aircraft movements. 

2.1.4 Data Integration and Calculation for Civil Aviation 

The sections above have described the methods used to calculate fuel and emissions for 

each flight. In this section, the description covers the data integration and calculation 

software which performs the actual calculation and generates the gridded data. In outline, 

this process requires that flight data for each individual flight are taken from the Air Traffic 

Movements Database (Section 2.1.1). Representative aircraft and engines are allocated to 

each flight in accordance with the table generated from the output of the Aircraft 

Representation, Profiling and Fuel Prediction Module (Section 2.1.2). Take off weight is 

calculated from the great circle mission length plus allowances for diversion and delay. 

Emissions are calculated for each flight using emissions indices, i.e. emission per kg of fuel 

used, for each representative aircraft. These emission indices (EIs) were generated in the 

Emissions Parameterisation Module and they vary with Mach number, throttle setting and 

altitude (Section 2.1.3). The resultant data provide emissions and fuel used for each leg of 

each flight. A leg represents a portion of a flight for which identifiable data are available. 

There are about 30 legs per flight on average and the geographical location of each leg is 

known through its 4-D start and finish coordinates (latitude, longitude, altitude and time). 

This process is shown diagrammatically in Figure 19: 


Figure 19: Data integration process 

Plotting these data on a grid of longitude, latitude and altitude is carried out on a flight-byflight 

basis, allocating emissions to each of the geographical grid cells through which the 

flight passes. Cell size is selectable within AERO2k. The chosen output cell size of 1 deg by 1 

deg by 500ft represents a manageable data size whilst giving the resolution required for 

climatologists. If required, the AERO2k tool can provide higher resolution grids until 

limitations are reached due to resolution of the input data. At this stage, distance flown in 

each grid cell is also calculated. 

Dependant upon the form of output data required, the 42 tables of daily flight-by-flight 

emissions data may be queried and manipulated. No general automation of this process 

has been implemented within AERO2k. 

For the AERO2k 2002 emissions inventory, the 42 data tables are geographically gridded 

and then annualised to provide the main gridded output. Additional MS Access queries are 

also run on the data to provide aircraft-type, regional and global totals for distance-flown, 

fuel-used and emissions. 

Emissions gridding is carried out by analysing each flight segment in turn from the chosen 

database of flights. For analysis of the 2002 data, this database would consist of one of the 

42 representative days. For each flight segment, the crossing-points into and out of each 

grid cell are computed using navigational formulae. The emissions for each flight segment 

are than allocated into each cell accordingly. The process is repeated for every flight to 

produce the total (daily) gridded data. 

To produce the 6-hourly data for the diurnal variation analysis, representative days are 

divided into 4 six-hourly periods before gridding. 

To annualise the gridded data from each of the 42 representative days, each actual day of 

the year 2002 is allocated to a representative day in accordance with Table 4. 

Each of the 42 representative days therefore represents a whole number of actual days and 

it is a simple but data-intensive process to multiply the data for each of the representative 

days by the appropriate whole number. The sum of these 42 days multiplied by the number 

of representative days that they represent then forms the total distance-flown, fuel-used 

and emissions for civil aviation for 2002. 

Output from the gridding process is a table, approximately 0.5Gb in size, containing the 

fuel-used, emissions and distance flown in each of the 3.24 million cells (for the 1deg x 

1deg x 500ft grid). Many of these cells are of course empty. For AERO2k, the 2002 data are 

presented in the form of 12 monthly tables of gridded emissions together with four tables 


each representing a week-averaged figure for the 4 six-hourly period of a day. The data are 

presented on the AERO2k website at http://www.cate.mmu.ac.uk/aero2k.asp. 

The gridded emissions described above represent the main output of the AERO2k project. 

However the annualised data provide a rich source of data to provide further insight into 

civil aviation emissions. 

To assist policymakers and other stakeholders, a small number of data queries have been 

undertaken on the 42 representative days to provide totalled data for each fuel used and 

emission type, set against representative aircraft-type plus a range of global and regional 

breakdowns. 

As an example, regional data are available within the airports table, each airport being 

assigned to one of seven global regions defined by Eurocontrol. Hence flights in each 

representative day can be assigned, along with their emissions, to any region. According to 

whether the flight terminates in the same or a different region, the emissions can be 

assigned to intra- or inter-regional flights and then annualised for 2002. As a future 

application for AERO2k, similar analysis could be carried out for individual countries on a 

domestic/international flight basis. 

Results from these data queries are reported in Section 3. 

2.2 2002 Emissions from Military Flights 

The 2002 military emissions inventory has been compiled entirely separately from the civil 

inventory. Whilst it had initially been hoped to assemble military flight inventories, fuel and 

emissions parameters as part of a single flight inventory, national security prevented the 

same quality of data being made available to AERO2k. Rather than dilute the civil aviation 

data with approximations for military aviation, the two gridded inventories have been 

compiled and presented separately. Despite these reservations, it is believed that the 

military data represent a significant step forward in knowledge of global emissions from 

military aviation. A 3-dimensional military aviation gridded inventory has been constructed 

to include fuel, carbon monoxide (CO), unburnt hydrocarbons (HC), nitrogen-oxides (NOx), 

carbon dioxide CO2 and water vapour H2O. No attempt has been made to estimate 

particulate data due the shortage of reliable information. 

2.2.1 Approach 

The paucity of the military movements data, in particular radar data, the lack of emissions 

certification data for military engines, the wide diversity in missions and their 

characteristics, the wide variety of aircraft types and confidentiality of specific 

performance, lead to a general lack of availability of sufficient data. This finding forces the 

adoption of an approach for military aviation emissions and movement inventories that is 

quite different to the civil aviation approach, while maintaining a similar overall work 

breakdown. This overall work breakdown (for both civil and military inventories) comprises 

the following steps: 

• Air traffic movements database construction, (for the military inventory on a per 

country basis, for the civil inventory on radar track records). 

• Aircraft representation profiling and fuel prediction yielding typical flight profiles of 

typical military missions. This includes the development of conversion factors to 

convert representative aircraft and representative missions into any aircraft-enginemission 

combinations. 

• Emission parameterisation, for military aircraft including addressing specific military 

issues such as afterburning. These parameterisation methods are then used to 

generate the emissions along flight profiles. 


2.2.2 Air traffic and emissions forecasting. 

Given the general lack of radar data on military movements a comparable approach to the 

civil inventory compilation cannot be directly followed. As an alternative, the foundation 

for the military movement database is: 

• The aircraft type, engine type and number in fleet per aircraft type per country. 

• The mission(s) to be performed by aircraft type. 

• The utilisation of aircraft expressed in flying hours per year. 

• Selection of a limited set of representative aircraft and missions. 

• Selection of suitable scaling factors to convert specific aircraft and mission into any 

aircraft-mission. 

• Simulation of fuel consumption and emissions along the flight path of representative 

aircraft-missions. 

For a limited number of countries having a high number of aircraft and flights, there is 

additional information compiled on: 

• The location of air force bases 

• The type and number of aircraft allocated to each of these air force bases. 

• Military airspace lateral and altitude limits. 

The above-described information allows the building of a movements database. The data 

handling process is depicted in Figure 20. The green boxes denote data. The grey ellipses 

denote data processing or linking. 

The large centre grey boxes denote specific software tools that are embedded in the data 

processing. The E-MISSION (military (e-) mission analysis) program is effectively a coupling 

of the NLR Gas turbine Simulation Program GSP and a military mission performance 

program to calculate time, fuel and emission profiles from military aircraft performance, 

engine characteristics and mission profiles. The Flight Profile tool is a MATLAB tool to 

combine all relevant military information (fleet inventory, airspace, bases, utilisation, fuel 

and emission profiles) and processes the information into a 3-dimensional grid for 

emissions and fuel distribution. This grid is then the interface to the emissions grid building 

and forecasting parts. 

Fleet per Country 

4 

Aircraft - Engine 

types & Missions 

13a 

13b 

13c 

7 

Reference 

AC types 

Reference 

Engines 

Reference 

Missions 

2 

Utilisation 8 

Gasturbine 

Simulation 

Program 

Figure 20: Overview of database processing 

E-Mission tool 

11 

10 

Air Force Bases 

5 

Number 

of Flights 

Aircraft-Engine 

Conversion 

Factors 

Military Airspace 

14 

Reference Flight 

& Emissions 

Profiles 

15 

WP1: 

(Military) 

Mov. DB 

WP1: 

Fuel & Em. 

Grids & 

Profiles DB 

Legend: Outputs 


1 

3 

6 

9 

12 

Military Movements DB 

Mission DB 

Flight-Profile tool 

16 

Inputs 

Process

2.2.3 Inventory of military aircraft 

The military component inventories include all aircraft types as listed in [NLR, 2004]. This 

inventory includes not only the front-line aircraft types such as fighters, bombers and 

attack aircraft, but also includes helicopters, support, tanker intelligence, liaison, special 

operations as well as executive and transport aircraft. The inventory is based on owning 

country and includes aircraft assets from all branches of the military as well as guard, 

reserve and paramilitary forces where applicable. The inventory is categorised by country, 

aircraft type, mission(s) and region and distinguishes about 1500+ aircraft derivative types 

and 380 compound missions. 

Many military aircraft are designed to perform multiple missions, either true multi-role, or 

in swing-role. Typically 2 or 3 missions can be distinguished for such aircraft. Typical split 

between the roles, and reference missions are assumed. 

2.2.4 Deployment and Utilisation of Military Aircraft 

For the allocation of fuel use and emissions into the three dimensional space, it is assumed 

that aircraft typically fly within their home territory. One exception to this is the NATO area 

in Western Europe where it is assumed that NATO aircraft could be operated (if range 

permits) throughout the NATO airspace. Although NATO member states, Greece and Turkey 

are exempt from this assumption. 

For the NATO countries having the largest military aircraft fleet, i.e. Spain, Germany, United 

Kingdom, France, Italy, and the United States, the aircraft have been allocated to their 

airbases. A total of 422 air force bases and 192 airspace bases have been included in the 

movement database, and are depicted in Figure 21 and Figure 22. Some countries have 

airbases located in a foreign country (only shown if within Europe). 

40° N 

50° N 

10 ° W 

60° N 

0 ° 


10 ° E 

Figure 21: (NATO) airbases for specific countries in Europe 

20 ° E

30 ° N 

150 ° 

W 

45 ° N 

60 ° N 

135 ° W 

Figure 22: Airbases in the USA 

120 ° W 

Conversion from aircraft count in an inventory into number of flights or flying hours 

requires an estimate of the aircraft utilisation (i.e. the number of flights or flying hours per 

aircraft). For the purpose of this study, the USAF planning methods have been applied. In 

this method, there is a distinction between the Total Active Inventory (TAI) and the Primary 

Aircraft Inventory (PAI). The Total Active Inventory comprises aircraft assigned to operating 

forces for mission, training, test, or maintenance. Not all of these aircraft have operational 

status: maintenance requirements, backup and spare aircraft reduce the effective 

operational number of aircraft. This TAI inventory includes primary, backup, and attrition 

aircraft. 

The Primary Aircraft Inventory (PAI) is the number of aircraft assigned to meet Primary 

Aircraft Authorisation (PAA), i.e. authorised number of aircraft to perform the unit’s 

operation mission. PAA is generally some fraction of the total aircraft inventory. Aircraft not 

part of the PAI, are usually undergoing (scheduled or unscheduled) maintenance, 

modifications, inspection, or repair. The ratio of operational aircraft to the total possessed 

aircraft depends on the type of aircraft. Bombers, large transport and electronic surveillance 

and/or reconnaissance platforms tend to have a higher ratio of operation aircraft to total 

possessed aircraft. 

Not all states necessarily use their aircraft at the same rate as the US. Some unclassified 

data exist to substantiate non-US military aircraft utilisation. Hence, estimates were made 

from the US utilisation rates according to [Landau, 1994]. 

105 ° W 

2.2.5 Military aircraft types and reference aircraft 

Military aircraft and engines are not subjected to international emissions standards and 

certification. As a consequence there is hardly any data publicly available on fuel and 

emissions characteristics on military engines. However, for a limited number of (military) 

aircraft, the performance and (engine) emissions characteristics, sometimes even including 

the transient behaviour, are available in sufficient detail to warrant modelling with NLR’s 

mission analysis model and gas turbine simulation tool GSP. 

Apart from the availability of sufficient aircraft, engine and mission data, other aspects 

relevant to the selection of the reference aircraft are the volume of aircraft in the fleet, and 

the mission to be performed. The mission analysis model (E-mission) is used to determine 

the required engine performance along a mission profile and the latter is to generate the 

specific emissions and fuel performance. An example of the resulting missions, fuel 

consumption and emissions profiles are shown in Figure 23. 


° 

90 

W 

° W 

75 

° W 

60

altitude [m] 

15000 

10000 

Fuelflow [kg/s] 

HC [g/s] 

5000 

Escort using F-15 Eagle 

0 0.5 1 1.5 2 

x 10 4 

0 

20 

time [s] 

15 

10 

5 

0 0.5 1 1.5 2 

x 10 4 

0 

200 

time [s] 

150 

100 

50 

0 0.5 1 1.5 2 

x 10 4 

0 

time [s] 

altitude [m] 

NO x [g/s] 

CO [g/s] 

15000 

10000 

0 0.5 1 1.5 2 

x 10 6 

0 

400 

distance from base [s] 


5000 

300 

200 

100 

0 0.5 1 1.5 2 

x 10 4 

0 

400 

time [s] 

300 

200 

100 

0 0.5 1 1.5 2 

x 10 4 

0 

time [s] 

Figure 23: Escort mission profiles, altitude, speed, fuel consumption and emissions as a 

function of time 

2.2.6 Mission types and reference mission types for military flights 

Military aircraft are designed to fulfil one dedicated or more (multi-role or swing-role) 

missions. For the purpose of this inventory, the multi-role or swing-role missions are split 

into individual missions with a distribution in number of flights per individual mission. 

Different aircraft types can usually perform the same kind of mission, with the main 

differences limited to range, speeds, operating altitude, ordnance delivered or weaponry 

and effectiveness. In standard operations during peacetime, effectiveness, ordnance and 

weaponry do not affect the fuel and emission characteristics to a significant extent. For the 

purpose of this study, differences in aircraft range, speed and operating altitudes are taken 

into account wherever data are available. 

Because of a general lack of data on non-NATO countries, a limited set of missions is 

derived from standardised NATO/US-Navy missions [Gallaher, USAF, 1977], [USAF, 1989]. 

These missions are typically defined by generic values for power setting (e.g. mil thrust, 

afterburning thrust), speeds (e.g. cruise at mil power, dash speed) or time (e.g. 20 minutes 

on station). For the selected reference aircraft types, the generic characteristics are 

converted into quantifiable numbers. A total of 11 missions for fixed wing aircraft and a 

total of 6 for helicopters have been selected for this inventory. 

All missions have a basic scheme shown in Figure 24.

Takeoff 

Climb 

Cruise 

Loiter 

Figure 24: Mission design template 

In-Flight 

Refuelling 

Mission Specific 

Requirements 

Air to Air combat 

Ground Attack 

Weapon Delivery 

Reconnaissance 

Terrain Following 

Anti-Submarine search 

etc. 

Descent 

Approach 

Landing 

Once the speed, altitude and throttle settings are defined along a mission, the next step is 

to evaluate the fuel burn and emissions (NOx, CO and HC) along the missions over time for 

this specific aircraft-engine-mission combination, with the known reference aircraft 

performance and engine characteristics. 

For this purpose, the NLR Gas turbine Simulation Program (GSP) is coupled to a mission 

profile program to convert and combine mission profiles into a fuel (and optionally 

emissions) profiles. The calculation of the fuel burn and emissions is described in AERO2k 

deliverable Report D13b [Broomhead, 2004]. Finally, the resulting fuel burn and emissions 

profiles are converted from the specific aircraft-engine-mission into some other aircraftengine-missions 

using the performance indicators. 

2.2.7 Military fuel consumption and emissions profile allocation in airspace 

Once aircraft and missions are analysed and the fuel consumption and emissions along a 

flight profile are known as a function of altitude and time, these emissions and fuel 

consumption are allocated in a three dimensional space representing the airspace 

surrounding the earth globe. For the vertical distribution of fuel and emissions, the mission 

profiles provide sufficient information (although a correction for the air base ground level 

still needs to be included). These mission profiles do not supply any information on the 

distribution in the horizontal plane i.e. the geographical location. A three-way approach has 

been defined to allocate the emissions in 3-dimensional space. 

Which of the three geographic aggregation levels is applied is dependent on the availability 

of data on the location of air force bases and aircraft allocated and military airspace 

locations as well as the country area relative to aircraft range. Each level demands a 

different approach of allocating the fuel and emissions into space: 

Country profile: The flights, fuel consumption and emissions are spread out in longitude 

and latitude over the home-country using the scaled fuel and (e-) mission profiles for each 

aircraft and mission type in the fleet. Such profile is the single option if no information is 

available on the location of air force bases and the numbers and types of aircraft. This 

profile is also the preferred approach in case the area of the country is small compared to 

the range of the aircraft. Even in the case of detailed data on air force bases etc., this profile 

serves to account for ferry flights, base rotations etc. This type of movements requires the 

aircraft numbers, missions and utilisation to be known or estimated. Integration over all 


aircraft types, missions and utilisation yields a fuel and emissions altitude distribution on a 

per country basis. 

Airfield profile: The flights, fuel consumption and emissions can be allocated/linked to air 

force bases, aircraft types and mission. In this case the flying time, fuel and emissions 

distribution are confined to areas within the concentric circles around air force bases. The 

area in which the emissions are confined is based on the typical mission range of the 

aircraft type under consideration. Furthermore, the missions are confined again to the 

countries’ borders (or ‘friendly’ airspace e.g. NATO). For some major countries, the locations 

of the air bases are known as well as the number and type of aircraft stationed, and their 

associated typical missions. This type of approach allows a regionalised or local fuel and 

emission altitude distribution. 

Airspace profile: The flights, fuel consumption and emissions distributions are based on 

flights between air force bases and dedicated airspace that are well within reach of the 

aircraft type and mission located at that air force base. In this case it is assumed that a 

significant part of the flight is spent within this airspace with a ferry to and from the air 

force base. This type is effectively a point to point flight and allows a spot or local fuel and 

emission altitude distribution. 

In cases where all relevant data are available, and the airspace profile can be used, usually 

there is a mix of all three of the cases above to cover variations in operations, with 

emphasis on the airspace and airfield profiles. 

Each of the three different movement types (country, airfield and airspace profiles) require 

a different approach to (global) grid aggregation of the fuel, time and emissions 

distribution. For all three cases, the vertical distribution is according to the aircraft type and 

mission type specific fuel and emissions profiles. 

All of these profiles are finally converted from data produced at the level of country, airfield 

or airspace profiles into a 3-dimensional grid that spans the earth and is comparable to the 

civil aviation inventory. 

Results from the military aviation inventory are described in Section 3. 

2.3 Forecast for 2025 

In this section, the method used to develop the 2025 forecast for civil aviation is described. 

The starting point for calculation of fuel usage and emissions in 2025 is capacity. Data was 

obtained from AERO2k modelling of the 2002 missions for the base year and information 

provided by Airbus for the forecast year. The quantity of fuel, for the forecast year, was 

calculated from capacity and traffic efficiency that was assumed to improve year-on-year to 

reflect current and predicted trends. The emissions performance of newly introduced 

aircraft is assumed to improve in line with progressively more stringent levels relative to 

CAEP4 – the fuel efficiency effect of increases in pressure ratio are accommodated. 

Changes in other emissions are assumed to be in line with changes in fuel consumption. 

For each representative aircraft, in the base year fleet, the engine emissions certification 

parameter is described by the ICAO certification specifier Dp/Foo 11 . Dp/Foo is plotted against 

overall engine pressure ratio (PR) and improved technology is demonstrated by increased 

margin against limit values. The position of the various stringencies - CAEP2 (31 December 

2003 and earlier), CAEP4 (post 1 January 2004), and stringencies relative to CAEP4, are 

shown in Figure 25. Note that CAEP4-64% is equivalent to CAEP2-80%, an aspirational 

11 where Dp is the total mass of emissions produced during a landing / take off cycle (LTO) and 

Foo is maximum sea level thrust 


emissions performance target set out by ACARE in their Vision 2020 12 to direct research to 

achieve an 80% reduction in NOx. By inference this suggests an equivalent reduction in 

Dp/Foo. The ‘characteristic’ values of Dp/Foo for all of the engines in the emissions databank 

are shown in Figure 25. 

NOx Dp/F oog/kN 

120 

100 

80 

60 

40 

20 

For compliance engine 

Dp/Foo must not exceed 

regulatory line 

CAEP2 

CAEP4 

CAEP2-70% 

CAEP4-64% 

At PR30 stringency is a 

% of CAEP4 at PR30 

then parallel to CAEP4 

Figure 25: Regulatory Dp/Foo to CAEP2, CAEP4 and stringencies to CAEP4 

2.3.1 Aircraft additions and retirements 


CAEP2 

CAEP4 

CAEP4-5% 

CAEP4-10% 

CAEP-15% 

CAEP4-20% 

CAEP4-30% 

CAEP4-40% 

CAEP4-50% 

CAEP4-64% 

Characteristic 

Dp/Foo all engines 

Using the world’s 2002 fleet of “representative” aircraft types, there are four groups of 

aircraft; those that are already out of production and that will retire before 2025, those that 

are going to be out of production by 2025 but still flying at that date, those that will be in 

production in 2025, and new aircraft such as the A380 that are currently under construction 

at the beginning of the forecast period. 

The rules governing retirement age were taken from the retirement policy given in a report 

showing that at least half of the narrow-bodied aircraft would be retired by age 27 and 

wide-bodied by 35. Some aircraft were flying to age 45; however these were not taken into 

account because of the difficulty in identifying these aircraft from the 2002 data. For this 

reason where aircraft are retired, the date of retirement was calculated at these set times 

after introduction into the fleet: the movement of passenger types into the freighter fleet 

or their extended lives as a result were not explicitly modelled. The model “replaces” 

retired aircraft with new representative types in essentially the same seat band, dividing 

the outgoing aircraft’s capacity in ASK equally amongst the incoming replacements. 

Aircraft in the other three groups are modelled to comply with all of the emissions 

stringencies from their effective date, and are introduced into the fleet as if they were ‘new’ 

aircraft. 

These concepts of replacing retiring aircraft and assuming others would always comply 

with stringencies allows the establishment of a history of capacity, fuel and emissions for 

the fleet as a whole so that it can predict the quantity of fuel used and NOx emitted for 

forecast years. The relative proportion of aircraft retiring and new entrants affects the rate 

at which the fleet turns over, and hence the speed with which fleet NOx performance is 

improved. 

12 EUROPA: ACARE Vision 2020: 

http://europa.eu.int/comm/research/growth/aeronautics2020/pdf/aeronautics2020_en.pdf

2.3.2 Forecasting: Capacity - fuel - emissions 

Capacity is the product of the actual distance travelled between departure and arrival 

airports, frequency, and the seating capacity of the individual aircraft. This may be reduced, 

mathematically, to the sum for all representative aircraft of the product of total distance 

per representative aircraft type and that aircraft’s seating capacity. The 2002 database 

yielded the actual distance each representative aircraft flew and the number of missions, 

which was supplemented by a list of the seating capacity of each representative aircraft. 

The base year capacity was therefore derived yielding a total global capacity for 2002 of 

4.787 10 12 ASK, equivalent to 2.585 10 12 ASNM. 

The capacity forecast for 2025 was based on data provided by Airbus and included the 

predicted growth of traffic (revenue passenger km) and the number of aircraft required to 

satisfy it. The predicted traffic growth was converted into a predicted capacity growth, 

based on a load factor increase from 0.7 in 2002 to 0.74 in 2025. This represents an 

industry consensus on the improvement in aircraft load and passenger management over 

the forecast period. This therefore results in the capacity growth being slightly lower than 

the traffic growth. This information for 2025 gives a snapshot of the fleet and the capacity 

it will perform in ASK, but does not contain the age profile (and therefore retirement 

predictions) needed to underpin the fuel and NOx calculations. The growth values, 

regionally, based on the Airbus information were applied to the base year data, and 

summed to provide a global value, this suggested total capacity was 1.243 10 13 ASK, 

equivalent to 6.712 10 12 ASNM. 

These values are not identical to those used in the DTI forecast that supported IPCC, 

therefore other data sources were investigated, and the results are shown in Figure 26. The 

Airbus Global Market Forecast (GMF) data 13 for 2000 and 2020 are almost the same as the 

DTI forecast that supported the IPCC. Although the growth rates are derived from the same 

source, the base year starting point is different, being lower than either the DTI or the 

Airbus GMF forecasts and reflects, possibly, the reaction following the terrorist events of 

September 2001 after which air traffic declined more severely than at the Gulf War of 

1990/1991. Although traffic is recovering, in some regions airlines have increased yield by 

running fewer missions at higher load factors – thus reducing capacity, but traffic growth 

may be similar. 

Traffic ASK 10 8 

140000 

120000 

Capacity 

100000 

80000 

AERO2K Capacity 2025 

AIRBUS GMF 2020 

AIRBUS GMF 2000 

60000 

ICAO 2001 

ORACLE 1998 

40000 

ORACLE 2000 

OAG MAX 2002 

20000 

OAG MAX 2002 + 10% for charter traffic 

AERO2K Capacity 2002 

0 

DTI Log Historic 

1995 2000 2005 2010 2015 2020 2025 2030 

Figure 26: Global capacity and trends 

Year 

The model that UK DTI used to support IPCC is shown in Figure 26. However, for AERO2k 

the trend shown in Figure 26 had to be normalised to align the base year (2002) data that 

13 Airbus: Global Market Forecast 2001-2020, 

http://www.airbus.com/pdf/media/gmf2001.pdf 


originated from EUROCONTROL and forecast year (2025) data. Here the AERO2k data 

included schedule passenger, charter, and freight flights. To generate the normalised curve, 

the UK DTI model builds up a year-by-year change in fleet capacity, based on the base year 

data, the aircraft delivered, retired, and fleet growth. From this an annual growth rate can 

be determined, this was 4.234 percent. At a global level this is justifiable although capacity 

in and between individual regions may follow different patterns. The rate at which aircraft 

are replaced also affects the speed with which compliant aircraft are introduced when 

stringency changes. Typically the rate of introduction of new aircraft is approximately 5% 

to 7% of capacity, and retirement and attrition (crashes) is approximately 1% to 2%. 

Capacity has to be converted into a quantity of fuel and emissions for 2025, and this is 

accomplished using the “Traffic Efficiency” method developed previously by UK DTI to 

support the IPCC [IPCC, 1999]. Further, more detailed, alternatives have been considered as 

a result of improved data and modelling capability. Using projected delivery and 

retirement data for aircraft, the DTI model generates a year-by-year change in capacity, fuel 

used and NOx emitted. The link between capacity and fuel is ‘traffic efficiency’ – defined 

here as the quantity of ASK performed per kg of fuel used, and the link between NOx 

emitted and fuel is the fleet emissions index for NOx (EINOx), , grams of NOx produced per 

kg of fuel used. The fleet EINOx trend is influenced by introduction into the fleet of ‘new’ 

aircraft that comply with the latest emissions certification standards (stringency): this 

drives the year-on-year trend in EINOx of the new aircraft fleet. 

2.3.3 Regional growth factors 

The 2002 and Airbus forecast data are based on capacity between individual city pairs and 

therefore the quantity of fuel used and NOx emitted within a region and between regions 

can be defined. 

The difference in capacity between the base and forecast years enabled the DTI to prepare 

matrices of regional growth factors for multiplying capacity, fuel and NOx. This method 

does not account for the possible introduction of new airports and assumes all future 

traffic grows only along existing routes. In the base year the 2002 passenger and freight 

data were combined whilst in the forecast year the AIRBUS data identified these market 

sectors individually. There was, therefore, a need to identify freight movements and 

convert the freight capacity into an equivalent passenger capacity given in ASK. 

2.3.4 Freighter capacity 

The freight capacity is currently growing faster than passenger capacity, and therefore 

there was a need to identify it and apply the appropriate growth rates. The 2002 mission 

database included freighter movements but did not identify them directly. Therefore a 

method based on timetable data from OAG was used. For each movement the OAG data 

includes aircraft type and capacity. Capacity for freighters is given in Available Tonne Miles 

(or km) but this had to be converted into an equivalent passenger capacity because the 

base year data included freighter movements that were not individually identified. The 

conversion into seating capacity of the representative passenger aircraft, took into account 

the difference in seating offered on over and under 2900nm missions. DTI prepared these 

data at the city pair level, and therefore could identify flows within and between regions. 

The percentage of freight from the OAG data was applied to the AERO2K data, at a regional 

level. The global total capacity offered by freight aircraft in the base year converted into 

ASK as described above was 3647 10 8 ASK. Total capacity calculated from the AERO2k 2002 

data was 47873 10 8 ASK and therefore freight formed 7.6 percent of the total, which is 

approximately the same percentage as the OAG data. 

Airbus forecast regional freighter growth in RTK and freighter movements for the forecast 

year and converted this into an equivalent ASK, this capacity identified movements 

between region city pairs. As with the scheduled passenger data, to ensure consistency 

between base and forecast years the freighter growth values were applied to the base year 


data to generate new forecast data. The freight capacity for the forecast year was 12717 

10 8 ASK, compared against the total capacity of 124262 10 8 ASK, and therefore freight 

formed 10.2 percent of the total. This demonstrates the fact that the growth rate of freight 

is greater than passenger capacity. 

2.3.5 Fuel consumption and trends 

Fuel consumption for the fleet is calculated from total annual capacity (in ASK) using the 

concept of ‘traffic efficiency’, defined above. The fleet traffic efficiency in the base year was 

calculated as 31.91 ASK/kg. This may be compared with the previous DTI forecast for IPCC 

for 2002 of 34.09 ASK/kg (equivalent to 18.4 ASM/kg). Over time aircraft and engine design 

has improved and aircraft are using less fuel, i.e. traffic efficiency has improved. Flying 

shorter distances between airports through improvements in air traffic management (ATM) 

will also improve TE by reducing the quantity of fuel used. A literature search suggests an 

improvement in TE of between 1.0 and 2.9% per year [Lee Joosung, 2001], and the rates 

used previously by DTI for the IPCC fall in this band [Greene, 1996]. For that work the DTI 

used an increase in ASM per kg of fuel of 1.3% pa to 2009, 1.0% pa from 2010 to 2020 and 

0.5% pa thereafter, and these values have been modelled again here. 

An alternative approach to TE has also been investigated to provide regional variation. This 

approach considers the aspects of TE improvement in the following areas: 

• Improvements in Airframe/Engine technology of replacement aircraft; 

• Increased efficiency of new aircraft (growth); 

• Improvements on existing aircraft (upgrades etc); 

• Improvements to ATM. 

By considering the 2002 world fleet at a regional level (2002 regional fleet make-up), and 

using the replacement information stated previously, the effect of this on TE per region 

may be estimated, over the forecast period. 

A simplification is used to assess the effect that additional new aircraft have on regional TE. 

Historically, improvements for new production aircraft have averaged 1 – 2% per year in 

fuel efficiency. Using a conservative value of 1% pa in this case as a scenario for the 23 year 

forecast period, and the changes in capacity at a regional level, an estimation for changes to 

TE due to new aircraft may be obtained. This is clearly a very simplified approach, and other 

factors may be required in the future to provide more accurate data. However, this will 

produce an indication of variations at a regional level that are not shown through a 

generalised global approach to TE. This approach assumes that all growth is accommodated 

by new aircraft entering the fleet (as distinct from, for example, returning from “desert 

storage”) has been used to validate and inform the global TE values adopted for this work. 

Improvements to the fleet’s fuel efficiency for existing aircraft (through modifications or inlife 

upgrades) are difficult to predict, but a constant global improvement value of around 

0.4% pa, based on suggested improvements considered feasible during the forecast 

timeframe, is incorporated. 

Finally, by comparing the base year GC distance and actual distance travelled at a regional 

level, an estimation of the potential improvement that may be achieved through ATM 

developments over the forecast period may be introduced to the model. 

Thus, from a starting point of a trend in capacity, a trend in fuel used between the base and 

forecast years was established to enable global fuel consumption to be predicted for the 

forecast year. 

Where aircraft were retired and replaced a means had to be devised to assess the change to 

the quantity of fuel that would be used so that the new quantity of NOx could be 

determined. The modelling approach assumed the capacity in ASK of retiring aircraft was 

shared equally by the replacements. The fuel used by the replacements was calculated by 

applying individual aircraft traffic efficiency to each replacement aircraft’s share of the 


total capacity. In this way, the ASK remained the same, but the quantity of fuel changed to 

reflect the change in aircraft. The quantity of fuel used was used only to trend NOx 

produced by the replacement aircraft that performed the same ASK as before. 

2.3.6 Stringency and EINOx 

The CAEP4 limit mandated from 1 January 2004 is 16.25% lower than CAEP2 at an engine 

pressure ratio of 30 (see Figure 25) and CAEP are considering further regulatory 

stringencies. Where currently the CAEP4 regulatory line has a kink point at PR30, reflecting 

an increase in the regulatory requirement from this point, potential candidate stringencies 

also have a kink point at PR30 and the certification requirement may depend on whether 

engine pressure ratios is above or below PR30. For example, below PR30 the proposals 

being considered are a straight percentage of the CAEP4 limit value, whilst above PR30, the 

percentage at PR30 is taken, converted into an absolute reduction in Dp/Foo, and then that 

value runs parallel to the CAEP4 line. 

The conceptual work described in [Addleton, 2004] established a mathematical relationship 

between EINOx of ‘new’ aircraft on entry into service and stringency relative to CAEP2 

levels. The analysis investigated the effect of changes in Dp/Foo on individual aircraft/engine 

combinations to predict changes in individual aircraft EINOx based on the quantity of fuel 

consumed remaining the same. That is, in the event the NOx regulatory limit is tightened a 

manufacturer would make his engine comply with the new rule. The NOx performance 

would be improved, but it is assumed at this stage that the fuel consumption remains 

unchanged. NOx emitted was summed for all aircraft, and when based on the original 

quantity of fuel consumed, yielded EINOx for a ‘new’ fleet. This EINOx was applied as a 

global figure to all new aircraft introduced in a given year to satisfy the growth in capacity 

(and retirements), and aircraft introduced in a given year retained that EINOx value until 

retirement. 

This analysis investigated, mathematically, the effect of changes in stringency and changes 

in pressure ratio. A rise in pressure ratio is likely to result in reduced fuel consumption, 

although the analysis was unable to determine any definitive effect of changes in pressure 

ratio on changes in fuel economy at this time – fuel efficiency improvements are therefore 

addressed through traffic efficiency trends. The best indicator for changes in EINOx proved 

to be Dp/Foo on an engine-by-engine basis, but there are benefits and caveats as follows: 

• Previous work carried out by UK DTI suggests that, for individual engines, a reduction 

in Dp/Foo might lead to a similar or marginally larger reduction in whole mission and 

cruise EINOx. The inference is that improvements may be larger than implied by the 

reduction in Dp/Foo. 

• Globally, differences in Dp/Foo without reference to a specific engine are not a good 

indicator of fleet EINOx trends. For example, if an engine from one manufacturer 

replaces an engine made by another, the change in NOx performance may not be the 

same as the ratio of the two Dp/Foo values – the engine with the higher Dp/Foo may 

yield lower overall NOx values due to the shape of its NOx characteristic curve 

(relating engine fuel flow to EINOx at the ICAO certification points). 

• Reductions in EINOx only apply to engines newly introduced into the fleet and not the 

fleet as a whole. 

2.3.7 Modelling stringency 

Although the initial conceptual work included reference to the EC LOW-NOx programme 14 , 

an engine manufacturer participating in EC-sponsored CYPRESS project 15 contributed views 

14 CORDIS RTD-PROJECTS: LOWNOX I - LOW EMISSION COMBUSTER TECHNOLOGY, Project Reference: 

AERO0016, 1990-1992. 


egarding the range of stringencies to be modelled, based on what might be feasible over 

the forecast period. 

Note should be taken: 

• that when a stringency is introduced into the model, it is assumed to apply only to 

those aircraft entering service after the date of applicability and not the whole fleet. 

• the levels of stringencies modelled do to not imply a commitment on ICAO CAEP to 

introduce them – they are simply used as one regulatory scenario. 

• That the dates do not necessarily mean that a regulatory stringency has already been, 

or even will be, recommended by IACO CAEP; a stringency is a modelling convenience 

to establish a trend in EINOx based on possible scenarios. 

The effect of stringencies relative to CAEP4 were modelled for each certificated engine in 

the representative fleet and assigned dates for introducing stringency into the model. 

Where an engine did not meet a regulatory stringency because its Dp/Foo was above a level 

in the scenario, it was assumed to be improved by its manufacturer to just comply with the 

regulatory rule as shown in Figure 27. This figure also demonstrates the effect of increasing 

pressure ratio. In practice, when an engine model fails a stringency, the manufacturer 

designs it to meet a standard that is more severe (ie, lower emissions) than the stringency it 

failed so that it may remain in production and comply with possible future (even more 

stringent) legislation that may demand even lower Dp/Foo limits. 

Figure 27: Effect of increased pressure ratio on possible reduction in Dp/Foo 

This is the rationale for using a CAEP4-20% step in 2004 instead of CAEP4, as many engines 

that are likely to be brought into service from that date are already at that level. Further, 

aircraft were assumed to meet whatever stringency was modelled until they retire. 

Not all of the fleet of representative aircraft were selected. To determine the effect of 

stringency only those aircraft whose engines have ICAO certificated data were selected. 

Thus jets and turboprops whose engines are uncertificated were excluded from the analysis 

because their emissions source data was not from certification sources. However, trends 

15 CYPRESS: EC Contract No: G4RD-CT-2000-00383. Future Engine Cycle Prediction and Emissions 

Study, publication pending 


for these unregulated jets and turboprops were assumed to be the same as the trends 

modelled for the regulated jets. 

Using concepts developed in Annex A, stringencies were modelled to predict fleet EINOx 

based on information contained in the CYPRESS report and advice from an engine 

manufacturer in a scenario as follows: 

• 2002 – no alteration in current emission performance of engines 

• 2004 – CAEP4 - 20% based on many aircraft are already compliant to CAEP4 

• 2010 – CAEP4 - 40%, engine PR rises by 5.0%, based on the ANTLE work 

undertaken by RR 16 

• 2012 – CAEP4 - 40%, engine PR rises by 10% 

• 2016 – CAEP4 - 50%, engine PR rises by 15% 

• 2020 – CAEP4 - 64%, engine PR rises by 20% - working towards an ACARE 17 goal 

of CAEP2 – 80% (CAEP4 – 76%) 

• Data from individual aircraft/engine combinations aggregated to fleet level for 

aircraft entering service 

Changes in EINOx are assumed to be directly proportional to changes in Dp/Foo on an 

engine-by-engine basis. The effect of introducing these stringencies is shown in 

Table 12. 

Year 2002 2004 2010 2012 2016 2020 

Stringency relative to CAEP4 % 0 20 40 40 50 64 

Dp/Foo relative to CAEP4 % 0 80 60 60 50 36 

Rise in PR relative to base year % 0 0 5 10.0 15.0 20.0 

EINOx of a new fleet 13.171 10.887 9.013 9.639 8.84 7.457 

Table 12: Dp/Foo relative to CAEP and possible dates effective in the model 

2.3.8 EINOx – effect of new aircraft on fleet 

Each change in stringency is assumed to precipitate a change in the certificated NOx 

performance of new aircraft introduced into the fleet from the date the stringency 

becomes effective, and hence their EINOx values. Fuel consumption improvements 

are accommodated by ongoing improvements in traffic efficiency, (the effect of 

changes to PR are also incorporated in this way). Therefore, on an engine-by-engine 

basis, the change in margin between an engine’s characteristic Dp/Foo and a level of 

stringency can be used to determine the change in EINOx for that engine. By 

applying these individual changes in EINOx to the (unchanged) quantity of fuel used 

by each new aircraft, a new total quantity of NOx for the new fleet can be calculated. 

Since the quantity of NOx from each new aircraft entering the fleet is now different 

(lower), a new EINOx can be established for representative aircraft entering the fleet 

by summing their total NOx and fuel and calculating EINOx from their total NOx and 

total fuel. This is done on an individual aircraft type basis - a straight average of the 

EINOx values for the representative aircraft does not give the EINOx for newly 

introduced aircraft. 

Such calculations were performed for all of the stringencies and their associated 

changes in pressure ratio. At each change in stringency, aircraft are introduced at the 

new level of stringency, and continue to be introduced at that level until the model 

16 

ROLLS-ROYCE: http://www.rolls-royce.de/News/2001/E_01-06-16_DRAFT_Parisantle.PDF 

17 

European Aeronautics: A Vision for 2020: 

http://europa.eu.int/comm/research/growth/aeronautics2020/en/index.html 


changes the stringency level again. Additional continuous reductions in NOx 

emissions occur as a result from improved traffic efficiency (reflecting the industry 

trend of reduced fuel consumption and hence reduced NOx) on a year-on-year basis. 

The analysis suggests that for newly introduced aircraft, EINOx would reduce from 

13.17g/kg fuel to 7.457g/kg by 2025, based on estimated data. By incrementally 

introducing aircraft into the fleet, whole fleet EINOx values were calculated to be 

13.17 and 10.12 g/kg fuel for 2002 and 2025 respectively. The difference shows how 

fleet EINOx lags behind the introduction of stringency because of the long time 

aircraft are retained in service. 

2.3.9 Calculation of fuel used and emissions for 2025 

For civil aviation, the distance flown, fuel and NOx traffic efficiency factors described 

above are applied to the 2002 data to produce forecast data for 2025. A summary of 

the growth factors is provided in Table 13 to Table 15 . 

Asia & 

Pacific 

Eastern & 

Southern 

Africa 

Europe & 

North 

Atlantic 

Middle 

East 

North, 

Central 

America & 

Caribbean 

South 

America 

Western & 

Central 

Africa 

Global 

Total 

Asia & 

Pacific 

Eastern 

& 

Southern 

Africa 

Europe 

& North 

Atlantic 

Middle 

East 

North, 

Central 

America & 

Caribbean 

South 

America 

3.7283 2.2887 4.1458 2.4616 3.3865 2.9955 

Western 

& 

Central 

Africa 

2.2807 2.4532 2.8373 2.6219 2.5304 2.8726 2.7222 

4.1356 2.9268 3.4719 2.0839 2.6004 3.2508 2.8788 

2.5901 2.6771 1.9725 2.7625 2.7466 2.7415 

3.1119 2.4758 2.5305 2.8342 1.6934 2.7030 2.5065 

2.7500 2.8331 3.0860 2.8108 2.7670 2.8547 

2.4678 2.8929 2.7553 2.4995 2.8537 2.4269 

Table 13: Capacity multiplier 2002 to 2025 (scheduled passenger, charter, and freight 

flights) 



Total 

2.5956

Asia & 

Pacific 

Eastern & 

Southern 

Africa 

Europe & 

North 

Atlantic 

Middle 

East 

North, 

Central 

America & 

Caribbean 

South 

America 

Western & 

Central 

Africa 


Total 

Asia & 

Pacific 

Eastern 

& 

Southern 

Africa 

Europe 

& North 

Atlantic 

Middle 

East 

North, 

Central 

America & 

Caribbean 

South 

America 

2.4876 0.9430 1.6408 1.3549 1.3934 1.2267 

Western 

& 

Central 

Africa 

0.9017 3.1178 1.2249 1.7812 1.2198 0.9444 1.5496 

1.5933 1.2400 3.7435 1.3987 1.2949 1.5791 1.7079 

1.4332 1.8539 1.3369 1.8310 1.1827 1.4844 

1.2620 0.8255 1.2805 1.2555 1.8521 1.7722 1.1302 

1.1632 0.9162 1.5355 1.9149 2.7523 1.9490 

1.9662 1.7644 1.6844 1.0398 1.9374 2.5210 

Table 14: Fuel multiplier 2002 to 2025 (scheduled passenger, charter, and freight flights) 

Asia & 

Pacific 

Eastern & 

Southern 

Africa 

Europe & 

North 

Atlantic 

Middle 

East 

North, 

Central 

America & 

Caribbean 

South 

America 

Western & 

Central 

Africa 


Total 

Asia & 

Pacific 

Eastern 

& 

Southern 

Africa 

Europe 

& North 

Atlantic 

Middle 

East 

North, 

Central 

America & 

Caribbean 

South 

America 

1.6695 0.6167 1.2306 0.8586 0.9995 0.8251 

Western 

& 

Central 

Africa 

0.5854 2.5348 0.8910 1.2250 1.0514 0.6179 1.2541 

1.1524 0.8733 3.2598 0.9922 0.8731 1.0443 1.2601 

0.9089 1.2887 0.9858 1.1787 0.7412 1.0460 

0.8957 0.6480 0.8654 0.7865 1.6266 1.2739 0.7003 

0.8113 0.5860 1.0211 1.4311 2.3752 1.4749 

1.5864 1.3316 1.3479 0.7688 1.4689 2.1111 

Table 15: NOx multiplier 2002 to 2025 (scheduled passenger, charter, and freight flights) 



Total 

2.0834 


Total 

1.6008

Each 2002 flight is queried to identify its start and finish region. The appropriate 

regional fuel or NOx factor is then applied to the fuel and emissions on each flight leg 

to produce a 2025 data file containing 2002 flights with revised data appropriate to 

2025. Distance flown is not recalculated at this detail level. These flights are then 

aggregated, either using the gridding software to produce 2025 gridded data or by 

data queries to produce global and regional data. These results are described in 

Section 3. 

For military aviation, no separate forecast has been produced for 2025. It is 

recommended that the 2002 data are used for 2025 (See Section 2.4.3). 

2.4 Validation, Uncertainty and Sensitivity Analysis 

Validation of AERO2k has been carried out, in stages, throughout the development 

and integration of the AERO2k inventory and forecast. This section describes the key 

conclusions from the validation, uncertainty and sensitivity work. 

2.4.1 Validation of the 2002 Civil Aviation Inventory 

Air Traffic Movements 

The air traffic movement database contains a comprehensive coverage of IFR 

movements for the ECAC area and North America using ATC data. Significant inputs 

were made to avoid double-counting flights. However, the complexities of flight 

identification and real world data errors mean that some uncertainties due to the 

absence or double counting of flights are unavoidable. For the remainder of global 

aviation, schedule data was used, thereby omitting non-schedule (e.g. charter) flights 

and including cancelled flights. These uncertainties are difficult to estimate as no 

source was found to estimate the total number of flights in the world. As mentioned 

by Attilio Costaguta, Chief of ICAO statistics section: “While in terms of revenuetonne 

kilometres, ICAO has a fairly good coverage, this is not the case for aircraft 

movements. Data for smaller regional or domestic airlines are generally not 

submitted to ICAO. Thus the number of departures shown may be significantly 

smaller than those which actually took place.” [Costaguta, 2001]. 

In order to assess the uncertainties present in the inventory, a qualitative assessment 

is summarised in Table 16. It shows the dependencies, relationships, limitations and 

assumptions made for each of the modelling steps. 

Modelling 

steps 

Import of 

AMOC 

headers 

Import of 

ETMS 

headers 

Update of 

maximum 

stop time 

Import of 

AMOC legs 

Dependencies Relationships Limitations Assumptions 

AMOC traffic.txt 

file 

ETMS flight.txt file 

AMOC headers 

and AMOC 

- Airport table 

- Aircraft table 

- ETMS airport 

table 


OID that makes 

the link between 

Capacity of the simulator 

that generates AMOC 

traffic.txt file 

Lack of arrival time for 

AMOC data 

Capacity of the simulator 

that generates AMOC 

Data generated 

are 

representative 

of all flight 

plans registered 

in the ECAC 


area 


are 

representative 

of all flights for 

North America 

Use of last 

event time

Import of 

ETMS legs 

Merge of 

AMOC and 

ETMS 

Import of 

schedule data 

headers 

Import of 

schedule data 

legs 

Existing 

trajectory 

Profiles and 

waypoints 

flight.txt file AMOC headers 

and legs 

ETMS headers and OID that makes 

ETMS 

the link between 

flight_chord.txt ETMS headers 

file 

and legs 

Consistency check 

Assessment 

Great Circle Profiles 

Merge of 

scheduled 

data with 

AMOC and 

ETMS 

Schedule data 

recorded in Back 

Aviation database 

Merge of AMOC 

and ETMS data 

- ETMS headers and 

legs 

- CARAT (NIMA 

data) 



- Airline table 


- List of city-pairs 

(AMOC, ETMS, 

Back Aviation) 


flight.txt file 

- Data missing in fields 

- Rounding of the eventtime 

to the minute 

- Trajectory kinks due to 

erroneous speed and 

flight level 

- Variation of the 

number and distribution 

of the aircraft position 

along the trajectory 

according to the sources 

- Criteria defined for 

assessing the duplication 

of flights 

- Quality of the existing 

trajectory 

- Representatives of a 

week of traffic (February 

2002) 

- Shortest trajectory 

created by CARAT, not 

necessary the trajectory 

used 

- Aircraft grouping system 

Defined number of 

positions calculated 

Criteria defined for 

assessing the duplication 

of flights 


are 

representative of 

all scheduled and 

commercial 

flights declared 

by airlines in the 

world 

Air traffic 

management 

conditions 

applied to air 

traffic in the rest 

of the world. 

Great Circle 

represent a 

trajectory 

Table 16: Dependencies, relationships, limitations and assumptions made for each 

modelling steps 


Although the number of flights contained within the inventory may be less than the 

actual number flown, the capture of IFR flights from US and European radar data and 

the use of timetable data for the rest of the world make it extremely likely that 

almost all flights by larger aircraft will have been captured. 

However, previous inventory work had shown that avoiding duplication of flights 

within the flight database is not a simple task. Code sharing, alternative airline and 

airport codes, merging datasets from flight movement and timetable information 

plus errors in the source data all contribute to the possible duplication of flights. As 

described in section 2.1.1, considerable work was carried out to blend the information 

from the various sources without the introduction of duplicates. To supplement this, 

further checks on the flights database were carried using additional criteria using the 

AERO2k data integration software. 

These additional checks of the flights database verified two features. Firstly that the 

flights in the database are all unique and have not been duplicated, and secondly that 

the number of flights in the database for each country broadly matches estimates 

made by airport operators and air traffic control. By looking at samples of data for a 

particular area, four main types of possible error in the data were examined. These 

were: 

• Airport location identifiers varying between IATA codes and ICAO codes, 

for example HNL and PHNL for Honolulu airport, 

• Timetable and air-traffic data recording slightly different take-off and 

arrival times, 

• Call sign errors 

− Where code-sharing airlines swap the airline identifying part of the 

call sign (the alphabetical part), for example, AAL307 to EGF307. 

− Where one of the source databases had recorded the airline and 

flight components of the call sign in different fields and these had 

been merged badly, for example, ELY002 became ELY2. 

• Where the aircraft had been changed, possibly somewhere in the 

intervening period between a schedule being submitted and take-off. So 

often, an Embraer E135 in the schedule data was replaced by an Embraer 

E145 for the actual flight. 

Sample data were checked and where any type of error was identified, improvements 

were made to the flight database generation software and a complete new flights 

database was supplied which contained none of these duplications. 

Aircraft representation and Fuel Profiling 

Piano validation 

The aircraft performance package selected to provide fuel usage predictions for 

AERO2k is PIANO [PIANO, 2004]. PIANO (Project Interactive Analysis and 

Optimisation) is a professional software tool for commercial aircraft analysis. PIANO 

has a history of prior use in inventory projects, i.e. ANCAT/EC2 [Gardner, 1998], and 

for projects such as the EC 5 th Framework project NEPAIR [Norman, 2003]. PIANO is 

open source rather than an inaccessible in-house code, and has the flexibility to 

predict performance at any flight altitude, speed and aircraft mass. Detailed flight 

performance evaluations in PIANO are derived from first principles, based on 

aerodynamics, aircraft mass, and engine characteristics. PIANO calculates the mass of 

mission fuel plus reserves that are required for a given mission distance and payload 

mass. The climb, cruise and descent segments of a mission are all analysed using 

detailed step-by-step techniques. The aircraft models have generic engines typical of 

the technology and thrust requirements of the class and therefore provide a typical 


Aircraft 

representation of the fuel burn of an aircraft type. PIANO generates fuel profiles 

covering the entire flight cycle, though in AERO2k it will only be used to provide data 

for operation above 3000ft. 

The PIANO software tool has been favourably compared to external sources such as 

BADA data from Eurocontrol and to real airline operational data. It should be noted 

that all validation work is based on statistically small samples of data. These provide 

“snapshots” of actual operation against which to compare the PIANO tool. It should 

be appreciated that in the world’s fleet of aircraft there are innumerable different 

combinations of aircraft type and variant, weights, configurations, engines and 

operational variables. Therefore the aim of this validation was to provide reasonable 

confidence but noting that exceptions and differences in performance will always 

occur. 

PIANO comparison with ANCAT/EC2 data 

Some validation of PIANO was carried out in the ANCAT/EC2 project [Gardner, 1998]. 

Predicted mission fuel consumption data from flight planning tools from British 

Airways and Virgin are quoted in the ANCAT/EC2 report. In Table 17 and Table 18, 

these data are compared to some of the PIANO models to be used in AERO2k. 

Mission 

distanc 

e 

Cruise 

altitude 

(nm) FL 

(ft / 100) 

TOW 

(kg) Mission 

fuel (kg) 

BA 

CAPRA 18 AERO2k PIANO ANCAT/EC2 PIANO 

Mission 

fuel (kg) 

AERO2k / 

CAPRA % 

Mission 

fuel (kg) 

ANCAT / 

CAPRA % 

A320-200 1000 350 69939 6510 6445 99.0 6127 94.1 

A320-200 1000 370 64029 6038 5995 99.3 6029 99.9 

B737-200 1000 310 51861 6558 6471 98.7 6034 92.0 

B737-400 500 350 55842 3510 3574 101.8 3510 100.0 

B737-400 1000 350 53988 6078 6051 99.6 5788 95.2 

B737-400 1500 350 58222 9238 9046 97.9 8947 96.8 

B747-200B 3000 310/350 305083 70474 64955 92.2 67083 95.2 

B747-400 3000 350/390 291925 60968 58361 95.7 56582 92.8 

B757-200 500 350 92595 4980 4559 91.5 4644 93.3 

B757-200 2500 350 98468 19853 18184 91.6 19190 96.7 

B767-300ER 500 350/370 126618 5969 6225 104.3 5679 95.1 

B767-300ER 4000 350/390 167382 44577 43550 97.7 41635 93.4 

DC-10-30 19 

2000 350 199767 34456 33321 96.7 32690 94.9 

F100 500 350 41956 3031 3085 101.8 2969 98.0 

Mean = 97.7 Mean = 95.5 

Table 17: Comparison of PIANO to British Airways data from ANCAT/EC2 

The data in Table 17 show an overall improvement for AERO2k compared to 

ANCAT/EC2. This is due mainly to the PIANO aircraft models used for AERO2k. It 

should also be noted that PIANO Version 2.5 was used for ANCAT/EC2 whereas PIANO 

Version 3.9 will be used for AERO2k, although using different PIANO versions should 

not make an appreciable difference to the results. 

In Table 17 it is noticed that there is a poor match for the two flights for the B757, 

with PIANO underestimating by approximately 8.5%. The reason for this could be due 

to the use of a different aircraft variant or different engines compared to that used in 

CAPRA, or perhaps some operational reason that leads to a substantially increased 

18 Data from the British Airways CAPRA model is for 15 different missions using 10 aircraftengine 

combinations. CAPRA is used in performance evaluation and is linked to BA’s fuel 

monitoring system. 

19 Lockheed L-1011 PIANO model used for this aircraft for AERO2k. 


fuel burn. One point of interest in Table 17 is the comparison made for the DC-10. In 

AERO2k the representative type for this aircraft is the Lockheed L-1011, and it was 

this model that was used to generate the PIANO fuel data in the above table. It can be 

seen that the result is apparently better than that of the DC10 model used in 

ANCAT/EC2. 

Aircraft Mission 

distance 

B747- 

200 

B747- 

400 

(nm) 

Cruise 

altitude VIRGIN 

FL Trip Fuel 

(ft/100) (kg) 

AERO2k PIANO 

60.9% payload 

Trip Fuel AERO2k/V 

(kg) IRGIN % 

AERO2k PIANO 70% 

payload 

Trip Fuel AERO2k/V 

(kg) IRGIN % 

ANCAT/EC2 PIANO 

Trip Fuel 

(kg) 

ANCAT/ 

VIRGIN % 

1000 370 23000 21974 95.54 22348 97.17 22692 98.66 

2000 330/370 44000 40711 92.53 41541 94.41 42860 97.41 

3000 350 68000 62511 91.93 63714 93.70 65145 95.80 

4000 330/370 92000 83636 90.91 85690 93.14 88976 96.71 

5000 330/370 118000 107885 91.43 110585 93.72 115294 97.71 

mean = 92.47 mean = 94.43 mean = 97.26 

1000 370 20000 21255 106.28 21606 108.03 20332 101.66 

2000 370 39000 39710 101.82 40421 103.64 38621 99.03 

3000 350/390 59000 58619 99.35 60110 101.88 58157 98.57 

4000 330/370 81000 80246 99.07 81946 101.17 79458 98.10 

5000 330/370 104000 103207 99.24 105563 101.50 102339 98.40 

mean = 101.15 mean = 103.25 mean = 99.15 

Table 18: Comparison of PIANO to Virgin data from ANCAT/EC2 

The data in Table 18 show a good correlation for the B747-400 for flight distances 

greater than 1000nm. The difference for the shorter flight is probably due to a 

difference in the climb fuel predicted by PIANO and Virgin. The comparison for the 

B747-200 is less good, and again could be due to the use of a different aircraft 

variant, different engines or some operational reason that leads to a substantially 

increased fuel burn. In this case an alternative reason might be that the aircraft is 

typically operated with a much higher payload mass than that used in PIANO for this 

comparison. 

It is not known to what extent the flight planning models incorporate “real world” 

effects such as deterioration or winds. Regardless, the comparison of PIANO to within 

3% on average to both the BA data and the Virgin B747-400 is good. 

PIANO Comparison with BADA data 

BADA (Base of Aircraft Data) is an aircraft performance database produced and used 

by Eurocontrol [BADA, 2003]. BADA consists of sets of ASCII files containing 

performance and operating procedure data for 267 different aircraft types. Of these, 

87 datasets were developed using reference sources such as flight manuals and 

operating manuals. The remaining 180 types are linked to and are represented by one 

of the other 87 aircraft. The main application for BADA is trajectory simulation and 

prediction within the domain of Air Traffic Management. The Operations 

Performance Model of BADA defines the aircraft type, mass, flight envelope, 

aerodynamics, engine thrust and fuel consumption. Data from BADA Version 3.3 have 

been compared to PIANO data for a number of different aircraft types. An example is 

given in Figure 28 for a Boeing B747-400 aircraft. Fuel consumption rates in terms of 

nautical miles (nm) travelled per kg of fuel consumed are compared at a number of 

different altitudes (flight levels) at different aircraft masses. 


Fuel consumption rate (nm per kg fuel) 

0.07 

0.065 

0.06 

0.055 

0.05 

0.045 

0.04 

0.035 

0.03 

190000 240000 290000 340000 390000 

Aircraft mass (kg) 

BADA410 

BADA390 

BADA370 

BADA350 

BADA330 

BADA310 

BADA290 

BADA280 

BADA260 

BADA240 

PIANO410 

PIANO390 

PIANO370 

PIANO350 

PIANO330 

PIANO310 

PIANO290 

PIANO280 

PIANO260 

PIANO240 

Figure 28: Comparison of PIANO to BADA data for a Boeing B747-400 at a number of 

different altitudes (legend shows flight levels) for different aircraft masses 

The B747-400 in Figure 28 shows a very good match between PIANO and BADA, with 

the arithmetic mean difference of all data points of +0.37% with a standard deviation 

of 3.10. PIANO and BADA produce similar results for most aircraft. However, it is 

typical that there are some differences somewhere within the cruise flight regime. 

When comparisons are made for other aircraft, these differences can become 

apparent at different flight conditions for different aircraft, some are at lower flight 

altitudes with low aircraft mass, others at higher altitudes. The mean percentage 

difference values were calculated for all aircraft where there were data available in 

BADA to match the AERO2k representative aircraft type PIANO models. These are 

shown in Table 19. 


Aircraft type Representative type code Piano as % of BADA Standard deviation 

A300 600R A306 101.71 2.20 

A310-300 A310 104.77 4.38 

A319 A319 100.88 2.77 

A320-200 A320 102.34 2.30 

A321-100 A321 101.31 2.79 

A330-300 A330 110.62 3.13 

A340-300 A340 102.17 3.71 

ATR72 AT72 112.79 4.25 

B727-200 B722 89.10 0.85 

B737-200 B732 102.98 13.09 

B737-800 B738 126.95 8.81 

B747-200B B742 106.56 3.61 

B747-400 B744 100.37 3.10 

B757-200 A752 110.51 4.34 

B767-300ER B763 94.99 2.75 

B777-200 B772 115.79 5.39 

BAC1-11 BA11 83.74 1.48 

BAe 146 BA46 101.99 7.45 

Dassault Falcon 900 F900 91.41 4.30 

DC9 DC9 112.17 13.41 

Fokker F100 F100 156.06 14.73 

Fokker F50 F50 97.53 4.27 

Fokker F70 F70 107.22 2.38 

Lockheed L1011-500 L101 91.23 7.68 

MD11 MD11 105.70 2.62 

MD80 series MD80 91.64 3.11 

Mean = 104.71 

Table 19: Comparison of PIANO data to BADA 

The arithmetic mean of the performance data for each aircraft incorporates up to ten 

different cruise altitudes and three aircraft weights, similar to those shown in Figure 

28. The arithmetic mean of all types analysed in this exercise shows PIANO fuel burn 

data are less than 5% higher than that of BADA. The standard deviation is 13.98. 

Approximately 35% of the aircraft models agree to within 3%, approximately 50% 

agree to within 7%, and 85% to within 13%. It should be noted that whilst the brief 

description of these aircraft types for BADA and PIANO are the same, e.g. B737-800, 

the actual aircraft variant upon which the models are based may have significant 

differences, such as different maximum take-off weights, payload capabilities (due to 

interior fitments) and different engines. 

In Table 19 PIANO estimates fuel burn for the B757 on average10.5% higher than 

BADA, while in Figure 17, PIANO underestimates the fuel burn for this aircraft by 

approximately 8.5%. The exact reason for this is not known but it is anticipated that it 

is due to differences in the variant modelled as described in the paragraph above. 

Airline operational data 

PIANO was compared to airline operational data. Data for two aircraft types were 

available and were analysed. The results for each aircraft type were comparable but 

data for one of the types only covered two routes (city pairs). Therefore the results 

presented are for the one aircraft type for which data for a large number of routes 

were available. The aircraft type used for comparison in this section is a medium size 

airliner which is typically used on all route lengths, i.e. short haul through to long- 


haul routes. The aircraft type and the operator are subject to commercial sensitivity, 

so they cannot be disclosed. The flights encompassed mission ranges from 75 to over 

4000 n.miles and cruise flight altitudes from 17000 to 41000 feet. 

The operational flight data were recorded by on-board flight data recorders and 

provided data on longitude, latitude, altitude, aircraft weight, engine fuel 

consumption rates, flight speed (Mach), outside temperature and wind direction and 

speed. All these data were recorded at intervals of approximately four seconds 

throughout each flight. From these data the fuel burn and other information for 

particular flight segments could be calculated. The flights considered included 

destinations to and from all points of the compass from the airline’s home base. 

There was therefore no particular bias on direction or on prevailing winds. The 

analysis of the data included corrections for the effect of winds by calculating an 

‘equivalent headwind’ from the recorded wind direction and speed. This gave a better 

indication of the equivalent still air distance that was flown, and allowed comparison 

to the great circle distance between origin and destination airports. There was no 

correction made for ambient temperatures that were different from ISA standard 

temperatures for the given flight altitude. 

PIANO was used to replicate as closely as possible the flight segments from the 

operational data. For this exercise the aircraft mass was matched as closely as 

possible, as was the distance travelled (compared to the distance corrected for wind 

effects from the operational data) and the flight speed. It should be noted that the 

long-range cruise (LRC) speed is used in the inventory (99% SAR). 

Comparisons were made for aircraft take off weight (showing the 60.9% payload, 

used in Aero2k, gave the best fit with airline data), climb and descent (showing good 

agreement with “normal” climb and descent data, excepting outlying data probably 

due to exceptional items such as air traffic delays) and cruise. An example of cruise 

data is shown in Figure 29. 

Fuel mass for flight segment (kg), PIANO 

40000 

35000 

30000 

25000 

20000 

15000 

10000 

5000 

0 

0 10000 20000 30000 40000 

Fuel mass for flight segment (kg) operational data 

PIA NO and 

operational data 

1-1 line 

Figure 29: Operational fuel burn for individual flight segments compared to equivalent 

calculated PIANO fuel burn data 

In Figure 29, cruise data for 162 flight segments from the 77 operational flights were 

compared to calculations made with PIANO for equivalent flight segments. The flight 

segments were each portions of flights that were carried out at a constant altitude, 

and encompassed distances from 15 to 2500 nm at cruise flight altitudes from 17000 


to 41000 feet. The mean of calculated PIANO fuel burn data is 97.77% of the 

operational data. The goodness of fit (R 2 ) 20 is 0.997. 

In summary, PIANO has been shown to produce fuel burn data that agrees with fuel 

burn data from reference sources (other prediction programmes and operational 

flight data), without any systematic errors. Comparison to operational flight data has 

shown an especially good match at cruise where most fuel is consumed. Small 

discrepancies between the PIANO predictions and the reference sources can be 

accounted for by a number of reasons: 

• manoeuvres in flight (such as turns), 

• engine and airframe deterioration, 

• sensor error on the operational flight data, 

• modelling inaccuracies of the other prediction programmes (the airline route 

planning systems and BADA), 

• the use of different aircraft variants and/or aircraft weights by PIANO and the 

reference sources. 

Validation of Representative aircraft types 

The selection of representative aircraft and engines was described in [Norman, 2002] 

and was based on consideration of number of seats, engine technology, maximum 

take-off weight (MTOW) and configuration, and number of aircraft in the fleet. 

Analysis of one day’s flight movements (4 th February 2002) has been used to confirm 

the selection of aircraft types by scrutiny of the numbers of aircraft movements by 

each type, indicating the relative importance of accuracy of models (Table 20). Some 

aircraft types are responsible for only a very small proportion of movements. These 

tend to be older aircraft types such as the Boeing 707 (B703) and Lockheed Tristar 

(L101). 

20 Goodness of Fit (R 2 ) is the coefficient of determination, defined as 1 – (sum of the squares about 

the mean)/(sum of the squared errors). As a fit becomes more ideal, the R² values approach 1.0. 


Representative Total Percentage of Representative Total Percentage of 

type movements total 

type movements total 

A306 919 0.96 B772 877 0.92 

A310 386 0.40 BA11 26 0.03 

A319 2380 2.50 BA46 3 

2455 2.57 

A320 4758 4.99 C130 2 

84 0.09 

A321 1029 1.08 C550 1 

3805 3.99 

A330 465 0.49 DC9 2094 2.20 

A340 396 0.42 E145 3 

10383 10.89 

A34R 20 0.02 F100 3 

1529 1.60 

AT72 2 

3503 3.67 F2TH 1 

1098 1.15 

B703 100 0.10 F50 2 

8722 9.14 

B712 639 0.67 F70 3 

888 0.93 

B722 1349 1.41 F900 1 

497 0.52 

B732 2061 2.16 GLF4 1 

603 0.63 

B734 10357 10.86 L101 276 0.29 

B736 3638 3.81 L188 2 

47 0.05 

B738 2204 2.31 MD11 259 0.27 

B742 438 0.46 MD80 5206 5.46 

B744 957 1.00 MD90 460 0.48 

B752 3167 3.32 SF34 2 

15094 15.82 

B763 2141 2.24 YK42 3 

76 0.08 

Table 20: Numbers of movements by each aircraft type for 4th February 2002 ( 1 bizjets; 

2 turboprops; 3 regional jets; everything else - ‘large jets’) 

Maximum take-off weight 

The mean maximum take-off weight (MTOW) for all aircraft represented by each 

representative aircraft type was calculated from JP fleets data [BUCHair, 2002]. These 

mean MTOW values are therefore based on the number of aircraft of each type in the 

world fleet. This data were compared to the MTOW data for the PIANO models being 

used for the representative aircraft types (Figure 30). 

PIANO model MTOW (kg) 

450000 

400000 

350000 

300000 

250000 

200000 

150000 

100000 

50000 

+10% 

0 

0 50000 100000 150000 200000 250000 300000 350000 400000 450000 

Average MTOW (kg) from fleet data 

-10% 

Figure 30: PIANO model MTOW against mean MTOW, all aircraft 


There is a good comparison in Figure 30 between the mean take-off weight data of 

actual aircraft and the PIANO models being used, as indicated in the statistics in Table 

21. 

Data values 

Mean MTOW (PIANO as percentage of mean value) 102.48% 

Standard deviation 7.12 

R 2 value 0.996 

Table 21: Data statistics for Figure 30 

Comparison to other aircraft 

The 40 representative aircraft types used in AERO2k represent several hundred 

different aircraft types and variants. It is possible within PIANO to compare the 

performance of the representative types to a large number of other aircraft types and 

variants to give an impression of their ‘representativeness’. 

The other PIANO models used in the comparisons in this section are those provided, 

without guarantee, with the standard version of PIANO. They have not been validated 

in the way the representative types have in AERO2k. The models provided with PIANO 

do not include all possible aircraft types and variants, only a selection. Finally, there is 

no weighting to account for the numbers of movements by each type of aircraft. 

Therefore this section serves as an indicator of basic trends only. 

This exercise derives from the premise that aircraft weight and distance flown are 

dominating factors to the mass of fuel consumed in flight. Specific groups of aircraft 

are plotted together to show how good the chosen representative aircraft models are, 

on an MTOW basis. Definite trends can be seen for different mission distances of 

total mission fuel burn against MTOW. Aircraft have been separated by ‘generation’ 

into newer and older aircraft types: older technology aircraft tend to consume 

significantly more fuel. The trend lines shown on the plots are for the representative 

aircraft. Figure 31 shows the fuel burn for all aircraft for a mission distance of 500nm. 

Figure 32 and Figure 33 show similar data but are for mission distances of 2000nm 

and 3500nm respectively. 

Fuel burn (kg) 

16000 

14000 

12000 

10000 

8000 

6000 

4000 

2000 

0 

0 100000 200000 300000 400000 

MTOW (kg) 

500nm 'current' aircraft type 

500nm 'old' aircraft type 

500nm current representative type 

500nm old representative type 

Figure 31: Fuel burn against MTOW for 500nm mission, comparing representative types 

and represented types (using PIANO models) 



50000 

45000 

40000 

35000 

30000 

25000 

20000 

15000 

10000 

5000 

0 

0 100000 200000 300000 400000 

MTOW (kg) 





Figure 32: Fuel burn against MTOW for 2000nm mission, comparing representative 

types and represented types (using PIANO models) 


90000 

80000 

70000 

60000 

50000 

40000 

30000 

20000 

10000 

0 

0 100000 200000 300000 400000 

MTOW (kg) 





Figure 33: Fuel burn against MTOW for 3500nm mission, comparing representative 

types and represented types (using PIANO models) 

It can be seen in Figure 31 to Figure 33 that there is a definite correlation between the 

trendline for the representative aircraft types and the aircraft data at all mission 

distances, for both older and newer generation aircraft types. Goodness of fit (R 2 ) 

values for Figure 31 to Figure 33 are shown in Table 22. 

500nm 

2000nm 

3500nm 

(Figure 31) 

(Figure 32) 

(Figure 33) 

‘Current’ aircraft 0.97024 0.96799 0.94484 

‘Old’ aircraft 0.96037 0.92445 0.94342 

Table 22: Goodness of fit (R2) for Figure 31 to Figure 33 


Figure 34 shows the fuel burn for selected short range and selected medium range 

aircraft for a mission distance of 500nm. 


7000 

6000 

5000 

4000 

3000 

2000 

1000 

0 

0 20000 40000 60000 80000 100000 120000 

MTOW (kg) 



Airbus A319 

Airbus A320-200 


Avro RJ 85 

B737-200 

B737-400 

B737-700 

B737-800 

B757-200 

Douglas MD-82/88 

Embraer EMB-145 

Fokker F100 

FokkerF70 

Figure 34: Fuel burn against MTOW for 500nm mission for selected short range aircraft 

types 

Figure 34 demonstrates a reasonable correlation between the representative types 

and the other aircraft, with R 2 for the ‘current’ aircraft types of 0.91 and for the older 

aircraft types of 0.74. It should be noted that there are a very wide variety of design 

and size of aircraft in the short-range aircraft group shown in Figure 34, leading to 

more variation in performance and therefore a relatively lower R 2 value. 

Figure 35 and Figure 36 show the fuel burn for selected medium to long-range 

aircraft types for mission distances of 2000nm and 3500nm respectively. 


50000 

45000 

40000 

35000 

30000 

25000 

20000 

15000 

10000 

5000 

0 

50000 150000 250000 350000 450000 

MTOW (kg) 



Airbus A300 600R 




B707-320C 

B747-200B 

B747-400 

B757-200 

B767-300ER 

B777-200 

Douglas DC 10-30 

MD-11 

Figure 35: Fuel burn against MTOW for 2000nm mission for selected medium-long 

range aircraft types 



90000 

80000 

70000 

60000 

50000 

40000 

30000 

20000 

10000 

0 

150000 200000 250000 300000 350000 400000 450000 

MTOW (kg) 





B707-320C 

B747-200B 

B747-400 

B767-300ER 

B777-200 

Douglas DC 10-30 

MD-11 

Figure 36: Fuel burn against MTOW for 3500nm mission for selected long range aircraft 

types 

Figure 35 and Figure 36 again demonstrate reasonable levels of correlation between 

the representative types and the other aircraft, as shown in Table 23. 

2000nm (Figure 35) 3500nm (Figure 36) 

‘Current’ aircraft 0.94 0.85 

‘Old’ aircraft 0.90 - 

Table 23: Goodness of fit (R2) for Figure 35 and Figure 36 

The good correlations in this section show that range and take-off weight (or aircraft 

mass) are dominant factors in fuel usage and that the representative aircraft types 

that are being used are representative of a much broader range of aircraft types and 

variants. 

Russian/Former Soviet Union aircraft 

The selection of representative aircraft used sample flight movement data and JP 

Fleets data [BUCHair, 2002]. The sample flight movement data only contained data 

for flights originating or terminating in Europe and North America so very few flights 

by aircraft produced in Russia/Former Soviet Union (FSU) were recorded. Analysis of 

JP Fleets showed there to be very large numbers of Russian/FSU-produced aircraft in 

the world fleet although the number of these aircraft still in service could not be 

determined. Thus there was some uncertainty over the selection of representative 

types for Russian/FSU aircraft. As PIANO contains very few models of aircraft 

produced in Russia/FSU, it was decided that these aircraft would be represented by 

similar western aircraft, the selections being made based on technology age, 

configuration and MTOW. Only limited validation could be carried out, although 

validation using one of the few aircraft to have a model in PIANO (Ilyushin IL96) 

showed fuel burn agreement within 10%. The numbers of movements by 

Russian/FSU aircraft are relatively low in comparison to the total number of 

movements (approximately 1% of the total number of all movements). This lessens 

the impact on the end inventory of the reduced ability to validate the models for 

these aircraft. 


Emissions Parameterisation 

The correlation methods described for emissions of CO, hydrocarbons and soot are 

the only known algorithms that combine a physical background with available 

measurement data to an individual formula for each engine. These methods have 

revealed good agreement with the few available measurement data. In contrast, the 

data for non-volatile particulate numbers are based only on a general correlation and 

can only be regarded as a preliminary estimate. Further research is needed in this 

area to characterise particulate emissions, including volatile particles, both on the 

ground and at altitude. 

There are several NOx correlations available which are based on different approaches. 

Since there will be no internal combustor data available in AERO2k, only the more 

simple methods come into consideration of which the p3-T3 method and the DLR fuel 

flow method are widely known and accepted. The p3-T3 method delivers highly 

accurate results, if the pressure exponent and the p3-T3 relation of each engine are 

known. Unfortunately these data are not available for AERO2k. Therefore the DLR fuel 

flow method, which is based purely on published data from the ICAO database, and 

has demonstrated good accuracy, has been used in AERO2k. 

Examples of validation evidence for these models have been presented in Section 

2.1.3 and are more fully examined in [Plohr, 2004]. 

Data integration and calculation 

This section describes the additional checks carried out to validate the data 

integration software that forms the heart of AERO2k. These checks are in addition to 

the validation carried on the individual modules described above. 

Comparison of individual flights, flight legs and overall results with FAA SAGE Inventory 

Separately from the contracted AERO2k work, a comparison is being undertaken 

under the stewardship of ICAO between AERO2k and the US FAA inventory SAGE. The 

SAGE inventory is still under development and this comparison is scheduled to 

continue beyond the current AERO2k project. At the time of writing, the comparison 

work is not available for publication. However, much work has been undertaken as 

part of this comparison and, to date, the comparison of these two major inventory 

projects has shown substantially similar results despite a number of detail 

differences in approach. 

Fuel Used Validation by random sampling of flights 

Validation checks have been carried out on the AERO2k output on an individual flight 

basis. The following paragraphs describe a validation check that the fuel-used output 

from AERO2k agrees with the PIANO modelling data on which it was based. 

Flight data for the week beginning 5 th February 2002 were processed using the 

AERO2k program, to produce three different ratios namely: 

− The fuel burn per nautical mile travelled, 

− The take-off mass as a function of the distance travelled, 

− The speed as minutes per nautical mile. 

The GC distance between the departure and arrival airports was added allowing any 

major detours to be spotted. Of the thousands of flights examined, only one flight 

showed any detectable error in the distance flown, in that it had flown 189% of the 

GC distance to get to its destination. The ratios discussed below were all examined to 

see if there was anything else unusual about this flight, but there were no other 

anomalies. It is possible that since this was a one-hour flight, the GC route and the 

route taken by the aircraft to fly along designated flight corridors were vastly 

different – perhaps an ATC routing or diversion issue. 


In order to have a frame of reference when examining the AERO2k results, mission 

tables were prepared in PIANO for each representative aircraft type that was 

examined. The assumption was made that the aircraft were flying at a speed that 

gave 99% of their specific air range, 60.9% of the max payload, and altitudes that 

were typical of the selected flights were chosen, as well as a range of distances that 

would allow a good comparison with the selected flights. The results from the 

mission tables and AERO2k were then plotted to compare the two and to find any 

AERO2k results that deviated from the “norm”. Three graphs were plotted for each 

aircraft type: fuel burn per nautical mile (n.mile), the take-off weight per n.mile, and 

the speed, measured as n.mile per minute. An example of each of the graphs is 

pictured below in Figure 37 to Figure 39. 

Fuel used (kg) 

8000 

7000 

6000 

5000 

4000 

3000 

2000 

1000 

0 

Fuel used v Distance 

0 200 400 600 800 1000 1200 

GC distance (n.miles) 

Figure 37: Fuel used vs. distance results for the MD80 

T/O mass (kg) 

70000 

60000 

50000 

40000 

30000 

20000 

10000 

0 

Take off weight v Distance 

Piano 


A2K 

0 200 400 600 800 1000 1200 

Distance (n.miles) 

Figure 38: Take off weight vs distance to be travelled for the MD80. 

Piano 

A2K

Journey time (min) 

180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

Distance v Time 

Piano 

0 200 400 600 800 1000 1200 

Distance (n.miles) 

Figure 39: Time taken to complete the journey compared to the distance of the flight 

for the MD80. 

In order to find out if there were any systematic problems with the size of the aircraft, 

the AERO2k representative aircraft examined were grouped by size into small, 

medium and large categories. 

In the small category, the flights that had been randomly selected were all relatively 

short, low flights, so they were difficult to simulate in PIANO. This meant that in 

general there was quite a large deviation from the PIANO trends for each of the 

aircraft. Closer examination, for example by checking just the cruise sections of a 

flight, or by inputting the exact journey distance and altitudes revealed no problems 

and that the AERO2k results were in good agreement with PIANO. It should of course 

be remembered that the flights will never precisely match the PIANO data, as in 

AERO2k the aircraft fly far more complex missions than can be simply simulated in 

PIANO. The fuel burn, flight time and take off weight are far more likely to be affected 

by small in-flight changes in smaller aircraft than in larger aircraft as they have a 

proportionally greater effect. 

The medium and large aircraft both showed a closer correlation to the PIANO data 

than the small aircraft. The only real deviation from the expected results was for 

some A340 and 737-400 flights. In both cases the root cause of the problem was 

traced to the point times in the flights and these were corrected using further data 

traps within the AERO2k data integration software. This data check confirmed the 

decision made early in the project not to rely on the known unreliability of flight 

point times as a fundamental source of aircraft speed data but to use the timing data 

only for temporal ordering of the flight leg data. 

Fuel-used checks on a flight leg basis 

The majority of fuel usage and emissions from civil aviation occurs during the cruise 

phase. Constant-altitude cruise data from selected flights have been compared to 

PIANO data for similar aircraft weights and speeds at the same altitude to confirm 

the correct implementation of the PIANO-derived data within the AERO2k dataintegration 

module. As an additional check, fuel-used data have also been calculated 

using the BADA performance tool [BADA, 2003]. The results from each of the three 

calculations (AERO2k, PIANO, and BADA) are shown in Figure 40 to Figure 43 for the 

A320, A340, B737 and B747 aircraft. 


A2K

Fuel consumption (nm/kg) 

0.250 

0.230 

0.210 

0.190 

0.170 

0.150 

0.130 

A320-200 

45000 50000 55000 60000 65000 70000 75000 

Aircraft Weight (kg) 

AERO2K FL330 99%SAR 

PIANO FL330 M0.78 

BADA FL330 M0.78 

AERO2K FL370 99%SAR 



Figure 40: Fuel consumption comparisons between AERO2k, PIANO and BADA for A320- 

200 


0.100 

0.095 

0.090 

0.085 

0.080 

0.075 

A340-300 

0.070 

150000 160000 170000 180000 190000 200000 210000 


AERO2k FL370 99%SAR 



AERO2k FL390 99% SAR 



Figure 41: Fuel consumption comparisons between AERO2k, PIANO and BADA for A340- 

300 



0.21 

0.19 

0.17 

0.15 

0.13 

0.11 

0.09 

0.07 

0.05 

B737-800 

47000 57000 67000 77000 







Figure 42: Fuel consumption comparisons between AERO2k, PIANO and BADA for B737- 

800 


0.065 

0.06 

0.055 

0.05 

0.045 

0.04 

0.035 

0.03 

B747-400 

190000 240000 290000 340000 390000 

Aircraft mass (kg) 




Figure 43: Fuel consumption comparisons between AERO2k, PIANO and BADA for B747- 

400 

The figures demonstrate the operation of the AERO2k data integration software 

which uses look up tables derived from PIANO. The AERO2k fuel-used data are 

calculated at 99% of the Specific Air Range (SAR) speed, with recalculation of aircraft 


weight for every flight leg, and every 300km if the flight leg is longer than 300km. 

This is compared with PIANO and BADA data calculated at a constant Mach Number 

(close to but not equal to 99% SAR). The fuel consumption from PIANO and AERO2k 

data are close, with steps in the AERO2k data where weight is not recalculated for 

300km. This demonstrates the correct implementation of the PIANO performance 

lookup tables within AERO2k. 

The difference between PIANO and BADA data is simply the difference in the 

assumptions used to derive the respective PIANO and BADA performance models. 

These differences were examined in AERO2k Deliverable D8 [Norman, 2004], 

concluding that across the range of representative aircraft, PIANO compared 

favourably with BADA and other available operational data. 

Checks on a Global, Regional and National Basis 

Previous inventories have published verified results for fuel used, NOx and in some 

cases other emissions, for both civil and military emissions. Whilst AERO2k includes 

considerably more detail calculation and also updated technical information, it would 

be expected that the AERO2k results would be comparable with previous results 

unless there has been some technical development either in the aircraft fleet or in the 

emissions indices. 

A comparison of results for annual fuel-used and emissions with previous inventories 

is shown in Table 24. Emissions indices for these results are shown in Table 25. 

Civil Total Mass (Tg) 

Aero2k NASA1999 NASA1992 ANCAT 1992 DLR 1992 

Fuel Used 156 128 113.85 114.2 112.24 

NOx 2.06 1.69 1.44 1.6 1.6 

CO 0.507 0.685 1.29 n/a n/a 

HC 0.063 0.189 0.26 n/a n/a 

Particulate Mass 0.0039 n/a n/a n/a n/a 

Particulate Number 4.03E+25 n/a n/a n/a n/a 

Table 24: Comparison of AERO2k fuel-used and emissions global totals with previous 

inventories 

Civil EIs (g/kg) 


Fuel Used - - - - 

NOx 13.2 13.2 12.6 14 14.2 

CO 3.25 5.4 11.3 n/a n/a 

HC 0.4 1.5 2.3 n/a n/a 


Particulate Number 

(/kg) 

2.58E+14 n/a n/a n/a n/a 

Table 25: Comparison of AERO2k fuel-used and emissions EI with previous inventories 

The fuel used figure reflects the doubling in air traffic since 1992, offset by the fuel 

efficiency improvements, suggested as being in the region of 1% per year. Increasing 

pressure ratio, plus other technology improvements have allowed significant 

improvements in combustion efficiency in the last 25 years (Figure 44) and these 

engines are now dominating the civil aircraft fleet. Hence the significant reduction in 

CO and HC EIs. Despite improvements in NOx control technology, the effect of 

increasing pressure ratios has offset these, resulting in a slight increase in NOx EI over 

the decade. Examination of certification data [ICAO 1995] from newly certificated 


engines over the period confirms this trend. From a validation point of view, whilst 

not an exhaustive check, these results fall within the expected band. 

% of ICAO Standards 

450 

400 

150 

100 

50 

0 

Ref: The Boeing Company 

Hydrocarbons 

Carbon monoxide 

Pre81 81-91 91-present 

Year of Engine Certification 

Figure 44: Decrease in certificated HC and CO emissions by period of engine certification 

2.4.2 Validation of 2002 Military Aviation Emissions 

The military aviation database contains comprehensive coverage of fleet inventory. In 

many cases the actual state of airworthiness needs to be estimated. In addition, the 

number of movements and flying hours per aircraft (type) are fairly well known in 

case of NATO countries (making up the majority of aircraft movements), but for other 

countries, in particular the former Soviet states and China, these data are not known 

and can only be estimated. 

In addition, there is considerable uncertainty on where the emissions are actually 

released into the earth's atmosphere. Missions, even if they have the same purpose 

(for training) may have considerable differences in actual profiles. The calculated CO 

and HC emissions depend for a great deal on the accuracy of the estimated use of 

afterburning. There is little and conflicting information on the amount of 

afterburning used across the range of operations at world scale. Considering that this 

inventory assumes afterburning whenever opportunity is there, the current values of 

CO and EI-CO will be relatively close to the upper limit of the feasible range. 

2.4.3 Validation of 2025 Aviation Emissions 

The civil aviation emissions modelled for 2025 are, obviously, not based on actual 

data but are the result of forecast demand growth and technology assumptions. 

These assumptions are based on DTI’s work for CAEP and on Airbus’ expectation of 

aviation market growth. Whether they actually occur will depend on a number of 

factors both external and internal to aviation. The Airbus work is based on that 

manufacturer’s view of the future aviation demand and the aircraft size categories 

needed to meet that demand. As such the 2025 demand tends toward a European 


view, which is appropriate for this EC project. However it is recognised that these are 

other views of regional and global aviation demand growth and these views could, in 

future work, be used as the input to a further AERO2k emissions forecast. In fact, the 

global averaged demand growth of 4.2% per year is close to the 4.3% used in the 

CAEP/6 FESG forecast to 2020 [FESG, 2003]. Coincidentally, this FESG growth figure 

has recently been amended to 4.2% to 2020 [CAEP, 2004]. Whilst regional emphasis 

may vary within these forecasts, the AERO2k growth to 2025 is in line with other 

global forecasts. It does, however, remain a forecast based on assumptions, not a 

certainty. 

Similarly, the models used by DTI to generate the fleet rollover and resultant fleet 

technology improvements are the models accepted by CAEP FESG for their globally 

accepted forecast work for the CAEP process. The input data for these models, in 

terms of technology improvements, are based on the data referenced in Section 2.3 

have been checked with a major European engine manufacturer and accepted as 

being a reasonable forecast assuming continued pressure on NOx and fuel 

consumption. Once again, whether these assumptions represent what actually occurs 

is subject to events both inside and outside aviation. 

For military aviation, by its very nature, forecasting global distribution and quantity 

of emissions from military aviation over a 20+ year timescale is less predictable than 

forecasting emissions from civil aviation. Military aviation is not subject to growth in 

line with economic prosperity but is more prone to variation from one or more global 

and regional political scenarios. Given that military aviation generally contributes 

only around 10% of total aviation emissions, it was considered that providing globally 

distributed data for military emissions for 2025 was so subject to these scenario 

variations that any particular forecast could not be validated within the overall scope 

of AERO2k. Hence a generalised assumption was made that the calculated 2002 

values should be used for 2025 military emissions. 

2.4.4 Uncertainties 

As with any inventory, there are a significant number of assumptions made to allow 

quantification of the data and to keep the data to manageable proportions. This 

section highlights some of the more significant uncertainties that arise from those 

assumptions. 

Key Background Assumptions 

Improved routing 

Previous inventories assumed a great circle 21 route was flown between origin and 

destination airports and also assumed a typical cruise altitude. The flight movement 

data for AERO2k provide longitude, latitude and altitude data at a number of 

intervals throughout each flight. This allows appropriate fuel and emissions data for 

the actual flight route and altitude to be selected for the inventory. It will still be 

assumed that a great circle will be flown between each waypoint through the flight. 

This approach is a significant improvement on previous inventories as there can be a 

large difference between the actual route flown and the great circle route. The 

reasons for these differences can be due to air traffic control (congestion, flight 

corridors etc), restrictions on airspace (military areas or overfly rights for some 

countries) or weather (turbulence, storms) or avoiding/taking advantage of prevailing 

winds. 

The AERO2k flight movement data cannot cover every part of every flight in great 

detail, however. There are two aspects to this, a large-scale and a small-scale effect: 

21 The great circle distance is the shortest distance between two points on a sphere. 


The large-scale effect occurs when there are large distances between the waypoints. 

The intervals between waypoints can be spaced far apart in some regions of the 

world. The fine detail of the flight route in this region is not available but AERO2k still 

offers a significant improvement compared to using a great circle between origin and 

destination airports. 

The small-scale effect concerns parts of the flight where the intervals between 

waypoints may be small but the aircraft is performing rapid or frequent changes in 

course or altitude. These primarily affect climb or descent segments of the flight and 

it is not possible to capture precise details about the time or location of direction or 

altitude changes. The effects of these will have the largest impact on short flights 

where the climb phase and holding during descent contribute to a larger percentage 

of the total flight fuel burn. Analysis of the operational flight data used for validation 

in Section 2.4 shows a mean true distance travelled of 4.5% farther than great circle 

routes. 

Wind 

The effect of wind on aircraft during flight is, for a given flight speed, to alter the 

speed relative to the ground. Flying into a headwind means aircraft effectively fly 

slower relative to the ground, the actual distance flown (equivalent still air distance) 

increases, taking more time to reach the destination and consequently consuming 

more fuel. Flying with a tailwind has the opposite effect. A side-wind will push the 

aircraft off its intended course so a corrected course will be necessary to compensate. 

Most of the time the wind will act at an angle relative to the direction of flight so the 

effect will be a composite of the components of head/tail and sidewinds. In practice 

flight speeds may be altered to try to mitigate the effects to some extent. 

The AERO2k flight movement data consist of radar track data for flights over North 

America and the north Atlantic, and modelled flight trajectories for the rest of the 

world. The radar track data intrinsically incorporate the effects of winds as they 

impact directly on the flight times. For the rest of the world the trajectory model 

assumes still air. 

Computational methods exist to calculate the effect of winds using ‘equivalent 

headwinds’ [Sawyer, 1950] and meteorological statistics. However the computational 

effort required to implement such methods into the inventory would be prohibitive. 

Instead, an assumption is made for the flight speeds to be used (see Section 2.1.2). 

Whilst the effect of winds is only partially included in the movement database 

through the flight track data, the predicted flight trajectories do try to account for the 

presence of prevailing winds on some routes and minimise their effects where 

possible by predicting flight routes and altitudes which take advantage of tailwinds 

or minimise headwinds. 

Analysis of the operational flight data used for validation shows that winds at cruise 

altitudes can affect the equivalent still air flight distance by as much as ±20%. The 

mean difference from this data is an increase in equivalent still air distance of 1.5%. 

Atmosphere 

The use of ISA (International Standard Atmosphere) standard day temperatures for 

the AERO2k inventory was agreed as an a priori assumption early in the project. There 

is an effect on overall aircraft performance with changes in ambient temperature, 

and this affects the fuel consumption and emissions produced. This is primarily due 

to the effect of inlet air temperature on engine performance. The complications of 

modelling the changes of ambient temperature on a seasonal basis and with latitude 

were judged to be too great within the scope of the project as fuel burn and 

emissions datasets would have to be replicated for each different temperature band. 

Global mean variation from ISA standard day temperatures with height for the 

period 1958 to 2001 are shown in Appendix A [Ulbrich, 2004]. Even on this annually- 


averaged basis, there is considerable deviation from standard ISA temperatures with 

aircraft flying in areas of ISA ± 20deg. However, the predominance of flights, between 

30°N and 60°N, lie within the ISA ± 5deg band , suggesting that the overall deviation 

of fuel burn from ISA-derived performance levels will be small (

During flight a number of manoeuvres are performed which cannot be accurately 

modelled based on the flight movement data or the PIANO aircraft performance tool. 

Between and during the climb, cruise and descent segments the aircraft will make 

turns and accelerate (or decelerate), which will require more fuel than for straight 

and level flight. The effect of these manoeuvres on total fuel burn is, however, 

considered to be small over a flight distance. 

An additional manoeuvre to be considered is step-climbs made during cruise. These 

predominantly take place on longer routes where an aircraft burns a considerable 

amount of fuel. As the aircraft gets lighter the optimum altitude for minimum fuel 

consumption increases, and the pilot will seek permission from air traffic control to 

increase the flight level to match the aircraft’s optimum altitude as closely as 

possible. The increased fuel consumption during the step climb is usually far offset by 

the reduction in fuel consumption at the higher altitude. 

The flight movement data do not provide the resolution to give the exact time and 

location where step climbs take place. The fuel allocation method therefore selects 

cruise fuel burn data appropriate to the altitudes at each waypoint throughout each 

flight rather than making independent assumptions about when a step-climb took 

place and allocating additional fuel to account for the manoeuvre. Any error 

introduced by not including a specific climb fuel allowance will be smaller than the 

error from not changing the cruise flight altitude at the appropriate point in the 

cruise. Both of these errors will still be considerably lower than those for using 

average cruise altitudes, as used in previous inventory approaches. 

Deterioration 

Deterioration of engines and the airframe leads to increased fuel consumption. Over 

time, a gas turbine engine’s performance will be reduced due to deterioration of its 

component parts, due to tip clearances losses and accumulation of deposits on blade 

surfaces. The engine then has to be worked harder to produce the required thrust. 

The result is increased fuel consumption rates, which can show marked increases over 

the service life of an engine. On the airframe, surface irregularities, badly fitting seals, 

control surface alignment and instrumentation errors can lead to increased drag, 

which again results in increased fuel consumption rates. 

The EC 4 th Framework project AEROCERT [Norman, 2001] found deterioration of 

engine performance encountered in in-service engines would result in typical 

maximum fuel consumption increases of 3 to 4%. The rate of deterioration of the 

performance of an airframe is slow compared to that of an engine. Cumulative 

deteriorations on an airframe may, over many years, represent an overall efficiency 

loss that equates to up to 2% of cruise fuel burn. These results are comparable to 

typical increases in fuel consumption due to deterioration of between 2 and 6% 

stated by Eurocontrol [Mykoniatis and Martin, 1998]. This report also states that, in 

practice, engine performance degradation has only a small overall impact on total 

fuel burn, even on long distance flights. This is because an aircraft will not typically 

have all of its engines in a highly deteriorated state and because airframe 

deterioration builds up only slowly over many years. 

From consideration of the information above, it is estimated that the total increase in 

fuel consumption across the fleet due to deterioration will not be more than about 

3% compared to a brand new aircraft. This increased fuel consumption is implicitly 

included in the data from airlines and in the published performance data that are 

used for validation in Section 2.4.1. Therefore it is assumed that the effects of 

deterioration are adequately represented in the AERO2k inventory. 

Flight Uncertainties 

AERO2k has sought to include all global flights under IFR rules performed in 2002. The 

decision to omit non-IFR flights (primarily general aviation) immediately introduces 

an element of underestimate; of the order of 2% of the total civil aviation fuel used 


data. The capture of data for non-IFR flights in detail is a major and disproportionate 

task. It was therefore excluded from AERO2k in the project specification. 

Capture of data on IFR flights was based on the complex task of merging three flight 

databases – Europe (ECAC actual flight tracked data), North America (AMOC actual 

flight tracked data) and Back Aviation global timetable data. Whilst attempts were 

made to gain actual flight data for the rest of the word, this was not available within 

the project timescales and budget. This remaining timetabled fraction represents less 

than 30% of global aviation. The AERO2k data therefore do not include nontimetabled 

(e.g. charter) flights outside Europe and North America. The effect of such 

omission will vary around the globe, dependant upon the proportion of nontimetabled 

IFR flights in a particular region. The major charter flight concentration in 

Europe has however been included and the overall impact is estimated to be a small 

(

Representative Aircraft and Fuel Profiling Uncertainties 

AERO2k uses 40 representative aircraft to represent a world fleet of over 20000 

aircraft of over 200 different types (and many variations within these types). This 

methodology therefore represents an approximation, for which the uncertainties for 

individual aircraft were dealt with in AERO2k Deliverable Reports D7 [Norman 2002] 

and D8 [Norman, 2004]. 

For the overall emissions inventory, all major types of civil aircraft have been 

included, omitting only small aircraft (which are unlikely to appear in IFR flights) and 

rare larger types (which by their small numbers and/or low utilisation do not cause 

significant global emissions e.g. Antonov AN124). 

For these major types, representative engines have been selected on the basis of their 

NOx emissions. This methodology assumes that competitive engine types on the 

same aircraft will have similar fuel consumption (within 1% or 2%); as a key issue in 

global aviation, NOx is then the next distinguishing parameter for which data are 

available. The process for selection using average NOx is covered in D7 [Norman, 

2002] and D8 [Norman, 2004] and in Section 2.1.2 above, concluding that for the 

global inventory, the overall NOx emissions will represent a best estimate (subject of 

course to the engine modelling and NOx parameterisation used to calculate NOx at 

altitude). Other non-fuel related emissions will have a higher degree of uncertainty 

(primarily HC and CO) as these will depend upon the performance of the particular 

chosen representative engine. The emission levels of both these parameters are 

primarily dependant upon engine generation (i.e. entry into service date). The 

representative engines chosen are also dependant upon entry into service; hence this 

HC and CO improvement trend will be captured in the emissions data. It is recognised 

that this process does not provide the same degree of precision as it does for NOx. 

However, given the current relative importance of these emissions, the NOx-based 

approach is considered to be most appropriate for the representative engines. 

Additionally, it should be recognised that uncertainties over operational practice 

(particularly actual idle settings) are likely to be greater than any averaging effect 

from the choice of representative engine. In terms of impact, most HC and CO 

emissions from aviation are emitted during the landing and take-off cycle and, whilst 

important in terms of local air quality, the remaining emissions at altitude are 

currently considered by climatologists to be of lesser importance compared to other 

aviation effects at altitude. 

For the representative engines, fuel flows for the LTO cycle have been taken from the 

certification engine test data and thus represent a close approximation to actual fuel 

flows (subject to background assumptions in Section 2.4.4). However, fuel flows at 

altitude are a closely guarded commercial secret and thus must be calculated from 

available aircraft and engine performance models. Uncertainties in this process have 

been covered extensively in AERO2k Deliverable Report D8 [Norman, 2004]. It is 

apparent from the evidence presented in D8 that there is considerable variation in 

the fuel flows modelled by different methods. The report concludes that the PIANObased 

methods produce fuel burn data agreeing with reference sources without any 

systematic errors, with especially good match at the important cruise conditions. 

The method used in the AERO2k emissions inventory model for assigning fuel data to 

the flight profiles was described in Section 2.1.2. A number of a priori background 

assumptions were made in this part of the process (Section 2.4.4). Although the 

impact of a number of these background assumptions cannot be accurately 

modelled, and they have therefore deliberately not been accounted for in AERO2k, it 

is apparent that, taken together, they are likely to represent a bias effect on the 

AERO2k results. Most of the impacts are thought to increase fuel and emissions at 

altitude ie winds ~+1.5%, holding/stacking

altitude may be a small underestimate and data users may wish to make their own 

assessment of these effects. Conversely, the implicit assumptions in the CAEP-derived 

LTO cycle are to overestimate emissions. Although much of this overestimate has 

been eliminated from AERO2k by use of alternative airport- and aircraft-specific 

times-in-mode, there are still specific operational issues such as reduced thrust takeoff 

and idle setting (often 5g/kg) 15% 

CO and HC (EI < 5g/kg) 0,3g/kg (1g/kg exceptionally) 

DLR Soot Soot mass 10% (30% exceptionally) 

DLR fuel flow NOx 5% 

The correlation methods used for emissions of CO, hydrocarbons and soot are the 

only known algorithms that combine a physical background with available 

measurement data to an individual formula for each engine. Since these methods 

have revealed good agreement with the few available measurement data, they have 

been used for AERO2k. 

Aero2k does not hold any internal combustion data, so although several NOx 

correlations, based on different approaches exist, only the more simple methods are 

available to the model. Of the simpler methods, the DLR fuel flow method and the p3- 

T3 method are the most widely known and accepted. However, the p3-T3 method 

requires the pressure exponent and the p3-T3 relation of each engine to be known, 

and this information is not available in Aero2k. The DLR fuel flow method, on the 

other hand, produces accurate results based purely on published data from the ICAO 

database, so it was chosen as the method for calculating NOx in Aero2k. 

For particulate numbers, this work in AERO2k is a first attempt to make a global 

quantification in the form of an inventory. Due to the lack of measured data of the 

individual engine types, this function based on correlating non-volatile particle 

numbers to particle mass has been used to determine the particle number emission 

indices of the representative engines. It is obvious that this procedure is not suited to 

deliver accurate results for individual flights, but delivers the best estimate possible 

with the few available data for a number of aircraft movements. The estimate of 

particle numbers must therefore be regarded as an “order-of-magnitude” estimate 

that can be significantly improved if further research is carried out on the 

characteristics of particle emissions from gas turbines. 

Data Integration Uncertainty 

Data integration is generally a precise process – taking flight data, matching 

representative aircraft and engines, calculating fuel flow and emissions and 

allocating these to flights, regions and geographical grids etc. There are, however, a 

small number of assumptions in the data integration, which are included to reduce 

computing time or to increase accuracy. The main assumptions are: 

LTO emissions are assumed to occur at the airport coordinates 


LTO times (to and from 3000ft) are assumed to be in accordance with the typical LTO 

times contained in the look-up table for the particular airport. Where actual flight 

data are available for the flight phases less than 300ft, these are not used. 

Airports are assumed to be at sea-level 22 

Aircraft weight is recalculated only at each data point or every 300nm, whichever is 

smaller (see Figure 41 and Figure 42, which indicate the impact of this assumption) 

Aircraft speed is not calculated from distance flown divided by time. Instead, the 

aircraft is assumed to fly at 99% of Specific Air Range (SAR) [Norman, 2004]. This 

assumption is in line with airline practise and avoids erroneous calculation of speed 

due to errors in the time data, deviation from great circle distance and wind effects. 

Climb and descent fuel does not depend upon the geographical distance covered but 

only on the change of altitude of the climb/descent and the weight of the aircraft. 

Where precise values are not available in the look-up tables, values for fuel and 

emissions are calculated by interpolation. 

The impact of these assumptions on data will become apparent only if detailed 

queries are made on the data. However, for the overall inventory results, the impact 

of these assumptions is small and normally negligible. 

2.4.5 Sensitivities 

Representative Days, Weeks and Year 

AERO2k global gridded emissions have been calculated from the best available flight 

data from 6 representative weeks in 2002. In using representative weeks to reduce 

data collection and processing time (and even in using a single year), there is a risk 

that data may be affected by individual events that are not “typical” of aviation over 

the longer term. Whilst there is a small increase in uncertainty introduced by use of 

the representative weeks method, there is a significant improvement in the utility of 

the tool thorough reduced data processing times. 

For 2002 as a whole, the after effects of the events of 9/11 were being felt in the 

downturn in aviation. At the end of 2002, the SARS outbreak was appearing on the 

world scene and again just beginning to affect traffic in terms of passenger numbers. 

A football (soccer) World Cup was held in Japan and Korea in June 2002. All these 

factors are reflected in the air traffic represented in AERO2k but they do demonstrate 

that the traffic for any year is unique to that year. Different effects were apparent in 

2001 eg the immediate affects of 11 Sept, and in 2003 e.g. the Iraq War and the full 

SARS outbreak. 

A similar effect is present in the representative weeks, which is more difficult to 

quantify. The representative weeks have been annualised using flight number data 

for every day of the year. Seasons were found to be the strongest effect and the 

annualisation process has been carried out on this basis. However, short-term effects 

in 2002 can cause data for individual days within the “representative days” to be 

affected. The 6 representative weeks (42 days) within the AERO2k 2002 data have 

been examined for consistency and two events were identified. On Wednesday 11 

22 In reality this would impact time to climb to cruise altitude and the overall aircraft 

performance. This is not expected to have a significant impact on the overall inventory result, 

as the total fuel consumed during all LTO operations is small relative to total aviation fuel 

(

Sept 2002 the flight data showed a significant fall in the number of flights compared 

to the weekly patterns for the other representative weeks. Examination of the data 

showed the main fall was in flights to or from the USA with almost full recovery by 

the next day. A similar effect was seen toward the end of the December week (2 to 9 

Dec) with a considerable dip in the number of US originating flights. Weather records 

show a major snowstorm along the US eastern seaboard from 5 December onwards 

with over 150mm of snow falling in one hour at Washington Dulles airport. 

Consideration was given to taking account of these events. However, they are not 

totally unique events and are representative of many weather, sport and cultural 

events occurring during the year that change the overall volume of actual air traffic. 

There is a remaining concern that the Sept 11 anniversary would be one of the larger 

effects and its effect may be exaggerated by its use in the representative week. 

Operational Practice and Other Unquantifiable effects 

The various uncertainties involved in aircraft operation have been mentioned in a 

number of earlier sections in this report. Together, the varying operational procedures 

for take off settings, cruise speeds, fuel tankering etc, bring uncertainty to the 

calculation. It has been the policy in AERO2k not to try and make approximate 

allowances for factors that are not fully understood and it is left to the user to 

account for these if required. In summarising the various effects, the vast majority of 

these suggest that AERO2k represents a small underestimate in terms of fuel, CO2 

and H20 emissions. For NOx, CO and HC emissions, the underestimate in terms of fuel 

may be offset by an overestimate of LTO emissions due to use of the ICAO published 

values which represent maximum rather than average emissions. 


Further analysis using the detail data contained within AERO2k 

The current design of the AERO2k software is, in accordance with the aims of the 

project, focussed on providing global gridded data for use by climatologists. Although 

featuring many enhancements compared to previous inventories, like them, it 

includes a range of assumptions that are designed to provide this climate data within 

the limitations of affordable computing power. As a consequence, there are 

limitations in the extent to which the unpublished data within the 2002 flight 

database should be used for analysis at a more microscopic level. For example, 

subdivision of the data to country and even major airport level should retain 

adequate level of accuracy. However, the global omission of non-IFR flights 

(representing around 2% of global fuel burn) would result in unrepresentative data if 

analysis were taken for small domestic airports where non-IFR flights dominate. 

Because of the use of representative aircraft, detailed analysis by individual aircraft 

type can only be carried out with care However good representation by aircraft 

weight, seat-class range can be carried out successfully. 

In a similar way, the focus on global climate effects does not require a high degree of 

accuracy in the LTO region. AERO2k contains a number of enhancements such as 

airport specific LTO times-in-mode compared to previous inventories. However, 

airport specific studies using the data would need to be undertaken with care in order 

to include specific operational practices such as reduced thrust take-off and 

controlled descent approach. 

Regional Variation Sensitivity 

Flight data used to construct the 2002 inventory consists of two styles of data. The 

first is actual flight tracked data from Europe and North America, which represents an 

accurate representation of actual latitude, longitude and altitudes flown. The second, 

for the remainder of the world, is based on simulated trajectories formed from 

timetable data enhanced with knowledge of aircraft routings and flight profiles. As a 

consequence of this difference, there is a difference in the degree of accuracy of 

gridded data between these two regions. Perhaps of particular relevance to 

climatologists is the absence of actual (as opposed to simulated) flight trajectories in 

the southern hemisphere. Whilst estimates, particularly of altitude, have been made 

based on typical airline practice, there is a small degree of uncertainty over the 

vertical distribution of flights, and therefore emissions in the southern hemisphere. 

Whether this uncertainty is significant will depend upon the altitude sensitivity of 

climate impacts in these regions. 

There is also a small difference in the percentage of flights covered between the same 

regions. For Europe and North America, flight tracking picks up both timetabled and 

non timetabled flights. Non-timetabled IFR flights for the remainder of the world, 

such as charter, business or VIP flights are calculated to represent a small proportion 

of fuel-used and emissions [Norman, 2004]. Nevertheless, if making inter-regional 

comparisons, this small but difficult to quantify difference in source data will become 

larger. 

Sensitivity Summary 

In summary it is concluded that with the additional representative aircraft, flight 

inventory and updated emissions data, AERO2k represents a significant and 

important step forward over previous inventories. The core figures of fuel-used, CO2, 

H2O and NOx have confirmed and refined data from previous inventories, 

demonstrating that continued growth in aviation since the mid-1990s is still 

outstripping the considerable advances in fuel-saving and emissions reduction 

technology. 


Overall, the predicted fuel-used, CO2, H2O and NOx figures are considered highly likely 

to represent a small underestimate of the likely actual figures, resulting from the 

deliberate decision not to try to include effects which could not be quantified within 

the current work programme of the AERO2k project (e.g. winds, tankering). 

Additionally, the lack of complete flight data for parts of the world and the deliberate 

omission of non-IFR flights are also likely to represent a small underestimation. 

For CO and HC emissions, uncertainties in source data show a greater variation on an 

engine-to-engine basis as well as between engine types [ICAO 1995]. Together with 

the approximation of using representative engines selected on a NOx-basis and the 

significant reductions possible from operational measures at airports, the 

uncertainties in CO and HC from individual flights is significant. However, most HC 

and CO emissions from aviation are emitted during the landing and take-off cycle 

and, whilst important in terms of local air quality, the remaining emissions at altitude 

are currently considered by climatologists to be of lesser importance compared to 

other aviation effects at altitude. Nevertheless, when compared to previous 

inventories, the AERO2k results clearly demonstrate the continued improvement in 

HC and CO emissions achieved with recent aviation combustion technology. 

The new information on particulates is subject to a greater degree of uncertainty, 

primarily due to the lack of measured data and scientific understanding of the 

formation of particulates in gas turbines. Only a small number of engine 

measurements are available for validation and application and a generic method has 

been used to cover all engines. It is obvious that this procedure is not suited to deliver 

accurate results for individual flights, but delivers the best global estimate possible 

given the paucity of available data on this subject. 


3 Results 

AERO2k results fall into three categories: inventory results for 2002 civil aviation, 

inventory results for 2002 military aviation and a civil aviation forecast for 2025. 

Within each of these categories, the results are presented as global gridded data, as 

global totals, regional totals and totals by representative aircraft. Distribution of 

emissions by altitude is also shown. 

3.1 Results for 2002 Civil and Military Aviation 

3.1.1 Gridded data for 2002 

The main results from AERO2k for 2002 are the global gridded data for fuel-used, 

emissions and distance flown. These data are described in outline here and are 

published on the AERO2k WebPages at http://www.cate.mmu.ac.uk/aero2k.asp. 

The gridded data consist of: 

• 12 tables, one for each month of 2002, each containing the civil aviation 

emissions, fuel used and distance flown in a global grid of resolution 1 deg by 1 

deg by 500ft 

• tables showing the civil aviation emissions, fuel used and distance flown over 4 

successive 6-hour periods 

• 1 table showing annual military aviation emissions, fuel used and distance 

flown in a global grid of resolution 1 deg by 1 deg by 500ft. 

These data are intended for use by atmospheric scientists for the estimation of 

climate impact of current and future aviation. 

For the civil aviation gridded tables, although the data are given at a vertical 

resolution of 500 feet, these data need to be treated with care. The majority of the 

flight data provide altitude information to 3 significant figures (e.g. FL302 

representing 30200ft). As air traffic convention provides for vertical separation by 

constraining actual cruise flight close to the round thousands of feet altitude, much 

of the cruise data are of the form of round thousands of feet e.g. “FL300” 

representing 30000ft. With a grid vertical resolution of 500ft, this results, for 

example around 30000ft, in a large emissions value falling into the 30000ft to 

30499ft cell with very few in the 29500ft to 29999ft cell. 

In some senses, this is a real effect, with emissions concentrated in horizontal bands 

1000ft apart. These bands are blurred by the effects of slight variations from the 

target flight altitude, by errors and uncertainties in radar information and even in the 

position of the engines on the aircraft. However, to which of the cells (for example 

just above or just below 30000ft) the emissions should be allocated is somewhat 

arbitrary. Further blurring will occur with dispersion of the emissions but this is a 

matter for atmospheric scientists. 

For users that require it, a much smoother altitude profile can be obtained by 

decreasing the gridding resolution to 1000ft, using an initial 500ft cell at ground 

level. Subsequent cells will cover 500 to 1500ft etc, thereby enveloping flight around 

each round-thousand-feet flight level. The 500ft resolution has been retained in the 

data presented to assist those seeking greater resolution to assist in quantification of 

cirrus and contrail effects. 

For military data, the effect is very different. Here actual flight data are not available 

and all data are based on typical flight plans, for latitude, longitude and for altitude. 

Hence, differences in emissions values at 500ft resolution do not represent an actual 


difference from actual military flights. This is one of a number of reasons for 

publishing the civil and military gridded data separately. 

3.1.2 Other Results - 2002 

To complement the global gridded results, further analysis has been carried out to 

provide “headline” and summary data for 2002 fuel and emissions 23 . 

Global Emissions – Civil and Military Aviation 

The global consumption of fuel, emissions of CO2, H20, NOx, CO, HC, particulate mass, 

particulate numbers and distance flown for 2002 is given in Table 26. 

Distance 

CO2 H2O CO NOx HC Soot Particles 

Fuel Used 

Flown Produced Produced Produced Produced Produced Produced Produced 

(10 3 million 

n. miles) 


Civil 

24 


17.9 156 492 193 .507 2.06 .063 .0039 

4.03 X 

10 25 

Military 


n/a 19.5 61.0 24.1 .647 .178 .066 n/a n/a 

Total n/a 176 553 217 1.15 2.24 .129 n/a n/a 

Table 26: 2002 annual fuel and emissions - civil and military aviation 

Fuel used by civil aviation 25 in 2002 is shown to have risen above 150Tg. Military 

aviation uses a further 19.5Tg, representing just over 11% of the total fuel used. CO2 

emissions from aviation exceeded half a billion tons in 2002, representing about 2% 26 

of global anthropomorphic CO2 emissions. NOx emissions rose above 2Tg for civil 

aviation with military aviation contributing a further 8% of the total. In contrast, the 

continued effect of significant improvements in combustion efficiency to better than 

99% in more recent aircraft has stemmed any increase in CO and HC emissions. The 

new calculation of military emissions to include reheat operation, which is relatively 

inefficient in combustion terms, shows values of CO and HC emissions for military 

aviation to be on a similar level to those of the civil fleet. These military figures for CO 

and HC should be regarded as maxima as there is some uncertainty over the actual 

afterburner use in service throughout the globe. Finally, the figure for the number of 

particles produced should be regarded as a first estimate for the purposes of climate 

effect assessment. 

Emissions indices for the civil and military fleets are shown in Table 27, emphasising 

the combustion efficiency and afterburning effects on CO and HC emissions 

described above. A figure of 8.71 kg of fuel used per nautical mile flown for the global 

civil aviation fleet is also given. 

23 

Fuel, NOx, CO2, H20, CO, HC, Particulate mass (soot), Particulate number, distance flown 

24 

Civil aviation includes IFR flights only 

25 

Civil aviation includes IFR flights only 

26 

http://www.eia.doe.gov/emeu/cabs/carbonemiss/chapter1.html 


Civil 


Military 


Fuel Used 

per n.mile 

EI CO2 EI H2O EI CO EI NOx EI HC EI Soot EI Particles 

(kg/n.mile) (g/kg) (g/kg) (g/kg) (g/kg) (g/kg) (g/kg) 

(number/ 

kg) 

27 8.71 3150 1238 3.25 13.2 0.4 0.025 2.6 x 1014 

n/a 3150 1238 33.1 9.1 3.4 n/a n/a 

Table 27: 2002 annual emissions indices- civil and military aviation 

Putting the civil aviation results into some form of transport productivity measure, 

ICAO Circular 299-AT/129 [ICAO, 2003] quotes data for passengers, freight and mail 

carried in 2002 (scheduled traffic only). Assuming AERO2k has captured 75% of global 

non-scheduled traffic from the radar tracking data and that scheduled traffic 

represents 88% of all traffic [ICAO, 2003], AERO2k suggests global average data for 

IFR flights as shown in Table 28. 

Average 

payload 

(tonnes) 

CO2 H 

Fuel Used 

2O CO NOx HC Soot Particles 

Produced Produced Produced Produced Produced Produced Produced 

per 

per per per per per per per 

Payload 

Payload Payload Payload Payload Payload Payload Payload 

Tonne.km 

Tonne.km Tonne.km Tonne.km Tonne.km Tonne.km Tonne.km Tonne.km 

(kg 

fuel/tonne. 

km) 

(kg 

CO2/tonne. 

km) 

(kg 

H2O/tonne. 

km) 

(kg 

CO2/tonne. 

km) 

(kg 

NOx/tonne. 

km) 

(kg 

HC/tonne. 

km) 

(kg 

soot/tonne. 

km) 


(number of 

particles/ 

tonne.km) 

Civil 

Aviation 28 13.03 0.36 1.14 0.45 0.00117 0.00477 0.00015 9.03E-06 9.33E+13 

Table 28: 2002 annual emissions indices per payload tonne - civil aviation 

A comparison of the fuel used and emissions results with those from previous 

inventories is contained in Section 3.1.2. 

Global Spatial and Temporal Distribution of Civil Aviation Emissions for 2002 

The AERO2k gridded data provides the detailed global spatial and temporal 

distribution of fuel used, emissions and distance flown. Figure 45 to Figure 47 show 

three visualisations of the gridded data. 

27 Civil aviation includes IFR flights only 

28 Civil aviation includes IFR flights only

Figure 45: Visualisation of fuel used for one day 

Figure 45 shows a 3-D global representation of fuel used in one day. The vertical scale 

has been exaggerated to assist in the visualisation. Figure 46 is the same plot with 

the areas of lower concentration removed. Areas of high aviation fuel usage over the 

three traditional areas of North America, Europe and the Far East are revealed. 

Figure 46: Visualisation of fuel used for one day - Isosurfaces for higher concentrations 

Figure 47 focuses on Europe, in this case visualising NOx levels in each grid cell. NOx 

from westbound flights has been removed to give a 3D view of the centre of Western 

Europe. Climb out and cruise paths area clearly shown. 


Figure 47: Visualisation of NOx emissions - Isosurfaces for higher concentrations 

Figure 45 to Figure 47 visualise fuel used and emissions for flights over a single day. 

However, global distribution of aviation emissions varies by season as well as by 

altitude and by the emission itself. Whilst seasonal variations are primarily due to 

demand and meteorological variations (such as the position of the jet stream), much 

larger variations are due to the production rate of each emission at various power, 

altitude and speed conditions. As a result, most CO and HC emissions occur below 

3000ft whilst NOx emissions are biased toward cruise altitudes. These results are 

shown graphically in Figure 48 and Figure 49. Seasonal variation for fuel used is 

shown in Figure 50. Further tabulation of 2002 fuel consumed, emissions and 

distance flown by altitude is contained in Appendix C. 


Altitude (ft) 

altitude (ft) 

60,000 

50,000 

40,000 

30,000 

20,000 

10,000 

0 

60,000 

50,000 

40,000 

30,000 

20,000 

10,000 

Comparison of fuel used and emissions at altitude - 8th-13thApril 2002 

0 200 400 600 800 1000 1200 1400 1600 

Fuel Used or Emissions (Mg) 

Fuel CO2 H2O 

Figure 48: 2002 civil aviation emissions of fuel, CO2 and H2O by altitude 

0 

Emissions at altitude - 8th-13th April 2002 

0 1 2 3 4 5 6 

Emissions (Mg) 

CO NOx HC Soot 

Figure 49: 2002 civil aviation emissions of NOx, CO, HC and soot by altitude 


Altitude (ft) 

60000 

50000 

40000 

30000 

20000 

10000 

Seasonal fuel used at altitude 

0 

0 100 200 300 400 500 600 700 

Fuel Used (Mg) 

April Sept October February 

Figure 50: Seasonal variation of 2002 civil aviation fuel usage by altitude 

For military aviation in 2002, the lack of detailed data on military flight schedules 

results in considerably increased uncertainty within these data compared to the civil 

database. For that reason, no seasonal breakdown of military emissions is presented. 

However, the globally accumulated vertical distribution of fuel consumption, NOx, 

HC, CO as well as time specified by altitude band is given in Figure 51. 


HeightBand [*1000 m] 

25 

20 

15 

10 

5 

World results: Fuel and Time 

Fuel profile [kg] 

Time profile [s] 

0 0.5 1 1.5 2 

x 10 10 

Fuel [kg], Time [s] 

0 20 40 60 

Emission Index [g/kg] 


HeightBand [* 914.4 m] 

25 

20 

15 

10 

5 

World results: Emission Index profile 

EI-HC profile [g] 

EI-CO profile [g] 

EI-NO x profile [g] 

Figure 51: 2002 military fuel burn, flight time and emissions indices by altitude 

Figure 51 shows that peak fuel burn occurs in the 9-14 km altitude band. In terms of 

emission indices, NOx emissions peak in the 0-1 km altitude band) and approximately 

20-25 km bands. At low altitudes, takeoff and climb out are mainly contributing. At 

very high altitude, engine operating conditions also increase NOx production. It 

should be noticed that at low altitude, military aircraft NOx production is relatively 

low because of the use of afterburning that ‘kills’ quite some NOx. Hence the peak is 

less pronounced compared to the civil inventory. 

The considerable CO and HC emissions indices throughout the altitude bands 

correspond with the use of afterburning during manoeuvres and/or climb. 

It should be noted that the number of aircraft capable of operating beyond 14-km 

altitude is rare in terms of aircraft types and numbers as well as flights. If operating in 

these altitude bands, engine-operating conditions are near or at the edges of the 

operating envelope. In many cases, this results in relatively high emissions per kg fuel 

used. 

Based on the movement database aircraft inventory, location, the reference aircraft 

and aircraft-reference aircraft conversion factors, missions and utilisation data, the 

fuel use, flight time and emissions are allocated into a world spanning, threedimensional 

grid, with a cell size of 1deg x 1 deg x 1000 ft. Figure 52 shows the 

world-gridded fuel distribution, aggregated over altitude. For enhanced view, fuel 

consumption levels below a threshold value are not included, and appear as white 

(uncoloured) areas. Figure 53 shows a focus of Europe. The underlying grid measures 

1 deg. by 1deg.

Figure 52: Military global fuel consumption distribution, aggregated over altitude. 

Figure 53: Military fuel consumption distribution, aggregated over altitude, focussed on 

Europe 


© 

© 

10 

8 

6 

4 

2 

10 7 

0 

2003 

. 

5 107 

4.5 

4 

3.5 

3 

2.5 

2 

1.5 

1 

0.5 

0 

2003 

.

0% 

3% 

10% 

Regional Distribution of fuel consumed, emissions and distance flown for civil 

aviation 

Whilst emissions occur along the entire path of each flight, emissions can be 

attributed to the origin and destination of each flight. The following seven figures 

(Figure 54 to Figure 60) show the annual distribution of civil aviation distance flown, 

fuel consumed and NOx emissions between seven regions used in AERO2k. These 

regions are: 

• Asia and Pacific 

• Eastern and Southern Africa 

• European and North Atlantic 

• Middle East 

• North American, Central American and Caribbean 

• South American 

• Western and Central Africa 

Differences between the proportions of distance flown and fuel used shown in the 

charts can be primarily attributed to differences in average aircraft size and route 

lengths. NOx emissions tend to closely follow fuel consumption at this level of 

analysis. A detailed listing of the countries allocated to each region is contained in 

Appendix B. Numerical values for distance flown, fuel used and all emissions are 

tabulated in Appendix D. 

Distance Flown 

11% 

0% 

ASIA AND PACIFIC To ASIA AND PACIFIC 

76% 

ASIA AND PACIFIC To EASTERN AND SOUTHERN AFRICA 

ASIA AND PACIFIC To EUROPEAN AND NORTH ATLANTIC 

ASIA AND PACIFIC To MIDDLE EAST 

ASIA AND PACIFIC To NORTHAMERICAN CENTRAL AMERICAN 

AND CARIBBEAN 

ASIA AND PACIFIC To SOUTHAMERICAN 

3% 

14% 

1% 

16% 

Fuel Used 

0% 



66% 




ASIA AND PACIFIC To NORTHAMERICAN CENTRAL 

AMERICAN AND CARIBBEAN 

ASIA AND PACIFIC To SOUTH AMERICAN 

3% 

13% 

1% 

16% 

NOx 

0% 


67% 


ASIA AND PACIFIC To EUROPEAN AND NORTHATLANTIC 


ASIA AND PACIFIC To NORTHAMERICAN CENTRAL 



Figure 54: Regional distance flown, fuel consumption and NOx emissions for flights 

departing from the Asia and Pacific region


51% 

36% 

4% 

0% 

1% 

5% 

3% 

EASTERN AND SOUTHERN AFRICA To ASIA AND PACIFIC 

EASTERN AND SOUTHERN AFRICA To EASTERN AND SOUTHERN 

AFRICA 

EASTERN AND SOUTHERN AFRICA To EUROPEAN AND NORTH 

ATLANTIC 

EASTERN AND SOUTHERN AFRICA To MIDDLE EAST 

EASTERN AND SOUTHERN AFRICA To NORTHAMERICAN 

CENTRAL AMERICAN AND CARIBBEAN 

EASTERN AND SOUTHERN AFRICA To SOUTH AM ERICAN 

EASTERN AND SOUTHERN AFRICA To WESTERN AND CENTRAL 

AFRICA 

72% 

55% 

Fuel Used 


27% 

4% 

0% 

1% 

9% 

4% 


EASTERN AND SOUTHERN AFRICA To EASTERN AND 

SOUTHERN AFRICA 

EASTERN AND SOUTHERN AFRICA To EUROPEAN AND 

NORTH ATLANTIC 

EASTERN AND SOUTHERN AFRICA To MIDDLE EAST 



EASTERN AND SOUTHERN AFRICA To SOUTH AM ERICAN 

EASTERN AND SOUTHERN AFRICA To WESTERN AND 

CENTRAL AFRICA 

56% 

NOx 

24% 

5% 

0% 

1% 

4% 

10% 


EASTERN AND SOUTHERN AFRICA To EASTERN AND 


EASTERN AND SOUTHERN AFRICA To EUROPEAN AND 

NORTHATLANTIC 

EASTERN AND SOUTHERN AFRICA To M IDDLE EAST 



EASTERN AND SOUTHERN AFRICA To SOUTHAMERICAN 

EASTERN AND SOUTHERN AFRICA To WESTERN AND 



departing from the Eastern and Southern Africa region 


5% 

13% 

6% 

1% 

2% 

1% 

EUROPEAN AND NORTHATLANTIC To ASIA AND PACIFIC 

EUROPEAN AND NORTHATLANTIC To EASTERN AND SOUTHERN 

AFRICA 

EUROPEAN AND NORTHATLANTIC To EUROPEAN AND NORTH 

ATLANTIC 

EUROPEAN AND NORTHATLANTIC To MIDDLE EAST 

EUROPEAN AND NORTHATLANTIC To NORTH AMERICAN 


EUROPEAN AND NORTHATLANTIC To SOUTHAMERICAN 

EUROPEAN AND NORTHATLANTIC To WESTERN AND CENTRAL 

AFRICA 

54% 

Fuel Used 

6% 

21% 

3% 

3% 

1% 

12% 

EUROPEAN AND NORTHATLANTIC To ASIA AND PACIFIC 

EUROPEAN AND NORTHATLANTIC To EASTERN AND 


EUROPEAN AND NORTHATLANTIC To EUROPEAN AND 

NORTHATLANTIC 

EUROPEAN AND NORTHATLANTIC To MIDDLE EAST 

EUROPEAN AND NORTHATLANTIC To NORTH AMERICAN 


EUROPEAN AND NORTHATLANTIC To SOUTHAMERICAN 

EUROPEAN AND NORTHATLANTIC To WESTERN AND 


49% 

NOx 

7% 24% 

3% 

3% 

1% 

13% 

EUROPEAN AND NORTH ATLANTIC To ASIA AND PACIFIC 

EUROPEAN AND NORTH ATLANTIC To EASTERN AND 


EUROPEAN AND NORTH ATLANTIC To EUROPEAN AND 

NORTH ATLANTIC 

EUROPEAN AND NORTH ATLANTIC To MIDDLE EAST 

EUROPEAN AND NORTH ATLANTIC To NORTHAMERICAN 

CENTRALAMERICAN AND CARIBBEAN 

EUROPEAN AND NORTH ATLANTIC To SOUTH AMERICAN 

EUROPEAN AND NORTH ATLANTIC To WESTERN AND 

CENTRALAFRICA 


departing from the European and North Atlantic region

50% 


MIDDLE EAST To ASIA AND PACIFIC 

32% 

2% 

2% 

0% 

14% 

MIDDLE EAST To EASTERN AND SOUTHERN AFRICA 

MIDDLE EAST To EUROPEAN AND NORTH ATLANTIC 

MIDDLE EAST To MIDDLE EAST 

MIDDLE EAST To NORTHAMERICAN CENTRAL AMERICAN AND 

CARIBBEAN 

MIDDLE EAST To WESTERN AND CENTRAL AFRICA 

47% 

Fuel Used 



31% 

2% 

16% 

4% 

0% 


MIDDLE EAST To EUROPEAN AND NORTHATLANTIC 


MIDDLE EAST To NORTHAMERICAN CENTRAL AMERICAN 

AND CARIBBEAN 


44% 

NOx 

33% 


2% 

17% 




4% 

0% 

MIDDLE EAST To NORTHAMERICAN CENTRAL AMERICAN 

AND CARIBBEAN 



departing from the Middle East region 


87% 1% 

0% 

0% 

4% 

0% 

8% 

NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN To 

ASIA AND PACIFIC 


EASTERN AND SOUTHERN AFRICA 


EUROPEAN AND NORTHATLANTIC 


MIDDLE EAST 


NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN 


SOUTH AMERICAN 


WESTERN AND CENTRALAFRICA 

74% 

Fuel Used 

0% 

15% 

0% 

2% 

0% 

9% 






EUROPEAN AND NORTH ATLANTIC 


MIDDLE EAST 


NORTHAMERICAN CENTRAL AMERICAN AND CARIBBEAN 




WESTERN AND CENTRAL AFRICA 

70% 

0% 

NOx 

18% 

0% 

2% 

0% 

10% 

NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN To 





EUROPEAN AND NORTH ATLANTIC 


MIDDLE EAST 


NORTH AMERICAN CENTRALAMERICAN AND CARIBBEAN 






departing from the North and Central American and Caribbean region

68% 


18% 

SOUTH AMERICAN To ASIA AND PACIFIC 

0% 

0% 

0% 

14% 

SOUTH AMERICAN To EASTERN AND SOUTHERN AFRICA 

SOUTH AMERICAN To EUROPEAN AND NORTHATLANTIC 

SOUTH AMERICAN To NORTHAMERICAN CENTRAL AMERICAN 

AND CARIBBEAN 

SOUTH AMERICAN To SOUTHAMERICAN 

SOUTH AMERICAN To WESTERN AND CENTRALAFRICA 

3% 

6% 

56% 

22% 

Fuel Used 

56% 

SOUTHAMERICAN To ASIA AND PACIFIC 


22% 

0% 

0% 

0% 

SOUTHAMERICAN To EASTERN AND SOUTHERN AFRICA 

SOUTHAMERICAN To EUROPEAN AND NORTHATLANTIC 

SOUTHAMERICAN To NORTHAMERICAN CENTRAL AMERICAN 

AND CARIBBEAN 

SOUTHAMERICAN To SOUTHAMERICAN 

SOUTHAMERICAN To WESTERN AND CENTRAL AFRICA 

23% 

NOx 

49% 

SOUTHAMERICAN To ASIA AND PACIFIC 

26% 

0% 

1% 

1% 

SOUTHAMERICAN To EASTERN AND SOUTHERN AFRICA 

SOUTHAMERICAN To EUROPEAN AND NORTHATLANTIC 

SOUTHAMERICAN To NORTHAMERICAN CENTRAL AMERICAN 

AND CARIBBEAN 

SOUTHAMERICAN To SOUTHAMERICAN 

SOUTHAMERICAN To WESTERN AND CENTRAL AFRICA 


departing from the South American region 


0% 

29% 

6% 

WESTERN AND CENTRAL AFRICA To EASTERN AND SOUTHERN 

AFRICA 

WESTERN AND CENTRAL AFRICA To EUROPEAN AND NORTH 

ATLANTIC 

WESTERN AND CENTRAL AFRICA To MIDDLE EAST 

WESTERN AND CENTRAL AFRICA To NORTHAMERICAN CENTRAL 


WESTERN AND CENTRAL AFRICA To SOUTHAMERICAN 

WESTERN AND CENTRAL AFRICA To WESTERN AND CENTRAL 

AFRICA 

62% 

3% 

Fuel Used 

11% 0% 

19% 

5% 


AFRICA 


ATLANTIC 


WESTERN AND CENTRAL AFRICA To NORTHAMERICAN 

CENTRALAMERICAN AND CARIBBEAN 



AFRICA 

63% 

3% 

NOx 

12% 0% 

17% 

5% 


AFRICA 


ATLANTIC 


WESTERN AND CENTRAL AFRICA To NORTHAMERICAN 




AFRICA 


departing from the Western and Central Africa region 

It should be noticed that in order to produce the forecast for 2025, factors from the 

DTI/Airbus growth and technology development forecast (Section 2.3) have been 

applied to regional data for 2002.

Distance Flown, Fuel Consumed, and Emissions for Civil Aviation, distributed by 

representative aircraft type 

Table 29 shows the distribution of the calculated fuel consumed, emissions and 

distance flown for each of the 40 representative aircraft types used within AERO2k. 

All major aircraft types within the global civil aviation fleet have been allocated to 

one of these representative types. 

Representative Distance Flown Fuel Used 

Aircraft 

CO2 

Produced 

H2O 

Produced 

CO 

Produced 

NOx 

Produced 

HC 

Produced 

Soot 

Produced 


Particles 

Produced 

Nautical miles 

-9 (Gg) (Gg) (Gg) (Gg) (Gg) (Gg) (Gg) x10-23 

x10 

A306 0.27 3447 10862 4267 10.13 48.76 0.78 0.084 8.18 

A310 0.20 2091 6588 2588 6.04 26.77 0.43 0.045 5.21 

A319 0.48 3128 9850 3873 14.60 39.16 2.32 0.032 2.77 

A320 1.14 7455 23481 9229 30.38 100.68 4.50 0.085 7.48 

A321 0.24 1797 5660 2224 6.53 28.27 0.79 0.025 2.25 

A330 0.34 4185 13197 5181 7.26 72.88 1.89 0.049 5.67 

A340 0.43 5931 18703 7342 9.34 95.42 0.76 0.066 8.37 

A34R 0.03 456 1426 564 7.67 3.14 1.46 0.029 3.49 

AT72 0.23 621 1957 769 3.15 6.75 0.02 0.087 5.51 

B703 0.10 1419 4433 1757 28.72 8.86 5.62 0.094 10.87 

B712 0.07 441 1389 546 1.97 4.90 0.01 0.005 0.39 

B722 0.37 4164 13125 5155 10.27 39.70 1.99 0.175 16.43 

B732 0.31 2211 6966 2737 6.78 21.26 1.20 0.100 8.43 

B734 1.72 11817 37196 14629 62.08 124.09 3.33 0.148 15.29 

B736 0.73 4547 14330 5629 13.14 58.57 1.23 0.084 7.33 

B738 0.73 4813 15172 5958 10.84 66.74 1.02 0.096 9.37 

B742 0.42 9152 28869 11330 9.17 150.44 1.31 0.182 24.26 

B744 1.11 23206 73161 28729 48.85 292.75 3.54 0.428 58.87 

B752 1.15 9094 28657 11258 27.16 123.84 2.11 0.240 26.65 

B763 1.24 13979 44066 17306 33.69 187.13 2.55 0.268 33.01 

B772 0.68 10312 32527 12766 10.67 224.00 1.06 0.148 19.07 

BA11 0.00 12 37 14 0.09 0.12 0.01 0.001 0.05 

BA46 0.21 1300 4088 1609 9.22 9.58 1.07 0.046 3.99 

C130 0.03 204 643 253 0.88 1.37 0.27 0.013 1.12 

C550 0.61 987 3109 1222 4.15 7.39 0.53 0.034 3.00 

DC9 0.34 3308 10412 4095 16.85 21.94 2.58 0.162 12.52 

E145 1.10 3180 9999 3937 23.45 23.30 2.82 0.111 10.04 

F100 0.16 1028 3231 1273 8.57 8.34 1.05 0.037 3.01 

F2TH 0.23 464 1459 575 3.87 6.02 0.35 0.016 1.65 

F50 0.40 1236 3891 1530 5.93 14.48 0.02 0.183 11.47 

F70 0.11 588 1849 727 3.38 5.36 0.62 0.030 2.54 

F900 0.11 275 865 341 2.92 2.98 0.25 0.009 0.96 

GLF4 0.16 581 1827 719 3.53 4.88 0.64 0.022 2.22 

L101 0.22 3617 11379 4478 22.93 65.93 4.49 0.084 10.09 

L188 0.01 78 245 96 0.24 0.40 0.02 0.004 0.31 

MD11 0.30 4480 14124 5547 10.68 54.26 0.75 0.086 11.63 

MD80 0.98 8187 25806 10136 20.54 85.03 3.89 0.338 31.15 

MD90 0.07 574 1807 710 2.46 7.77 0.37 0.007 0.54 

SF34 0.85 1476 4645 1827 8.07 12.02 5.42 0.262 17.23 

YK42 0.02 204 643 252 0.43 2.99 0.05 0.008 0.82 

Table 29: 2002 Fuel, emissions and distance flown by representative aircraft type – civil 

aviation

The 40 representative types can be divided into four categories – large jet, regional 

jet, turboprop and business jet. Allocating the distance flown, fuel and emissions into 

each category gives the following category totals, plus fuel consumption and 

emission indices (Figure 61 and Table 30 and Table 31). It is left to the reader to 

allocate efficiency factors representing the amount of freight and passengers carried 

by each representative type and aircraft category, with the caveat that attempts to 

substitute different categories of aircraft on any route can only be done with full 

knowledge of route length, demand and other detailed operational factors. 

Business Jet 

Turboprop 

Regional Jet 

Large Jet 

Totals 

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 

Distance Flown Fuel Used NOx Produced Soot Produced 

Figure 61: Distribution of distance flown, fuel used, NOx and soot production between 

civil aircraft categories in 2002 

Distance 

Flown 

(n.miles 

x10 -9 ) 

Fuel 

Used 


(Gg) (Gg) (Gg) (Gg) (Gg) (Gg) (Gg) (x10 -23 ) 

Large Jet 13.68 143823 453596 177995 428.81 1952.59 50 3.061 339.43 

Regional Jet 1.6 6302 19874 7799 45 49.57 5.61 0.232 20.36 

Turboprop 1.52 3623 11427 4478 18.27 35.02 5.75 0.549 35.64 

Business Jet 1.11 2307 7275 2855 14.47 21.27 1.77 0.081 7.83 

Table 30: 2002 Fuel, emissions and distance flown by representative aircraft category – 

civil aviation 


Emissions 

Indices 

Fuel 

consumption 

(kg/nm) 


(g/kg 

fuel) 

(g/kg 

fuel) 

(g/kg 

fuel) 

(g/kg 

fuel) 

(g/kg 

fuel) 

(g/kg 

fuel) 


(number/kg 

fuel x 10 -20 ) 

Large Jet 10.51 3154 1238 2.98 13.58 0.35 0.02 2.36 

Regional Jet 3.94 3154 1238 7.14 7.87 0.89 0.04 3.23 

Turboprop 2.38 3154 1238 5.04 9.67 1.59 0.15 9.84 

Business Jet 2.08 3153 1238 6.27 9.22 0.77 0.04 3.39 

Table 31: 2002 Fuel consumption and emissions indices by representative aircraft 

category – civil aviation 

Comparison of 2002 results with other Inventory results 

Previous global emissions inventories have estimated global aviation emissions for 

different years using different detailed information sources, assumptions and 

methodologies – different both to each other and to AERO2k. Because of these 

differences, detailed analysis comparing the results raises a number of uncertainties. 

Nevertheless, each inventory is attempting to quantify global emissions from 

aviation for a particular year. Global totals for civil and military aviation from four of 

these recent inventories (DLR 1992 [Schmitt, 1997], ANCAT/EC2 1992 [Gardner, 

1998], NASA 1992 [Baughcum, 1996] and NASA 1999 [Sutkis, 2001]) are compared 

with the AERO2k results in Table 32 and displayed graphically in Figure 62 to Figure 

65. 

Emissions - Total 

Mass 

(Tg) 

Aero2k NASA1999 29 NASA1992 ANCAT 1992 DLR 1992 

Fuel Used 156 128 113.85 114.2 112.24 

NOx 2.06 1.69 1.44 1.6 1.6 

CO 0.507 0.685 1.29 n/a n/a 

HC 0.063 0.189 0.26 n/a n/a 


Particulate Number 4.03E+25 n/a n/a n/a n/a 

EIs (g/kg) 


Fuel Used - - - - 

NOx 13.2 13.2 12.6 14 14.2 

CO 3.25 5.4 11.3 n/a n/a 

HC 0.4 1.5 2.3 n/a n/a 


Particulate Number 

(/kg) 

2.58E+14 n/a n/a n/a n/a 

Table 32: 2002 Global fuel, emissions and emissions indices for civil aviation 

The civil aviation fuel-used figure reflects the increase in air traffic since 1992, offset 

by the fuel efficiency improvements, suggested as being in the region of 1% per year. 

29 The NASA 1999 figure shown includes only scheduled flights

Increasing pressure ratio, plus other technology improvements have allowed 

significant improvements in combustion efficiency in the last 25 years and these 

engines are now dominating the civil aircraft fleet (Figure 44). Hence the significant 

reduction in CO and HC EIs. Conversely, for NOx, the effect of increasing pressure 

ratios has largely offset significant improvements in NOx control technology, 

resulting in only a slight decrease in NOx EI over the decade (compared to the 

ANCAT/DLR results). 

Emissions from military aviation have a greater degree of uncertainty when 

compared to the civil aviation data. Understandable secrecy surrounding the 

performance and flight profiles requires a degree of approximation. Military fuelused, 

emissions and EIs are shown in Table 33. 

Emissions – Total 

Mass 

(Tg) 


Fuel Used 19.5 n/a 25.55 17.08 17.1 

NOx 0.18 n/a 0.23 0.2 0.2 

CO 0.647 n/a 0.29 n/a n/a 

HC 0.066 n/a 0.06 n/a n/a 

EIs (g/kg) 


Fuel Used - - - - - 

NOx 9.1 n/a 8.9 11.9 11.8 

CO 33.1 n/a 11.2 n/a n/a 

HC 3.4 n/a 2.4 n/a n/a 

Table 33: 2002 Global fuel, emissions and emissions indices for military aviation 

The data show military fuel-used at around 12% of the civil aviation fuel consumed 

whilst lower average pressure ratios lead to lower NOx EIs and hence total military 

NOx at around 9% of the civil aviation emission. In contrast, military emissions of CO 

and HC are calculated to be on a par with civil aviation emissions. Whilst these results 

are highly sensitive to assumptions on afterburner usage and performance, they do 

highlight the significant improvements made within civil aviation in this area. 

Figure 62 to Figure 65 present this tabulated data in graphical form. 


Fuel Used (Tg) 

180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

1970 1975 1980 1985 1990 1995 2000 2005 

Figure 62: 2002 Global fuel consumed 30 

NOx Emissions (Tg) 

3 

2 

1 

0 

1970 1975 1980 1985 1990 1995 2000 2005 

Figure 63: 2002 Global NOx emissions 31 

30 The NASA 1999 figure shown includes only scheduled flights 


NASA 

DLR 

ANCAT 

AERO2K 

Military 

NASA 

Military 

DLR 

ANCAT 1992 

AERO2K 


CO Emissions (Tg) 

1 

0.5 

0 

1970 1975 1980 1985 1990 1995 2000 2005 

Figure 64: 2002 Global CO emissions 32 

HC Emissions (Tg) 

0.35 

0.3 

0.25 

0.2 

0.15 

0.1 

0.05 

0 

1970 1975 1980 1985 1990 1995 2000 2005 

Figure 65: 2002 Global HC emissions 33 

3.2 Results for 2025 Civil and Military Aviation 

3.2.1 Gridded Data for 2025 

NASA 

NASA 

Military 

AERO2K 

Military 

AERO2K 

As for 2002, the main results from AERO2k for 2025 are the global gridded data for 

fuel-used, emissions and distance flown, published on the AERO2k WebPages at 

http://www.cate.mmu.ac.uk/aero2k.asp. The data consist of 12 tables, one for each 

month of 2025, each containing the civil aviation emissions, fuel used and distance 

flown in a global grid of resolution 1 deg by 1 deg by 500ft. Comments in Section 

3.1.1 regarding the vertical resolution also apply to this data. 

There are no separate results for military aviation in 2025. In any analysis of total 

2025 emissions, is recommended that the results for 2002 are used. This conclusion is 

explained further in Section 2.4.3. 




3.2.2 Other Results - 2025 

As described in Section 2.3, results for 2025 are based on factored 2002 results and 

represent just one possible scenario for global aviation. In addition to the global and 

regional results from AERO2k, results from other scenario work are presented for 

comparison. 

2025 Global Emissions from Civil Aviation 

The global consumption of fuel, emissions of CO2, H20, NOx, CO, HC, particulate mass 

(soot), particulate numbers and distance flown for 2025 is given in Table 34. 

Distance 

Flown 

(10 3 million n. 

miles) 

Fuel Used 

CO2 

Produced 

H2O 

Produced 

CO 

Produced 

NOx 

Produced 

HC 

Produced 

Soot 

Produced 



Particles 

Produced 


2025 36.1 327 1029 404 1.15 3.308 0.1447 0.0087 8.54 x 10 25 

Table 34: 2025 Annual fuel and emissions - civil aviation 

The assumptions used to model the 2025 emissions provide for aviation in 2025 

satisfying a demand increase averaging 2.6 times the 2002 figure over the globe, with 

a move toward larger, more fuel efficient aircraft. As a result of this 2.6 times increase 

in the number of available-seat-kilometres, fuel used by civil aviation 34 in 2025 has 

risen above 300Tg, doubling the figure from 2002. CO2 emissions from civil aviation 

have also doubled, exceeded a billion tons in 2025. In contrast, NOx emissions rose 

only 1.6 times, resulting from improvement in fleet NOx technology. The effect of 

significant improvements in combustion efficiency seen in the 2002 figures in the 

form of reducing CO and HC emissions is not seen in the 2025 figures, as any further 

improvements are small and are not seen to keep pace with the traffic demand 

increase. Again, the figure for the number of particles produced should be regarded 

as a first estimate for the purposes of climate effect assessment. 

AERO2k emissions indices for the 2002 and 2025 civil aviation fleets are shown in 

Table 35. 

Fuel Used 

per n.mile 

EI CO2 EI H2O EI CO EI NOx EI HC EI Soot 

EI 

Particles 

(kg/n.mile) (g/kg) (g/kg) (g/kg) (g/kg) (g/kg) (g/kg) (/kg) 

2002 8.72 3150 1238 3.25 13.2 0.40 0.03 2.6 x 10 14 

2025 9.06 3150 1238 3.52 10.1 0.44 0.03 2.6 x 10 14 

Table 35: 2002 Annual emissions indices- civil and military aviation 

A comparison of these forecasts with those from previous inventories is described 

later in this section. 

Global Spatial and Temporal Distribution of Civil Aviation Emissions for 2025 

For the 2025 forecast, AERO2k has not attempted to model changes in air traffic 

management. Excepting the changes in frequency of each flight, dependent upon the 

regional growth and technology change factors, the profile of emissions by altitude is 

not significantly changed on a global scale. The greater effect is the change in global 

distribution of the emissions toward the high growth regions. This effect is seen in 

the gridded data and also in the traffic flows in the following Figure 66, which should 

be compared with Figure 54 to Figure 60 in Section 3.1.2. 

34 Civil aviation includes IFR flights only

11% 

0% 

2% 


11% 0% 


76% 




ASIA AND PACIFIC To NORTHAMERICAN CENTRAL AMERICAN 

AND CARIBBEAN 


42% 


38% 


2% 

15% 




3% 

0% 

MIDDLE EAST To NORTHAM ERICAN CENTRAL AMERICAN AND 

CARIBBEAN 



39% 

48% 


4% 

0% 

1% 

3% 

5% 

EA STERN AND SOUTHERN AFRICA To ASIA AND PACIFIC 

EA STERN AND SOUTHERN AFRICA To EASTERN AND SOUTHERN 

AFRICA 

EA STERN AND SOUTHERN AFRICA To EUROPEAN AND NORTH 

ATLANTIC 

EA STERN AND SOUTHERN AFRICA To MIDDLE EAST 

EA STERN AND SOUTHERN AFRICA To NORTHAMERICAN 


EA STERN AND SOUTHERN AFRICA To SOUTHAMERICAN 

EA STERN AND SOUTHERN AFRICA To WESTERN AND CENTRAL 

AFRICA 

80% 


0% 

7% 

0% 

11% 

2% 

0% 

NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN To ASIA 

AND PACIFIC 

NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN To 

EA STERN AND SOUTHERN AFRICA 


EUROPEAN AND NORTHATLA NTIC 


MIDDLE EAST 


NORTH AMERICAN CENTRAL AMERICAN AND CARIBBEAN 





3% 

61% 


5% 

0% 

26% 

5% 


AFRICA 


ATLANTIC 


Figure 66: Regional distance flown, for 2025 

WESTERN AND CENTRAL AFRICA To NORTH AMERICAN CENTRAL 

AM ERICAN AND CARIBBEAN 

WESTERN AND CENTRAL AFRICA To SOUTH AMERICAN 


AFRICA 

76% 


3% 10% 

7% 

1% 

2% 

1% 

EUROPEAN AND NORTHATLA NTIC To ASIA AND PACIFIC 

EUROPEAN AND NORTHATLA NTIC To EASTERN AND SOUTHERN 

AFRICA 

EUROPEAN AND NORTHATLA NTIC To EUROPEAN AND NORTH 

ATLANTIC 

EUROPEAN AND NORTHATLA NTIC To MIDDLE EAST 

EUROPEAN AND NORTHATLA NTIC To NORTH AMERICAN 


EUROPEAN AND NORTHATLA NTIC To SOUTH AMERICAN 

EUROPEAN AND NORTHATLA NTIC To WESTERN AND CENTRAL 

AFRICA 

67% 


18% 

SOUTH AMERICAN To ASIA AND PACIFIC 

0% 

0% 

0% 

15% 

SOUTH AMERICAN To EASTERN AND SOUTHERN AFRICA 

SOUTH AMERICAN To EUROPEAN AND NORTHATLANTIC 

SOUTH AMERICAN To NORTHAMERICAN CENTRAL AMERICAN 

AND CARIBBEAN 

SOUTH AMERICAN To SOUTHAM ERICAN 

SOUTH AMERICAN To WESTERN AND CENTRAL AFRICA

Distance Flown, Fuel Consumed and Emissions for Civil Aviation in 2025, by 

representative aircraft category 

Figure 67 shows the distribution of the calculated fuel consumed, emissions and 

distance flown for the 4 categories of civil aircraft types used within AERO2k. These 

data compare with the 2002 data in Figure 61 showing the proportion of distance 

flown by large jets increasing to over 90% although technical improvements have 

maintained their share of NOX and soot emissions at the 2002 proportions. 

Business Jet 

Turboprop 

Regional Jet 

Large Jet 

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 

Fuel Used NOx Produced Soot Produced 

Figure 67: Distribution of distance flown, fuel used, NOx and soot production between 

civil aircraft categories in 2025 

Comparison of 2025 Results with other Inventory Results and Forecasts 

Previous global emissions inventories have also forecast global aviation emissions for 

future years. Each forecast uses different detail information sources, assumptions, 

methodologies and also different assumptions to underpin the forecast itself. As for 

the 2002 data, because of these differences, detail comparison between the forecasts 

contains numerous uncertainties as to the source of those differences. Nevertheless, 

each inventory is attempting to forecast global emissions from aviation. Global 

forecasts from three of these recent inventories were produced for the year 2015 (DLR 

1992 [Schmitt 1997], ANCAT/EC2 1992 [Gardner 1998], NASA 1992 [Baughcum 

1996]. These forecasts are presented together with the inventory data and the 

AERO2k 2025 forecast in Figure 68 to Figure 71. 



350 

300 

250 

200 

150 

100 

50 

0 

1970 1980 1990 2000 2010 2020 2030 

Figure 68: 2025 Global fuel consumed 

NASA 


DLR 

ANCAT 

AERO2K 

Military 

For total civil aviation fuel used, the AERO2k 2025 forecast shows the expected 

increase in the total quantity of fuel used described in Section 2.3 above. The increase 

is less severe than that forecast in the NASA and ANCAT/EC2 studies, the reasons for 

which require further study outside the scope of the AERO2k project. 


5.0 

4.0 

3.0 

2.0 

1.0 

0.0 

1970 1980 1990 2000 2010 2020 2030 

Figure 69: 2025 Global NOx emissions 

NASA 

DLR 

ANCAT 

AERO2K 

Military 

As with fuel used, NOx emissions are forecast to continue to rise despite the 

introduction of lower NOx technologies as new aircraft enter the fleet. In a fairly 

aggressive NOx-reduction scenario, the assumptions in AERO2k include a 

combination of this improved technology, plus continued regulation to moderate the 

increase in NOx emissions. This contrasts with the much less aggressive NOxreduction 

scenario used in the 2015 forecasts made by NASA and ANCAT in the mid- 

1990s.

CO Emissions (Tg) 

1.5 

1.0 

0.5 

0.0 

1970 1980 1990 2000 2010 2020 2030 

Figure 70: 2025 Global CO emissions 

HC Emissions(Tg) 

0.4 

0.3 

0.3 

0.2 

0.2 

0.1 

0.1 

0.0 

1970 1980 1990 2000 2010 2020 2030 

Figure 71: 2025 Global HC emissions 

NASA 

AERO2K 

Military 

NASA 

AERO2K 

Military 

For CO and HC, AERO2k has simply assumed that there is little or no further 

improvement in the respective emission indices. Hence the reductions seen up to 

2002 as 1960/70s combustion technology aircraft are retired are overtaken by 

increases in traffic volume. 

Comparison of 2025 results with other Scenarios 

In addition to the forecasts from previous inventory work described in the preceding 

section, there have been a number of other attempts to quantify future emissions 

from aviation using scenario tools to generate estimations of 2002 traffic demand 

and technology improvement, each constrained by factors such as airport capacity. 

Predictions beyond 2025 are increasingly uncertain and the use of scenarios permits a 

range of assumptions and possible outcomes to be accommodated. In this section, 

the AERO2k 2025 forecast is compared with three such scenario studies – ICAO FESG 35 

35 Forecasting and Econocmics Sub-group 


[ICAO, 1999], EDF 36 (Vendantham and Oppenheimer, 1994, 1998) and CONSAVE 37 

[Berghof, 2004]. 

In the ICAO FESG forecast, three demand scenarios are generated (high, medium and 

low) together with high and low technology assumptions for each demand scenario, 

all based on market maturity concepts. In the EDF forecast, a logistic model is used 

focusing particularly on demand growth in developing countries. Finally, the 

preliminary results from CONSAVE use four varied socio-political scenarios to describe 

four extremes of aviation growth. The AERO2k results are plotted against each of 

these scenarios in turn. 

Comparison with FESG Scenarios 

Based on data from historical growth trends, the ICAO FESG Scenarios use a single 

global growth model of traffic demand based on a logistics growth curve function, 

without constraints. Hence the FESG scenarios to a large extent reflect assumptions 

of no fundamental change in the trends within society or within aviation itself. GDP 

and aviation demand growth rates are based on Boeing forecasts to 2015 and the 

IPCC IS92 scenarios thereafter. Three growth scenarios representing high, medium 

and low growth are illustrated. Within each of the 3 demand growth scenarios, there 

are 2 technology scenarios representing higher and lower achievement in terms of 

embodiment of fuel consumption and emissions reduction technology through the 

fleet. 

Against these FESG scenarios, the AERO2k forecasts for global fuel consumption, and 

hence CO2, lie slightly above the medium growth scenario. In contrast, the AERO2k 

NOx forecast lies below the medium growth scenario, perhaps reflecting the AERO2k 

assumption of ongoing technical success in meeting a series of increased NOx 

stringency measures. Further analysis requires detailed examination of the scenarios 

beyond the scope of this report. 


800 

700 

600 

500 

400 

300 

200 

100 

0 

High 

Demand 

Medium 


Low 


1960 1980 2000 2020 2040 2060 

Figure 72: Comparison of global aviation fuel used with FESG scenarios 

36 Environmental Defense Fund 

37 Constrained Scenarios on Aviation and Emissions 

NASA 

DLR 

ANCAT 

AERO2K 

Military 

FESG 



12.0 

10.0 

8.0 

6.0 

4.0 

2.0 

High Demand 

Med Demand 

Low Demand 

0.0 

1960 1980 2000 2020 2040 2060 

Figure 73: Comparison of global aviation NOx emissions with FESG scenarios 

Comparison with EDF Scenarios 

NASA 

DLR 

ANCAT 

AERO2K 

Military 

FESG 

The US-based Environmental Defence Fund has produced forecasts for 2020 and 2050 

(and 2100) [Vendantham and Oppenheimer 1994, 1998] based on the six IPCC 

scenarios (IS92a-f) combined with two demand sets – base and high. In terms of 

general level of activity, the base cases are similar to the FESG scenarios. The high 

growth scenarios model substantially higher developing-world-driven aviation 

growth up to market saturation around 2040. Five of these cases (2 base cases, 3 high 

cases) are plotted in Figure 74 and Figure 75 for CO2 (in terms of Tg of carbon 38 ) and 

NOx together with the equivalent results for AERO2k. 

AERO2K 

Figure 74: Comparison of global aviation CO2 emissions with EDF projections (from IPCC 

1999) 

38 Multiply Tg carbon by 44/12 to obtain Tg CO2 


AERO2K 

Figure 75: Comparison of global aviation NOx emissions with EDF projections (from 

IPCC 1999) 

AERO2k results for both 2002 and 2025 appear to fall below the EDF IS92C base 

prediction for CO2 and close to it for NOx. However care is needed in making such 

comparisons. AERO2k includes only civil IFR and military flights whilst the EDF 

predictions have also attempted to account for general aviation, fully global charter 39 

business and other operational effects. Nevertheless, comparison between the FESG, 

EDF and AERO2k forecasts for NOx demonstrates the scenario-dependent variability 

even in these business-as-usual based scenarios. 

Comparison with CONSAVE 

At time of writing early results from the EC Framework 5 research project CONSAVE 

are becoming available. Using the Dutch AERO model (which has no link other than 

name to AERO2k), CONSAVE has taken four global economic and socio-political 

scenarios and modelled aviation in each of these scenarios [Berghof, 2004]. The four 

scenarios modelled are: 

− Unlimited Skies (ULS) in which aviation has thrived through economic growth and 

through having no unacceptable environmental impact (i.e. effects are resolved 

elsewhere). 

− Regulatory Push & Pull (RPP) where the regulatory bodies actively control, push 

and guide the aviation developments towards the future. 

− Fractured World (FW) where regional protectionism and antagonism has a 

significant adverse impact on the demand and supply for aviation. 

− Down to Earth (DtE) in which society becomes environmentally protective and at 

the same time loses interest in (long distance) (air) travel. 

All four CONSAVE scenarios share a common development up to the year 2005, 

harmonised with the ICAO/FESG forecasts. The scenarios start to diverge significantly 

from 2010 onwards. These scenarios include constrained conditions where the 

aviation system has responded to pressures of limited infrastructure, high fuel prices, 

and, in one case, kerosene-to-hydrogen (LH2) transition pushes the aviation system to 

39 Aero2k has charter from Europe and North America. 


its limits. Built on the newest "background" material in fields external to transport, 

they are intended to set a framework for assessment of the long-term development 

in aviation. 

CONSAVE models the aviation growth under each of these four scenarios, predicting 

CO2 and NOx emissions for the years 2000, 2020 and 2050. These results are 

compared with AERO2k results for 2002 and 2025 in Figure 76 to Figure 80. 

Demand (billion RTK) 

4500 

4000 

3500 

3000 

2500 

2000 

1500 

1000 

500 

0 

1990 2000 2010 2020 2030 2040 2050 2060 

Unlimited Skies Regulatory Push & Pull (LH2) 

Regulatory Push & Pull (All Kero) Fractured World 

Downto earth AERO2k 

Figure 76: Comparison of global civil aviation demand with CONSAVE scenarios 

Fuel used (Tg) 

900 

800 

700 

600 

500 

400 

300 

200 

100 

0 

1990 2000 2010 2020 2030 2040 2050 2060 




Figure 77: Comparison of global civil aviation fuel used with CONSAVE scenarios 


CO2 emissions(Tg) 

3000 

2500 

2000 

1500 

1000 

500 

0 

1990 2000 2010 2020 2030 2040 2050 2060 




Figure 78: Comparison of global civil aviation CO2 emissions with CONSAVE scenarios 40 

H2O emissions(Tg) 

2000.0 

1800.0 

1600.0 

1400.0 

1200.0 

1000.0 

800.0 

600.0 

400.0 

200.0 

0.0 

1990 2000 2010 2020 2030 2040 2050 2060 



Dow n to earth AERO2k 

Figure 79: Comparison of global civil aviation H2O emissions with CONSAVE scenarios 

40 Note: In the RP&P(LH2) scenario, no allowance is shown for CO2 emitted during hydrogen 

production. Dependent upon production technology, such emissions could be significant. 

Similarly water vapour emissions the RP7P(LH2) scenario would be higher than in all the other 

scenarios. 


NOx emissions(Gg) 

8000 

7000 

6000 

5000 

4000 

3000 

2000 

1000 

0 

1990 2000 2010 2020 2030 2040 2050 2060 



Dow n to earth AERO2k 

Figure 80: Comparison of global civil aviation NOx emissions with CONSAVE scenarios 

A full analysis of the CONSAVE results will be published in the CONSAVE project 

report [Berghof, 2004]. However, an initial comparison with AERO2k shows 

passenger/freight demand for the AERO2k 2025 scenario to lie between the 

technology driven, relatively unregulated “Unlimited Skies” scenario and the more 

regulated “Regulatory Push & Pull” scenario. This matches the scenario modelled in 

AERO2k which assumes relatively high technical success in an environmentally 

regulated scenario. 

In terms of fuel and CO2, the AERO2k scenario models proportionally higher usage 

and emissions, but results still lie between the two CONSAVE scenarios. For NOx, the 

assumptions in AERO2k are more aggressive with emissions lying close to those in the 

CONSAVE “Regulatory Push & Pull” scenario. This is consistent with the assumptions 

in Section 2.3. Further analysis would require a detailed comparison of the aircraft 

fleet and emissions assumptions in the two projects, which is outside the scope of 

this report. However, the comparison of AERO2k, which focuses on emissions 

calculation, and CONSAVE, which focuses on scenarios, shows the results to be 

compatible. 


4 Conclusions 

This report has described the production of a global gridded aviation emissions 

inventory for 2002 and a forecast of emissions for the year 2025, for both civil and 

military aviation. In addition, the report presented total “headline” figures for global 

and regional aviation fuel used and emissions for the 2002 inventory year and the 

2025 forecast year. 

Global inventories of aircraft fuel usage and emissions are required for the 

quantification of environmental effects of aviation. For the key climate impact 

assessment models, the input data for individual emissions need to be placed on a 

global grid. In order to meet this requirement, this EC 5th Framework Programme 

project ‘AERO2k’ has developed a new and improved global inventory of aviation fuel 

usage and emissions. Additional parameters (e.g. particle emissions and distance 

travelled/grid cell) are now needed for the climate modelling community in addition 

to the previously provided gas phase species of carbon dioxide (CO2), carbon 

monoxide (CO), hydrocarbons (HCs) and NOx. These new parameters have been 

added to the civil aviation inventory in AERO2k. 

To provide new aviation emissions data on a global gridded basis, AERO2k has taken 

the best available civil and military flight information for the year 2002. For civil 

aviation, this included radar tracked flight data from North America and Europe 

showing actual latitude, longitude and altitude along the flight path. Routing 

information was used to place timetabled flights from the rest of the world onto a 

global grid. Using 40 representative aircraft, fuel used for each flight was calculated 

using performance data from the PIANO aircraft performance tool. Employing the 

latest publicly available information on emissions factors, emissions were calculated 

based on aircraft height, weight and speed, throughout the flight. New information 

on non-volatile particulate emissions has been added to provide a first gridded 

estimate of particulate emissions from civil aviation. Extensive validation activities 

have been performed to ensure the integrity of the data processing and output. 

The production of these gridded data included a number of challenges, principally in 

generating a robust 2002 flights database from a range of input sources, in grouping 

and modelling the representative aircraft and engines, in parameterising the 

emissions and in combining the data to generate the global gridded data. Within 

each stage, the assumptions made were balanced between the requirements for 

accuracy and the need to keep the data and computing requirements within 

manageable proportions. Wide ranging validation activities have been performed to 

ensure the integrity of the assumptions, data processing and output. Key validation 

activities have been reported in this and other AERO2k reports. Based on this 

validation work, the results for AERO2k are believed to represent an important step 

forward over previous inventories, although the absolute accuracy of the data is 

difficult to determine without considerable further work beyond the scope of the 

project. Estimates of uncertainties have been provided for individual elements of the 

work where available. Because of the decision not to include effects which could not 

be quantified (eg winds, tankering) and the omissions of non-timetable flights 

outside Europe and North America, it is probable that the total figures given 

represent a slight underestimate of the actual total emissions from civil aviation. 


Gridded results have been published on the World Wide Web at 

http://www.cate.mmu.ac.uk/aero2k.asp. Analysis of the data has also been carried 

out to produce the global and regional results contained in this report. Key headline 

results for 2002 were: 

Distance 

CO2 H 

Fuel Used 

2O CO NOx HC Soot Particles 

Flown Produced Produced Produced Produced Produced Produced Produced 

(10 3 million 

n. miles) 


Civil 

Aviation 41 17.9 156 492 193 .507 2.06 .063 .0039 

4.03 X 

10 25 

Military 


n/a 19.5 61.0 24.1 .647 .178 .066 n/a n/a 

Total n/a 176 553 217 1.15 2.24 .129 n/a n/a 

For civil aviation in 2002, the fuel-used and NOx results are in line with results from 

previous inventory work. Annual fuel-used continues to increase despite efficiency 

improvements in newer aircraft. The complex CO2/NOx trade-off related to engine 

pressure ratio is evidenced by the continuing increase in annual NOx emissions and 

substantially unchanging value of NOx emitted per kg of fuel-used (EI NOx). 

Conversely, the benefits of improving combustion efficiency in recent aircraft are now 

seen in a substantial reduction in annual CO and HC emissions and even greater 

reductions in fleet EI CO and HC for civil aviation. 

This reduction in CO and HC emissions from civil aviation can be contrasted with the 

estimated equivalent emissions from military aviation. Unlike previous inventories, 

the AERO2k military inventory has attempted to take full account of reheat operation 

with the outcome that estimates of CO, and to a lesser extent HC emissions from 

military aviation are increased compared with those earlier inventories. Whilst the 

AERO2k figures for military CO and HC should be regarded as maxima because of the 

difficulty in obtaining accurate reheat usage data, the implication is that military 

aviation is now a major source of these particular aviation emissions. This contrasts 

with earlier inventory results. 

As previously mentioned, new data, not available from previous inventories, are 

presented in the form of particulate mass and number and in distance flown in each 

grid cell. Both these parameters have increasing importance in the assessment of 

aviation climate impacts from contrail and cirrus formation. For particulates, it is 

recognised that the scientific understanding of particulate emissions from aircraft 

engines at altitude is in its infancy. Whilst using the best available information from 

DLR, leaders in measurement of these emissions, the particle number data presented 

must be regarded as a first estimate. 

To generate a forecast for 2025, a scenario has been developed within AERO2k in 

which demand growth and technology improvements are based on estimates by 

Airbus and UK DTI. These estimates produce regional factors, globally averaging a 

multiplication of capacity by 2.6, fuel used by 2.1 and NOx emitted by 1.6 times over 

the 23 years from 2002 to 2025. This represents a continuous demand growth 

scenario at around 4.2% per annum with particularly successful embodiment of NOx 

reduction technology in new aircraft over the period. 

Using the regional demand and technology factors generated by this scenario, the 

2002 flight dataset was processed to for forecast gridded data for 2025. This gridded 

41 Civil aviation includes IFR flights only 


data is published alongside the 2002 data at http://www.cate.mmu.ac.uk/aero2k.asp. 


Distance 

Flown 

(10 3 million n. 

miles) 

Fuel Used 

CO 2 

Produced 

H 2O 

Produced 

CO 

Produced 

NO x 

Produced 

HC 

Produced 

Soot 

Produced 



Particles 

Produced 


2025 36.1 327 1029 404 1.15 3.308 0.1447 0.0087 8.54 x 10 25 

For 2025, the scenario confirms the challenge faced by civil aviation in mitigating the 

mass of emissions resulting from meeting the increased passenger and freight 

demand. Despite assumptions of increased average aircraft size and continuing 

success in introducing fuel saving and emissions reduction technology, fleet rollover 

timescales and the demand growth rate itself are still leading to significant increases 

in global emissions. Over the 23 year period from 2002 to 2025, satisfying a demand 

increase of 2.6 times results in a doubling of fuel consumed and hence of CO2 and 

H2O emissions. With ongoing regulation and technical success, there is an increase in 

NOx by the lower factor of 1.6 times – but still an increase. The scenario also suggests 

an increase in CO, HC and particulate emissions as significant technology 

improvements run out in this area. The redistribution of these emissions around the 

globe as a result of the regionally differential growth rates is contained in the global 

gridded data. 

In conclusion, AERO2k has provided a significant update of the quantity and location 

of emissions from global aviation. The use of radar tracked data for Europe and North 

America has considerably enhanced the knowledge of the actual global position 

(latitude, longitude and altitude) at which these emissions occur. Increased numbers 

of representative aircraft compared to previous inventories have improved the 

accuracy of the estimation, as has the updating of emissions parameters based on 

latest available research. The provision of particulate number estimates and distance 

flown per grid cell are firsts for global inventories. These new data give atmospheric 

scientists the opportunity to evaluate the likely impact of aviation on climate through 

aviation-induced contrails and cirrus clouds. Combined with the improved and 

updated gridded data for the gaseous emissions, the overall AERO2k 2002 and 2025 

gridded data provide a major new data source for aviation climate impact 

assessment.

5 Recommendations for Further Work 

There is a significant opportunity to capitalise on the FP5 investment in AERO2k in 

order to meet the increasing need for substantiated aviation emissions data and its 

climate effect. Within the terms of the AERO2k project, the software has been 

developed to run data for 2002 on a global basis as a “one-shot” exercise to provide 

global data. However, development and further exploitation of the AERO2k model 

would provide authoritative global, regional and national emissions data essential to 

inform debate on the environmental effects of aviation in Europe and globally and 

evaluate the means to regulate and control those emissions. AERO2k model has the 

potential to become a major policy analysis tool for evaluating global, regional 

national and major airport emissions from actual flights and from future aviation 

scenarios. The vast majority of the work required to produce such a tool has already 

been completed within the initial AERO2k project described in this report. 

Future work falls into two main categories, namely the exploitation and where 

appropriate, development of AERO2k to provide global/regional emissions data and 

secondly, the integration of AERO2k into a suite of compatible modules modelling the 

interrelationships between climate effect, air quality, noise and economics within 

aviation. 

To develop the AERO2k software requires a short programme to improve the data 

search and analysis capability. Designed as a one off-activity, AERO2k is currently a 

loosely connected group of utilities implemented in MS Access 2000 requiring 

considerable programming effort and time to retrieve new information. To allow cost 

effective use of the wealth of data incorporated in AERO2k, it is required to bring 

together these utilities into a client/server database application. In addition, 

effectiveness needs to be enhanced by: 

− Addition of a user interface to improve cost effectiveness of query and 

scenario input 

− Implementation of a module to allow user-defined scenario changes by 

adjustment of the flight database and application of further traffic efficiency 

factors to those flights. 

− Consideration of the requirement for addition of future aircraft types and 

new destination pairs. 

− Automation of changes to search criteria including variable regions, group of 

countries (eg EC25) and individual States. 

With this additional functionality, AERO2k can be rerun with different scenario and 

propulsion technology data to provide revised 2002 and 2025 data for different 

scenarios – these could be different technology assumptions, ATC improvements, 

cruise height changes and so on. If appropriate, latest information could be used to 

update current AERO2k assumptions and source data (e.g. engine models, emissions 

parameters) 

Additionally, AERO2k has been built to provide global data, but has immense 

potential to provide detail data (airport, country, air quality etc). The overall 

programme is in place but to allow detail queries to provide valid information, it 

would be necessary to: 


− Increase the number of weeks data 

− Increase the number of aircraft models and engine combinations 

− Improve detail of the LTO assumptions such as reduced throttle settings, idle 

settings 

− Incorporate additional tail number and fleet data to provide improved granularity of 

fleet make up (to complement addition of aircraft models) 

Similarly, the current coverage of AERO2k includes only “quantifiable” effects and 

only IFR flights. To complete the overall aviation emissions picture, an estimation of 

these effects (e.g. winds, tankering) and the emissions contribution of non-IFR flights 

are required. 

AERO2k is based on a flights database supplied from the Eurocontrol infrastructure, 

in collaboration with the FAA. In cooperation with Eurocontrol, further research in 

improving the method of flight data collection and analysis should be encouraged. In 

particular, further work linked to the intrinsic quality of the data would be required to 

gain in data quality, quality which would read through to the emissions estimates 

themselves. There is a need for an international effort to harmonize airports, aircraft 

and airline coding and for all parties to apply a unique code system. The increasing 

number of code-sharing flights leads to their possible duplication as the same flight 

appears under different callsigns. A recognised system for identifying code-sharing 

callsigns would need to be put in place by the airlines. The use of a unique identifier 

linked to the tail number for example would also facilitate greatly the identification 

of flights and the merging of trajectories. The automatic report of flight information 

would avoid data typing errors such as an “O” typed instead of a “0” and so would 

reduce the number of flights listed as inconsistent. 

In order to gain full international acceptance for the results from AERO2k, it is 

recommended that AERO2k is subjected to scrutiny alongside the FAA SAGE (System 

for Assessing Global Emissions) model for approval by CAEP, with the objective of 

providing an authoritative European assessment tool for global aviation emissions. 

Maintaining such approval would require maintenance of the AERO2k model to 

include additional base years, new aircraft and new emissions data as they became 

available. 

Such an approval would strengthen the use of AERO2k to support the development of 

UNFCCC fuel used and emissions reporting by States, by provision of AERO2k data to 

improve lower tier methodologies and as a higher tier methodology in itself. 

Provision of data for additional years would allow short term comparison of aviation 

trends, assist in UNFCCC methodology improvement and actual national reporting. 

On this last point, use of AERO2k as a higher tier methodology for calculation of 

national emissions offers EC states considerable cost savings and improvements in 

accuracy compared to the manual methods currently used 

As a separate issue, an evaluation of the actual climate impact is the natural followon 

from AERO2k emissions data. Integration of the AERO2k model with one or more 

climate impact models to allow cost effective production of the relative climate 

impacts of modelled scenarios. Similarly the integration with AQ, ATC, noise and 

economics models is an important issue in the sustainable aviation debate and an 

investigation of candidates and approach to providing a European inter-relationship 

modelling capability is required, based on use of loosely connected but compatible 

models. 


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7 Acronyms and abbreviations 

ACARE Advisory Council for Aeronautics Research in Europe 

AERO NLR’s Aviation Emissions and Evaluation of Reduction Options Model 

AERO2k 

The EC 5th Framework Programme project ‘AERO2k’ developing a new and 

improved global inventory of aviation fuel usage and emissions for 2002 

and a forecast of emissions for 2025 

AMOC ATFM (Air Traffic Flow Management) Modelling Capability 

The ANCAT/EC2 global aircraft emissions inventories produced by the 

ANCAT/EC2 

ECAC/ANCAT/EC Emissions Inventory Database Group; ANCAT: Group of 

experts on the Abatement of Nuisances Caused by Air Transport; ECAC: 

European Civil Aviation Conference 

AQ Air Quality 

ASK Available seat kilometres 

ASNM Available seat nautical miles 

ATC Air Traffic Control 

BADA 

Base of Aircraft Data: aircraft performance database produced and used by 

Eurocontrol 

BPR Bypass Ratio 

CAEP Committee on Aviation Environmental Protection 

CFMU Central Flow Management Unit 

CO Carbon monoxide 

CO2 

Carbon dioxide 

DLR Deutsche Forschungsanstalt für Luft- und Raumfahrt e.V, Germany 

DTI UK department of Trade and Industry 

EC European Commission 

ECAC European Civil Aviation Conference 

EDF Environmental Defence Fund 

EI 

Emissions Index (normally expressed as weight of emission per weight of 

fuel used 

ETMS Enhanced Traffic Management System 

FAA Federal Aviation Administration 

FESG Forecasting and Economics Sub Group 

FL Flight level 

FSU Former Soviet Union 

Gb Gigabyte (= 10 9 bytes) 

Gg Gigagramme (= 10 9 grammes = 1000 tonnes) 

GC Great circle distance, the shortest distance between two points on a sphere 

GIS Geographic Information System 

GMT Greenwich Mean Time 

GSP NLR’s Gas Turbine Performance Programme 

HC Hydrocarbons 

H2O Water (normally in vapour form) 

IATA International Air Transport Association 

ICAO International Civil Aviation Organisation 

IFR Instrument flight rules 

WMO/UNEP Intergovernmental Panel on Climate Change (jointly 

IPCC 

established by the World Meteorological Organisation and the United 

Nations Environment Programme) 

ISA International Standard Atmosphere 

Jet A-1 A specification for aviation kerosene 

LTO The Landing and Take-Off cycle 


Mg Mega grammes (=10 6 grammes = 1 tonne) 

MS Microsoft 

n/a Not applicable 

NLR 

Nationaal Lucht -en Ruimtevaartlaboratorium 

Laboratory), Netherlands 

(National Aerospace 

n.mile Nautical mile 

NOx 

oxides of nitrogen, NO2 + NO 

OAG OAG Worldwide Ltd – a provider of airline schedule data 

PIANO 

PIANO (Project Interactive Analysis and Optimisation) is a professional 

software tool for commercial aircraft analysis 

PR Pressure ratio (overall engine) 

SAGE 

The US FAA’s SAGE (System for Assessing Aviation’s Global Emissions) 

project 

SAR Specific air range 

SARS Severe Acute Respiratory Syndrome 

SLS Sea Level Static 

R 2 

Goodness of Fit (or coefficient of determination) defined as 1 – (sum of the 

squares about the mean)/(sum of the squared errors) 

Tg Teragrammes (=10 12 grammes = 1 Megatonne) 

UNFCCC United Nations Framework Convention on Climate Change 

USAF United States Air Force 

UTC Coordinated Universal Time 

VBA Visual Basic for Applications 

WP Work Package 


A Appendix A 

A.1 Annual deviation of temperature from ISA standard (Ulbrich 2004) 

Height in 

hPa 

Temperature 


B Appendix B 

B.1 Definition of Geographic Regions 

The following tables show the allocation of individual countries to regions for the 

purposes of presentation of AERO2k results. 

B.1.1 Asia and Pacific 

American Samoa Kiribati Papua New Guinea 

Australia Laos Philippines 

Bangladesh Macau Polynesia (French) 

Bhutan Malaysia Samoa 

British Indian Ocean Territory Maldives Singapore 

Brunei Darussalam Marshall Islands Solomon Islands 

Cambodia; Kingdom of Micronesia South Korea 

China Midway Atoll (USA) Sri Lanka 

Christmas Island Mongolia Taiwan 

Cocos (Keeling) Islands Myanmar Thailand 

Cook Islands Nauru Tonga 

East Timor Nepal Tuvalu 

Fiji New Caledonia (French) USA 

Guam New Zealand USA Minor Outlying Islands 

Guam (USA) Niue Vanuatu 

Hong Kong Norfolk Island Vietnam 

India North Korea Wake Island (USA) 

Indonesia Northern Mariana Islands Wallis And Futuna Islands 

Japan Palau 

B.1.2 Eastern and Southern Africa 

Angola Madagascar Somalia 

Botswana Malawi South Africa 

Burundi Mauritius Swaziland 

Comoros Mayotte Tanzania 

Djibouti Mozambique Tanzania United Republic of 

Eritrea Namibia Uganda 

Ethiopia Reunion (French) Zambia 

Kenya Rwanda Zimbabwe 

Lesotho Seychelles 


B.1.3 European and North Atlantic 

Albania Great Britain Poland 

Algeria Greece Portugal 

Andorra; Principality of Greenland Romania 

Armenia Hungary Russian Federation 

Austria Iceland Slovak Republic 

Azerbaidjan Ireland Slovenia 

Belarus Italy Spain 

Belgium Kazakhstan 

Svalbard and Jan Mayen 

Islands 

Bosnia-Herzegovina Kyrgyz Republic (Kyrgyzstan) Sweden 

Bulgaria Latvia Switzerland 

Croatia Lithuania Tadjikistan 

Czech Republic Luxembourg Tunisia 

Denmark Macedonia Turkey 

Estonia Malta Turkmenistan 

Faroe Islands Moldavia Ukraine 

Finland Monaco United Kingdom 

France Morocco Uzbekistan 

Georgia Netherlands Yugoslavia 

Germany Netherlands Antilles 

Gibraltar Norway 

B.1.4 Middle East 

Afghanistan; Islamic State of Jordan Qatar 

Bahrain Kuwait Saudi Arabia 

Cyprus Lebanon Sudan 

Egypt Libya Syria 

Iran Oman United Arab Emirates 

Iraq Pakistan Yemen 

Israel Palestinian Territory 

B.1.5 North American, Central American and Caribbean 

Anguilla Dominican Republic Puerto Rico 

Antigua And Barbuda El Salvador Saint Kitts & Nevis Anguilla 

Aruba Grenada Saint Lucia 

Bahamas Guadeloupe (French) Saint Pierre and Miquelon 

Barbados Guatemala Saint Vincent & Grenadines 

Belize Haiti Trinidad and Tobago 

Bermuda Honduras Turks and Caicos Islands 

Canada Jamaica USA 

Cayman Islands Martinique (French) Virgin Islands (British) 

Costa Rica Mexico Virgin Islands (USA) 

Cuba Montserrat 

Dominica Nicaragua 


B.1.6 South American 

Argentina Ecuador Paraguay 

Bolivia Falkland Islands Peru 

Brazil French Guyana Suriname 

Chile Guyana Uruguay 

Colombia Panama Venezuela 

B.1.7 Western and Central Africa 

Benin Equatorial Guinea Mali 

Burkina Faso Gabon Mauritania 

Cameroon Gambia Niger 

Cape Verde Ghana Nigeria 

Central African Republic Guinea 

Saint Tome (Sao Tome) And 

Principe 

Chad Guinea Bissau Senegal 

Congo Ivory Coast (Cote D'Ivoire) Sierra Leone 

Congo; The Democratic 

Republic Of The 

Liberia Togo 


C Appendix C 

C.1 2002 fuel consumed, emissions and distance flown by altitude 

Tabulation of 2002 fuel consumed, emissions and distance flown by altitude covers 

one week in each of four months, spread though the year. These months broadly 

cover the 4 seasons. Altitudes are given for the top of each altitude band (ie “3000ft” 

covers emissions from 0 to 3000ft). 

Weekly Fuel Used (Mg) 

Altitude (ft) April Sept October February 

3000 347 374 363 341 

6000 114 121 123 113 

9000 127 134 134 132 

12000 141 141 142 161 

15000 122 129 130 131 

18000 117 125 125 121 

21000 107 117 116 108 

24000 107 115 115 111 

27000 120 130 130 124 

30000 183 182 184 189 

33000 350 360 378 343 

36000 537 586 566 496 

39000 434 510 484 410 

42000 69 99 82 73 

45000 3.042 2.968 2.840 2.791 

48000 0.646 0.649 0.613 0.468 

50000 0.015 0.021 0.011 0.009 

Table C1 – 2002 weekly fuel consumed – civil aviation – distribution by altitude and 

time of year 

Weekly CO2 Produced (Mg) 


3000 1090 1170 1140 1070 

6000 359 381 386 358 

9000 400 421 423 417 

12000 444 446 447 508 

15000 385 408 409 414 

18000 368 394 395 380 

21000 337 368 367 340 

24000 338 363 363 351 

27000 379 411 410 391 

30000 577 575 579 594 

33000 1100 1140 1190 1080 

36000 1690 1850 1780 1560 

39000 1370 1610 1530 1290 

42000 219 311 259 231 

45000 9.580 9.350 8.950 8.790 

48000 2.030 2.040 1.930 1.470 

50000 0.047 0.066 0.035 0.029 

Table C2 – 2002 weekly CO2 emissions– civil aviation – distribution by altitude and time 

of year 


Weekly H2O Produced (Mg) 


3000 429 463 450 422 

6000 141 150 152 140 

9000 157 165 166 164 

12000 174 175 176 200 

15000 151 160 161 163 

18000 145 155 155 149 

21000 133 145 144 134 

24000 133 143 143 138 

27000 149 161 161 153 

30000 227 226 228 233 

33000 433 446 468 424 

36000 664 725 701 614 

39000 537 631 599 507 

42000 86 122 102 91 

45000 3.770 3.670 3.520 3.450 

48000 0.800 0.803 0.759 0.579 

50000 0.018 0.026 0.014 0.011 

Table C3 – 2002 weekly H20 emissions – civil aviation – distribution by altitude and 

time of year 

Weekly CO Produced (Mg) 


3000 3.730 4.040 3.920 3.680 

6000 0.290 0.314 0.311 0.291 

9000 0.285 0.306 0.300 0.292 

12000 0.307 0.319 0.311 0.337 

15000 0.269 0.294 0.288 0.277 

18000 0.260 0.287 0.282 0.259 

21000 0.250 0.281 0.273 0.245 

24000 0.259 0.287 0.281 0.264 

27000 0.303 0.350 0.331 0.314 

30000 0.465 0.477 0.473 0.492 

33000 0.819 0.892 0.896 0.813 

36000 1.170 1.310 1.240 1.100 

39000 0.783 0.926 0.865 0.745 

42000 0.117 0.162 0.131 0.127 

45000 0.013 0.013 0.012 0.011 

48000 0.00310 0.00300 0.00279 0.00224 

50000 0.00008 0.00011 0.00004 0.00005 

Table C4 – 2002 weekly CO emissions – civil aviation – distribution by altitude and time 

of year 


Weekly NOx Produced (Mg) 


3000 4.620 5.010 4.900 4.540 

6000 1.830 1.950 1.960 1.810 

9000 2.000 2.120 2.110 2.060 

12000 2.170 2.240 2.240 2.400 

15000 1.890 2.020 2.020 1.990 

18000 1.760 1.900 1.910 1.790 

21000 1.590 1.740 1.740 1.590 

24000 1.510 1.630 1.630 1.530 

27000 1.510 1.640 1.650 1.540 

30000 2.120 2.130 2.150 2.180 

33000 4.000 4.090 4.340 3.940 

36000 6.450 7.000 6.770 5.980 

39000 5.540 6.490 6.210 5.230 

42000 0.899 1.290 1.090 0.945 

45000 0.029 0.028 0.027 0.028 

48000 0.00569 0.00551 0.00557 0.00398 

50000 0.00012 0.00016 0.00009 0.00007 

Table C5 – 2002 weekly NOx emissions – civil aviation – distribution by altitude and 

time of year 

Weekly HC Produced (Mg) 


3000 0.5010 0.5420 0.5240 0.4960 

6000 0.0405 0.0440 0.0427 0.0409 

9000 0.0405 0.0436 0.0417 0.0420 

12000 0.0440 0.0458 0.0438 0.0484 

15000 0.0385 0.0423 0.0407 0.0395 

18000 0.0376 0.0417 0.0399 0.0372 

21000 0.0346 0.0387 0.0369 0.0338 

24000 0.0342 0.0379 0.0362 0.0347 

27000 0.0378 0.0446 0.0408 0.0394 

30000 0.0563 0.0585 0.0574 0.0612 

33000 0.0877 0.0994 0.0977 0.0877 

36000 0.1230 0.1390 0.1290 0.1180 

39000 0.0713 0.0874 0.0810 0.0667 

42000 0.0111 0.0158 0.0126 0.0120 

45000 0.0017 0.0018 0.0016 0.0016 

48000 0.00046 0.00050 0.00041 0.00037 

50000 0.00001 0.00002 0.00001 0.00001 

Table C6 – 2002 weekly HC emissions – civil aviation – distribution by altitude and time 

of year 


Weekly Soot Produced (Mg) 


3000 0.01585 0.01708 0.01638 0.01573 

6000 0.00468 0.00499 0.00489 0.00469 

9000 0.00500 0.00527 0.00512 0.00528 

12000 0.00539 0.00541 0.00526 0.00618 

15000 0.00428 0.00455 0.00443 0.00454 

18000 0.00386 0.00417 0.00404 0.00390 

21000 0.00319 0.00352 0.00342 0.00317 

24000 0.00276 0.00298 0.00291 0.00286 

27000 0.00282 0.00312 0.00303 0.00294 

30000 0.00393 0.00394 0.00394 0.00414 

33000 0.00638 0.00664 0.00692 0.00628 

36000 0.00824 0.00899 0.00861 0.00770 

39000 0.00555 0.00647 0.00612 0.00525 

42000 0.00079 0.00112 0.00092 0.00086 

45000 0.0000485 0.0000486 0.0000455 0.0000444 

48000 0.0000104 0.0000107 0.0000095 0.0000077 

50000 0.0000002 0.0000003 0.0000001 0.0000002 

Table C7 – 2002 weekly particulate mass emissions – civil aviation – distribution by 

altitude and time of year 

Weekly Particles Produced x10 -22 


3000 7.69 8.28 7.94 7.63 

6000 3.45 3.66 3.66 3.47 

9000 3.78 3.99 3.94 3.99 

12000 4.19 4.23 4.18 4.76 

15000 3.63 3.87 3.83 3.87 

18000 3.56 3.85 3.78 3.63 

21000 3.20 3.52 3.46 3.20 

24000 3.06 3.29 3.25 3.18 

27000 3.44 3.79 3.70 3.58 

30000 5.22 5.24 5.24 5.48 

33000 9.46 9.86 10.30 9.29 

36000 13.20 14.50 13.80 12.30 

39000 9.19 10.70 10.10 8.68 

42000 1.310 1.840 1.510 1.410 

45000 0.080 0.080 0.075 0.073 

48000 0.0169 0.0173 0.0154 0.0126 

50000 0.0004 0.0006 0.0002 0.0003 

Table C8 – 2002 weekly particulate number – civil aviation – distribution by altitude 

and time of year 


Weekly Distance Flown (n.miles x10 -6 ) 


3000 2.25 2.35 2.44 2.07 

6000 87.20 90.64 95.38 76.65 

9000 62.89 64.23 67.79 59.86 

12000 70.75 68.46 72.17 74.53 

15000 68.50 70.74 74.50 65.88 

18000 72.30 75.84 79.78 66.72 

21000 69.76 74.94 78.56 63.77 

24000 72.66 76.47 79.89 68.51 

27000 82.40 87.73 91.59 77.29 

30000 114.04 114.22 119.89 105.53 

33000 186.82 195.01 207.44 167.80 

36000 252.32 276.08 280.09 215.86 

39000 207.72 245.90 247.34 184.49 

42000 48.01 60.39 56.48 47.24 

45000 5.98 5.49 5.92 4.64 

48000 1.41 1.30 1.39 0.86 

50000 0.032 0.036 0.034 0.016 

Table C9 – 2002 weekly distance flown – civil aviation – distribution by altitude and 

time of year 


D Appendix D 

D.1 Annual Values of Distance Flown, Fuel Consumed and Emissions for Civil 

Aviation Flights between Regions 

Asia and Pacific Departures 

Distance 

Flown 

Fuel Used CO2 H2O CO NOx HC Soot Particles 

Arrival Region 

n.miles 

-9 

x10 

(Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10-23 

Asia and Pacific 2.160 22500 70910 27860 65.120 338.100 7.081 0.5157 48.85 

Eastern and Southern 

Africa 

0.013 214 676 266 0.298 3.214 0.026 0.0036 0.52 

European and North 

Atlantic 

0.279 4933 15560 6107 9.202 66.950 0.795 0.0826 12.00 

Middle East 

North American, 

0.075 938 2959 1162 1.732 15.050 0.205 0.0154 1.95 

Central American and 

Caribbean 

0.328 5550 17500 6871 10.040 78.980 0.877 0.0930 13.62 

South American 0.002 34 106 42 0.045 0.553 0.003 0.0005 0.07 

Table D1 – 2002 Regional fuel, emissions and distance flown for departures from Asia & 

Pacific region 

Eastern and Southern Africa Departures 


Distance 

Fuel Used 

Flown 

n.miles x10 


- 

9 (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10 -23 



Africa 

0.113 617 1944 764 1.919 7.686 0.279 0.0258 1.96 


Atlantic 

0.079 1254 3956 1553 2.081 17.620 0.186 0.0205 2.99 

Middle East 0.009 98 310 122 0.205 1.490 0.029 0.0018 0.21 

North American, Central 

American and Caribbean 

0.001 8 27 11 0.027 0.101 0.003 0.0002 0.02 


Western and Central 

Africa 

0.007 92 291 114 0.203 1.156 0.017 0.0017 0.23 

Table D2 – 2002 Regional fuel, emissions and distance flown for departures from 

Eastern and Southern Africa region 


European and North Atlantic Departures 

Distance 

Flown 

Fuel 

Used 



n.miles 

-9 

x10 




Africa 

0.075 1227 3869 1519 2.006 17.55 0.184 0.0215 2.99 


Atlantic 

3.679 24020 75630 29740 110.900 278.90 14.240 0.7584 68.33 

Middle East 


0.255 2665 8399 3299 7.380 37.69 0.978 0.0517 6.46 


Caribbean 

0.653 9123 28760 11290 18.060 137.10 1.887 0.1586 21.62 



Africa 

0.044 532 1677 659 1.538 7.331 0.193 0.0106 1.37 


European and North Atlantic region 

Middle East Departures 

Distance 

Flown 

n.miles 


x10 

Fuel 

Used 


-9 (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10-23 



Africa 

0.010 99 311 122 0.208 1.483 0.030 0.0018 0.21 


Atlantic 

0.265 2771 8734 3431 7.963 37.900 1.056 0.0531 6.75 

Middle East 


0.170 1811 5705 2242 5.826 28.530 0.736 0.0480 3.95 


Caribbean 

0.013 210 662 260 0.280 3.458 0.023 0.0035 0.49 


Africa 

0.002 27 84 33 0.066 0.366 0.008 0.0006 0.08 


Middle East region 


North American, Central American and 

Caribbean Departures 

Distance 

Flown 

Fuel 

Used 



n.miles 

-9 

x10 




Africa 

0.005 96 302 119 0.171 1.233 0.012 0.0018 0.26 


Atlantic 

0.674 9260 29200 11460 18.340 139.300 1.946 0.1563 21.77 

Middle East 


0.014 216 681 267 0.366 3.540 0.040 0.0035 0.51 


Caribbean 

7.370 46850 147600 58000 196.200 541.100 27.450 1.4780 135.00 



Africa 

0.003 48 150 59 0.096 0.810 0.010 0.0009 0.13 

Table D5 – 2002 Regional fuel, emissions and distance flown for departures from North 

American, Central American and Caribbean region 

South American Departures 

Distance 

Flown 

n. miles 


x10 

Fuel 

Used 


-9 (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) (Mg) x10-23 



Africa 

0.001 27 85 33 0.027 0.431 0.003 0.0005 0.07 


Atlantic 


0.086 1202 3790 1488 1.843 18.240 0.156 0.0174 2.53 


Caribbean 

0.114 1187 3740 1469 3.454 16.040 0.449 0.0230 3.11 



Africa 

0.0001 0.70170 2.21200 0.86870 0.00169 0.00938 0.00013 0.00001 0.00160 

Table D6 – 2002 Regional fuel, emissions and distance flown for departures from South 

American region 


Western and Central Africa 


Distance 

Flown 

Fuel 

Used 


Produced Produced Produced Produced Produced Produced Produced 

nautical miles 

-9 

x10 


Eastern and 

Southern Africa 

0.005 46.46 147 57.51 0.107 0.590 0.010 0.0009 0.11 

European and 

North Atlantic 

0.047 537.3 1693 665.10 1.360 7.239 0.161 0.0094 1.28 

Middle East 


0.002 26.28 83 32.53 0.066 0.351 0.009 0.0006 0.08 

Central American 

and Caribbean 

0.005 91.64 289 113.40 0.201 1.370 0.020 0.0017 0.24 

South American 0.0001 1.032 3.26 1.28 0.002 0.014 0.00017 0.000018 0.0024 

Western and 

Central Africa 

0.025 166.3 524 205.90 0.711 2.017 0.095 0.007537 0.55 


Western and Central Africa region 


Initial distribution list 

External 

QinetiQ 

Dr Ing. D Koertzer DG Research-H.3 Aeronautics, EC 

Mr T Elliff, EUROCONTROL 

Mr R M Gardner, DfT 

Prof. D S Lee, Manchester Metropolitan University 

Dr C Marizy, Airbus France 

Dr J Middel, NLR 

Mr M Plohr, DLR Köln 

Mr P Newton, DTI 

Dr D Raper, Manchester Metropolitan University 

Prof. Dr R Sausen, DLR Oberpfaffenhofen 

Information Resources 

Mr C J Eyers, QinetiQ 

Mr D A Addleton, QinetiQ/DTI 

Mr K D Brundish, QinetiQ 

Mr A Stapleton, QinetiQ 

Mr M Savill, QinetiQ 


Report documentation page 

Originator’s Report Number QinetiQ/04/001113 

Originator’s Name and Location 

Customer Contract Number and Period 

Covered 

Chris Eyers 

QinetiQ, Cody Technology Park, Farnborough, 

Hampshire, GU14 0LX 

G4RD-CT-2000-00382 

Customer Sponsor’s Post/Name and Location Dr.-Ing Dietrich Knoerzer, EC DG Res 

Report Protective Marking and 

any other markings 

Date of issue Pagination No. of 

references 

Unlimited 31 Dec 2004 Cover + 144 60 

Report Title 

AERO2k Global Aviation Emissions Inventories for 2002 and 2025 

Translation / Conference details (if translation give foreign title / if part of conference then 

give conference particulars) 

- 

Title Protective Marking Unlimited 

Authors 

Downgrading Statement - 

Secondary Release Limitations - 

Announcement Limitations - 

C Eyers, P Norman, J Middel, M Plohr, S Michot, K 

Atkinson, R Christou. 

Keywords / Descriptors Aviation fuel usage and emissions 

Abstract 

This report describes the work undertaken within the EC 5 th Framework Programme AERO2k 

WP1 to produce an inventory of aviation emissions for the year 2002 and a forecast for 

2025. The report summarises development of the inventory and forecast and includes 

validation of the results. The results from this work are presented as global and regional 

results in this report whilst gridded emissions data are available on the World Wide Web at 

http://www.cate.mmu.ac.uk/aero2k.asp. The gridded data is thereby available for use by 

atmospheric scientists to assess aviation effects on climate; the global and regional results 

provide a firm foundation for policy and scenario work. 

Abstract Protective Marking: Unlimited 

This form meets DRIC-SPEC 1000 issue 7 


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