Assessment of CO2 Estimation Methods for UK Aviation - Climate change Essay Example

 

 

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Abstract

 

 

Climate change is now constantly in the media, with public awareness and scientific understanding (as well as the urgency of the situation) growing daily.  But although climate change will have a damaging effect on the whole planet and is a compelling reason not to expand airports, the greenhouse gas emissions from an airport (unlike the noise and air pollution) have no immediate impact on the local community.

Although there are several methodologies for estimation of other gases such as NO2, CO, there are very few methodologies available specifically for CO2. It is most often found missing from most of the studies done on emission from aircrafts. However CO2 is an important greenhouse gas and contributes heavily to the emission. Hence it is necessary to identify a methodology that can be applied to estimate CO2 emission accurately.

This research attempts to use the existing methodologies that have been evolved such as RASCO, FLEM, IPCC. The research identifies the methodological steps that are relevant for CO2 emission estimation and then develops a methodology specifically for aircraft emission. The research looks at the data available in the various studies that can be applied generic to the aircraft and then look at the specific parts for the Manchester Airport.

 

The results presented in this thesis are based on my own research in the Department of Environmental and Geographical Sciences, The Manchester Metropolitan University.  All assistance received from other individuals and organisations has been acknowledged, and full reference is made to all published and unpublished sources used.

This thesis has not previously been submitted for a degree at any other institution

J. E. Mape

 

1      Introduction

Aircraft emit gases and particles directly into the upper troposphere and lower stratosphere where they have an impact on atmospheric composition.  These gases and particles alter the concentration of atmospheric greenhouse gases, including carbon dioxide (CO2), ozone (O3), and methane (CH4); trigger formation of condensation trails (contrails); and may increase cirrus cloudiness, all of which contribute to climate change.  The principal emissions of aircraft include the greenhouse gases carbon dioxide and water vapour (H2O). Other major emissions are nitric oxide (NO) and nitrogen dioxide (NO2) (which together are termed NOx), sulphur oxides (SOx), and soot.  The total amount of aviation fuel burned, as well as the total emissions of carbon dioxide, NOx, and water vapour by aircraft, are well known relative to other parameters important to this assessment.  The climate impacts of the gases and particles emitted and formed as a result of aviation are more difficult to quantify than the emissions; however, they can be compared to each other and to climate effects from other sectors by using the concept of radiative forcing. Because CO2 has a long atmospheric residence time (≈100 years) and so becomes well mixed throughout the atmosphere, the effects of its emissions from aircraft are indistinguishable from the same quantity of CO2 emitted by any other source.  The other gases (e.g., NOx, SOx, water vapour) and particles have shorter atmospheric residence times and remain concentrated near flight routes, mainly in the northern mid-latitudes.  These emissions can lead to radiative forcing that is regionally located near the flight routes for some components (e.g., ozone and contrails) in contrast to emissions that are globally mixed (e.g., CO2and CH4).  The global mean climate change is reasonably well represented by the global average radiative forcing, for example, when evaluating the contributions of aviation to the rise in globally averaged temperature or sea level. However, because some of aviation’s key contributions to radiative forcing are located mainly in the northern mid-latitudes, the regional climate response may differ from that derived from a global mean radiative forcing. AERONOX (1995)

 

The impact of aircraft on regional climate could be important, but has not been assessed in this report.  O3 is a greenhouse gas.  It also shields the surface of the Earth from harmful ultraviolet (UV) radiation, and is a common air pollutant.  Aircraft-emitted NOx participates in ozone chemistry.  Subsonic aircraft fly in the upper troposphere and lower stratosphere (at altitudes of about 9 to 13 km), whereas supersonic aircraft cruise several kilometres higher (at about 17 to 20 km) in the stratosphere. (Barrett J et al,2003) O3 in the upper troposphere and lower stratosphere is expected to increase in response to NOx increases and CH4 is expected to decrease.  At higher altitudes, increases in NOx lead to decreases in the stratospheric ozone layer. Ozone precursor (NOx) residence times in these regions increase with altitude, and hence perturbations to ozone by aircraft depend on the altitude of NOx injection and vary from regional in scale in the troposphere to global in scale in the stratosphere.  Water vapour, SOx (which forms sulfate particles), and soot play both direct and indirect roles in climate change and ozone chemistry. (IATA, 2001)

1.1  Statement of the problem

 

CO2 is an important constituent that contributes to the climate change. Further, aviation industry having a high growth potential, is one of the most important industry that influences the amount of CO2. In the analysis of air pollution at airports, CO, NO, NO2, SO2, O3 and hydrocarbon particles are analysed. CO2 emissions are not measured. This research is intended to bridge the gap and provide a reliable methodology for estimation of CO2 at Manchester airport.

1.2    Purpose of the Study

The purpose of the study is to determine an appropriate emission evaluation methodology for CO2 that is suitable for aviation and apply it to Manchester airport.

 

1.3    Research Hypotheses

The research hypothesis is the following

The IPCC and AERO methodology can be applied to a specific airport with modifications
Different research methodologies such as IPCC, AERO, DTLR can be combined to provide an effective methodology
1.4     Limitations of the Study

The research has the following limitations:

The research is limited to only CO2 emission
The research focuses on Manchester airport only and does not look at certain aspects of aviation that may be applicable in bigger or smaller airports
The research takes approximations on certain aircrafts where the details are not available easily.
The research is limited in time and is not tracked for advances in technologies or changes in aviation patterns after the study period causing the result to be valid only for the research period.
The research does not take into account specific incidents or developments that increases or decreases the importance of Manchester airport and its functioning and hence the CO2 emission impact.
The research does not take into account any substitution of airports that may be driven by political or economic reasons.
 

 

2      Literature Review

The following literature reviews are carried out as part of this research:

§  Henderson, SC, Wickrama  (1999) Aircraft emissions: Current Inventories and Future Scenarios, Chapter 9 in Aviation and the Global Atmosphere, A Special Report of IPCC 10 (Intergovernmental Panel on Climate Change)

§  RASCO – Regional Airport Route Analysis, Aviation & Travel Consultancy, July 2001

§  Aviation Emissions and Evaluation of Reduction Options (AERO) – 2000

§  DTI (2003) Our energy future – creating a low carbon economy

 

2.1   Policy Review – Current Inventories and Future Scenarios

 

 

Climate policy review and assessment

‘Contraction and Convergence’ was chosen as the post-Kyoto policy to be employed within this project following a decision by the UK Government to adopt the 60% target as based on the RCEP’s recommendation.  As mentioned previously, this target is itself based on a Contraction and Convergence policy.  However, to understand this policy option in the context of the others available, a literature review was carried out prior to the commencement of the project.

 

CCOptions model

Since 1996, the GCI has encouraged awareness of contraction and convergence as a practical interpretation of their philosophical principle that “every adult on the planet has an equal right to emit greenhouse gases”.  Contraction and convergence is an international framework for assuming an equitable contribution to the arrest of global greenhouse gas emissions, with all nations working together to establish and achieve an overall yearly emissions target, contraction. Moreover, all nations converge towards equal per-capita emissions by a specified year, convergence.  By simultaneously contracting and converging, such a mechanism requires all nations to impose targets from the outset (Cameron, 2003).  In light of the growing support for the GCI’s emission reduction regime, the GCI have produced a simple spreadsheet model – CCOptions – to allow policymakers and researchers to investigate the impact of varying the contraction and convergence years, as well as the target CO2 stabilisation levels.

 

Model Description

Many climate models are complex, multi-dimensional programmes that represent the fluid dynamics, thermodynamics, chemistry and radiative effects within and between the atmosphere, oceans and biosphere.  In contrast, CCOptions attempts neither to model the atmosphere, nor the carbon-cycle.  Instead, it uses the outputs and data from the UK’s Hadley Centre general circulation model (GCM) and IPCC reports as a scientific basis for determining global and national emissions trajectories.  The strengths of the CCOptions model as a policy tool lie therefore in its internal simplicity and the direct correlation between its clear policy-relevant outputs.  The CCOptions model distributes the global carbon budget between nations, depending on an atmospheric carbon dioxide concentration target and individual nations’ populations, to reveal national carbon emission reduction targets for each year up until 2200.  By keeping the model simple, policymakers can readily interpret its results, using them to set yearly targets for their own nation’s carbon emissions.

Input data

The CCOptions model comprises a set of Excel worksheets and accompanying  documentation.  The main worksheet contains details of a proposed contraction and convergence scenario, as well as graphs and data representing the key results.  Supplementary worksheets contain input data and the results for individual nations.  The input data for the model come in two forms; the raw historical carbon dioxide and population data for each nation, and the experimental parameters.  Fundamental to the model is a comprehensive list of all nations’ respective population and carbon dioxide emissions data.  Gathering such data is a non-trivial exercise due, for example, to national boundary changes, poor carbon dioxide accounting etc. The nations that are used in the latest version (Version 8.6) of CCOptions are those included in the CO2 Information Analysis Centre’s (CDIAC) 2003 listing.  The CO2 data for all nations is therefore taken from the CDIAC database (CDIAC, 2004) giving values in million tonnes of carbon for each year between 1800 and 2000.  The model includes a nation labelled ‘other’ to which the difference between the CDIAC’s estimate of total global emissions and the sum total of all the nations’ emissions is allocated.

 

The population data used in CCOptions is taken from the UN median population figures and forecasts (UN, 2002) and lists annual values for each nation between 1950 and 2050.  The list of nations includes one labelled ‘other’ intended to account for a number of small islands.  In relation to both the ‘other emissions’ and the ‘other population’, the GCI claim that because both values are very small, they do not significantly effect the calculations for carbon emissions within the model.  An important characterisation of the model is the stabilisation of the population figures at a chosen date between 2000 and 2050.  The purpose of this ‘cut-off population date’ is to reduce any incentive for a particular nation to increase their population and thereby their emissions allocation (each nation’s emissions targets being based on their population).  Clearly the appropriateness of adopting a population stabilisation date is open to argument, however given that population forecasts only exist up to 2050, the GCI consider maintaining a constant global population beyond 2050 an acceptable and appropriate simplification. (European Commission, 1999)

Review of  aircraft emission studies

Global passenger air travel, as measured in revenue passenger/km, is projected to grow by about 5% per year between 1990 and 2015, whereas total aviation fuel use—including passenger, freight, and military is projected to increase by 3% per year, over the same period, the difference being due largely to improved aircraft efficiency.  Projections beyond this time are more uncertain so a range of future unconstrained emission scenarios is examined in this report.

 

All of these scenarios assume that technological improvements leading to reduced emissions per revenue passenger-km will continue in the future and that optimal use of airspace availability (i.e., ideal air traffic management) is achieved by 2050.  If these improvements do not materialize then fuel use and emissions will be higher.  It is further assumed that the number of aircraft as well as the number of airports and associated infrastructure will continue to grow and not limit the growth in demand for air travel.  If the infrastructure was not available, the growth of traffic reflected in these scenarios would not materialize.  IPCC (1992) developed a range of scenarios, IS92a-f, of future greenhouse gas and aerosol precursor emissions based on assumptions concerning population and economic growth, land use, technological changes, energy availability, and fuel mix during the period 1990 to 2100.

 

Scenario IS92a is a midrange emissions scenario.  Scenarios of future emissions are not predictions of the future.  They are inherently uncertain because they are based on different assumptions about the future, and the longer the time horizon the more uncertain these scenarios become.  The aircraft emissions scenarios developed here used the economic growth and population assumptions found in the IS92 scenario range.  The other aircraft emissions scenarios were built from a range of economic and population projections from IS92a-e.  These scenarios represent a range of plausible growth for aviation and provide a basis for sensitivity analysis for climate modeling.  However, the high growth scenario Edh is believed to be less plausible and the low growth scenario Fc1 is likely to be exceeded given the present state of the industry and planned developments. (Bowen, J, 2000)

 

Aviation is growing at a very rapid rate and forecasts predict that the aviation industry will be very much larger in the future than it is today.  A much larger aviation industry will have a much greater environmental impact.

 

Recent Growth

Aviation has the highest growth rates of all modes of transport. Between 1970 and 1995 the number of kilometres flown by passengers worldwide grew by 360% – from 551 billion to 2,537 billion.

UK aviation has grown almost as fast over the same period.  In 1970 31.6 million passengers passed through UK airports. By 1995 this figure had risen to 129.6 million passengers.  This is an increase over 25 years of 310%.  Over the same period, the number of flights in and out of UK airports increased by 166%.  Freight aviation grew even more rapidly. Between 1960 and 1995 freight aviation increased in global tonne kilometres by 2,200%. (Berkely Hanover Consulting, 2000)

 

Future Growth

Passenger aviation

All the forecasts for passenger aviation predict substantial growth, but the forecasts show a wide range of growth rates. Many of these forecasts have been developed in conjunction with the Inter Governmental Panel on Climate Change, or IPCC, as part of its work on predicting the future impact of aviation on climate change.  The IPCC uses one particular forecast – the ‘base case’ forecast – as the main model for its calculations on climate change.  For the 20-year period between 1995 and 2015 the ‘base case’ forecast predicted growth of 122%.  In 1995 the worldwide total of passenger kilometres flown was 2,537 billion.  By 2015, according to the forecast, the worldwide total of passenger kilometres would be 5,639 billion. By 2050 that figure would have risen by 450% (compared to 1995).  The worldwide total of passenger kilometres would be 13,934 billion.  To get the best picture of the likely rates of growth within the aviation industry, it is necessary to look at a full range of forecasts.  These forecasts use different methodologies and work with a range of assumptions about economic and population growth. Naturally, the growth rates vary, but the overall picture is clear.  These forecasts show passenger aviation by 2015 growing by somewhere between 81% and 280%.  The lowest forecast estimates 4,596 billion passenger kilometres by 2015 and the highest forecast estimates 9,647 billion passenger kilometres by 2015 (Little, AD, 2000).

 

The IPCC ‘base case’ forecast of growth of 122% is at the lower end of this range.  It is likely that the ‘base case’ forecast is an underestimate.  Between 1970 and 1995 the highest growth rates in aviation occurred in Asia, where there was an increase of 1,870%.  The bulk of future growth will take place in parts of the world experiencing rapid economic expansion: primarily Asia, Eastern Europe and, to some extent, Latin America.  Nevertheless the UK is one of the most important aviation markets in Europe and future air-passenger growth can be expected to have very significant consequences.  The Department of Transport has produced a forecast for the number of passengers passing through UK airports each year by 2015.  This forecast predicts a rise from the 1995 figure of 138%.  This would increase the number of passengers passing through UK airports each year from 130 million to 310 million.  That is another 180 million passengers; about three times the current passenger throughput of Heathrow airport. (Bishop, S  et al, T, 2003)

 

Freight aviation

In the ten years up to 1995 the figure for worldwide tonne kilometres of freight more than doubled to 83.1 billion.  In the UK air freight grew even faster.  Over the same period it increased by 170% to 4.1 billion tonne kilometres.  In 1994 the US Environmental Defence Fund estimated that civil freight accounted for almost 18% of global aviation fuel usage.  Air freight is expected to grow rapidly in the future.  One reason is that traditional patterns of supply, where local consumption is met by local production, are giving way to global supply lines.  Another reason is that two of the world’s most populous countries, China and India, are moving into strongly liberalised and deregulated styles of economic activity.  At present most air freight is carried on passenger aircraft (Maddison, D, et al, 1996).

 

But forecasts predict that by 2050 there could be as many as 19,000 freight aircraft, making up 31% of the total commercial fleet.

 

Recreational flying

Recreational flying accounts for 2.8% of global aviation fuel usage.  The UK already has a large number of recreational airfields: forty-two alone in the counties of Norfolk, Suffolk, Essex and Cambridgeshire.  There is intense pressure to develop small airfields, which has resulted in numerous planning enquiries.  Many of these recreational airfields are a source of local concern because of the noise and air pollution they generate.  Recreational flying can be expected to grow rapidly, giving rise to significant environmental impacts.  Disposable incomes continue to rise in the advanced industrial economies and leisure pursuits continue to become more specialised, expensive and exotic.  In this context recreational flying can be expected to increase dramatically.  This growth will affect the local and the wider environment.

 

Emissions of CO2 by aircraft were 0.14 Gt C/year in 1992.  This is about 2% of total anthropogenic CO2 emissions in 1992 or about 13% of CO2 emissions from all transportation sources.  The range of scenarios considered here projects that aircraft emissions of CO2 will continue to grow and by 2050 will be 0.23 to 1.45 Gt C/year.  For the reference scenario (Fa1) this emission increases 3-fold from six different scenarios for aircraft fuel use.  Emissions are given in Gt C [or billion (109) tonnes of carbon] per year.  To convert Gt C to Gt CO2 multiply by 3.67.  Aircraft emissions of CO2 represent 2.4% of total fossil fuel emissions of CO2 in 1992 or 2% of total anthropogenic CO2 emissions by 2050 to 0.40 Gt C/year, or 3% of the projected total anthropogenic CO2 emissions relative to the mid-range IPCC emission scenario (IS92a). For the range of scenarios, the range of increase in CO2 emissions to 2050 would be 1.6 to 10 times the value in 1992. (Mason, KJ et al, 1995)

Concentrations of and radiative forcing from CO2 today are those resulting from emissions during the last 100 years or so. The CO2 concentration attributable to aviation in the 1992 atmosphere is 1 ppmv, a little more than 1% of the total anthropogenic increase.  This percentage is lower than the percentage for emissions (2%) because the emissions occurred only in the last 50 years.  For the range of scenarios, the accumulation of atmospheric CO2 due to aircraft over the next 50 years is projected to increase to 5 to 13 ppmv.  For the reference scenario (Fa1) this is 4% of that from all human activities assuming the mid-range IPCC scenario. (Mason, KJ, 2001)

Climate Change and Air Travel

Climate change is now constantly in the media, with public awareness and scientific understanding (as well as the urgency of the situation) growing daily.  But although climate change will have a damaging effect on the whole planet and is a compelling reason not to expand airports, the greenhouse gas emissions from an airport (unlike the noise and air pollution) have no immediate impact on the local community.

 

For this reason, it can be hard for residents and campaigners to visualise or feel threatened by greenhouse gases from their local airport, and they may tend to underplay it in their campaigns. To try to overcome this, a number of groups have estimated the emissions of CO2, the most important greenhouse gas, associated with their local airport.  There are no statistics, official or otherwise, on the emissions from each UK airport, so groups have had to devise their own methods of calculation.  They have used national statistics which refer to the total of UK airports as well as information on their local airport, such as number of passengers, number of flights, aircraft types and routes flown.  A figure of 9.8 million tonnes of carbon emissions emitted by aircraft from all UK airports in 2005 was given in answer to a Parliamentary Question (Hansard, 8 December 2005).  Half the emissions of aircraft flying from UK airport to foreign airports have been included in this figure, the other half, reasonably enough, are apportioned to return flights from the far end.  One tonne of carbon is equivalent to 3.67 tonnes of CO2, so 9.8 tonnes of carbon is equivalent to 36 mt of CO2. (Dentener, FJ,1997)

Estimates have now been made for 4 UK airports:

·                     Bristol 0.7 mt pa

·                     Gatwick 5.0

·                     Stansted 2.2

·                     Heathrow 13.9

 

Such figures have more impact if they can be related to other things.  Bristol, for example, has compared the airport emissions with those from all Bristol’s road traffic and the amount of forest one would need to store the CO2.  All local groups can play this game and, by doing so, give a local context and perspective to airport emissions.

 

Aircraft are not the only sources of air pollution at local levels.  Passengers, airlines, airport companies both landside and airside, as well as aircraft maintenance areas, all contribute to total levels of air pollution within a 15-20 km radius of the airport.  The main pollutants are VOCs, PM10, SO2 and NOx, and they are identified as causing health problems for local residents and airport workers.  They also contribute significantly to local air pollution.  Whilst many symptoms are not particularly visible, long-term exposure poses a great health risk.  At ground level, particulate matter emitted by aircraft and airport vehicles can cause higher incidences of localised health problems such as asthma and pulmonary disease.

 

Surface access to airports also causes significant emissions as most people tend to travel by private car.  Some countries in Europe have efficient public transport systems which link major cities to airports.  In Switzerland, for example, 65 per cent of passengers use public transport to and from the airport.  This is contrasted with a figure of less than 10 per cent for the UK with the exception of Heathrow and Gatwick which have direct rail links (DETR, 2000).  Also, low cost airlines use regional airports located far from city centres.  They create greater surface transport emissions as people tend to drive to the terminals.  More efficient public transport could alleviate some of these air pollution problems and so surface access strategies should be considered by policy-makers.

If air travel demand increases as predicted and there is no shift to public transport, congestion around airports is likely to increase.  This burden is borne by local businesses and residents.  Therefore, policies such as local transport plans are clearly needed to address these local transport issues.  Airport operating companies can take steps to reduce impacts by using cleaner fleets, bussing in employees and advocating the use of public transport through information campaigns. (IPCC, 1999)

Airport Impacts

A number of impacts are associated with the operation of airports.  Firstly, a significant amount of land is required to build the runways, terminals, car parks, services areas and transport networks.  Airports are typically located on the outskirts of the cities near to the countryside, and as more and more capacity is required to meet demand for aviation more land is used, encroaching further on the countryside with the direct loss of important habitats and possible reduction in biodiversity.  Furthermore, losses could be caused by the pollution emanating from the airport including the waste generated by millions of passenger movements and by the airport employees’ commute.  In addition, airport operations such as the maintenance and servicing contribute to local air quality and surface water pollution.  For example, during freezing weather aircraft need de-icing fluid, part of which ends up washed into local water sources.

 

One simple way to get an approximate estimate is to start from the figures given by the Department for Transport that all aircraft from UK airports emitted 9.8 million tonnes (Mt) of carbon in 2005.  That is equivalent to 36 Mt of CO2.  Gatwick handled 32 million passengers compared to a UK total of 217 million.  The distance flown by planes from Gatwick is probably about equal to the national average; less than from Heathrow but more than from other airports.  That suggests that aircraft from Gatwick on their outward journeys emitted about 5 Mt of CO2.  A second method of calculation is to start from the amount of aviation fuel taken on board aircraft at Gatwick.  In 2004 this was 2.4 billion litres which would weigh roughly 2 million tonnes.  Every tonne of aviation fuel produces 3.15 tonnes of CO2.  Thus the fuel taken on board at Gatwick produces 6.3 million tonnes of CO2.  That figure needs adjusting to allow for the fact that some aircraft fill up at Gatwick for both the outward and for the return journeys.  On the other hand aircraft belonging to foreign airlines may fill up abroad for both journeys. (This calculation is unduly kind to BAA. They charge a commission on all aircraft fuel loaded at Gatwick, and therefore in theory ought to take some responsibility for ALL the CO2 produced when that fuel is burnt, whether on an outward or inward flight.)

 

According to an aviation expert, for most of the Mediterranean and tourist destinations Gatwick fuel is fairly close in price to the destination price, particularly for a Gatwick-based airline which will achieve a volume discount.  Most of the leisure flights to these points are operated on aircraft that can comfortably carry round trip fuel without the economic disadvantage of having to burn off too much to carry it.  For flights to say the Middle East, Egypt and North Africa it may be advantageous to uplift fuel at destination and for long haul flights to USA etc the fuel required will eliminate the capability of carrying much, if any, of the return fuel load.  ‘At a rough guess, I would say that 60% of departures from Gatwick, accounting for about 40% of the fuel uplift, are carrying fuel for the return journey’.  This suggests that the CO2 emissions caused by the outward flights are around 4.5 Mt, broadly confirming the figure obtained by the first method.

A third, but more complicated, method is to add up the mileage of all the routes flown, and assess the emissions caused by each type of aircraft both on take-off and when cruising.  This method has been used by the Stop Stansted Expansion campaign, and their results are consistent with our results above. (Bleijenberg, A, et al, 1998)

 

Step 1) Fuel consumption: We arrived at a fuel burn figure of 3.1 tonnes per hour as the weighted average for  passenger aircraft operating in and out of Stansted.  This average is mostly derived from Ryanair’s fleet of (mostly) Boeing 737-800 aircraft and Easyjet’s fleet of (mostly) Airbus A319s.  Ryanair accounts for 60% of all Stansted scheduled passenger traffic and Easyjet for 27%.  Both fleets will very soon be standardised on these modern and fuel efficient aircraft types and we have assumed that they already are, which makes our baseline slightly conservative. Note however, that whilst Ryanair and Easyjet account for 87% of scheduled Stansted traffic, the remaining 13% generally operate older, less fuel efficient aircraft.  Also, Stansted now has some longer haul scheduled services, for example to New York and Washington where larger aircraft are used, burning far more fuel per trip.  There are also charter flights to consider.  These only account for 4.2% of total passenger flights at Stansted but they push up the average slightly because they generally operate larger, less modern, aircraft types consuming more fuel per hour.  Thus the 3.1 tonne figure is slightly higher than the average for Ryanair and Easyjet alone  (Brockhagen, et al, 1999).

 

Step 2) Trip duration: An analysis based on the BAA Stansted flight timetable (scheduled and charter flights) enabled us to calculate the average duration for a passenger flight at Stansted of 96 minutes (1.6 hours).  Here again, short haul services by Ryanair and Easyjet dominate this schedule but charter and long haul flights push up the average.

 

Step 3) Fuel burn per trip: From the above, this works out to 4.96 tonnes of fuel used per trip (3.1 x 1.6). As a reality check, this is only slightly higher (as we would expect) than the fuel usage figure which can be derived from information published by Ryanair on total annual trips and total annual tonnes of fuel purchased.

 

Step 4) Conversion to CO2: When one tonne of kerosene is burnt, it produces 3.11

tonnes of CO2. This is an internationally accepted conversion factor.  Hence an average Stansted flight generates 15.4 tonnes of CO2 (4.96 x 3.11).

 

Step 5) Commercial passenger flights: Stansted handled a total of 178,414 commercial flights in 2005 of which 166,767 were commercial passenger flights (scheduled and charter) and the remainder were freight.  At 15.4 tonnes per flight, this equates to 2.6m tonnes of CO2 for the passenger flights.

 

Step 6) Freight flights Stansted handled 11,647 freight flights in 2005.  Freight aircraft operate much larger (and generally older) aircraft and mostly operate on long haul routes – bringing in fruit, flowers, vegetables and all manner of high value goods from all corners of the world and a Boeing 747 freighter will use about 120 tonnes of fuel on a trip from Hong Kong.  We do not have precise data for average trip length/duration but we estimate an average 8 hour trip and an average fuel burn of 8 tonnes per hour.  Overall, for freight flights, I have estimated an average fuel burn of 64 tonnes per flight which equates to 199 tonnes (64 x 3.11) of CO2 per flight = 2.3m tonnes of CO2 for 11,647 flights.

 

Step 7) Non Commerical Flights: Stansted handles a further 15,097 flights in 2005 – i.e. on top of the 178,414. Non-commercial flights consist of aircraft carrying less than 10 passengers, business jets, aircraft repositioning, training and testing flights, diplomatic, military, Queens Flight, and rotary wing flights.   An aircraft’s fuel usage is dramatically higher (up to 10 times) during the first 20 minutes of a flight, whilst climbing to its cruising altitude, but even so, we have assumed that the contribution of non-commercial flights to CO2 emissions to be relatively small.  We have estimated this nominally at 2 tonnes of fuel per flight = 6.22 tonnes of CO2 = 0.1m tonnes. (IWW, INFRAS, 1995)

 

Step 8) Sub-total: Adding (5), (6) and (7) together we arrive at a figure of 5.0m tonnes of CO2 emissions. However, there are some further calculations to be done:

 

Step 9) Radiative Force Index (RFI): This is defined in the HMT/DfT paper, “Aviation and the Environment; Using Economic Instruments” as the ratio of total radiative forcing to that from CO2 emissions alone.  Total radiative forcing induced by aircraft is the sum of all forcings, including direct emissions (e.g. CO2, soot) and indirect atmospheric responses (e.g. CH4,O3, sulphate, contrails).  RFI is a measure of the importance of aircraft-induced climate change caused by all emissions, not just the contribution from the release of fossil carbon alone. According to the 1999 IPCC report “Aviation and the Global Atmosphere”  the RFI for aircraft is 2.7. Thus the 5.0m tonnes of CO2 shown at Step (8) becomes 13.5m tonnes when expressed as ‘ CO2 equivalent’.

 

Step 10) Divide by 2: The Government’s approach is to divide aircraft emissions on international flights by 2, based on the argument that emissions should be split 50:50 between the country of origin and the country of destination.  Despite the fact that it seems a bit unreasonable to hold the Maldives responsible for 50% of the carbon emissions created by all the Jumbo jets delivering and collecting hoardes of British holidaymakers, we apply the 50:50 rule to our calculations.  In fact, we apply this for domestic flights, so that a trip between Stansted and Edinburgh results in the emissions being allocated 50:50 to each airport. This reduces the Stansted’s CO2 emissions in 2005 to the equivalent of 6.75m tonnes.

 

Step 11) Surface Access Transport: This relates to the carbon emissions arising from passengers and employees travelling to and from the airport by car, bus, train etc.  We estimate all this to be about 2% of the Stansted total for aircraft emissions based on wider Department for Transport analysis – i.e. 135,000 tonnes.  There is also a small quantity of on-site airport emissions.

 

The Intergovernmental Panel on Climate Change has used two different metrics for assessing climate change: radiative forcing and the global warming potential. A newly developed metric – the global temperature potential is also discussed.

 

Radiative forcing

The radiative forcing is defined as “the change in the energy balance of the lower atmosphere by a climate change mechanism” and is measured in units of Watts per square metre (W/m2).  The ‘climate change mechanism’ is typically the emission of a greenhouse gas (e.g. CO2  from human activity), or a collection of different gases (e.g. all greenhouse gases from the agricultural sector).  Radiative forcing can therefore be used to examine the influence of the aviation sector as a whole, including all atmospheric effects.

Radiative forcing is usually determined between two different points in time (e.g. the change in the energy balance of the lower atmosphere between pre-industrial times and the present). CO2 concentrations have increased from 280 ppm in 1750 to 365 ppm in 1998, resulting in an extra 1.46 W/m2 being trapped within the earth’s atmosphere.  Radiative forcing does not examine the influence of a single flight in the present day; instead it calculates the total influence of all historic aviation emissions.

Radiative Forcing Index

The radiative forcing index (RFI) is an extension of the concept of radiative forcing, and is simply the radiative forcing of a gas with respect to that of CO2.  For aviation emissions the radiative forcing of the different atmospheric effects can be calculated separately. The radiative forcing index is the ratio of the total radiative forcing compared to that of CO2 alone.

 

The IPCC calculate the change in radiative forcing of aviation emissions since pre- aviation times to be 0.049 W/m2.  This corresponds to a radiative forcing index of 2.7 as the total radiative forcing of 2.7 times that of CO2 alone (0.018 W/m2).  However, a recent study (TRADEOFF) has updated this figure and a value of 1.9 is now the best-quantified estimate of radiative forcing index of aviation emissions.

 

Global Warming Potential

The global warming potential (GWP) is the most commonly used climate change metric and is used for assessing different greenhouse gases under the Kyoto protocol and other international policy instruments.

It is defined as ‘the [cumulative] radiative forcing of one kilogram of emitted gas relative to one kilogram of reference gas’.  In practice the reference gas is always CO2, so the global warming potential is a measure of the warming effect of other greenhouse gases relative to CO2.  Typically, a 100-year time horizon is used although shorter and longer timescales are possible to calculate.  Unfortunately, the global warming potential is not a suitable measure of the influence of aviation emissions.  This is because the global warming potential examines the effect of one tonne of emitted gas, yet the climate impacts of one tonne of aviation emissions will depend on other factors due to the impact of short-lived gases (especially nitrogen oxides), formation of contrails at altitude.  These effects depend on the location, altitude, temperature, season, light intensity and the concentrations of other pollutants in the atmosphere.  Attempts have been made to quantify the global warming potential of aviation emissions, but results vary widely due to the models used, and assumptions made about the climate impact of emissions of nitrogen oxides.  No accurate measure of the global warming potential of aviation emissions has yet been calculated (Li, MZF, 1998).

 

Global Temperature Potential

The global temperature potential (GTP) is analogous to the global warming potential, but instead of using radiative forcing as a measure of climate change, it uses the average surface temperature change.  It too considers the influence of a pulse emission of one tonne of gas, and is typically considered over a 100-year timeframe.

The GTP is capable of modelling the influence of short-lived species, and so is applicable for aviation emissions. Emissions of ground-level greenhouse gases have a GTP very similar to the GWP as the temperature response of ground level emissions is directly related to the change in radiative forcing. Unfortunately, work on this metric is still in its infancy, and more studies will need to be undertaken to quantify the influence of aviation emissions more exactly.

 

 

 

 

 

 

 

 

 

 

 

2.2   RASCO Methodology

 

RASCO study involves analysis of a large range of issues. It employs a number of research disciplines to estimate the impact of UK’s regional capacity and activity. RASCO methodology involves the following aspects:

§  Passenger traffic forecasting models

§  Freight traffic forecasting models

§  Mail traffic forecasting

§  Regional airport route analysis

§  Analysis of ‘thin’ routes

§  Airport catchment analysis

§  Propensity to fly analysis

§  Airport capacity assessment

 

2.3.1     Passenger traffic forecasting

RASCO employs Second Passenger Allocation Model (SPAM) model. The main purpose of using SPAM is to distribute the national traffic forecasts to individual airports in the period and extrapolate to 2030.

The RASCO work uses SPAM that is recalibrated on 1998 taken as the base year and the DETR 2000 Air Traffic Forecasts for national air traffic demand.  The earlier RAS studies, on the other hand, used SPAM calibrated on 1993 data and, for national air demand input, the DETR 1997 Air Traffic Forecasts.  Most of the RASCO runs used midpoint demand in the DETR 2000 Air Traffic Forecasts, which are national unconstrained forecasts to 2020.  Since the horizon of the RASCO forecasts is 2030, the DETR 2000 Air Traffic Forecasts were extended from 2020 to 2030 – this was at a somewhat lower growth rate than over the 2010-2020 periods so as to reflect greater maturity of the air travel markets by this period.  (Regional Airport Route Analysis, Aviation & Travel Consultancy, 2001)

The SPAM model forecast is calculated in the following categories:

§  Long haul traffic;

§  Internatinal to international interlining;

§  No-frills carriers;

§  Domestic end-to-end traffic and

§  Regional propensity to fly.  (DETR, 2000)

The DETR 2000 Air Traffic Forecasts indicate that the long haul traffic growth is faster than the short haul traffic. The Manchester airport has about 17% of the long haul traffic according to DETR. This is expected to increase to 20% in Manchester by 2030.

The international-to-international interlining consists of international passengers who take connecting international flights at UK airports. This category is significant enough  for Manchester airport to be counted at the moment, but does not emerge as a major category in 2030 according to the DETR estimates.

 

Annual movements can be broken down into the following components:

• peak month movements

• peak day movements

• peak day capacity

• peak hour capacity

Of the components, only the last is solely operational; the rest also depend on demand considerations. Both operational and demand factors need to be taken into account in calculating annual capacity. This is the approach that has been followed in the RASCO study.  The RASCO appraisal framework assesses scenarios and projects in terms of the Government’s five core objectives for transport:

• safety;

• economy;

• environment;

• accessibility; and

• integration.

While the appraisal indicators examined by RASCO are compatible with the five objectives outlined above, for ease of reference we have grouped indicators under the following headings:

 

Appraisal Indicators

• Impacts on Safety

• Public Safety Zones

• Airspace

• Impacts on the Economy • Financial and Economic Impacts

• Wider Economic Benefits

• Employment

• Impacts on People (Environment)

• Noise

• Air Quality

• Impacts on the Natural and Built Environment

• Greenhouse Gases and Climate Change

• Ecology, Landscape and Heritage

• Impacts on Surface Access (Accessibility)

• Road and Rail Access

• Car Parking

• Impacts on Regional Planning (Integration)

• Urbanisation

• Regeneration

The appraisal broadly follows the approach adopted in Stage One of the SERAS study, on which there was wide spread consultation and subsequent discussion with the RASCO Reference Groups.

 

3.2      Public Safety Zones

These are areas of land at the ends of runways at the busiest airports within which development is restricted in order to control the number of people on the ground who are at risk of death or injury if an aircraft crashes on take-off or landing.  This measure is primarily designed to ensure there is a presumption in terms of planning against new development within PSZs (with the exception of low-density development such as car parking).  Following a government review of Public Safety Zone policy launched in 1994, the DfT have been working to establish PSZs based on risk contour modelling for the 20 airports for which zones had already been established over the years and the establishment of zones at those additional airports where modelling indicates sufficient risk. (NB PSZs have not yet been formally designated in Northern Ireland.)  In order to define risk contours, analysis is based on numbers and types of aircraft movements 15 years ahead.  This analysis takes account of evidence drawn from historical data that different types of aircraft have different crash rates, as well as other factors.  The risk contours will be remodelled about every 7 years.  They will also be remodelled if a significant expansion of an airport is approved which has not already been assumed in the modelled risk contours.  Public safety zones correspond closely to the areas within which the modelled individual risk of death per year by aircraft accident exceeds 1 in 100,000.  Individual risk near an airport is calculated by reference to three components:

 

• the annual probability that a crash will occur near a given airport, i.e. the product of aircraft crash rates and the number of movements;

• the theoretical distribution of these crashes; and

• the theoretical size of the crash area and the likelihood of fatal injuries within it.

 

3.3      Risk Classification Risk Management Action (i.e. PSZ Policy)

Greater than 1 in 10,000 Intolerable risk area

• no individual required to be exposed to Individual Risk greater than 1 in 10,000

• household & industry to be relocated as risk intolerable

• existing and proposed transport links (but not termini) can remain. Traffic discouraged from stopping.

 

3.4      Air Quality

As part of the RAS studies, an assessment of the impact on air quality around airports of the postulated growth scenarios was undertaken.  The analysis has been carried out in two stages.  The impact of helicopters on air quality was also included in this assessment.  This section of the RASCO Report only covers a summary of the full methodological approach to air quality assessment.

 

The study considered the impact of three growth scenarios in 2015 and 2030, with 1999 as the baseline year.  Air quality was judged against current National Air Quality (NAQS) objectives (as incorporated in the Air Quality Regulations 2000), focusing on the pollutants nitrogen oxides and particles. The three scenarios considered in each future year can be briefly characterised as follows:

• ‘Environmental’ (E): assumes no infrastructure development other than what was already in the planning system at the time the work was undertaken – early 2000;

• ‘High Growth’ (H): growth according to demand (high demand), with constraints only applied to airports in the south east;

• ‘Base Case’ (B): mid-point demand, with constraints only applied to airports in the south east.

 

For a fuller explanation, the reader is directed to the following supporting document. Air Quality at UK Regional Airports – A report produced for DTLR by AEA Technology plc, December 2000.  The study evaluated the air quality implications of the growth scenarios under the assumption that the airport infrastructure and surface access network remain the same as at present.  Clearly, in many instances, infrastructure changes would be needed to accommodate the envisaged airport activity but no speculations were made as to what these changes might be.  Thus the analysis addressed the question of what air quality problems would arise if the airports grew without changes to the present spatial arrangement of runway(s), roads and surrounding residential areas.  Manchester Airport is the only instance where the study considered a change to the airport infrastructure, in that the future cases take account of the operation of Runway 2 in Stages.

 

Stage A was a screening analysis, which provided a way of identifying airport/scenario combinations that will not generate exceedences of air quality objectives and thus did not require detailed airport-specific dispersion modelling.  Airport/scenario combinations that did not satisfy the screening criteria in Stage A would not necessarily lead to air quality exceedences, but needed to be assessed in more detail, using dispersion modelling techniques, in Stage B.  Stage A adopted a conservative approach to the estimation of concentrations and, in some instances, the resulting values may be significant overestimates in relation to the calculated emissions.

 

Receptors: Two classes of receptors are differentiated:

• ‘background’ receptors – these are far enough from roads that they do not receive a significant above-background contribution from the nearby road links; and

• ‘near-road’ receptors – these are close enough to the nearest road links that they receive a significant above-background contribution from those links.

For screening purposes, properties out to a maximum of 200m from a road were considered potentially at risk of receiving a significant contribution from the road, but in practice the distance within which there is a significant contribution might be much smaller, depending on traffic volume, vehicle fleet mix, meteorology, and so on. In addition, on-airport receptors were distinguished from off-airport receptors.

 

Pollutants:

The pollutants included in the analysis are:

• Nitrogen oxides, comprising nitric oxide and nitrogen dioxide; and

• Particles with a diameter of less than 10 microns.

Although other airborne pollutants are emitted at airports, the two noted here are of particular concern in a number of areas of the UK.  Neither of these pollutants is unique to airports, being emitted by a variety of sources, in particular road vehicles and power stations. On an airport the chief sources of such emissions are aircraft, road vehicles and airside support vehicles/plant.

 

Contributions

From a methodology viewpoint, the total airborne concentration of a specified pollutant is considered as two components:

• The contribution from ‘aircraft-related’ sources, defined as aircraft exhaust emissions, APU (Auxiliary Power Unit) emissions typically from airport stand activities and emissions from airside support vehicles; and

• The contribution from all other sources, including landside road vehicles and sources that contributes to general background levels in the region of the airport.

For near road receptors, the contributions from nearby road links were considered separately.

 

Aircraft related emissions

The emissions from a given aircraft engine during a particular phase (mode) of the Landing and Take-Off (LTO) cycle are given by the product of the time-in-mode, the fuel flow rate which is measured in kilogrammes per second, and the emission factor which relates to the volume of pollutant produced by the volume of fuel burned for the particular engine thrust setting engaged during the mode.  The total annual LTO-cycle emissions at an airport is then the sum over all engines on a particular aircraft, summed over all aircraft movements in a year at the airport (with landing and take-off parts of the cycle counting as separate movements).

Airside vehicle emissions:

Estimates of the emissions from airside support vehicles and plant in a given year were based on the simple assumption of a fixed amount of pollutant per passenger throughput, on the grounds that the level of information available on this source at most airports would not support a more sophisticated methodology. At first sight it might appear that scaling emissions by the number of aircraft movements rather than passengers might be preferable, but the approach adopted is based on the assumption that larger aircraft will generate more airside activity.  The quantities of emissions per passenger in the current year were based on past analyses carried out for Stansted and Gatwick airports.

The ‘passenger number’ values used in calculating airside vehicle emissions were enhanced to include a passenger equivalent for freight aircraft. In fact, the total passenger number was obtained from the processed aircraft movement data broken down by aircraft size band (which includes freight movements) by assigning the mean number of seats in the band to every aircraft and assuming 70% of the available seats are occupied.

Screening Criteria: The estimates of aircraft-related emissions obtained as described above are used in both Stages A and B.  Where the stages differ is in the derivation of the contribution to ground-level annual-mean concentrations resulting from those emissions.

Greenhouse Gases / Climate Change

Two ‘packages’ of future airport development scenarios have been considered under the analysis undertaken for RASCO.  They were selected to represent alternative scenarios of no additional runways anywhere and a scenario that assumes new runways in both the south east and the regions – a high capacity scenario.  In all cases it is assumed that there are no constraints other than demand to all runways, new and existing, operating at their maximum capacity by 2030.  The assessment of the CO2 impacts of these packages has been undertaken for 2030.  The assessment takes account of CO2 from some but not all associated sources.  The fact that the packages appraised have different capacities and different abilities to accommodate potential demand means that a full comparison of CO2 impacts would have to be very wide ranging.  For example, the constrained package will be least able to accommodate demand at UK airports, encouraging a range of alternative behaviour, which could include the following: trip suppression; trips by surface modes (road, rail, ferry) replacing domestic air trips or trips to the near continent; more air trips and flights through regional airports; more use of hub airports outside the UK; more flights between hub airports outside the UK and world destinations.  The scale of these responses and their CO2 implications could not always be predicted. The assessment of CO2 impacts was therefore confined to the principal aircraft and surface access sources. No attempt was made to assess changes in flight patterns for either package (ENDS, 2002).

 

Measurement of Aircraft-related CO2

Principal inputs to the estimation of aircraft-related CO2 were:

• SPASM model outputs for passenger ATMs and mppa;

• ATMs by aircraft size – again derived from SPASM model outputs – with aircraft size banded into one of 6 categories;

• Freight tonnages and ATMs from the DTLR Freight Forecasting model, with a manual construction of aircraft size distributions across key airports with average load factors applied.

 

CO2 emissions were determined from this data using the “QinetiQ” civil aircraft performance package and the ICAO engine emissions databank.  Representative data were produced for each of the aircraft types designated in the lists of aircraft movements, for each of the forecast cases.  The CO2 emissions data were totalled and presented for each of the selected regions served by the airports under consideration in the study.

 

Aircraft types

The large list of aircraft types provided by CAA was unmanageable.  Aircraft types were aggregated into groups with similar performance and capabilities, and one aircraft with characteristics representative of all the aircraft in each group was selected.  The representative types used were the ANCAT25 representative aircraft types, where possible.  In some cases 25 ANCAT/EC2 Global aircraft emissions inventories for 1991/92 and 2015 the ANCAT types were unsuitable (e.g. types that will be discontinued from service by 2015/2030), so alternative representative types were chosen where necessary.

 

Representative passenger aircraft Seat band class

The representative types chosen are among the most numerous in each of the classes in terms of movements per day in the movement list and are generally newer aircraft types in each market sector. Where there are options of maximum take-off weights for a representative type, the higher gross weight version was selected.  The engines assumed for the representative types were those that are most common on those aircraft in today’s fleet.  The fleet mix contains some future aircraft types in addition to types current in today’s fleet.  The fleet mix shows only small numbers of movements by the newer types. As the representative types are, on the whole, newer types from today’s fleet, any fuel burn benefits of the future types will be countered by the increased fuel burn of the older types that are represented. A business jet (Dassault Falcon 2000) represented the very small aircraft, which included small piston and turboprop aircraft and business jets.  The aircraft performance programme was not able to model such small non-jet powered aircraft.  Whilst the comparative fuel consumption for these aircraft is substantially different, the overall effect will be negligible due to the very small numbers of movements of these aircraft at the airports being considered in this study.  A similar method of selecting representative types was used for the freighter aircraft, with the corresponding freight class into which they fall.

 

Representative freighter aircraft freight class

For the regions and individual airports within those regions served by the airports under consideration, Great circle (GC) distances from the airports (in practice Heathrow was assumed) to the destination airports were determined.  The use of GC distances may underestimate flight distances by 10-11% for short haul sectors in congested airspace (such as much of Europe).  Deviation is generally much less on long-haul routes, and the average world-wide distance underestimate by GC may be around 6.5%.  However, within the forecast timescale (2030) efficiency gains made through direct/free routing of air traffic may go some way to off-setting this.  For very short flights, a minimum flight distance of 200 nm was imposed. Wind effects were not accounted for. West-bound flights consume 10-20% more fuel than the average, but the east-west differences will largely balance out.  Average distances from the airports to each region were calculated; as data would not be available for splits of flights to individual destination airports (the arithmetic mean of the distances to the airports in each region was calculated).  Determining fuel burn / CO2 based on an average distance can be shown to have negligible effect compared to averaging the fuel burn for flights to each distance separately.

 

Load factors

The passenger load factor data used was a single figure forecast for all movements from each airport to each destination airport.  The average load factors of movements from all the airports to each destination airport were determined.  These average load factors were then used to determine average load factors for all flights to each destination region.  It can be shown that using this average has a negligible effect on fuel burn / CO2 compared to a more rigorous method.  For freighter aircraft, the load factor used was assumed to be 50%.

 

Aircraft data and fuel use

The load factors determined previously were used to calculate payloads.  Actual payload weights were determined as the multiple of the load factor (as a percentage) and the aircraft’s maximum available payload.  As load factors varied according to destination region, a payload was calculated for each representative aircraft type for each of the destination regions. Fuel burn data was obtained from the aircraft performance model:

 

• using the class category average load factor for the destination region, as calculated previously.

• using average region distances, as calculated previously.

• using typical international reserves for all aircraft:

• diversion distance 200 nautical miles (nm), at typical cruise speed and altitude,

• hold time 30 minutes, hold altitude 5000 ft,

• 5% mission fuel contingency.

• representative flight altitudes (flight levels, FL) were chosen for all flights based on mission distance:

• distances less than 1000 nm: FL310 (short haul flights constrained by ATC)

• 1000 nm to 2000 nm: FL350 (medium haul, less constrained routes)

• greater than 2000 nm: FL350/FL390 (long haul, little ATC restriction, and allowed to step-climb during cruise)

• one exception to these flight altitudes was the Falcon 2000 business jet, all flights of which were carried out at FL410.

• fuel burn data was for operations from start of roll to touchdown; other ground operation fuel burn data were obtained separately, from the emissions databank. (RASCO, 2001)

 

Some aircraft could not achieve the flight distances specified.  This was generally for medium-long distance flights using the lower class aircraft, where the average distance to the destination region was in excess of the aircraft’s maximum range.  In some cases the shorter distance destinations within a region were achievable, and so fuel data for flights to those destination were used instead. Where this was not possible, and where another aircraft in the class could make the mission, that aircraft was assumed to operate all the flights from that class to the region.  The fuel burn data from all the representative types to the destination regions were further reduced before the next stage.  The percentage splits of the numbers of aircraft in each class travelling to the destination regions were applied to the fuel burn data, to obtain an average flight fuel burn data from each class to each destination.

 

Gross fuel burn and CO2 calculations.

Aircraft mission fuel burn was multiplied by the number of movements, and converted to CO2 emissions. This stage draws together the movement data and the fuel burn data to produce the CO2 emissions data, disaggregated by class and destination region.

• Movements from each airport to each destination region were summarised and totalled.

 

• Flight fuel burn data (in kg) for an average, single movement (start of roll to touch-down) of an aircraft in each class, to each destination region, were imported from the previous stage.

• Corresponding data for ground movements obtained from the emissions databank is added to the flight fuel burn data, to produce a block fuel burn figure. 26 minutes of idle operation is assumed for each aircraft type at all South East airports but a lower figure of 5 minutes at regional airports – reflecting smaller airport size and lower levels of congestion. Fuel burn rate data from the emissions databank (using nearest equivalent engine for some engines not in the databank) for taxi/idle power was multiplied by the number of engines on the aircraft and by the time at that power setting. The 26 minutes of operation at SE airports is representative of the combined time to taxi out to depart and taxi back to gate after landing. 26 minutes is used in the ICAO certification process for taxi/idle operation, and is consistent with average taxi/idle times at very large and/or congested airports, such as Heathrow. It was assumed that all the airports under consideration in the South East will be tending towards this situation by 2015/2030.

• The individual flight block fuel burn data for each class and originating airport to each destination region are multiplied by the number of flights of that class to each region, to produce a figure for total fuel burn (in kg).

• In the final summary table the domestic emissions data are multiplied by two. This is on the premise that the data generated in this study represent fuel burn/ CO2 data produced by the UK.  The ‘ownership’ of emissions produced by international flights is split equally between the originating region and the UK, whereas domestic air travel is wholly ‘owned’ by the UK.  The movement numbers used in this part of the study have been for departures from airports only. Thus only 50% of the total traffic has been considered (all flights have a reciprocal inbound flight).  For domestic routes, the UK owns both segments, and hence total emissions must be adequately represented.

All fuel burn data is also multiplied by a factor which gives the CO2 output (in kg).  The factor used (3.15) is based on a typical aviation fuel hydrogen:carbon ratio.  This factor for conversion of fuel to CO2emissions was agreed by ICAO/CAEP/WG429. (RASCO, 2001)

 

Measurement of Surface Access-related CO2

Two sources of surface access-related CO2 were estimated.  The first source was passenger-related surface access trips.  Only car-related emissions were estimated.  To make this calculation forecast air passenger trips between each SPASM model district and each UK airport represented in SPASM were taken.  Trips were multiplied by the distances between District centres and airports represented in the SPASM surface access models.  Passenger trips were converted into estimated car km by multiplying by 0.9 (to represent an assumed car share of all such trips) and dividing by 1.1 (to represent an assumed car occupancy).  These were then multiplied by an assumed 2030 CO2 rate of 147 grams per car km (147 tonnes per million vehicle km) based on future vehicle fleet and emission assumptions taken from the UK Emissions Factor Database and the DMRB as appropriate.   The second source was freight-related surface access trips. Estimated freight tonnage movements between each SPASM model district and the 14 largest freight airports were estimated (these account for around 95% of UK air freight traffic).  Freight tonnages were multiplied by the distances between district centres and the airports as represented in the SPASM 29 ICAO/CAEP/WG4 (operational issues) meeting, June 2000.  Freight tonnages were converted into estimated freight movements km by assuming an average load factor of 1 tonne per trip.  As most air freight movements use Light Goods Vehicles (LGVs), freight km were also assumed to generate CO2 at the rate of 146.7 tonnes per million vehicle km. (RASCO, 2001)

 

 

Impacts on Surface Access

The surface access trips to/from an airport by both passengers and employees are clearly an important aspect of any forecast growth.  As such, the impact of forecast air passenger and freight growth on traffic volumes on key routes into and out of airport sites has been examined.  By combining the impact of airport growth on the highway network with the potential for rail schemes to attain the target mode shares it was possible to provide an indication of the impact of growth at an airport on surface access issues.  The combined analysis concentrated on the two main surface access issues facing regional airports if they are to develop in a sustainable manner in the period to 2030 when growth is forecast to average 4-5% pa:

 

• the need to increase catchment area and improve market penetration within established areas of influence to enhance accessibility, and

• the requirement to encourage alternatives to car and address congestion on transport infrastructure (particularly road and car parking) serving the airport, if Government policy in the 1998 Transport White Paper on airport surface access is to be met. (Dings, JMW et al 2002)

 

The results from the analysis were used to:

• define the common strategic surface access issues forecast for regional airports and possible responses to these;

• outline the options potentially available to address these issues and discuss the necessary preconditions for doing so;

• highlight the implications of these options for the surface access strategies of different regional airports and detail those schemes, at those airports with a forecast passenger throughput above 5.0 mppa by 2030, to be considered in the RASCO study; and

• develop key questions for the Reference Groups.

 

For each policy scenario and for each forecast year 2015 and 2030 passenger trips and freight related movements were obtained.  It should be stressed from the outset that this analysis of surface access need is essentially of a broad-brush nature given the need to produce a consistent approach across 23 regional airports under a range of future year demand scenarios.  It is also the case that we have not been able to reflect various important study conclusions that have been reached, or several major corridor studies that are underway or planned over the course of the work programme.  Clearly, more detailed assessment of the impacts on both the local and strategic transport networks will be needed before major airport development options are progressed (Olsthoorn, X, 2001).

 

Highway Modelling

Trip Generation

Road surface trip generation was based on assumptions regarding:

• the number of transit passengers;

• the split between business and leisure passengers;

• mode share for both business and leisure passengers;

• vehicle occupancy;

• employment numbers, mode share and vehicle occupancy for employees;

• business, retail and servicing trips related to the airports; and

• freight trips generated per tonne of freight.

 

These assumptions can be specific to individual airports if sufficient data is available.  Alternatively they can be based on the typical characteristics of an airport relative to its size.

 

Distribution of Trips To Links

Trips were distributed to the principal highway network on a judgmental basis and overlaid on top of non-airport traffic. Future year background traffic (non-airport traffic) was forecast by factoring up mid-point DTLR National Road Traffic Forecasts with airport traffic removed.  The volume to capacity ratio for each of the key links was then calculated.

CO2 emissions from aircraft can be calculated from a knowledge of the amount of fuel consumed during the flight. However, unlike terrestrial transport, fuel consumed does not scale linearly with distance travelled due to the extra fuel burn required to lift the plane up to cruising altitude, and the necessity to carry large quantities of fuel for long distance flights.  A model has been developed to determine emissions accurately. The emissions of CO2 from an individual flight will depend on many different factors including distance travelled, weather conditions (head or tail wind), cargo load, passenger load and flight altitude. Obviously, for an individual seeking to offset, these conditions will be unknown.  The model therefore uses averaged data to determine emissions.  Whilst the emissions from an individual flight may be under or over that determined by the model, any errors will cancel each other out over multiple flight offsets.

 

Calculating fuel burn

The fuel burn is attributed to different sections of the flight, which each use fuel at different rates. Emissions occur during:

• The Landing and Take Off cycle (LTO) which includes all activities near the airport that take place below the altitude of 3000 feet (1000 m).  This consists of taxi-out, take-off and climb out, and at the end of the flight, the landing approach and taxi-in.  This is the fuel required to get the aircraft into the air (and down again) and are constant irrespective of flight length.  Ascents require a much more intense fuel burn than cruising at constant altitude.

• The Climb, Cruise and Descent cycle (CCD) is defined as all activities that take place at altitudes above 3000 feet (1000 m). This fuel use accounts for the bulk of the flight distance, and naturally varies with flight length.

 

The proportions of LTO to CCD will vary between flights, with short-haul flights (e.g. Heathrow to Amsterdam) having a much larger contribution from LTO than a transatlantic flight (e.g. Heathrow to Tokyo).  For simplicity of calculation, and because most passengers do not know the exact make of aircraft on which they are flying, representative aircraft have been chosen to calculate the fuel burn. Boeing 737s, the most popular aircraft ever produced, flew 14.8% of intra-EU flights in 1984 and so have been chosen as the representative model for short-haul flights.  Short-haul flights are defined as those less than 3500 km. Historically, medium to long-haul flights have been flown on Boeing 747s, but in recent times Airbus A340s have attracted a significant proportion of the market share.  The Airbus is newer and more efficient, and so produces less CO2 per kilometre than the Boeing.  The model has assumed that medium- and long-haul flights (>3500 km) are considered as an average of Airbus A340 and Boeing 747 emissions (ICAO, 1995).

 

Tables of the amount of fuel consumed are available for all major types of aircraft as published by the European Environment Agency (2003).

 

Calculating CO2 emissions

When fuel oil is burned, it is converted to CO2 and water vapour.  Combustion of one kilogram of fuel oil yields 3.15 kilograms of CO2 gas. CO2 emissions are therefore 3.15 times the mass of fuel burned.

In an offsetting model such as this, it is important to attribute only the emissions for which the passenger is directly responsible to that passenger. This has two effects:

• The commercial freight load of the plane is ignored. Commercial freight loads are estimated to be 10% of the total weight of the plane for long-haul flights,5 so only 90% of emissions are attributed to the passengers.

• Emissions are allocated per seat. The number of seats on standard models of aeroplanes are readily available.

 

Model output

The model gives a series of curves of CO2 emissions per seat as a function of distance travelled.  Departure and destination airports are selected from a database, which returns the longitude and latitude of the respective locations.  The length of flight is then calculated using trigonometry, and the corresponding emissions determined from the appropriate curve.

It can be seen that there is a discontinuity between the aircraft used for short-haul versus medium to long-haul flights. The smaller, lighter Boeing 737’s use less fuel per kilometre.  As soon as larger heavier aeroplanes with a greater fuel loads are used, flights become less efficient and emissions per seat greater.

For very short flights, carbon efficiency is low, as the fuel burn required for the landing and take-off cycle is the major component of emissions.  This drops away such that flights over 2000 km in a Boeing 737 are the most efficient flights (of those considered in this model).  For larger aircraft on medium and long-haul flights, the landing and take-off cycle is not so critical, and the climb, cruise and descent cycle forms the major part of the fuel burn.  There is a slight decrease in flight efficiency with increased distance, due to the greater fuel load that must be carried.

 

Applying a metric for aviation emissions

Now that the CO2 emissions are known, a metric must be applied to account for the full environmental effect of aviation emissions.  A metric is a mechanism for transformation of emissions of gases with different effects on climate into one common scale. In simple terms, the metric is a measure of how many tonnes of CO2 emissions should be avoided at ground level, as opposed to emitting one tonne of aviation emissions at high altitude.  The full climate impact of aviation is deemed to be between 2 and 4 times greater than CO2 alone, but the exact value is dependent on which parameter is chosen to be measured by the metric.  There are many different possible metrics for comparing greenhouse gases, some of which have become commonly used.  However, it is only possible to use some of these to examine aviation emissions.

 

What to measure?

The mass of CO2 emissions can be calculated as outlined above, but quantifying their effect on the environment is more complex, and depends on what is used as a measure of the climate impact. This is true for any greenhouse gas emissions, not just the special case of aviation.  The ‘chain of influences’ gives a range of possible measures of climate impact that could all theoretically be used as the basis for a metric. The greenhouse gas emissions will alter the atmospheric concentration, which in turn alters the energy balance of the atmosphere (known as radiative forcing).  The radiative forcing is the driving force behind climate change, but because the atmosphere is a complex system, the effects of radiative forcing on the climate are not linear. Furthermore, there are many measures of climate change including effects on temperature, rainfall, average wind-speed and sea level rise.  These changes in climate have impacts on society including agriculture, land use, energy consumption.  Ultimately, these societal changes can be quantified in terms of financial impacts.

 

Any of these parameters can be used as a measure of the environmental influence of greenhouse gas emissions.  However, there is a trade-off to be made when determining which of these parameters to utilise as a measure of climate change. The further down the chain one goes, the more relevant it becomes to people and society, but the less well science and computer modelling can quantify it.  To date, science and policy has adopted radiative forcing (and derivatives thereof) as metrics of climate change.  However, climate models are becoming sophisticated enough to start quantifying the influence on temperature, a more easily understood parameter for the layperson, and it is expected that metrics based on temperature will become more commonly used in future.

Extent or rate?

Once the measure of environmental impacts has been decided, some assessment must also be made as to whether it is the extent or rate of change that is the most important.  For example, the extent of ice cover will be influenced by the extent of temperature rise.  Conversely, the ability of ecosystems to adapt to climate change is determined by the rate of temperature rise.  It is becoming increasingly apparent that both extent and rate of climate change are important, and that both are unprecedently high.  However, it is the extent that has been most widely studied and utilised.

 

Timeframes

Climate change metrics can operate over different timeframes. Some are instantaneous, whilst others give the summed effect up to a chosen point in the future.  Because different greenhouse gases have different lifetimes in the atmosphere, the choice of metric timeframe is critical in determining the relative importance of different gases.  Other metrics examine the influence of historical emissions.

Sustained or pulse emissions?

A further difference between metrics is whether they consider a ‘pulse’ emission (i.e. the instantaneous emission of 1 tonne of gas) or sustained emissions (emission of a profile of emissions over a specified timeframe).  Sustained emissions metrics may be more policy relevant, but pulse emissions are more useful for carbon trading and offsetting projects.

 

 
2.3  Aviation Emissions and Evaluation of Reduction Options (AERO) – 2000

 

The Dutch Civil Aviation Authority took the initiative to analyse the policy, assess the problems related to air pollution from aircraft engines and to identify measures to reduce the emissions. As part of its activities, an extensive global information and modelling system was developed. This model is called AERO modelling system (AERO-MS).

The AERO-MS is a policy-testing tool to evaluate the consequences of actions or responses to emission related measures. The estimates are calculated for the year 1992 with extrapolation till 2015.

The CO2 emissions from the aircraft is estimated by AERO with the calculation of +/- 3.17 kg per fuel burnt. The study takes into account the efficiency improvements in the aircraft technololgies  as well as  increase in traffic. The contribution of C02 from aviation is expected to increase up to 2.1% of the total emissions of C02 by the year 2015.

Computational Steps

The computational steps consists of a sequence of steps that assess the environmental and economic impacts of aircraft engine emissions. The steps include the following:

 

Computation of aircraft technology and fleet built-up
Computation of air transport demand according to the passengers and freight, supply according to the capacity offered and aircraft flights.
Costs of air transport
Revenues of air transport
Direct economic impacts of air transport
Aircraft flight paths, fuel-use and emissions
Atmospheric emissions from ground surface sources
Atmospheric concentrations of key substances and related environmental effects.
(AERO, 2000)

Overview of computational steps in AERO Modelling System (AERO, 2000)

The steps can be summarised as below:

Aircraft technology and fleet build up: The aircraft technology has direct impact on the fuel use and hence the emission characteristics of different aircraft types. The fuel-use and the emission characteristics is expressed in terms of ‘technology age’ of the aircraft type.
The supply and demand of air transport in terms of passengers and freight and the frequency and capacity of services offered is matched with the passenger fairs/ freight fares to estimate the demand and supply. The comparison of the number of flights follows from the volumes of passengers and freight transported.
The aircraft operating costs are then associated with the flights by aircraft type and technology level. The considered variable cost components are fuel costs, route charges, airport charges, flight and cabin crew costs, maintenance costs, capital and finance costs. Other constituents of operating costs include volume related costs such as costs of ground-handling, sales, ground facilities and overhead.
Revenues from the air transport directly from the number of passengers and freight transported.
The direct economic affects of air traffic including the effects on airlines such as operating costs, revenues, results and other macro-economic effects such as airline-related employment and contributions to gross value added.
Fuel-use and emissions for each flight is computed by taking into account the specification of flight paths, the geographical flight specification and the technical characteristics by aircraft type and technology level.
The effects of emissions from other ground sources are considered. A global inventory is made with emissions from other such sources.
The effects of aircraft engine emissions on atmospheric concentrations are determined for substances such as CO2, NOx and ozone. The impact is estimated based on the effective UV doses as well as changes in radiative forcing (global warming potential).
Flights and Emissions Model (FLEM)

 

Flights and Emissions Model (FLEM) provides framework to calculate the aircraft engine emissions and distribution of the emission in the global atmosphere. The model calculates three dimensional flight paths of aircraft flying from flight origin to destination. The emissions during the flight profiles are calculated based on local atmospheric parameters and momentary throttle settings.  These parameters and settings further depend on the flight parameters such as speed, weight and altitude. The calculated emissions are distributed across a three-dimensional grid that spans across the global atmosphere. Summation across all fights provides total emissions per grid cell.

For the computation of aircraft engine emissions, FLEM uses inputs from other models such as aviation activity in the base year from the Unified Database and for future distribution, it is determined by the ADEM model.

 

FLEM computation scheme

(AERO, 2000)

 

FLEM computes emission levels for three levels: the Base, Datum and Policy cases.

2.4   UK Government Energy and  Aviation Policy

 

In February 2003, the UK’s Department for Trade and Industry published its Energy White Paper entitled ‘Our energy future – creating a low carbon economy’ (DTI, 2003).  In the following December, the UK’s Department for Transport published ‘The Future of Air Transport’ referring to aviation policy across the UK.  The incompatibility and conflicts of interest of the two white papers were immediately apparent.  On the one hand, the UK Government wishes to push the economy towards reducing carbon dioxide emissions by 60% by 2050, and on the other, it wishes to meet the growing demand for aviation.  The question therefore, is whether or not the two goals can be met simultaneously.

 

4.1      UK Energy White Paper

The Energy White Paper set a target of reducing total UK carbon dioxide emissions by 60% from ‘current’ levels by 2050 (DTI, 2003).  The White paper essentially accepted the analysis of the RCEP in their 22nd report Energy – The Changing Climate (RCEP, 2000).

Contraction and convergence profile for the UK, demonstrating the origin of the 60%  target. They argued that a ‘contraction and convergence’ policy is required for international control of CO2 emissions, a consequence of which is a requirement for a 60-90% reduction in such emissions by industrialised countries by 2050.  The principal objective of the RCEP (and by association the end point) is to avoid “dangerous climate change” by ensuring the global mean temperature increase does not exceed 2°C.  Based on the best scientific evidence available at that time, this was linked with an atmospheric concentration of CO2 of 550ppmv.  This is understood by the UK Government and RCEP as being consistent with the goal of the Framework Convention on Climate Change (UNFCCC, 1992).  When looking further at the detail of the end point, it can be seen that this target is for domestic emissions only, and excludes international aviation and shipping emissions.  However, as this target is based on a global CO2 concentration target, omitting certain sectors is not an option, if 550 ppmv Contraction & Convergence Profile for the UK The ultimate goal is to ensure that the UK plays its part in reducing the possibility of dangerous climate change.

 

4.2      UK Aviation White Paper

The Aviation White Paper was written to address the pressures caused by the increasing demand to travel by air, whilst at the same time meeting commitments to protect the environment (DfT, 2003b). The paper states that the UK’s economy depends on air travel, with many businesses, in both manufacturing and service industries relying heavily on this mode of transport. Furthermore, visitors are said to be crucial to UK tourism, airfreight has doubled in the last 10 years and 200,000 people are employed in the aviation industry, with three times as many jobs supported by it indirectly. According to the UK Government, all of the above put pressure on airports, some of which are at, or fast approaching, capacity. Therefore, the UK Government states that the white paper sets out a measured and balanced approach that provides a strategic framework for the development of air travel over the next 30 years. The programme of airport expansion proposed in the Aviation White Paper has stimulated considerable and ongoing debate on the appropriate scale of the aviation industry (DfT, 2003b). According to the paper, “all the evidence suggests that the growth in popularity and importance of air travel is set to continue over the next 30 years”.  In 2003, some 200 million passengers passed through UK airports, a figure that is predicted to rise to between 400 and 600 million by 2030 (DfT, 2003b), if sufficient capacity is provided. This implies an annual rate of increase of between 2.6% and 4.2%.  Implications of such high growth for CO2 emissions and climate change are far reaching.   If it is assumed that the underlying structure of the aviation industry remains unchanged (i.e. routes, load factors, air-traffic management, fleet and engine efficiency) then an increase in passenger numbers would result in a proportional increase in carbon emissions.  However, reductions in the amount of carbon emitted per passenger-km are likely to arise from a combination of load factor improvement, aircraft design, aircraft size, air transport management and engine efficiency.  The IPCC Special Report on aviation (1999) estimates that a combination of these improvements up to 2050 will be equivalent to a 1.2% increase in seat kilometres travelled per kg fuel consumed per year.  This value is a mean of the efficiency improvements estimated by the IPCC in their seven scenarios. A slightly lower rate, of 1% per year, has been suggested and used by the DfT in the Aviation White Paper. However, even a 1.2% per year fuel efficiency improvement still leaves a 1.4% to 3% increase in emissions from the aviation industry each year.

 

Given the long lifetime of new aircraft, in the region of 30 years, the Government’s current “predict and provide” approach to aviation leaves the UK wedded to a future of increasing emissions from the sector.  To illustrate the range of carbon emissions forecast by the DfT, figure 5 plots their ‘worst’, ‘central’ and ‘best’ emissions cases, taken from documentation produced to support the Aviation White Paper (DfT, 2004).  The ‘worst case’ forecast assumes limited fuel efficiency improvements, limited fleet renewal, and no economic instruments and are based on the ‘high capacity’ case developed within the Economic Instruments paper (DfT, 2003a), but with three, rather than two, additional runways built in the South East of England, as well as, so-called, unconstrained capacity in the regions.  The ‘central case’ figures are again based on the ‘high capacity’ data, but incorporating fuel efficiency improvements envisaged by the IPCC (IPCC, 1999) and by the Advisory Council for Aeronautics Research in Europe (ACARE).

 

Finally, the ‘best case’ estimates use economic instruments to produce an additional 10% fuel efficiency saving from 2020 onwards, with half of that in 2010.   For the ‘worst’ case, emissions increases due to growth within the aviation industry will be off-set by efficiency improvements from 2030 onwards.  In other words, from 2030, growth has either reduced to around 1% per year, to match efficiency improvements, or efficiency gains have significantly increased to match growth.  Such a picture seems unlikely as growth within the aviation industry has consistently been much higher than the UK’s GDP growth, and therefore these emission forecasts require further analysis.  Furthermore, the DfT’s forecasts need to be considered in relation to contraction and convergence targets and profiles.

 

The Aviation White Paper suggests, by way of its mid-level forecast, that growth in the UK,  a country with a relatively mature aviation industry, will average 3.3% per year between today and 2030. This figure is based on a growth of around 3.8% per year in terms of passenger numbers until 2020, then a further growth of 1.8% per year from 2020 to 2030.  According to the Aviation White Paper, these growth figures are based on the assumption that there will be continued growth within the short-haul market, a recovery following the events of 11 September of the long-haul market and cheaper airfares due to enhanced competition which will be enough to offset any effect of an environmental charge. It is unclear therefore, why this growth figure should reduce between 2020 and 2030.  DfT’s high-level forecast shows average growth of 4% per year up to 2030 – 4.5% between 2000 and 2020, and around 2.7% from 2020 to 2030.  Historically, growth in passenger numbers at UK airports has been around 5.8% from 1973 to 2003, (CAA, 2004) substantially higher than DfT’s future projections. Moreover, the Eurostat dataset (Layos, 2005) suggests that the current rate of growth in the UK is actually 6.4%, based on the trend between 1993 and 2001 (eliminating the short-term effects following the events of 11 September), again, significantly larger than the 3-4% assumption used in the white paper. ACARE assume 50% fuel efficiency improvement between 2000 and 2050. To incorporate this, 15% is assumed to be between 2000 and 2030, with a further 25% occurring between 2030 and 2050.  The remaining 10% is already factored into the original DfT figures and arise from assumed improvement in operational measures in aviation.

3          Methodology

 

The following section discusses this project’s methods and processes, covering a number of different aspects. The methodology was derived by analysing into the various models currently available. The following models have been referred to arrive at the proposed model

RASCOM model
AERO FLEM model
The calculation methodology was evolved and is described as below:

1.        Identify the different aircraft types that operate through Manchester airport:

This includes domestic, international and transit aircrafts. The make and the engine types of each carrier which uses the Manchester airport is identified at this stage. The engine types are cross referenced with the IPCC inventory to identify the details of emission for the engine types.

2.        Select high plane counts (eg 13,000) for 1999:

The plane counts provides estimate of the number of flights for each category of the aircrafts.

3.        Estimating the emission for each engine type: In this step, the list of planes and their engines are listed. The number of engines for each make of the plane is identified. The engine type for each type of plane is identified.

Four modes of operations are identified for each engine type. If the engine types are not certain, a high and low fuel flow is taken. This value is used for the calculation in this case.

4.               Calculation of time for each mode:

Landing and Take Off cycle (LTO cycle) operations of aircraft are usually divided into two main parts:

§  The Landing/Take-off (LTO) cycle consists of all activities near the airport that take place below the altitude of 3000 feet. Hence it includes the taxi-in and out, take-off, climb-out, and approach landing. (Rypdal, 2003)

§  Cruise includes all activities that take place at altitudes above 3000 feet. Cruise includes climb to cruise altitude, cruise, and descent from cruise altitudes.

There are six possible operating modes in a standard aircraft LTO:

·                    Approach;

·                    Taxi/idle in;

·                    Taxi/idle out;

·                    Take off;

·                    Climbout; and

·                    Reverse thrust (if applicable). (Rypdal, 2003)

For each mode an average time of operation is identified according to the ICAO Engine Exhaust Emissions Databank. This value is taken for each type of engine for the different modes. To arrive at the fuel usage at each stage, the fuel usage value in kg/sec is multiplied by the number of seconds for that mode (240, 1560 etc). (Rypdal, K, 2003)

5.        Calculation of CO2 emission estimate:

The CO2 emission estimate is evaluated based on the fuel to CO2 conversion rate specified by ICAO. All fuel burn data is multiplied by a factor which gives the CO2 output (in kg). The factor used (3.15) is based on a typical aviation fuel

hydrogen:carbon ratio. (ICAO,1993) This factor for conversion of fuel to CO2 emissions is according to adopted value by ICAO/CAEP. (Henderson, S. et al, 1999)

6.        Calculation of CO2 emissions for each plane:

The CO2 emission of each engine is then multiplied with the number of engines to arrive at the emission estimate for each plane.

7.        Calculation of the emission estimate of each plane:

The number of movements of each plane is multiplied by the emission estimate of each plane to get the total emission of the plane in 1999 for that plane type.

8.        Calculation of total emission at Manchester Airport in 1999:

The total emissions of each plane type in 1999 is added up to get the total emission of Manchester Airport in 1999.

 

 
4                                                Research Findings and Analysis

 

The data collection was carried out in Manchester Airport for the research. The research steps according to the methodology adopted was followed to arrive at the final result.

The research findings can be summarised as follows:

1.        Identification of the different aircraft types that operate through Manchester airport:

It was found that 28 types of aircrafts used Manchester airport. It consisted of various types of engine types. A list of the aircraft types, engine types and engine numbers are provided in the appendix.

2.        Select high plane counts (eg 13,000) for 1999:

The plane counts provides estimate of the number of flights for each category of the aircrafts. The highest values were found for Boeing 757 (21,261) followed by Airbus A320 (13,031). In total there were 144,372 flights recorded across the different types of aircrafts.

3.        Estimating the emission for each engine type:

In this step, the list of planes and engine types are identified. The complete list can be found in the Appendix B.

4.        Calculation of time for each mode:

The ICAO Engine Exhaust Emissions Databank provides emission factors for 4 possible thrust settings. Reverse thrust has not been included because of the difficulty in determining its usage.

 

Mode
Thrust Setting (S)
Approach:

From 1000m to touchdown
30%
Taxi and Idle:

Engine start up on stand

Pushback and taxi out

Holding at runway head

Touchdown to start of taxi in

Taxi in to stand

Engine shutdown on stand
7%
Take off:

Start-of-roll to wheels off
100%
Climb out:

Climb to 450 m
100%
Climb out:

Climb from 450 m to 1000m
85%
Table 5.1 – Association between aircraft modes and power settings (Underwood et al, 1994).

 

The time an aircraft operates in each mode is dependent on a number of factors and where possible it is recommended that site-specific data regarding times in mode are used in estimating emissions.  Approach and climb out times in mode may vary but taxi and idle times are very site specific and are dependent on the length of taxiways and queuing delays etc.  The time spent in take-off mode is fairly standard and does not vary much from location to location.  The ICAO has derived an International LTO cycle from times in mode information averaged from a large number of international airports (Table 5.2).

Mode of operation
International LTO
Approach
240
Idling
1560
Take off
40
Climb out
132
Summary of aircraft times in mode (seconds).

 

Times in mode for approach, climb out and take off have been taken from the international LTO.  Site specific times in mode data could be obtained by manual extraction from the Airport’s noise and track system (MANTIS) but the work required to do this is considered disproportionate to any improved accuracy.  Site specific idling times have been used because of their greater variability between airports.

Taxi times are dependent on the runway in use and the stand to/from which the aircraft manoeuvres.  Data was extracted from the Airport’s MIS database on the percentage use in 1999 of aircraft stands.  In order to keep a manageable number of taxi routes in the spatial representation of emissions, stands were grouped as shown in Table 5.3.

To/From Stands
Percentage Use
T2
20.10
T1C
19.68
T1B
23.58
T3A 16-18
9.491
T3A 41-46
9.491
T3 50s
11.47
Other
6.19
Percentage use of aircraft stand groupings.

 

The percentage use of T3A stands was taken together and the split has been assumed to be half.

Emissions have been apportioned evenly across stand areas for Terminal 2, Terminal 1 Pier C, Terminal 1 Pier B, Terminal 3 stands 16 to 18, Terminal 3 stands 41 to 49 and Terminal 3 stands 50 to 56.  Taxi emissions under the category of other have been apportioned to Stands 63 to 71.

The taxi times of each aircraft in the LTO cycle are available on the MIS database.  The fields used are actual time and chock time.  On arrival, actual time corresponds to touchdown and this has been used as the  start time for taxi.  On departure actual time corresponds to wheels off.  It is therefore necessary to subtract the take off time (40 s) from all departure taxi times.  The idle & taxi times are shown in Table 5.4.

 

Runway
Mode
Time (seconds) To/From Stands
T2
T1C
T1B
T3A

16-18
T3A

41-46
T3 50S
24R
Taxi In
442
379
305
314
298
323
Taxi Out
996
889
742
702
701
707
06L
Taxi In
444
371
277
253
244
248
Taxi Out
1008
918
845
882
915
954
Taxi times calculated from MIS database.

 

It is possible to calculate an average idling time for both runway 24R and 06L from the data in Table 5.4.  This gives an idling time at the Airport of 1180 s.  This can be compared to the International LTO idling time of 1560 s which is considerably greater.

The time spent on stand has been assumed to be 180 seconds for aircraft taxiing out (i.e. pushback) and 45 seconds for taxiing in aircraft based on physical observations of a small sample of movements.  Table 5.5 presents the revised idling times.

 

Runway
Mode
Time (seconds) To/From Stands
T2
T1C
T1B
T3A

16-18
T3A

41-46
T3 50S
24R
Taxi In
397
334
260
269
253
278
Stand
225
225
225
225
225
225
Taxi Out
816
709
562
522
521
527
06L
Taxi In
399
326
232
208
199
203
Stand
225
225
225
225
225
225
Taxi Out
828
738
665
702
735
774
Times in mode for aircraft idle/taxi according to stand used.

 

Further the values of the times are determined from the observation of the different types of aircrafts. A combination of estimated values and observation is used for the calculation of the final values.

 

5.        Calculation of CO2 emission estimate:

The fuel flow of the aircraft measured in kg/sec is multiplied by the time of each segment of the flight and aggregated to find the fuel used per engine of the aircraft.

The time for each mode were not available for all the aircraft types. In this case, a default value of 35, 85, 70 and 175 seconds respectively was used.  The result provided the total fuel usage for the aircraft for the time of operation at the Manchester Airport. The detailed values can be found in the Appendix B

Total estimate of CO2 emission at Manchester Airport:
The fuel consumption is multiplied by a constant value of 3.15 to get the total emission from the fuel consumption. This is applied to the number of engines of the aircraft. Further this is multiplied by the number of flights of each type.

A total value of 478675681.79 kg is obtained for the Manchester Airport.

 

Methodological Issues

The following methodological issues were faced while using the adopted methodology were the following:

The main difficulty in applying the methodology is to get the correct values for the time fuel usage by the different types of aircrafts.
This value of the fuel usage by aircrafts was based on standard values based on ICAO. This does not reflect the improvements in the engines and hence may provide a higher value.
The aircraft type is not distinguished between domestic and international and this may have differences in the flight emission.
 
5                                                Conclusion

 

The research looked into finding the emission estimation method at Manchester Airport. The purpose of the research was to identify a methodology for calculation of CO2 emission. The research was successful in identifying a methodology that was put to test. The methodology was able to arrive at the CO2 emission with the data obtained from observation at Manchester Airport.

Specifically, there were two  research hypothesis that formed the basis of the research.

The RASCOM and AERO methodology can be applied to a specific airport with modifications
It was found that the two methodologies could be used to arrive at a methodology that can be applied.

Different research methodologies such as IPCC, AERO, DTLR can be combined to provide an effective methodology
It was possible to take specific parts of the methodology and data pertaining to aircrafts and use it in the research. Hence this hypothesis also held true for the research.

Next steps

As next steps for the research, the following activities are to be adopted:

Verify the results of the research with similar research to ensure that the values obtained are accurate and if any adjustments are needed to be done to the methodology.
To find the applicability of the model in other airports, it is necessary to test the model in different environments. This is an important step in adopting the model.
The model needs to be applied for Manchester Airport again to determine whether all the assumptions adopted are valid.

 

 

6                                                Reference and Bibliography

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Dings, JMW, Wit, RCN, Leurs, BA, Davidson, MD (CE) Fransen, W (2002) External costs of aviation (Main report) Delft, 2002 (February) Main report 77 pp.

 

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Henderson, SC, and Wickrama, UK (1999) Aircraft emissions: Current Inventories and Future Scenarios, Chapter 9 in Aviation and the Global Atmosphere, A Special Report of IPCC 10 (Intergovernmental Panel on Climate Change), eds. Penner, JE, Lister, DH, Griggs, DJ, Dokken, DJ, and McFarland, M, Cambridge University Press, Cambridge, UK, 291–332, 1999.

 

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IPCC (1999) Aviation and the Global Atmosphere, A Special Report of IPCC 10 (Intergovernmental Panel on Climate Change), eds. Penner, JE, Lister, DH,

 

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Maddison, D, Pearce, D, Johansson-Stenman, O, Calthrop, E, Litman, T and Verhoef, E (1996) The true costs of road transport, Earthscan, London.

 

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Mason, KJ (2000) The propensity of business travellers to use low cost airlines, Journal of Transport Geography, Volume 8, Issue 2, June, Pages 107-119.

 

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Minnis, P, Schumann, U, Doelling, DR, Gierens, KM and Fahey, DW (1999) “Global distribution of contrail radiative forcing”. Geophys. Res. Ltrs., 26, July 1, 1999, pp.1853-1856.

 

 

RASCO (2001) Regional Airport Route Analysis, Aviation & Travel Consultancy, and the daily, monthly and seasonal profiles.

 

Olsthoorn, X (2001) Carbon dioxide emissions from international aviation: 1950-2050. Journal for Air Transport Management. Vol 7 pp 87-93.

 

ICAO (1995): Engine exhaust emissions databank. First edition. Doc 9646-AN/943.

 

Rypdal, K (2003) Good practice guidance and uncertainity management in Natonal greenhouse gas inventories, 97-102.

 

 

 
7                                                 Appendix A

 

 

 

 
8                                                 Appendix B – Calculations

 

Manchester International Airport 1999

Aircraft Type
Aircraft Code
Engine Type
MODE 1
MODE 2
MODE 3
MODE 4
Fuel Flow – T/O
Fuel Flow – C/O
Fuel Flow – App
Fuel Flow – Idle
Number of Engines
Plane Count in 1999
Total CO2 Emission Estimate
1
SAAB 2000
S20
AE3007C

0.28
0.234
0.091
0.039
2
1,704
4701266.476
2
ASK
P38
unknown

0.348
0.29
0.23
0.083

1,222

3
Boeing 747SP
74L

0.416
1.12
0.369
0.06
4
875
2414089.3
4
Lockheed L1011 Tristar pax
L10

0.23
1.95
0.508
0.037
3
1,844
5087520.764
5
BAe Jetstream 41
J41

no data

0.14
2.78
0.647
0.014
2
4,756
13121609.95
6
BAe Jetstream 31
J31

no data

0.05
3.61
0.786
0.08
2
1,648
4546764.761
7
Fokker F70
F70

0.68
4.44
0.925
0.146
2
1,893
5222709.765
8
Fokker F50 jet
F50

1.31
5.27
1.064
0.08
2
2,725
7518163.819
9
Fokker 100
F10
TAY650-15
34.96
94.38
60.96
185.64
1.94
6.1
1.203
0.014
2
5,498
15168757.68
10
Embraer RJ145
EM4

2.57
0.234
1.342
0.12
2
1,370
866540.9415
11
Dehavilland Dash 8
DH8

3.2
0.287
1.481
0.226
2
6,984
4417461.267
12
Dehavilland Dash 3
DH3
unknown

3.83
0.34
0.091
0.332

1,091
690070.195
13
Canadair Regionaljet
CRJ

4.46
0.393
0.68
0.438
2
2,922
1848199.001
14
British Aerospace (BAC) One Eleven 500
B15

1.164
0.446
1.269
0.544
2
1,183
748261.2655
15
British Aerospace ATP
ATP

no data

0.32
0.499
1.858
0.65
2
9,959
6299183.384
16
Airbus Industrie A300-600 pax
AB6
PW4158
99.24
264.53
163.80
329.16
0.28
0.552
2.447
0.756
2
1,473
931689.64
17
Boeing 737
73K

0.24
0.605
3.036
0.862
2
9,623
62305210.52
18
Boeing 767-300
763
CF6-80C2B6

0.2
0.234
3.625
0.968
2
5,926
38368562.56
19
Boeing 767-300
763
PW4060
105.88
275.22
168.72
332.28
0.16
0.32
4.214
1.2
2
5,926
38368562.56
20
Boeing 757
757

48.64
134.46
82.10
162.34
0.12
0.406
4.803
0.38
2
21,261
97019853.56
21
Boeing 757-200
752
PW2040
70.44
191.14
118.32
241.80
0.08
0.492
5.392
0.068
2
4,165
19006052.87
22
Boeing 757-200
752
RB211-535C
72.00
195.36
129.60
312.00
0.04
0.234
5.981
0.039
2
4,165
21673687.07
23
Boeing 737-500
735
CFM56-3-B1
37.84
104.54
69.60
177.84
4.0
2.5
2.5
2.2
2
6,371
18229207.23
24
Boeing 737-400
734
CFM56-3C-1
46.16
125.93
80.64
193.44
7.7
3.9
2.5
2.3
2
5,056
16557832.71
25
Boeing 737-400
734
CFM56-3B-2
42.24
115.80
75.36
185.64
6.0
3.0
2.5
2.2
2
5,056
15551010.2
26
Boeing 737-200
732

2
4,312

27
Airbus A321
321
CFM56-5B2
57.04
152.86
90.24
185.64
8.2
10.1
0
0
2
5,043
15511025.4
28
Airbus A320
320
CFM56-5B4
46.64
126.85
78.24
166.92
10.0
11.7
0
0
4
13,031
40080145.16
29
British Aerospace 146
145
ALF502R-5

2
7,290
22422243.74

TOTAL

144,372
478,675,681.79
 

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