Air Departure Tax reduction in Scotland: noise impact assessment

4. Noise impact prediction methodology

4.1. Overview

4.1.1. This section describes the technical approach to the forecasting of changes in noise levels. The methods applied to determine potential noise impacts as a result of proposed reductions in ADT are as follows:

• Noise modelling methodology – sets out the methodology to predict aircraft noise contours.
• Determination of baseline Air Traffic Movements (ATM) – sets out how baseline ATMs were derived from 2016 flight logs. 
• Determination of future scenario ATMs – sets out the method how ATMs for future assessment scenarios were determined from forecast passenger numbers.
• Application of noise mitigation in predictions – sets out the consideration of potential future reductions in aircraft noise in predictions.
• Population mapping – sets out the methodology for identifying receptors within aircraft noise contour bands.

4.2. Noise modelling methodology

4.2.1. Noise modelling was undertaken using commercially available software called Integrated Noise Model (INM). INM calculates aircraft noise from ATM data which is applied to approach and departure flight paths. Further details on INM are presented in Appendix A.

4.2.2. INM has the capacity to output a variety of noise level parameters depending on the input information. As described in Section 1.1.10, the following parameters are used to describe aircraft noise levels:

• L_Aeq,16h – average sound level over a 16-hour daytime period (07:00hrs to 23:00hrs) using the average daily aircraft movements from the 92-day summer period;
• L_night – average sound level over an 8-hour night-time period (23:00hrs to 07:00hrs) using the average daily aircraft movements from the 92-day summer period.

4.2.3. The output of the noise model is a series of contour lines which link geographical points experiencing equal noise in a similar way to isobars link points of equal barometric pressure or relief contours link areas of equal elevation on a topographic map. These noise contours define areas within which noise levels are above the specified value. For example a contour labelled 51 dB L_Aeq,16h indicates that all areas within that contour will be exposed to daily average aircraft noise levels of 51 dB L_Aeq,16h or above.

4.3. Determination of baseline air traffic movements

4.3.1. A study of 2016 ATMs was undertaken using 2016 ATM data that were sourced from each airport. Although 2016 noise contours would be provided by each airport as part of Round 3 of the END, it was considered preferable to model 2016 ATMs as part of this assessment to ensure that a consistent methodology was applied when modelling baseline and future scenario noise contours. This study covers objective (b) set out in paragraph 2.3.1.

4.3.2. Due to the potential for discrepancies in how 2016 ATM data could be analysed and differences in aircraft noise data in INM and the Civil Aviation Authority’s Aircraft Noise Contour (ANCON) noise modelling software, the baseline noise contours presented in this report should not be considered comparable with those provided for Round 3 of the END. As Round 3 mapping has not been publicised at the time of undertaking this assessment, there was no way of mitigating this; so the reader is reminded of the technical differences. A detailed methodology of how 2016 ATM data was analysed is presented in Appendix A.

4.3.3. Flight paths were input into the noise model using information obtained from the National Air Traffic Services website^[25]. Where detailed information about Standard Instrument Departure (SIDs) routes was not available, END Round 2 noise contour maps were referred to for provision of SID information. Flight paths for each airport are presented in Figures A.1 to A.5 of Appendix A.

4.3.4. Military and rotary wing aircraft contribute to noise at Aberdeen and Prestwick airports. These movements would be unaffected by changes to ADT as they are non-chargeable aircraft, so have been omitted from predictions.

4.4. Determination of future scenario ATMs

4.4.1. The future assessment year has been taken as 2022 as it was considered that the effect of a reduction in ADT will be in a steady state by this point. Passenger data was provided by Peter Brett Associates (PBA) under three different ADT scenarios with different Pass Through (the extent to which an ADT change is reflected in lower air fares) assumptions. PBA have conducted a separate study^[26] into the economic impact of potential reductions in ADT and every effort has been made to ensure that this study is consistent with the PBA work. The nine scenarios (not including the baseline scenario or ‘Do nothing’/existing tax levels option) provided in the PBA study are as follows:

• Scenario 1a – 100% cut in Band A (Full Pass Through);
• Scenario 1b – 100% cut in Band A (Partial Pass Through);
• Scenario 1c – 100% cut in Band A (Zero Pass Through);
• Scenario 2a – 100% cut in Band B (Full Pass Through);
• Scenario 2b – 100% cut in Band B (Partial Pass Through);
• Scenario 2c – 100% cut in Band B (Zero Pass Through);
• Scenario 3a – 50% cut in Band A & Band B (Full Pass Through);
• Scenario 3b – 50% cut in Band A & Band B (Partial Pass Through);
• Scenario 3c – 50% cut in Band A & Band B (Zero Pass Through).

4.4.2. Although there may be changes to flight paths and airport infrastructure (i.e. new runways) in the future, no such changes have been made at the time of issue of this report. Consequently, no information is available on potential future operational conditions and noise modelling of future scenarios uses existing flight paths and airport layouts as the best possible basis.

4.4.3. Future ATMs have been calculated with reference to the baseline 2016 ATM data. Forecast passenger data provided by PBA covers the years from 2017 to 2022. The forecast passenger data were simplified into groups defined by operator type to provide a means of correlating passenger numbers with ATMs:

• UK scheduled operators;
• EU scheduled operators;
• Overseas (non-EU) scheduled operators;
• UK charter operators; and
• EU charter operators.

4.4.4. Scheduled services are defined as those performing to a published timetable with charter services referring to all air traffic movements other than scheduled services. 

4.4.5. The change in passengers broadly affects ATMs depending on the operator category they are assigned to. UK and EU scheduled operators tend to operate Code C aircraft (e.g. A320, B737) that are medium sized. Overseas scheduled operators tend to operate long haul Code E aircraft (e.g. A330, B777) that can carry a greater number of passengers. Charter operators tend to operate small aircraft that carry a low number of passengers. Consequently, a large increase in charter passengers will have a large increase in ATMs compared to a corresponding large increase in overseas scheduled passengers that can be handled by a smaller increase in ATMs.

4.4.6. ATM data was provided from each airport for 2016 whereas PBA passenger forecasts started from 2017. It was assumed that the ATM data from 2016 would broadly correlate with the 2017 passenger forecasts. Consequently, representative ATMs for the 2022 assessment scenarios were generated from the 2016 ATM data based on the change in passengers between 2017 and 2022. The methodology applied for calculating future scenario ATMs is presented in Appendix B. 

4.4.7. The scenarios that have been considered in this assessment are: 1a, 1c, 2a, 2c, 3a, and 3c (as described in Section 4.4.1). These scenarios represent the ‘Full Pass Through’ and the ‘Zero Pass Through’ scenarios. This enables an understanding of the potential range of changes in noise contours at each airport as a result of each ADT change option. A summary of the total projected yearly passengers (PAX) and annual average ATMs for each scenario are presented in Table 1. 

4.4.8. It should be noted that, as the current APD exemption for all passengers flying on aircraft departing from airports within the Highlands & Islands is expected to remain in place under ADT, passenger forecasts have not been undertaken for Inverness Airport. However, baseline 2022 noise contours have been calculated for Inverness Airport using a combination of DfT ATM forecasts^[27] and historical CAA ATM data^[28]. A discussion on how changes in passenger numbers affect the summer average ATMs at each airport along with full details of summer average ATMs for each aircraft variant are presented in Appendix B.

Table 1 Projected Passengers and ATMs for Each Tax/Pass Through Scenario

Scenario	ABZ		EDI		GLA		INV		PIK
	Yearly[29] PAX	Average Daily ATMs	Yearly PAX	Average Daily ATMs	Yearly PAX	Average Daily ATMs	Average Daily ATMs	Yearly PAX	Average Daily ATMs
Baseline 2017	2,943,686	178.6	12,558,738	379.1	9,305,969	309.6	72.6	682,964	152.3
Baseline 2022	3,049,512	184.8	13,987,106	405.2	9,600,513	319.2	83.1	693,731	153.2
1a	3,130,663	189.5	14,702,412	425.6	10,019,302	342.5	-	770,733	159.3
1c	3,138,205	191.6	15,664,092	449.6	10,588,591	352.3	-	870,042	167.3
2a	3,070,868	185.8	14,050,224	407.0	9,687,174	327.3	-	693,731	153.2
2c	3,142,359	189.2	14,267,537	415.6	10,036,505	333.2	-	693,731	153.2
3a	3,100,766	187.7	14,376,318	416.3	9,853,238	327.9	-	732,232	156.3
3c	3,153,249	191.2	15,105,630	434.6	10,414,268	346.1	-	794,480	161.2

4.5. Application of noise mitigation in predictions

4.5.1. Mitigation of aircraft noise is covered by the International Civil Aviation Organisation’s (ICAO) Balanced Approach to Aircraft Noise Management^[30]. The Balanced Approach explores various measures to address noise problems at airports through consideration of four principal elements:

• Reduction of noise at source.
• Land use planning and noise management.
• Noise abatement and operational procedures.
• Operating restrictions.

4.5.2. The most effective means of reducing aircraft noise is through reduction of noise at source.

4.5.3. There is a continued drive in the aircraft industry to reduce noise generated by aircraft movements. In 2017, ICAO Chapter 14^[31] standard of aircraft were introduced which included noise criteria which all new civil aircraft should achieve. Consequently, aircraft fleets in the 2022 future assessment year are likely to generate lower levels of noise than current aircraft fleets. As no information is available on how flight operators may phase into service new aircraft variants, assumptions cannot easily be made as to the percentage of fleet that may be upgraded. Thus, for the purposes of the analysis, aircraft fleets in the 2022 scenarios are considered equivalent to the fleets operating in 2016.

4.5.4. Aircraft noise may also be mitigated through optimisation of operational procedures. Improvements in aircraft technology allow aircraft noise to be managed through measures such as steeper aircraft approach angles and management of approach/ departure profiles (flap settings, speed and reverse thrust). Airports may or may not adopt optimised operational procedures in future. However, as no information is available on how they may be implemented at each airport, this study assumes that operational procedures will remain consistent for future scenarios.

4.5.5. As no potential future mitigation measures have been included in the predictions of future aircraft noise, the noise impacts forecast in this work are likely to be over-estimates. However, it is not possible to say to what extent these values are over-estimated. 

4.6. Population mapping

Baseline mapping

4.6.1. The receptors considered in the assessment of noise impacts are exclusively residential in character. Although other types of receptors (e.g. schools, hospitals, theatres etc.) may be similarly affected by changes in aircraft noise, it was considered reasonable for this study to only consider residential receptors as they make up the majority of the affected sensitive receptors around airfields.

4.6.2. The baseline population mapping was undertaken using the baseline noise contours which were created for each airport. Ordnance Survey building footprints were identified where they intersected the noise contours. Using Ordnance Survey (OS) AddressBase Premium data, residential buildings were selected and address data that were located within these building footprints were identified. 

4.6.3. Noise levels from the noise contour bands described above were assigned to each residential property (or household) within each noise contour. Based on household occupation projections undertaken by National Records for Scotland^[32] (NRS), a figure of 2.14 persons was taken as the average population for a Scottish household in 2017. Aggregate scores for total population currently affected by noise levels within the noise bands were then calculated.

Future mapping

4.6.4. For each airport, research was carried out to identify future housing developments in the vicinity. Housing development data was collated from Local Authorities where possible, and digitised from their Local Development Plans (LDPs) in a small number of cases where data could not be provided. A maximum development capacity number was taken from the LDPs if this was not already provided and attributed to each housing development area. A Geographic Information System (GIS) assessment was then made to identify where residential properties had already been built (and were already contained within the OS AddressBase Premium data) and the difference between this number and the capacity number was used to identify how many additional properties should be added. Points were generated in a random pattern within these development areas, to represent the future property locations.

4.6.5. A GIS model was created to run the population assessment for the future scenarios, and this identified residential properties within the contours as per the baseline process, and additionally future housing locations within the noise contours. The model also attributed a noise level to each point. 

4.6.6. As per the baseline, the noise level was aggregated to provide a count for each noise level for each airport. A 2022 average household size figure of 2.07 referenced from NRS household projections^[33] was applied as a factor to each property to calculate the total number of people forecast to be affected within each noise band.