Aircraft Noise at Source

The International Civil Aviation Organisation (ICAO) promotes a balanced approach to airport noise management consisting of four principle elements, namely: reduction of noise at source; land-use planning and management; noise abatement operational procedures; and aircraft operation restrictions. Each element represents a method of managing noise that, based on the particulars of a given airport, can be used in combination to manage or reduce aircraft noise. One of the most tangible of these four elements in managing aircraft noise levels is the noise at source (that produced by the aircraft itself). Over the last 30 years significant efforts have been aimed at producing quieter planes and, where other elements of the balanced approach have been exhausted or are not feasible, the continued advancement of aircraft technology, producing ever quieter planes, is often seen as a pre requisite to accommodating increased numbers of aircraft movements.

In the UK, like many other countries, the noise levels of civilian aircraft are classified based on the certification standards adopted by ICAO, the rules for which are set out in Annex 16 to the Convention of International Civil Aviation. So as not to inhibit safety or performance the ICAO standards do to themselves aim to drive legislation, but provide a classification of existing technology which airport operators or government regulators may adopt for implementation of their own environmental policies and practices. As technology has improved new certifications have been developed, such that Annex 16 consists of various chapters each corresponding to different stages of aircraft development. The first standard, Chapter 2, targets aircraft certificated prior to 6 October 1977, Chapter 3 aircraft certified from 6 October 1977 and Chapter 4, the most recent, aircraft certified from 1 January 2006. The ICAO standards are applied when an aircraft design is first approved for operational use and do not prevent the use of existing designs, classified under a different standard, being used for the production of new aircraft. However, where an older aircraft (originally classified under, say, Chapter 3) meets the requirements of a later, more stringent, classification (say Chapter 4) manufactures may opt to re-certificate the aircraft designs.

As mentioned above, Annex 16 standards are not designed to correspond with restrictions on the use of a given aircraft per se; however, they do offer a bench mark where by noisier aircraft can be phased out or restricted - on 01 April 2002 European legislation banned the operation of 'Chapter 2' aircraft to/from all European airports.

Although the different body/engine configurations and different series of the same design of aircraft can make it difficult to differentiate a Chapter 2 aircraft from a Chapter 3 or a Chapter 3 from a Chapter 4, for illustrative purposes below are some examples of aircraft belonging to different chapters:

Chapter 2 Aircraft (no longer in use within Europe): Boeing 737-200 (in production until 1988); 747-100 (the world's first 'jumbo jet', in production until 1986)

Chapter 3 Aircraft (characterised by more modern, quieter, jet aircraft): Boeing 737-300 (in production until 1999); Boeing 737-400 (in production until 2000); Boeing 747-400 (in service since 1989), Boeing 777 (in service since 1995); and Airbus A319 (in service since 1996).

Chapter 4 (at least one third quieter than those currently certified to the Chapter 3 standard, IATA): Airbus A380 (in service since 2007); Boeing 737-600 (in service since 1998).

The ICAO standards represent a useful means of categorizing aircraft (with regard to noise level); however, within each Chapter there is naturally going to be variation between aircraft - a 428 seat 747 'jumbo jet' is significantly more disruptive that an 80 seat Fokker 70, both of which are Chapter 3 aircraft. Indeed, Chapter 3 aircraft that exceed Chapter 3 noise standards but have not yet been reclassified as Chapter 4, may actually be quieter than a Chapter 4 aircraft.

Variation in noise level between different aircraft has important implications for the creation of a noise map based on ground observations. If a continuous series of measurements are taken at a given location, over time the average of those measurements will tend toward the true mean noise level experienced at that location; however, just a single measurement, which might observe a ‘quiet’ or ‘noisy’ aircraft, is prone to be un representative of the typical level of noise.

When considering such a spatially distributed phenomenon as aircraft noise it is not practical to take a continuous series of measurements at all locations. This is especially true in the case with the lhrnoisemap project where data collection is opportunistic, based on volunteered geographical information (VGI). The accuracy of this method must therefore rely either on accommodating this uncertainty or assuming repeated sampling of the same location will occur over time - helping the sample become more representative of the true mean at that location. Although the latter is preferable, to achieve repeated samples comprehensively requires a very large number of observations (together with an according time span), and to a greater or lesser degree methods of accommodating the uncertainty will have to be given consideration.

Even if repeated sampling were comprehensively achieved this makes the additional assumption that these observations are random, namely that the incidence of ‘noisy’ aircraft balances those of ‘quiet’. Rather than aiming to achieve a random sample another approach might aim to sample only a selected class of the total population (i.e. only record the noisy aircraft). Doing so would mean that a resulting visualization would represent only that class of the population rather than the population itself; however, would result in more standardised samples, thus reducing the need for repeated sampling. To facilitate this approach in the final analysis of data volunteers are asked to include the type of aircraft in the description of their sample. Whilst it is not realistic to identify most aircraft types the Boeing ‘jumbo’ 747 represents one that, if visible, is recognisable to many people. In the analysis of the data being able to identify a particular type of aircraft, representative of a single class within the population, will provide a means of performing a more rigorous analysis and possibly a method of validating other results. (As mentioned above, although all 747s operating at Heathrow are classified as Chapter 3 aircraft, it must be acknowledged that different series' of the aircraft produce different levels of noise, relative though to the differences between small aircraft and large aircraft this might be considered negligible)

References

Aircraft Noise, International Civil Aviation Organization Air Transport Bureau:

http://www.icao.int/env/noise.htm

Balanced Approach to Noise Management around Airports, IATA:

http://www.iata.org/NR/rdonlyres/5176A849-8FD6-4844-AB79-110F7D7789D2/0/BalancedApproachtonoisemanagementaroundairports.pdf

Emissions Impossible, Aviation Environment Federation:

http://www.hacan.org.uk/resources/reports/emissions.impossible.pdf

Noise Certification Standards, IATA:

http://www.iata.org/NR/rdonlyres/F57F6C76-4DB7-4A99-AC87-B2724AED97F9/0/Noisecertificationstandards.pdf

AudioBoo

AudioBoo is an iPhone Social Networking application that allows users to make digital noise recordings on their iPhone then post them on the AudioBoo website where they can be shared with other users. The recordings, referred to as ‘Boos’, can be made anywhere and, although they can not be saved, may be published remotely subject to network reception.

Using the iPhone’s location awareness Boos are geotagged so that, on the AudioBoo website, the sound file / media player is accompanied by an embedded Google Map showing the location where the recording was made (from this the position's latitude and longitude can be extracted). In addition to the sound recording and its location the website also provides the time at which the recording was made, the author, and the title of the boo.

AudioBoo represents an alternative approach to conducting a participatory noise survey using mobile phones: rather than measuring the level of a noise event in situ AudioBoo offers the facility to record the event its self, geographically reference its location and publish it remotely to a location where it can be readily accessed and shared.

Applying a predetermined tag (lhrnoise) to each Boo means that, using the website’s search facility, Boos collected and published for the LhrNOISEsurvey can be easy identified. Recorded as MP3 files software capable of Acoustic Analysis can be used to analyse the recordings and identify the levels of aircraft noise experienced. Transferring the analysis of the noise events from the 'front end' (the phone) to the 'back end' (me) provides a significantly more user friendly method of collecting data and facilitates an approach to the analysis of noise levels with the potential of distinguishing aircraft noise from other noise sources (such as wind of traffic) that might otherwise distort the measurement.

Possibly the greatest advantage of AudioBoo is its capacity to communicate peoples experiences of aircraft noise as real audio, rather than just numbers. The map below shows markers identifying the location of recordings, hyperlinking them to the AudioBoo website means the respective recording can be heard.

Second noise survey

In a second noise survey a similar methodology was used to that of the first (record location and noise level separately then correlate manually); however, the dBMeter Pro iPhone App by The Java Works was used in preference to NoiseMeter. This was both because dBMeter Pro offered better functionality (including the ability to measure noise on a decibel scale and export measurements as CSVs via email) and because an update to the NoiseMeter App rendered it unsuitable for noise measurements of the type required by this project - a lesson in the dangers of reliance on third party applications without any service level guarantee.

With aircraft taking off in an westerly direction from the northern runway the levels of noise pollution experienced were universally high, indeed, at a number of locations the maximum sound level of the passing aircraft reached the maximum that can be measured using dBMeter Pro inconjuction with the iPhone - found to be 103.3 dB(lin).



At each location a number of measurements were made and an average calculated. The accuracy of the average value is dependent on the samples used in its calculation forming a representative, random, sample of all aircraft noise events experienced at that location. Multiple readings were used; however, it is likely that the sample size was not sufficient to avoid the average being biased by a greater proportion of large, noisy, aircraft during the period of observation at a given location or small, quieter, aircraft during the period of observation at another. Because of this the average values given in the survey results must be interpreted with care; however, the ability of the phone to measure noise events is a great encouragement.

First noise surveys

To begin exploring how mobile phones could be used to survey aircraft noise I visited the village of Cranford, located at the end of Heathrow's northern runway, to take some samples using my iphone. Using the NoiseMeter app by Treascovery I was able to measure noise levels and with the GPS Tracker app by Instamapper I had a means of recording location (both applications were downloaded onto my iphone via Apple’s App Store).

NoiseMeter does not record in dB but instead uses its own scale of intensity ranging from 0 to 150. Once activated the application displays the real time reading together with the average and maximum experienced during that period of measurement. It should be stressed that I neither calibrated nor tested the accuracy of the NoiseMeter and it is not envisaged that this app be used beyond these conceptual trials.

NoiseMeter does not permit noise readings to be saved so each observation was recorded manually using the phone’s notepad, nor does it possess ‘location awareness’, so, before making each noise reading the location was tagged using GPS Tracker. These locations were later downloaded and manually matched with their respective noise reading.

Starting in Cranford my intention was to move away from the airport, periodically taking noise reading and tagging my location, to investigate whether the (expected) decrease in noise levels could be detected using my phone.



The above map, with markers to the north east of the airport, show where measurements were taken. It is pretty obvious that my plan to take multiple measurements at progressively increased distances from the airport was a little ambitious and, with measurements only being taken in a small geographical area, the survey was inconclusive in determining whether the the phone could detect variability in the noise levels. Despite this the trial was successful in bringing to my attention a number of important points:

The phone's microphone is quite sensitive and ambient noise, including wind and traffic, can easily mask the noise signature of aircraft - as with many types microphone wind caused particular problems, with even a light gust registering twice the noise intensity of a landing aircraft (the need to find a sheltered location contributed to why I only had time to survey two locations);

Different aircraft cause different levels of noise (it was for this reason that five samplings were taken at each location and the average taken); and

Landing aircraft cause less noise than those taking off (on the afternoon that I visited aircraft were approaching from the east to land on the northern runway).

Beginings...

With the location awareness capabilities (GPS, A-GPS) increasingly found in modern smart phones I am very interested in the potential use of mobile phones as personal data collecting devices. I believe the capability to geograhically reference sensed information offers exciting opportunities, empowering individuals to survey and investigate phenomenon in their local environment. As an application of this concept I am researching how Citizen Sensing might be applied to measure and record aircraft noise levels in the communities surrounding London's Heathrow Airport.