Chapter 5—The Person-Events Index—Assessing the Total Noise Load of Airports
- 5.1 Background
- 5.2 The Person-Events Index (PEI)
- 5.3 The Average Individual Exposure (AIE)
- 5.4 Worked Example—Sydney Airport
- 5.5 Discussion
The conventional method of assessing the noise loads generated by competing airport development or operating options (e.g. new runway and/or flight path configurations), for example in an EIS process, is to compute and compare the number of persons living within specified noise exposure zones.
In some cases comparisons of population distributions across noise zones give a clear picture of overall noise exposure trends or differences. However, in many cases the conclusions are not clear-cut and decision-makers are faced with a dilemma when considering options. Which imposes the greatest noise load? Option A where there is a small number of persons exposed to a high noise load or Option B where there is a large number of persons exposed to a low noise load.
Option A type approaches are superficially attractive since they involve impacting the least number of people. However, this may not necessarily be the option with the most desirable outcome since the outcome may have been achieved by concentrating the noise on a small number of people rather than by reducing the total noise load. This data interpretation dilemma is only likely to be exacerbated in the future as community pressures force noise reporting to be extended to lower noise levels and the number of noise zones under consideration to be increased.
One common way to rank, or assess changes in, data sets which have subsets which vary independently is to use some form of single figure index which ‘summarises’ the overall data (e.g. the Dow Jones Index). In a similar vein an index termed the Person-Events Index (PEI) has been developed by the Department of Transport and Regional Services to summarise population noise exposure data sets.
The index has deliberately been expressed in a form which gives non-experts (e.g. decision-makers & community representatives) some feel for the magnitude of the noise load.
The index is not intended to replace existing noise indicators but to supplement them. An index assists interpretation of data; detailed examination of the base data always needs to be made if definitive conclusions are to be drawn when comparing two noise exposure data sets.
The PEI allows the total noise load generated by an airport to be computed by summing, over the exposed population, the total number of instances where an individual is exposed to an aircraft noise event above a specified noise level over a given time period.
For example, if a departure off a specific runway at an airport by a particular aircraft type leads to 20,000 persons being exposed to a single event noise level greater than 70 dB(A) then the PEI(70) for that event would be 20,000. If there were a further similar event the PEI(70) would double to 40,000 since there would have been that number of instances where a person was exposed to a noise level louder than 70 dB(A). The PEI is therefore expressed by the following formula:
PEI(x) = S P N N
x = the single event threshold noise level expressed in dB(A)
P N = the number of persons exposed to N events > x dB(A)
The PEI is summed over the range between N min (a defined cut-off level) and N max (the highest number of noise events louder than x dB(A)persons are exposed to during the period of interest).
By summing all the single events at an airport, say for an average day, a total PEI(70) (or PEI(80), etc) can be developed. The PEI(70) is the total number of instances on the average day where a person is exposed to a noise event greater than 70 dB(A) and is a measure of the total noise load generated by the airport. For example, the worked example in Section 5.4 shows that for Sydney Airport in the ‘noise sharing’ configuration the PEI(70) is approximately 6.9 million (when considering the population within the 40 events per day contour).
The PEI in itself does not indicate the extent to which the noise has been distributed over the exposed population. For example, a PEI(70) of 2 million for an airport could mean that one person has been exposed to two million events in excess of 70 dB(A) (assuming this were possible), or that two million people have each received one event or it could be arrived at by any other combination of the two factors. A summary of the noise distribution is provided by the Average Individual Exposure (AIE) which is given by the formula:
AIE = PEI/total exposed population
For example, in the worked example shown in the next section, at Sydney Airport in the ‘noise sharing’ configuration approximately 100,000 persons are exposed to 40 or more noise events greater than 70 dB(A) on the average day. The PEI(70) = 6.9 x 10 6 and hence the AIE is ((6.9 x 10 6 )/10 5 ) = 69. This shows that for the exposed population under consideration(ie those persons living within the 40 events per day contour) the average individual exposure is approximately 70 events per day louder than 70 dB(A).
The AIE therefore gives the average individual noise exposure in the number of events greater than the specified noise level over the specified time. When comparing options at a particular airport the AIE indicates the extent to which the noise is concentrated or shared.
Over the past five years there have been three very distinct operating regimes at Sydney Airport. Prior to November 1994 the Airport operated with two crossing runways. During 1995 and the first few months of 1996 the Airport operated almost exclusively on the two north-south parallel runways. Since March 1996 noise sharing arrangements under the Airport’s Long Term Operating Plan have been in place. These distinct operating regimes give a good opportunity to demonstrate how the PEI/AIE enables changes in noise exposure patterns to be examined. It also allows a comparison to be made with ANEF data.
Table 5.1 below summarises the noise exposure patterns for the three operating scenarios using the ANEF & PEI/AIE.
Table 5.1: Comparison of Indices
|Population >30||Population >20|
- See Appendix C for details of the derivation of the data in the table.
- The PEI & AIE in the table are based on the 40 events/day contour as this broadly equates to the 20 ANEF contour at Sydney Airport.
It is very illustrative to compare the information on the pre-parallels and parallels regimes shown in the table. One of the key noise arguments in favour of introducing the parallel runways was that it would lead to a significant reduction in the number of persons within the 20 ANEF contour. This in fact did take place as shown in the Table. However, the table also shows that if the project had been assessed using the PEI a somewhat different picture would have emerged. The Table shows that by going to parallel runways the noise load expressed in PEI in fact increased by more than 20%, rather than decreased as implied by the ANEF. The change in the Average Individual Exposure (AIE) from 87 to 122 gives a very simple indication of the magnitude to which the noise was concentrated on the smaller number of persons exposed. The table also illustrates that the total noise load of the Airport under the current noise sharing arrangements is similar to that existing under the pre-parallels arrangements but that the noise is significantly less concentrated than under either of the two previous arrangements.
The worked example shows that the PEI assists in the interpretation of noise exposure distributions when considering different operating arrangements at an airport. The index permits a rapid overview to be made of noise exposure information and reveals a somewhat different picture to prima facie conclusions based solely on the total numbers of persons exposed.
The EIS for the Second Sydney Airport contains a PEI/AIE analysis which is used to analyse differences in the noise exposure patterns between Sydney Airport and the three options for the proposed new airport [ref 18].
Table 5.2 shows how the PEI/AIE allows a comparison to be made of the total noise loads generated by different airports.
Table 5.2: PEI/ AIE Comparison between Australian Airports
|Airport||No of persons exposed to
> 10 events/day
> 70 dB(A)
|Major international airport||190,000||27.0||142.0|
- The information for the major international airport relates to the number of persons exposed to more than 40 events a day louder than 70 dB(A). The airport asked that it not be identified in this paper.
It can be seen that the total noise load generated by Sydney Airport, when expressed in PEI, far exceeds that at other Australian airports. To place these figures in context the PEI generated when one B747-200 takes off to the north on the main runway at Sydney Airport is approximately 250,000. This is similar to a whole day’s noise load at Brisbane Airport. By the same token the average daily PEI at Sydney is significantly lower than that shown for the major international airport. While the PEI is high for Sydney by Australian standards the Average Individual Exposure is relatively low and is, in fact, similar to the other Australian airports. This highlights the relevance of the noise sharing policy to the airport which generates by far the largest aggregate noise load of any Australian airport. Nevertheless, sight should not be lost of the fact that there are a significant number of persons exposed to more than 100 events louder than 70 dB(A) on the average day at Sydney. By way of contrast, while the AIE at Adelaide (for persons living within the 10 events/day contour) is higher than that at Sydney, the maximum individual noise load is less than 100 events a day.
Compared to conventional approaches the PEI has advantages in that, because it is not based on the common equal energy indicators, it throws a different light on noise exposure data and presents the ‘picture’ in a way that can be readily understood by the non-expert. It is arithmetic and therefore shows differences between scenarios much more starkly than logarithmic indices which dampen any differences. It is very easy to carry out computations with the PEI and both the PEI and AIE express the information in a way that can be ‘visualised’ (ie they refer to a number of noise events).
The PEI is particularly useful for computing partial noise loads. A PEI can be calculated, with relative ease, for one or a small number of movements to give a figure that has some ‘meaning’. This allows rapid ‘first-cut’ comparisons to be made between different airport scenarios (e.g. comparing noise exposure patterns from different flight paths or different aircraft types on the same flight path). Appendix C includes a PEI/AIE analysis of the two main operating modes at Sydney Airport to illustrate how the index can be used to analyse partial noise loads.
As demonstrated by the worked example, the Average Individual Exposure(AIE) is particularly useful when comparing different operating scenarios at a particular airport a particular airport a particular airport a particular airport a particular airport as it gives a very quick and easily understood indication of the extent to which the airport’s noise load is concentrated or spread. As such it acts as a flag to warn when there are unusual aspects to a noise distribution (e.g. very high concentration) which warrant further examination. The AIE needs to be used with caution when comparing noise exposure patterns between different between different between different between different between different airports airports airports airports airports and in these circumstances the PEI is a more useful tool.
While noise practitioners are able to accurately interpret noise exposure data, experience has shown that decision-makers and community representatives are strongly attracted to scenarios which result in the least number of people being in the contours. However, it is very easy for the inexperienced eye to be confounded by a situation where the minimisation of persons within noise contours has been achieved by concentrating noise on a relatively small number of people (without any reduction, or even an increase, in the total noise load).
At the very least the PEI is a good device for &squo;sanity checking’ when assessing noise exposure patterns and for assisting decision-makers and community members to understand the implications of noise exposure data.