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Applied Acoustics xxx (2010) xxx–xxx
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Applied Acoustics
journal homepage: www.elsevier.com/locate/apacoust
Technical Note
Aircraft noise annoyance estimation: UK time-pattern effects
Peter Brooker *
Aviation Consultant, London, UK
a r t i c l e
i n f o
Article history:
Received 28 March 2009
Received in revised form 19 January 2010
Accepted 21 January 2010
Available online xxxx
Keywords:
Annoyance
Statistics
Surveys
Exposure-response
a b s t r a c t
An improvement appears to be possible in estimating UK aircraft noise annoyance. This is based on a
more detailed analysis and modelling of the data supporting the present UK aircraft noise policies. There
is empirical evidence that people’s real-life annoyance at aircraft noise is in part determined by its timepatterns. People benefit from Heathrow’s regular and predictable alternation cycles on westerly operations, equivalent to an effective dB(A) Leq value for that operational mode some 2-dB less than the measured dB(A) Leq. This correction is statistically controlled for people whose work/business is connected
with Heathrow. The implications of a time-pattern correction would be significant for UK airport noise
contours.
Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction
Is it possible to improve the methods for estimating UK aircraft
noise annoyance for airports in the UK? It is suggested here that
some improvement is possible, based on a more detailed analysis
and modelling of the data supporting the present UK aircraft noise
policies. The starting point is the nature of the noise ‘climate’ at
Heathrow, from which most of the evidence on annoyance has
been gathered.
Heathrow airport’s operations have complex restrictions, to a
large degree reflecting the need to reduce its noise impact. One
the most important measures is ‘runway alternation’. Heathrow’s
parallel runways 27L and 27R operate in so-called segregated
mode, i.e. one runway for arrivals, the other for departures. Runway alternation requires aircraft to land on one runway from
morning to mid-afternoon and on the other for the rest of the
day. While the airport is operating westerly, the morning/afternoon rota changes in a predictable fashion from day to day. This
means that residents under westerly flightpaths always get periods
of reduced noise, in either the first or second half of the day. There
is no easterly alternation: the so-called ‘Cranford agreement’ requires landings on the northern runway (09L) and takeoffs on
the southern one (09R).
The noise benefits of runway alternation are well recognised,
for example, in Godfrey’s [1] evidence to the Heathrow Terminal
5 public inquiry. Given the positive views expressed about runway
alternation, any prospect of its disappearance would be viewed
very negatively. However, this was an option proposed as part of
* Tel.: +44 20 8777 1718.
E-mail address: [email protected].
the future development of the airport DfT [2], HACAN [3]. The runways would be operated in ‘mixed mode’, i.e. both runways would
be used simultaneously for departures, with arrivals being interleaved between them, so there would be no predictable periods
of respite from the noise. The recent government decision DfT [4]
rejected mixed mode operations, but also said that easterly alternation should replace the Cranford agreement – so alternation remains an important factor in decision-making.
How much effect does alternation have on the calculated noise
exposure around Heathrow used by DfT (ERCD [5])? The answer is
‘None’, because the UK’s standard noise exposure contours do not
adjust for the time-pattern. (NB: there does not appear to have a
great deal of research into the annoyance effects of time-patterns
of noises – see De Coensel [6].) The lack of quantification of runway
alternation’s effects on people’s annoyance means there is no basis
for assessing the real impact of a potential move from segregated
to mixed mode on Heathrow residents. The following is an attempt
to produce such quantification, using the original data on which
DfT based their current aircraft noise assessments.
2. ANIS results and subsequent dB(A) Leq-based government
policies
ANIS is the abbreviation for the UK’s ‘Aircraft Noise Index Study’
reported at Brooker et al. [7]. This study consisted of face-to-face
social surveys at small sites – termed ‘communities’ – around several UK airports. The people in each site received approximately
the same aircraft noise exposure, measured at a central point in
the site. 2097 people were interviewed at 26 sites, i.e. about 80
per site, in the summers of 1980 and 1982. The average response
rate was 69%. Annoyance from aircraft noise was measured by a
0003-682X/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.apacoust.2010.01.010
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P. Brooker / Applied Acoustics xxx (2010) xxx–xxx
variety of direct and constructed scales suggested by the research
literature. Very detailed noise concurrent measurements were
used to construct a large set of noise exposure metrics. Finally,
there was a programme of detailed statistical analyses of the community-level data, mainly using step-wise multiple regression
models.
Table 1 presents the extracts from the ANIS data used in the
analyses here. (Brooker et al. [7] presents very extensive data tabulations; the raw data is no longer retained by the Civil Aviation
Authority) The main result of the study was that the A-weighted
equivalent continuous sound level measured over the week before
the social survey, i.e. dB(A) Leq24h(1 W), a noise energy measure,
would be an appropriate index. Following publication of the ANIS
Report, consultation, and some further work, the decision to use
a 16-h dB(A) Leq16h(3 M) for three summer months for the UK aircraft noise index was announced in 1990. The 16-h variant was
preferred because DfT policy is that night-time operations should
be treated separately from day and evening operations; the former
seen as reflecting sleep disturbance rather than annoyance. The
average over the three summer months aims to reflect the average
noise climate, i.e. matching the variations in runway modal usage.
DfT made the policy choice to use 57 dB(A) Leq16h(3 M) as the level
of noise exposure marking the approximate onset of significant
community annoyance.
Among the conclusions of ANIS were:
3. Modelling the annoyance effects of runway alternation in
ANIS
People’s general experience of annoyance could be assessed by
asking how annoyed they were by aircraft noise, a good measure
being the proportion of people saying they are ‘very much
annoyed’ (termed here ‘%VMA’).
Why did ANIS not investigate runway alternation? Why it was
not investigated subsequently? There are a variety of reasons, several connected with the difficulty of modelling the effect. Can the
annoyance effects of runway alternation be estimated?
The only major statistical ‘confounding factor’, i.e. a social or
economic variable that affected responses markedly, was
respondents’ economic connection with the airport, more precisely ‘the proportion of people surveyed who worked at, or
had business with, the airport’.
Airport dependent factors were not detected.
This study focused on the annoyance reactions in communities,
so the variables examined were noise measures of various kinds
and community socio-economic measures (e.g. percentage of manual workers). Personal attitudinal (e.g. reported noise sensitivity)
and demographic (e.g. age dependency) factors were averaged
out, although these are known to be significant individual modifying factors (e.g. see Miedema and Vos [8]).
DfT [2] uses the aircraft noise index in a variety of important
policy ways, most obviously in producing contours of equal
dB(A) Leq16h(3 M) values around airports to indicate changes in
the noise environment. For example, airport operators are expected to ‘offer to purchase those properties suffering from both
a high level of noise (69 dB(A) Leq16h(3 M) or more) and a large increase in noise (3 dB(A) Leq16h(3 M) or more)’.
Table 1
Dataset extracted from ANIS work.
Site
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Variable name
Name
Harlesden A
Harlesden B
Willesden
Woodham
Chiswick
Feltham A
Feltham B
Hounslow
Isleworth
Colnbrook
Hounslow W
Hounslow C
Stanwell I
Stanwell II
Stanwell III
Stanwell IV
Ifield
Horley
Manchester
Aberdeen
Luton A
Luton B
Ealing
Egham
Slough
Sheen
2
%VMA
10.6
16.7
10.6
6.4
6.7
52.3
51.7
40.2
43.7
28.9
31.6
25.4
3.8
4.1
9.9
6.1
3.3
17.8
17.1
6.1
25.0
29.1
37.7
29.9
30.6
29.2
3
%VMA
dB(A) Leq week
Work at airport (%)
Alternation benefit
56.7
56.7
51.7
49.6
54.4
68.3
68.3
69.1
68.2
68.8
64.0
62.1
61.0
56.0
62.4
61.1
53.9
61.9
57.3
55.9
59.4
59.4
60.1
65.3
65.8
62.6
6
1.5
4.6
0.0
6.4
2.7
2.3
3.4
13.4
7.0
18.1
13.2
8.5
18.8
11.3
22.2
35.4
11.1
11.1
1.3
2.0
2.5
5.1
3.9
11.7
8.3
0.0
7
0
0
0
1
1
0
0
1
1
1
0
0
1
1
1
1
0
0
0
0
0
0
0
1
1
1
8
Leq
WorkAp
Z
Day
Evening
16 h
57.3
57.3
52.7
50.3
55.0
69.1
69.1
70.1
68.4
68.9
64.5
62.1
61.8
56.6
62.8
61.6
54.1
62.4
57.6
57.0
60.0
60.0
60.4
66.1
66.0
62.4
4
54.4
54.4
44.4
46.0
52.1
64.5
64.5
63.1
67.3
68.4
61.8
61.9
57.1
53.6
60.7
59.2
53.3
59.6
56.4
47.4
56.6
56.6
59.0
61.6
65.1
63.0
5
Notes: All the data (rounded to one decimal place) come from Brooker et al. [7]. By column:
By column:
2 – Site locations shown in Fig. 5.1 of Ref. [7].
3 – %VMA is the percentage stating they are ‘very much annoyed’ by aircraft noise (Table C2).
4, 5 – Day and evening dB(A) Leqs for week prior to survey (Table C2).
6 – dB(A) Leq16(1 W), calculated from columns 4 and 5.
7 – Percentage of respondents working at or having business with the airport (Table C2).
8 – 1 If westerly operations are the dominant ‘alternation benefiting’ Heathrow mode (Fig. 5.1 in Ref. [7] et seq).
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In the planning of ANIS, runway alternation effects were seen as
second-order contributions to disturbance reactions. The main
aims of the study were to disentangle the effects of changes in
the noise level and number of aircraft heard, and to get some statistical confidence that the variation with the number of aircraft
was not markedly stronger than implicit in dB(A) Leq16h(1 W).
Even to do these two tasks required careful design of the location
of survey sites around the airports, with large samples of people
to establish statistically stable estimates of annoyance, coupled
with a large noise measurement and analysis programme.
Heathrow’s alternation procedures are not typical of most multi-runway airports. Most of the large ones use mixed mode because
of its higher runway capacity compared with segregated mode. DfT
funded several university-based studies on airport noise indices
both before and after the ANIS work. The literature searches associated with these studies did not produce research outputs dealing
with Heathrow-like airports, i.e. similar kind of planned alternation operations affecting large populations. A later, smaller-scale,
UK study – by different authors – broadly confirmed its results,
but did not generate new understandings about alternation effects
[9]. The only large-scale study, a cut-down version of ANIS coupled
with an economic assessment, did not produce reliable results [10]
– but, in any case, its specification did not include any examinations of alternation effects.
What avenue is open for discovering something useful about
alternation? The only remaining possibility therefore seems to be
a further examination of the ANIS data, the underpinning for the
current policy, in an attempt to ‘force out’ all its potential information. The following sequence of statistical facts and inductive reasoning is offered:
(i) An ANIS site’s dB(A) Leq values are largely determined by
the noisiest mode of runway operation, if this occurs reasonably frequently.
(ii) The study design of ANIS focused on the dominant ‘aircraft
noise energy mode’ at each site (inter alia this helped ensure
measurement accuracy), in particular for westerly
operations.
(iii) Taking (i) and (ii) together, there are ANIS sites whose dB(A)
Leq values were dominated by the contribution of their Heathrow westerly (alternating) mode of operation.
(iv) People’s annoyance is highly correlated with the dB(A) Leq
they experienced in the recent past – e.g. the previous week.
(v) Public inquiry evidence and discussions with residents who
gain from the effects of Heathrow runway alternation indicate that it is viewed by them as very beneficial.
(vi) If (v) is meaningful, the hypothesis is that runway alternation effects on annoyance are ‘large’, so the expectation is
that this will be apparent in people’s annoyance ratings for
benefiting sites.
(vii) If (iii)–(vi) are valid, runway alternation effects will be
detectable in the annoyance ratings versus 1-week dB(A)
Leq at those Heathrow sites dominated by westerly
operations.
(viii) Therefore, it is possible to derive some kind of Heathrow
alternation decibel correction for such sites.
Variants of this reasoning have been examined, but either do
not generate testable hypotheses or require data that is not available. For example, as indicated above, the study design did not control statistically for alternation patterns in the period prior to the
survey: alternation therefore has to be measured simply rather
than by trying to construct variables from recent swapover patterns. The steps are not deductively watertight (note words such
as ‘largely’, ‘focused’, ‘correlated’ and ‘hypothesis’) – they suggest
that this would be a useful approach. Thus, the conclusion is that
3
there will be different relationships with dB(A) Leq16h(1 W) for
the westerly ‘Alternation benefiting’ Heathrow dataset and the rest
of the ANIS dataset (‘Other’ sites).
Table 1 summarises the ANIS dataset. The first two columns
show the reference number and name of the site. The third column
presents the ‘very much annoyed’ percentages – %VMA. Columns 4
and 5 are the day and evening dB(A) Leq16h(1 W) values for the
week before the social survey, with column 6 being the calculated
dB(A) Leq16h(1 W) for the DfT 16-h period, i.e. combining the previous two columns. Column 7 is the percentage of respondents
working at or having business with the airport – WorkAp.
Table 1’s final column indicates if the site benefits from westerly alternation, with a zero if it does not and one if it does. The
assessment of which sites do benefit uses some cautious general
principles, rather than trying to invent numerical criteria. The
aim is to identify sites where an alternation effect would very
probably exist (this is a cautious approach, i.e. will tend to underestimate the effects of alternation):
Obviously, the non-Heathrow sites are all at 0.
Heathrow sites under or near to easterly departure routes, but
not close to westerly routes, will also be at 0.
Heathrow sites under or near to westerly departure routes, but
not close to easterly routes, will be at 1.
But some sites have more complex noise climates. For people’s
annoyance responses to benefit from westerly alternation, i.e. to
get a ‘1’ score, two tests are proposed, on the basis that the reasoning above is correct. First, the westerly noise climate must dominate the easterly one. Thus, the criteria for ‘dominate’ must
include relative dB(A) Leq16h(1 W) values for the different operational modes. Second, alternation must actually benefit the site
location in terms of the noise exposure experienced. The key question is whether the noise climates before and after the westerly
mode ‘swapover’ each day differ significantly. But this will not always be the case at a westerly-affected site. The obvious example
is a site located mid-way between the alternative westerly routeings. Thus, a site to the east of the airport and mid-way between
the two approach paths would not get any alternation noise benefit: there would be no swapover effect – a resident would continue
to hear the same kinds of noise levels, but coming from the south
rather than the north and vice versa.
The ANIS Report (Brooker et al. [7]) presents information on the
location of the sites used in the study and their noise climates. In
particular, Fig. 5.1 (p. 92) shows sites to the east of the airport
(note that the arrival routes are not displayed in the Figure: aircraft
fly straight in for at least the last 15 km to the runway, i.e. the
flightpath is the extended centreline). Two sites of interest are
Hounslow W(est) and Hounslow C(entral). These are about midway between the westerly approach paths, so the alternation impacts would not match the ideal pattern. Moreover, as Heathrow’s
runways are some 1400 m apart, there is substantial lateral attenuation of landing aircraft noise levels. These two sites are also affected by easterly operations, with a route going to the northeast (the easterly departure routes shown from the northern runway are rarely used). In addition, aircraft departure paths are in
practice spread about the nominal routeing shown, so aircraft
can be much closer to those survey locations than the displayed
routeing alone would suggest. The conclusion is that Hounslow
West and Hounslow Central should not be counted as benefiting
from runway alternation.
There are other sites with complex noise climates shown in figure. For example, Isleworth and Hounslow are exposed to both
westerly and easterly modes, with roughly comparable dB(A)
Leq16h(1 W) values. However, Fig. 5.1 in Ref. [7] shows that both
lie directly under a westerly approach path, and so gain significant
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Fig. 1. Raw ANIS data taken from Table 1 blue triangles – Alternation set, red circles
– Other sites separate quadratic fits. (For interpretation of the references to colour
in this figure legend, the reader is referred to the web version of this article.)
alternation benefits. For the purposes of the analysis here, it is assumed that they do benefit, i.e. both are allocated a ‘1’ score in column 8 of Table 1.
Fig. 1 shows a scatterplot of %VMA against dB(A) Leq16h(1 W).
The Figure also shows quadratic fits to the Alternation and Other
datasets. Polynomial fits using Ordinary Least Squares are often
used for this kind of gently increasing response data. However,
textbooks (e.g. Quinn and Keough [11]) document the problems
with polynomial fits, including multicollinearity of the independent variables and potential overfitting (i.e. too many parameters
relative to the amount of data, causing random variation in the
data to appear as a systematic effect). The simplest way of preventing this is to limit the degree of polynomial that which produces
the largest Adjusted-R2 statistic. This is both a good measure of
model goodness of fit and a useful stopping criterion to check
new variables when comparing model efficiencies. It is suitable
for the nested model comparisons examined here. Other sequential
F-tests and Akaike’s Information Criterion could also be used, with
generally similar results – e.g. see Quinn and Keough [11]. For the
Fig. 1 data, the calculations show that quadratic fits are better than
linear and cubic fits, with marked non-linearity at the left of the
data range.
The difference between the two sets is very marked over most
of the data range. It appears that the Alternation curve is simply
shifted to the right from the Other curve. From a policy perspective, the interest is in Leq values above about 57 dB(A) Leq16h(1 W).
In this region, the distance between the curves is roughly constant,
so it is worth examining this part of the response relationship in
more detail.
Fig. 2 uses the same data as Fig. 1, but datapoints in the markedly non-linear region below 55 dB(A) Leq16h(1 W) are omitted. All
the results that follow use this restricted dataset, i.e. close to or
above the policy threshold, and so conclusions about the response
curves are limited to the policy range of the Leq16h(1 W) values displayed. Figure shows the UK policy threshold as a vertical line.
There are two interpolation lines drawn for both the Alternation
and Other datasets: linear and quadratic fits. The lines are very
close, and in both cases the linear fit has the highest Adjusted-R2
statistic, which indicates that adding the second-power term does
not improve the fit. The shift in the Figure from the Other set to the
Alternation line is about 4 dB. Is this the true effect?
Fig. 2. Linear and quadratic fits to >55 dB(A) Leq16h(1 W) ANIS data taken from
Table 1 blue triangles – Alternation set, red circles – Other sites full and dashed/
dotted lines are linear and quadratic fits to the two datasets. (For interpretation of
the references to colour in this figure legend, the reader is referred to the web
version of this article.)
datasets. For Fig. 2, the Other sites have a mean WorkAp of about
5% but the mean of the Alternation sites is about 15%. This possible
effect needs to be tested statistically.
Social survey data are often analysed using regression techniques applicable to several different groups of interview subjects,
here the data from two kinds of sites. Analyses of this type of data
use Analysis of Covariance (ANCOVA). The main interest is to
examine to what extent the regression relationships differ between
the groups, by using appropriate ‘dummy’ variables to compare
regression fits from the different groups. A dummy variable takes
on a finite number of values such that each value represents a different group or category – 0 and 1 for two groups. These values
have no meaning numerically – they are group indicators.
To test hypotheses about the different data groups, the statistical model to be tested must describe relationships between a response variable y and the dose variable x, i.e. %VMA and dB(A)
Leq16h(1 W) here, for the two groups, indexed by the dummy variable Z. A general linear model is of the form:
yi ¼ b0 þ b1 xi þ b2 Z i þ b3 xi Z i þ b4 W i þ ei
The suffix i means the ith observation, i.e. the site number; y is
the %VMA. x is the dB(A) Leq16h(1 W) (chosen to align with the DfT
dB(A) Leq measure); Z is a dummy variable, 1 if the site is a Westerly alternation and 0 otherwise; W is a statistical confounding
factor, here the percentage of respondents who worked at, or had
business with, the airport – ‘WorkAp’; e is the error term. For ordinary least squares statistical testing of hypotheses, e should be
from a zero mean and constant variance Gaussian distribution.
The Gaussian approximation should be tenable over a wide range
of x-values, as %VMA is a binomially distributed ratio variable
and the typical sample size for an observation is about 80 people.
A binomial distribution also implies that the variance does not vary
too much over most of the range of x (for x markedly >0 here), i.e.
approximate homoscedasticity. This meets the conditions for Central Limit theorem convergence, and so statistical testing using
standard multiple regression methods would be appropriate.
This model provides linear regression models for the two
groups of sites by letting the dummy variable take the values 0
and I respectively, i.e.:
4. Statistical testing of ANIS data
A major concern is if the effects of one or more statistical confounding factors could have distorted the relationships in Fig. 2.
Respondents’ economic connection with the airport, WorkAp in
column 7 of Table 1, could have a major effect on the observed
scatterplot, as its values are markedly different between the two
Other: yi = b0 + b1xi + b4Wi + ei
Westerly: yi = (b0 + b2) + (b1 + b3)xi + b4Wi + ei
The hypothesis of coincidence, i.e. no difference between the
two datasets, is that both the slope parameters and the intercept
parameters agree for the two groups, i.e. it is that:
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Table 2
Parameter estimates from a linear (ANCOVA) model for %VMA: intercept and slope
dummies and WorkAp, >55 dB(A) Leq.
Variable
Estimate
Standard
error
t-Statistic
2-Tail
p-value
dB(A) Leq16h(1 W)
Z
Z dB(A) Leq16h(1 W)
WorkAp
Constant
Adjusted-R2
3.273
21.964
0.463
0.712
168.853
0.8591
0.394
38.343
0.604
0.178
23.959
8.30
0.57
0.77
4.01
7.05
0
0.5743
0.4542
0.0009
2.00E06
Table 3
Parameter estimates from a linear (ANCOVA) model for %VMA: intercept dummy and
WorkAp, >55 dB(A) Leq.
Variable
Estimate
Standard error
tStatistic
2-Tail p-value
dB(A) Leq16h(1 W)
Z
WorkAp
Constant
Adjusted-R2
3.071
7.311
0.682
156.697
0.8623
0.290
3.033
0.171
17.742
10.60
2.41
3.98
8.83
0
0.0268
0.0009
0
b2 ¼ b3 ¼ 0
This linear model is applied to the data in Table 1, using free
web software [12]. The statistical test results based on the regression are shown in Table 2. Although both dB(A) Leq16h(1 W) and
the WorkAp variables contribute to a good fit, neither dummy variable is statistically significant at anywhere near the 5% level, because of multicollinearity.
The model has more terms than needed to fit the data – it is
overspecified. Removing either of the Z-dependent variables
slightly increases the value of the Adjusted-R2 statistic. As the main
policy focus is in the effects of changes in dB(A) Leq16h(1 W), the
simplest way forward is to use just the intercept variable (i.e.
b2Z) term. The regression with just this single dummy is shown
in Table 3. For this variant, all the variables are significant at greater than the standard percent figure. Removing any of the variables
in this model reduces the value of the Adjusted-R2 statistic, so the
conclusion is that this is a good statistical fit to the data.
From Table 3, the shift between the fitted Alternation and Other
lines, controlling statistically for the WorkAp variable, is 2.4 dB, i.e.
7.31/3.07. At places that benefit from runway alternation, and with
the same percentage of people who do not work at, or have business with the airport, annoyance responses are about the same
as at the non-alternation locations which have dB(A) Leq16h(1 W)
values some 2-dB lower. This 2-dB effect is therefore an ‘alternation decibel correction’ (ADC).
5. Are the ADC analysis and estimations robust?
Several rational criticisms and questions could be posed about
this analysis and the robustness of the ADC. A selection is answered very briefly, in no particular order.
Are there obvious weaknesses in the statistical modelling? A variety of sensitivity questions and variant models were explored.
First, quadratic rather than linear forms were examined for the Tables 2 and 3 regressions, but did not perform better in Adjusted-R2
terms. Note that these tables represent interpolation fits. Second,
Q–Q plots of the Tables 2 and 3 regressions appear reasonable,
i.e. the error terms are approximately Gaussian. However, an
examination of the residual values showed some evidence of heteroscedasticity, consistent with the binomial distribution for the
smallest %VMA values. A variant model transforming %VMA by
the arcsine-square root transform was therefore used for variance
5
stabilization to convert the Tables 2 and 3 data to an approximate
homoscedastic Gaussian with constant variance [13]. The Z-variable measuring alternation was again statistically significant and
of a similar magnitude, the ADC being 2.6 dB (and with predicted
responses greater than zero). Third, the literature on Adjusted-R2
indicates that it is an adequate but ‘soft’ criterion, i.e. it tends to allow models with more variables than some other criteria, but the
cases examined here did not in fact lead to overfitting.
There could be better models: would logistic or probit analyses
improve on polynomial fitting, in particular as the predicted values
are then constrained to range between 0% and 100%? It is quite
possible that a more complex formulation might be better, but
the interpolation modelling here is conceptually simple and uses
textbook statistical testing methods and criteria. Logistic and probit models do not generate markedly improved fits to aircraft noise
annoyance data, e.g. see Brooker [14] for references to recent major
European and USA curve-fitting studies. The requirement that the
fitted function asymptotically approaches 0% and 100% is very
powerful – the concern is that it could affect the goodness of the
interpolation fit in the region of greatest policy interest. (As an
aside, why should a polynomial interpolate annoyance responses
– which are gently-increasing – reasonably well, given that it is
‘known’ with logistic fits that for high dB(A) Leq values the proportion of highly annoyed should be 100%? One possible answer is
‘population sorting’. For example, sensitivity differences mean that
people who would be highly annoyed tend to move away from the
highest noise exposure locations. This would tend to reduce the
proportion of high annoyance responses at higher Leq values, so
that an ‘underlying’ logistic curve might flatten out at those
values.)
Is %VMA an appropriate measure for people’s annoyance? Brooker
[15] discusses the use of %VMA and (e.g.) ‘average’ annoyance,
showing that %VMA has good statistical properties. FAA [16] is a
recent discussion of %VMA and other measures.
The analysis uses old data. The same data supports the existing
DfT policy. There are no more recent good quality, large-sample
datasets available, but where UK data was been gathered in ways
comparable with ANIS, it showed broad consistency with the ANIS
dB(A) Leq16h(1 W) relationships [9]. ANIS data is in line with
worldwide study results, given typical traffic growth conditions
near to airports using a particular runway configuration (e.g. see
[17], which emphasizes the large differences arising from different
study methodologies). There may be some upward changes in
annoyance versus dB(A) Leq16h(1 W) (or related indices) over the
last 25 years, but these are statistically weak (e.g. Brooker [17].
There is no obvious reason why the nature of people’s responses
to time-pattern effects such as alternation should change over
time.
Some of the sites in the study are not at Heathrow. The aim of the
study was to find good evidence for a UK index. ANIS did not find
airport-dependent effects, and the noise indices used internationally do not include airport-specific parameters.
Could other statistical confounding variables have marked effect on
the analyses? The only other socio-economic variable that appeared
in the step-wise regressions of Brooker et al. [7] was the percentage of non-manual workers in the community. When this variable
is added to the regression in Table 3, its coefficient is not statistically significant at even the 10% level and the Adjusted-R2 statistic
is reduced.
An ANCOVA using westerly/easterly sites as a dummy variable
also shows similarly statistically significant relationships – so
why focus on alternation? Statistically, an operational direction
(easterly/westerly) dummy variable would show similar effects
in an ANCOVA, simply because that variable happens to be highly
correlated with the Alternation dummy, given the large proportion
of Heathrow sites. However, westerly/easterly is no more than a
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P. Brooker / Applied Acoustics xxx (2010) xxx–xxx
label about the mode of operation: it is not a description of the characteristics of a noise climate. A runway alternation effect matches
the expressed perceptions of the people exposed to the aircraft
noise about the value of alternation – psychologists’ ‘face validity’.
Why does the analysis use 1-week dB(A) Leq16h(1 W) rather
than DfT’s standard of an average dB(A) Leq16h(3 M), over the summer 3 months? The aim in the ANIS regressions was to find the
best combination of physical noise parameters to match with people’s annoyance. That turned out to be the 1-week variant, i.e.
dB(A) Leq16h(1 W). The DfT’s 3-month version is intended to indicate the average annoyance over the summer, i.e. by averaging
the dB(A) Leq16h(3 M) values over the whole period. For policy reasons, DfT chose to exclude night-time contributions: this was not a
recommendation in the ANIS Report, but the values of dB(A)
Leq24h(3 M) and dB(A) Leq16h(3 M) are highly correlated.
Could there be differences between people’s annoyance reactions to arrivals and departures, given that they have different
noise characteristics? Such differences are possible, but these
parameters are not included in the current noise indices used in
other countries. Several other countries have large populated areas
near to airports subject to a great variety of numbers of arrivals
and departures, so such an effect might be detectable.
The modal split between westerly and easterly operations observed
in ANIS was atypical and this could have distorted responses. This is
certainly a possible effect, but the average modal split, both for
all sites and the Alternation/Other subsets, was about 60%, i.e. lower than the yearly average but minor in dB(A) Leq16h(1 W) terms.
Surely, the ‘true’ ADC is likely to be less simple than a fixed 2-dB
estimate? The effects of alternation on the noise climate will
diminish with the distance from the airport, because of increasing
flight path dispersion and greater aircraft height, so the ADC probably increases with overall dB(A) Leq16h(1 W). There is certainly a
possibility of more complex relationships, but note the difficulty
of distinguishing statistically between highly correlated dummy
variables.
Is it appropriate to ‘adjust’ real dB(A) Leq values? The aim is to get
the best annoyance contours. dB(A) Leq is a physical measure: it
has no special ‘reality’ in terms of annoyance: it is a valuable index
only to the extent that it matches annoyance empirically. It is
therefore legitimate to modify dB(A) Leq16h(3 M) to produce a
new kind of unit, if this would deliver a better match with annoyance. Adjusted versions of dB(A) Leq are already used in some
countries’ indices, with the aim of taking better account of the
annoyance/sleep effects of evening and night flights, e.g. the Ldn
and Lden indices, although the evidence for such weightings is
not statistically conclusive (e.g. see discussion in the FAA 2009 Forum [16]).
Is a 2-dB effect significant as regards government policy? There are
two simple comparisons. First, compare the 2-dB with the UK DfT
[2] policy statements, which refer to 3 dB being a ‘large increase in
noise’. Second, the Ldn and Lden indices noted above have differences of 2-dB or less (e.g. [18], Table A1 in particular).
What lessons are there for the design of future aircraft noise studies? The design of a study that, inter alia, would estimate alternation effects still appears to be methodologically very difficult –
and hence likely to be resource intensive. When a questionnaire
is administered, presumably at a specific time for each respondent,
what precise questions would be asked about the current, very recent and ‘average’ noise exposure? If these questions were asked
on different days in the same survey area then the recent noise patterns for respondents would be likely to be different. Should some
people be interviewed in the quiet period and others in the noisy
period? What would these answers be compared with – are there
similar sites for which people get the same dB(A) Leq16h(1 W) value but which have the noise events spread over the whole time
period?
6. Official UK noise contour implications
There is an obvious question about the implications of these results. Is the implication that official UK noise contours have been
understating the extent of aircraft noise annoyance? The answer is
that there are pluses and minuses in its effects. The problem identified here is that the benefits of runway alternation at Heathrow
detected in the ANIS data are not available at other airports. Overall, the noise contours present a reasonable picture of the total aircraft noise annoyance in the UK, because ANIS’s design had a
roughly representative spread of affected populations. However,
the implications of the ADC estimate are potentially significant
for UK noise contours at individual airports. These contours should
show comparable lines of equal annoyance around each airport,
but without the ADC they do not. A correction of 2-dB would have
a major impact on contour areas and location.
The starting point for the simplest changes to improve the match
of dB(A) Leq16h(3 M)-based contours to people’s annoyance, fall in
to two classes. Except at Heathrow, airport contours are currently
calculated correctly in terms of relative annoyance. dB(A)
Leq16h(3 M) remains an appropriate annoyance measure, i.e. requires no adjustment. However, as the responses for Other sites
in Table 3 is 1 dB higher than the average annoyance responses
for all the data, non-Heathrow airport ‘onset’ contours should at
56 dB(A) Leq16h(3 M) rather than 57 dB(A) Leq16h(3 M), and similarly for higher value contours. Heathrow calculations for easterly
contributions are calculated correctly, but the dB(A) Leq16h(3 M)
contributions for westerly modes should be reduced by 2-dB. A
modified annoyance measure – call it dB(A) Leq16h(3 M)* – would
add easterly mode contributions of dB(A) Leq16h(3 M) to westerly
mode contributions of (dB(A) Leq16h(3 M) – 2). Again, for consistency, the published onset contours should be at 56 dB(A)
Leq16h(3 M).
However, the answer has to be more complex. The problems are
those sketched in Section 3. To get the full westerly alternation
annoyance benefit at a particular place, it appears necessary both
for the westerly mode to have the dominant effect on the noise climate there and for alternation to benefit that location in noise
terms. The former means that the contouring output would need
to be examined to ensure that the 2-dB ADC had been included
only where the westerly mode was fully dominant. The latter
means that places where the alternation swapover has little effect
should not get any ADC adjustment. Both of these therefore generate an interpolation problem, as the contouring estimation moves
from locations requiring a full ADC to those not requiring it.
Improved Heathrow annoyance contours would therefore require four ingredients: good estimate of ADC; criterion for westerly
mode to be the dominant noise contributor; criterion for the alternation swapover to be significant, i.e. a full ADC value; and interpolation model from locations with full ADC to non-ADC locations.
None of these is insuperable, but they would require further detailed work to set the various criteria, and subsequently would
add to the complexity of contour computations. One process would
be to compute dB(A) Leq16h(3 M) estimates at grid points for each
of the airport operating modes; to adjust dB(A) Leq16h(3 M) values
at grid points where the dominance and alternation significance
criteria are applicable, and then to use a geostatistical technique
such as kriging to interpolate consistently between the ‘patchwork’
of computed grid points.
7. Conclusions
Empirical UK evidence is presented of additional complexities
in people’s real-life annoyance at aircraft noise, arising from its
time-patterns. For the UK noise policy region of about 57 dB(A)
Please cite this article in press as: Brooker P. Aircraft noise annoyance estimation: UK time-pattern effects. Appl Acoust (2010), doi:10.1016/
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P. Brooker / Applied Acoustics xxx (2010) xxx–xxx
Leq16h and above, statistical reanalysis of past social survey work of
people’s annoyance reactions indicates a benefit from Heathrow’s
regular and predictable alternation cycles on westerly operations.
The robustness of these analyses and inferences is discussed.
Annoyance response data for people living at places dominated
by westerly runway alternation, compared with ‘normal’ operational modes, show a statistically significant shift – a Heathrow
alternation correction (ADC) – of about 2-dB. The ADC takes account of the significant statistical confounding factor: ‘the proportion of people who do not work at, or have business with the
airport’.
The implications are potentially significant for UK noise contours at individual airports. These contours should show comparable lines of equal annoyance around each airport, but do not take
into account ADC effects. Improved Heathrow contours would require: good estimate of ADC; criterion for westerly mode to be
the dominant noise contributor; criterion for a significant alternation swap over; and interpolation model from locations with full
ADC to non-ADC locations.
Acknowledgements
I would very much like to thank former Civil Aviation Authority
colleagues for commenting on draft versions of this paper, and the
journal’s reviewers for making some interesting comments and
posing key questions for me to answer.
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j.apacoust.2010.01.010