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Aircraft noise annoyance estimation: UK time-pattern effects
Applied Acoustics 71 (2010) 661–667
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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 13 February 2010 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]. 0003-682X/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apacoust.2010.01.010
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
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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
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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:
P. Brooker / Applied Acoustics 71 (2010) 661–667 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
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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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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)
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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.
References [1] Godfrey V. Proof of evidence: the vital importance of runway alternation at Heathrow. Heathrow Association for the Control of Aircraft Noise (HACAN), HAC 60; 1997.. [2] DfT (Department for Transport). Adding capacity at Heathrow airport – public consultation; 2007.
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[3] HACAN. Adding capacity at Heathrow airport: response from HACAN; 2008. http://www.hacan.org.uk/resources/briefings/hacan.response.to.consultation. pdf>. [4] DfT. Decision on adding capacity at Heathrow airport; 2009.. [6] De Coensel B. Introducing the temporal aspect in environmental soundscape research. PhD dissertation. Dept. of Information Technology, Faculty of Engineering Sciences, Ghent University, Belgium; 2007. ISBN:978-90-8578133-2. . [7] Brooker P, Critchley JB, Monkman DJ, Richmond C. United Kingdom Aircraft Noise Index Study (ANIS): main report DR report 8402, for CAA on behalf of the Department of Transport, CAA, London; 1985. . [8] Miedema HME, Vos H. Demographic and attitudinal factors that modify annoyance from transportation noise. J Acoust Soc Am 1999;105(6):3336–44. [9] Ollerhead JB, Diamond I. Social surveys of night-time effects of aircraft noise. Noise and Man’ 93: Noise as a Public Health Problem. In: Sixth international congress, INRETS, Arcueil, France, vol.3; 1993. p. 373–6. [10] Brooker P. ANASE unreliable – owing to design-induced biases. Acoust Bull 2008(January/February):26–31. [11] Quinn GP, Keough MJ. Experimental design and data analysis for biologists. 1st ed. Cambridge University Press; 2002. [12] Wessa P. Multiple regression (v1.0.26) in free statistics software (v1.1.23-r4). Office for Research Development and Education; 2008. . [14] Brooker P. Finding a good aircraft noise annoyance curve. Acoust Bull 2008;33(4):36–40. July/August. [15] Brooker P. Measuring community annoyance from aircraft noise. Acta Acust United Acust 2009;95(3):573–7. [16] FAA (Federal Aviation Administration, Office of Environment and Energy). Summary report of the international forum on aircraft noise impacts research, Ottawa, Canada; August 27, 2009. [17] Brooker P. Do people react more strongly to aircraft noise today than in the past? Appl Acoust 2009;70(5):747–52. [18] Miedema HME, Oudshoorn CGM. Annoyance from transportation noise: relationships with exposure metrics DNL and DENL and their confidence intervals. Environ Health Perspect 2001;109(4):409–16.
Contents lists available at ScienceDirect
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 13 February 2010 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]. 0003-682X/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apacoust.2010.01.010
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
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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
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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:
P. Brooker / Applied Acoustics 71 (2010) 661–667 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
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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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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)
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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.
References [1] Godfrey V. Proof of evidence: the vital importance of runway alternation at Heathrow. Heathrow Association for the Control of Aircraft Noise (HACAN), HAC 60; 1997.
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[3] HACAN. Adding capacity at Heathrow airport: response from HACAN; 2008. http://www.hacan.org.uk/resources/briefings/hacan.response.to.consultation. pdf>. [4] DfT. Decision on adding capacity at Heathrow airport; 2009.