Activity interference and noise annoyance

Journal of Sound and Vibration (1985) 103(2), 237-252

ACTIVITY INTERFERENCE F. L. HALL,

S.

M.

AND NOISE ANNOYANCE

TAYLOR

AND

S. E. BIRNIE

Department of Geography, McMaster University, Hamilton, Ontario, Canada L8S 4K 1 (Received 19 June 1984, and in revised form 27 November 1984)

+

Debate continues over differences in the dose-response functions used to predict the annoyance at different sources of transportation noise. This debate reflects the lack of an accepted model of noise annoyance in residential communities. In this paper a model is proposed which is focussed on activity interference as a central component mediating the relationship between noise exposure and annoyance. This model represents a departure from earlier models in two important respects. First, single event noise levels (e.g., maximum levels, sound exposure level) constitute the noise exposure variables in place of long-term energy equivalent measures (e.g., 24-hour L,, or Ldn). Second, the relationships within the model are expressed as probabilistic rather than deterministic equations. The model has been tested by using acoustical and social survey data collected at 57 sites in the Toronto region exposed to aircraft, road traffic or train noise. Logit analysis was used to estimate two sets of equations. The first predicts the probability of activity interference as a function of event noise level. Four types of interference are included: indoor speech, outdoor speech, difficulty getting to sleep and awakening. The second set predicts the probability of annoyance as a function of the combination of activity interferences. From the first set of equations, it was possible to estimate a function for indoor speech interference only. In this case, the maximum event level was the strongest predictor. The lack of significant results for the other types of interference is explained by the limitations of the data. The same function predicts indoor speech interference for all three sourcesroad, rail and aircraft noise. The results for the second set of equations show strong relationships between activity interference and the probability of annoyance. Again, the parameters of the logit equations are similar for the three sources. A trial application of the model predicts a higher probability of annoyance for aircraft than for road traffic situations with the same 24-hour L,,. This result suggests that the model may account for previously reported source differences in annoyance.

1. INTRODUCTION Recent studies of the dose-response relationship between transportation noise and annoyance consistently show differences in the relationship for different sources of noise (e.g. aircraft, road traffic and trains) [l-3]. The existence of these differences is seen as a problem: noise ought to have the same effect regardless of the source. Although some have suggested that the difference can be attributed to personal attributes toward different noise sources, there are good reasons to explore acoustical considerations more fully before turning to secondary factors. The starting point for this paper is the observation that the findings cited above have invariably come from studies in which measures of daily average sound levels (e.g., Ld,,, Leq(24hj, NEF) have been used. These averaging measures obscure the very real differences in the variation over time of different types of noise. As a result, noise environments which are very different when compared in terms of descriptors of the actual noisy events can in fact be nominally equivalent in terms of 24-hour average levels. This paper describes the development of an annoyance model based on single event noise descriptors. A trial application of the model is also 237 0022460X/85/220237

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Q I985 Academic Press Inc. (London) Limited

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reported to show that different probabilities of annoyance are predicted for two sources (aircraft and road traffic) with the same 24-hour L,, This conceptual model is based on the proposition, consistent with results of previous studies, that noise leads to annoyance through its interference with activities, particularly those involving speech communication and sleep. A logical implication of the model is that specific noisy events will be most closely associated with annoyance, and that therefore the noise levels from those events are important, rather than 24-hour average levels. Hence it is possible that a focus on event levels will be able to resolve some of the differences between studies which have been found when a 24-hour average level has been used. The full model and its ramifications are discussed in section 2. The analytical approach arises from the nature of the model. Because it is event-based, a probabilistic model of response is appropriate, rather than a deterministic one. That (or annoyance) is, the mathematical model expresses the probability of activity interference occurring at any given noise level. A logit function is used to calculate those probabilities. A probabilistic approach such as this has apparently been used only a few other times in noise-related work [e.g., 4, 51. In section 3, the logit approach and the reasons for its selection are outlined. Some readers may choose to omit this technical discussion and proceed directly to the empirical analysis beginning in section 4. The remaining sections of the paper describe the initial test of the model. The data acquisition and data reduction are briefly described in section 4. Noise and social survey data on activity interference and annoyance were collected at 57 sites in Southern Ontario exposed to aircraft, road traffic or train noise. The next section presents the estimation of the equations which represent the links between noise event levels and activity interference, and then between activity interference and annoyance. In section 6, these results are combined to estimate a path linking noise and annoyance. The final section contains the conclusions about the model with particular reference to the implications of the test results for explaining source differences in dose-response relationships.

2. A MODEL OF NOISE ANNOYANCE In 1970 Borsky [6] published a model of the community effects of noise in which a sequence of links leading from noise exposure to activity interference to annoyance and finally to behavioural reactions (e.g., complaint action) was proposed. A similar conceptualization of the noise-annoyance chain is found in the models suggested by Sorensen [7] and by Guski and Rohrmann [8]. In a previous paper [9], one of the present authors developed and tested a path model of aircraft noise annoyance in which activity interference variables, specifically speech interference and sleep disturbance, were the immediate antecedents of annoyance. The results of a path analysis confirmed the significance of their effects on annoyance. There is, therefore, a conceptual and empirical basis for a model of noise annoyance in which activity interference variables are included as the primary intervening factors. In the model proposed here this same structure is adopted, with noise leading to activity interference, which then leads to annoyance. We recognize that the origins of annoyance are not necessarily limited to the activity interference effects of noise. Fidel1 [lo], for example, suggested that annoyance may also arise because the very presence of a noise is disliked, regardless of its intensity and, therefore, the probability of activity interference. He concluded that an interference-based process and inherent aversion are likely both involved in annoyance reactions and that the two mechanisms are probably not separable in quantitative terms. In our modelling approach we choose to focus on activity interference effects to determine the extent to which they alone can account for source differences in annoyance.

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Four types of activity interference have been most often included in community response studies, and are therefore used in our model. Two deal with speech interference, outdoors and indoors; and two are related to sleep, difficulty getting to sleep, and awakening. Hence the model is as shown in Figure 1: depending on what an individual is doing at any particular time, noise can result in interference with any one of the four activities. Taken together, an individual’s experience of activity interference leads to annoyance.

Figure

1. Conceptual

model.

Elementary logic suggests that, for a model such as this, daily average noise levels are not appropriate. Activities occur as discrete events, at particular times and places. They are not interrupted by an average noise level; they can be interrupted by specific noisy events occurring at the same time. Thus a logical consequence of this model is a focus on noisy events, and the sound levels associated with those events. Unfortunately, the existing literature cannot take one much further than this elementary logic in identifying specific event levels to be used. For speech interference, most laboratory work has dealt with steady, not variable noise levels. An exception is the experimental study by Pearsons [ll] in which the effect of time-varying traffic noise on speech communication and annoyance was examined. He concluded that L,, is an adequate predictor of speech interference for traffic noise distributions for which L,,-L,, values range from 0.4 to 7.8 dB. Nevertheless the 1981 report by CHABA Working Group 83 [12], on “the effects of time varying noise on speech intelligibility indoors”, concluded that the Working Group (of which Pearsons was a member) could not recommend specific functions to relate levels of time varying noises to loss of intelligibility. For sleep interference, there are at least a few positive results. Horonjeff et al. [ 131 report reasonable success in predicting behavioural awakening with a time-integrated measure of detectability. While this is an unusual noise measure, it at least serves to relate noise to awakening, which most previous studies had been unable to do [14]. For trouble getting to sleep, there is very little previous work even addressing the topic. Thus, previous research on the noise to interference links in the proposed model provides little guidance on appropriate event noise measures. The discussion of the model so far implies causal relationships between noise, activity interference and annoyance. The claim of causality cannot be directly supported by statistical analysis of field survey data, since it is impossible to construct a controlled experiment of the kind which could demonstrate causality clearly. The causal logic can, however, be tested in the negative [9]. If there is no association among the components of the model, then the claim for causal links is greatly weakened. If statistical associations do exist, the causal links are not proven. In this case, however, the model can be regarded as normative [ 151: that is, as describing what interference and annoyance ought to occur in response to specific noise events. The utility of a normative model lies in the logical framework it provides for consistent assessments of noise impacts. Even as a normative model, the one shown in Figure 1 is clearly only a partial model. It is focussed on acoustical variables to the exclusion of personal and situational variables

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which a number of other studies have found to be important, and any effect of the noise other than activity interference is omitted. There are two main reasons for the acoustical focus. The first reason stems from practical considerations. Noise control involves the reduction of noise at source or by means of barriers. Evaluating the effectiveness of these often very costly programs depends on being able to calculate the community benefit (e.g., annoyance reduction). Such calculations require equations in which acoustical measures are the predictors of community response. The second reason is essentially an hypothesis following from the arguments given earlier that 24-hour average levels do not represent the noise environment as it is experienced. This implies that in previous studies the effect of acoustical variables may have been seriously underestimated relative to the effects of personal and situational characteristics. Analysis of the noise-annoyance relationship with use of other acoustical measures (such as those based on noisy events) may lead to a revised and more accurate estimate of the importance of noise. In suggesting this, we are, however, well aware of the sentiment that too many years of acoustical research were wasted trying to develop the perfect noise measure: that the profession would be further ahead now if one measure had been selected and used consistently. We are not attempting to develop new acoustical indices. Rather, we are making use of existing ones in a different way, to try to provide a further explanation of the effects of noise. The event levels which will be considered in the analysis are all commonly used measures, such as the maximum A-weighted level, and the sound exposure level ( LAE). Given, then, the focus of the model on specific noise events and the activity interference resulting from them, what is the appropriate mathematical structure? Earlier empirical work on dose-response functions shows that a probabilistic approach is more appropriate than a deterministic one. Consider the case of speech interference. As the noise levels increase, the probability of error rises, or the percentage of syllables correctly identified decreases. There is not a discontinuous break at any particular sound level, below which all the signal is intelligible and above which none is. A probabilistic approach has an added advantage for modelling the noise-annoyance link: the analytical model which results can be sensibly applied to either individuals or groups. For individuals, the results represent probabilities of annoyance. For groups, the probability is interpretable as the percentage (expected to be) annoyed, or highly annoyed. Thus a probabilistic analytical model can complement existing studies of aggregate response, and can also provide a disaggregate or individual level interpretation of noise effects. The model can be expressed symbolically as the following set of equations: probability

(interference,

occurs) = hj( L) ;

(1)

herej indicates one of the four interference types, and L is some noise level (e.g., maximum level) caused by a specific event, as measured indoors or outdoors as appropriate for the interference;

P (annoyance at the noise source) =f, ({P interferencej occurs}, nd, n,) ;

(2)

here { } indicates the set of interferences and nd and n, are the numbers of day-time and night-time events respectively; if the results of these two equations are combined, then P (annoyance of

P

(annoyance

at source)

=f,({hj(L)},

nd,

n,),

at source) =f2( L, n& n,).

(3)

The first equation simply expresses the probability of interference occurring as a function of the event noise level. The second equation predicts annoyance at the noise source as

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a function of the set of four probabilities of particular interference occurrences, plus number of events. The final equation expresses the fact that, as a consequence of the first two, it may be possible to work back through the two previous steps to arrive at a simple expression

for annoyance

at source

as a function

of event level and number

of events.

3. ESTIMATION PROCEDURES The selection of a procedure assumptions about the statistical

to estimate the equations just described depends upon form of the relationship between noise level as the dose and annoyance as the response. Ideally, one could appeal to a theory of noise annoyance as the basis for a decision. Unfortunately, a decision has to be made in the absence of theory. Some inferences can be drawn from psychophysical research. Previous doseresponse studies are also useful, specifically those in which the percentage (highly) annoyed has been modelled, because, as already stated, this is the aggregate expression of the individual level probabilities contained in our model. A major focus in the psychophysics literature has been the measurement of sensory, including auditory, thresholds [ 161. The statistical relationship between, for example, the percentage of words correctly heard and the intensity of the speech stimulus (in dB) can be described by an S-shaped curve similar to the ogive of the cumulative normal probability distribution. The extension of this kind of finding to the analysis of annoyance lies in a common concern with the definition of threshold functions. Stevens has made the connection by suggesting that the purpose of a community noise survey might be to determine “at what noise level, on the average, we cross the threshold from noisy to unacceptable” [16, p. 1751 (or equally, from not annoying to annoying). The identification of an ogive function in the psychophysical analysis of auditory thresholds therefore provides one possible functional form for estimation of interference and annoyance probabilities. The evidence from dose-response models of the percentage (highly) annoyed also supports a non-linear function. Schultz [17] described the average dose-response curve for percent highly annoyed by a cubic regression function which he argued may be the outcome of a combination of arousal and loudness power law functions. The support for this relatively complex curve in which the rates of change differ for different noise level ranges is, however, weakened by the absence of many data points above 80% highly annoyed. It is quite plausible that the addition of these data points would support a simpler ogive function. Some analysts have estimated linear equations even when scatterplots of the data strongly suggested non-linearities. Fields and Walker [l], for example, deleted a number of data points at zero percent affected prior to performing a regression analysis “over the range of noise levels exhibiting a linear noise/annoyance relationship”. Estimation of a non-linear function in this case could have accommodated the full range of data points. The scatterplots they reported again suggest the appropriateness of an ogive. Clearly, then, some functional form which yields an ogive is preferable. Three commonly used distributions which yield such functions are the cumulative normal (probit model), the Cauchy (arctan model) and the logistic distribution (logit model) [18, p. 561. All three give very similar results, but the logit model has very clear computational advantages over the other two [ 16, p. 581, and so it is the only one discussed further here. The general form of the logit model is P(y) = e@“/( 1 +ea’“), where P(y) is the probability of an outcome of the dependent variable, x is the value(s) of the independent variable(s), and B is the logit function coefficient(s), possibly including a constant term. Derivation of this equation for noise response has been described by

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HALL,

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M.

TAYLOR

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BIRNIE

McCafferty [19]: The equation defines a logistic curve describing the change in the outcome probability as a function of changes in the independent variables. The resulting curve is asymptotic to both P = 0 and P = 1 and therefore permits the inclusion of all observed values of the dependent variable for estimation of the p values. To understand the behaviour of the logit model more fully, consider the following example. Assume that the dependent variable is the probability of being highly annoyed by road traffic noise and the only independent variable is the 24-hour L,,(in other words, the sort of dose-response function which has been commonly reported in the literature). The model is then P(HA)=[exp(a+bL)]/[l+exp(a+bL)], where P(HA) is the probability of being highly annoyed, a is a constant, b is the coefficient the terms in the logit equation the “logit”, on L,and L is the 24-hour L,,. By rearranging or log of the odds expression (which is more easily interpreted for certain key values) can be defined as ln[P/(l-P)]=a+bL. One value of particular interest is the noise level at which there is a 50% chance of high annoyance occurring. For that, In [P/( 1 - P)]= In (0*5/0*5) = In 1= 0, and thence a + bL = 0 implies L = -u/b.Thus,the level at which half of the population is disturbed is defined by the ratio of the only two coefficients of the model. The rate of change of probability with a change in L is dP/dL

=

b exp (a + bL)/[ 1 + exp (a + bL)]*,

which obviously changes with noise level, and also depends on both coefficients of the model. Two example curves are displayed in Figure 2, and certain key values for them are also shown. Both have been constrained to have the same value for L at P = 0.5, to emphasize the effect of different “slopes” of the curve.

0.8 -

P 0”

-

x O-6 i c?

Figure 2. Example logit functions, showing effect of varying the coefficient on Leq, for constant a/b ratio. Function 1: a =-25.0; b=0.37; P(HA) at L=50,0.00; P(HA) at L=SO, 0.99; L for P=0.5,67.6dB; dP/dL at P=O.5, O@)/dB. Function 2: a=-10.14; b=0.15; P(HA) at L=50, 0.07: P(HA) at L=80, 0.87: L for P = 0.5, 67.6 dB; dP/dL at P = 0.5, O.O4/dB.

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The usual approach to estimating coefficients for a logit model is maximum likelihood estimation. For the analysis reported in this paper, BMDPLR was used. Multivariate models are both plausible and practical for these analyses, and are utilized. In all of the analyses in this paper, a binary dependent variable is used, measuring the presence or absence of a particular property or characteristic (e.g., highly annoyed/not highly annoyed by noise). The independent variables must have interval properties. Conversion of nominal or ordinal variables to dummy interval variables allows the use of measures with lower order properties. Several of the subsequent analyses involve the use of dummy independent variables (e.g., the occurrence or non-occurrence of activity interference used to predict the probability of source annoyance). Comparison of predicted probabilities and observed outcomes determines the goodness of fit of the logit model. Coefficients estimated for each independent variable included in the equation indicate its contribution to the prediction of outcomes on the dependent variable. To summarize, then, we have introduced a new, detailed model of how annoyance at noise arises, based on the activity interference caused by specific noisy events. For logical reasons, this model is most appropriately probabilistic, rather than deterministic. A tractable probabilistic model can be constructed by using logit analysis which has the advantage of producing a sensible shape for the resulting curve, unlike a linear model. It has the added advantage of providing a multivariate procedure for assessing the relative contributions of each of a set of independent variables for predicting outcome probabilities of the dependent variable. 4. DATA ACQUISITION In order to test the model, noise levels for specific events are necessary, both indoors and outdoors, plus social survey data on specific types of activity interference. A limited set of appropriate data were collected during the summer of 1981, in Southern Ontario. A total of 406 interviews were obtained, and detailed indoor and outdoor noise measurements, were collected at 79 of the houses. This section describes the site selection procedures, the interview, and the noise measurements. 4.1. SITE SELECTION Data were obtained from 57 sites. Fourteen were under a flight path approaching Toronto International Airport, 14 were adjacent to a rail line, and 29 were beside a highway or a major arterial. All of the sites were composed solely of single family homes, in order to avoid problems with inter-residence noise. In addition, each site had fairly high noise levels outside. There were two reasons for this choice. First, a major objective of the data collection effort was to measure the acoustical insulation of the houses. To do this, noise measurements had to be taken simultaneously indoors and outdoors during a noise event. It was necessary for the event to be intrusive enough (i.e., louder than the ambient level inside) for measurements to be taken indoors. Second, since a major focus of the research was activity interference, noise levels indoors had to be high enough for such interference to be possible. Site selection was influenced to a great extent by practical issues. The number of potential rail sites was limited because of the prevalence of duplex and other multi-family dwelling types along the rail lines. The sites used are located in Hamilton, Mississauga, Etobicoke, North York and Markham. They cover a wide range of traffic levels, and are affected by various types of movements (freight, passenger, commuter). The road traffic sites are all adjacent (front or back facing) to either an expressway or a heavily travelled arterial road. The aircraft sites are all located at the south end of runway 15-33 at Toronto

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International Airport. The reasons for selecting all of the aircraft sites in one area were twofold. First, scheduling noise measurements to coincide with the rotation of runway use was difficult. The selected area contained the largest concentration of population at fairly high noise levels around the airport, and these sites were the closest to many of the road and rail sites. These factors made scheduling easier. Second, during July and August (after completion of our interviews in that area) the airport was undergoing an extensive runway maintenance program. At that time another runway was closed, which resulted in use of runway 15-33 almost continually. This presented an opportunity to schedule noise measurements without having to worry about a rotation in runway use during the measurements. 4.2.

INTERVIEW

PROCEDURES

A total of 406 interviews were conducted at the 57 sites. Of those, the majority (225) were at road sites, with the remainder split between rail (90) and air (91) locations. The interview was designed to provide information which would allow the investigation of the links between noise level, interference, and annoyance. Therefore, the focus of the interview was on how the respondent’s activities were interrupted by noise. In order to obtain this information a “scenario” approach was used. The interviewer asked if the noise ever interfered with things that the respondent was doing in or around the house. For each interference volunteered, he/she was asked to describe the situation in terms of time, place, and activity. Appropriate probes were used to aid recall of any interferences not initially volunteered. The number of scenarios reported ranged from 0 to 5. Two questions in the interview pertained to sleep interruption. The respondents were asked if they had experienced difficulty getting to sleep over the past three months because of noise. If the answer was yes, they were asked to indicate the frequency of occurrence. In the second question the respondents were asked whether or not they were awakened once asleep. Again frequency of occurrence was obtained, as the number of times per week or month and the number of times per night this happened. For each respondent an overall annoyance rating for the source was obtained by using a five-point ordinal (unnumbered) scale. The scale points were as follows: not at all annoying; slightly annoying; moderately annoying; very annoying; and extremely annoying. Questions concerning habits of the respondent (e.g., time spent at home) were asked since these habits could affect the noise levels actually experienced by the respondent. In order to analyze the survey data, the variable number of scenarios per respondent had to be summarized, to one set of information. This was accomplished by developing four scenario variables, based on the activity interferences of interest. These were speech indoors, speech outdoors, difficulty getting to sleep, and awakening. For the two sleep variables the occurrence and frequency information was transcribed directly from the questionnaire. For the speech variables the appropriate information had to be obtained from the scenarios. Any scenario involving conversation, telephone, watching television, listening to the radio, or entertaining was considered to be a speech activity. The information from the interview specified whether the interference took place indoors or outdoors, and these were kept separate. If the respondent gave at least one speech scenario then speech interference (either indoors or outdoors) was said to have occurred. If there was no scenario of a particular type, the occurrence variable was coded as “no”. 4.3.

NOISE

MEASUREMENTS

Simultaneous order to permit

indoor and outdoor measurements calculation of residential acoustical

during noisy events were required, in insulation. This, in turn, was needed

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for calculation of indoor levels for events other than the few which could be measured. In order to minimize the inconvenience to the residents, an attempt was made to limit the time at any one site to 45 minutes. Because of the time required per house, problems in scheduling measurements when residents were available, and poor weather during the summer, measurements were obtained at only 79 households. Of these, 44 were at highway or arterial locations, 20 under a flight path for Toronto International Airport, and 15 adjacent to a rail line. The noise measurements taken were intended to characterize the events that each respondent was exposed to: for example, an aircraft flyover or train passby. Indoor and outdoor measurements were taken simultaneously, for a limited sample of events at each location. For the aircraft sites, additional information was available from the FAA Integrated Noise Model (version 3). Therefore, for those sites, the worst and average events outdoors were characterized by using the INM (with a correction for open field VS.middle-of-the-yard location, and a half-second integration time), and the corresponding indoor levels were obtained by applying the indoor-outdoor reduction as measured in the field. For the rail sites, sampling of events indoors and outdoors simultaneously was used. For road locations, events were also sampled (for these two types of sites a one second integration time was used). However, since all of the road sites were at highway or heavily travelled arterial locations, distinct events were not expected to be identifiable. Therefore, 30 seconds of data were considered to be “an event”, thereby representing a time period similar to that for the air and rail sites (6 “events” were sampled for each road site). The indoor noise event levels obtained for all three sources were the maximum A-weighted level (l/2 s or 1 s Ley), the average event and worst case event Leq, the average event and worst case event sound exposure level, and five time-above measures (for indoor thresholds 45, 50, 55, 60 and 65 dB(A)). To complete the characterization of the noise environment two additional measures were used. The 24-hour L,, was obtained by using 24-hour monitoring for the road and rail locations, and the INM for the air sites. The number of events (daily average) was also used. For all of the air sites, this was 35 per day, with general aviation flights excluded. The rail traffic figures ranged from 6 to 109 movements per day. For the road locations the average annual daily traffic level (AADT) published by the Ministry of Transportation and Communications (Ontario) was used. The total vehicle count per day ranged from 25 500 to 162 000 for the locations used in the analysis. 5. ESTIMATION

OF THE

MODEL

Once the data had been collected and reduced, it became obvious that a single sample design would not permit testing of all aspects of the model. In particular, with one focus on the way the several types of activity interference combine to produce annoyance at a noise source, it was essential to include sites where most types of interference have a high probability of occurrence. This we did. However, such a sample will have quite high outdoor noise levels, and will be inappropriate for estimating equations for outdoor speech interference. For this and other reasons, the analysis reported here tests only some of the components of the model. The first task in testing the model, from both Figure 1 and equation (l), was the estimation of equations predicting the probability of interference as a function of noise level, for four types of activity interference. In this part of the analysis the data for the 79 respondents for whom detailed noise event exposure levels were available was used. Only one function yielded a statistically significant logit equation, that for indoor speech interference.

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The probable reasons for lack of success in estimating functions for the other three types of interference deserve mention. For outdoor speech interference, the reason has already been suggested. For indoor levels to be high enough to cause speech interference (e.g., maximum levels of 70 dB) the outdoor level would be extremely high (from 25 to 30 dB higher). As a result, outdoor speech interference was almost certain to occur, if the respondent were outdoors at home. This led to time at home and time outdoors being the only good predictors of outdoor speech interference, rather than any noise measure. This does not mean outdoor speech interference is not a function of event noise levels; it means only that these data cover too small a range of levels to be able to identify the effect. For both awakening and being kept from sleep, the reasons for failure to find functions are less obvious. One possible explanation may stem from the fact that all of the noise measurements were taken in the living room of the house, and none were in the respondent’s bedroom. Another explanation is that the noise measures we used are not appropriate. Horonjeff et al. [ 131 for example, suggested time-integrated detectability as a potentially valuable measure for predicting sleep effects. The complication introduced by individual variations in adaptation to noise intrusion of sleep may also be a factor. Details of the estimation of the equation for indoor speech interference are given in the Appendix. The equation itself is as follows: P (interference)

=

exp (-5.69 + 0.098 max level) 1+

exp (-5.69 + O-098 max level)’

The logistic curve described by the equation is shown in Figure 3. (No data points are included because the response data were O/l dichotomies (i.e., speech interference not I.0 aJ

:

I

I

I

I

1

0.9-

?

oa-

; al ;

c

0.7-

f

0.6-

:: g

0,5-

0 :

0,4-

0 0,32. C E 0.2:: c

O.l-

L mOr (dB(A)l

Indoors

Figure 3. Logit function for indoor speech interference.

reported/reported). It would be possible to calculate the proportions of respondents reporting speech interference for different noise intervals and show these as data points around the curve except that for these data the sample size is too small at any particular noise level for this to give a meaningful result.) The function allows one to calculate the maximum indoor level at which the probability of interference exceeds 0.5, namely 58 dB. The slope of the logistic curve at P = 0.5 is 0.0245: that is, for every 1 dB increase in the indoor maximum level the probability of

ACTIVITY INTERFERENCE

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AND NOISE ANNOYANCE

speech interference increases by O-0245. The slope is therefore quite shallow, indicating that the probability of interference is not very sensitive to changes in the maximum level. The second task in testing the model was to estimate an equation to predict source annoyance as a function of the set of four activity interferences, as shown in Figure 1 and equation (2). Data for all 406 respondents could be used because noise measures do not enter the analysis. The dependent variable was dichotomized as highly annoyed/not highly annoyed at the source. The independent variables were also dichotomies representing reported occurrence/non-occurrence of each activity interference and the frequency of awakening. Equations were estimated for each of three sources and for all sources combined to determine whether or not the interference components of annoyance differ by source (see the Appendix for a detailed description of the analysis). TABLE I Logic analysis of source annoyance

Independent variables

All cases

Air

Rail

Road

Occurrence Speech indoors Speech outdoors Getting to sleep Awakening

0.83*** 1.31*** 1.30*** 0.71**

1.20* 1.31*

1.90*** 1.22* 2.01*** -

1 .oo** 2.05*** 1.30*** 1.23**

Frequency Awakening/month Awakening/night

0.70* 1.23*

-

-

-

-

Constant

_2.31***

-0.949***

-3.23***

-2.68***

% Prediction Rho square

73 0.26

73 0.15

76 0.32

82 0.37

1.17* -

*** Significant at 0.001; ** Significant at 0.01; * Significant at 0.05.

The equations (see Table 1) show some variation by source but the evidence is not conclusive that the composition of annoyance is source dependent. As a result, a combined source equation was used for the application of the model described in the next section. The coefficients for all equations are positive confirming that the probability of annoyance is greater if activity interference occurs and, in the case of awakening, if it occurs more frequently. 6. APPLICATION

OF THE

MODEL

In this section, the model is used to predict annoyance at aircraft and road traffic situations which have the same 24 hour Leg The objective of this application is to determine whether or not the model predicts different probabilities of annoyance for the two situations. Specifically, one wants to determine whether or not the predicted probabilities are consistent with previous findings [2], which showed for the same 24-hour L,, higher annoyance for aircraft noise than for road traffic noise. Application of the model requires seven probability functions: four to predict the probability of each of the four types of activity interference as a function of noise level, two to predict the frequency of awakening (per month and per night) and one to predict source annoyance as a function of combined interferences. Only two of these could

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be estimated in the analyses previously described: the probability of indoor speech interference and the probability of source annoyance. A function for outdoor speech interference was estimated from the indoor function. In doing this, it was assumed that people are 5 dB more “tolerant” of outdoor noise as a result of expecting and adapting to a noisier outdoor environment. The noise measure used in the equation was the maximum outdoor level. A function for awakening was estimated from the data reported by Horonjeff et al. [13]. The equation predicts the probability of awakening as a function of LAE This calculation required a double estimation from the data of Horonjeff et al. (see reference [20]) with the result that the function must be regarded as uncertain. Previous studies provide no basis for estimating functions for difficulty getting to sleep, and for frequency of awakening. Given the inability to obtain functions from our own data, we were therefore forced to omit these variables from this application of the model. As a result, it was necessary to re-estimate the function for source annoyance including as independent variables only those three interferences which could be predicted from noise levels. These procedures led to the four equations shown in Table 2. An example of calculations with these equations appears in Table 3. In order to have good event-level data, the TABLET

Logit functions for application of model

The model to be applied here consists of 4 equations. All four equations involve calculation of probabilities by using a logistic function, of the form P = e//(1 + e’), where f is a function (referred to as a “logit”) as follows: (1) for probability of indoor speech interference, P,, f, = -5.69 + 0.098 (maximum indoor A-weighted level) (2) for probability

of outdoor speech interferences,

Pz,

fi = -6.19 + 0,098 (maximum outdoor A-weighted level) (3) for probability

of awakening,

P3,

f3 = -13.0+0*2 (4) for probability

of annoyance,

(indoor average event LAE)

P4,

f,=-2.145+0.977P,+1*353P,+1.585P,

TABLET

Event levels and probabilities for road trajic and aircraft situations with 24-hour L,, of 63 dB(A)

Variables Indoor maximum level (dB(A)) P, (speech interference) Outdoor maximum level (dB(A)) Pz (speech interference) Indoor average L,, (dB(A)) PJ (awakening) P4 (annoyance)

Road traffic site 411 41.0

Aircraft site 132

-

64.2 0.64

0.16 75.0

99.2 0.97

0.76 60.5

58.2 0.20 0.34

0.29 0.56

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aircraft and road traffic sites were selected from the current data set. In general the 24-hour L,, at road sites was higher than at aircraft sites, so identical 24-hour L,, values could be obtained only for 63 dB(A). The probabilities of each of the three types of interference (Table 3) are higher at the aircraft site. Consequently, the probability of high annoyance is also higher for the aircraft site, O-56 compared with 0.34 at the road traffic site. These probabilities can also be interpreted as the fraction of people who will be affected, and, in particular, the percent highly annoyed. When these predictions are compared with the dose-response functions previously estimated by using a different data set [2], there is good consistency in that both sets of results show a difference of roughly 20% Leqc24hj of 63 dB(A). The only discrepancy is the general level of annoyance, which is about 20% lower in the previous study than that predicted from the present data. The second important result from this application is that the model also shows where the differences in annoyance responses originate, and thereby provides a better basis for noise control assessment. This model permits the evaluation of certain types of noise control measures, such as reduction of certain types of flights, or insulation of residences, with more sensitivity than previous approaches provided. The results of this application provide support for the model. The evidence indicates that predicting annoyance from activity interferences, which in turn are a function of single event noise levels, can potentially account for the differences in dose-response relationships for different sources previously reported when the 24-hour L,, was used. Admittedly, this is a restricted, trial application. Two of the equations used to define the model, for outdoor speech interface and awakening, are quite speculative. Nevertheless, these results are an encouraging beginning and strongly support refinement of the model and more extensive testing, which is the focus of work in progress. We are aware that the model does not give a complete explanation for the differences: other avenues need to be explored as well. There is first of all the aversiveness of the noise, as suggested by Fidel1 [lo]. There is also a refinement to the noise effect model proposed recently by Gjestland [21]. He has suggested, on the basis of preliminary experimental tests, that the annoyance contribution of any single event is affected by the intensity and temporal proximity of previous events. The contribution of an event can be reduced or even eliminated if immediately preceded by an event of much greater intensity. The extent of this “masking” effect is potentially source-related due to source differences in time-histories (e.g., freeway traffic compared with infrequent aircraft movements).

7. CONCLUSIONS In this paper we have argued for a new approach to the modelling of noise annoyance which can potentially account for the source differences previously reported. We have proposed a model containing three basic components: single even? noise levels, activity interferences and annoyance. The links between them can be expressed mathematically as a set of probabilistic equations. Logit analysis provides an appropriate procedure for estimating these equations. Several positive results emerge from the analysis. First, the estimation of an equation to predict indoor speech interference as a function of maximum indoor noise level represents a major step forward. No function for predicting speech interference in the presence of time-varying noise had previously been found, and the most recent effort to find one had ended rather pessimistically [12]. The equations show that a maximum indoor level of 58 dB is the level at which 50% of the people report speech interference. We recognize that this conclusion is limited to noises behaving in a “somewhat consistent

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manner” [22]. This finding is an advance but needs to be checked against other information (e.g., implied communication distance for 95% speech intelligibility) before being applied in determining guidelines for acoustical insulation, for example. Second, there is the successful estimation of equations to predict annoyance as a function of combined interference effects. The strength of those results provides strong support for the overall model, and also helps to identify the relative importance of the several interference effects. Third, for the indoor speech equation there is no significant difference by source. This is an important result because it implies that for any specific event, all noise predicts speech interference in the same way, and A-weighted decibels are a reasonable basis for measurement. Finally, the results from the trial application of the model show that when the set of equations is combined, annoyance at road traffic and aircraft situations with the same 24-hour L_r is predicted to be different. This implies that the model provides a potential explanation for previously reported source differences in annoyance.

ACKNOWLEDGMENTS The assistance of Peter G. Groves and Janice E. Thomson in conducting the analysis is very gratefully acknowledged. Financial support for this work came from several sources. Data acquisition was funded by the Natural Sciences and Engineering Research Council of Canada (through strategic grant G0306). The analysis was funded primarily through the unsolicited proposals program of Supply and Services Canada, with the assistance of Transport Canada Energy Research Funds and the National Research Council of Canada.

REFERENCES 1. J. M. FIELDS and J. G. WALKER 1982 Journal ofSound and Vibration 81, 51-80. Comparing the relationships between noise level and annoyance in different surveys. the Acoustical 2. F. L. HALL, S. E. BIRNIE, S. M. TAYLOR and J. E. PALMER 1981 Journal Society of America 70, 1690-1698. A direct comparison of community response to road traffic noise and to aircraft noise. 76,1161-l 168. Community response to F. L. HALL 1984JournaZoftheAcousticaZSocietyofAmerica noise: is all noise the same? J. W. SARGENT, M. I. GIDMAN, M. A. HUMPHREYS and W. A. UTLEY 1980 Journal of Sound and Vibration 70, 557-572. The disturbance caused to school teachers by noise. D. N. M. STARKIE and D. M. JOHNSON 1975 The Economic Value of Peace and Quiet. Farnborough, Hants: Saxon House. P. N. BORSKY 1970 in Transportation Noises. (J. D. Chalupnik editor. Seattle: University of Washington Press, 219-277. The use of social surveys for measuring community responses to noise environments. 1981 The National Institute of Environmental Medicine, Stockholm, Working I. S. MRENSEN Paper. Annoyance due to noise exposure. 1981 Journal ofEnvironmental Policy 2, 183-212. Psychological 8. R. GUSKI and B. ROHRMANN aspects of environmental noise. 9. S. M. TAYLOR 1984 Journal of Sound and Vibration 96, 243-260. A path model of aircraft noise annoyance. 10. S. FIDELL 1984 in Noise and Society (D. M. Jones and A. J. Chapman editors). New York: John Wiley, 247-277. Community response to noise. 11. K. PEARSONS 1978 Noise Control Engineering 10, 108-119. The effect of time-varying traffic noise on speech communication and annoyance. 12. CHABA 1981 Committee on Hearing, Bioacoustics and Bio-Mechanics, Report of Working Group No. 83. The effects of time-varying noise on speech intelligibility indoors.

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and D. M. GREEN 1982 Journal ofSound 13. R. D. HORONJEFF,S. FIDELL, S. R. TEFFETELLER and Vibration 84, 327-336. Behavioural awakening as functions of duration and detectability of noise instructions in the home. 14. J. S. LUKAS 1977 USEPA Report No. 600/l-77-010. 15. 16. 17. 18. 19. 20.

21. 22.

Measures of noise level: their relative accuracy in predicting objective and subjective responses to noise during sleep. S. FIDELL 1979 American Society for Testing and Materials, Special Technical Publication 692. Speech interference and community annoyance. S. S. STEVENS 1975 Psychophysics. New York: John Wiley. T. J. SCHULTZ 1978 Journal of the Acoustical Society of Ametica 64,377-405. Synthesis of social surveys on noise annoyance. T. A. DOMENCICH and D. MCFADDEN 1975 Urban Travel Demand; a Behavioural Analysis. New York: American Elsevier. D. MCCAFFERTY1978 M.A. Thesis, Department of Geography, McMaster University, Hamilton, Ontario. Modelling the effect of non-acousticvariables on individual response to community noise. F. L. HALL, S. M. TAYLOR and S. E. BIRNIE 1983 Development of a Model to Predict the Eflects of Environmental Noise on Residential Communities. Report for supply and service Canada. T. GJESTLAND 1984 in Proceedings, FASE84. (J. Tro editor). Trondheim: ELAB, 339-342. A new strategy for assessing noise annoyance. D. L. JOHNSON 1984 Personal communication.

APPENDIX: A.l.

NOISE

AS A PREDICTOR

ESTIMATION OF LOGIT EQUATIONS OF

INDOOR

SPEECH

INTERFERENCE

This section describes the estimation of a logit model to predict the probability of indoor speech interference. Of the 79 respondents used in this part of the analysis, 35 (44%) reported speech interference indoors. This figure is made up of 12 (60%) of 20 at the aircraft sites, 8 (53%) of 15 at the rail sites and 15 (34%) of 44 at the road traffic sites. Discriminant analysis was used to screen the noise variables to identify the best predictor of interference to use in the logit analysis. Six variables emerged as significantly related to indoor speech interference: maximum level, worst event Leq, average event L,, worst among event L,, average event LAE, and time above 55 dB(A). Due to the covariation these six, only maximum level entered the discriminant function. Maximum level was therefore the noise variable used to estimate the logit function. Dummy variables for source were added to the discriminant function (with maximum level) to test the assumption that source specific functions are not necessary. The assumption was confirmed; neither dummy variable was significant. The results of the logit analysis provide the following equation (both coefficients are highly significant (p < 0.001)): P (interference)

=

exp (-5.69 1+ exp (-5.69

+ O-098 max level) +0*098 max level)’

The function correctly predicts interference for 68% of respondents. The goodness of fit is however quite low (rho square = O-173). A second analysis was performed including the number of events and an interaction term (maximum level X number of events). Neither variable was significant and their inclusion did not improve either the goodness of fit or the predictive power of the function. The same result was obtained when the log of the number of events was used. This is not surprising, since the number of events occurring at all sites is high enough to ensure that the respondents would experience some noisy events. Therefore, for these data, the relationship for predicting the occurrence of interference depends only on level. One further logit analysis was performed to determine whether the results obtained by using maximum level were superior to those achievable by using 24 hour L,, as the noise

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variable. The finding was conclusive: 24 hour L,, had no significant effect and in fact did not satisfy conditions for entry into the logit function. This evidence supports the logic of the argument for using single event descriptors as the noise variables in annoyance models. A.2. ACTIVITY

INTERFERENCE

AS A PREDICTOR

OF

SOURCE

ANNOYANCE

objective of the second part of the analysis was to estimate a logit equation to )predict annoyance at the source as a function of activity interference. Data from the full sample of 406 respondents were used for this purpose. The dependent variable was defined as a dichotomy distinguishing those highly annoyed and not highly annoyed at the noise source (i.e., aircraft, trains or road traffic). Of the 406 respondents, 177 (44%) reported being highly annoyed, which included 38 (42%) of 91 at the aircraft sites, 29 (32%) of 90 at the rail sites and 110 (49%) of 225 at the road sites. The independent variables were also dichotomies separating those reporting and not reporting each of the four types of activity interference (i.e., speech indoors, speech outdoors, difficulty getting to sleep, awakening). The reported frequencies of interference were also included as dichotomous independent variables. Four logit equations were estimated, one for all sources combined and one for each of the three separate sources (see Table 1). In the equation for combined sources, all four interference occurrence variables and both frequency variables are significant. All of the coefficients are positive confirming that the probability of being highly annoyed at the source is greater if activity interference occurs. The sizes of the coefficients show that the strongest predictors of annoyance are interference with outdoor speech and getting to sleep and the frequency of awakening per night. Seventy-three per cent of the cases are correctly classified (as highly annoyed/not highly annoyed) by the equation. Together with the rho square (O-26), this indicates a moderately good fit to the data. The equations for the separate sources show the strongest results in the road traffic case. All four interference occurrence variables again enter plus frequency of awakening (per month). Annoyance is correctly predicted for 82% of the respondents. Outdoor speech interference is the strongest predictor of annoyance. The results are slightly weaker in the rail case. Three variables enter the equation. Interference with indoor speech and getting to sleep are the most significant contributors to annoyance. The equation for aircraft is the weakest. The two sleep interference variables enter with awakening being the marginally stronger predictor. These differences suggest that at rail and aircraft sites high annoyance is most strongly related to the interference of indoor activity, especially sleep, whereas at the road traffic sites both indoor and outdoor activity interference contribute to the probability of respondents being highly annoyed. Nevertheless, the evidence here is not conclusive that the composition of annoyance differs significantly by source. Possible differences in the relationships between interference and annoyance were further investigated by re-estimating the equation for combined sources with inclusion of two dummy variables for the source. The road traffic dummy had a significant and positive coefficient. This finding indicates that factors other than activity interference result in a higher probability of annoyance at the road traffic sites. A plausible factor based on previous studies [ 81 is the relative absence of quiet periods at road sites compared with either rail or aircraft sites. The