Do short-term temporal variations of noise exposure explain variance of noise annoyance?

Transcription

1 Do short-term temporal variations of noise exposure explain variance of noise annoyance? Mark BRINK 1 ; Reto PIEREN 2 ; Maria FORASTER 3 ; Danielle VIENNEAU 3 ; Ikenna EZE 3 ; Emmanuel SCHAFFNER 3, Harris HERITIER 3 ; Christian CAJOCHEN 4 ; Nicole PROBST-HENSCH 3 ; Martin ROOSLI 3, Jean-Marc WUNDERLI 2 1 Swiss Federal Office for the Environment, Bern, Switzerland 2 Empa Swiss Federal Laboratories for Materials Science and Technology, Duebendorf, Switzerland 3 Swiss Tropical and Public Health Institute, Basel, Switzerland 4 Centre for Chronobiology, Psychiatric Hospital of the University of Basel, Switzerland ABSTRACT It has been suggested that, in addition to energetic measures, the effects of noise can be better explained by considering the variation of noise level over time and the frequency distribution of event-related acoustic measures, e.g. the maximum sound pressure level. To this end, a new acoustic descriptor called Intermittency Ratio (IR) has been developed within the framework of the SiRENE study (Short and long-term Effects of Transportation Noise Exposure). The descriptor reflects the "eventfulness" of a noise exposure situation and may be used as an additional exposure metric alongside established ones like the Leq. In this paper, preliminary experiences with the metric are reported. It is explored whether IR is sufficiently decorrelated from the Leq to be used as a complementary measure, whether the intermittent characteristics of noise as expressed in the new metric correlates with the subjectively perceived intermittency of noise exposure at the homes of Swiss residents, and whether IR can potentially contribute to the explanation of noise annoyance and self-reported sleep disturbance in socio-acoustic surveys. Keywords: Noise exposure, Intermittent noise, Annoyance I-INCE Classification of Subjects Number(s): 62, INTRODUCTION Noise exposure from transport infrastructures is one of the most widespread sources of environmental stress and discomfort in daily life. Noise is an important public health factor, with recent burden of disease estimates ranking it a major environmental health risk besides air pollution in Europe (1). A recent review (2) summarized the current state of knowledge about auditory and non-auditory effects of noise. Typically, epidemiological studies on noise effects as well as many annoyance surveys consider noise exposure as equivalent continuous levels over longer time periods (predominantly by using Leq-based metrics like the Ldn, Lden, LNight or LDay). However, their explanatory power regarding annoyance or disturbance effects (especially sleep disturbance) is often limited. A relevant question is whether additional information on the temporal structure of noise, in particular, the presence of clearly noticeable single events (3) or calm periods in between (4) could increase explained variance of exposure-response models. Also, regarding reactions of sleepers to noise events in the night-time, probabilities of e.g. awakening reactions are more clearly correlated with acoustic characteristics of single noise events than with average measures (5, 6). It can be concluded that for health impact studies, which integrally evaluate the effects of noise during day and night, Leq-based exposure measures may not be sufficient to explain effects satisfactorily and should be complemented by 1 mark.brink@bafu.admin.ch 1

2 additional measures that account for the temporal structure of the noise exposure. However, it was so far unclear how this is best parametrized in a metric that is not (or at least not highly) correlated with the Leq, but takes into account the emergence or "prominence" of single noise events, regardless of the long-term average energetic level. As part of the currently ongoing SiRENE study on short and long term effects of transportation noise exposure in Switzerland, a noise exposure metric was developed that expresses that proportion of the acoustical energy contribution in the total dose which is created by individual "noise events" (7, 8). The metric intends to yield an integral description of the eventfulness (or: "intermittency") of noise exposure situations, taking into account both number and magnitude of noise events. The metric was thus called Intermittency Ratio (IR) and is defined in a way which allows it to be calculated relatively easily based on traffic data and geometry. One goal of the SiRENE study is to elucidate the effect of the intermittency of transportation noise situations (as expressed in the new metric) on annoyance responses as well as sleep disturbances (self-reported in the field (this paper) and objectively measured in the sleep laboratory (9)) and on cardiovascular morbidity and mortality (1). To this end, one part of SiRENE comprises a socio-acoustic survey carried out in entire Switzerland on annoyance reactions and other self-reported health and quality of life outcomes. To further validate the IR metric, the survey also included a rating scale that aimed at measuring the subjective perception of intermittency of noise. In this paper, preliminary survey results are reported. It is explored whether IR is sufficiently decorrelated from the Leq to be used as a complementary measure, whether the intermittent characteristics of noise as expressed in the new metric correlates with the subjectively perceived intermittency of noise exposure at the homes of Swiss residents, and whether IR can potentially contribute to the explanation of noise annoyance and self-reported sleep disturbance in socio-acoustic surveys. 2. METHODS 2.1 Basic principle of calculating the Intermittency Ratio IR (IR) Highly intermittent traffic noise consists of subsequent pass-bys of vehicles (cars, aircraft, trains...) which acoustically stand out from the background (noise) by a certain degree. We define such parts of the level-time course as "noise events". For an integral characterization of the "eventfulness" of an exposure situation over a longer period of time we introduce the event-based sound pressure level L eq,t,events, which accounts for all sound energy contributions that exceed a given threshold. This event-based sound pressure level L eq,t,events can now be compared to the overall (total) sound pressure level L eq,t,tot, i.e., the average level of all noise sources that acoustically account for a particular exposure situation. The Intermittency Ratio IR is defined as the ratio of the event-based sound energy to the overall sound energy: IR 1.1L eq,t,events 1.1L eq,t,tot 1 = 1.1(L eq,t,events L eq,t,tot ) 1 [%] A single event only contributes to L eq,t,events if its level exceeds a given threshold K: K L eq,t,tot + C [db] This threshold K is defined relative to the long-term average of the overall sound pressure level L eq,t,tot and an offset C. The latter is the only free parameter within the definition of IR and has presently been set based on numerical simulations of various traffic situations to C = 3 db. By definition, IR only takes values between and 1% (including % and 1%). An IR of higher than means that more than half of the total sound dose is caused by "distinct" pass-by events. In situations with only events that clearly emerge from background noise (e.g. a receiver point near a railway track), IR gets close to 1%. In contrast, constantly flowing traffic, e.g. from a distant highway, only yields small IR values. The IR metric can be calculated for any time period. In the present exercise, IR during day (IR 16h,Day ), IR at night (IR 8h,Night ), and IR over 24 h (IR 24h ) are used. We generally assume that IR is useful in characterizing the different forms of, particularly, road 2

3 traffic noise, from highly intermittent small city streets to highways that display an almost constant flow of traffic. The calculation principle of the metric is documented in (7). 2.2 Calculation of noise exposure and Intermittency Ratio IR for all residential buildings in Switzerland In the present study, transportation noise exposure assessment was carried out for the sources road, rail, and air traffic in entire Switzerland for the reference year 211. The noise exposure assessment in this study was carried out on a unprecedented level of detail (11). Noise exposure was calculated for up to 3 facade points per building facade and storey in 24 one-hour time slices. This allowed the calculation of any kind of Leq-based noise metric (such as Ldn, Lden, LDay, LNight etc.) for the most exposed or least exposed facade (or any other facade, for that matter). Hourly-level noise exposure assessment comprised the 1-hour-LAeq, the number of events, and of course, the Intermittency Ratio IR. The exposure assessment in the SiRENE project encompassed all Swiss buildings (1.8 Mio.) with a total of 54 Mio. facade points. This calculation provided the sampling frame for the survey, whose participants were randomly selected from the entire country based on noise exposure characteristics of their dwelling (see below). 2.3 Factorial design and sampling The factorial design employed for the sampling of dwelling units accounted for three sources (road, rail, air), two time periods (LAeq,16h,Day and LAeq,8h,Night), three categories of IR ( -33%, 33-66%, 66-1%), and ten LAeq,16h,Day resp. LAeq,8h,Night exposure categories (2.5 db steps, from the lowest (<45 db(a)) to the highest (>65 db(a)) category). This results in 3x2x3x1 (=18) factor levels (cells). For each cell, we randomly selected 1 dwelling units and corresponding individual (postal) addresses based on a joint recordset of building and census data provided by the Swiss Federal Statistical Office. As the sample was drawn from official register data, representativity of the survey sample for the Swiss population in each cell can be regarded as very high. The employed stratification and wide range of exposure levels provides optimal exposure contrast and equally sized cells that are necessary to derive statistically sound exposure-effect relationships. Sample size calculations for the survey were carried out previously. Expecting a response rate of at least, we posted 4 x 45 questionnaires (total N=18), with a cover letter, that stated that the survey was about noise. Although for each dwelling unit, the minimum, maximum and average facade point level was calculated, the following analyses account for the maximum (loudest) facade point, and the corresponding IR (at that same facade point). 2.4 Survey questionnaire For the survey, we implemented a mixed-mode approach using postal questionnaires and the option for completion of the survey by online questionnaire. About of the sample completed the survey online. To be able to control for time of the year- and seasonal effects, the survey was split in 4 waves, which were spaced 3 month apart (the surveying months were: November 214, February 215, May 215, August 215). As part of Wave 3 and to later assess a potential non-response bias, 224 short CATI-based non-responder interviews were carried out in total among initial non-responders. They answered a few questions from the original questionnaire over the phone. The survey consisted of a 7-pages long questionnaire that contained questions on noise annoyance, sleep disturbances, health status, psychosocial variables, time use, sleeping habits, coping with noise strategies, and many additional variables. The degree of noise annoyance was assessed by both the 5-point verbal ICBEN scale with the verbal marks "not at all", "slightly", "moderately", "very", "extremely", and the 11-point numerical ICBEN scale, separately for road traffic, railway, and aircraft noise (12). Similarly, the percentage of highly sleep disturbed (HSD) individuals was assessed using a ICBEN-type 5-point scale, separately for the three noise sources. Ticking one of the three top scale points on the 11-point scale denominated 'HA' status, ticking one of the two top scale points on the 5-point scale on sleep disturbances 'HSD' status. In order to validate the IR metric, for which no previous experience existed beforehand, a 7-item self-report scale was employed which was developed to assess the "perceived" intermittency of the 3

4 transportation noise situation at the homes of survey respondents. The scale was used in Wave 3 and 4 and was constructed after an initial roundtable generation of 22 items which were devised to describe the eminent features of intermittency of transportation noise, as believed to be expressed in the IR metric. After performing a reliability/item analysis on an initial test sample (N=33), 7 items which had to be answered on a 6-point scale ranging between "don't agree" to "fully agree" were chosen for the scale. In the survey, respondents were first asked to indicate which noise source prevailed at their home and then answered the 7 items for that noise source. The scale items (translated from the German original) read as: (a) (b) (c) (d) (e) (f) (g) I feel the noise at my home fluctuates quite strongly I can easily identify individual vehicles or aircraft out of the noise The noise I am exposed to at home can vary greatly from minute to minute The noise at my home is very irregular At home, every day I can hear many individual passing vehicles or overflights when I'm outdoors The loudness of the noise at my home is constantly changing The noise at my home is rather volatile 3. RESULTS 3.1 Response From the originally 18' questionnaires sent out, we received back 5592 (Wave 1: 1289, Wave 2: 1482, Wave 3: 1419, Wave 4: 1419), of which 4398 were filled out on paper, and 1194 completed online of the responders were male, 2943 were female. 3.2 Distribution of IR in the sample Fig. 1 displays the distribution of IR (over 24h) -values in the sample. As consequently for all sources, IR was set to the value "" if the criteria for maximum propagation distances to all sound sources were exceeded and therefore no Leq was determined (e.g. receiver points far away from airports or railway lines), the lowest category (-1%) received very high numbers of cases, especially for railway and aircraft noise No of obs 8 6 No of obs 2 15 No of obs IR Category IR category IR category Road traffic Railways Aircraft Figure 1 Distribution of IR values (categories) in the survey sample 3.3 Correlation between IR and Leq The (as large as possible) decorrelation from the Leq is an important feature of a noise metric that aims at explaining those parts of the variance of noise effects which are not captured in the Leq (or related measures). Therefore we first inspected correlations in the survey sample between Leq and IR at day and night for the three transportation noise sources road, rail, and air (Fig. 2). Importantly, the correlations shown account for the calculation at the facade point with the highest LAeq of the respondent's dwelling and the corresponding IR value at that same facade point (this is not necessarily also the facade point with the highest IR of the dwelling). 4

5 IR,16h Day corresp. Facade Point [%] 1 IR,16h Day corresp. Facade Point [%] IR,16h Day corresp. Facade Point [%] 1 3 LAeq,16h Loudest Facade Point [db] Day: Rail (r=.62, N=2694) LAeq,8h Loudest Facade Point [db] Night: Road (r=.25, N=541) 8 IR,8h Night corrsp. FP [%] 8 IR,8h Night corrsp. FP [%] Day: Air (r=.63, N=2559) LAeq,16h Loudest Facade Point [db] LAeq,16h Loudest Facade Point [db] Day: Road (r=.24, N=5587) IR,8h Night corrsp. FP [%] LAeq,8h Loudest Facade Point [db] Night: Rail (r=.7, N=3482) LAeq,8h Loudest Facade Point [db] Night: Air (r=.59, N=2377) Figure 2 Scatterplots incl. regression line (red) of LAeq,16h,Day and facade-point-corresponding IR16h,Day (top row); Scatterplots incl. regression line (red) of LAeq,8h,Night and facade-point-corresponding IR8h,Night Correlations in the present survey sample are clearly nonzero for all the sources, but also decidedly smaller than correlations between standard noise metrics. The decorrelation works rather well for road traffic noise, the scattering is rather wide, whereas for the clearly "event -ish" noise sources railways and aircraft, IR and Leq correlate higher. 3.4 IR and self-reported perception of noise situations as intermittent As part of the questionnaire in the Waves 3 and 4, a self-report rating scale on "perceived" intermittency comprising 7 items was employed. The 7 item values (between 1...6) were averaged to obtain the "subjective intermittency perception score", "1" denominating no perception of intermittency at all, "6" very high intermittency. While the scale exhibited a good internal consistency with a Cronbach's α of.84 (N=2417), the correlations with IR (over 24 hours) was low on average. Obviously those items that directly addressed the perceiveability of single events/passbys (e.g. "At home, every day I can hear many individual passing vehicles or overflights when I'm outdoors") better correlated with IR than more general descriptions of the noise situation. The scale was again subjected to a post-hoc item analysis, which revealed that some items less correlated with the total scale value than on average, and some did not well correlate with the IR (over 24 hours). We dropped the items (d) ("noise irregular"), and (g) ("noise rather volatile") from the scale to build the new scale with items (a), (b), (c), (e), (f). Fig.s 3 and 4 display the score values of the overall score and individual scale items against IR (over 24h and at night), categorized in 1% steps of IR. 5

6 Subjective Intermittency Perception Score IR (over 24h) - corresp. source Score Value Item (a) Item (b) Item (c) Item (e) Item (f) Subjective Intermittency Perception Score IR (over 24h) - all sources Score Value Item (a) Item (b) Item (c) Item (e) Item (f) Figure 3 Left: Mean Subjective Intermittency Perception Score plotted against IR (over 24 h) of the corresponding (self-reported) source, incl. 9 confidence intervals. Right: Mean Subjective Intermittency Perception Score plotted against IR (over 24 h) of all sources, incl. 9 confidence intervals. 6 6 Subjective Intermittency Perception Score Score Value Item (a) Item (b) Item (c) Item (e) Item (f) Subjective Intermittency Perception Score Score Value Item (a) Item (b) Item (c) Item (e) Item (f) IR (at night) - corresp. source IR (at night) - all sources Figure 4 Left: Mean Subjective Intermittency Perception Score plotted against IR (at night) of the corresponding (self-reported) source, incl. 9 confidence intervals. Right: Mean Subjective Intermittency Perception Score plotted against IR (at night) of all sources, incl. 9 confidence intervals. There is only little indication that intermittency, as calculated in the IR metric, is related to the individual perception of the survey participants of noise situations as being intermittent. Further sensitivity analyses will reveal if there are specific exposure situations where the two measures correlate better, e.g. in a subset of situations where respondents chose rather extreme values. 3.5 Exposure-Response Relationships for annoyance (%HA) and sleep disturbance (%HSD) In the following, the relationships between average exposure level, intermittency of noise, and the probability for being highly annoyed (HA) and being highly sleep disturbed (HSD) are investigated. Average exposure is either expressed as Lden or LNight at the most exposed facade. Intermittency is expressed as IR over 24h (IR 24h ), and IR at night (IR 8h,Night ), at the corresponding facade point. The corresponding logistic regressions were estimated using the generalized linear model. 3.6 Exposure-response relationships for %HA A primary goal of the present survey was to derive the exposure-%ha functions for the three transportation noise sources and compare them with commonly used curves for noise impact assessment. Fig. 5 depicts the (crude model) bivariate exposure-response relationship for %HA due to 6

7 % HA (11-pt scale) road, rail, and aircraft noise in the survey sample, including for comparison the so called "EU curves" (13). The curves were drawn based on the parameter estimates of the logit term. For each model, only those cases were included where the Lden of the respective source was greater than db. 8% 7% 6% Air 4% 3% 2% 1% % L den [db(a)] Rail Road EU-Curve Aircraft EU-Curve Road EU-Curve Rail Figure 5: Exposure-response curves for %HA for road, rail, and aircraft noise in the present sample, incl. 9 confidence intervals. Bivariate logistic regression model (Road N=5364; Rail N=4188; Air N=3189) Fig. 5 reveals an increase of %HA as compared to the "EU curves" (1), whose empirical groundwork is now 2 or more years old. The shift to the left is particularly pronounced for aircraft noise. The curve for aircraft noise is also higher than that from an earlier Swiss study carried out in the vicinity of Zurich airport back in 21/23 (14). The trend over the last decades of increasing aircraft noise annoyance at a given exposure level truly seems to be a persistent phenomenon which was recently also demonstrated in the NORAH-Study around several airports in Germany (15). Interestingly, road and railway noise exposure display almost identical exposure-response curves, questioning the justification of the "railway bonus" as it is commonly implemented in rating levels of railway noise (also in Switzerland). The reasons for the quite vast increase of the percentage highly annoyed by aircraft noise, as compared to the corresponding EU curve, remain to be further investigated. 3.7 Modification of exposure-effect functions through different magnitudes of IR in the logistic regression context In addition to standard noise exposure metrics (Lden, LNight) as main predictors, the further analyses aimed at elucidating the effect of different levels of intermittency on annoyance and sleep disturbance. To this end, two sets of logistic regression models were built using the generalized linear model (GLZ): The first set regressed %HA on Lden and IR 24h and their interaction term, the second regressed %HSD on LNight and IR 8h,Night and their interaction term as well. For all models, the cases with IR values below 1% were excluded. Fig. 6 shows the modelled relationship between Lden and %HA for three (arbitrarily chosen) levels of IR (1%,, and 1%). Fig. 7 shows the relationship between LNight and %HSD for three (arbitrarily chosen) levels of IR (1%,, and 1%). The IR-levels 1% and 1% represent the lowest and highest value IR can take with the current model and data. The curves were drawn based on the parameter estimates, which are jointly tabulated in Table 1. 7

8 % HSD (5-pt scale) % HSD (5-pt scale) % HA (11-pt scale) % HA (11-pt scale) % HA (11-pt scale) % HA (11-pt scale) 4 low IR (1%) 4 low IR (1%) 4 low IR (1%) 4% medium IR () 4% medium IR () 4% medium IR () 3 high IR (1%) 3 high IR (1%) 3 high IR (1%) 3% 3% 3% 2% 2% 2% 1% 1% 1% % % % Road traffic noise L den [db(a)] Railway noise L den [db(a)] Aircraft noise L den [db(a)] Figure 6: %HA for road (left), rail (mid), and aircraft noise (right) as a function of Lden and IR (over 24h). The curves are based on logistic regression results from Table 1. For all sources, cases with IR <1% were excluded. 4 4% 3 low IR (1%) medium IR () high IR (1%) 4 4% 3 low IR (1%) medium IR () high IR (1%) 4 4% 3 3% 3% 3% 2% 2% 2% low IR (1%) 1% 1% 1% medium IR () high IR (1%) % % % Road traffic noise L Night [db(a)] Railway noise L Night [db(a)] Aircraft noise L Night [db(a)] Figure 7: %HSD for road (left), rail(mid), and aircraft noise (right) as a function of LNight and IR (at night). The curves are based on logistic regression results from Table 1. For all sources, cases with IR <1% were excluded. Evaluating the curves in Fig. 6 and 7, the general trend seems to be that higher intermittency, expressed in higher IR values, leads to slightly higher values of %HA and %HSD. This trend is reversed with road traffic noise, where low levels of IR elicit more HA responses and more HSD responses up to about 6 db(a) LNight. The results for railway noise at night and aircraft noise at night clearly point in the expected direction, namely that intermittent noise exposure situations (at the same average level) produce stronger self-reported sleep disturbances. It remains to be investigated if particularly for railway and aircraft noise exposure the collinearity between Lden/LNight and IR in the overall sample might be too high to clearly discern the effects of average level versus effects of intermittency. For road traffic noise, where the decorrelation between average level and IR works relatively well, a more constant noise or flow of traffic seems to elicit stronger reactions, confirming early research on the issue (16). Table 1 Parameter estimates of logistic regression models (a) %HA as a function of Lden and IR 24h Road traffic noise Railway noise Aircraft noise Effect B SE exp(b) p Effect B SE exp(b) p Effect B SE exp(b) p Intercept Intercept Intercept Lden Lden Lden IR 24h IR 24h IR 24h Lden x IR 24h Lden x IR 24h Lden x IR 24h (b) %HSD as a function of LNight and IR 8h,Night Road traffic noise Railway noise Aircraft noise Effect B SE exp(b) p Effect B SE exp(b) p Effect B SE exp(b) p Intercept Intercept Intercept LNight LNight LNight IR 8h,Night IR 8h,Night IR 8h,Night LNight x IR8 h,night LNight x IR 8h,Night LNight x IR 8h,Night

9 3.8 Explained variance differences in the prediction of the ICBEN 11-pt annoyance score Whether the inclusion of IR as a predictor alongside Leq-based measures increases explained variance was investigated by linear regression of the annoyance score and the calculation of the adjusted R-square statistic (as, with logistic regression, the ordinary least squares approach to R-squared is not possible, and alternative ("pseudo") measures like e.g. the Nagelkerke R-squared are not always easy to interpret). We compared (crude) linear models without IR as a predictor with models specifying Lden + IR + Lden IR 24h, and report the whole model adjusted R 2. Results of this exercise are tabulated in Table 2. Table 2 Linear regression model and fit statistics to compare exposure-annoyance models with and without accounting for intermittency (a) Annoyace rating (11-pt scale) as a function of Lden Road traffic noise Railway noise Aircraft noise (Whole model adjusted R 2 =.151) (Whole model adjusted R 2 =.263) (Whole model adjusted R 2 =.216) Effect: F p Effect: F p Effect: F p Intercept Intercept Intercept Lden Lden Lden (b) Annoyance rating (11-pt scale) as a function of Lden and IR 24h Road traffic noise Railway noise Aircraft noise (Whole model adjusted R 2 =.155) (Whole model adjusted R 2 =.266) (Whole model adjusted R 2 =.22) Effect: F p Effect: F p Effect: F p Intercept Intercept Intercept Lden Lden Lden IR 24h IR 24h IR 24h Lden x IR 24h Lden x IR 24h Lden x IR 24h. 1. The results from Table 2 indicate that accounting for IR only very slightly increases the annoyance score variance explanation, as expressed in the R-squared statistic in the linear regression context, at least if one accounts for IR 24h and the interaction Lden IR 24h in the model. To conclude on the gain of explained variance including the IR without interaction in the models, corresponding models will be tested in a further analysis step (not yet reportet here). 4. SUMMARY AND CONCLUSIONS The present paper presented very preliminary results of an effort to establish a new metric alongside energetic measures in the modeling of noise effects, particularly annoyance and self-reported sleep disturbances. Decorrelation of the new metric from the Leq in a stratified survey sample worked relatively well with road traffic noise, but less so with railway and aircraft noise, the latter being relatively intermittent sources "by nature" where high intermittency is normally (and positively) correlated with average exposure measures. However, we were able to demonstrate that the correlation of IR with the Lden (in this case) is far smaller than between standard metrics, which opens an avenue to use the new metric in more sophisticated noise effect models. Contrary to expectation, calculated IR was not strongly associated with the subjective (self-reported) perception of the intermittency of noise exposure at the homes of survey respondents. Also, IR seems not to 'per se' and systematically increase or decrease annoyance (or HSD) resp onses. Situations with high IR values are, on the one hand, characterized by dominant single events which are likely to cause higher attention and therefore presumably also higher annoyance. The same situations on the other hand feature longer periods with quietness which might have the opposite effect and reduce annoyance. Our first results indicate that the latter mechanism might be responsible for lower percentages of HA in intermittent road traffic situations (cp. Fig. 6). Exposure situations that are characterized by rather intermittently occurring noise events like e.g. airplane overflights, feature more and longer noise-free intervals, but clearly also more pronounced single events, that could trigger physiological reactions (e.g. heart rate or blood pressure increases) in the nighttime. The consideration of IR therefore might yield a better additional variance explanation in 9

10 epidemiological studies on long-term health effects or, in particular, field or laboratory studies on sleep disturbances involving objectively measured reactions to noise events. The present analyses for the night time did not (yet) account for the least exposed facade; doing so could possibly alter the results of the IR effect on self reported sleep disturbances (and maybe annoyance reactions as well). Corresponding analyses remain to be carried out. The reported preliminary analyses essentially used most of the available cases in rather simple models that were targeted at one source only and omitted e.g. the potential modifying effects of concurrent noise sources (i.e. that are audible at the same time). It will be the object of a range of further sensitivity analyses to more clearly elucidate the role of IR in the explanation of annoyance and self-reported sleep disturbance effects. ACKNOWLEDGEMENTS We are most grateful to all of the survey respondents for taking time out of their busy lives to provide answers to our questions. The study was undertaken in the framework of the SiRENE (Short and Long Term Effects of Transportation Noise Exposure) project, which is funded by the Swiss National Science Foundation SNF-SIRENE (CRSII3_147635). The authors gratefully acknowledge the financial support from SNF, and the scientific input and advice of colleagues working on the project REFERENCES 1. Hanninen O, Knol AB, Jantunen M, Lim TA, Conrad A, Rappolder M, et al. Environmental burden of disease in Europe: assessing nine risk factors in six countries. Environ Health Perspect. 214;122(5): Basner M, Babisch W, Davis A, Brink M, Clark C, Janssen S, et al. Auditory and non-auditory effects of noise on health. The Lancet. 213;383(9925): De Coensel B, Botteldooren D, De Muer T, Berglund B, Nilsson ME, Lercher P. A model for the perception of environmental sound based on notice-events. Journal of the Acoustical Society of America. 29;126(2): Miedema H, Oudshoorn C. Annoyance from transportation noise: Relationships with exposure metrics DNL and DENL and their confidence intervals. Environmental Health Perspectives. 21;19(4): Basner M, Muller U, Elmenhorst EM. Single and combined effects of air, road, and rail traffic noise on sleep and recuperation. Sleep. 211;34(1): Brink M, Lercher P, Eisenmann A, Schierz C. Influence of slope of rise and event order of aircraft noise events on high resolution actimetry parameters. Somnologie. 28;12: Wunderli JM, Pieren R, Habermacher M, Vienneau D, Cajochen C, Probst-Hensch N, et al. Intermittency ratio: A metric reflecting short-term temporal variations of transportation noise exposure. Journal of exposure science & environmental epidemiology. 215: Wunderli JM, Pieren R, Vienneau D, Cajochen C, Probst-Hensch N, Roosli M, et al., editors. Parameter study on IR, a metric reflecting short-term temporal variations of transportation noise exposure. Paper presented at the ; 216; Hamburg. 9. Rudzik F, Thiesse L, Pieren R, Wunderli JM, Brink M, Probst-Hensch N, et al., editors. Effects of Continuous and Intermittent Transportation Noise on Sleep Fragmentation. Paper presented at the ; 216; Hamburg. 1. Héritier H, Vienneau D, Foraster M, Eze I, Brink M, Cajochen C, et al., editors. Source-specific transportation noise mortality from heart failure and myocardial infarction in Switzerland. Paper presented at the ; 216; Hamburg. 11. Karipidis I, Vienneau D, Habermacher M, Köpfli M, Brink M, Probst-Hensch N, et al. Reconstruction of historical noise exposure data for environmental epidemiology in Switzerland within the SiRENE project. Noise Mapping Volume 1, Issue 1, ISSN (Online) X, DOI: 12478/noise-214-2, July Fields JM, De Jong RG, Gjestland T, Flindell IH, Job RFS, Kurra S, et al. Standardized general-purpose noise reaction questions for community noise surveys: Research and a 1

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