Most studies published to date consider single noise sources and the reported noise metrics are not informative about the peaking characteristics of the source under investigation. Our study focuses on the association between cardiovascular mortality in Switzerland and the three major transportation noise sources-road, railway and aircraft traffic-along with a novel noise metric termed intermittency ratio (IR), expressing the percentage contribution of individual noise events to the total noise energy from all sources above background levels. We generated Swiss-wide exposure models for road, railway and aircraft noise for 2001. Noise from the most exposed façade was linked to geocodes at the residential floor height for each of the 4.41 million adult (>30 y) Swiss National Cohort participants. For the follow-up period 2000-2008, we investigated the association between all noise exposure variables [L(Road), L(Rail), L(Air), and IR at night] and various cardiovascular primary causes of death by multipollutant Cox regression models adjusted for potential confounders including NO. The most consistent associations were seen for myocardial infarction: adjusted hazard ratios (HR) (95% CI) per 10 dB increase of exposure were 1.038 (1.019-1.058), 1.018 (1.004-1.031), and 1.026 (1.004-1.048) respectively for L(Road), L(Rail), and L(Air). In addition, total IR at night played a role: HRs for CVD were non-significant in the 1st, 2nd and 5th quintiles whereas they were 1.019 (1.002-1.037) and 1.021 (1.003-1.038) for the 3rd and 4th quintiles. Our study demonstrates the impact of all major transportation noise sources on cardiovascular diseases. Mid-range IR levels at night (i.e. between continuous and highly intermittent) are potentially more harmful than continuous noise levels of the same average level.

Most environmental epidemiology studies model health effects of noise by regressing on acoustic exposure metrics that are based on the concept of average energetic dose over longer time periods (i.e. the Leq and related measures). Regarding noise effects on health and wellbeing, average measures often cannot satisfactorily predict annoyance and somatic health effects of noise, particularly sleep disturbances. It has been hypothesized that effects of noise can be better explained when also considering the variation of the level over time and the frequency distribution of event-related acoustic measures, such as for example, the maximum sound pressure level. However, it is unclear how this is best parametrized in a metric that is not correlated with the Leq, but takes into account the frequency distribution of events and their emergence from background. In this paper, a calculation method is presented that produces a metric which reflects the intermittency of road, rail and aircraft noise exposure situations. The metric termed intermittency ratio (IR) expresses the proportion of the acoustical energy contribution in the total energetic dose that is created by individual noise events above a certain threshold. To calculate the metric, it is shown how to estimate the distribution of maximum pass-by levels from information on geometry (distance and angle), traffic flow (number and speed) and single-event pass-by levels per vehicle category. On the basis of noise maps that simultaneously visualize Leq, as well as IR, the differences of both metrics are discussed.

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Noise exposure prediction models for health effect studies normally estimate free field exposure levels outside. However, to assess the noise exposure inside dwellings, an estimate of indoor sound levels is necessary. To date, little field data is available about the difference between indoor and outdoor noise levels and factors affecting the damping of outside noise. This is a major cause of uncertainty in indoor noise exposure prediction and may lead to exposure misclassification in health assessments. This study aims to determine sound level differences between the indoors and the outdoors for different window positions and how this sound damping is related to building characteristics. For this purpose, measurements were carried out at home in a sample of 102 Swiss residents exposed to road traffic noise. Sound pressure level recordings were performed outdoors and indoors, in the living room and in the bedroom. Three scenarios—of open, tilted, and closed windows—were recorded for three minutes each. For each situation, data on additional parameters such as the orientation towards the source, floor, and room, as well as sound insulation characteristics were collected. On that basis, linear regression models were established. The median outdoor–indoor sound level differences were of 10 dB(A) for open, 16 dB(A) for tilted, and 28 dB(A) for closed windows. For open and tilted windows, the most relevant parameters affecting the outdoor–indoor differences were the position of the window, the type and volume of the room, and the age of the building. For closed windows, the relevant parameters were the sound level outside, the material of the window frame, the existence of window gaskets, and the number of windows.