Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Most noise-exposed populations especially in the vicinity of airports cite sleep disturbance as a common complaint. Pro- tection of a particular sleep period is necessary for overall quality of life. Sleep may be quite sensitive to environmental factors, especially noise, because external stimuli are still processed by the sleeperâs sensory functions, although there may be no conscious perception of their presence. The large amount of research published during the last 30 years has produced considerable variability and often con- troversial results. For example, in establishing the effect of aviation noise on health, the absence of one internationally accepted exposure-effect or dose-response relationship is largely the result of a lack of one obvious âbest choiceâ research methodology, as well as to the complex interactions of many factors that influence sleep disturbance, including the differences of the noise source and the context of the living environment. Current exposure-response relationships use either awakenings or body movements to describe sleep disturbance. Several studies suggest that either sound exposure level (SEL) or maximum noise level (Lmax) are better predictors of sleep disturbance than long-term weighted averages [equiv- alent sound level (Leq)], day-evening-night average noise levels (Lden), community noise equivalent level (CNEL), DNL, or equivalent noise level for night (Lnight). A survey of the literature also shows large differences between results from numerous laboratory studies and those from epidemio- logical or experimental studies made in real, in-home situa- tions. The landmark study by Ollerhead et al. (1992) clearly identified a difference between laboratory and in-home studies of sleep disturbance, with the in-home data showing it takes considerably more noise to awaken people than data collected in the laboratory studies, and that the agreement between actimetrically determined arousals and electro- encephalogram (EEG)-measured arousals were very good (Ollerhead et al 1992). It summarized by stating that âonce asleep, very few people living near airports are at risk of any substantial sleep disturbance resulting from aircraft noise, even at the highest event noise levels.â Later studies by Horne et al. (1994) document a landmark in-home field study that demonstrated dose-response curves based on laboratory data greatly overestimated the actual awakening rates for aircraft noise events. In 1995, Fidell found that SELs of individual noise intrusions were much more 12 closely associated with awakenings than long-term noise exposures (Fidell et al. 1995). These findings do not resemble those of laboratory studies of noise-induced sleep interference, but agree with the results of other field studies. Importantly, the study also concludes the relationship observed . . . between noise metrics and behavioral awakening responses suggest instead that noise induced awakening may be usefully viewed as an event-detection process. Put another way, an awak- ening can be viewed as the outcome of a de facto decision that a change of sufficient import has occurred in the short-term noise environment to warrant a decision to awaken (Fidell et al. 1995). This is an important observation that leads to suspicion of any assumption about the independence of noise events made in the pursuit of estimating total awakenings. In 1989, a comprehensive database representing 25 years of both laboratory and field research on noise-induced sleep disturbance was the basis for an interim curve to predict the percent of exposed individuals awakened as a function of in- door A-weighted SEL (Finegold et al. 1992). This curve was adopted by FICON in 1992. Since publication of the FICON report (Federal Interagency Committee on Noise 1992), sub- stantial field research in the area of sleep disturbance has been completed. The data from these studies show a consistent pat- tern, with considerably less percent of the exposed population expected to be behaviorally awakened than laboratory studies had demonstrated. As a result, the Federal Interagency Com- mittee on Aviation Noise (FICAN) published a new recom- mendation in 1997. Interestingly, the FICAN curve does not represent a best fit of the study data, but rather is constructed to represent the out boundary of the data (FICAN 1997). In summary, although the most common metrics for assessing the impacts of DNL, Lden, or CNEL already contain a 10-dB penalty for night-time noises, there are circumstances where a separate analysis of the impacts of night-time trans- portation noise is warranted. There are, however, different definitions of sleep disturbance and different ways to measure it, different exposure metrics that can be used, and consistent differences in the results of laboratory versus field studies. At the present time, very little is known about how, why, and how often people are awakened during the night, although it is generally acknowledged that the âmeaning of the soundâ to the individual, such as a child crying, is a strong predictor of awakening. Although different models can estimate various metrics, there is substantial controversy associated with how to CHAPTER FOUR SLEEP DISTURBANCE AND AVIATION NOISE
13 apply and interpret these studies. Current research has focused on measuring in-home sleep disturbance using techniques not available in 1985. In-home sleep disturbance studies clearly demonstrate that it requires more noise to cause awakenings than was previously theorized based on laboratory sleep disturbance studies. Recent studies have cautioned about the over-interpretation of the data. This is contrasted with recent efforts to estimate the population that will be awakened by aircraft noise around airports. Research may not yet have suf- ficient specificity to estimate the population awakened for a specific airport environment or the difference in population awakened for a given change in an airport environment.
