EEG power spectral responses to wind farm compared with road traffic noise during sleep: A laboratory study.

Wind turbine noise is dominated by low frequencies for which effects on sleep relative to more common environmental noise sources such as road traffic noise remain unknown. This study examined the effect of wind turbine noise compared with road traffic noise on sleep using quantitative electroencephalogram power spectral analysis. Twenty-three participants were exposed to 3-min samples of wind turbine noise and road traffic noise at three sound pressure levels (33, 38 and 43 dBA) in randomised order during established sleep. Acute (0-30 s) and more sustained (30-180 s) effects of noise presentations during N2 and N3 sleep were examined using spectral analysis of changes in electroencephalogram power frequency ranges across time in 5-s intervals. Both noise types produced time- and sound pressure level-dependent increases in electroencephalogram power, but with significant noise type by sound pressure level interactions in beta, alpha, theta and delta frequency bands (all p < 0.05). Wind turbine noise showed significantly lower delta, theta and beta activity immediately following noise onset compared with road traffic noise (all p < 0.05). However, alpha activity was higher for wind turbine noise played at lower sound pressure levels (33 dBA [p = 0.001] and 38 dBA [p = 0.003]) compared with traffic noise during N2 sleep. These findings support that spectral analyses show subtle effects of noise on sleep and that electroencephalogram changes following wind turbine noise and road traffic noise onset differ depending on sound pressure levels; however, these effects were mostly transient and had little impact on conventionally scored sleep. Further studies are needed to establish if electroencephalogram changes associated with modest environmental noise exposures have significant impacts on sleep quality and next-day functioning.

Annoyance due to amplitude modulated low-frequency wind farm noise: A laboratory study.

This study tested for differences in perceived annoyance and loudness between road traffic noise (RTN) and wind farm noise (WFN) with amplitude modulation (AM) and tonality. Twenty-two participants, who were primarily university students with no previous exposure to WFN and aged between 19 and 29 (mean, 22 years old; standard deviation, 2) years old with normal hearing, underwent a laboratory-based listening test. Each participant rated perceived annoyance and loudness of WFN and RTN samples played at sound pressure levels (SPLs) ranging from 33 to 48 dBA. Probability modeling revealed that participants were the largest source of variability in ratings of perceived annoyance and loudness while noise type and SPL were relatively minor sources. Overall, no differences were found between WFN and RTN perceived annoyance or loudness ratings. On the other hand, no substantial differences in annoyance were found between low-frequency tonal AM and mid-to-high-frequency AM or "swish" WFN.

A systematic review and meta-analysis of wind turbine noise effects on sleep using validated objective and subjective sleep assessments.

Little is known about the potential impacts of wind turbine noise (WTN) on sleep. Previous research is limited to cross-sectional studies reporting anecdotal impacts on sleep using inconsistent sleep metrics. This meta-analysis sought to comprehensively review studies evaluating the impact of WTN using widely accepted and validated objective and subjective sleep assessments. Search terms included: "wind farm noise", "wind turbine noise", "wind turbine sound", "wind turbine noise exposure" AND "sleep". Only original articles published in English published after the year 2000 and reporting sleep outcomes in the presence of WTN using polysomnography, actigraphy or psychometrically validated sleep questionnaires were included. Uniform outcomes of the retrieved studies were meta-analysed to examine WTN effects on objective and subjective sleep outcomes. Nine studies were eligible for review and five studies were meta-analysed. Meta-analyses (Hedges' g; 95% confidence interval [CI]) revealed no significant differences in objective sleep onset latency (0.03, 95%  CI -0.34 to 0.41), total sleep time (-0.05, 95%  CI -0.77 to 0.67), sleep efficiency (-0.25, 95%  CI -0.71 to 0.22) or wake after sleep onset (1.25, 95%  CI -2.00 to 4.50) in the presence versus absence of WTN (all p > .05). Subjective sleep estimates were not meta-analysed because measurement outcomes were not sufficiently uniform for comparisons between studies. This systematic review and meta-analysis suggests that WTN does not significantly impact key indicators of objective sleep. Cautious interpretation remains warranted given variable measurement methodologies, WTN interventions, limited sample sizes, and cross-sectional study designs, where cause-and-effect relationships are uncertain. Well-controlled experimental studies using ecologically valid WTN, objective and psychometrically validated sleep assessments are needed to provide conclusive evidence regarding WTN impacts on sleep.

Establishing the acute physiological and sleep disruption characteristics of wind farm versus road traffic noise disturbances in sleep: a randomized controlled trial protocol.

Study Objectives: Despite the global expansion of wind farms, effects of wind farm noise (WFN) on sleep remain poorly understood. This protocol details a randomized controlled trial designed to compare the sleep disruption characteristics of WFN versus road traffic noise (RTN). Methods: This study was a prospective, seven night within-subjects randomized controlled in-laboratory polysomnography-based trial. Four groups of adults were recruited from; <10 km away from a wind farm, including those with, and another group without, noise-related complaints; an urban RTN exposed group; and a group from a quiet rural area. Following an acclimation night, participants were exposed, in random order, to two separate nights with 20-s or 3-min duration WFN and RTN noise samples reproduced at multiple sound pressure levels during established sleep. Four other nights tested for continuous WFN exposure during wake and/or sleep on sleep outcomes. Results: The primary analyses will assess changes in electroencephalography (EEG) assessed as micro-arousals (EEG shifts to faster frequencies lasting 3-15 s) and awakenings (>15 s events) from sleep by each noise type with acute (20-s) and more sustained (3-min) noise exposures. Secondary analyses will compare dose-response effects of sound pressure level and noise type on EEG K-complex probabilities and quantitative EEG measures, and cardiovascular activation responses. Group effects, self-reported noise sensitivity, and wake versus sleep noise exposure effects will also be examined. Conclusions: This study will help to clarify if wind farm noise has different sleep disruption characteristics compared to road traffic noise.

Environmental noise-induced cardiovascular responses during sleep.

STUDY OBJECTIVES: This study was designed to test the utility of cardiovascular responses as markers of potentially different environmental noise disruption effects of wind farm compared to traffic noise exposure during sleep. METHODS: Twenty participants underwent polysomnography. In random order, and at six sound pressure levels from 33 dBA to 48 dBA in 3 dB increments, three types of wind farm and two types of road traffic noise recordings of 20-s duration were played during established N2 or deeper sleep, each separated by 20 s without noise. Each noise sequence also included a no-noise control. Electrocardiogram and finger pulse oximeter recorded pulse wave amplitude changes from the pre-noise onset baseline following each noise exposure and were assessed algorithmically to quantify the magnitude of heart rate and finger vasoconstriction responses to noise exposure. RESULTS: Higher sound pressure levels were more likely to induce drops in pulse wave amplitude. Sound pressure levels as low as 39 dBA evoked a pulse wave amplitude response (Odds ratio [95% confidence interval]; 1.52 [1.15, 2.02]). Wind farm noise with amplitude modulation was less likely to evoke a pulse wave amplitude response than the other noise types, but warrants cautious interpretation given low numbers of replications within each noise type. CONCLUSIONS: These preliminary data support that drops in pulse wave amplitude are a particularly sensitive marker of noise-induced cardiovascular responses during. Larger trials are clearly warranted to further assess relationships between recurrent cardiovascular activation responses to environmental noise and potential long-term health effects.

An experimental investigation on the impact of wind turbine noise on polysomnography-measured and sleep diary-determined sleep outcomes.

STUDY OBJECTIVES: Carefully controlled studies of wind turbine noise (WTN) and sleep are lacking, despite anecdotal complaints from some residents in wind farm areas and known detrimental effects of other noises on sleep. This laboratory-based study investigated the impact of overnight WTN exposure on objective and self-reported sleep outcomes. METHODS: Sixty-eight participants (38 females) aged (mean ± SD) 49.2 ± 19.5 were recruited from four groups; N = 14, living <10 km from a wind farm and reporting WTN related sleep disruption; N = 18, living <10 km from a wind farm and reporting no WTN sleep disruption; N = 18, reporting road traffic noise-related sleep disruption; and N = 18 control participants living in a quiet rural area. All participants underwent in-laboratory polysomnography during four full-night noise exposure conditions in random order: a quiet control night (19 dB(A) background laboratory noise), continuous WTN (25 dB(A)) throughout the night; WTN (25 dB(A)) only during periods of established sleep; and WTN (25 dB(A)) only during periods of wake or light N1 sleep. Group, noise condition, and interaction effects on measures of sleep quantity and quality were examined via linear mixed model analyses. RESULTS: There were no significant noise condition or group-by-noise condition interaction effects on polysomnographic or sleep diary determined sleep outcomes (all ps > .05). CONCLUSIONS: These results do not support that WTN at 25 dB(A) impacts sleep outcomes in participants with or without prior WTN exposure or self-reported habitual noise-related sleep disruption. These findings do not rule out effects at higher noise exposure levels or potential effects of WTN on more sensitive markers of sleep disruption. CLINICAL TRIAL REGISTRATION: ACTRN12619000501145, UTN U1111-1229-6126. Establishing the physiological and sleep disruption characteristics of noise disturbances in sleep. https://www.anzctr.org.au/. This study was prospectively registered on the Australian and New Zealand Clinical Trial Registry.

The effect of wind turbine noise on polysomnographically measured and self-reported sleep latency in wind turbine noise naïve participants.

STUDY OBJECTIVES: Wind turbine noise (WTN) exposure could potentially interfere with the initiation of sleep. However, effects on objectively assessed sleep latency are largely unknown. This study sought to assess the impact of WTN on polysomnographically measured and sleep diary-determined sleep latency compared to control background noise alone in healthy good sleepers without habitual prior WTN exposure. METHODS: Twenty-three WTN naïve urban residents (mean ± SD age: 21.7 ± 2.1 years, range 18-29, 13 females) attended the sleep laboratory for two polysomnography studies, one week apart. Participants were blind to noise conditions and only informed that they may or may not hear noise during each night. During the sleep onset period, participants were exposed to counterbalanced nights of WTN at 33 dB(A), the upper end of expected indoor values; or background noise alone as the control condition (23 dB(A)). RESULTS: Linear mixed model analysis revealed no differences in log10 normalized objective or subjective sleep latency between the WTN versus control nights (median [interquartile range] objective 16.5 [11.0 to 18.5] vs. 16.5 [10.5 to 29.0] min, p = .401; subjective 20.0 [15.0 to 25.0] vs. 15.0 [10.0 to 30.0] min, p = .907). CONCLUSIONS: Although undetected small effects cannot be ruled out, these results do not support that WTN extends sleep latency in young urban-dwelling individuals without prior WTN exposure.

K-complexes are a sensitive marker of noise-related sensory processing during sleep: a pilot study.

STUDY OBJECTIVES: The primary aim of this study was to examine dose-response relationships between sound pressure levels (SPLs) and K-complex occurrence probability for wind farm and road traffic noise. A secondary aim was to compare K-complex dose-responses to manually scored electroencephalography arousals and awakenings. METHODS: Twenty-five participants underwent polysomnography recordings and noise exposure during sleep in a laboratory. Wind farm and road traffic noise recordings of 20-sec duration were played in random order at 6 SPLs between 33 and 48 dBA during established N2 or deeper sleep. Noise periods were separated with periods of 23 dBA background noise. K-complexes were scored using a validated algorithm. K-complex occurrence probability was compared between noise types controlling for noise SPL, subjective noise sensitivity, and measured hearing acuity. RESULTS: Noise-induced K-complexes were observed in N2 sleep at SPLs as low as 33 dBA (Odds ratio, 33 dBA vs 23 dBA, mean (95% confidence interval); 1.75 (1.16, 2.66)) and increased with SPL. EEG arousals and awakenings were only associated with noise above 39 dBA in N2 sleep. K-complexes were 2 times more likely to occur in response to noise than EEG arousals or awakenings. Subjective noise sensitivity and hearing acuity were associated with the K-complex occurrence, but not arousal or awakening. Noise type did not detectably influence K-complexes, EEG arousals, or awakening responses. CONCLUSION: These findings support that K-complexes are a sensitive marker of sensory processing of environmental noise during sleep and that increased hearing acuity and decreased self-reported noise sensitivity increase K-complex probability.