Wind farm noise (WFN) exposure effects on sleep remain poorly understood. This study compared the probability of electroencephalographically (EEG) defined arousal from established sleep following WFN versus road traffic noise (RTN) onset. Sixty-eight adults were studied in a sleep laboratory on one night with repeated 20-s WFN and RTN exposures. Following ≥ 2 min of established sleep and ≥ 20-s between noise exposures, pre-recorded WFN or RTN samples were reproduced at sound pressure levels (SPLs) of 30, 40, and 50 dBA in random order. The primary outcome was the probability of EEG-defined arousal events (> 3 s EEG shifts to faster frequencies) following the onset of each noise exposure. Awakening responses (> 15 s EEG frequency shifts) were also evaluated. Noise type, SPL, and sleep stage effects on arousal and awakening response probabilities were evaluated using mixed effects logistic regression analyses. Of 68 participants, 62 (mean ± SD aged 49 ± 20 years, 35 females) had sufficient replicates of noise exposure data for analysis. Arousal response probabilities were low, particularly in deep sleep, but showed a significant noise type-by-SPL interaction (χ2 = 13, p = 0.001), with marginally but significantly lower WFN compared to RTN arousal probabilities at 40 dBA (mean [95% CI]: 2.1 [1.5, 2.9] vs. 3.2 [2.4, 4.2]%, p = 0.016) and 50 dBA (5.0 [4.0, 6.2] vs. 8.6 [6.9, 10.6]%, p < 0.001). Awakenings were infrequent (< 4% at 50 dBA) but showed similar effects. These findings show that acute WFN onset is marginally less sleep disruptive than road traffic noise events of equivalent SPL ≥ 40 dBA.

Abstract The impact of overnight wind farm noise exposure on sleep is unclear and traditional manual sleep scoring may not be sensitive enough to detect subtle changes in sleep. This study used quantitative EEG (qEEG) to assess subtle effects of noise on sleep under well-controlled laboratory conditions. Sixty-eight individuals (Males: Females; 30:38) aged (Median [IQR]) 55.5yrs [31.3-66.3], from four groups (rural residents with and without WFN-related complaints, control rural residents, and urban traffic noise-exposed residents) underwent overnight polysomnography during six different noise exposure nights in random order. Noises included 20sec and 3min intermittent road traffic and wind farm noise exposures (full night averages 42dBA and 32dBA, respectively), continuous and intermittent wind farm noise at 25dBA, and a quiet control night (background noise,19dBA). Traditional polysomnography sleep metrics, qEEG power in delta (0.5-4Hz) to beta (32Hz) frequencies, and K-complex density (events/min) were compared between noise conditions, groups and sleep stages using likelihood ratio tests. Wake after sleep onset, time spent in N1 sleep and relative beta activity were higher, whilst time spent in N3 was reduced on the 20 second noise exposure night compared to control (all p<0.05). Full night average qEEG outcomes were not different between nights. K-complex density was significantly increased during both 20sec and 3min intermittent noise exposure conditions compared to control (p<0.001). Full night qEEG outcomes do not appear to be more sensitive to noise related sleep disturbance than traditional sleep scoring methods. However, K-complex density assessments may be particularly useful in future studies of noise effects on sleep.

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.

Abstract Introduction Environmental noise could negatively impact the sleep and well-being of nearby residents. This study tested for overnight noise exposure effects on the cortisol awakening response (CAR), a potential marker of noise-related stress responses, and for differences in CAR and hair cortisol between different prior noise exposure groups. Methods A randomised controlled laboratory trial was conducted in 68 individuals from four groups; including WFN-exposed residents n=14 with and n=18 without WFN-related complaints, n=18 quiet rural control and n=18 urban traffic-noise exposed residents. Across six nights, after an initial adaptation night, participants were exposed in random order to different noise conditions, which included intermittent WFN and road traffic noise (RTN), WFN at average exposure levels throughout wake, sleep or both and a quiet control night. Salivary CAR responses were evaluated from 5 serial saliva samples collected following awakening. Hair cortisol levels were also collected. Mixed effects models were used to examine group and night effects on CAR and group effects on hair cortisol. Results There was a significant main effect of condition on CAR (p=0.038), but no significant pairwise differences between the control night and noise exposure nights, and no further differences between nights or groups on CAR or hair cortisol concentrations. Conclusions Acute in-laboratory noise-related sleep disturbance at the levels used in this study do not appear to alter acute cortisol awakening responses. A better understanding of chronic noise exposure effects and stress responses is important to help clarify and mitigate potential environmental noise exposure effects on nearby residents.

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.

STUDY OBJECTIVES Wind turbine noise 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 wind turbine noise on polysomnographically-measured and sleep diary-determined sleep latency compared to control background noise alone in healthy good sleepers without habitual prior wind turbine noise exposure. METHODS Twenty-three wind turbine noise naïve urban residents (mean±standard deviation 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 wind turbine noise 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 wind turbine noise versus control nights (median [interquartile range] objective 16.5 [11.0 to 18.5] versus 16.5 [10.5 to 29.0] minutes, p = 0.401; subjective 20.0 [15.0 to 25.0] versus 15.0 [10.0 to 30.0] minutes, p = 0.907). CONCLUSIONS Although undetected small effects cannot be ruled out, these results do not support that wind turbine noise extends sleep latency in young urban dwelling individuals without prior wind turbine noise exposure.

SummaryWind 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 (allp < 0.05). Wind turbine noise showed significantly lower delta, theta and beta activity immediately following noise onset compared with road traffic noise (allp < 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.

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.