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.

Amplitude modulated wind farm noise relationship with annoyance: A year-long field study.

This paper presents results from a one-year study of indoor annoyance and self-reported sleep times for two participants located near different wind farms. Continuous measurements of outdoor and indoor noise and meteorological conditions were taken at each location for the duration of the study. In at least 50% of the annoyance recordings, participants described noise as "swish" or "swoosh." Furthermore, the majority of the annoyance recordings occurred at nighttime and in the early morning. The third quartile of A-weighted indoor sound pressure level [SPL(A)], between 27 and 31 dBA, was associated with an 88% increased probability of annoyance compared to the lowest reference quartile, which was between 12 and 22 dBA [odds ratio and 95% confidence intervals, 7.72 (2.61,22.8), p < 0.001]. The outdoor SPL(A) was also predictive of annoyance but only between 40 and 45 dBA. The outdoor prevalence of amplitude modulation (AM), defined as the percentage of time that AM was detectable by an algorithm for each annoyance period, was also associated with annoyance. Self-reported sleep efficiency (time spent asleep relative to time in bed available for sleep) was significantly associated with nighttime annoyance (ß = -0.66, p = 0.02) but only explained a small fraction of the variance (R2 = 5%).

Numerical simulation of blade-passage noise.

Numerical simulations are used to investigate the noise generated by the passage of a rotor blade past a fixed object (the blade-passage effects), which was studied by simulating a three-bladed rotor that is supported by a vertical cylindrical tower. To isolate the blade-passage effects, no incoming wind was introduced in the simulation. The symmetric blade was set to zero pitch angle relative to the plane of rotation and two blade-tower distances were investigated. The sliding mesh method was used to simulate the rotation of the blades and Curle's acoustic analogy was used to predict the noise generated from the simulated flow data. Intense force fluctuations occur during the interaction on both the tower and the passing blade, and these are the primary sources of blade-passage noise. The contribution of the force fluctuations on the support tower to blade-passage noise, which previously had been ignored, was revealed to be more significant than that of the blades. The numerical model successfully predicts the noise spectra, which are validated by the very good agreement with experimental measurements. The simulations provide a framework to better understand blade-tower interaction noise in various applications.

Experimental investigation of trailing edge noise from stationary and rotating airfoils.

Trailing edge noise from stationary and rotating NACA 0012 airfoils is characterised and compared with a noise prediction based on the semi-empirical Brooks, Pope, and Marcolini (BPM) model. The NACA 0012 is symmetrical airfoil with no camber and 12% thickness to chord length ratio. Acoustic measurements were conducted in an anechoic wind tunnel using a stationary NACA 0012 airfoil at 0° pitch angle. Airfoil self-noise emissions from rotating NACA 0012 airfoils mounted at 0° and 10° pitch angles on a rotor-rig are studied in an anechoic room. The measurements were carried out using microphone arrays for noise localisation and magnitude estimation using beamforming post-processing. Results show good agreement between peak radiating trailing edge noise emissions of stationary and rotating NACA 0012 airfoils in terms of the Strouhal number. Furthermore, it is shown that noise predictions based on the BPM model considering only two dimensional flow effects, are in good agreement with measurements for rotating airfoils, at these particular conditions.

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.

Beyond traditional wind farm noise characterisation using transfer learning.

This study proposes an approach for the characterisation and assessment of wind farm noise (WFN), which is based on extraction of acoustic features between 125 and 7500 Hz from a pretrained deep learning model (referred to as deep acoustic features). Using data measured at a variety of locations, this study shows that deep acoustic features can be linked to meaningful characteristics of the noise. This study finds that deep acoustic features can reveal an improved spatial and temporal representation of WFN compared to what is revealed using traditional spectral analysis and overall noise descriptors. These results showed that this approach is promising, and thus it could provide the basis for an improved framework for WFN assessment in the future.

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.

A novel EEG marker predicts perceived sleepiness and poor sleep quality.

STUDY OBJECTIVES: To determine if a novel EEG-derived continuous index of sleep depth/alertness, the odds ratio product (ORP), predicts self-reported daytime sleepiness and poor sleep quality in two large population-based cohorts. METHODS: ORP values which range from 0 (deep sleep) to 2.5 (fully alert) were calculated in 3s intervals during awake periods (ORPwake) and NREM sleep (ORPNREM) determined from home sleep studies in the HypnoLaus (N = 2162: 1106 females, 1056 males) and men androgen inflammation lifestyle environment and stress (MAILES) cohorts (N = 754 males). Logistic regression was used to examine associations between ORPwake, ORPNREM, and traditional polysomnography measures (as comparators) with excessive sleepiness (Epworth sleepiness scale >10) and poor sleep quality (Pittsburgh sleep quality index >5) and insomnia symptoms. RESULTS: High ORPwake was associated with a ~30% increase in poor sleep quality in both HypnoLaus (odds ratio, OR, and 95% CI) 1.28 (1.09, 1.51), and MAILES 1.36 (1.10, 1.68). High ORPwake was also associated with a ~28% decrease in excessive daytime sleepiness in the MAILES dataset. ORPNREM was associated with a ~30% increase in poor sleep quality in HypnoLaus but not in MAILES. No consistent associations across cohorts were detected using traditional polysomnography markers. CONCLUSIONS: ORP, a novel EEG-derived metric, measured during wake periods predicts poor sleep quality in two independent cohorts. Consistent with insomnia symptomatology of poor perceived sleep in the absence of excessive daytime sleepiness, ORPwake may provide valuable objective mechanistic insight into physiological hyperarousal.

A Novel Electroencephalogram-derived Measure of Disrupted Delta Wave Activity during Sleep Predicts All-Cause Mortality Risk.

Rationale: Conventional markers of sleep disturbance, based on manual electroencephalography scoring, may not adequately capture important features of more fundamental electroencephalography-related sleep disturbance. Objectives: This study aimed to determine if more comprehensive power-spectral measures of delta wave activity during sleep are stronger independent predictors of mortality than conventional sleep quality and disturbance metrics. Methods: Power spectral analysis of the delta frequency band and spectral entropy-based markers to quantify disruption of electroencephalography delta power during sleep were performed to examine potential associations with mortality risk in the Sleep Heart Health Study cohort (N = 5,804). Adjusted Cox proportional hazard models were used to determine the association between disrupted delta wave activity at baseline and all-cause mortality over an approximately 11-year follow-up period. Results: Disrupted delta electroencephalography power during sleep was associated with a 32% increased risk of all-cause mortality compared with no fragmentation (hazard ratio, 1.32 [95% confidence interval, 1.14-1.50]), after adjusting for total sleep time and other clinical and lifestyle-related covariates, including sleep apnea. The association was of similar magnitude to a reduction in total sleep time from 6.5 hours to 4.25 hours. Conventional measures of sleep quality, including wake after sleep onset and arousal index, were not predictive of all-cause mortality. Conclusions: Delta wave activity disruption during sleep is strongly associated with all-cause mortality risk, independent of traditional potential confounders. Future investigation into the potential role of delta sleep disruption on other specific adverse health consequences such as cardiometabolic, mental health, and safety outcomes has considerable potential to provide unique neurophysiological insight.

Directivity of blade-tower interaction noise.

This paper presents a combined experimental and numerical study that characterises the directivity of blade-tower interaction (BTI) noise. Numerical computations were performed using a hybrid approach combining unsteady Reynolds-averaged Navier-Stokes equations and Curle's acoustic analogy, allowing the noise from the blades and the tower to be computed separately. The noise directivity of the blade and the tower components have a dipole pattern and a monopole-like pattern, respectively; hence, the resulting BTI noise directivity resembles an oval. Partial cancellations between the blade and tower components are also shown to affect the BTI noise directivity.

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.

Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning.

STUDY OBJECTIVES: K-complexes (KCs) are a recognized electroencephalography marker of sensory processing and a defining feature of sleep stage 2. KC frequency and morphology may also be reflective of sleep quality, aging, and a range of sleep and sensory processing deficits. However, manual scoring of K-complexes is impractical, time-consuming, and thus costly and currently not well-standardized. Although automated KC detection methods have been developed, performance and uptake remain limited. METHODS: The proposed algorithm is based on a deep neural network and Gaussian process, which gives the input waveform a probability of being a KC ranging from 0% to 100%. The algorithm was trained on half a million synthetic KCs derived from manually scored sleep stage 2 KCs from the Montreal Archive of Sleep Study containing 19 healthy young participants. Algorithm performance was subsequently assessed on 700 independent recordings from the Cleveland Family Study using sleep stages 2 and 3 data. RESULTS: The developed algorithm showed an F1 score (a measure of binary classification accuracy) of 0.78 and thus outperforms currently available KC scoring algorithms with F1 = 0.2-0.6. The probabilistic approach also captured expected variability in KC shape and amplitude within individuals and across age groups. CONCLUSIONS: An automated probabilistic KC classification is well suited and effective for systematic KC detection for a more in-depth exploration of potential relationships between KCs during sleep and clinical outcomes such as health impacts and daytime symptomatology.