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Physiological effects of wind turbine noise on sleep

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In accordance with the EU energy policy, wind turbines are becoming increasingly widespread throughout Europe, and this trend is expected to continue globally. More people will consequently live close to wind turbines in the future, and hence may be exposed to wind farm noise. Of particular concern is the potential for nocturnal noise to contribute towards sleep disturbance of nearby residents. To examine the issue, we are implementing a project titled Wind Turbine Noise Effects on Sleep (WiTNES). In a pilot study described in this paper, we performed an initial investigation into the particular acoustical characteristics of wind turbine noise that might have the potential to disturb sleep. Six young, healthy individuals spent 5 nights in our sound exposure laboratory. During the final 3 nights of the study, the participants were exposed to wind turbine noise, which was synthesised based on analysis of field measurements. Exposures involved periods of different amplitude modulation strengths, the presence or absence of beats, different blade rotational periods, and outdoor LAEq,8h=45 or 50 dB with indoor levels based on the windows being fully closed or slightly open. Physiological measurements indicate that nights with low frequency band amplitude modulation and LAEq,8h=45 dB, slightly open window (LAEq,8h=33 dB indoors) impacted sleep the most. The presence of beats and strong amplitude modulation contributed to sleep disturbance, reflected by more electrophysiological awakenings, increased light sleep and wakefulness, and reduced REM and deep sleep. The impact on sleep by these acoustic characteristics is currently the focus of interest in ongoing studies.
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PROCEEDINGS of the 22nd International Congress on Acoustics
Wind Farm Noise: Paper ICA2016-440
Physiological effects of wind turbine noise on sleep
Michael G. Smith(a), Mikael Ögren(b), Pontus Thorsson(c), Eja Pedersen(d) and
Kerstin Persson Waye(e)
(a) University of Gothenburg, Sweden, michael.smith@amm.gu.se
(b) University of Gothenburg, Sweden, mikael.ogren@amm.gu.se
(c) Chalmers University of Technology Sweden, pontus.thorsson@akustikverkstan.se
(d) Lund University, Sweden, eja.pedersen@arkitektur.lth.se
(e) University of Gothenburg, Sweden, kerstin.persson.waye@amm.gu.se
Abstract
In accordance with the EU energy policy, wind turbines are becoming increasingly widespread
throughout Europe, and this trend is expected to continue globally. More people will consequently
live close to wind turbines in the future, and hence may be exposed to wind farm noise. Of
particular concern is the potential for nocturnal noise to contribute towards sleep disturbance of
nearby residents. To examine the issue, we are implementing a project titled Wind Turbine Noise
Effects on Sleep (WiTNES). In a pilot study described in this paper, we performed an initial
investigation into the particular acoustical characteristics of wind turbine noise that might have
the potential to disturb sleep. Six young, healthy individuals spent 5 nights in our sound exposure
laboratory. During the final 3 nights of the study, the participants were exposed to wind turbine
noise, which was synthesised based on analysis of field measurements. Exposures involved
periods of different amplitude modulation strengths, the presence or absence of beats, different
blade rotational periods, and outdoor LAEq,8h=45 or 50 dB with indoor levels based on the windows
being fully closed or slightly open. Physiological measurements indicate that nights with low
frequency band amplitude modulation and LAEq,8h=45 dB, slightly open window (LAEq,8h=33 dB
indoors) impacted sleep the most. The presence of beats and strong amplitude modulation
contributed to sleep disturbance, reflected by more electrophysiological awakenings, increased
light sleep and wakefulness, and reduced REM and deep sleep. The impact on sleep by these
acoustic characteristics is currently the focus of interest in ongoing studies.
Keywords: wind turbine, sleep, polysomnography
22nd International Congress on Acoustics, ICA 2016
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Physiological effects of wind turbine noise on sleep
1 Introduction
According to the European Wind Energy Association, there was almost 13 000 MW of wind power
installed across the EU in 2015 [1]. This represents a 6.3% increase over the previous year.
Annoyance from wind turbine noise has previously been evaluated, primarily in cross-sectional
studies [2]. However, long term health consequences, including sleep disturbance, have not been
studied and the physiological effects are not known. Present debate on one side argues that
“sound from wind turbines does not pose a risk of... any adverse health effect in humans” [3]. On
the other side, there have been claims for symptoms including impairment of mental health [4].
Studies such as [4] have been subject to criticism, both in terms of the conclusions drawn from
the data and the experimental design itself. Whilst many claims of adverse effects are anecdotal,
sleep disturbance is one of the issues most frequently reported and supported by previous cross-
sectional studies [5].
There is ample evidence illustrating that adequate sleep is necessary for maintaining good health.
Disturbed sleep can hence be of consequence for immediate and long-term health [6]. Night time
noise has the potential to adversely affect sleep, which has been recognised by the World Health
Organisation and reflected by their publication of night time noise limits [7]. The Environmental
Noise Directive (2002/49/EC) recognises that community noise is potentially harmful and so
requires that all EU member states map the noise exposure of their populations. Despite this,
wind turbines are often erected in quiet rural areas, where sleep disturbance due to wind turbine
noise is reported more frequently [8]. However, reported effects of this noise on sleep may be
biased by perceived annoyance and so objective measures of sleep structure and other
physiological response, for instance cardiovascular effects, are clearly needed.
Although objective measures have been made on the human effects of numerous environmental
sources, particularly traffic noise [6,9,10], the majority of studies on the effects of wind farm noise
have used only subjective means, and only using calculated equivalent sound levels in dBA at
the façade based on simplified sound radiation and propagation models. A notable recent study
has however examined wind turbine noise using wrist actigraphy [11]. Compared to traffic noise
where much research has been performed, little is known regarding how noise from wind turbines
objectively influences sleep. The aim of the current project is therefore to determine whether noise
from wind turbines can impact on human sleep, and how any such impacts are manifested.
2 Methods
2.1 Overall study design
An experimental study using a within-subject design was implemented to investigate the effect of
wind turbine noise on sleep. Prior to the study described in this paper, an initial explorative pilot
study was performed. In this pre-pilot, which involved six young and healthy participants, it was
found that EEG awakenings occurred more frequently during nights with indoor noise levels of 34
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dBA (window closed) than nights with 30 or 34 dBA (window slightly open). Indications were also
found that amplitude modulation, rotational speed of the turbine blades and/or the presence of
strong “beats” contributed to sleep fragmentation, reflected by EEG arousals, awakenings and
changes to a lighter sleep stage. These findings served as the basis for the development of the
specific characteristics of wind turbine noise examined in the present work.
2.2 Study protocol
Participants slept for 5 consecutive nights in a sound environment laboratory, which is furnished
to imitate a typical home environment. The first night served as a habituation to the environment
and the sleep measurement apparatus. The second night was an exposure-free control night to
measure baseline sleep. Nights 3-5 were exposure nights where wind turbine noise (WTN) was
introduced (see 2.4). Participants arrived at the lab by 20:00 each evening and were instructed to
begin trying to fall asleep at 23:00. They were woken by an automated alarm call at 07:00 each
morning. Low level artificial background noise simulating ventilation noise was introduced into the
bedrooms throughout the study.
Sleep was measured using polysomnography (PSG) using standard electrode placements and
sampling and filter frequencies [12]. PSG data were manually scored by a single sleep
technologist according to current guidelines [12]. Electroencephalogram (EEG) arousals were
classed according to current criteria [13]. Arousals of longer than half an epoch (>15s) were
classed as awakenings.
Participants completed questionnaires within 15 minutes of awakening each morning. The
questions and their validation against objective sleep measurements are reported elsewhere, but
summarily include items on sleep quality and disturbance, nocturnal restoration, and subjective
sleep assessment [14]. The current paper focusses on objective measures of sleep only, and
therefore the questionnaire data will not be reported.
2.3 Participants
Six young healthy persons took part in the study (mean age 24 years SD±2.3, mean BMI 20.7
SD±0.4, 5 women). All were students recruited via public advertising, provided informed written
consent prior to the start of the study, and were free to stop taking part at any time. They were
financially compensated for participating. Each individual underwent a hearing test to a screening
level of 15 dB HL from 125 Hz to 8 kHz using pure tone audiometry.
2.4 Noise exposures
Noise exposure was synthesised based upon extensive analysis of recorded wind turbine sound
signals. The development of these synthesised exposures is outside the scope of the current
paper, but are presented here in summary and will be described in detail in a journal article that
is currently under preparation. Three 8-hour periods of WTN were generated, corresponding to
the three study exposure nights (Night A, Night B, Night C). Each individual hour of each night
contained a 2*2*2 arrangement of high and low turbine rotational periods, weak and strong
amplitude modulation, and the presence or absence of strong beats. Each hour therefore
contained eight distinct noise scenarios, each of which was 400s in duration. The presentation
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order of these eight scenarios varied in an 8×8 Latin square design across the eight study hours
of each night (23:00 to 07:00), with each scenario only preceding and following any other scenario
only once. Every hour concluded with 400s free of WTN. A detailed overview of the noise
scenarios is given in Table 1. A description of the differences between Night A, Night B and
Night C are also given in Table 1 (window closed or slightly open, outdoor noise level, and/or the
frequency bands of the amplitude modulation).
Table 1 Noise scenarios implemented across the three exposure nights
Night
Noise level
Period
AM
strength
AM
frequency
bands
Strong
beats
Night A
Window slightly
open
LAEq,1h,outdoor=45 dB
1.
Weak
MF
No
LAEq,1h,indoor=33 dB
2.
Weak
MF
No
3.
Strong
MF
No
4.
Strong
MF
No
5.
Weak
MF
Yes
6.
Weak
MF
Yes
7.
Strong
MF
Yes
8.
Strong
MF
Yes
9.
No turbine noise background only
Night B
Window slightly
open
LAEq,1h,outdoor=45 dB
1.
Weak
LF
No
LAEq1h,,indoor=33 dB
2.
Weak
LF
No
3.
Strong
LF
No
4.
Strong
LF
No
5.
Weak
LF
Yes
6.
Weak
LF
Yes
7.
Strong
LF
Yes
8.
Strong
LF
Yes
9.
No turbine noise background only
Night C
Window closed
LAEq,1h,outdoor=50 dB
1.
Weak
LF
No
LAEq1h,,indoor=30 dB
2.
Weak
LF
No
3.
Strong
LF
No
4.
Strong
LF
No
5.
Weak
LF
Yes
6.
Weak
LF
Yes
7.
Strong
LF
Yes
8.
Strong
LF
Yes
9.
No turbine noise background only
RPM=Rotations per minute (of turbine blade); AM=Amplitude modulation; MF=Middle Frequencies (500
2500 Hz); LF=Low Frequencies (80500 Hz)
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2.5 Statistical analysis
All analyses were performed in IBM SPSS 22. Data were analysed in a repeated-measurement
ANOVA. Given the exploratory nature of the work, all statistical tests with p<0.1 are reported.
Post hoc analysis was performed even if no significant main effect was observed. Corrections for
multiple comparisons were abdicated in order to minimise the likelihood of missing potentially
important findings [15].
3 Results
3.1 Differences between nights
The mean values of sleep macrostructure variables which were found to differ between at least
two nights (p<0.1) are given in Table 2. Compared to the control night,
participants in Night A took 7.2 minutes longer to fall asleep, had a reduction in continual
time in N2 (“intermediate”) sleep of 10.6 minutes, and had a reduction in continual time in
slow wave (“deep”) sleep of 5.4 minutes.
participants in Night B had an average increase of wakefulness of 5.8 minutes,
corresponding to a reduction in sleep efficiency of 2.6%, and had a reduction in the time
spent in slow wave sleep of 4.8%.
participants in Night C had a reduction in continual time in N2 sleep of 11.4 minutes, had
a reduction in continual time in slow wave sleep of 9.2 minutes and had a reduction in the
time spent in slow wave sleep of 0.8%.
Participants in Night A and Night C had a reduction in continual time in N2 sleep of 8.4 and 9.2
minutes respectively than in Night B. Participants in Night B woke up for the first time 31 minutes
earlier than in Night C.
No effects were seen for rapid eye movement (REM) sleep latency, SWS latency, sleep period
time, maximum uninterrupted REM or N1 (“light”) sleep duration, %N1, %N2, %REM, number or
frequency of EEG arousals, number or frequency of EEG awakenings, or number or frequency of
sleep stage changes.
Generally, sleep was least fragmented during the night with no WTN (the control night), with a
higher percentage of SWS, lower wakefulness, longer sustained SWS and N2, and shorter sleep
latency compared to nights where a difference was found. However, differences between
exposure nights are rather limited, with lower sustained N2 in Night B than Night A, and an earlier
first awakening in Night B than Night C. Based on these PSG macrostructure variables, it appears
that of all the exposure nights, the condition with the most deleterious effects on sleep was Night
B, which had low frequency band amplitude modulation and LAEq,8h=45 dB, slightly open
window (LAEq,8h=33 dB indoors). Taking all of the above together, the results suggest that there
is some evidence that nights with WTN can contribute to sleep disturbance, but how exactly this
contribution occurs is unclear.
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Table 2 Differences in sleep macrostructure between nights. Mean values and standard deviations (±)
are shown for sleep variables for which at least one significant (p<0.01) difference was found between
nights.
Variable
Statistics
Control
Night A
Night B
Night C
Main effect
Post-hoc
SL (min)
10.3±8.4
17.5±10.6
17.0±11.4
21.3±25.5
-
Con:A (p=0.064)
WASO (min)
14.5±6.5
15.0±7.2
20.3±7.3
17.9±10.0
-
Con:B (p=0.031)
First awakening
(min)
39.8±30.0
58.8±51.4
26.3±34.7
57.3±59.6
-
B:C (p=0.076)
Max N2 (min)
38.3±8.0
27.7±6.6
36.1±9.0
26.9±5.7
F(3,15)=8.792
p=0.001
Con:A (p=0.005)
Con:C (p=0.005)
B:A (p=0.029)
B:C (p=0.011)
Max SWS (min)
40.2±10.3
34.8±10.2
32.9±16.9
31.0±8.9
-
Con:A (p=0.079)
Con:C (p=0.035)
SE (%)
94.8±1.9
93.2±3.1
92.2±2.1
91.8±7.2
-
Con:B (p=0.073)
SWS (%)
22.8±4.9
21.7±5.3
18.0±3.7
22.0±4.0
F(3,15)=4.070
p=0.027
Con:B (p=0.055)
Con:C (p=0.050)
SL=Sleep Latency following lights-out; WASO=Wakefulness After Sleep Onset; First awakening=Time of
first EEG awakening following sleep onset; Max N2=Maximum uninterrupted period in sleep stage N2;
Max SWS=Maximum uninterrupted period in Slow Wave Sleep; SE=Sleep Efficiency; SWS=Percentage
of time asleep in Slow Wave Sleep
3.2 Differences between sound character periods
Sound character periods 1 to 8, plus the quiet period, were compared across the control and
exposure nights. The following main effects were found:
Time awake in period 7: F(3,15)= 3.325, p=0.048 (see Figure 1A)
Percentage of time in N1 sleep in period 6: F(3,15)= 3.201, p=0.054 (see Figure 1B)
Percentage of time in N1 sleep in period 7: F(3,15)= 2.568, p=0.093 (see Figure 1B)
Percentage of time in SWS sleep in period 4: F(3,15)=11.454, p<0.001 (see Figure 1B)
Percentage of time in REM sleep in period 8: F(3,15) =3.041, p=0.062 (see Figure 1B)
Post-hoc comparisons between nights for periods where main effects were found are given in
Table 3.
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A
B
Figure 1A Time awake in sound character period 7 1B Distribution of time in difference sleep stages in
different sound character periods between nights. Error bars indicate 95% confidence intervals.
Table 3 Significant post hoc effects between nights for sleep macrostructure variables in periods with the
same sound character. No significant post hoc effects were found for TST or %N2. Only periods where a
main effect was observed are shown.
Variable
Sound character period
1
2
3
4
5
6
7
8
9
Wake
(min)
Con:A p=0.018
Con:C p=0.017
N1 (%)
Con:B p=0.029
Con:C p=0.079
B:C p=0.006
Con:C p=0.036
SWS (%)
Con:B
p=0.009
A:B p=0.006
B:C p=0.001
REM (%)
Con:B
p=0.053
Data for EEG arousals, awakenings and sleep stage changes (SSCs) for all three exposure nights
were pooled for all sound periods sharing common sound character. Main effects were found for
the frequency of SSCs (F(5,25)=2.299, p=0.075) and awakenings (F(5,25)=2.146, p=0.093). No
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effects were found for arousals. From Figure 2 it appears that SSCs occur more frequently during
periods with weak AM and no beats, and that awakenings occur more frequently during periods
with strong AM and beats. These findings were not statistically significant however.
Figure 2 Frequency of SSCs and awakenings during periods with high and low RPM, strong and weak
amplitude modulation, and beats or no beats. Data presented are summed from all three exposure nights.
Error bars indicated 95% confidence intervals.
3.2.1 Summary of effects of sound character periods
In summary, the effects of individual sound character periods on different sleep parameters are:
Effects on N1: Period 7 (low RPM, strong AM, beats), Period 6 (high RPM, weak AM,
beats)
Effects on W time: Period 7 (low RPM, strong AM, beats)
Effects on SWS: Period 4 (high RPM, strong AM, no beats)
Effects on REM: Period 8 (high RPM, strong AM, beats)
Effects on SSCs and awakenings: Low/high AM, no beats/beats
4 Discussion
In this study, the effects of wind turbine noise on sleep were investigated using physiological
measures. There is some evidence that compared to control nights with no noise, sleep during
nights with WTN had a reduced amount of SWS, more time spent awake, increased sleep latency
and a reduction in sustained SWS and N2 sleep. The amount of REM sleep, SWS and WASO
were affected by sound characters with strong amplitude modulation. N1 (“light”) sleep was more
prevalent during noise with beats. SWS in particular has been identified as important for
declarative memory in humans [16]. Furthermore, SWS is considered to be important for physical
restoration [17] and is accordingly prioritised after sleep deprivation [18]. REM sleep is believed
to be important for cognition [19]. Despite the observed physiological disruptions, it is unclear at
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present whether the size of these effects of WTN on SWS and REM would interfere with any
associated biological processes.
During sleep, the body reacts more strongly to an abrupt change in acoustic environment than a
gradual change. For instance, awakening probability is linked with noise rise time [20]. As such it
was hypothesised if WTN had negative effects on sleep, such effects would manifest during high
rotational speeds, strong amplitude modulation and the presence of beats. The indication, albeit
non-significant, that the frequency of SSCs was lower during periods of high RPM, lower during
periods of strong amplitude modulation and lower during periods with beats could therefore
appear surprising. However, during periods of strong amplitude modulation and periods with
beats there seems to be a higher frequency of awakenings, which do not include changes to a
wake stage. In other words, rather than the participants simply changing sleep stage, full
awakenings seemed to occur instead during these periods.
Although the frequency of awakenings and sleep stage changes were influenced by the strength
of AM, the total number of awakenings and sleep stage changes did not differ between across
any of the experimental nights, including the control. This means that in order for these reactions
to occur at a higher rate during certain times, they are not occurring during other times when they
may have spontaneously appeared as part of the natural rhythm of sleep. These awakenings and
SSCs are being redistributed throughout the night, which may have implications for certain
neuronal processes performed during sleep, such as the clearance of waste products that
accumulate during wakefulness that has been demonstrated in animal studies [21].
This small-scale experiment served as a pilot study, and was therefore limited by a number of
factors. The participants cannot be considered as representative of the population who are
exposed to WTN at home. Together with the small sample size, the conclusions drawn should
not be taken outside of the context of the work; to provide input for future work with a more
appropriate study sample, size, and exposure design. The chosen noise levels were higher than
those recommended in Sweden [22], but were not unrealistically high for other countries, in order
to increase the likelihood of inducing a physiological response. The rationale for this decision was
to increase the expected effect size so as to better detect what elements of the sound character
contributed to response in this small pilot study. An ongoing study has the aim of examining sleep
under the influence of noise from wind turbines at more commonly occurring levels.
5 Conclusions
Physiological measurements indicate that nights with low frequency band amplitude modulation
and LAEq,8h=45 dB, slightly open window (LAEq,8h=33 dB indoors) impacted sleep the most. In
particular, amplitude modulation and the presence of beating were important constituents of the
wind turbine noise contributing to sleep disruption.
Acknowledgments
We thank Stamatina Kalafata, Hanna Hertzberg, Natalie Bogicevic and Nicholas Lindholm for
their assistance in conducting the study. The work was funded by the Swedish Research Council
for Environment, Agricultural Sciences and Spatial Planning (FORMAS) grant number 2013-745.
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... However, there is a noticeable inconsistency regarding the effects of WFN on sleep (National Health and Medical Research Council 2015;Micic et al. 2018). Insignificant effects of WFN on sleep were reported in some studies (Michaud et al. 2013;Jalali et al. 2016), while another objective study has reported that WFN can have some effects on sleep (Smith et al. 2016). Michaud et al. (2013) measured the objective sleep response due to WFN using actigraphy. ...
... Nevertheless, Smith et al. (2016) showed that WFN at high SPLs containing AM can have an effect on sleep in a more controlled environment of WFN characteristics through listening tests. However, the number of participants was limited to six. ...
Conference Paper
Full-text available
Despite the significance of listening tests in identifying the human response to wind farm noise (WFN), little attention has been paid to methodological approaches relevant to WFN listening tests to date. Moreover, evidence on the potential adverse effects of WFN is still not well established. This paper thereby sheds light on the different quantification approaches of human response to WFN characteristics. There is also a discussion on the quality of current evidence regarding the effect of WFN on annoyance and sleep disruption. In the context of listening tests, separating WFN characteristics can be beneficial in many ways. Firstly, acceptable threshold levels for each component of WFN for daytime annoyance and night-time sleep disturbance can be quantified. Second, the most annoying characteristic of WFN can be identified. Third, the most annoying characteristics of WFN can be identified by combining two or more components of WFN. Finally, this helps determine the sufficiency of current penalties applied to WFN characteristics.
... While considerably lower than traffic noise, uncertainty remains concerning chronic sleep disturbance effects of WTN. Worstcase WTN at > 31.9 dB (A) indoors appears to disturb subjective and objective measures of sleep [91,92]. Thus, it remains important to confirm if and how much objective and/or subjective sleep disturbance effects are attributable to WTN, and the mechanisms through which these may occur. ...
... dB (A) indoors, equivalent to 45 dB (A) outdoor, with 1-2 and 7-9 dB amplitude modulation), compared to control conditions. The same group are currently investigating effects on sleep macro-structure using three simulated WTN exposure nights, and field-based PSG studies to investigate sleep in wind turbine-exposed participants in their home environment [91]. ...
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Adequate sleep is important for good health and well-being, and inadequate sleep leads to impaired attention and performance. Persistent poor sleep is also associated with cognitive and metabolic impairment, cardiovascular problems and diminished psychological well-being. Recent growth in wind farm developments has been associated with community complaints regarding sleep disturbance, annoyance and a range of health issues that some attribute to wind farms. Wind turbines create aerodynamic and mechanical noise that, if sufficiently loud, has the potential to disturb residents’ sleep, particularly for those living in close proximity. According to the World Health Organisation (WHO), noise effects on sleep are expected to occur with outside noise levels > 40 dB (A). On the other hand, the WHO guidelines also state that “when prominent low-frequency components are present, measures based on A-weighting are inappropriate”, so uncertainty remains regarding which alternative noise measures and noise limits are most appropriate to mitigate community impacts of wind farm noise on sleep. In Australia, dwellings are typically located > 1 km from the nearest wind turbine where wind farm noise becomes more biased towards lower frequencies (\(\le \) 200 Hz) at low sound pressure levels (\(<\sim \) 40 dB (A) outside) that may or may not be audible inside a dwelling. Nevertheless, as with any environmental noise, wind farm noise has the potential to disturb sleep, via frequent physiological activation responses and arousals affecting the micro-structure of sleep, and the overall macro-structure of sleep, including total sleep time potentially reduced by difficulty falling asleep and returning to sleep following awakenings for whatever reason. Over time, chronic insomnia could potentially develop in individuals with greater sensory acuity and/or those prone to annoyance from environmental noise. However, it is unclear if and how much sleep is disturbed by the relatively low sound pressure levels relevant to wind turbine noise. Good empirical evidence to investigate these plausible mechanisms is sparse. In this paper, we describe the psychophysiological mechanisms that underlie sleep disturbance in response to noise, review current evidence regarding the effects of wind farm noise on sleep, evaluate the quality of existing evidence and identify evolving research in this area.
... In both cases the effects of other parameters, such as differences in the audible-range frequency spectra, appeared to be stronger; as mentioned above, low-frequency character in WTN AM has been highlighted as a possible cause of increased annoyance [53]. In a recent laboratory study, Smith et al [62] investigated the effect of AM WTN on objective parameters of sleep (N = 6), finding evidence that the night with 'strong, low-frequency' AM at an indoor exposure level of 33 dB L Aeq,1h showed most sleep fragmentation and the least amount of slow wave sleep, compared with the control night. The corresponding outdoor equivalent level was 45 dB(A), though the degree of 'masking sound' in the stimuli (eg from wind/vegetation noise) is unclear. ...
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WSP | Parsons Brinckerhoff led a research project on behalf of the UK Government, reviewing the human exposure-response to amplitude-modulated (AM) wind turbine noise (WTN). The review included identifying the potential effects on health, and recommendation of a scheme for use in development planning to control the potential impact of AM WTN on communities situated near to wind farms. This paper focuses on the findings of the review, including effects on community annoyance and health, with reference to the results of recent field studies. The control scheme for AM is described, and emerging measures for mitigation are discussed. Also examined is the range of non-acoustic factors that influence responses to WTN, and potential future approaches to addressing these complex issues are considered.
... A report [32] commissioned by the Scottish Government, which is investing in wind energy to a heroic degree, grudgingly accepts that wind turbine noise interferes with sleep. A recent Swedish study, conducted [33] on healthy volunteers in a sleep laboratory, has shown that the noise produced by wind turbines, particularly low frequency band amplitude modulation, is disruptive to sleep. This was indicated by an increase in electro-physiological awakenings, lighter sleep with more wakefulness, and reduced deep sleep and Rapid Eye Movement sleep. ...
... The noise levels in the present study are rather on the high side, and such levels are unlikely to occur regularly if WTN limits are met [12]. Furthermore, the acoustical characteristics of the noise (amplitude modulation and presence of beats) were chosen based on an initial pilot study designed to provide indications on potentially deleterious acoustical characteristics of WTN [13], and as such the exposures in the present study likely represent a worst-case scenario. It is not a simple task to determine any long-term health consequences of infrequent yet acute disturbance, since the body can compensate for poor sleep in subsequent nights [14]. ...
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Onshore wind turbines are becoming increasingly widespread globally, with the associated net effect that a greater number of people will be exposed to wind turbine noise (WTN). Sleep disturbance by WTN has been suggested to be of particular importance with regards to a potential impact on human health. Within the Wind Turbine Noise Effects on Sleep (WiTNES) project, we have experimentally investigated the physiological effects of night time WTN on sleep using polysomnography and self-reporting protocols. Fifty participants spent three nights in the sound exposure laboratory. To examine whether habituation or sensitisation occurs among populations with long-term WTN exposure, approximately half of the participants lived within 1km of at least one turbine. The remaining participants were not exposed to WTN at home. The first night served for habituation and one WTN-free night served to measure baseline sleep. Wind turbine noise (LAEq,indoor,night=31.9 dB) was introduced in one night. This exposure night included variations in filtering, corresponding to a window being fully closed or slightly open, and variations in amplitude modulation.
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Study objectives Assess the physiologic and self-reported effects of wind turbine noise (WTN) on sleep. Methods Laboratory sleep study (n=50 participants: n=24 living close to wind turbines, n=26 as a reference group) using polysomnography, electrocardiography, salivary cortisol and questionnaire endpoints. Three consecutive nights (23:00-07:00): one habituation followed by a randomized quiet Control and an intervention night with synthesized 32 dB LAEq WTN. Noise in WTN nights simulated closed and ajar windows and low and high amplitude modulation depth. Results There was a longer REM sleep latency (+16.8 min) and lower amount of REM sleep (-11.1 min, -2.2%) in WTN nights. Other measures of objective sleep did not differ significantly between nights, including key indicators of sleep disturbance (sleep efficiency: Control 86.6%, WTN 84.2%; wakefulness after sleep onset: Control 45.2 min, WTN 52.3 min; awakenings: Control n=11.4, WTN n=11.5) or the cortisol awakening response. Self-reported sleep was consistently rated as worse following WTN nights, and individuals living close to wind turbines had worse self-reported sleep in both the Control and WTN nights than the reference group. Conclusions Amplitude modulated continuous WTN may impact on self-assessed and some aspects of physiologic sleep. Future studies are needed to generalize these findings outside of the laboratory, and should include more exposure nights and further examine possible habituation or sensitization.
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Study objectives: To investigate the association between self-reported and objective measures of sleep and wind turbine noise (WTN) exposure. Methods: The Community Noise and Health Study, a cross-sectional epidemiological study, included an in-house computer-assisted survey and sleep pattern monitoring over a 7 d period. Outdoor WTN levels were calculated following international standards for conditions that typically approximate the highest long-term average levels at each dwelling. Survey data were collected between May and September 2013 from adults, aged 18-79 y (606 males, 632 females) randomly selected from each household and living between 0.25 and 11.22 kilometers from operational wind turbines in two Canadian provinces. Self-reported sleep quality over the past 30 d was assessed using the Pittsburgh Sleep Quality Index. Additional questions assessed the prevalence of diagnosed sleep disorders and the magnitude of sleep disturbance over the previous year. Objective measures for sleep latency, sleep efficiency, total sleep time, rate of awakening bouts, and wake duration after sleep onset were recorded using the wrist worn Actiwatch2® from a subsample of 654 participants for a total of 3,772 sleep nights. Results: Participant response rate for the survey was 78.9%. Outdoor WTN levels reached 46 dB(A) with an arithmetic mean of 35.6 and a standard deviation of 7.4. Self-reported and objectively measured sleep outcomes consistently revealed no apparent pattern or statistically significant relationship to WTN levels. However, sleep was significantly influenced by other factors, including, but not limited to, the use of sleep medication, other health conditions (including sleep disorders), caffeine consumption, and annoyance with blinking lights on wind turbines. Conclusions: Study results do not support an association between exposure to outdoor WTN up to 46 dB(A) and an increase in the prevalence of disturbed sleep. Conclusions are based on WTN levels averaged over 1 y and, in some cases, may be strengthened with an analysis that examines sleep quality in relation to WTN levels calculated during the precise sleep period time.
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Wind turbine noise exposure and suspected health-related effects thereof have attracted substantial attention. Various symptoms such as sleep-related problems, headache, tinnitus and vertigo have been described by subjects suspected of having been exposed to wind turbine noise. This review was conducted systematically with the purpose of identifying any reported associations between wind turbine noise exposure and suspected health-related effects. A search of the scientific literature concerning the health-related effects of wind turbine noise was conducted on PubMed, Web of Science, Google Scholar and various other Internet sources. All studies investigating suspected health-related outcomes associated with wind turbine noise exposure were included. Wind turbines emit noise, including low-frequency noise, which decreases incrementally with increases in distance from the wind turbines. Likewise, evidence of a dose-response relationship between wind turbine noise linked to noise annoyance, sleep disturbance and possibly even psychological distress was present in the literature. Currently, there is no further existing statistically-significant evidence indicating any association between wind turbine noise exposure and tinnitus, hearing loss, vertigo or headache. Selection bias and information bias of differing magnitudes were found to be present in all current studies investigating wind turbine noise exposure and adverse health effects. Only articles published in English, German or Scandinavian languages were reviewed. Exposure to wind turbines does seem to increase the risk of annoyance and self-reported sleep disturbance in a dose-response relationship. There appears, though, to be a tolerable level of around LAeq of 35 dB. Of the many other claimed health effects of wind turbine noise exposure reported in the literature, however, no conclusive evidence could be found. Future studies should focus on investigations aimed at objectively demonstrating whether or not measureable health-related outcomes can be proven to fluctuate depending on exposure to wind turbines.
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Wind farms are a new source of environmental noise. The impact of wind turbine noise on health and well-being has not yet been well-established and remains under debate. Long-term effects, especially, are not known, because of the short time wind turbines have been operating and the relatively few people who have so far been exposed to wind turbine noise. As the rate of new installations increases, so does the number of people being exposed to wind turbine noise and the importance of identifying possible adverse health effects. Data from three cross-sectional studies comprising A-weighted sound pressure levels of wind turbine noise, and subjectively measured responses from 1,755 people, were used to systematically explore the relationships between sound levels and aspects of health and well-being. Consistent findings, that is, where all three studies showed the same result, are presented, and possible associations between wind turbine noise and human health are discussed. © 2011 Institute of Noise Control Engineering.