Resource Documents: Noise (558 items)
Documents presented here are not the product of nor are they necessarily endorsed by National Wind Watch. These resource documents are provided to assist anyone wishing to research the issue of industrial wind power and the impacts of its development. The information should be evaluated by each reader to come to their own conclusions about the many areas of debate.
Effect of modulation depth, frequency, and intermittence on wind turbine noise annoyance
Christina Ioannidou, Sébastien Santurette and Cheol-Ho Jeong
J. Acoust. Soc. Am. 139, 1241 (2016); http://dx.doi.org/10.1121/1.4944570
Amplitude modulation (AM) may be an important factor for the perceived annoyance of wind turbine noise (WTN). Two AM types, typically referred to as “normal AM” (NAM) and “other AM” (OAM), characterize WTN AM, OAM corresponding to having intermittent periods with larger AM depth in lower frequency regions than NAM. The extent to which AM depth, frequency, and type affect WTN annoyance remains uncertain. Moreover, the temporal variations of WTN AM have often not been considered. Here, realistic stimuli accounting for such temporal variations were synthesized such that AM depth, frequency, and type, while determined from real on-site recordings, could be varied systematically. Listening tests with both original and synthesized stimuli showed that a reduction in mean AM depth across the spectrum led to a significant decrease in annoyance. When the spectrotemporal characteristics of the original far-field stimuli and the temporal AM variations were taken into account, the effect of AM frequency remained limited and the presence of intermittent OAM periods did not affect annoyance. These findings suggest that, at a given overall level, the AM depth of NAM periods is the most crucial AM parameter for WTN annoyance.
Introductory remarks for special issue on wind turbine noise
Paul Schomer and Sanford Fidell
J. Acoust. Soc. Am. 139, 1430 (2016); http://dx.doi.org/10.1121/1.4942436
The effects of wind turbine noise (WTN) on residential populations have become a matter of considerable popular and technical controversy in recent years. Because fewer resources have been devoted to scientific study of WTN effects than to other forms of environmental noise, much speculation and debate still surrounds the origins and nature of effects of WTN.
This special issue presents findings of a thorough cross-sectional field study of community response to wind turbines conducted by Health Canada. The reported study is notable for its scale, design, care in execution, and sophistication of analysis. It assesses both subjective and objective end points, and it identifies limits to the generalizability of reported findings. Substantial quantities of supplementary data, which accompany the articles, may be accessed electronically through the ASA website. The URLs for this material may be found in the individual articles.
As noted by the authors, interpretations of study findings are subject to limitations inherent to the design itself. Most notably, cross-sectional studies cannot establish causal relationships, nor can the Health Canada study be used to make inferences about the presence of health effects that may occur at very low prevalence rates. The current findings cannot be generalized to settings in which A-weighted WTN levels exceed 46 dB, the upper limit of WTN exposure investigated. The study likewise offers no insight about long term changes in community reaction to WTN beyond the observation that suggests annoyance with WTN does not appear to level off or subside after a year of exposure.
Beyond annoyance, the Health Canada study indirectly suggests that if health effects do exist, they would occur at very low prevalence rates, and that future work in this area could benefit from carefully executed case-control studies in addition to longitudinal studies. Case-control studies would provide an opportunity to study WTN impacts from areas with very low population densities. This is not possible in large-scale cross-sectional studies that aim to assess impacts on a larger population.
A rather strong finding to emerge from this study is that there appears to be a sharp break point at 35 dB where the odds of reporting high annoyance with WTN increase by a factor of 10, and continue to increase further at the highest WTN level category assessed.
This finding lends support to a criterion of meaningful WTN effect at about 35 dB. Such a criterion would be based on the level at which attitudes change, rather than a sleep based limit. The community tolerance level (CTL), analyzed as a part of the paper that models annoyance, provides a good way to compare WTN annoyance to the annoyance caused by more common community noises, such as road traffic. The authors show the close correspondence between the present study and four earlier European studies, lending further support to the use of CTL for comparative analyses
The study further shows that the noise emitted by wind turbines is clearly not the only annoying feature attributed to wind turbines. Annoyance with wind turbines was also related to visual impacts, shadow flicker, and blinking lights. Participants were also found to be concerned for their physical safety. That concern, in turn, was related to annoyance. These findings imply that amelioration of community reactions to wind turbines should consider these factors collectively.
The noise metric that best predicts community response to WTN remains another open question. The Health Canada study examined both A- and C-weighted metrics, which were found to be highly correlated. This may mean only that the several models of wind turbines included in this study all have similar spectral characteristics. The high correlation does not mean that C-weighted assessments may be replaced by A-weighted analyses. Concerns about low frequency noise are best addressed by metrics that are most sensitive to low frequency exposures.
Although A-weighted noise metrics may correlate with community responses to wind turbine noise, this does not necessarily make them the preferred metrics for use in this application. Indeed, the statistical association between A-weighted WTN levels and annoyance in the Health Canada study was especially weak: the base model accounted for only about 9% of the variance when only WTN noise levels were considered. The strength of the model only increased to 58% after other “non-A-weighted” variables were added.
The Health Canada study has clearly advanced understanding of WTN effects, but much remains to be learned.
Wind turbine sound power measurements
Stephen E. Keith, Katya Feder, Sonia A. Voicescu, Victor Soukhovtsev, Allison Denning, Jason Tsang, Norm Broner, Werner Richarz and Frits van den Berg
J. Acoust. Soc. Am. 139, 1431 (2016); http://dx.doi.org/10.1121/1.4942405
This paper provides experimental validation of the sound power level data obtained from manufacturers for the ten wind turbine models examined in Health Canada’s Community Noise and Health Study (CNHS). Within measurement uncertainty, the wind turbine sound power levels measured using IEC 61400-11 [(2002). (International Electrotechnical Commission, Geneva)] were consistent with the sound power level data provided by manufacturers. Based on measurements, the sound power level data were also extended to 16 Hz for calculation of C-weighted levels. The C-weighted levels were 11.5 dB higher than the A-weighted levels (standard deviation 1.7 dB). The simple relationship between A- and C- weighted levels suggests that there is unlikely to be any statistically significant difference between analysis based on either C- or A-weighted data. [download PDF]
Wind turbine sound pressure level calculations at dwellings
Stephen E. Keith, Katya Feder, Sonia A. Voicescu, Victor Soukhovtsev, Allison Denning, Jason Tsang, Norm Broner, Tony Leroux, Werner Richarz and Frits van den Berg
J. Acoust. Soc. Am. 139, 1436 (2016); http://dx.doi.org/10.1121/1.4942404
This paper provides calculations of outdoor sound pressure levels (SPLs) at dwellings for 10 wind turbine models, to support Health Canada’s Community Noise and Health Study. Manufacturer supplied and measured wind turbine sound power levels were used to calculate outdoor SPL at 1238 dwellings using ISO [(1996). ISO 9613-2−Acoustics] and a Swedish noise propagation method. Both methods yielded statistically equivalent results. The A- and C-weighted results were highly correlated over the 1238 dwellings (Pearson’s linear correlation coefficient r > 0.8). Calculated wind turbine SPLs were compared to ambient SPLs from other sources, estimated using guidance documents from the United States and Alberta, Canada. [download PDF]
Exposure to wind turbine noise: Perceptual responses and reported health effects
David S. Michaud, Katya Feder, Stephen E. Keith, Sonia A. Voicescu, Leonora Marro, John Than, Mireille Guay, Allison Denning, D’Arcy McGuire, Tara Bower, Eric Lavigne, Brian J. Murray, Shelly K. Weiss and Frits van den Berg
J. Acoust. Soc. Am. 139, 1443 (2016); http://dx.doi.org/10.1121/1.4942391
Health Canada, in collaboration with Statistics Canada, and other external experts, conducted the Community Noise and Health Study to better understand the impacts of wind turbine noise (WTN) on health and well-being. A cross-sectional epidemiological study was carried out between May and September 2013 in southwestern Ontario and Prince Edward Island on 1238 randomly selected participants (606 males, 632 females) aged 18–79 years, living between 0.25 and 11.22 km from operational wind turbines. Calculated outdoor WTN levels at the dwelling reached 46 dBA. Response rate was 78.9% and did not significantly differ across sample strata. Self-reported health effects (e.g., migraines, tinnitus, dizziness, etc.), sleep disturbance, sleep disorders, quality of life, and perceived stress were not related to WTN levels. Visual and auditory perception of wind turbines as reported by respondents increased significantly with increasing WTN levels as did high annoyance toward several wind turbine features, including the following: noise, blinking lights, shadow flicker, visual impacts, and vibrations. Concern for physical safety and closing bedroom windows to reduce WTN during sleep also increased with increasing WTN levels. Other sample characteristics are discussed in relation to WTN levels. Beyond annoyance, results do not support an association between exposure to WTN up to 46 dBA and the evaluated health-related endpoints. [download PDF]
Personal and situational variables associated with wind turbine noise annoyance
David S. Michaud, Stephen E. Keith, Katya Feder, Sonia A. Voicescu, Leonora Marro, John Than, Mireille Guay, Tara Bower, Allison Denning, Eric Lavigne, Chantal Whelan, Sabine A. Janssen, Tony Leroux and Frits van den Berg
J. Acoust. Soc. Am. 139, 1455 (2016); http://dx.doi.org/10.1121/1.4942390
The possibility that wind turbine noise (WTN) affects human health remains controversial. The current analysis presents results related to WTN annoyance reported by randomly selected participants (606 males, 632 females), aged 18–79, living between 0.25 and 11.22 km from wind turbines. WTN levels reached 46 dB, and for each 5 dB increase in WTN levels, the odds of reporting to be either very or extremely (i.e., highly) annoyed increased by 2.60 [95% confidence interval: (1.92, 3.58), p < 0.0001]. Multiple regression models had R2’s up to 58%, with approximately 9% attributed to WTN level. Variables associated with WTN annoyance included, but were not limited to, other wind turbine-related annoyances, personal benefit, noise sensitivity, physical safety concerns, property ownership, and province. Annoyance was related to several reported measures of health and well-being, although these associations were statistically weak (R2 < 9%), independent of WTN levels, and not retained in multiple regression models. The role of community tolerance level as a complement and/or an alternative to multiple regression in predicting the prevalence of WTN annoyance is also provided. The analysis suggests that communities are between 11 and 26 dB less tolerant of WTN than of other transportation noise sources. [download PDF]
Self-reported and measured stress related responses associated with exposure to wind turbine noise
David S. Michaud, Katya Feder, Stephen E. Keith, Sonia A. Voicescu, Leonora Marro, John Than, Mireille Guay, Allison Denning, Tara Bower, Paul J. Villeneuve, Evan Russell, Gideon Koren and Frits van den Berg
J. Acoust. Soc. Am. 139, 1467 (2016); http://dx.doi.org/10.1121/1.4942402
The current study was the first to assess stress reactions associated with wind turbine noise (WTN) exposure using self-reported and objective measures. Randomly selected participants, aged 18–79 yr (606 males; 632 females), living between 0.25 and 11.22 km from wind turbines, were exposed to outdoor calculated WTN levels up to 46 dBA (response rate 78.9%). Multiple regression modeling left the great majority (77%–89%) of the variance in perceived stress scale (PSS) scores, hair cortisol concentrations, resting blood pressure, and heart rate unaccounted for, and WTN exposure had no apparent influence on any of these endpoints. PSS scores were positively, but weakly, related to cortisol concentrations and resting heart rate (Pearson r = 0.13 and r = 0.08, respectively). Across WTN categories, modeled mean PSS scores ranged from 13.15 to 13.84 (p = 0.8614). Modeled geometric means for hair cortisol concentrations, resting mean systolic, diastolic blood pressure, and heart rate were 150.54–191.12 ng/g (p = 0.5416), 113.38–116.82 mmHg (p = 0.4990), 67.98–70.34 mmHg (p = 0.5006), and 68.24–70.71 bpm (p = 0.5223), respectively. Irrespective of WTN levels, diastolic blood pressure appeared to be slightly (2.90 mmHg 95% CI: 0.75,5.05) higher among participants highly annoyed by blinking lights on turbines (p = 0.0081). Collectively, the findings do not support an association between exposure to WTN up to 46 dBA and elevated self-reported and objectively defined measures of stress. [download PDF]
Estimating annoyance to calculated wind turbine shadow flicker is improved when variables associated with wind turbine noise exposure are considered
Sonia A. Voicescu, David S. Michaud, Katya Feder, Leonora Marro, John Than, Mireille Guay, Allison Denning, Tara Bower, Frits van den Berg, Norm Broner and Eric Lavigne
J. Acoust. Soc. Am. 139, 1480 (2016); http://dx.doi.org/10.1121/1.4942403
The Community Noise and Health Study conducted by Health Canada included randomly selected participants aged 18–79 yrs (606 males, 632 females, response rate 78.9%), living between 0.25 and 11.22 km from operational wind turbines. Annoyance to wind turbine noise (WTN) and other features, including shadow flicker (SF) was assessed. The current analysis reports on the degree to which estimating high annoyance to wind turbine shadow flicker (HAWTSF) was improved when variables known to be related to WTN exposure were also considered. As SF exposure increased [calculated as maximum minutes per day (SFm)], HAWTSF increased from 3.8% at 0 ≤ SFm < 10 to 21.1% at SFm ≥ 30, p < 0.0001. For each unit increase in SFm the odds ratio was 2.02 [95% confidence interval: (1.68,2.43)]. Stepwise regression models for HAWTSF had a predictive strength of up to 53% with 10% attributed to SFm. Variables associated with HAWTSF included, but were not limited to, annoyance to other wind turbine-related features, concern for physical safety, and noise sensitivity. Reported dizziness was also retained in the final model at p = 0.0581. Study findings add to the growing science base in this area and may be helpful in identifying factors associated with community reactions to SF exposure from wind turbines. [download PDF]
Author: Cooper, Steven
Abstract: In the olden days of acoustics (predigital), low frequency analysis used analogue narrow band filters and cathode ray oscilloscopes for special problems leading to the general use of peak values. Analogue filters have time constants that can affect the derived rms values requiring caution where high crest factors are involved. Modern narrowband digital analysis is based on an FFT [fast Fourier transform] of the time signal to extract the periodic function that occurs in the time domain that are then displayed as discrete peaks in the frequency domain. FFT analysis of turbines show discrete infrasound peaks at peaks at multiples of the blade pass frequency in addition to sidebands in the low frequency range spaced at multiples of the blade pass frequency. Are these signals actually there or are they a product of modern day analysis. Is the infrasound signature a clue to a different area of investigation? The paper will show the results of testing to compare old fashioned and modern day analysis.
170th Meeting of the Acoustical Society of America
Jacksonville, Florida, 02-06 November 2015
Author: Sugimoto, Takanao; Koyama, Kenji; Kurihara, Yosuke; and Watanabe, Kajiro
Abstract: This paper describes the development of a new sensor which uses a condenser microphone and a new system containing it as an element. The back of the microphone is covered with a seal chamber, which expands the frequency characteristic of the microphone to the infrasonic region. In addition, a windscreen is fitted to the sensor to reduce or eliminate wind noise. We developed a measurement system with this new sensor, installed it at a wind farm, and measured infrasound. The measurement results confirmed that the measurement system worked normally and could measure infrasound generated by wind turbines. Moreover, it was confirmed that the equivalent continuous sound level is highly correlated with the average rotor speed of a wind turbine.
Figure 7 shows the measurement result of October 25, 2007 19:16 as a sample, and Fig. 8 shows the result calculated by Eq. (2) and the calibration result of a G frequency weighting sound pressure level.
Figure 10 shows the relationship of the equivalent continuous sound level for 80 seconds (calculated from 240 measurement results by using Eq. (3)) and the average rotor speed.
Society of Instrument and Control Engineers Annual Conference 2008
August 20-22, 2008, University of Electro-Communications, Chofu, Tokyo, Japan
Exposure-response relationship of wind turbine noise with self-reported symptoms of sleep and health problems: A nationwide socioacoustic survey in Japan
Author: Kageyama, Takayuki; Yano, Takashi; Kuwano, Sonoko; Sueoka, Shinichi; and Tachibana, Hideki
Abstract: The association of wind turbine noise (WTN) with sleep and physical/mental health has not been fully investigated. To investigate the relationship of WTN with the prevalence of self-reported symptoms of sleep and health problems, a socioacoustic survey of 1079 adult residents was conducted throughout Japan (2010-2012): 747 in 34 areas surrounding wind turbine plants and 332 in 16 control areas. During face-to-face interviews, the respondents were not informed of the purpose of the survey. Questions on symptoms such as sleeplessness and physical/mental complaints were asked without specifying reasons. Insomnia was defined as having one or any combination of the following that occurs three or more times a week and bothers a respondent: Difficulty initiating sleep, difficulty maintaining sleep, premature morning awakening, and feeling of light overnight sleep. Poor health was defined as having high scores for health complaints, as determined using the Total Health Index, exceeding the criteria proposed by the authors of the index. The noise descriptor for WTN was LAeq,n outdoor, estimated from the results of actual measurement at some locations in each site. Multiple logistic analysis was applied to the LAeq,n and insomnia or poor health. The odds ratio (OR) of insomnia was significantly higher when the noise exposure level exceeded 40 dB, whereas the self-reported sensitivity to noise and visual annoyance with wind turbines were also independently associated with insomnia. OR of poor health was not significant for noise exposure, but significant for noise sensitivity and visual annoyance. The above two moderators appear to indicate the features of respondents who are sensitive to stimuli or changes in their homeostasis.
Noise Health. 2016 Mar-Apr;18(81):53-61.