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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.


Date added:  January 3, 2017
TechnologyPrint storyE-mail story

Grid-scale fluctuations and forecast error in wind power

Author:  Bel, G.; Connaughton, C.P.; Toots, M.; and Bandi, M.M.

Abstract:
Wind power fluctuations at the turbine and farm scales are generally not expected to be correlated over large distances. When power from distributed farms feeds the electrical grid, fluctuations from various farms are expected to smooth out. Using data from the Irish grid as a representative example, we analyze wind power fluctuations entering an electrical grid. We find that not only are grid-scale fluctuations temporally correlated up to a day, but they possess a self-similar structure—a signature of long-range correlations in atmospheric turbulence affecting wind power. Using the statistical structure of temporal correlations in fluctuations for generated and forecast power time series, we quantify two types of forecast error: a timescale error (eτ) that quantifies deviations between the high frequency components of the forecast and generated time series, and a scaling error (eζ) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations for generated power. With no a priori knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error (eτ) and the scaling error (eζ).

G Bel
Department of Solar Energy and Environmental Physics, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Israel

C P Connaughton
Centre for Complexity Science, University of Warwick, Coventry, U.K.

M Toots and M M Bandi
Collective Interactions Unit, Okinawa Institute of Science and Technology Onna, Okinawa, Japan

Published 1 February 2016 • New Journal of Physics, Volume 18, February 2016
doi: 10.1088/1367-2630/18/2/023015

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Date added:  January 2, 2017
TechnologyPrint storyE-mail story

New insights into fluctuations of wind energy, with implications for engineering and policy

Author:  Okinawa Institute of Science and Technology Graduate University

Summary:
The amount of energy generated by renewables fluctuates depending on the natural variability of resources at any given time. The sun isn’t always shining, nor is the wind always blowing, so traditional power plants must be kept running, ready to fill the energy gap at a moment’s notice. Because the grid has no storage, and unlike coal or nuclear, there is no control over the fluctuating production of renewable energy, the energy they produce has to be consumed straight away, or risk collapsing the electrical grid. On particularly windy days, for example, surges in power generated by wind turbines have been known to overwhelm the electrical grid, causing power outages. To avoid this, operators of large power plants sometimes resort to paying consumers to use electricity on particularly sunny and windy days when there is too much excess power in the system, in order to balance the supply and demand of energy at the grid.

FULL STORY

The amount of energy generated by renewables fluctuates depending on the natural variability of resources at any given time. The sun isn’t always shining, nor is the wind always blowing, so traditional power plants must be kept running, ready to fill the energy gap at a moment’s notice. Because the grid has no storage, and unlike coal or nuclear, there is no control over the fluctuating production of renewable energy, the energy they produce has to be consumed straight away, or risk collapsing the electrical grid. On particularly windy days, for example, surges in power generated by wind turbines have been known to overwhelm the electrical grid, causing power outages. To avoid this, operators of large power plants sometimes resort to paying consumers to use electricity on particularly sunny and windy days when there is too much excess power in the system, in order to balance the supply and demand of energy at the grid.

Dealing with the peaks and troughs of intermittent renewable energy will become increasingly challenging as governments try to phase out of more stable coal-powered energy sources in the coming decades. In order to mitigate or manage these fluctuations in renewable energy, we need to understand the nature of these fluctuations better. Professor Mahesh Bandi, head of the Collective Interactions Unit at the Okinawa Institute of Science and Technology Graduate University (OIST) has used turbulence theory combined with experimental wind plant data to explain the statistical nature of wind power fluctuations in a single-author paper published in Physical Review Letters.

Wind speed patterns can be depicted as a wind speed spectrum on a graph. In 1941, Russian physicist Andrei Kolmogorov worked out the spectrum of wind speed fluctuations. Subsequently, it was shown that the spectrum for wind power follows the exact same pattern. However, until now, it was simply assumed that these spectra were identical due to the relationship between power and speed, where power equals wind speed cubed. But this proved to be a red herring. Professor Bandi has shown for the first time that the spectrum of wind power fluctuations follows the same pattern as wind speed fluctuations for a different reason.

Kolmogorov’s 1941 result applies to measurements of wind speed made at several distributed points in space at the same time. But wind power fluctuations at a turbine are measured at a fixed location over an extended time period. The two measurements are fundamentally different, and by carefully accounting for this difference, Professor Bandi was able to explain the spectrum of wind power fluctuations for an individual turbine.

We can think of turbulence as a ball of air, or an ‘eddy’, of fluctuating wind speed. Long time-scale, low frequency eddies can span hundreds of kilometres. Inside these large eddies are shorter time-scale, high frequency eddies that might span a few kilometres. Therefore, if all of the turbines in the same wind plant fall within the same short and long time-scale eddies, the energy they produce fluctuates as if the entire plant were one giant turbine. This is exactly what Professor Bandi found when he looked at the wind power fluctuations of all of the turbines in a wind plant in Texas.

In fact, even geographically dispersed wind plants can exhibit correlated fluctuations in power if they fall within the same short and long time-scale eddies. However, as the distance between wind plants increases, their power fluctuations start to decouple from each other. Two geographically dispersed wind plants might encounter the same long time-scale wind speed fluctuations whilst encountering completely distinct shorter time-scale wind speed fluctuations.

In the past, some scientists have underestimated the problem of turbulence, arguing that the power produced by geographically dispersed wind turbines in windy and calm locations at any one point in time will average out when they reach a centralised grid. However, Professor Bandi’s findings show for the first time, that this phenomenon, known as ‘geographic smoothing’, only works to a certain extent.

The power generated by geographically dispersed turbine plants averages at high frequencies, because while one plant might fall within the short time-scale eddy, the other might not. In other words, the surge in power output at one plant is averaged out by a trough in power output from another, far-away plant at high frequencies. But because the plants still fall within the same long time-scale eddy, the power they produce will have correlated fluctuations at low frequencies, which generate the most power. A surge in power at one wind turbine plant will coincide with the surge at a far-away plant within the same long time-scale eddy, meaning that the power they feed to the grid cannot be averaged out. This means that there is a natural limit to how much one can average fluctuations in wind power; a limit beyond which fluctuations can continue to wreak havoc on the grid. Using data from 20 wind plants in Texas and 224 wind farms in Ireland Professor Bandi showed that this limit exists in reality.

“Understanding the nature of fluctuations in wind turbine power has immediate implications for economic and political decision making,” says Professor Bandi.

Due to the variability of renewables, coal-fired power plants providing back-up energy are kept running in case of sudden power outages, meaning that more energy is produced than needed. This means that ‘green’ energy is still contributing to carbon emissions, and there is an associated cost of maintaining reserve energy, that will only increase as the proportion of renewables increases in the years to come. The discovery of a limit in geographical smoothing, articulated by Professor Bandi, will enable better estimates of the operative amount of reserves that needs to be maintained.

This discovery will also impact environmental policy. By considering the limit for averaging fluctuations of power, combined with the availability of different renewable resources such as sun, wind and waves in a particular area, policy-makers will be better equipped to work out optimal combinations of different energy sources for specific regions

“Understanding the nature of fluctuations for wind turbines could also open up other avenues of research in other fluctuating systems,” says Professor Bandi.

Science Daily, December 31, 2016, https://www.sciencedaily.com/releases/2016/12/161231184935.htm

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Date added:  December 21, 2016
Europe, WildlifePrint storyE-mail story

Patterns of migrating soaring migrants indicate attraction to marine wind farms

Author:  Skov, Henrik; et al.

Abstract:
Monitoring of bird migration at marine wind farms has a short history, and unsurprisingly most studies have focused on the potential for collisions. Risk for population impacts may exist to soaring migrants such as raptors with K-strategic life-history characteristics. Soaring migrants display strong dependence on thermals and updrafts and an affinity to land areas and islands during their migration, a behaviour that creates corridors where raptors move across narrow straits and sounds and are attracted to islands. Several migration corridors for soaring birds overlap with the development regions for marine wind farms in NW Europe. However, no empirical data have yet been available on avoidance or attraction rates and behavioural reactions of soaring migrants to marine wind farms. Based on a post-construction monitoring study, we show that all raptor species displayed a significant attraction behaviour towards a wind farm. The modified migratory behaviour was also significantly different from the behaviour at nearby reference sites. The attraction was inversely related to distance to the wind farm and was primarily recorded during periods of adverse wind conditions. The attraction behaviour suggests that migrating raptor species are far more at risk of colliding with wind turbines at sea than hitherto assessed.

Henrik Skov, Mark Desholm, Stefan Heinänen, Johnny A. Kahlert, Bjarke Laubek, Niels Einar Jensen, Ramūnas Žydelis, Bo Præstegaard Jensen

Published 21 December 2016.
Biology Letters, volume 12, issue 12
DOI: 10.1098/rsbl.2016.0804

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Date added:  December 19, 2016
HealthPrint storyE-mail story

Response to McCunney et al: Wind turbines and health: an examination of a proposed case definition

Author:  McMurtry, Robert; and Krogh, Carmen

Some people living in the environs of industrial wind turbines (IWTs) report experiencing adverse health effects (AHE/IWT). Reported effects include annoyance, sleep disturbance, stress-related health impacts and reduced quality of life. In some cases, families have effectively abandoned their homes, been billeted by wind energy developers or have negotiated financial agreements with developers.[1]

McMurtry and Krogh[2] presented Diagnostic criteria for adverse health effects in the environs of wind turbines to assist medical practitioners presented with patients reporting AHE/IWTs. Noise and Health published the views of McCunney et al.[3] that presented critical commentary on these AHE/IWT diagnostic criteria as well as its predecessor, which were presented by McMurtry.[4]

In this response, McMurtry and Krogh present a critical analysis of the commentary contained in the presentation of McCunney et al.

References cited to support the content of this response include the following:

Noise & Health 2016;18(85):399–402

‘Key Points’

McCunney et al. asserted that they contrasted the McMurtry and Krogh criteria with the Institute of Medicine guidelines for the development of clinical guidelines and itemized ‘key points’[3] in the two tables reproduced below.

In the work of McCunney et al., Table 2 states ‘[a]ll of the symptoms and conditions in the case definition are common and nonspecific, and have numerous causes’.[3] The medical significance of this table item remains a mystery as numerous medical conditions have common symptoms. The third-order criteria presented in the case definition by McMurtry and Krogh[2] are symptoms consistent with the ‘well-known stress effects of exposure to noise’,[1,5] which ‘are familiar to environmental noise control officers and other “on the ground” professionals’.[5]

McCunney et al.’s co-authors, Drs. Colby and McCunney and others, were paid by the wind industry to prepare a report that examined the potential health impacts of wind turbines.[6] Their report shows Colby et al. stating that their authors ‘undertook extensive review, analysis, and discussion of the large body of peer reviewed literature on sound and health effects in general, and on sound produced by wind turbines’.[5] On the basis of this review, Colby et al. determined the documented symptoms of ‘wind turbine syndrome’ (sleep disturbance, headache, tinnitus, ear pressure, dizziness, vertigo, nausea, visual blurring, tachycardia, irritability, problems with concentration and memory, and panic episodes associated with sensations of internal pulsation or quivering when awake or asleep) ‘are not new’ and have been published previously in the context of ‘annoyance’[5] and are the ‘well-known stress effects of exposure to noise’.[5]

The key aspect is that these symptoms cluster in some affected individuals exposed to IWTs. It is important that a simple method be available for family practitioners to assess appropriately the range of symptoms reported. McMurtry and Krogh have outlined such a method.

The next item listed by McCunney et al. in Table 2 states ‘[t]he case definition lacks the requirement to confirm reports of symptoms with medical records and diagnostic studies’.[3] This statement is erroneous. McMurtry and Krogh have specified that a licensed practitioner must conduct a thorough history, physical examination and investigation and rule out alternative explanations before ‘presumed diagnosis’ of AHE/IWT is warranted.[2] To achieve the category of ‘confirmed diagnosis’ McMurtry and Krogh have specified that additional investigative procedures are required and have proposed sophisticated investigative procedures, such as ‘measurements electrophysiologically and by biomarkers’.[2] Reader feedback has since commented that given the accumulation of evidence for a causal link between sleep disturbance and cardiovascular disease, it is not prudent to wait for serious events to accrue. Therefore, it would be expedient to measure intermediate phenotypes, such as biomarker, which are known to lie along the patho-physiological pathway between health and disease.

In the article by McCunney et al., the third item that ‘[a]ll medical information, with two exceptions, is subjective’[3] in Table 2 is irrelevant. It is a normal clinical practice for physicians to diagnose and treat patients displaying subjective symptoms. For example, this practice assists with incidence and surveillance of arthritis[7] and management of postoperative and other pain such as migraine headache.

McCunney et al. expressed concern that ‘[t]he case definition does not meet essential criteria for clinical guidelines, most notably by lack of committee involvement in the development of the guidelines, as the AHE/IWT reflects two authors perspective’.[3] McCunney et al. then present the reader with contradictory information in Table 3 stating that the case definition was ‘developed by one author’.[3] As described by McMurtry[4], the origin of the criteria and their genesis stemmed from a 3-day symposium during which 11 experts presented their perspectives.[8] The symposium proceedings were published in a special edition of a peer-reviewed journal.[9] McCunney et al. omitted the disclosure of this information.

McCunney et al. alleged that McMurtry and Krogh ‘lack of indication of potential conflicts of interest’[3] and stated that the conflicts of interest statement was ‘[n]ot done’.[3] McCunney et al. provided no explanation or reference to support these allegations.

As health professionals, McMurtry[10] and Krogh[11] understand the obligation of authors to declare conflicts of interest. Had McCunney et al. approached McMurtry and Krogh with these unsupported allegations, they would have been advised there is no undeclared conflict of interest. McMurtry and Krogh declare they: complied fully with the Journal of the Royal Society of Medicine conflict of interest requirements; and at no time have McMurtry or Krogh: been paid to serve as experts on IWTs; received funding or grants to publish on IWTs; received financial remuneration for their services or research on IWTs.

McMurtry and Krogh would have further advised that the obligation to state potential conflicts of interest would also extend to the authors of McCunney et al. Any statement of potential conflicts of interest should include Drs. McCunney, Mundt and Colby relationships with the wind industry including, but not limited to, payments received from the wind industry to serve as experts and/or prepare reports for the wind industry that examined the potential health impacts of wind turbines. McCunney et al. disclosed that some of its authors served as experts in several litigation matters on behalf of wind farm developers and wind turbine manufacturers.[3] This declaration by McCunney et al. is incomplete as it omits disclosure of payments received for other services, such as payments to prepare report(s) for the wind industry that examined the potential health impacts of wind turbines.[6]

The criticism by McCunney et al. of research based on self-reports including that of the validated questionnaires is puzzling given such a practice in research is widely utilized in medical and psychological clinical research. The World Health Organization LARES study provided a relevant example of research that employed a health questionnaire filled in by/for individuals (including children)[12] and concluded ‘for chronically strong annoyance a causal chain exists between the three steps health – strong annoyance – increased morbidity’.[13]

Drs. Colby and McCunney themselves also have cited research based on self-reporting questionnaires (i.e. Møller and Lydolf, 2002; Mirowska and Mroz, 2000) in support of the Colby et al. determination that ‘wind turbine syndrome’ symptoms are the ‘well-known stress effects of exposure to noise’.[5]

McCunney et al. state in Table 3, ‘Data collection method given: Not done’.[3] Once again, McCunney et al. have presented readers with erroneous information. Under the heading ‘Methods’ McMurtry and Krogh state:

‘A revised case definition was developed through a variety of methods including a review of self-reporting surveys published in the peer-reviewed literature and other sources; interviews and correspondence with neighbours reporting health effects; incident reports posted on the Internet; testimony under oath during judiciary proceedings of neighbours reporting health effects; personal dialogue with physicians; and grey literature. We searched PubMed and Google Scholar for articles published since 2000 that included the terms “wind turbine health”, “wind turbine survey”, “wind turbine symptoms”, “wind turbine self reports” and “wind turbine noise”. A PubMed search with the search term “case definition” obtained additional background relating to case definitions for emerging diagnostic challenges’.[2]

Another item listed by McCunney et al. in Table 3 concerned quality of evidence. The quality of the papers quoted in McMurtry and Krogh is affirmed by other peer-reviewed publications such as the meta-analysis presented by Onakpoya et al.[14] McMurtry and Krogh citations included the clinical review of Jeffery et al., which confirmed the reported ‘effects from exposure to IWTs are consistent with well-known stress effects from persistent unwanted sound’.[1] Also cited was the 2010 review written by a member of the Canadian Wind Energy Association (CanWEA), which concluded ‘The audible sound from wind turbines, at the levels experienced at typical receptor distances in Ontario, is nonetheless expected to result in a non-trivial percentage of persons being highly annoyed. As with sounds from many sources, research has shown that annoyance associated with sound from wind turbines can be expected to contribute to stress related health impacts in some persons’.[15]

The final item listed by McCunney et al. in Table 3 stated ‘Evidence supporting recommendations: Not done’.[3] This statement has the potential to mislead readers as McMurtry and Krogh provided citations throughout to support its content. McMurtry and Krogh’s recommendation that a licensed practitioner conduct a thorough history, physical examination and investigation and rule out alternative explanations is consistent with ‘the requirement to confirm reports of symptoms with medical records and diagnostic studies’[3] listed by McCunney et al. in Table 2.

Combinatorics

McCunney et al. presented a display of ‘combinatorics’ claiming ‘[t]he point of this exercise was to quantitatively evaluate the scientific validity of the proposed case definition if it were to be used as intended’.[3]

Beginning a mathematical exercise with erroneous assumptions will inevitably lead to misleading results as has occurred in McCunney et al. McMurtry and Krogh’s[2] diagnostic criteria required that all of the four first-order criteria be either positive or not. If all of the four diagnostic criteria were not positive, then no further investigation was indicated. If all four were positive, then the next step was to advance to the second-order criteria, which required that three of the four be positive before establishing a ‘probable diagnosis’. Third-order criteria were not required to achieve ‘probable diagnosis’. Investigative steps were next required to determine if ‘presumed diagnosis’ was warranted. The calculations presented by McCunney et al. do not reflect these case definition requirements and are simply irrelevant.

The ‘risks to patients’[3] claimed in the conclusions of McCunney et al. is not supportable. McMurtry and Krogh have published in a medical journal as the case definition is intended to be used by licensed medical practitioners trained in diagnostic procedures. The case definition requires application of professional medical judgement and diligence including the conduct of a thorough history, physical examination and investigation to rule out alternative explanations. It is not intended to be used in mechanical mathematical exercises absent of medical diagnostic diligence. McCunney et al. cannot claim its exercise quantitatively evaluates ‘the scientific validity of the proposed case definition if it were to be used as intended’.[3] McCunney et al.’s exercise in math has the potential to mislead readers as it fails to use the case definition as it is presented and intended.

Discussion

McMurtry and Krogh invited, and have received, commentary, which to date has been positive and constructive. Critical commentary can be informative when accurate, and relevant. Conversely, commentary based on erroneous and irrelevant information has the potential to mislead readers and should be viewed with caution.

McCunney et al. should have focused their commentary on the current version of the case definition. Instead, McCunney et al. erratically jump between the 2014 ‘revised case definition’[2] and the superseded McMurtry[4] with much of its commentary focused on the latter.

It has been acknowledged that the manuscript by McCunney et al. was ‘rejected’ by a peer-reviewed Canadian medical journal before being accepted by Noise and Health.[16]

The references cited in this analysis support the conclusion that the examination by McCunney et al. contains erroneous and irrelevant commentary, which has the potential to mislead readers.

This critical analysis of McCunney et al. cites references that confirm that reported AHE/IWT are consistent with well-known stress effects from persistent unwanted sound.[1,5] Annoyance is an acknowledged health effect.[17–19] A causal chain exists between strong annoyance and increased morbidity,[13] and chronically strong annoyance must be classified as a serious human health risk.[20]

The CanWEA has published a media release, which advised those impacted by wind turbine annoyance stating:

‘The association has always acknowledged that a small percentage of people can be annoyed by wind turbines in their vicinity. … When annoyance has a significant impact on an individual’s quality of life, it is important that they consult their doctor’.[21]

McMurtry and Krogh’s diagnostic criteria is a tool provided to assist practicing physicians who are presented with such patients. The first- and second-order criteria identified individuals who domicile with IWTs in their vicinity and who were experiencing symptoms including annoyance or an impact on their quality of life. These criteria were in keeping with the acknowledgements and advice contained in CanWEA’s 2011 media release. Third-order criteria were used to enhance the understanding of patients’ illness experience and were not essential for satisfying the Diagnostic Criteria.

The content of the article by McCunney et al. suggests that its authors may not have understood the procedure presented for diagnosing patients suffering from ‘annoyance’ and the ‘well-known stress effects of exposure to noise’. While this response does not address all the weaknesses contained in the analysis by McCunney et al., it is our hope it will help clarify understanding of this diagnostic tool. We invite readers to explore the work of McMurtry and Krogh, and as always we welcome constructive commentary.

References

1 Jeffery RD, Krogh CM, Horner B. Industrial wind turbines and adverse health effects. Can J Rural Med 2014;19:21-6. PubMed PMID: 24398354. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24398354.[Last accessed 2016 Nov 15].
2 McMurtry RY, Krogh CM. Diagnostic criteria for adverse health effects in the environs of wind turbines. JRSM Open 2014;5:1-5. doi: 10.1177/2054270414554048. PMID: 25383200 [PubMed] PMCID: PMC4221978. Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=Diagnostic+criteria+for+adverse+health+effects+in+the+environs+of+wind+turbines. [Last accessed 2016 Nov 15].
3 McCunney RJ, Morfeld P, Colby WD, Mundt KA. Wind turbines and health: An examination of a proposed case definition. Noise Health 2015;17:175-81. Available from: http://www.noiseandhealth.org/text.asp?2015/17/77/175/1606782. [Last accessed 2016 Nov 15].
4 McMurtry RY. Toward a case definition of adverse health effects in the environs of industrial wind turbines: Facilitating a clinical diagnosis. Bull Sci Technol Soc 2011;31:316. doi: 10.1177/0270467611415075. Available from: http://bst.sagepub.com/content/31/4/316. [Last accessed 2016 Nov 15].
5 Colby WD, Dobie R, Leventhall G, Lipscomb DM, McCunney RJ, Seilo MT et al. Wind Turbine Sound and Health Effects: An Expert Panel Review. Prepared for American Wind Energy Association and Canadian Wind Energy Association; 2009. Available from: http://www.canwea.ca/pdf/talkwind/Wind_Turbine_Sound_and_Health_Effects.pdf. [Last accessed 2016 Nov 15].
6 Technical Hearing before the State of Vermont Public Service Board heldFebruary 10, 2011. Docket Number 7628. p. 37.
7 Helmick CG, Felson DT, Lawrence RC, Gabriel S, Hirsch R, Kwoh CK et al. Arthritis and Rheumatism, Part I, vol. 58. American College of Rheumatology; 2008. p. 15-25. doi: 10.1002/art.23177. Available from: http://www.commed.vcu.edu/Chronic_Disease/arthritisimpactjan08.pdf. [Last accessed 2016 Nov 15].
8 The Society for Wind Vigilance. First International Symposium on the Global Wind Industry and Adverse Health Effects. Picton, Ontario, Canada. October 29–31, 2010. Available from: http://www.windvigilance.com/international-symposium. [Last accessed 2016 Nov 15].
9 SAGE Publications. Bulletin of Science, Technology and Society (BSTS). Table of Contents – August 2011, 31(4). Available from: http://bst.sagepub.com/content/31/4.toc. [Last accessed 2016 Nov 15].
10 McMurtry R. Order of Canada, June 30, 2011. Available from: http://www.gg.ca/document.aspx?id=14175. [Last accessed 2016 Nov 15].
11 Cheryl Gallant Presents Carmen Krogh With Diamond Jubilee Medal, February 11, 2014. Available from: http://cherylgallant.com/2014/02/11/cheryl-gallant-presents-carmen-krogh-diamond-jubilee-medal/. [Accessed 2016 Nov 15].
12 World Health Organization. Large Analysis and Review of European Housing and Health Status (LARES). Preliminary Overview; 2007.
13 Niemann H, Maschke C. WHO LARES: Final Report: Noise Effects and Morbidity. Geneva, Switzerland: World Health Organization; 2004.
14 Onakpoya IJ, O’Sullivan J, Thompson MJ, Heneghana CJ. The effect of wind turbine noise on sleep and quality of life: A systematic review and meta-analysis of observational studies. Environ Int 2015;82:1-9. Available from: http://www.sciencedirect.com/science/article/pii/S0160412015001051. [Last accessed 2016 Nov 15].
15 HGC. Low Frequency Noise and Infrasound Associated with Wind Turbine Generation Systems. A Literature Review. Ontario Ministry of Environment; 2010. RFP December 2010.
16 Case Nos.: 15-011. Dingeldein v. Ministry of Environment and Climate Change. Environmental Review Tribunal. Transcript of Dr. McCunney, May 11, 2015. p. 71.
17 Health Canada, It’s Your Health, Community Noise Annoyance 2005, (September). ISBN # H50-3/192-2005E-PDF. Catalogue # 0-662-41546-9, © Her Majesty the Queen in Right of Canada, represented by the Minister of Health; 2005. Available from: http://www.hc-sc.gc.ca/hl-vs/alt_formats/pacrb-dgapcr/pdf/iyh-vsv/life-vie/community-urbain-eng.pdf. [Last accessed 2016 Nov 15].
18 Michaud DS, Keith SE, McMurchy D. Noise annoyance in Canada, Noise Health 2005;7:39-47. PMID: 16105248 [PubMed – indexed for MEDLINE]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16105248. [Last accessed 2016 Nov 15].
19 Berglund B, Lindvall T, Schwela DH. Guidelines for Community Noise. Geneva, Switzerland: World Health Organization 1999.
20 Niemann H, Bonnefoy X, Braubach M, Hecht K, Maschke C, Rodrigues C et al. Noise-induced annoyance and morbidity results from the pan-European LARES study. Noise Health 2006;8:63-79.
21 The Canadian Wind Energy Association. The Canadian Wind Energy Association Responds to October 14, 2011 Statement by Wind Concerns Ontario, Media Release (2011, October 14). PDF Available on request.

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