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Estimating annoyance to calculated wind turbine shadow flicker is improved when variables associated with wind turbine noise exposure are considered
Author(s) -
Sonia A. Voicescu,
David S. Michaud,
Katya Feder,
Leonora Marro,
John Than,
Mireille Guay,
Allison Denning,
Tara Bower,
Frits van den Berg,
Norm Broner,
Éric Lavigne
Publication year - 2016
Publication title -
the journal of the acoustical society of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 187
eISSN - 1520-8524
pISSN - 0001-4966
DOI - 10.1121/1.4942403
Subject(s) - annoyance , turbine , confidence interval , wind power , environmental science , flicker , noise (video) , statistics , wind speed , odds ratio , meteorology , medicine , mathematics , audiology , computer science , engineering , physics , mechanical engineering , electrical engineering , artificial intelligence , image (mathematics) , loudness , operating system
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.

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