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Derivation and application of a composite annoyance reaction construct based on multiple wind turbine features

OBJECTIVES: Noise emissions from wind turbines are one of multiple wind turbine features capable of generating annoyance that ranges in magnitude from not at all annoyed to extremely annoyed. No analysis to date can simultaneously reflect the change in all magnitudes of annoyance toward multiple win...

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Autores principales: Michaud, David S., Marro, Leonora, McNamee, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6019414/
https://www.ncbi.nlm.nih.gov/pubmed/29981033
http://dx.doi.org/10.17269/s41997-018-0040-y
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author Michaud, David S.
Marro, Leonora
McNamee, James
author_facet Michaud, David S.
Marro, Leonora
McNamee, James
author_sort Michaud, David S.
collection PubMed
description OBJECTIVES: Noise emissions from wind turbines are one of multiple wind turbine features capable of generating annoyance that ranges in magnitude from not at all annoyed to extremely annoyed. No analysis to date can simultaneously reflect the change in all magnitudes of annoyance toward multiple wind turbine features. The primary objective in this study was to use principal component analysis (PCA) to provide a single construct for overall annoyance to wind turbines based on reactions to noise, blinking lights, shadow flicker, visual impacts, and vibrations evaluated as a function of proximity to wind turbines. METHODS: The analysis was based on data originally collected as part of Health Canada’s cross-sectional Community Noise & Health Study (CNHS). One adult participant (18–79 years), randomly selected from dwellings in Ontario (ON) (n = 1011) and Prince Edward Island (PEI) (n = 227), completed an in-person questionnaire. Content relevant to the current analysis included the annoyance responses to wind turbines. RESULTS: The first construct tested in the PCA explained 58–69% of the variability in total annoyance. Reduced distance to turbines was associated with elevated aggregate annoyance scores among ON and PEI participants. In the ON sample, aggregate annoyance was effectively absent in areas beyond 5 km (mean 0.12; 95% CI 0.00, 1.19), increasing significantly between (2 and 5] km (mean 2.13; 95% CI 0.92, 3.33), remaining elevated, but with no further increase until (0.550–1] km (mean 3.37; 95% CI 3.02, 3.72). At ≤ 0.550 km, the average overall annoyance was 3.36 (95% CI 2.03, 4.69). In PEI, aggregate annoyance was essentially absent beyond 1 km; i.e., (1–2] km (mean 0.21; 95% CI 0.00, 0.88); (2–5] km (mean 0.00; 95% CI 0.00, 1.37); > 5 km (mean 0.00; 95% CI 0.00, 1.58). Annoyance significantly increased in areas between (0.550 and 1] km (mean 1.59; 95% CI 1.02, 2.15) and was highest within 550 m (mean 4.25; 95% CI 3.34, 5.16). CONCLUSION: The advantages and disadvantages to an aggregated annoyance analysis, including how it should not yet be considered a substitute for relationships based on changes in high annoyance, are discussed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.17269/s41997-018-0040-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-60194142018-07-11 Derivation and application of a composite annoyance reaction construct based on multiple wind turbine features Michaud, David S. Marro, Leonora McNamee, James Can J Public Health Quantitative Research OBJECTIVES: Noise emissions from wind turbines are one of multiple wind turbine features capable of generating annoyance that ranges in magnitude from not at all annoyed to extremely annoyed. No analysis to date can simultaneously reflect the change in all magnitudes of annoyance toward multiple wind turbine features. The primary objective in this study was to use principal component analysis (PCA) to provide a single construct for overall annoyance to wind turbines based on reactions to noise, blinking lights, shadow flicker, visual impacts, and vibrations evaluated as a function of proximity to wind turbines. METHODS: The analysis was based on data originally collected as part of Health Canada’s cross-sectional Community Noise & Health Study (CNHS). One adult participant (18–79 years), randomly selected from dwellings in Ontario (ON) (n = 1011) and Prince Edward Island (PEI) (n = 227), completed an in-person questionnaire. Content relevant to the current analysis included the annoyance responses to wind turbines. RESULTS: The first construct tested in the PCA explained 58–69% of the variability in total annoyance. Reduced distance to turbines was associated with elevated aggregate annoyance scores among ON and PEI participants. In the ON sample, aggregate annoyance was effectively absent in areas beyond 5 km (mean 0.12; 95% CI 0.00, 1.19), increasing significantly between (2 and 5] km (mean 2.13; 95% CI 0.92, 3.33), remaining elevated, but with no further increase until (0.550–1] km (mean 3.37; 95% CI 3.02, 3.72). At ≤ 0.550 km, the average overall annoyance was 3.36 (95% CI 2.03, 4.69). In PEI, aggregate annoyance was essentially absent beyond 1 km; i.e., (1–2] km (mean 0.21; 95% CI 0.00, 0.88); (2–5] km (mean 0.00; 95% CI 0.00, 1.37); > 5 km (mean 0.00; 95% CI 0.00, 1.58). Annoyance significantly increased in areas between (0.550 and 1] km (mean 1.59; 95% CI 1.02, 2.15) and was highest within 550 m (mean 4.25; 95% CI 3.34, 5.16). CONCLUSION: The advantages and disadvantages to an aggregated annoyance analysis, including how it should not yet be considered a substitute for relationships based on changes in high annoyance, are discussed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.17269/s41997-018-0040-y) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-04-11 /pmc/articles/PMC6019414/ /pubmed/29981033 http://dx.doi.org/10.17269/s41997-018-0040-y Text en © Crown in Right of Canada 2018. This article is an open access publication 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Quantitative Research
Michaud, David S.
Marro, Leonora
McNamee, James
Derivation and application of a composite annoyance reaction construct based on multiple wind turbine features
title Derivation and application of a composite annoyance reaction construct based on multiple wind turbine features
title_full Derivation and application of a composite annoyance reaction construct based on multiple wind turbine features
title_fullStr Derivation and application of a composite annoyance reaction construct based on multiple wind turbine features
title_full_unstemmed Derivation and application of a composite annoyance reaction construct based on multiple wind turbine features
title_short Derivation and application of a composite annoyance reaction construct based on multiple wind turbine features
title_sort derivation and application of a composite annoyance reaction construct based on multiple wind turbine features
topic Quantitative Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6019414/
https://www.ncbi.nlm.nih.gov/pubmed/29981033
http://dx.doi.org/10.17269/s41997-018-0040-y
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