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Enregistrement W2278250271

Exploring the Association between Proximity to Industrial Wind Turbines and Self-Reported Health Outcomes in Ontario, Canada

2014· dissertation· en· W2278250271 sur OpenAlexaboutno aff
Claire Paller

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2014
Typedissertation
Langueen
DomaineHealth Professions
ThématiqueNoise Effects and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWind powerTurbineScale (ratio)Relevance (law)PsychologyStatisticsEngineeringGeographyEnvironmental scienceMathematicsCartographyPolitical scienceElectrical engineeringMechanical engineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Background: Wind turbines are a form of renewable energy, which generate electricity from wind energy, a practice dating back over 100 years. More recently, large-scale wind energy developments have started to employ one or several industrial wind turbines, which produce the majority of wind energy in Ontario. The production of electricity from the movement of industrial wind turbine motor blades creates both mechanical and aerodynamic noise. This type of environmental noise is a growing public health concern, especially for residents living close to industrial wind turbines. A body of evidence now exists to suggest that industrial wind turbine noise can impair health and contribute to annoyance and sleep disturbance. However, in Ontario, little is known about how industrial wind turbines impact people living in their vicinity. 
\n
\nObjectives: This investigation was a cross-sectional study involving eight Ontario communities that contain greater than ten industrial wind turbines. The objectives of this study were to explore the association between proximity to industrial wind turbines and self-reported health effects, specifically quality of life (both physical and mental health) and sleep disturbance, in residents living close to wind turbines. Dose-response relationships were also explored in an attempt to investigate acceptable exposure levels and appropriate setback distances for industrial wind turbines. 
\n
\nMethods: Eight wind farms in Ontario were selected for analysis. For this cross-sectional study, the ‘Quality of Life and Renewable Energy Technologies Study’ survey was used to measure the impact of industrial wind turbines on health. Using Canada Post’s Unaddressed Admail Service, surveys were sent to 4,876 residences near industrial wind turbines in these eight communities. Survey responses were sent back to the University of Waterloo and data from the surveys were used for analysis. Descriptive analyses were performed and multiple regression models were run to investigate the effect of the main independent variable of interest (distance to nearest industrial wind turbine) on the various outcome variables. Descriptive statistics, including means and standard deviations were performed on a number of dependent and independent variables including age, sex, time in home, number of industrial wind turbines within 2,000 meters and sleep and health outcomes. 
\n
\nResults: In total, 412 surveys were returned (8.45% response rate); 16 of these survey respondents did not provide their home address. Therefore, 396 surveys were included in the analysis. The mean self-reported distances of survey respondents to wind farms was 2,782 meters +-3,950 meters (range: 0.40-55,000 meters). The mean calculated distance from residence to the closest industrial wind turbine was 4,523 meters +-4,420 meters (range: 316-22,661 meters). The difference between the calculated and perceived distance measurements was statistically significant (P<0.001) with survey respondents reporting that they live, on average, 1,741 meters closer to wind farms than they actually do. The relationship between Pittsburgh Sleep Quality Index and ln(distance) was found to be statistically significant (P=0.01) when controlling for age, gender and county, meaning that as distance increased (move further away from an industrial wind turbine), Pittsburgh Sleep Quality Index decreased (i.e. sleep improved) in a logarithmic relationship. Among the eight Wind Turbine Syndrome index variables, the relationship between vertigo and ln(distance) was statistically significant (P<0.001) when controlling for age, gender, and county. Additionally, the relationship between tinnitus and ln(distance) approached statistical significance (P=0.08) when controlling for age, gender and county. Both vertigo and tinnitus were worse among participants living closer to industrial wind turbines. 
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\nConclusion: Study findings suggest that industrial wind turbines could have an impact on health. Using a sample of rural Ontario residents (although not necessarily representative of the target population), this study explored the quality of life (both physical and mental health) and sleep disturbance of residents living in the vicinity of industrial wind turbines. However, because of study limitations, there are many questions still to be answered before firm conclusions can be drawn. Based on the findings of this study it is recommended that further studies be carried out to examine the effects of low-level stressors, such as industrial wind turbine noise, on health. Specifically, study findings suggest that future research should focus on the effects of industrial wind turbine noise on sleep disturbance and symptoms of inner ear problems. Although the study findings could suggest that there is a possible association between various health outcomes and how far someone lives from an industrial wind turbine, it is important to remember that there are limitations to these conclusions.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,310
Score d'incertitude au seuil0,713

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,063
Tête enseignante GPT0,280
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2014
Routes d'admission1
Résumé présentoui

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