Exploring the Association between Proximity to Industrial Wind Turbines and Self-Reported Health Outcomes in Ontario, Canada
Bibliographic record
Abstract
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. \n \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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".