ABSTRACT: On a decibel-for-decibel basis, wind turbine noise is commonly judged as significantly more problematic than most other community noise sources. As a relatively new source of community noise, however, methodological issues remain as to how wind farm noise should be measured, and how data should be collected to afford valid health assessments of turbine noise. Maintaining public health while ensuring that wind farm developments are not unnecessarily blocked has created a tension between the communities asked to host wind farms and those developers wishing to build them. Between them stand local and state regulatory authorities who are increasingly required to judge the risks and benefits of wind farms based on scant data, or technical arguments that go far beyond their expertise. Issues with measurement include, but are not limited to, terrain effects, seasonal and meteorological effects, the validity of averaging, single microphone vs. array recordings, coherent addition of periodic noise sources, level measurements vs. dynamic measurements, selection of frequency weightings, and the effects of thermal stratification on wind shear. Individual responses to wind farm noise are barely related to current acoustical indices and can instead be deconstructed from a set of interacting factors, including noise sensitivity, attachment to place, age, and procedural fairness. A further issue centres on how ‘health’ should be defined, and the best outcome measures to use when judging the impacts of turbine noise. This paper identifies current and advanced wind turbine noise prediction, measurement and assessment issues and uses examples of individual experiences of turbine noise to emphasise the importance of “getting it right”.
Noise Measurement Services Pty Ltd, Brisbane, Australia
School of Public Health, Auckland University of Technology, Auckland, New Zealand
Presented at the annual convention of the Australian Acoustics Society, 2nd-4th November 2011, Queensland, Australia
Download original document: “Wind turbine noise: why accurate prediction and measurement matter”
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