Prediction and management of aircraft noise annoyance around Canadian airports

Noise pollution is a serious environmental problem affecting millions of people world-wide. Aircraft are one source of transportation noise that impacts residents in communities surrounding airports and flight paths. Noise is the biggest nuisance of airport operations and has resulted in complaints, protests, and even legal action. The burden of noise is likely to increase in the coming decades as quieter aircraft technologies lag traffic growth projections, and rapid urbanization narrows the buffer between airports and residential areas. Numerous physical and psychological effects of aircraft noise exposure have been studied, the most common of which is annoyance. Noise annoyance has been identified as a primary health effect endpoint of environmental noise exposure and has also been identified as an aggravating factor to other suspected health effect endpoints. Noise annoyance is also the primary metric used in aircraft noise regulations and guidelines that aim to reduce the effects of aircraft noise on individuals. Managing noise annoyance is the goal of most noise mitigation efforts. Failure to do so can result in prolonged conflicts between airports and their neighbours. To prevent severe annoyance, competent authorities across the world have taken initiative to study the phenomenon and improve methods for its prediction and management. This is a complex task due to the nature of noise annoyance, which does not strictly and closely correlate to noise exposure metrics. More insight into the non-stimulus-related variables, or non-acoustic factors, is required to effectively predict and mitigate annoyance. Acquiring this level of understanding requires large cross-sectional studies that revise and calibrate annoyance and noise metrics, noise thresholds, and guidelines as well as identify non-acoustic contributors to annoyance. Canada has not undertaken this initiative, often relying on international findings to inform its noise policy. This is problematic as annoyance trends evolve with time and location, thus the annoyance prediction and mitigation employed in one country or even community, may not be appropriate at another time and in a different setting. The goal of this research is to improve noise annoyance prediction and understanding, particularly in Canada, in order to facilitate for the management of community expectations. The original hypothesis implored the creation of new metrics that would better correlate to annoyance thus enhance its prediction. Following an extensive review of Canada’s current system for noise annoyance prediction, the Noise Exposure Forecast (NEF), it was determined that the NEF metric is adequate, yet its application and interpretation are flawed and outdated. As a result, the system fails to reflect true community response to noise at various noise exposure levels. To improve the understanding, prediction and ultimately mitigation of annoyance, this research conducted a thorough review of the NEF system including but not limited to noise and annoyance metrics, noise thresholds, noise contours and community response prediction guidelines. In addition, two community surveys were executed in the vicinity of Toronto Pearson International Airport to establish the prevalence of severe noise annoyance and by way of that create a regional dose-response relationship. The surveys also identified non-acoustic variables associated with annoyance. This work contributes to the modernization of Canadian state of the science relating to aircraft noise annoyance and sets the basis for further nationwide research. Resulting from this work was a comparative analysis between the NEF metric and other land use planning metrics (Lden, DNL), a regional dose-response relationship, an updated noise exposure threshold for the onset of significant annoyance, recommendations for revisions to the guidelines for the prediction of community response to aircraft noise, revised noise contour modelling method for the purpose of annoyance prediction, and a statistical model identifying acoustic and non-acoustic predictors of severe annoyance. The above discussed outcomes will provide an updated set of tools to be used in the prediction and management of aircraft noise annoyance around Canadian airports.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.