Predictors of noise annoyance in noisy and quiet urban streets

Abstract

Objectives Although noise annoyance is a major public health problem in urban areas, there is a lack of published data on predictors for noise annoyance in acoustically different urban environments. The aim of the study was to assess the predictive value of various factors on noise annoyance in noisy and quiet urban streets. Methods Equivalent noise levels [Leq (dBA)] were measured during day, evening and night times in all of the streets of a central Belgrade municipality. Based on 24-hour noise levels, the streets were denoted as noisy (24-hour Leq over 65 dBA), or quiet (24-hour Leq under 55 dBA). A cross-sectional study was performed on 1954 adult residents (768 men and 1186 women), aged 18–80 years. Noise annoyance was estimated using a self-report five-graded scale. In both areas, two multivariate logistic regression models were fitted: the first one with nighttime noise indicators and the other one with parameters for 24-hour noise exposure. Results In noisy streets, the relevant predictors of high annoyance were: the orientation of living room/bedroom toward the street, noise annoyance at workplace, and noise sensitivity. Significant acoustical factors for high noise annoyance were: nighttime noise level [OR = 1.02, 95%CI = 1.00–1.04 (per decibel)], nighttime heavy traffic [OR = 1.01, 95%CI = 1.00–1.02 (per vehicle)]; or day–evening–night noise level (Lden) [OR = 1.03, 95%CI = 1.00–1.07 (per decibel)]. In quiet streets, the significant predictors were: noise sensitivity, the time spent at home daily, light vehicles at nighttime or heavy vehicles at daytime. Conclusions Our study identified subjective noise sensitivity as a common annoyance predictor, regardless of noise exposure. Noise levels were important indicators of annoyance only in noisy streets, both for nighttime and 24-hour exposure. We propose that noise sensitivity is the most relevant personal trait for future studies and that nighttime noise levels might be as good as Lden in predicting annoyance in noisy urban areas.