Influencing Factors Identification and Prediction of Noise Annoyance—A Case Study on Substation Noise

Guoqing Di, Yihang Wang, Yao Yao, Jiangang Ma and Jian Wu
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Guoqing Di: College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Yihang Wang: College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Yao Yao: College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Jiangang Ma: State Grid Shaanxi Electric Power Research Institute, Xi’an 710054, China
Jian Wu: State Grid Shaanxi Electric Power Research Institute, Xi’an 710054, China

IJERPH, 2022, vol. 19, issue 14, 1-14

Abstract: Noise-induced annoyance is one person’s individual adverse reaction to noise. Noise annoyance is an important basis for determining the acceptability of environmental noise exposure and for formulating environmental noise standards. It is influenced by both acoustic and non-acoustic factors. To identify non-acoustic factors significantly influencing noise annoyance, 40 noise samples with a loudness level of 60–90 phon from 500–1000 kV substations were selected in this study. A total of 246 subjects were recruited randomly. Using the assessment scale of noise annoyance specified by ISO 15666-2021, listening tests were conducted. Meanwhile, basic information and noise sensitivity of each subject were obtained through a questionnaire and the Weinstein’s noise sensitivity scale. Based on the five non-acoustic indices which were identified in this study and had a significant influence on noise annoyance, a prediction model of annoyance from substation noise was proposed by a stepwise regression. Results showed that the influence weight of acoustic indices in the model accounted for 80% in which the equivalent continuous A-weighted sound pressure level and the sound pressure level above 1/1 octave band of 125 Hz were 65% and 15%, respectively. The influence weight of non-acoustic indices entering the model was 20% in which age, education level, noise sensitivity, income, and noisy degree in the workplace were 8%, 2%, 4%, 4%, and 2%, respectively. The result of this study can provide a basis for factors identification and prediction of noise annoyance.

Keywords: noise annoyance; non-acoustic factors; influence weight; prediction model; substation noise (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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