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

Author

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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)

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.

Suggested Citation

  • Guoqing Di & Yihang Wang & Yao Yao & Jiangang Ma & Jian Wu, 2022. "Influencing Factors Identification and Prediction of Noise Annoyance—A Case Study on Substation Noise," IJERPH, MDPI, vol. 19(14), pages 1-14, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8394-:d:859133
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    References listed on IDEAS

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    1. Sarah Weidenfeld & Sandra Sanok & Rolf Fimmers & Marie-Therese Puth & Daniel Aeschbach & Eva-Maria Elmenhorst, 2021. "Short-Term Annoyance Due to Night-Time Road, Railway, and Air Traffic Noise: Role of the Noise Source, the Acoustical Metric, and Non-Acoustical Factors," IJERPH, MDPI, vol. 18(9), pages 1-17, April.
    2. Sarah L. Benz & Julia Kuhlmann & Dirk Schreckenberg & Jördis Wothge, 2021. "Contributors to Neighbour Noise Annoyance," IJERPH, MDPI, vol. 18(15), pages 1-14, July.
    3. Guoqing Di & Kuanguang Lu & Xiaofan Shi, 2018. "An Optimization Study on Listening Experiments to Improve the Comparability of Annoyance Ratings of Noise Samples from Different Experimental Sample Sets," IJERPH, MDPI, vol. 15(3), pages 1-13, March.
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    Cited by:

    1. Chao Pan & Yunfa Wu & Sarula Chen & Yang Yang, 2023. "Indoor Environmental Comfort Assessment of Traditional Folk Houses: A Case Study in Southern Anhui, China," IJERPH, MDPI, vol. 20(4), pages 1-23, February.

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