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<p>Cities encompass a mixture of artificial, human, animal, and nature-based sounds, which through long and short-term exposures, can impact on physical and mental health. Yet, most epidemiological research has focused on only transportation noise, leaving a significant gap in understanding the health impacts of other urban sound types, especially in sub-Saharan Africa (SSA). We conducted a large-scale measurement campaign in Accra, Ghana, collecting audio recordings and sound levels from 129 locations between April 2019-June 2020. We classified sound types with a neural network model and then used Random Forest land use regression to predict prevalences of different sound types citywide. We then developed a composite metric integrating sound levels with the prevalence of sound types. Road traffic sounds dominated the urban core, while human and animal sounds were prominent in high-density and peri-urban areas, respectively. Our high-resolution approach provides a comprehensive characterization of the complexity of urban sounds in a major SSA city, paving the way for new epidemiological studies on the health impacts of exposure to diverse sound sources in the future.</p>

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Moving beyond the noise: geospatial modelling of urban sound environments in a sub-Saharan African city

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

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  • Sierra N. Clark,
  • Raphael E. Arku,
  • Majid Ezzati,
  • James Bennett,
  • Ricky Nathvani,
  • Abosede Sarah Alli,
  • James Nimo,
  • Josephine Bedford Moses,
  • Solomon Baah,
  • Allison Hughes,
  • Samuel Agyei-Mensah,
  • George Owusu,
  • Mireille Toledano,
  • Michael Brauer

摘要

Cities encompass a mixture of artificial, human, animal, and nature-based sounds, which through long and short-term exposures, can impact on physical and mental health. Yet, most epidemiological research has focused on only transportation noise, leaving a significant gap in understanding the health impacts of other urban sound types, especially in sub-Saharan Africa (SSA). We conducted a large-scale measurement campaign in Accra, Ghana, collecting audio recordings and sound levels from 129 locations between April 2019-June 2020. We classified sound types with a neural network model and then used Random Forest land use regression to predict prevalences of different sound types citywide. We then developed a composite metric integrating sound levels with the prevalence of sound types. Road traffic sounds dominated the urban core, while human and animal sounds were prominent in high-density and peri-urban areas, respectively. Our high-resolution approach provides a comprehensive characterization of the complexity of urban sounds in a major SSA city, paving the way for new epidemiological studies on the health impacts of exposure to diverse sound sources in the future.