Towards Urban Sustainability: Developing Noise Prediction Model in an Informal Setting

Abstract

Noise remains an important challenge, particularly in informal settings where planning and regulation are relatively weak. This study aims at developing a model to predict noise in a largely informal urban Kano, the second most populated city in Nigeria. Sound level meter (SLM) 200 TL was used to measure noise at locations covering different land use: residential, industrial, commercial, educational, and administrative areas. Data were collected for seven days, and each day measurements were taken for six hours: 8–10 a.m., 12–2 p.m. and 4–6 p.m. Land use, population density, residential division, traffic volume, and land cover were used to generate a noise model using weighted geographic regression. The findings revealed that noise in the area is higher than the permissible limits set by the WHO and Nigeria’s regulatory agency. The model identified population density as the most influencing factor, followed by land cover, traffic volume and distance to the road, then land use. Seventy three percent of the model’s residual are below five, indicating a significant association between noise and the variables used. The R2 ranges between 18% and 26% depending on the time of the day. Noise in the area can be effectively control by paying serious attention to city planning and enforcing traffic regulation measures.

Keywords

• sound level
• prediction
• city
• population
• land use
• urban planning