# Towards Urban Sustainability: Developing Noise Prediction Model in an Informal Setting
## 4 Discussion  
The mean levels in all locations were above the permissible limit. This finding agrees with [19], [6] that reported higher noise above the recommended levels in two Nigerian cities. As expected, that higher population is in congruence with higher noise pollution [37]. Noise is governed by several factors. Among the factors are population [38], land use [39; 40], distance from the road and traffic volume [41], and presence or absence of planning in the area [42]. Population is the most important factor explaining sound level. The next most contributing factors are traffic volume, distance from road, and land use with lowest \(p\)-value equal to 0.179 0.032, and 0.039, respectively. The least contributing factor is housing density with all \(p\)-values greater than 0.05. In the evening noise largely come from traffic sources which is very high, hence road is the most contributory factor. This finding corroborates [43] which stated that traffic is the most contributing pollution source in urban areas. In the morning, noise is largely contributed to by land use. The coefficient of determination ranges 16.3% and 26.8% for the afternoon and morning hours. The finding also reveals that apart from the factor used there may be other determinants of noise pollution, such as the ceremonial activities, wedding activity, and types building material, vegetation cover, culture, and societal values. Hence, there is a need to study noise pollution and noise explanation from people's perspectives. Indeed, the study of noise pollution in New York by [44] noted that explanations of noise pollution using land use and traffic data were found to be limited and have significant uncertainty, especially in large cities where building is diverse and natural environments heterogeneous. The uncertainty may even be more in Kano Metropolis where planning is generally lacking, regulation is poorly enforced, and land uses are too interwoven and mixed. The adjusted \(R^{2}\) points to the actual effect of explanatory variables subtracting influence arising directly from the addition of a new variable even if the variable has no effect. The standard error of an estimate of 10 indicates the expected difference between  
Figure 2: (**a**) Normality of observed noise level and (**b**) normality of predicted noise level. the observed and predicted noise level as 10 dB (A). While an R\({}^{2}\) square is quite small, the \(p\)-value indicates the significant effect of the independent variables (\(p\) = 0.000). Indeed, the coefficient of determination is larger than the 0.13 found by [33] in Western Cape, South Africa. As explained by [33], noise level is difficult to model with high level accuracy because of the influence of many factors, some of which are situational and characterized by high uncertainty. However, [45] found an \(R^{2}\) of 60.1% while modelling traffic noise in Dhanbad township in the eastern part of India. A study of noise pollution in New York, [44] also reported a significant effect of the explanatory variables on noise pollution using land use and traffic data; however, they noted limited and significant uncertainty, especially in large cities where building is diverse and natural environments heterogeneous. The uncertainty may even be higher in the Kano Metropolis where planning is generally lacking, regulations are poorly enforced, and land uses are interwoven and mixed. The study also indicates inequality in the noise level at a city level, with noise being higher in more informal area were residents are poor [46]. The residuals and other model parameters for samples' location gave more information about the model. From the appendix, 73% of the residuals are below 5, indicating the closeness between observed and predicted values. Only 9% of the residual are up to 10 dB, and most of these were observed in commercial and high-density residential areas where factors that can easily lead to wide variation in noise level exist. Moreover, the nature of noise being highly situational led to a wide gap in its prediction.