Characterization of high background radiation of terrestrial naturally occurring radionuclides in a mining region of senegal

Abstract

A survey of natural radioactivity has been carried out to estimate the concentration of naturally occurring radionuclides and radiological risk associated in the south East mining region, which present high natural background radiation. An in-situ gamma-ray spectrometer was used to map natural environmental gamma-emitting radionuclides. A combined inferential statistical and chemometrics of naturally occurring radionuclides were used for data modelling and characterization. The radiological data surveys were explored using inferential statistical, principal component analysis and a supervised support vector machine learning. First, one-way Analysis of variance, on-parametric Kruskal Wallis test, was applied allowing pairwise comparison of radionuclides levels in sampling sites, second, data was submitted to PCA to extract noise-free data and reduced data was analyzed with support vector machine. PCA results show that238 U contribution (56%) is dominant in the first principal component with40 K (35%). The second component, with the cumulative variance explained of 88%, is dominated by232 Th (68%) and40K (31%). Anova indicates that there is a significant difference in the mean mass concentration of samples type and soil activities are mostly lower. The best classification accuracy was 100% with the use of radial kernel density function. As potential uranium and gold mine site, these results will allow establishing both reference values for background radiation of the region and fingerprinting sources of naturally occurring radionuclides.