Radon risk mapping: A new geostatistical method based on Lorenz Curve and Gini index

In confined spaces such as living environments and workplaces, the concentration levels of radon (Rn222) can be very high as compared to the external environment. Since Rn has been classified as the second leading cause of lung cancer after cigarette smoking, to apply efficient locally based risk reduction actions, dense maps of indoor radon concentration are needed. These maps would provide information about the areas prone to high radon concentrations and therefore more dangerous to human health. The soil is the primary source of the Rn, hence the risk assessment and reduction for the radon exposure cannot disregard the identification of the local geology. In this regard, we propose an innovative method, based on the Gini index computation, for the realization of interpolated maps (kriging) to describe the distribution of concentration of Rn. To validate the method, a tool that simulates sets of radon concentrations is used, whose variability is, to the first order, controlled by a priori imposed different lithologies. A systematic comparison is made between the results achieved by means of a classically used geostatistical method and the proposed Gini-based tool. We show how, by using this latter tool, the kriging solutions appear to be more robust to resolve the different geogenic radon sources independently from the number of the available measurements.

Automated summary

In confined spaces, such as living environments and workplaces, high concentrations of radon may pose a health risk. Dense indoor radon concentration maps are needed for effective risk reduction measures tailored to local conditions. An innovative method based on Gini index computation is proposed to create interpolated maps and describe the distribution of radon concentration.