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.

在狭窄的空间（例如生活环境和工作场所）中，ra的浓度水平（Rn ²²²与外部环境相比）可能会很高。由于Rn已被列为仅次于吸烟的第二大肺癌诱因，因此要实施有效的局部降低风险措施，需要室内dense浓度的密集图。这些地图将提供有关ra气浓度高的区域的信息，因此对人类健康更加危险。土壤是Rn的主要来源，因此the的风险评估和减少不能忽略对当地地质的识别。在这方面，我们提出了一种基于基尼系数计算的创新方法，用于实现描述Rn浓度分布的内插图（克里金法）。为了验证该方法，使用了一种模拟sets浓度集的工具，该工具的变异性为：到一阶，由先验控制施加了不同的岩性。通过经典使用的地统计方法和所建议的基于Gini的工具对结果进行了系统的比较。我们展示了如何通过使用后一种工具，使克里金法解决方案看起来更健壮，能够独立于可用测量数量来解析不同的地质genic源。