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Indoor radon is an important risk factor for human health. Indeed radon inhalation is considered the second cause of lung cancer after smoking. During the last decades, in many countries huge efforts have been made in order to measuring, mapping and predicting radon levels in dwellings. Various researches have been devoted to identify those areas within the country where high radon concentrations are more likely to be found. Data collected through indoor radon surveys have been analysed adopting various statistical approaches, among which hierarchical Bayesian models and geostatistical tools are worth noting. The essential goal of this paper regards the identification of high radon concentration areas (the so-called radon prone areas) in the Abruzzo Region (Italy). In order to accurately pinpoint zones deserving attention for mitigation purpose, we adopt spatial cluster detection techniques, traditionally employed in epidemiology. As a first step, we assume that indoor radon measurements do not arise from a continuous spatial process; thus the geographic locations of dwellings where the radon measurements have been taken can be viewed as a realization of a spatial point process. Following this perspective, we adopt and compare recent cluster detection techniques: the simulated annealing scan statistic, the case event approach based on distance regression on the selection order and the elliptic spatial scan statistic. The analysis includes data collected during surveys carried out by the Regional Agency for the Environment Protection of Abruzzo (ARTA) in 1,861 random sampled dwellings across 277 municipalities of the Abruzzo region. The radon prone areas detected by the selected approaches are provided along with the summary statistics of the methods. Finally, the methodologies considered in this paper are tested on simulated data in order to evaluate their power and the precision of cluster location detection. [ABSTRACT FROM AUTHOR]

Ancient human settlements accumulate essential historical, archaeological, and geological information. An example is the St. Angel Cave, which preserves a Romanesque church and a complex of lustral tubs in the Eastern Maiella Massif (Central Apennines of Italy). Historical chronicles and archaeological data show that the church dates to the 10th–11th century. The archaeometry applied to the ceramic, coin, and wooden artefacts resulting from the excavation established a chronology of the periods of use and abandonment of the St. Angel Cave. The layering of architectural elements, changes in style, and alterations of the church structure account for two collapses. The first could be related to the poorly known 1209 earthquake. In addition, we describe the damage and changes to the structure and the use of space caused probably by the 1706 and 1933 earthquakes. [ABSTRACT FROM AUTHOR]