Indoor and Outdoor Natural Radioactivity in the Vulsini Volcanic District (Central Italy): Estimation of Doses and Radiological Risks

Terrestrial radioactivity for most individual is the major contributor to the total dose and is mostly provided by 238U, 232Th and 40K radionuclides. In particular indoor radioactivity is principally due to 222Rn, a radioactive noble gas descendent of 238U, second cause of lung cancer after cigarettes smoking. Vulsini Volcanic District is a well known quaternary volcanic area located between the northern Latium and southern Tuscany (Central Italy). It is characterized by an high natural radiation background resulting from the high concentrations of 238U, 232Th and 40K in the volcanic products. In this context, subduction-related metasomatic enrichment of incompatible elements in the mantle source coupled with magma differentiation within the upper crust has given rise to U, Th and K enriched melts. Almost every ancient village and town located in this part of Italy has been built with volcanic rocks pertaining to the Vulsini Volcanic District. The radiological risk of living in this area has been estimated considering separately: a. the risk associated with buildings made of volcanic products and built on volcanic rock substrates b. the risk associated to soil characteristics. The former has been evaluated both using direct 222Rn indoor measurements and simulations of “standard rooms” built with the tuffs and lavas from the Vulsini Volcanic District investigated in this work. The latter has been carried out by using in situ measurements of 222Rn activity in the soil gases. A radon risk map for the Bolsena village has been developed using soil radon measurements integrating geological information. Data of airborne radioactivity in ambient aerosol at two elevated stations in Emilia Romagna (North Italy) under the influence of Fukushima plume have been collected, effective doses have been calculated and an extensive comparison between doses associated with artificial and natural sources in different area have been described and discussed.

A new snowfall detection algorithm for high latitude regions based on a combination of active and passive sensors

Precipitation retrieval over high latitudes, particularly snowfall retrieval over ice and snow, using satellite-based passive microwave spectrometers, is currently an unsolved problem. The challenge results from the large variability of microwave emissivity spectra for snow and ice surfaces, which can mimic, to some degree, the spectral characteristics of snowfall. This work focuses on the investigation of a new snowfall detection algorithm specific for high latitude regions, based on a combination of active and passive sensors able to discriminate between snowing and non snowing areas. The space-borne Cloud Profiling Radar (on CloudSat), the Advanced Microwave Sensor units A and B (on NOAA-16) and the infrared spectrometer MODIS (on AQUA) have been co-located for 365 days, from October 1st 2006 to September 30th, 2007. CloudSat products have been used as truth to calibrate and validate all the proposed algorithms. The methodological approach followed can be summarised into two different steps. In a first step, an empirical search for a threshold, aimed at discriminating the case of no snow, was performed, following Kongoli et al. [2003]. This single-channel approach has not produced appropriate results, a more statistically sound approach was attempted. Two different techniques, which allow to compute the probability above and below a Brightness Temperature (BT) threshold, have been used on the available data. The first technique is based upon a Logistic Distribution to represent the probability of Snow given the predictors. The second technique, defined Bayesian Multivariate Binary Predictor (BMBP), is a fully Bayesian technique not requiring any hypothesis on the shape of the probabilistic model (such as for instance the Logistic), which only requires the estimation of the BT thresholds. The results obtained show that both methods proposed are able to discriminate snowing and non snowing condition over the Polar regions with a probability of correct detection larger than 0.5, highlighting the importance of a multispectral approach.