A benchmark for Monte Carlo simulations in gamma-ray spectrometry Part II: True coincidence summing correction factors.

The goal of this study is to provide a benchmark for the use of Monte Carlo simulation when applied to coincidence summing corrections. The examples are based on simple geometries: two types of germanium detectors and four kinds of sources, to mimic eight typical measurement conditions. The coincidence corrective factors are computed for four radionuclides.

Natural radionuclides in thermal mineral springs in Edipsos Greece.

Natural thermal and mineral therapeutic springs exist all over Greece. A radiological survey has been carried out in the municipality of Edipsos to study the naturally occurring alpha emitter radionuclides, 226Ra and the uranium isotopes. Thirteen thermal spring water samples and six tap water samples which were collected from five locations in the municipality of Edipsos, were analysed and measured by a-spectrometry.

Risk assessment for the optimization of the grid of a telemetric network monitoring system.

The goal of this work was to develop a methodology for risk assessment in case of an accident originating from a nuclear power plant, and consequently, to improve the relevant radiation monitoring network. In specific, the study involved risk estimation in Greece from a transboundary nuclear power plant accident. The tool employed was JRODOS (Java-based Real-time Decision Support), which is a system for off-site emergency management of radioactive material in the environment.

Coincidence summing corrections using PENELOPE/PENNUC Monte Carlo code for volume sources of different densities.

This work aims at providing a Monte-Carlo based methodology for calculating true coincidence correction (TCC) factors for volume sources of varying density. All simulations were carried out using the most recent version of Monte Carlo code PENELOPE. The main program PENMAIN was used for the calculation of full energy peak efficiencies.

Determining the probability of locating peaks using computerized peak-location methods in gamma-ray spectra as a function of the relative peak-area uncertainty.

The probabilities of locating peaks with a high relative peak-area uncertainty were determined empirically with nine types of peak-location software used in laboratories engaged in gamma-ray spectrometry measurements. It was found that it is not possible to locate peaks with a probability of 0.95, when they have a relative peak-area uncertainty in excess of 50%.