344. Application of Monte Carlo techniques to the calibration of airborne radioactivity monitoring systems. (December 2018)

• Record Type: Journal Article
• Authors: Sarnelli, A.; Mezzenga, E.; D'Errico, V.; Bianchini, D.; Negrini, M.
• Is Part Of: Physica medica. Volume 56(2018)Supplement 2
• Page Start: 270
• Publication Date: 2018-12
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2018.04.353
• Languages: English
• ISSNs: 1120-1797

Abstract

Abstract : Purpose: Air monitoring systems are mandatory in all areas potentially contaminated with airborne radioactivity, such as hot laboratories, treatment rooms, and radioactive waste storage areas. The calibration of such systems may be practically complicated or require the application of correction factors, obtained by means of approximated analytical calculations. Monte Carlo techniques provide an effective way to check the calibration and the impact of the approximations used in the calculation. Methods: The present work compares two different systems: a continuous air monitoring system, composed by a NaI detector located inside a chimney, and an air pumping system that measures the sample activity in a Marinelli beaker geometry, equipped with NaI detector. This setup allows to calibrate the continuous air monitoring system using the measurements provided by the Marinelli beaker system. A self-absorption correction factor, applied in the calibration of the Marinelli beaker system, is derived for gamma and beta+ radiation and compared with approximated analytical calculation available for gamma[1] . The cross-calibration of the two systems is complicated by the presence of F18 that sticks to the duct's walls, producing a background that continuously increases during the air flow, therefore the calibration constants are checked using an approximate analytical calculation and a Geant4 simulation. Results: Calibration factors are computed for ducts of different sizes and lengths, and validated using the IRST monitoring system. These coefficients represent a useful estimate in the calibration of continuous air monitoring systems in all cases where the cross-calibration of two systems is not possible. Conclusions: Monte Carlo techniques help in overcome challenging aspects in the calibration of air monitoring systems, providing reliable results that can be used to cross-check the calibration constants of the system in case of large experimental uncertainties.