Monte Carlo simulations of NaI(Tl) spectra for measurements of semi-infinite plumes.

For the past 10 y Health Canada has operated a Fixed Point Surveillance Network of NaI(Tl) detectors across Canada. Deployed for both emergency response and daily monitoring of airborne radiation in the environment, a spectral stripping method allowed measurement of certain isotopes well below the ambient dose rate. These include (133)Xe, (135)Xe and (41)Ar, typical of emissions from operating nuclear reactors. In an effort to increase the number of isotopes measured at these low levels a new technique of spectral fitting using spectral templates is being implemented. However, this requires very accurate spectral templates that can be difficult or impossible to obtain empirically for environmental measurements of airborne radio-isotopes. Therefore, a method of efficiently using Monte Carlo techniques to create these templates was developed.

Nuclear event zero-time calculation and uncertainty evaluation.

It is important to know the initial time, or zero-time, of a nuclear event such as a nuclear weapon's test, a nuclear power plant accident or a nuclear terrorist attack (e.g. with an improvised nuclear device, IND). Together with relevant meteorological information, the calculated zero-time is used to help locate the origin of a nuclear event. The zero-time of a nuclear event can be derived from measured activity ratios of two nuclides. The calculated zero-time of a nuclear event would not be complete without an appropriately evaluated uncertainty term. In this paper, analytical equations for zero-time and the associated uncertainty calculations are derived using a measured activity ratio of two nuclides. Application of the derived equations is illustrated in a realistic example using data from the last Chinese thermonuclear test in 1980.

Radiostrontium and radium analysis in low-level environmental samples following a multi-stage semi-automated chromatographic sequential separation.

Strontium isotopes, (89)Sr and (90)Sr, and (226)Ra being radiotoxic when ingested, are routinely monitored in milk and drinking water samples collected from different regions in Canada. In order to monitor environmental levels of activity, a novel semi-automated sensitive method has been developed at the Radiation Protection Bureau of Health Canada (Ottawa, Canada). This method allows the separation and quantification of both (89)Sr and (90)Sr and has also been adapted to quantify (226)Ra during the same sample preparation procedure. The method uses a 2-stage purification process during which matrix constituents, such as magnesium and calcium that are rich in milk, are removed as well as the main beta-interferences (e.g., (40)K, (87)Rb, (134)Cs, (137)Cs, and (140)Ba). The first purification step uses strong cation exchange (SCX) chromatography with commercially available resins. In a second step, fractions containing the radiostrontium analytes are further purified using high-performance ion chromatography (HPIC). While (89)Sr is quantified by Cerenkov counting immediately after the second purification stage, the same vial is counted again after a latent period of 10-14 days to quantify the (90)Sr activity based on (90)Y ingrowth. Similarly, the activity of (226)Ra, which is separated by SCX only, is determined via the emanation of (222)Rn in a 2-phase aqueous/cocktail system using liquid scintillation counting. The minimum detectable concentration (MDC) for (89)Sr and (90)Sr for a 200 min count time at 95% confidence interval is 0.03 and 0.02 Bq/L, respectively. The MDC for (226)Ra for a 100 min count time is 0.002 Bq/L. Semi-annual intercomparison samples from the USA Department of Energy Mixed Analyte Performance Evaluation Program (MAPEP) were used to validate the method for (89)Sr and (90)Sr. Spiked water samples prepared in-house and from International Atomic Energy Agency (IAEA) were used to validate the (226)Ra assay.

Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty.

A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of (131m)Xe, (133)Xe, (133m)Xe, and (135)Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naïve Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.

Low level noble gas measurements in the field and laboratory in support of the Comprehensive Nuclear-Test-Ban Treaty.

Over 300 daily environmental radioxenon samples were analyzed using French developed SPALAX for automatic sample preparation including high-resolution gamma-spectrometry. The 133Xe sensitivity was significantly better than 1 mBq/m3 (specified criterion for Comprehensive Nuclear-Test-Ban Treaty verification). Radioxenon analysis was extended to include the X-ray region by improved detector window, sample cell design, efficiency calibration, line shape fitting and background analysis. The resulting analysis offers a 4-16 fold improvement in sensitivity for 133mXe and 131mXe, respectively.