Assessment of a continuous air monitor for the detection of airborne transuranics

A study was performed to evaluate current algorithms used for detecting airborne transuranics in a continuous air monitor sampler and to develop an original algorithm that offers an improvement over the existing algorithms. When radon progeny decay they emit α particles that interfere with the detection of α particles from airborne transuranics.

The algorithms designed for detection of airborne transuranics predict the number of counts due to radon daughter a particles and subtract this from the counts in the spectrum. Canberra Nuclear Instruments' ASM 1000 continuous air monitor was used for collecting data in this study, and the algorithm used by the ASM 1000 to detect airborne transuranics was simulated. Contamination was simulated for twelve transuranics(²³²Th, ²³⁸U, ²³⁵U, ²³⁴U, ²³⁷Np, ²⁴²Pu, ²³⁹Pu, ²⁴³Am, ²³²U, ²²⁸Th, ²⁴¹Am, and ²³⁸Pu) and added to the radon daughter a particle spectrum collected by the ASM 1000.

The approach to detection of radioactive aerosols developed in this study models the radon progeny α particle spectrum as a superposition of gamma functions. When the spectrum was modeled with gamma functions, significant increases in instrument sensitivity were realized. A number of counts equal to a concentration equivalent to 8 derived air concentration (DAC) units of each transuranic was added to 131 radon progeny a particle spectra collected by the ASM 1000. The algorithm currently used in the instrument underestimates these counts by an average of 26% for ²³⁵U to an average of 89% for ²³²Th. The algorithm that describes the spectrum as a superposition of gamma functions underestimated these counts by an average of 13% for ²³²Th to an average of 25% for ²⁴¹Am.

Additionally, the method employed by the ASM 1000 to determine the lower limit of detection inflated the lower limit of detection and decreased the alarm sensitivity of the instrument. However, statistical measures for the lower limit of detection in the algorithm developed in this study improved the alarm sensitivity over that currently employed by the instrument.

This study also addresses the factors that influence α particle detection in continuous air monitor samplers. These factors include radon reduction screens, filter medium, source to detector distance, and relative size of source and detector.