Use of historical uranium air sampling data to estimate worker exposure potential to airborne radioactive particulate in a uranium processing facility
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Published on: January 30, 2018
M M Methner1, H A Feng, D F Utterback
1National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations and Field Studies, Health-Related Energy Research Branch, Cincinnati, Ohio, USA.
Abstract:
Historical industrial hygiene monitoring records from a uranium processing plant were collected and analyzed to characterize exposure potential to airborne radioactive particulate. More than 2,100 samples were collected during the period of 1954-1968. The data was organized by job title, plant number, and year of measurement. Laboratory analysis of air samples indicated a wide range of potential exposures to the alpha-emitting particulate. Logarithmic transformation of the data was necessary to approximate Gaussian distributions. Geometric Mean (GM) values were used as the measure of central tendency within years. GM values ranged from 23-49 disintegrations per minute per cubic meter of air sampled (dpm/m3) with the years 1963 and 1964 being significantly higher than other years (ANOVA: p < 0.05). When comparing exposure potential across plants, GM ranged from 20-68 dpm/m3, with plants 5 and 8 being significantly higher than the others (ANOVA: p < 0.05). Exposure potential for specific job titles across the plants varied widely. GM for clerks was the lowest (11 dpm/m3) while furnace operators were the highest (235 dpm/m3). Other job titles with potentially high exposures were chemical operators, forklift operators, machine operators, and furnace operators. This analysis indicates the magnitude and distributions of worker exposure to alpha-emitting airborne particulate. Additional analysis and epidemiologic studies are planned for this facility.
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Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

