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The behavior of the naturally occurring radionuclides 40K) 210Pbi 226Ra> 228Th> 228Ra> 235^a nd 238U in COal-fired power plants is described. The enrichment factors [EF = ([X] sample/[40K] sample)/([X] coal/[40K] coal)] for these nuclides in the finer stack fly ash particles indicate significant to at least slight enrichments for all the nuclides studied. These values range from 5.0 for 210Pb to 1.2 for 228Th. Uranium-235 and 238U EF values are both 2.8. Radium-226 and 228Ra EF values are 1.9 and 1.6, respectively. Thorium nuclides and 40K show little fractionation in the electrostatic precipitator-(ESP) collected fly ash and the bottom ash (slag or klinkers) when compared to the original coal. Lead-210 shows a definite depletion in the bottom ash and the ESP fly ash. Uranium also tends to be depleted in these samples. An explanation for the behavior of U and nuclides derived from the 238U decay chain includes a bimodal residence for this element in the coal.

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This study aimed to critically review methods for ranking risks related to food safety and dietary hazards on the basis of their anticipated human health impacts. A literature review was performed to identify and characterize methods for risk ranking from the fields of food, environmental science and socio-economic sciences. The review used a predefined search protocol, and covered the bibliographic databases Scopus, CAB Abstracts, Web of Sciences, and PubMed over the period 1993-2013. All references deemed relevant, on the basis of predefined evaluation criteria, were included in the review, and the risk ranking method characterized. The methods were then clustered-based on their characteristics-into eleven method categories. These categories included: risk assessment, comparative risk assessment, risk ratio method, scoring method, cost of illness, health adjusted life years (HALY), multi-criteria decision analysis, risk matrix, flow charts/decision trees, stated preference techniques and expert synthesis. Method categories were described by their characteristics, weaknesses and strengths, data resources, and fields of applications. It was concluded there is no single best method for risk ranking. The method to be used should be selected on the basis of risk manager/assessor requirements, data availability, and the characteristics of the method. Recommendations for future use and application are provided.