Evaluation of distribution coefficients and concentration ratios of 90Sr and 137Cs in the Techa River and the Miass River

Empirical data on the behavior of radionuclides in aquatic ecosystems are needed for radioecological modeling, which is commonly used for predicting transfer of radionuclides, estimating doses, and assessing possible adverse effects on species and communities. Preliminary studies of radioecological parameters including distribution coefficients and concentration ratios, for 90Sr and 137Cs were not in full agreement with the default values used in the ERICA Tool and the RESRAD BIOTA codes. The unique radiation situation in the Techa River, which was contaminated by long-lived radionuclides (90Sr and 137Cs) in the middle of the last century allows improved knowledge about these parameters for river systems. Therefore, the study was focused on the evaluation of radioecological parameters (distribution coefficients and concentration ratios for 90Sr and 137Cs) for the Techa River and the Miass River, which is assumed as a comparison waterbody. To achieve the aim the current contamination of biotic and abiotic components of the river ecosystems was studied; distribution coefficients for 90Sr and 137Cs were calculated; concentration ratios of 90Sr and 137Cs for three fish species (roach, perch and pike), gastropods and filamentous algae were evaluated. Study results were then compared with default values available for use in the well-known computer codes ERICA Tool and RESRAD BIOTA (when site-specific data are not available). We show that the concentration ratios of 137Cs in whole fish bodies depend on the predominant type of nutrition (carnivores and phytophagous). The results presented here are useful in the context of improving of tools for assessing concentrations of radionuclides in biota, which could rely on a wider range of ecosystem information compared with the process limited the current versions of ERICA and RESRAD codes. Further, the concentration ratios of 90Sr are species-specific and strongly dependent on Ca2+ concentration in water. The universal characteristic allows us to combine the data of fish caught in the water with different mineralization by multiplying the concentration of Ca2+. The concentration ratios for fishes were well-fitted by Generalized Logistic Distribution function (GLD). In conclusion, the GLD can be used for probabilistic modeling of the concentration ratios in freshwater fishes to improve the confidence in the modeling results. This is important in the context of risk assessment and regulatory

New stochastic carcinogenesis model with covariates: An approach involving intracellular barrier mechanisms

In this paper we present a new multiple-pathway stochastic model of carcinogenesis with potential of predicting individual incidence risks on the basis of biomedical measurements. The model incorporates the concept of intracellular barrier mechanisms in which cell malignization occurs due to an inefficient operation of barrier cell mechanisms, such as antioxidant defense, repair systems, and apoptosis. Mathematical formalism combines methodological innovations of mechanistic carcinogenesis models and stochastic process models widely used in studying biodemography of aging and longevity. An advantage of the modeling approach is in the natural combining of two types of measures expressed in terms of model parameters: age-specific hazard rate and means of barrier states. Results of simulation studies allow us to conclude that the model parameters can be estimated in joint analyses of epidemiological data and newly collected data on individual biomolecular measurements of barrier states. Respective experimental designs for such measurements are suggested and discussed. An analytical solution is obtained for the simplest design when only age-specific incidence rates are observed. Detailed comparison with TSCE model reveals advantages of the approach such as the possibility to describe decline in risk at advanced ages, possibilities to describe heterogeneous system of intermediate cells, and perspectives for individual prognoses of cancer risks. Application of the results to fit the SEER data on cancer risks demonstrates a strong predictive power of the model. Further generalizations of the model, opportunities to measure barrier systems, biomedical and mathematical aspects of the new model are discussed