A review and test of predictive models for the bioaccumulation of radiostrontium in fish.

Empirical relations between the (90)Sr concentration factor (CF) and the calcium concentration in freshwater aquatic systems have previously been determined in studies based on data obtained prior to the Chernobyl accident. The purpose of the present research is to review and compare these models, and to test them against a database of post-Chernobyl measurements from rivers and lakes in Ukraine, Russia, Belarus and Finland. It was found that two independently developed models, based on pre-Chernobyl empirical data, are in close agreement with each other, and with empirical data. Testing of both models against new data obtained after the Chernobyl accident confirms the models' predictive ability. An investigation of the influence of fish size on (90)Sr accumulation showed no significant relationship, though the data set was somewhat limited.

"Blind" testing of models for predicting the 90Sr activity concentration in river systems using post-Chernobyl monitoring data.

Two different models for predicting the time-dependent mobility of (90)Sr in river systems have been evaluated using post-Chernobyl monitoring data for five large Belarusian rivers (Dnieper, Pripyat, Sozh, Besed and Iput) in the period between 1990 and 2004. The results of model predictions are shown to be in good agreement (within a factor of 5) with the measurements of (90)Sr activity concentration in river waters over a long period of time after the accident. This verifies the relatively good accuracy of the generalised input parameters of these models which were derived primarily from measurements of (90)Sr deposited after atmospheric nuclear weapons testing (NWT). For the cases studied here, the simpler AQUASCOPE model performed just as well as the more complex "Global" model which used GIS-based catchment data as an input. The reasons for this are discussed. Exponential decay equations were also curve-fitted to the data for each river to help assess the uncertainties in the predictive models.