Temporal and spatial prediction of radiocaesium transfer to food products

A recently developed semi-mechanistic temporal model to is used predict food product radiocaesium activity concentrations using soil characteristics available from spatial soil databases (exchangeable K, pH, % clay and % organic matter content). A raster database of soil characteristics, radiocaesium deposition, and crop production data has been developed for England and Wales and used to predict the spatial and temporal pattern of food product radiocaesium activity concentrations (Bq kg-1). By combining these predictions with spatial data for agricultural production, an area's output of radiocaesium can also be estimated, we term this flux (Bq y-1 unit area-1). Model predictions have been compared to observed data for radiocaesium contamination of cow milk in regions of England and Wales which received relatively high levels of fallout from the 1986 Chernobyl accident (Gwynedd and Cumbria). The model accounts for 56 and 80% of the observed variation in cow milk activity concentration for Gwynedd and Cumbria respectively. Illustrative spatial results are presented and suggest that in terms of food product contamination areas in the north and west of England and Wales are those most vulnerable to radiocaesium deposition. When vulnerability is assessed using flux the spatial pattern is more complex and depends upon food product

Development of an approach to estimating mid- to long-term critical loads for radiocaesium contamination of cow milk in western Europe

This paper describes the application of the critical load methodology, developed to set emission targets for atmospheric pollutants, to radioecology. The critical load can be redefined within radioecology as the radionuclide deposition at which radionuclide activity concentrations in a specified food product will exceed the maximum permitted level. An empirically based approach is described which provides estimates of critical load values for cow milk in the mid- to long-term after an accident when soil-to-plant transfer of radiocaesium is largely responsible for plant radiocaesium contamination. The areas identified as being most potentially vulnerable to radiocaesium deposition using this approach are those with extensive areas of organic soils such as western Scotland, parts of Ireland, The Netherlands and Denmark. The classification of European soil types into soil groups with significantly different soil-to-plant transfer of radiocaesium, and the allocation of a transfer value to each soil group provide the greatest uncertainties within this approach. Potential problems and deficiencies affecting the estimation of parameter values are discussed

Predicting the transfer of radiocaesium from organic soils to plants using soil characteristics

A model predicting plant uptake of radiocaesium based on soil characteristics is described. Three soil parameters required to determine radiocaesium bioavailability in soils are estimated in the model: the labile caesium distribution coefficient (kdl), K+ concentration in the soil solution [mK] and the soil solution→plant radiocaesium concentration factor (CF, Bq kg−1 plant/Bq dm−3). These were determined as functions of soil clay content, exchangeable K+ status, pH, NH+4 concentration and organic matter content. The effect of time on radiocaesium fixation was described using a previously published double exponential equation, modified for the effect of soil organic matter as a non-fixing adsorbent. The model was parameterised using radiocaesium uptake data from two pot trials conducted separately using ryegrass (Lolium perenne) on mineral soils and bent grass (Agrostis capillaris) on organic soils. This resulted in a significant fit to the observed transfer factor (TF, Bq kg−1 plant/Bq kg−1 whole soil) (P<0.001, n=58) and soil solution K+ concentration (mK, mol dm−3) (P<0.001, n=58). Without further parameterisation the model was tested against independent radiocaesium uptake data for barley (n=71) using a database of published and unpublished information covering contamination time periods of 1.2–10 years (transfer factors ranged from 0.001 to 0.1). The model accounted for 52% (n=71, P<0.001) of the observed variation in log transfer factor

Spatial modelling of transfer of long-lived radionuclides from soil to agricultural products in the Chernigov region, Ukraine.

Within the RESTORE project (&lsquo;restoration strategies for radioactive contaminated ecosystems&rsquo;) funded by the European Commission Nuclear Fission Safety Programme, environmental models are being developed to identify regions that are vulnerable to increased radionuclide transfer as a consequence of the Chernobyl nuclear power plant accident and nuclear weapons testing at the Semipalatinsk test site in Kazakhstan. Since radionuclide transfer varies in space and time depending on deposition processes, soil type, land use, and resulting contamination in food products, the radionuclide transfer through food chains derived from a variety of ecosystems is analysed by the use of models embedded in a Geographical Information System. The Chernigov region in northern Ukraine was affected by the Chernobyl fallout resulting in deposition levels ranging from 15 to 300 kBq m&minus;2. GIS-based steady state and dynamic transfer models within an environmental decision support system (EDSS) were run for this region to model radiocaesium transfer from soil to various agricultural products on a collective farm level and on a district level within this region using spatial data sets of soil attributes, soil contamination and land use. Observed agricultural product contamination was available for comparison with model predictions. This paper presents examples of radiocaesium transfer from soil to fodder grass and potatoes to make an initial assessment of the radioecological situation in the Chernigov region to identify critical gaps in the model structure and data required for model input and validation. It highlights the feasibility of applying spatial and temporal data sets to make predictions of the present radioecological situation, as an alternative to approaches commonly used which categorise such data sets, thereby losing valuable information