Temporal and spatial prediction of radiocaesium transfer to food products

A recently developed semi-mechanistic temporal model is used to predict food product radiocaesium activity concentrations using soil characteristics available from spatial soil databases (exchangeable K, pH, percentage clay and percentage 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). 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/year per unit area). 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.

Self-help countermeasure strategies for populations living within contaminated areas of Belarus, Russia and Ukraine

Countermeasures have been effectively employed within intensive agricultural systems in areas of the Former Soviet Union (FSU) affected by the Chernobyl accident. However, ingestion doses continue to be elevated in some areas as a result of few foodstuffs which are collected from the wild or produced by the household. Forest fungi and berries, and milk from privately owned cattle are the most notable contributors to 137Cs intakes amongst these foodstuffs. In this paper we consider advice which would help affected populations to both understand the importance of these exposure routes and to reduce their exposure. In addition to the potential radiological benefits, self-help schemes are highly cost-effective and likely to have a positive psychological influence on populations living within contaminated areas of the FSU. Evidence to suggest that the transfer of radiocaesium to cow milk is considerably higher in the FSU than within western Europe and North America is 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 (kd1), 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, NH4+ 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.