Abstract. In this work TF values for90 Sr and 137 Cs were measured for reference plants grown in lysimeters containing soils representative of large agricultural areas in Brazil: Ferralsol, Nitisol and Acrisol. These results were discussed in the light of pedological analyses and results from the follow sequential chemical extraction protocol: 1) slightly acidic phase containing readily bioavailable elements; 2) easily reducible phase containing elements bound to Mn oxides; 3) oxidizable phase containing elements bound to labile organic matter; 4) alkaline phase containing mainly elements bound to Fe compounds; 5) resistant phase not potentially available to crops. These results showed that the main soil factors influencing the 137 Cs transfer to these soils were: exchangeable K, organic matter and iron oxides content. These results showed that 90 Sr plant uptake was influenced by exchangeable Ca. All these finds are in accord with previous studies and seems confirm the vulnerability of Ferralsol and Acrisol to 137 Cs contamination.
Abstract.Results of a sequential chemical extraction procedure for n? Cs in an acidic Oxisot showed that 3 years after the contamination, 40% of total concentration was considered readily bio-available, 20% bound to organic matter and 40% bound to Fe and Mn oxides. Four years after, the li7 Cs distribution in this soil have been changed as a consequence of changes in soil properties: 8% bio-available, 10% bound to organic matter, 43% bound to Fe and Mn oxides and 33% strongly bound to soil compounds. Changes in the ,37 Cs distribution in this soil were followed by reductions in soil to plant transfer factor (TF): TFojaati996 = 2,21 ± 1,30 (n=3) and r/'0uTO,2«w = 1,63 ± 0,38 (n=6). During the same period the soil properties of a basic Oxisol remained almost the same, consequently the geochemical distribution and soil to plant transfer factor for " 7 Cs did not change in this soil The geochemical distribution in an acidic Alfisol showed that Mn oxides are the main sink for this element and no ^Co was detected in the readily bio-available phase. In this soil, four years after contamination with 60 Co, it was not detected in plants.
The soil-plant transfer factor (Fv) is used methods in the computational models for radiological risk assessment by ingestion of radiocobalt-contaminated food. Different soil types, plants types and agricultural practices contribute to a wide dispersion of Fv values, indicating the need to study the criteria that influence root uptake in a regional view. In this scenario, Artificial Neural Networks (ANN) have become a possibility to predict Fv values based on critical pedological parameters. This work aims to apply ANN to evaluate the possibility of predicting Fv for 60Co in reference plants as a function of soil properties considered relevant for transfer processes in the soil-plant system. Through the systematic literature review, mineralogy, organic matter, texture, pH, CEC and nutrients were identified as soil properties that affect Fv values for 60Co. However, although these attributes were not always reported, still it was possible to create databases of Fv for 60Co in radish root and leaf, with pH, organic matter, and CTC as potential edaphic indicators. Learning sets were structured and due to the complexity of the search space and the small amount of available data, deep ANN with regularization (dropout) layers were required to achieve good prediction and avoid overfitting. The best model obtained showed good correlation in the validation and training set, considering the chosen parameters.
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