Development of a methodology for assessing radiological dose due to use of NORM sludge as fertilizer.

In Europe, the general obligation to recycle drives for increased reuse of residues containing Naturally Occurring Radioactive Material (NORM). In agriculture, this has led to the reuse of sludge produced by groundwater filtration facilities as a means of fertilization. In the frame of the RadoNorm project, a methodology was developed for dose assessment of agricultural workers and other members of the public living near agricultural fields in which NORM-containing sludge is applied.

Use of random forest algorithm for predictive modelling of transfer factor soil-plant for radiocaesium: A feasibility study.

A German dataset with soil-plant transfer factors for radiocaesium including many co-variables was analysed and prepared for the application of the Random Forest (RF) algorithm using the R libraries 'party', and 'caret'. A RF predictive model for soil-plant transfer factor was created based on 10 co-variables. These are, for example, taxonomic plant family, plant part, soil type and the exchangeable potassium concentration in the soil.

Assessing the exposure situations with naturally occurring radioactive materials across European countries by means of the e-NORM survey.

Despite the EU states being under the umbrella of the European Directive 2013/59/Euratom, a certain degree of heterogeneity may be noticed in the implementation of EU recommendations concerning regulation and handling of NORM into national legislation and practice. This is mainly a result of the still existing incomplete international knowledge about different phenomena related to NORM. Therefore, the attempt to advance the understanding of the behaviour and exposure of NORM is at the core of the European RadoNorm project.

Quantification of Conceptual Model Uncertainty in the Modeling of Wet Deposited Atmospheric Pollutants.

Conceptual model uncertainty and parameter uncertainty are dominant contributors to the total uncertainty of a radioecological model output. In the present study the focus is on conceptual model uncertainty, which is often not acknowledged. Conceptual model uncertainty is assessed by subtracting from the total uncertainty of the model output the propagated parameter uncertainty, obtained by means of Bayesian inference analysis.