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. Appropriate exposure scenarios were identified and modelled for each relevant NORM decay segment of both U-238 and Th-232 series, as well as for K-40. Dose assessments were performed using the software RESRAD-ONSITE with dose coefficients for external and internal exposure taken from the latest publications from the International Commission on Radiological Protection (ICRP). The objective was to develop a generic methodology to quantify exposure and to obtain screening values - so-called Operational Levels (OLs). These OLs pertain to the activity concentration of natural radionuclides (in terms of kBq kg-1) present in sludge that is reused in agriculture, for which dose criterion of 1 mSv year-1 is complied with. OLs can be used as screening tools by an authority/operator, even non-experts in the field of radiation protection. Results showed that the most critical decay segments are Ra-226+ and Ra-228+, for which OLs of the order of 1 kBq kg-1 were estimated. For all the other radionuclides, the calculated OLs are much higher than the activity concentrations typically found in sludge from water treatment facilities, and the radiological impact expected is well-below 1 mSv year-1. The methodology and results of this study could contribute to the update of the Clearance Levels and discharge levels reported in the European guidelines RP 122 Part II and RP 135, respectively.

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. The RF model results were compared with the results of two (semi-)mechanistic models. Of the more than 3000 entries in the original dataset, only about 1200 could be used, as this was the largest complete dataset with the largest number of co-variables available. The obtained RF predictive model can reproduce the experimental observations better than the two (semi)-mechanistic models, which are based on many assumptions and fixed parameter values. Model performance was quantified using the metrics of Root Mean Square Error (rmse) and Mean Absolute Error (mae). The RF model was able to reproduce the variability of the data by up to 6 orders of magnitude. The categorical co-predictors, especially taxonomic plant family and plant part, have a greater influence than the numerical co-predictors, such as pH and exchangeable soil potassium concentration. This feasibility study shows that RF is a promising tool to obtain predictive models for transfer factors. However, to build a widely applicable predictive model, a dataset is needed that contains at least thousands of entries for transfer factors and for the most important co-variables and considers a large parameter space.

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. Within this context, an international survey on NORM has been prepared. The goals of the survey were to gather information and data from European countries that will contribute making an updated and/or new (a) systematic overview of NORM exposure situations and their analysis with respect to different radiation protection aspects, (b) knowledge about applied radioecological models in a variety of NORM exposure situations to improve evaluation of possible exposure doses and risk for population and workers, as well as of environmental risk, and (c) overview of overall mitigation measures applied in NORM involving industries, and possible remediation activities applied at NORM affected legacy sites. The survey is built upon an extended list of NORM-involving industries and processes, covers general aspects of NORM, presence of multi-stressors, as well as practical procedures applied in management and regulation, also in the context of a circular economy. The survey responses were obtained from 19 countries. An analysis of survey responses proved that NORM control is still a complex issue for many countries, and the recently-introduced regulatory solutions require further interpretation for developing procedures and good practices. The present work provides a detailed analysis of the survey responses with respect to regulation, management and investigation of NORM.

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. The conceptual model uncertainty is quantified for two process-based models, which describe the interception of wet deposited pollutants under equilibrium and kinetic conditions, respectively. The natural variability due the chemical valence of the elements considered is accounted for in both models. Quantitative evidence has been obtained that the conceptual model uncertainty can contribute to the total uncertainty budget of the models for interception of wet deposited pollutants at least as much as, if not more than, parameter uncertainty.