Foliar interception of radionuclides in dry conditions: a meta-analysis using a Bayesian modeling approach

Soil and vegetation sampling during the early stage of Fukushima Daiichi Nuclear Power Plant accident and the implication for the emergency preparedness for agricultural systems

After the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, immediate soil and vegetation sampling were conducted according to the action plan of nuclear emergency monitoring; however, analysing the monitoring dataset was difficult because the sampling protocols were not standardised. In this study, the sampling protocols applied just after the FDNPP accident were reviewed, and the monitoring data were analysed. The detailed protocols and results can provide a sound basis for guidelines of soil and vegetation sampling for nuclear emergency monitoring. The activity concentrations of ¹³⁷Cs and ¹³¹I in weed samples measured immediately after the FDNPP accident were related to the air dose rate at 1 m. Consequently, vegetation sampling is recommended when the additional dose rate (above background) is higher than 0.1 μSv/h. To enhance the efficiency of a protective response in the case of a nuclear accident, predetermined sampling points for soil and vegetation sampling should be considered in the preparedness plan for nuclear emergencies. Furthermore, sampling and analytical measurement capacities (time, people, cost) during the early phase after nuclear emergencies need to be considered in the preparedness and action plan, and sampling and measurement exercises are highly recommended.

Identifiability of sorption parameters in stirred flow-through reactor experiments and their identification with a Bayesian approach

This paper addresses the methodological conditions -particularly experimental design and statistical inference- ensuring the identifiability of sorption parameters from breakthrough curves measured during stirred flow-through reactor experiments also known as continuous flow stirred-tank reactor (CSTR) experiments. The equilibrium-kinetic (EK) sorption model was selected as nonequilibrium parameterization embedding the K_d approach. Parameter identifiability was studied formally on the equations governing outlet concentrations. It was also studied numerically on 6 simulated CSTR experiments on a soil with known equilibrium-kinetic sorption parameters. EK sorption parameters can not be identified from a single breakthrough curve of a CSTR experiment, because K_d,1 and k^- were diagnosed collinear. For pairs of CSTR experiments, Bayesian inference allowed to select the correct models of sorption and error among sorption alternatives. Bayesian inference was conducted with SAMCAT software (Sensitivity Analysis and Markov Chain simulations Applied to Transfer models) which launched the simulations through the embedded simulation engine GNU-MCSim, and automated their configuration and post-processing. Experimental designs consisting in varying flow rates between experiments reaching equilibrium at contamination stage were found optimal, because they simultaneously gave accurate sorption parameters and predictions. Bayesian results were comparable to maximum likehood method but they avoided convergence problems, the marginal likelihood allowed to compare all models, and credible interval gave directly the uncertainty of sorption parameters θ. Although these findings are limited to the specific conditions studied here, in particular the considered sorption model, the chosen parameter values and error structure, they help in the conception and analysis of future CSTR experiments with radionuclides whose kinetic behaviour is suspected.

Interception of wet deposited atmospheric pollutants by herbaceous vegetation: Data review and modelling

Better understanding and predicting interception of wet deposited pollutants by vegetation remains a key issue in risk assessment studies of atmospheric pollution. We develop different alternative models, following either empirical or semi-mechanistic descriptions, on the basis of an exhaustive dataset consisting of 440 observations obtained in controlled experiments, from 1970 to 2014, for a wide variety of herbaceous plants, radioactive substances and rainfall conditions. The predictive performances of the models and the uncertainty/variability of the parameters are evaluated under Hierarchical Bayesian modelling framework. It is demonstrated that the variability of the interception fraction is satisfactorily explained and quite accurately modelled by a process-based alternative in which absorption of ionic substances onto the foliage surfaces is determined by their electrical valence. Under this assumption, the 95% credible interval of the predicted interception fraction encompasses 81% of the observations, including situations where either plant biomass or rainfall intensity are unknown. This novel approach is a serious candidate to challenge existing empirical relationships in radiological or chemical risk assessment tools.