Machine learning analysis of 137Cs contamination of terrestrial plants after the Fukushima accident using the random forest algorithm

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.

Use of machine learning and deep learning to predict particulate 137Cs concentrations in a nuclearized river

Cesium-137, discharged by nuclear installations under normal operations and deposited in watersheds following atmospheric testing and accidents (i.e. Chernobyl, Fukushima …), has been studied for decades. Thus, modelling of 137Cs concentration in rivers have been developed based on geochemical approaches and equilibrium assumptions (solid/liquid ratio) as this radionuclide has moved into rivers and oceans due to soil erosion. Recently a new approach is possible to model these concentrations with the popularization of data-driven models based on data acquired in the environment by monitoring networks. In this study, the concentrations of particulate cesium-137 measured near the mouth of the Rhône River (France), a highly nuclearized river, are simulated using two data-driven models, a Hierarchical Attention-Based Recurrent Highway Networks (HRHN) and a Random Forest Regressor (RF). The data-driven predictions were done using only hydrological data (water discharge and suspended solid fluxes) and industrial input of 137Cs. Although the data-driven models provided a better prediction than a recent empirical model, the best prediction (R2 = 0.71) was obtained with HRHN, a model that considers the temporal aspect of the monitoring data. The most important predictors were the hydrological data at the monitoring station and of the tributary that generate the most sediment flux (Durance River). In fact, the concentration of 137Cs in the perimeter of this study was more related to hydrology than to nuclear release, as there were few events with high 137Cs concentrations (concomitant nuclear release and low water discharge). However, the HRHN approach, which is more complex to implement than RF, can predict the concentrations of such events correctly despite their low representation of these events. The results of this study demonstrate the usefulness of data-driven models to assist monitoring programs by filling in gaps or helping to understand observed concentrations.

Tritium: Its relevance, sources and impacts on non-human biota

Tritium (³H) is a radioactive isotope of hydrogen that is abundantly released from nuclear industries. It is extremely mobile in the environment and in all biological systems, representing an increasing concern for the health of both humans and non-human biota (NHB). The present review examines the sources and characteristics of tritium in the environment, and evaluates available information pertaining to its biological effects at different levels of biological organisation in NHB. Despite an increasing number of publications in the tritium radiobiology field, there exists a significant disparity between data available for the different taxonomic groups and species, and observations are heavily biased towards marine bivalves, fish and mammals (rodents). Further limitations relate to the scarcity of information in the field relative to the laboratory, and lack of studies that employ forms of tritium other than tritiated water (HTO). Within these constraints, different responses to HTO exposure, from molecular to behavioural, have been reported during early life stages, but the potential transgenerational effects are unclear. The application of rapidly developing "omics" techniques could help to fill these knowledge gaps and further elucidate the relationships between molecular and organismal level responses through the development of radiation specific adverse outcome pathways (AOPs). The use of a greater diversity of keystone species and exposures to multiple stressors, elucidating other novel effects (e.g., by-stander, germ-line, transgenerational and epigenetic effects) offers opportunities to improve environmental risk assessments for the radionuclide. These could be combined with artificial intelligence (AI) including machine learning (ML) and ecosystem-based approaches.

Analysis of causal effects of 137Cs deposition on 137Cs concentrations in trees after the Fukushima accident using machine learning

Radioactive contamination of forests by long-lived radionuclides from nuclear accidents such as Chernobyl and Fukushima continues to be studied and quantitatively modeled. Whereas traditional statistical and machine learning (ML) techniques generate predictions by focusing on correlations between variables, quantification of causal effects of radioactivity deposition levels on contamination of plant tissues represents a more fundamental and relevant research goal. Modeling of cause-and-effect relationships is advantageous over standard predictive modeling, particularly by improving the generalizability of results to other situations, where the distributions of variables, including potential confounders, differ from those in the training data. Here we used the state-of-the-art causal forest (CF) algorithm to quantify the causal effect of ¹³⁷Cs land contamination after the Fukushima accident on ¹³⁷Cs activity concentrations in the wood of four common Japanese forest tree species: Hinoki cypress (Chamaecyparis obtusa), konara oak (Quercus serrata), red pine (Pinus densiflora), and Sugi cedar (Cryptomeria japonica). We estimated the average causal effect for the population, quantified how it was influenced by other environmental variables, and produced effect estimates at the individual level. The estimated causal effect was quite robust to various refutation methods, and was negatively influenced by high mean annual precipitation, elevation, and time after the accident. Wood subtype (e.g. sapwood, heartwood) and tree species made smaller contributions to the causal effect. We believe that causal ML techniques have promising potential in radiation ecology and can usefully expand the toolkit of modeling approaches available to researchers in this field.