The radioactive contamination in the ocean has raised significant concern on the environmental impact among Asian and Pacific countries since the Fukushima Daiichi Nuclear Power Plant accident (Mar 11, 2011). The first step in determining the contamination by the radioactive material is monitoring anomalies of environmental radioactivity of interest. As a result, each country has its own environmental radioactivity surveillance program. Strontium-90 (half-life 28.8 y) is one of the radionuclides of high interest in the environment, owing to its high fission production rate and biological accumulation resulting from similar chemical behavior with calcium. The level of Strontium-90 in the seawater is very low, with a global average of about 1 mBq kg-1. Consequently, it requires large volume of seawater sample, typically ranging from 40 L to 60 L. The purification of 90Sr from seawater sample is challenging due to the high salinity and presence of stable Sr (about 7 ppm). Therefore, the conventional method for determining 90Sr is time-consuming and labor-intensive work. The author reported an advanced method, which is a more analyst-friendly and simpler method compared to the current method, for the determination of 90Sr in seawater. This method focuses on the separation of 90Y, which is equilibrium with 90Sr, utilizing a commercialized extraction resin. As a result, it takes less than 3 hours to determine 90Sr in 50 L of seawater sample and requires less labor. Additionally, this approach could be applied to the analysis of 90Sr in radioactive waste

Radioactivity of radiostrontiums, Sr-89 and Sr-90, which are both pure beta-emitters, are generally measured via Cherenkov counting. However, the determination of Cherenkov counting efficiencies of radiostrontiums requires a complicated procedure due to the presence of Y-90 (also a pure betaemitter) which is the daughter nuclide of Sr-90. In this study, we have developed a machine learning approach using a linear regression model which allows an easier and simultaneous determination of the Cherenkov counting efficiencies of the radiostrontiums. The linear regression model was employed because total net Cherenkov count (Ct) from the three beta-emitters at time t after the separation of Y- 90, can be expressed as a linear combination of their respective time-varying radioactivities with their respective coefficients (parameters) being their counting efficiencies: Ct = εSr-90[ASr-90·exp(–λSr-90·t)] + εSr-89[ASr-89·exp(–λSr-89·t)] + εY-90[ASr-90·exp(1–λSr-90·t)], where ε is a counting efficiency, A is an initial activity, λ is a decay constant and t is time after the separation of Y-90, Thus, if we train the model with multiple Cherenkov counts measured from the three beta emitters, then we can obtain their estimates for counting efficiencies (so-called parameters) straightforward. For this, the model has been trained by two methods: Ordinary Least Squares (OLS) and Bayesian linear regression (BLR), for which two software packages, PyMC3 and Stan were employed to compare their performances. The results showed that the accuracy of the OLS was worse than that of the BLR. Particularly, the counting efficiency of Sr-90 was estimated to be smaller than 0, which is an unrealistic value. On the other hand, the estimates of the BLR gave realistic values which are close to the true values. Additionally, the BLR was able to provide a distribution for each counting efficiency (so-called “posterior”) from which various types of inference can be made including median and credible interval in the Bayesian statistics which is analogous to, but different from confidence interval in the Frequentist statistics. In the results of the BLR, the Stan package gave more accurate estimates than the PyMC3 package. Therefore, it is expected that counting efficiencies of the radiostrontiums including radioyttrium can be determined at the same time, more easily and accurately, by using the BLR with the Stan package and that the activities of radiostrontium also can be determined more easily by using the BLR if we know their counting efficiencies in advance. It is worth noting that the usage of the linear regression model in this study was different from the usual one where the trained model is used to predict a response value (count) from a set of unseen regressor values (activities).