During severe nuclear accidents, radioactive materials are expected to be released into the atmosphere. Estimating the source term plays a significant role in assessing the consequences of an accident to assist in actioning a proper emergency response. However, it is difficult to obtain information on the source term directly through the instruments in the reactor because of the unpredictable conditions induced by the accident. In this study, a deep learning-based method to estimate the source term with field environmental monitoring data, which utilizes the bagging method to fuse models based on the temporal convolutional network (TCN) and two-dimensional convolutional neural network (2D-CNN), was developed. To reduce the complexity of the model, the particle swarm optimization algorithm was used to optimize the parameters in the fusion model. Seven typical radionuclides (Kr-88, I-131, Te-132, Xe-133, Cs-137, Ba-140, and Ce-144) were set as mixed source terms, and the International Radiological Assessment System was used to generate model training data. The results indicated that the average prediction error of the fusion model for the seven nuclides in the test set was less than 10%, which significantly improved the estimation accuracy compared with the results obtained by TCN or 2D-CNN. Noise analysis revealed the fusion model to be robust, having potential applicability toward more complex nuclear accident scenarios.

A method was established for the determination of the elemental composition in plant samples using the characteristic argon fluorescence peak in the air as a normalization standard via low power total reflection X-ray fluorescence (LP-TXRF) spectrometry using polychromatic X-ray excitation. The source of the Ar peak was demonstrated both theoretically and experimentally. According to the theoretical study, the characteristic Ar peak mainly originates from the optical path of the incident and reflected X-rays and the standing wave field formed by the incident and reflected X-rays. Additionally, the experiments confirmed the main contribution of the standing wave fields formed by the incident and reflected X-rays to the Ar characteristic peaks, and the influence of reflector type and sample deposition amount on the Ar characteristic peaks was excluded. Three different plant certified reference materials (CRMs) were employed to evaluate the effectiveness of the proposed methodology for multi-elemental quantitative analysis. The results showed that the Ar-peak normalization method resulted in comparable accuracy and precision compared with the conventional internal standard method, and a long-term stability check under the same conditions showed the good stability of the argon peak.

This work describes a full field x-ray fluorescence element distribution imaging system with a combined collimating lens (CCL), which is more suitable for a higher x-ray energy range (12 keV–30 keV). The system consists of an optical-use charge-coupled device (CCD) camera coupled to a combined collimating lens (CCL), which includes pinhole collimator and x-ray window, x-ray tube, and sample room. The continuously variable magnification of 0.5–2 is achieved under a compact structure. The x-ray spectrum and two-dimensional element distribution mapping of the irradiated sample are obtained by processing a series of images acquired by using the CCD camera in a single photon counting mode. The energy resolution is 275 eV at the reference energy of 14.957 keV (yttrium Kα, Z = 39). The limit of detection is 46.41 ppm by measuring yttrium standard solution. The spatial resolution is 135 μm when using a 100 μm pinhole at the magnification of 1. Samples made by metal foils and mineral pigments are tested, and the results proved that the system was reliable when detecting elements of a high atomic number.

Teas of the same type in a small growing area are generally similar, and identification of their detailed geographic origin is challenging and interesting. In this study, a convenient method for distinguishing Pu-erh teas from nearby locations was developed by combining two spectroscopic techniques with chemometric methods. The distinction was based on the difference in the mineral elements and organic metabolites from tea infusions. First, Pu-erh tea was infused with water at 90 °C for 30 min. Total reflection X-ray fluorescence and ultraviolet-visible (UV-Vis) spectroscopies were used to determine the elements (P, S, K, Ca, Mn, and Ni) and organic metabolites (caffeine and polyphenols), respectively. Chemometric methods were applied to classify the origin based on the component. The proposed method was used to test samples collected from 10 mutually adjacent high-altitude villages in Mengku, Lincang, Yunnan, China. The developed user-friendly method was successfully used for the rapid evaluation of the composition, enabling the identification of the geographic origins of Pu-erh tea. Mn, caffeine, and polyphenols were found to be the key origin markers. Compared to principal component analysis, linear discriminant analysis models exhibited high discrimination performance with 100% predictive ability to distinguish the geographic origins of Pu-erh teas over a small production area. This rapid and simple method may be used to solve classification problems in tea quality control and management.