Uncertainty analysis in environmental radioactivity measurements using the Monte Carlo code MCNP5

High Purity Germanium (HPGe) detectors are widely used for environmental radioactivity measurements due to their excellent energy resolution. Monte Carlo (MC) codes are a useful tool to complement experimental measurements in calibration procedures at the laboratory. However, the efficiency curve of the detector can vary due to uncertainties associated with measurements. These uncertainties can be classified into some categories: geometrical parameters of the measurement (distance source-detector, volume of the source), properties of the radiation source (radionuclide activity, branching ratio), and detector characteristics (Ge dead layer, active volume, end cap thickness). The Monte Carlo simulation can be also affected by other kind of uncertainties mainly related to cross sections and to the calculation itself. Normally, all these uncertainties are not well known and it is required a deep analysis to determine their effect on the detector efficiency. In this work, the Noether-Wilks formula is used to carry out the uncertainty analysis. A Probability Density Function (PDF) is assigned to each variable involved in the sampling process. The size of the sampling is determined from the characteristics of the tolerance intervals by applying the Noether Wilks formula. Results of the analysis transform the efficiency curve into a region of possible values into the tolerance intervals. Results show a good agreement between experimental measurements and simulations for two different matrices (water and sand).Gallardo Bermell, S.; Querol Vives, A.; Ortiz Moragón, J.; Ródenas Diago, J.; Verdú Martín, GJ.; Villanueva López, JF. (2015). Uncertainty analysis in environmental radioactivity measurements using the Monte Carlo code MCNP5. Radiation Physics and Chemistry. 116:214-218. doi:10.1016/j.radphyschem.2015.05.023S21421811

7Be spatial and temporal pattern in southwest of Europe (Spain): Evaluation of a predictive model

This study presents a comprehensive statistical analysis of the cosmogenic radioisotope 7Be measured in surface air in ten stations over Spain for a 9 years period (from January 2006 to December 2014). Besides the analysis of 7Be seasonal and inter-annual variability, 7Be frequency distributions and its correlations with meteorological variables observed in the 10 sampling sites were investigated. The second part of this paper focuses on a feasibility study for the application of the Artificial Neural Networks (ANNs) to predict monthly 7Be activity concentrations using meteorological variables, PM10 concentrations and the sunspot number as input parameters. Notwithstanding the low correlations found between 7Be and input parameter, the performance of the ANNs, as evaluated by the relevant statistical parameters, demonstrates their capability to correctly predict 7Be monthly activities in the 10 Spanish sampling sites

Sources of the seasonal-trend behaviour and periodicity modulation of 7Be air concentration in the atmospheric surface layer observed in southeastern Spain

The components and the periodicity characterising airborne 7Be monthly concentration collected in Granada (SE Spain) between 2000 and 2018 are studied. These 19 years of measurements are particularly significant for the 7Be analysis as they cover the complete 24th solar cycle, 2008–2018. A Time Series Decomposition (TSD) technique has been applied to determine the different components present in the dataset: the trend, seasonal and irregular components. Then, the atmospheric parameters (Precipitation (Pp), Relative Humidity (RH), Temperature (Temp), Wind speed (WS), Total Cloudiness (TC), Low Cloudiness (LC)), teleconnection indices and the solar activity (Sun Spot Number, SSN) were used to justify the variability of each component. SSN and the teleconnection indices (Western Mediterranean Oscillation (WeMO), Atlantic Multidecadal Oscillation (AMO) and Quasi Biennial Oscillation (QBO)) are found to have a major impact on the trend component of 7Be. In turn, all the atmospheric parameters were found to have strong impact on the seasonal component. In order to observe the change in the impact of the parameters from the 24th solar cycle (from 2008 to 2018) to the complete time series (starting in 2000), the comparison between the two periods has been carried out. The results show that, over the 24th solar cycle, the SSN impacted predominantly on the trend component, whereas atmospheric parameters showed a slightly higher impact on the seasonal component. In addition, some atmospheric factors (Temp, RH, Pp, TC, LC) appeared to partially affect the irregular component. The analysis of the two phases of the 24th solar cycle shows that the influence of SSN is higher during the descending phase than during the ascending one. This behaviour is opposed to the one found for the 23rd solar cycle, which clarifies the special characteristics of the 24th cycle. Besides that, the Continuous Wavelet Transform (CWT) analysis was applied in order to extract the possible periodicities characterising 7Be data. The analysis revealed three sets of periodicities. Then, the Wavelet Coherence Analysis (WCA) method was particularly useful to study coherences between 7Be data and teleconnection indices. The 7Be concentrations in the large period (11–14 years) detected by CWT was found to be mainly modulated by WeMO, AMO and QBO, while the NAO modulates the smaller periods

Elaboration of novel adsorbent from Moroccan oil shale using Plackett–Burman design

The new adsorbents were prepared from Moroccan oil shale by chemical and physical process .In this study, experimental Plackett-Burman has been used as a screening method to study six factors for the development of materials to adsorbent basis of oil shale Moroccan. The factors have been identified by two levels, To Know temperature (°C), Processing time (min), mass ratio (m precursor/m acid), Pretreatment mixture the precursor with acid, origin of the raw material and type of the activating agent (H2SO4, H3PO4).And it was chosen as a response The maximum quantity of adsorption of the molecule of Methylene blue (Qads in mg/g) and the specific surface measure by the method bet (Sbet in m2/g), The predicted values were in agreement with the experimental values with a coefficient of determination (R2) of 0.98. The model has been validated by experiments subsequent to optimized conditions. The experimental data processing by software JMP 7 showed that the processing temperature The report of oil shale on the acid and activation time were the important effect on the maximal capacity of adsorption of methylene blue. The sample prepared at 237 °C during 215 min with pre-processing has a maximal capacity of adsorption equal to 54mg/g according to model of adsorption of Langmuir and SBET equal to 143 m2/g