Time series study of a 17-year record of (7)Be and (210)Pb fluxes in northern Taiwan using ensemble empirical mode decomposition

Time series analysis of a 22-year record of 7Be, 210Pb and gross alpha activities in the Canary Islands (Spain) using Principal Component and Multifractal Detrended Fluctuation Analyses

The long-term variability of a 22-year dataset of 7Be, 210Pb and gross alpha concentrations in surface aerosols collected in the Canary Islands has been analysed in this study. These "time series" were collected on a weekly basis. Various analytical techniques, including Principal Component Analysis (PCA), K-means clustering, correlation analyses, and back-trajectory were used to determine the variability of the data and assess the statistical importance of the source of the air masses reaching the study area. Monthly and annual variations for the time series were also studied. As expected, 7Be, 210Pb time series showed common variability, while gross alpha concentrations were strongly correlated with average PM10 concentration in air. The fractal properties of the time series were studied to gain a deeper understanding of the underlying structure and dynamics of the data. Multifractal Detrended Fluctuation Analysis (MF-DFA) and Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) techniques were also used to detect and analyse the multifractal characteristics of the 7Be, 210Pb, and gross alpha time series. Multifractality was observed, with values of 0.28, 0.67, and 0.61 for 7Be, 210Pb, and gross alpha, respectively. Long-range correlation was found to be the source of the observed multifractality in the three parameters. Multifractal detrended cross-correlation analysis supports the correlation between 7Be - Alpha, 210Pb - Alpha, and 7Be - 210Pb pairs. The results from this study will help model the transport and destiny of natural radionuclides in the atmosphere at this site. The evolution and interactions between 7Be, 210Pb, and gross alpha, reported herein occurred not just locally but also across extensive temporal domains, leading to the emergence of multifractal behaviour in their concentrations. These long-range behaviours/correlations might result from various factors such as atmospheric circulation patterns, global transport mechanisms, or large-scale environmental processes.

Predicting the monthly consumption and production of natural gas in the USA by using a new hybrid forecasting model based on two-layer decomposition

As an efficient, economical, and clean energy, natural gas plays an important role in the development of the new energy revolution. Accurate prediction of natural gas consumption and production can adjust energy deployment in advance, which can ensure the stable operation of natural gas. Considering the complex and non-linear characteristics of natural gas production and consumption data, this paper develops a new hybrid forecasting model (WPD-VMD-LSTM) based on the fuzzy entropy, variational mode decomposition (VMD), wavelet packet decomposition (WPD), and Long Short-Term Memory (LSTM). In this model, WPD and VMD undertake the tasks of primary and secondary decompositions, respectively; fuzzy entropy is used for the preprocessing process before the re-decomposition; and LSTM is used to predict the decomposed time series. In particular, the different criteria set by fuzzy entropy lead to the establishment of two prediction models. Then, two models are used to study monthly natural gas consumption and production in the USA. The results demonstrate that the proposed model performs significantly better than other comparable models and the target model has some practical value. Meanwhile, models may cope with different types of energy data, and models can accurately predict energy transformations with strong applicability, which can be applied to future energy forecasting in various fields. Finally, the constructed models are used to forecast the NGC and NGP in the USA in the next 3 years and make reasonable policy recommendations based on the forecast results.

Influence of long-range atmospheric transport pathways and climate teleconnection patterns on the variability of surface 210Pb and 7Be concentrations in southwestern Europe

The variability of the atmospheric concentration of the ⁷Be and ²¹⁰Pb radionuclides is strongly linked to the origin of air masses, the strength of their sources and the processes of wet and dry deposition. It has been shown how these processes and their variability are strongly affected by climate change. Thus, a deeper knowledge of the relationship between the atmospheric radionuclides variability measured close to the ground and these atmospheric processes could help in the analysis of climate scenarios. In the present study, we analyze the atmospheric variability of a 14-year time series of ⁷Be and ²¹⁰Pb in a Mediterranean coastal city using a synergy of different indicators and tools such as: the local meteorological conditions, global and regional climate indexes and a lagrangian atmospheric transport model. We particularly focus on the relationships between the main pathways of air masses and sun spots occurrence, the variability of the local relative humidity and temperature conditions, and the main modes of regional climate variability, such as the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO). The variability of the observed atmospheric concentrations of both ⁷Be and ²¹⁰Pb radionuclides was found to be mainly positively associated to the local climate conditions of temperature and to the pathways of air masses arriving at the station. Measured radionuclide concentrations significantly increase when air masses travel at low tropospheric levels from central Europe and the western part of the Iberian Peninsula, while low concentrations are associated with westerly air masses. We found a significant negative correlation between the WeMO index and the atmospheric variability of both radionuclides and no significant association was observed for the NAO index.