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.

The role of teleconnections and solar activity on the discharge of tropical river systems within the Niger basin.

The behavior of tropical river systems is driven by some internal and external factors. Understanding the role of these external forces, such as large-scale oscillations, on river discharge will provide insight into their dynamic complexities and modelling. In this study, the role of teleconnection patterns and solar activity on river discharges within the Niger basin was considered using both linear (correlation) and nonlinear (multifractal and joint recurrence analysis) statistical approaches. Correlation analysis suggests the existence of a linear relationship between tropical teleconnection patterns in the Atlantic and Pacific oceans with river discharge in the Niger basin. Nonlinear relationships were investigated using multifractal formalism and joint recurrence quantification analysis. A strong nonlinear relationship was observed between the teleconnection patterns and river discharge in Diola while other stations (Koulikoro, Ansongo, Niamey, Mopti, Kirango) showed no such relationship. The observation at Diola is attributed to its location (coastal) among other things. The multifractal strengths were found in the range of 0.58-2.86, suggesting fractal correlations between the parameters. There was no conclusive evidence of a linear and nonlinear relationship between solar activity and tropical river discharge within the Niger basin.

The Impact of the First and Second Waves of COVID-19 Pandemic in Nigeria.

In recent times, the COVID-19 pandemic has been the subject of global concern. It has so far claimed over 5.4 million lives globally, with over 291 million cases recorded worldwide as of 5 January 2022. It is known to have different waves and variants, thus making it difficult to handle/manage. This study investigates the impact of the first and second waves of COVID-19 in Nigeria, West Africa. The data used is for the 36 states of Nigeria archived at the National Centre for Disease Control from February 2020 to April 2021. Results from the study reveal that the highest number of COVID-19 cases during the first/second wave was recorded at Lagos (23,238/34,616), followed by the Federal Capital Territory (FCT) (6,770/12,911) and alternates between Plateau (3,858/5,170) and Kaduna (3,064/5,908). Similarly, the highest number of deaths (during the first/second wave) was also recorded in Lagos (220/219), followed by Edo (112/73), and then FCT (83/81). The Case Fatality Ratio (CFR) was observed to be higher mostly in northern Nigeria during the first wave and the southeast during the second wave of the pandemic. On the average, the number of cases/deaths recorded during the second wave was higher than those of the first wave, but a decrease in the CFR values was observed during the second wave. Higher values of COVID-19 cases/death were mostly recorded in Nigeria during; maximum relative humidity (RH) (>70%) with minimum Temperatures (<25°C), Low temperatures, and low RH which is mostly observed during the cold/dusty periods.

Association Between Weather Parameters and SARS-CoV-2 Confirmed Cases in Two South African Cities.

Several approaches have been used in the race against time to mitigate the spread and impact of COVID-19. In this study, we investigated the role of temperature, relative humidity, and particulate matter in the spread of COVID-19 cases within two densely populated cities of South Africa-Pretoria and Cape Town. The role of different levels of COVID-19 restrictions in the air pollution levels, obtained from the Purple Air Network, of the two cities were also considered. Our results suggest that 26.73% and 43.66% reduction in PM2.5 levels were observed in Cape Town and Pretoria respectively for no lockdown (Level 0) to the strictest lockdown level (Level 5). Furthermore, our results showed a significant relationship between particulate matter and COVID-19 in the two cities. Particulate matter was found to be a good predictor, based on the significance of causality test, of COVID-19 cases in Pretoria with a lag of 7 days and more. This suggests that the effect of particulate matter on the number of cases can be felt after 7 days and beyond in Pretoria.

Advanced Marine Predator Algorithm for Circular Antenna Array Pattern Synthesis.

The pattern synthesis of antenna arrays is a substantial factor that can enhance the effectiveness and validity of a wireless communication system. This work proposes an advanced marine predator algorithm (AMPA) to synthesize the beam patterns of a non-uniform circular antenna array (CAA). The AMPA utilizes an adaptive velocity update mechanism with a chaotic sequence parameter to improve the exploration and exploitation capability of the algorithm. The MPA structure is simplified and upgraded to overcome being stuck in the local optimum. The AMPA is employed for the joint optimization of amplitude current and inter-element spacing to suppress the peak sidelobe level (SLL) of 8-element, 10-element, 12-element, and 18-element CAAs, taking into consideration the mutual coupling effects. The results show that it attains better performances in relation to SLL suppression and convergence rate, in comparison with some other algorithms for the optimization case.

Multifractal analysis of air and soil temperatures.

Air and soil temperatures are important agrometeorological variables with several applications. Understanding the complex behavior of air and soil temperatures, as well as their interaction, will help in agricultural planning. Multifractal detrended fluctuation and multifractal cross-correlation analysis of air and soil temperatures were carried out in three locations (Akure, Abuja, and Bauchi) within a tropical country, Nigeria. Monthly and annual air and soil temperatures measured at 5 min intervals for a period of 1 year were obtained and analyzed for multifractality. There is evidence of seasonal dependence in the multifractal behavior of monthly soil temperature. Monthly temperatures (air and soil) were found to have higher degrees of multifractality than annual temperatures. Furthermore, latitudinal dependence was observed in the multifractal behavior of air and soil temperatures. The cross-correlation between air and soil temperatures also shows multifractality with persistence at the monthly scale and anti-persistence at the annual scale. This work has shed light on the complex relationship between air and soil temperatures, and the results will be useful in modeling the two variables.