Niger Delta University

This paper explores the interaction of 5G mmWave energy, specifically at frequencies above 24 GHz, with human tissues. It examines frequencies essential to 5G, including 24, 30, 35, 40, and 45 GHz, focusing on the skin, cornea, and enamel as candidates for investigation. The eye is particularly susceptible due to its surface location on the human body. Findings reveal that the penetration depth in the eye decreases from 7.331 µm at 24 GHz to 4.065 µm at 45 GHz, with the percentage of cornea tissue penetrated decreasing from 1.2 to 0.6% with frequencies. This result is unprecedented in existing literature. The results confirm that mmWaves do not penetrate beyond the cornea, emphasizing their surface-level effect on eye tissues. Another novel finding indicates that mmWaves attenuate entirely at the enamel, not affecting deeper dental structures, and significantly diminish at the skin’s epidermis without reaching the dermis, suggesting minimal penetration into deeper tissue layers. These discoveries introduce new, previously unreported data into the current research literature. Computational graphics for relative permittivity and conductivity versus frequency for the skin, cornea, and tooth enamel were generated. The resulting profiles are consistent with existing literature for other tissues, enhancing the reliability of the findings. Additionally, specific absorption rate values, computed using electric field measurements with an SMP2 meter at 900, 1800, and 2100 MHz, comply with the US Federal Communication Commission's SAR specifications of 1.6 W/kg.

Artisanal and small-scale gold mining is a critical livelihood for many in developing regions, yet it poses momentous environmental and health risks, principally through the release of naturally occurring radioactive materials. The study evaluates the annual effective dose received by artisans engaged in several mining activities at Itagunmodi and Iperindo in Osun State, Nigeria. The soil and rock samples were analysed for the activity concentrations of naturally occurring radionuclides (⁴⁰K, ²²⁶Ra, and ²³²Th) using gamma spectrometry. These concentrations were then used to determine the annual effective doses from different exposure scenarios, such as digging, panning, and crushing. The results revealed that artisans engaged in digging and panning in the Itagunmodi mine were exposed to an annual effective dose of 0.577 mSv/y, whereas those at the Iperindo mine received doses of 0.739 mSv/y for comparable tasks. Importantly, crushing operations at Iperindo were linked to greater exposure of 1.264 mSv/y. The cancer risks associated with these operations were determined using Monte Carlo simulation approach. The findings of this work provide crucial insights into occupational exposure to radiation among mining artisans and emphasize the necessity for implementing suitable safety measures to reduce health hazards related to radiation exposure in these mining sites.

Trace metal pollution is primarily driven by industrial, agricultural, and mining activities and presents complex environmental challenges with significant implications for ecological and human health. Traditional methods of environmental risk assessment (ERA) often fall short in addressing the intricate dynamics of trace metals, necessitating the adoption of advanced statistical techniques. This review focuses on integrating contemporary statistical methods, such as Bayesian modeling, machine learning, and geostatistics, into ERA frameworks to improve risk assessment precision, reliability, and interpretability. Using these innovative approaches, either alone or preferably in combination, provides a better understanding of the mechanisms of trace metal transport, bioavailability, and their ecological impacts can be achieved while also predicting future contamination patterns. The use of spatial and temporal analysis, coupled with uncertainty quantification, enhances the assessment of contamination hotspots and their associated risks. Integrating statistical models with ecotoxicology further strengthens the ability to evaluate ecological and human health risks, providing a broad framework for managing trace metal pollution. As new contaminants emerge and existing pollutants evolve in their behavior, the need for adaptable, data-driven ERA methodologies becomes ever more pressing. The advancement of statistical tools and interdisciplinary collaboration will be essential for developing more effective environmental management strategies and informing policy decisions. Ultimately, the future of ERA lies in integrating diverse data sources, advanced analytical techniques, and stakeholder engagement, ensuring a more resilient approach to mitigating trace metal pollution and protecting environmental and public health.