Speaker
Description
Background: Wireless communication devices are an integral part of daily life, leading to unknown radiofrequency electromagnetic fields (RF-EMF) exposure levels. Dose models quantify RF-EMF exposure from different sources by adding up absorbed energy in different exposure scenarios, but uncertainties persist regarding the input data. The GOLIAT project introduces a novel approach to estimate RF-EMF dose, incorporating uncertainty across multiple exposure scenarios, including 5G technology.
Methods: The GOLIAT dose model quantifies daily absorbed RF-EMF dose using normalized specific absorption rate (SAR) values, output power of near-field devices, and far-field exposure data for various exposure scenarios measured in the GOLIAT project or found in literature. A Monte Carlo Simulation (MCS) method quantifies the uncertainty by generating probability distributions for the input data in each scenario. An example scenario, native mobile phone call, was evaluated for somebody calling 8-minutes per day with mobile phone at the ear.
Results: The estimated brain dose results in a median dose of 94.42 mJ/kg/day (IQR: 37.37–233.54) and a mean dose of 177.20 mJ/kg/day (95% CI: 7.47–799.77), aligning with previous dose models.
Conclusion: Considering uncertainties in RF-EMF dose modelling illustrates the relevance of various parameters for this calculation and the limitation of simple exposure proxies for epidemiological research. The ability of the GOLIAT dose model of quantifying uncertainty across diverse exposure scenarios makes it a novel tool for evaluating potential health implications of RF-EMF exposure and its reliability.
