Effects of Radiofrequency Electromagnetic Field (RF-EMF) exposure on pregnancy and birth outcomes: A systematic review of experimental studies on non-human mammals

Interaction of 5G mid-band and mmWave electromagnetic fields with the murine fetus

Currently, fifth-generation (5G) systems are deploying in the mid-band (i.e., 3.5 GHz (GHz), with plans for standalone (SA) operation in the high-band (i.e., 26 GHz) soon. The literature reveals a lack of computational studies on flora and fauna. This study aims to address that gap and help validate experimental research on animal exposure to electromagnetic field (EMF). We aim to explore the radiofrequency (RF)-EMF absorbed by mouse fetuses through in silico analysis using the Finite Difference Time Domain (FDTD) technique. We utilize the commercial software XFdtd (Remcom) and conduct simulations at both 3.5 and 26 GHz. The investigation focuses on understanding the penetration depth to comprehend EMF absorption by the fetus. While the study indicates that RF-EMF absorption at 5G high-band frequencies is unlikely to pose significant risks to mouse uteruses and fetuses, there is a clear need for further investigation.

Gaps in Knowledge Relevant to the "ICNIRP Guidelines for Limiting Exposure to Time-Varying Electric, Magnetic and Electromagnetic Fields (100 kHz TO 300 GHz)"

ABSTRACT

In the last 30 y, observational as well as experimental studies have addressed possible health effects of exposure to radiofrequency electromagnetic fields (EMF) and investigated potential interaction mechanisms. The main goal of ICNIRP is to protect people and the environment from detrimental exposure to all forms of non-ionizing radiation (NIR), providing advice and guidance by developing and disseminating exposure guidelines based on the available scientific research on specific parts of the electromagnetic spectrum. During the development of International Commission on Non-Ionizing Radiation Protection's (ICNIRP's) 2020 radiofrequency EMF guidelines some gaps in the available data were identified. To encourage further research into knowledge gaps in research that would, if addressed, assist ICNIRP in further developing guidelines and setting revised recommendations on limiting exposure, data gaps that were identified during the development of the 2020 radiofrequency EMF guidelines, in conjunction with subsequent consideration of the literature, are described in this Statement. Note that this process and resultant recommendations were not intended to duplicate more traditional research agendas, whose focus is on extending knowledge in this area more generally but was tightly focused on identifying the highest data gap priorities for guidelines development more specifically. The result of this distinction is that the present data gap recommendations do not include some gaps in the literature that in principle could be relevant to radiofrequency EMF health, but which were excluded because either the link between exposure and endpoint, or the link between endpoint and health, was not supported sufficiently by the literature. The evaluation of these research areas identified the following data gaps: (1) Issues concerning relations between radiofrequency EMF exposure and heat-induced pain; (2) Clarification of the relation between whole-body exposure and core temperature rise from 100 kHz to 300 GHz, as a function of exposure duration and combined EMF exposures; (3) Adverse effect thresholds and thermal dosimetry for a range of ocular structures; (4) Pain thresholds for contact currents under a range of exposure scenarios, including associated dosimetry; and (5) A range of additional dosimetry studies to both support future research, and also to improve the application of radiofrequency EMF exposure restrictions in future guidelines.

Effect of 6 GHz radiofrequency electromagnetic field on the development of fetal bones

This study examined the impact of 6 GHz (0.054 W/kg SAR) Radiofrequency-Electromagnetic Field (RF-EMF) on prenatal bone development. In this study, 20 female and 20 male Wistar Albino rats divided into four groups. The Control group received no treatment, while in Group-I, only male rats were exposed to RF-EMF, female rats had no exposure. Group-II, both male and female rats received RF-EMF treatment. While in Group-III, only female rats were exposed to RF-EMF, male rats had no exposure. The exposure lasted 4 hours per day for 6 weeks. The rats were then allowed to mate within the group. After pregnancy, pregnant rats (Group-II and III) were exposed 4 hours per day for 18 days. On the 18th day of gestation, fetuses were removed and their weight and various lengths were measured. The skeletal system development of fetuses was examined with double skeletal staining method and assessed ossification in the extremities. In the study, fetal weights, head-tail length, occipital-frontal and parietal-parietal lengths significantly increased in all exposure groups when compared to the control group (p < 0.001). Although occipital-frontal length was smallest in Group-I, Group-II and Group-III were more higher than the control group (p < 0.001). The bones of the anterior and posterior extremities showed significant increases in length, ossification zone length, and ossification percentage in all experimental groups compared to the control group (p < 0.001). Our study showed that rats exposed to 6 GHz (0.054 W/kg) RF-EMF during the prenatal period had significant increases in bone development.

A Comparative Analysis of Explainable Artificial Intelligence Models for Electric Field Strength Prediction over Eight European Cities

The widespread propagation of wireless communication devices, from smartphones and tablets to Internet of Things (IoT) systems, has become an integral part of modern life. However, the expansion of wireless technology has also raised public concern about the potential health risks associated with prolonged exposure to electromagnetic fields. Our objective is to determine the optimal machine learning model for constructing electric field strength maps across urban areas, enhancing the field of environmental monitoring with the aid of sensor-based data collection. Our machine learning models consist of a novel and comprehensive dataset collected from a network of strategically placed sensors, capturing not only electromagnetic field readings but also additional urban features, including population density, levels of urbanization, and specific building characteristics. This sensor-driven approach, coupled with explainable AI, enables us to identify key factors influencing electromagnetic exposure more accurately. The integration of IoT sensor data with machine learning opens the potential for creating highly detailed and dynamic electromagnetic pollution maps. These maps are not merely static snapshots; they offer researchers the ability to track trends over time, assess the effectiveness of mitigation efforts, and gain a deeper understanding of electromagnetic field distribution in urban environments. Through the extensive dataset, our models can yield highly accurate and dynamic electric field strength maps. For this study, we performed a comprehensive analysis involving 566 machine learning models across eight French cities: Lyon, Saint-Étienne, Clermont-Ferrand, Dijon, Nantes, Rouen, Lille, and Paris. The analysis incorporated six core approaches: k-Nearest Neighbors, XGBoost, Random Forest, Neural Networks, Decision Trees, and Linear Regression. The findings underscore the superior predictive capabilities of ensemble methods such as Random Forests and XGBoost, which outperform individual models. Simpler approaches like Decision Trees and k-NN offer effective yet slightly less precise alternatives. Neural Networks, despite their complexity, highlight the potential for further refinement in this application. In addition, our results show that the machine learning models significantly outperform the linear regression baseline, demonstrating the added value of more complex techniques in this domain. Our SHAP analysis reveals that the feature importance rankings in tree-based machine learning models differ significantly from those in k-NN, neural network, and linear regression models.

Comparison of a radiofrequency electric and magnetic field source-based job-exposure matrix with personal radiofrequency exposure measurements

OBJECTIVES

Assessing occupational exposure to radiofrequency electromagnetic fields (RF-EMF) presents significant challenges due to the considerable variability in exposure levels within and between occupations. This spatial and temporal variability complicates the reliable evaluation of potential health risks associated with RF-EMF exposure in the workplace. Accurate assessment methods are crucial to understand the extent of exposure and to evaluate potential health risks, especially given the potential for higher exposures in occupational settings compared to the general population. This study compares the historical RF-EMF exposure estimates in the INTEROCC RF-EMF job-exposure matrix (RF-JEM) with recent personal measurement data collected in 2 countries as part of the OccRF-Health study, to assess the broader applicability of the RF-JEM.

METHODS

Weighted kappa (kw) coefficients and Spearman rank correlation tests were performed to assess the alignment between RF-JEM estimates and measurements for 8 h time-weighted average exposure intensity and prevalence estimates across various occupations. The comparisons were mainly based on 22 jobs having ≥5 measured workers in the OccRF-Health study.

RESULTS

Poor agreement was found for both exposure prevalence and intensity between both methods (kw < 0.1). RF-JEM values likely overestimated exposure levels for both electric (E) and magnetic (H) fields (mean percentage difference >194%) compared to current personal measurements.

CONCLUSIONS

Findings suggest that the INTEROCC-JEM likely overestimates current exposure intensity levels in the measured jobs. Adopting a semiquantitative JEM could also mitigate misclassification errors due to exposure variability, improving accuracy in exposure assessment. These findings indicate the need for more targeted personal measurements, including among highly exposed workers, and for potentially considering new exposure metrics to more accurately assess occupational RF-EMF exposures in occupational epidemiological research.

WHO to build neglect of RF-EMF exposure hazards on flawed EHC reviews? Case study demonstrates how "no hazards" conclusion is drawn from data showing hazards

We examined one of the first published of the several systematic reviews being part of WHO's renewed initiative to assess the evidence of associations between man-made radiofrequency electromagnetic radiation (RF-EMF) and adverse health effects in humans. The examined review addresses experimental studies of pregnancy and birth outcomes in non-human mammals. The review claims that the analyzed data did not provide conclusions certain enough to inform decisions at a regulatory level. Our objective was to assess the quality of this systematic review and evaluate the relevance of its conclusions to pregnant women and their offspring. The quality and relevance were checked on the review's own premises: e.g., we did not question the selection of papers, nor the chosen statistical methods. While the WHO systematic review presents itself as thorough, scientific, and relevant to human health, we identified numerous issues rendering the WHO review irrelevant and severely flawed. All flaws found skew the results in support of the review's conclusion that there is no conclusive evidence for nonthermal effects. We show that the underlying data, when relevant studies are cited correctly, support the opposite conclusion: There are clear indications of detrimental nonthermal effects from RF-EMF exposure. The many identified flaws uncover a pattern of systematic skewedness aiming for uncertainty hidden behind complex scientific rigor. The skewed methodology and low quality of this review is highly concerning, as it threatens to undermine the trustworthiness and professionalism of the WHO in the area of human health hazards from man-made RF-EMF.

Effects of paternal ionizing radiation exposure on fertility and offspring's health

Purpose

The intergenerational effects of ionizing radiation remain controversial. Extensive insights have been revealed for DNA mutations and cancer incidence in progeny, yet many of these results were obtained by immediate post-radiation mating. However, conception at short times after radiation exposure is likely to be avoided. After a long period of fertility recovery, whether unexposed sperm derived from exposed spermatogonia would challenge the health of the offspring is not yet clearly demonstrated.

Methods

Ten-week-old C57BL/6J males underwent whole-body acute γ irradiation at 0 and 6.4 Gy. Testes and sperm were collected at different times after radiation to examine reproductive changes. The reproductive, metabolic, and neurodevelopmental parameters were measured in the offspring of controls and the offspring derived from irradiated undifferentiated spermatogonia.

Results

Paternal fertility was lost after acute 6.4 Gy γ radiation and recovered at 10-11 weeks post irradiation in mice. The reproductive, metabolic, and neurodevelopmental health of offspring born to irradiated undifferentiated spermatogonia were comparable to those of controls.

Conclusion

The male mice could have healthy offspring after recovery from the damage caused by ionizing radiation.