Athanasios Manassas

To meet the ever‐growing demands of wireless communication and enable new applications, the 5th generation (5G) New Radio (NR) cellular system was developed to offer better connectivity, to reduce latency, and enable higher data transfer rates. The use of the 5G frequency spectrum is divided into two frequency ranges, Frequency Range 1 (5G NR FR1) from 0.41 GHz to 7.125 GHz, operating in the same frequency range as the 2G–4G cellular systems and WLAN systems, and Frequency Range 2 (5G NR FR2) from 24.25 to 52.60 GHz using millimetre waves (mmWaves) to enable fast transfer of large amounts of data. Since 2019, FR1 has been rapidly deployed in Europe. Until today, in Europe, FR2 has very limited applications. The objective of SEAWave project was to conduct two risk assessments, one for FR1 and one for FR2. Risk assessment procedure included evaluation of evidence of an association between skin cancer and exposure to FR1 and FR2 radiofrequency electromagnetic field (RF‐EMF), with the skin being the most exposed organ during use. Both assessments are based on: a comprehensive review of the scientific literature available before the end of the project and the consolidated findings from research studies generated within the SEAWave project. Experiments conducted within SEAWave project included: (i) In vivo experiments on Carcinogenesis‐susceptible (Car‐S) mice and Patched heterozygous knockout mice (Ptch1 +/−) to investigate the effects of prolonged exposure to 5G FR2 RF‐EMF at 27.5 GHz on the development of squamous cell carcinoma (SCC) and basal cell carcinoma (BCC), two of the most common skin tumours. In addition, skin inflammation and nociceptive signalling were analysed in dorsal skin samples taken from mice after early stage whole‐body exposure in the first 21 days of postnatal life and after 100 days and 6 months of exposure; (ii) Clinical trial where participants' inner arms were exposed to 5G FR2 RF‐EMF at 27.5 GHz to study transcriptomic changes in skin samples induced by 5G FR2 RF‐EMF exposure, with particular attention to inflammatory processes; (iii) In vitro experiments using different primary human skin cell lines, that is, light‐pigmented primary epidermal juvenile and adult keratinocytes (NHEK‐f.c. and NHEK‐c.) and light‐pigmented primary epidermal adult melanocytes (NHEM) exposed to 5G FR2 RF‐EMF at 27.5 GHz to study several endpoints including genotoxicity, aneugenicity, telomere measurement, DNA methylation and transcriptomics. The overall evidence for an association between skin cancer and 5G FR1 has been classified as ‘inconclusive’ based on the evaluations of human evidence, evidence from animal studies and mechanistic studies classified as ‘inadequate’. 5G FR1 evaluations were mainly based on literature review. The overall evidence for an association between skin cancer and 5G FR2 has been classified as ‘inconclusive’ based on the evaluations of human evidence, and evidence from animal studies and mechanistic studies classified as ‘inadequate’. 5G FR2 evaluations were mainly based on findings from the SEAWave project. All classifications were unanimous except for the 5G FR2 mechanistic evidence which a minority considered ‘limited’; even with limited evidence the overall assessment for 5G FR2 would have remained ‘inconclusive’. An impact evaluation completes the risk assessment, as for inconclusive evidence no quantitative risk assessment could be carried out.

Radiofrequency (RF) exposure assessment in indoor environments has attracted considerable interest due to the widespread use of wireless communication technologies and the increasing amount of time people spend indoors. In the framework of the SEAWave project, extensive frequency-selective measurements were performed in Greece to characterize environmental RF exposure across diverse indoor environments. A harmonized dataset comprising more than 26,000 measurement samples collected at 40 indoor locations was statistically analyzed, covering residential, commercial, educational, office/workplace, public/cultural, event/market, and transport environments. Transport environments exhibited the highest exposure levels (RMS = 0.856 V/m), whereas residential (0.122 V/m) and office/workplace (0.136 V/m) environments showed the lowest values. Cellular networks accounted for more than 80% of the total RF exposure in all environments, reaching 94.4% in transport environments, while Wi-Fi contributed between approximately 5.6% and 17% depending on the environment. Repeated measurements revealed environment-dependent differences in RF exposure, with the largest variations between measurement campaigns observed in public transport, commercial, and public environments, whereas no statistically significant relationship between floor level and RF exposure was observed. RF exposure varied across indoor environment categories, while an exploratory geographical comparison within Commercial/Retail environments did not reveal statistically significant differences among urban, suburban, and rural groups.

Millimeter wave (MMW) technology is increasingly used in telecommunications and medical applications, necessitating a deeper understanding of its interaction with human tissue, particularly skin. The limited penetration depth of MMWs makes the skin the primary site of energy absorption, requiring detailed dosimetric analysis. Traditional skin models often assume planar, layered structures, neglecting the natural undulating geometry of the epidermis and dermis. This study introduces a novel methodological framework for constructing realistic three-dimensional (3D) human skin models using Optical Coherence Tomography (OCT) and Ultrasound (US) imaging. The framework involves three key steps: (1) OCT and US image acquisition, (2) image segmentation and processing, and (3) synthesis of processed data to generate high-fidelity 3D skin models. Three anatomical sites—the volar forearm, hypothenar region, and fingertip—were selected for model development, representing variations in skin roughness and layer composition. The dielectric properties of the skin and subcutaneous layers were incorporated into finite-difference time-domain (FDTD) simulations at 27.5 GHz to evaluate the impact of skin roughness on electromagnetic field distribution. Our results demonstrate that the natural undulations of the dermoepidermal junction and air-skin interface significantly affect MMW energy absorption patterns. The roughness of these interfaces varies across anatomical sites, influencing local electric field distributions and leading to deviations from predictions based on planar models. The findings suggest that incorporating realistic skin geometries enhances dosimetric accuracy, which is critical for both safety assessments and optimizing MMW-based medical applications. This study provides an approach for individualized MMW dosimetry and underscores the importance of considering anatomical variations in electromagnetic exposure assessments. The framework was additionally extended to a nodular basal cell carcinoma case, revealing marginal changes in local field and SAR distributions and a moderate increase in reflected power compared to healthy skin.

This study explores the application of Artificial Intelligence (AI) in predicting electromagnetic field (EMF) exposure levels in urban environments. Machine Learning (ML) and Deep Learning (DL) models were developed to estimate the electric field (E, in V/m) at specific locations without requiring direct measurements. Measurements were conducted in Thessaloniki, Greece, covering a diverse urban landscape, including commercial and residential areas. The methodology involved collecting EMF data using high-precision equipment, validating and refining publicly available datasets, and extracting key features using Geographic Information System (GIS) tools. Essential input features included distances to the nearest base stations, their installation heights, classification of base stations regarding their emitted power, number of obstructing buildings, built area density, and line-of-sight (LOS) conditions. The AI models demonstrated strong predictive performance, with mean absolute errors (MAE) slightly above 0.3 V/m. The study highlights the importance of proper data preprocessing, feature selection, and integration of real-world measurements into AI-based prediction models. The results suggest that AI can serve as a reliable alternative for EMF exposure assessment, potentially reducing the need for costly and time-consuming field measurements while ensuring compliance with safety regulations.

This study presents an extensive analysis of electromagnetic field (EMF) exposure in Greek urban and suburban areas during the ongoing deployment of the 5G technology. A total of 400 in situ measurements of electric field strength were conducted at ground level across four distinct regions. The first part of the study provides a statistical analysis of the overall EMF exposure, highlighting the contributions of different cellular generations (2G–5G) to the total field. The results show that 4G networks are the dominant contributors, while the impact of 5G remains limited. The second part investigates the correlation between electric field levels in the 3.5 GHz band and the distance from 5G base stations (BS). Using geospatial analysis, we computed distances to the nearest BS and examined their relationship with measured EMF levels. The findings indicate a general inverse correlation between EMF levels and BS distance, although variations occur due to factors such as line-of-sight conditions and urban built environment characteristics. These results confirm that distance remains a key predictive feature for AI-based EMF exposure models, even in the case of 5G networks.

This study introduces a low-cost alternative method for mapping the electric field strength from 4G LTE base stations and identifies areas where this mapping is more accurate. A drive test campaign was conducted in the urban environment of Thessaloniki, Greece, using data obtained from three identical smartphones, each connected to a different mobile operator and an exposimeter. The smartphones used a mobile application to record Reference Signal Received Power (RSRP) values, while the exposimeter measured the electric field strength in selected frequency bands. In the first part, the variability of the received power over different periods within certain areas was studied, and the reasons for this variability were identified. In the second part, a linear factor was calculated to convert RSRP values into electric field strength using data from both the application and the exposimeter. The converted RSRP values were subsequently compared with the exposimeter data for validation. The results indicate that in areas where the variability of the received power is lower, the linear relationship between smartphone and exposimeter data is statistically stronger resulting in calculated electric field strength values are closer to the measured.

Owing to the advancement of wireless technologies, there is a strong public perception of increasing exposure to Radiofrequency (RF) electromagnetic fields (EMF). The aim of this study is to determine the evolution of EMF in the environment, and consequently, human exposure to them, over a period of about two decades, spanning from the end of 2003 until February 2022. The study is based on data collected by two non-ionizing radiation monitoring networks in Greece. The networks consist of fixed EMF sensors that register the RMS electric field value every 6 min, on a 24 h basis. We used the Seasonal–Trend decomposition method using (LOESS), known as the STL method to decompose the time series into trend, seasonal, and noise components. Additionally, since the sensors include frequency filters for separating the cellular frequencies, the recorded data were used to identify the exposure contribution by cellular networks in comparison to other EMF sources. The study indicates that RF-EMF do not explicitly decrease or increase but rather fluctuate over time. Similarly, the contribution of mobile cellular networks to the total field change over time.

The ever-increasing use of wireless communication systems during the last few decades has raised concerns about the potential health effects of electromagnetic fields (EMFs) on humans. Safety limits and exposure assessment methods were developed and are regularly updated to mitigate health risks. Continuous radiofrequency EMF monitoring networks and in situ measurement campaigns provide useful information about environmental EMF levels and their variations over time and in different microenvironments. In this study, published data from the five largest monitoring networks and from two extensive in situ measurement campaigns in different European countries were gathered and processed. Median electric field values for monitoring networks across different countries lay in the interval of 0.67–1.51 V/m. The median electric field value across different microenvironments, as evaluated from in situ measurements, varied from 0.10 V/m to 1.42 V/m. The differences between networks were identified and mainly attributed to variations in population density. No significant trends in the temporal evolution of EMF levels were observed. The influences of parameters such as population density, type of microenvironment, and height of measurement on EMF levels were investigated.

Non-ionising radiation-monitoring networks were initiated as a result of the public concerns about the potential health effects from telecommunication emissions. In the present study, the data acquired from such networks in Greece are used to assess the changes in the outdoor electromagnetic environment with respect to location and time. The study shows that there is a statistically significant difference between the urban (median electric field: 1.1 V m⁻¹) and the rural (median electric field: 0.3 V m⁻¹) installations of monitoring units and also shows that there is a median diurnal variation (daily maximum to minimum) of 20.2 and 33.8 % for the broadcasting and mobile telecommunication emissions, respectively. Moreover, there is a difference in the electric field between daytime and night, but not between morning and afternoon. The results are in line with previously published data from spot measurements, monitoring networks and personal exposimeter studies performed in several European countries.