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.

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.

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 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 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.