This exposure-assessment study proposes a Voronoi-diagram approach to visualize RF-EMF exposure across a city using personal exposimeter measurements of RMS electric field at seed points. Most mapped areas corresponded to about 1.9 V/m, with a maximum reported value of 11.4 V/m, all below the cited ICNIRP guideline level. The authors conclude the method is useful for communicating spatial variability, while also noting broader literature discussing potential health risks from EMF exposure.

This engineering/monitoring paper describes enhancements to the Serbian EMF RATEL system, which has continuously monitored RF-EMF since 2017, by adding drive test functionality to improve spatial coverage. The authors report preliminary quantitative drive test measurements and validation of the upgraded approach. The work emphasizes that characterizing spatial and temporal RF-EMF patterns can support exposure assessment relevant to public health risk evaluation.

This exposure-assessment study compares RF-EMF exposure maps produced using a broadband meter versus a personal exposimeter and aims to correct personal exposimeter readings to match broadband-based maps. The authors report that LOS/NLOS-specific correction factors reduce discrepancies, particularly improving LOS measurements affected by body shielding. A genetic algorithm is used to optimize correction factors and support scalable urban exposure mapping, with the authors noting that additional validation in other environments is needed.

This paper presents a machine-learning method to estimate ground-level electromagnetic radiation (electric field strength) in the near field of 5G base stations, using multiple technical and environmental input parameters. The authors report experimental performance with a mean absolute percentage error of about 5.89% and suggest the approach can reduce costs compared with on-site measurements. The work is positioned as supporting exposure management and base-station placement, while noting the need for careful EMF management due to potential health-risk links.

This paper proposes a time-averaging approach for analyzing long-term EMF exposure using time-series data from three sensors in a regulatory monitoring network. Sensors were installed at two kindergartens and one elementary school, and analyses reported daily/weekly patterns, differences between weekdays and weekends, and site-specific annual increases/decreases. The work emphasizes the value of continuous monitoring in sensitive areas, while not directly assessing health outcomes.

This exposure assessment reports 400 ground-level electric field measurements in Greek urban and suburban areas during 5G deployment. It finds that 4G contributes most to overall measured EMF exposure, while 5G currently contributes less. The study reports an inverse relationship between 3.5 GHz EMF levels and distance from 5G base stations, with urban areas showing higher levels than suburban areas.

This protocol and pilot study evaluated whether smartphone signal strength indicators can be converted into RF-EMF exposure estimates using derived formulas and regression models. The study reports a positive log-linear association between LTE RSSI and far-field (base station) exposure aggregated by location, while handset-related exposure at the ear and chest during data transmission showed negative log-linear trends with improving signal quality. The authors conclude the ETAIN 5G-Scientist app may support large-scale RF-EMF exposure assessment, but emphasize the need for more data to improve accuracy and address uncertainties in individual measurements.

This exposure assessment study introduces a cost-efficient add-on sensor attached to a smartphone to quantify auto-induced uplink RF-EMF transmission across 100–6000 MHz in multiple microenvironments. Activity-based surveys were conducted in seven European countries under non-user, maximum downlink, and maximum uplink scenarios. Reported power levels were lowest for non-user scenarios and higher during active use, with variation by country, urbanization, and setting. The authors frame the work as supporting future epidemiological research and planned validation against other tools.