This dosimetry intercomparison evaluated epithelial/absorbed power density and temperature rise in two high-resolution anatomical human face models under localized antenna exposures at 10 and 30 GHz. The study reports a statistical correlation between spatially averaged absorbed power density and temperature rise when appropriate averaging is applied. Antenna type/configuration was identified as the dominant contributor to variability, exceeding differences from averaging methods or anatomical models.

This exposure-assessment study proposes and validates a method to measure instantaneous RF exposure under typical base station load by generating defined data rates (low/medium/high) using iPerf and measuring channel power across services. Validation at four base stations suggests the approach is reliable across different times of day and loads, with reproducible results when averaging over 30 sweeps. Comparisons indicate iPerf-provoked constant data rates generally match exposure during real application usage, with few deviations beyond stated uncertainty.

This exposure/compliance assessment study collected 5- and 30-minute transmitted power data over one month from more than 400 5G FR2 beamforming base stations in urban Japan to evaluate the IEC 62232:2025 “actual maximum approach.” All measured transmitted powers were below configured maxima, with 30-minute averaged normalized maxima reported as < -8 dB, while 5-minute averaged maxima reached about -3 dB under low UE counts. The authors report that the actual maximum approach can avoid overestimation for longer averaging times, but may underestimate exposure for stations with three or fewer UEs if power is not monitored and controlled.

This engineering/modeling study developed a 3D myelin microstructure model using finite element analysis and high-resolution imaging to simulate local electromagnetic field distributions. It reports that myelin architecture substantially shapes the distribution of electromagnetic fields across neural tissues. The authors suggest these field variations could potentially serve as non-invasive indicators of myelin integrity and may support tracking neurodegenerative disease progression.

This exposure assessment used personal exposimeters to measure RF-EMF levels in the 2.4 GHz and 5.85 GHz Wi-Fi bands in airport terminals and during four international flights, including flights with and without onboard Wi-Fi service. Reported mean exposures varied by route but were described as substantially below an international reference level (10 W/m²). The authors conclude exposure is low while also recommending ongoing monitoring and precaution due to potential health concerns mentioned as emerging evidence.

This exposure assessment study compared RF-EMF measurements averaged over 1, 6, and 30 minutes using contiguous 1-minute data collected over 30 minutes at four indoor/outdoor sites across 15 frequency bands. Relative deviations between shorter averaging times and 30-minute averages were largely within ±3 dB. However, statistical comparisons of overall exposure variability between 1- or 6-minute and 30-minute averaging produced inconsistent results, with broadcast and most mobile services <2 GHz appearing broadly similar between 1- and 6-minute averaging.

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 analyzed smartphone-logged 4G LTE RSSI and GPS data from adults in France to identify determinants of downlink signal strength. RSSI varied with geospatial factors (distance to antennas, antenna density, urbanicity) and time of day, and was also influenced by technical smartphone parameters. The study reports an estimated electric field strength derived from RSSI, but notes high uncertainty in this conversion.

This paper presents a quantitative framework for selecting extremely low-frequency electromagnetic field (ELF-EMF) measurement instruments. It uses a weighted scoring matrix across six criteria and a logic-based flowchart to guide instrument choice based on operational needs. The framework is demonstrated in an occupational case study and is positioned as supporting transparent, adaptable device selection for occupational safety and public health.

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 applies a model-based clustering approach (Log-Normal Mixture Model) to continuous RF-EMF monitoring data from the Serbian EMF RATEL network in Novi Sad to characterize temporal exposure patterns. The analysis reports separation of night versus day exposure values and identification of daytime periods where exposure persists longer. The work is positioned as supporting improved understanding of when and where elevated exposures occur in urban environments.