Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Comment on: What is the radiation before 5G? A correlation study between measurements in situ and in real time and epidemiological indicators in Vallecas, Madrid, by I. López, N. Félix, M. Rivera, A. Alonso, and C. Maestú

We have read the article recently published by Lopez et al., 2020 (Lopez et al., 2021). This study aimed to find a possible relationship, if any, between exposure to RF-EMF with some health indicators such as sleep, headache, and fatigue collected through surveys, using maximum electromagnetic radiation peak-to-peak measurements. And after a detailed analysis of the study, we want to make some comments on said publication to clarify some aspects.

Comparative Analysis of User Exposure to the Electromagnetic Radiation Emitted by the Fourth and Fifth Generations of Wi-Fi Communication Devices

A suitable metric to describe human exposure to microwaves emitted by wireless communication devices is still incomplete. By using both theoretical analysis and experimental validation (in controlled and real deployed networks), we analyze and compare the specificity of exposure due to data transmissions in different configurations of fourth and fifth generation wireless fidelity (Wi-Fi) standards in the proximity of a mobile device. Measurements made use of the capability of the amplitude probability density incorporated in a real-time spectrum analyzer, proving its agility of highlighting different user exposure profiles. The results are presented comparatively and indicate that, in Wi-Fi networks, the modulation and coding scheme (MCS) should be used together with the duty cycle for an improved exposure assessment. The present work introduces the emitted energy density per bit in describing the user's exposure to Wi-Fi signals and proves its superiority in characterizing the true levels of exposure for the IEEE 802.11n and 802.11ac standards of communication.

Georeferencing of Personal Exposure to Radiofrequency Electromagnetic Fields from Wi-Fi in a University Area

In the last two decades, due to the development of the information society, the massive increase in the use of information technologies, including the connection and communication of multiple electronic devices, highlighting Wi-Fi networks, as well as the emerging technological advances of 4G and 5G (new-generation mobile phones that will use 5G), have caused a significant increase in the personal exposure to Radiofrequency Electromagnetic Fields (RF-EMF), and as a consequence, increasing discussions about the possible adverse health effects. The main objective of this study was to measure the personal exposure to radiofrequency electromagnetic fields from the Wi-Fi in the university area of German Jordanian University (GJU) and prepare georeferenced maps of the registered intensity levels and to compare them with the basic international restrictions. Spot measurements were made outside the university area at German Jordanian University. Measurements were made in the whole university area and around two buildings. Two Satimo EME SPY 140 (Brest, France) personal exposimeters were used, and the measurements were performed in the morning and afternoon, and on weekends and weekdays. The total average personal exposure to RF-EMF from the Wi-Fi band registered in the three study areas and in the four days measured was 28.82 μW/m2. The average total exposure from the Wi-Fi band registered in the ten measured points of the university area of GJU was 22.97 μW/m2, the one registered in the eight measured points of building H was 34.48 μW/m2, and the one registered in the eight points of building C was 29.00 μW/m2. The maximum average values registered in the campus of GJU are below the guidelines allowed by International Commission on Non-ionizing Radiation Protection (ICNIRP). The measurement protocol used in this work has been applied in measurements already carried out in Spain and Mexico, and it is applicable in university areas of other countries.

Comparison of statistic methods for censored personal exposure to RF-EMF data

Several studies have characterized personal exposure to RF-EMF, which allows possible effects on health to be studied. All equipment has a detection limit, below which we obtain nondetects or censored data. This problem is a challenge for researchers as it makes the analysis of such data complex. We suggest reconsidering the statistical protocols of the nondetects analysis by comparing four different methods. Three of them substitute censored data using different approaches: regression on order of statistics (ROS) to simulate data below the detection limit (Method 1), substituting nondetect values by the detection limit divided by 2 (Method 2), a naïve calculation (Method 3) using the detection limit as a valid measurement. The fourth method consists of considering censored data to be missing values (Method 4). This article examines how these methods affect the quantification of personal exposure. We considered data from 14 frequency bands from FM to WiMax measured in Albacete (Spain) for 76 days every 10 s by a personal exposimeter (PEM) Satimo EME Spy 140.Methods 3 and 2 gave similar mean and median values to Method 1, but both underestimated the mean values when high nondetects records occurred, which conditioned the physical description of the real situation. The mean values calculated by Method 4 differed from those obtained by Method 1 but were similar when the percentage of nondetects was below 20%.Our comparison suggests that nondetects can be neglected when the percentage of censored data is low to provide a more realistic physical situation.

A Topology-Based Approach to Improve Vehicle-Level Electromagnetic Radiation

The popularity of the electric vehicle (EV) brings us many challenges of electromagnetic compatibility (EMC). Automotive manufacturers are obliged to keep their products in compliance with EMC regulations. However, the EV is a complex system composed of various electromagnetic interferences (EMI), sensitive equipment and complicated coupling paths, which pose great challenges to the efficient troubleshooting of EMC problems. This paper presents an electromagnetic topology (EMT) based model and analysis method for vehicle-level EMI prediction, which decomposes an EV into multi-subsystems and transforms electromagnetic coupling paths into network parameters. This way, each part could be modelled separately with different technologies and vehicle-level EMI was able to be predicted by algebra calculations. The effectiveness of the proposed method was validated by comparing predicted vehicle-radiated emissions at low frequency with experimental results, and application to the troubleshooting of emission problems.

Personal Exposure to Radio Frequency Electromagnetic Fields among Australian Adults

The measurement of personal exposure to radiofrequency electromagnetic fields (RF-EMFs) is important for epidemiological studies. RF-EMF exposure can be measured using personal exposimeters that register RF-EMFs over a wide range of frequency bands. This study aimed to measure and describe personal RF-EMF exposure levels from a wide range of frequency bands. Measurements were recorded from 63 participants over an average of 27.4 (±4.5) hours. RF-EMF exposure levels were computed for each frequency band, as well as from downlink (RF from mobile phone base station), uplink (RF from mobile phone handsets), broadcast, and Wi-Fi. Participants had a mean (±SD) age of 36.9 ± 12.5 years; 66.7% were women; and almost all (98.2%) from urban areas. A Wi-Fi router at home was reported by 61 participants (96.8%), with 38 (61.2%) having a Wi-Fi enabled smart TV. Overall, 26 (41.3%) participants had noticed the existence of a mobile phone base station in their neighborhood. On average, participants estimated the distance between the base station and their usual residence to be about 500 m. The median personal RF-EMF exposure was 208 mV/m. Downlink contributed 40.4% of the total RF-EMF exposure, followed by broadcast (22.4%), uplink (17.3%), and Wi-Fi (15.9%). RF-EMF exposure levels on weekdays were higher than weekends (p < 0.05). Downlink and broadcast are the main contributors to total RF-EMF personal exposure. Personal RF-EMF exposure levels vary according to day of the week and time of day.