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Background: A preliminary job-exposure matrix (JEM) for radiofrequency electromagnetic fields (RF-EMF) was created based on self-reported occupational information from a multi-country population-based study of approximately 10,000 participants combined with available measurement data compiled in a source-exposure matrix (spot measurements). In order to address the limited personal occupational RF-EMF measurement data available in the literature, we performed a measurement campaign among workers in various occupations in Spain and France. Methods: Personal full-shift measurements were conducted using RadMan 2XT™ (Narda) devices. A worker diary was used to capture information on occupational and background sources of RF exposure during the shift. Inclusion of occupations to be measured was initially based on exposure prevalence and level information in the preliminary JEM and expert judgment. Results: Personal full-shift measurements were conducted among 333 workers representing 46 ISCO88 occupations. Exposure to electric (E) and magnetic (H) fields was infrequent with >99% of measurements below the detection limit of the device (≥1% of the 1998 ICNIRP standards). A total of 50.2% and 77.2% of workers were ever exposed to E and H fields respectively (having at least one recorded 1-second measurement above the detection limit). Workers in elementary occupations, technicians and associate professionals, plant and machine operators and assemblers had somewhat greater numbers of measurements above the detection limit, higher maximum values and longer exposure durations. A small proportion of measurements were ≥100% of the standards, though these exceedances were brief (generally a few seconds in duration). Female workers and workers reporting use of any RF-EMF emitting source were more likely to have a measured exposure to E and H fields. Conclusion: We conducted personal RF-EMF measurements among workers in various occupations in Spain and France. Overall, RF-EMF exposure ≥1 % ICNIRP was infrequent, despite some intermittent exposures ≥100% observed among workers in some occupations.

Wireless phones (both mobile and cordless) emit not only radiofrequency (RF) electromagnetic fields (EMF) but also extremely low frequency (ELF) magnetic fields, both of which should be considered in epidemiological studies of the possible adverse health effects of use of such devices. This paper describes a unique algorithm, developed for the multinational case-control MOBI-Kids study, that estimates the cumulative specific energy (CSE) and the cumulative induced current density (CICD) in the brain from RF and ELF fields, respectively, for each subject in the study (aged 10–24 years old). Factors such as age, tumour location, self-reported phone models and usage patterns (laterality, call frequency/duration and hands-free use) were considered, as was the prevalence of different communication systems over time.Median CSE and CICD were substantially higher in GSM than 3G systems and varied considerably with location in the brain. Agreement between RF CSE and mobile phone use variables was moderate to null, depending on the communication system. Agreement between mobile phone use variables and ELF CICD was higher overall but also strongly dependent on communication system. Despite ELF dose distribution across the brain being more diffuse than that of RF, high correlation was observed between RF and ELF dose.The algorithm was used to systematically estimate the localised RF and ELF doses in the brain from wireless phones, which were found to be strongly dependent on location and communication system. Analysis of cartographies showed high correlation across phone models and across ages, however diagonal agreement between these cartographies suggest these factors do affect dose distribution to some level. Overall, duration and number of calls may not be adequate proxies of dose, particularly as communication systems available for voice calls tend to become more complex with time.

Background: Exposure to radiofrequency electromagnetic fields (RF-EMF) is often measured with personal exposimeters, but the accuracy of measurements can be hampered as carrying the devices on-body may result in body shielding. Further, the compact design may compromise the frequency selectivity of the sensor. The aim of this study was to compare measurements obtained using a multi-band body-worn distributed-exposimeter (BWDM) with two commercially available personal exposimeters (ExpoM-RF and EmeSpy 200) under real-life conditions. Methods: The BWDM measured power density in 10 frequency bands (800, 900, 1800, 2100, 2600 MHz, DECT 1900 MHz, WiFi 2.4 GHz; with separate uplink/downlink bands for 900, 1800 and 2100 MHz); using 20 separate antennas integrated in a vest and placed on diametrically opposite locations on the body, to minimize body-shielding. RF-EMF exposure data were collected from several microenvironments (e.g. shopping areas, train stations, outdoor rural/ urban residential environments, etc.) by walking around pre-defined areas/routes in Belgium, Spain, France, the Netherlands and Switzerland. Measurements were taken every 1–4 s with the BWDM in parallel with an ExpoM-RF and an EmeSpy 200 exposimeter. We calculated medians and interquartile ranges (IQRs) and compared difference, ratios and correlations of geometric mean RF-EMF exposure levels per microenvironment as measured with the exposimeters and the BWDM. Results: Across 267 microenvironments, medians and IQR of total BWDM measured RF-EMF exposure was 0.13 (0.05–0.33) mW/m2. Difference: IQR of exposimeters minus BWDM exposure levels was −0.011 (−0.049 to 0.0095) mW/m2 for the ExpoM-RF and −0.056 (−0.14 to −0.017) for the EmeSpy 200; ratios (exposimeter/BWDM) of total exposure had an IQR of 0.79 (0.55–1.1) for the ExpoM-RF and 0.29 (0.22–0.38) for the EmeSpy 200. Spearman correlations were 0.93 for the ExpoM-RF vs the BWDM and 0.96 for the EmeSpy 200 vs the BWDM. Discussion and conclusions: Results indicate that exposimeters worn on-body provide somewhat lower total RF-EMF exposure as compared to measurements conducted with the BWDM, in line with effects from body shielding. Ranking of exposure levels of microenvironments showed high correspondence between the different device types. Our results are informative for the interpretation of existing epidemiological research results.

This paper applies Machine Learning (ML) algorithms to peer-reviewed publications in order to discern whether there are consistent biological impacts of exposure to non-thermal low power radio-frequency electromagnetic fields (RF-EMF). Expanding on previous analysis that identified sensitive plant species, we extracted data from 45 articles published between 1996 and 2016 that included 169 experimental case studies of plant response to RF-EMF. Raw-data from these case studies included six different attributes: frequency, specific absorption rate (SAR), power flux density, electric field strength, exposure time and plant type (species). This dataset has been tested with two different classification algorithms: k-Nearest Neighbor (kNN) and Random Forest (RF). The outputs are estimated using k-fold cross-validation method to identify and compare classifier mean accuracy and computation time. We also developed an optimization technique to distinguish the trade-off between prediction accuracy and computation time based on the classification algorithm. Our analysis illustrates kNN (91.17%) and RF (89.41%) perform similarly in terms of mean accuracy, nonetheless, kNN takes less computation time (3.38 s) to train a model compared to RF (248.12 s). Very strong correlations were observed between SAR and frequency, and SAR with power flux density and electric field strength. Despite the low sample size (169 reported experimental case studies), that limits statistical power, nevertheless, this analysis indicates that ML algorithms applied to bioelectromagnetics literature predict impacts of key plant health parameters from specific RF-EMF exposures. This paper addresses both questions of the methodological importance and relative value of different methods of ML and the specific finding of impacts of RF-EMF on specific measures of plant growth and health. Recognizing the importance of standardizing nomenclature for EMF-RF, we conclude that Machine Learning provides innovative and efficient RF-EMF exposure prediction tools, and we propose future applications in occupational and environmental epidemiology and public health., (Copyright © 2019 Elsevier Inc. All rights reserved.)

In this study, we studied the effect of 2.0 GHz radio frequency electromagnetic field (RF-EMF) and 50 Hz extremely low frequency electromagnetic field (ELF-EMF) exposure on prion generation and propagation using two budding yeast strains, NT64C and SB34, as model organisms. Under exposure to RF-EMF or ELF-EMF, the de novo generation and propagation of yeast prions [URE3] were elevated in both strains. The elevation increased over time, and the effects of ELF-EMF occurred in a dose-dependent manner. The transcription and expression levels of the molecular chaperones Hsp104, Hsp70-Ssa1/2, and Hsp40-Ydj1 were not statistically significantly changed after exposure. Furthermore, the levels of ROS, as well as the activities of superoxide dismutase (SOD) and catalase (CAT), were significantly elevated after short-term, but not long-term exposure. This work demonstrated for the first time that EMF exposure could elevate the de novo generation and propagation of yeast prions and supports the hypothesis that ROS may play a role in the effects of EMF on protein misfolding. The effects of EMF on protein folding and ROS levels may mediate the broad effects of EMF on cell function.

Background: We investigated whether radiofrequency electromagnetic fields (RF-EMF) from mobile phones and other wireless devices or by the wireless device use itself due to non-radiation related factors in that context are associated with an increase in health symptom reports of adolescents in Central Switzerland., Methods: In a prospective cohort study, 439 study participants (participation rate: 36.8%) aged 12-17 years, completed questionnaires about their mobile and cordless phone use, their self-reported symptoms and possible confounding factors at baseline (2012/2013) and one year later (2013/2014). Operator recorded mobile phone data was obtained for a subgroup of 234 adolescents. RF-EMF dose measures considering various factors affecting RF-EMF exposure were computed for the brain and the whole body. Data were analysed using a mixed-logistic cross-sectional model and a cohort approach, where we investigated whether cumulative dose over one year was related to a new onset of a symptom between baseline and follow-up. All analyses were adjusted for relevant confounders., Results: Participation rate in the follow-up was 97% (425 participants). In both analyses, cross-sectional and cohort, various symptoms tended to be mostly associated with usage measures that are only marginally related to RF-EMF exposure such as the number of text messages sent per day (e.g. tiredness: OR:1.81; 95%CI:1.20-2.74 for cross-sectional analyses and OR:1.87; 95%CI:1.04-3.38 for cohort analyses). Outcomes were generally less strongly or not associated with mobile phone call duration and RF-EMF dose measures., Conclusions: Stronger associations between symptoms of ill health and wireless communication device use than for RF-EMF dose measures were observed. Such a result pattern does not support a causal association between RF-EMF exposure and health symptoms of adolescents but rather suggests that other aspects of extensive media use are related to symptoms., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Introduction: Geospatial models have been demonstrated to reliably and efficiently estimate RF-EMF exposure from mobile phone base stations (downlink) at stationary locations with the implicit assumption that this reflects personal exposure. In this study we evaluated whether RF-EMF model predictions at the home address are a good proxy of personal 48h exposure. We furthermore studied potential modification of this association by degree of urbanisation., Method: We first used an initial NISMap estimation (at an assumed height of 4.5m) for 9563 randomly selected addresses in order to oversample addresses with higher exposure levels and achieve exposure contrast. We included 47 individuals across the range of potential RF-EMF exposure and used NISMap to re-assess downlink exposure at the home address (at bedroom height). We computed several indicators to determine the accuracy of the NISMap model predictions. We compared residential RF-EMF model predictions with personal 48h, at home, and night-time (0:00-8:00AM) ExpoM3 measurements, and with EME-SPY 140 spot measurements in the bedroom. We obtained information about urbanisation degree and compared the accuracy of model predictions in high and low urbanised areas., Results: We found a moderate Spearman correlation between model predictions and personal 48h (rSp=0.47), at home (rSp=0.49), at night (rSp=0.51) and spot measurements (rSp=0.54). We found no clear differences between high and low urbanised areas (48h: high rSp=0.38, low rSp=0.55, bedroom spot measurements: high rSp=0.55, low rSp=0.50)., Discussion: We achieved a meaningful ranking of personal downlink exposure irrespective of degree of urbanisation, indicating that these models can provide a good proxy of personal exposure in areas with varying build-up., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Background: Epidemiological studies on the potential health effects of RF-EMF from mobile phone base stations require efficient and accurate exposure assessment methods. Previous studies have demonstrated that the 3D geospatial model NISMap is able to rank locations by indoor and outdoor RF-EMF exposure levels. This study extends on previous work by evaluating the suitability of using NISMap to estimate indoor RF-EMF exposure levels at home as a proxy for personal exposure to RF-EMF from mobile phone base stations., Methods: For 93 individuals in the Netherlands we measured personal exposure to RF-EMF from mobile phone base stations during a 24h period using an EME-SPY 121 exposimeter. Each individual kept a diary from which we extracted the time spent at home and in the bedroom. We used NISMap to model exposure at the home address of the participant (at bedroom height). We then compared model predictions with measurements for the 24h period, when at home, and in the bedroom by the Spearman correlation coefficient (rsp) and by calculating specificity and sensitivity using the 90th percentile of the exposure distribution as a cutpoint for high exposure., Results: We found a low to moderate rsp of 0.36 for the 24h period, 0.51 for measurements at home, and 0.41 for measurements in the bedroom. The specificity was high (0.9) but with a low sensitivity (0.3)., Discussion: These results indicate that a meaningful ranking of personal RF-EMF can be achieved, even though the correlation between model predictions and 24h personal RF-EMF measurements is lower than with at home measurements. However, the use of at home RF-EMF field predictions from mobile phone base stations in epidemiological studies leads to significant exposure misclassification that will result in a loss of statistical power to detect health effects., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Objective: The motivation of this study is to evaluate the possible alteration of regional resting state brain activity induced by the acute radiofrequency electromagnetic field (RF-EMF) exposure (30min) of Long Term Evolution (LTE) signal., Methods: We designed a controllable near-field LTE RF-EMF exposure environment. Eighteen subjects participated in a double-blind, crossover, randomized and counterbalanced experiment including two sessions (real and sham exposure). The radiation source was close to the right ear. Then the resting state fMRI signals of human brain were collected before and after the exposure in both sessions. We measured the amplitude of low frequency fluctuation (ALFF) and fractional ALFF (fALFF) to characterize the spontaneous brain activity., Results: We found the decreased ALFF value around in left superior temporal gyrus, left middle temporal gyrus, right superior temporal gyrus, right medial frontal gyrus and right paracentral lobule after the real exposure. And the decreased fALFF value was also detected in right medial frontal gyrus and right paracentral lobule., Conclusions: The study provided the evidences that 30min LTE RF-EMF exposure modulated the spontaneous low frequency fluctuations in some brain regions., Significance: With resting state fMRI, we found the alteration of spontaneous low frequency fluctuations induced by the acute LTE RF-EMF exposure., (Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)