Radiofrequency radiation from nearby mobile phone base stations-a case comparison of one low and one high exposure apartment

Should Parents Allow Their Children Use Smartphones and Tablets? The Issue of Screen Time for Recreational Activities

Previous research has shown that children are more vulnerable to the adverse effects of radiofrequency electromagnetic fields (RF-EMFs) and blue light emitted from digital screens compared to healthy adults. This paper presents the findings of a cross-sectional study conducted in Yasuj, Iran, to investigate the screen time habits of children and adolescents and its potential impact on their health. A total of 63 participants, including 44 boys and 19 girls, were randomly selected for the study. The results showed that the average daily screen time for the children was 87.38 minutes, with a standard deviation of 49.58. When examining the specific purposes of screen time, it was found that the children spent an average of 17.54 minutes per day on screens for school assignments, 70 minutes per day for recreational purposes, and 23.41 minutes per day for contacting family, friends, and relatives. Our study highlights that a significant portion of the children's screen time was allocated to recreational activities. We observed some differences in screen time between girls and boys. Boys had a slightly higher overall daily screen time, primarily driven by more recreational screen time. However, girls spent slightly more time on screens for school assignments. The screen time for social interactions was similar for both genders. Our findings on the cognitive performance of children with different levels of screen time will be published in a separate paper.

Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens

BACKGROUND

Nowadays, there is a growing global concern over rapidly increasing screen time (smartphones, tablets, and computers). An accumulating body of evidence indicates that prolonged exposure to short-wavelength visible light (blue component) emitted from digital screens may cause cancer. The application of machine learning (ML) methods has significantly improved the accuracy of predictions in fields such as cancer susceptibility, recurrence, and survival.

OBJECTIVE

To develop an ML model for predicting the risk of breast cancer in women via several parameters related to exposure to ionizing and non-ionizing radiation.

MATERIAL AND METHODS

In this analytical study, three ML models Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) were used to analyze data collected from 603 cases, including 309 breast cancer cases and 294 gender and age-matched controls. Standard face-to-face interviews were performed using a standard questionnaire for data collection.

RESULTS

The examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity> 97.2%, mean specificity >96.4%, and average accuracy >97.1%).

CONCLUSION

Machine learning models can be used to effectively predict the risk of breast cancer via the history of exposure to ionizing and non-ionizing radiation (including blue light and screen time issues) parameters. The performance of the developed methods is encouraging; nevertheless, further investigation is required to confirm that machine learning techniques can diagnose breast cancer with relatively high accuracies automatically.

Comparative Analysis of Electric Field Strength, Magnetic Field Strength and Power Density around the Cell Phone Towers of Varying Characteristics with a Proposed Classification Facilitating Research on Human Population

The continuous exposure of electromagnetic field (EMF) radiation from cell phone towers may possibly have an influence on public health. Each cell phone tower is unique in terms of number of antennas and its associated attributes; thus, the radiation exposure varies from one tower to another. Hence, a standardized method for quantifying the exposure is beneficial while studying the effects of radiation on the human population residing around the cell phone towers. A mere collection of data or human samples without understanding the cell phone tower differences may show study results such as an increase or decrease in biological parameters. Those changes may not be due to the effects of EMF radiation from cell phone towers but could be due to any other cause. Therefore, a comparative study was designed with the aim of quantifying and comparing the electric field strength (EF), magnetic field strength (MF) and power density (PD) on four sides of cell phone towers with varying numbers of antennas at 50 m and 100 m. Further, an attempt was made to develop a PD-based classification for facilitating research involving human biological samples. Through convenience sampling, sixteen cell phone towers were selected. With the use of coordinates, the geographic mapping of selected towers was performed to measure the distance between the towers. Based on the number of antennas, the cell phone towers were categorized into four groups which are described as group I with 1-5 antennas, group II comprising of 6-10 antennas, group III consisting of 11-15 antennas and group IV comprised of towers clustered with more than 15 antennas. The study parameters, namely the EF, MF and PD, were recorded on all four sides of the cell phone towers at 50 m and 100 m. One-way ANOVA was performed to compare the study parameters among study groups and different sides using the Statistical Package for the Social Sciences (SPSS) version 25.0. The mean MF in Group IV was 2221.288 ± 884.885 μA/m and 1616.913 ± 745.039 μA/m at 50 m and 100 m respectively. The mean PD in Group IV at 50 m was 0.129 ± 0.094 μW/cm² and 0.072 ± 0.061 μW/cm² at 100 m. There was a statistically significant (p < 0.05) increase in the MF and PD at 50 m compared to 100 m among cell phone tower clusters with more than 15 antennas (Group IV). On the other hand, a non-significant increase in EF was observed at 50 m compared to 100 m in Group II and IV. The EF, MF and PD on all four sides around cell phone towers are not consistent with distance at 50 m and 100 m due to variation in the number of antennas. Accordingly, a PD-based classification was developed as low, medium and high for conducting research involving any biological sample based on quantile. The low PD corresponds to 0.001-0.029, medium to 0.03-0.099 and high to 0.1-0.355 (μW/cm²). The PD-based classification is a preferred method over the sole criteria of distance for conducting human research as it measures the true effects of EMF radiation from the cell phone towers.

Base transceiver station antennae exposure and workers' health

BACKGROUND

With the rapid development of technologies related to the communications industry, human exposure to electromagnetic fields has increased during recent decades. The study aimed at investigating the effect of exposure to waves emitted from the base transceiver stations (BTS) on workers' health.

METHODS

240 workers (120 BTS maintenance workers (case group) and 120 office staff (control group)) participated in the study. In order to determine the general health conditions in two groups, along with electromagnetic waves exposure measurement, the general health questionnaire (GHQ) was completed and the data on blood parameters were assessed.

RESULTS

The mean age and job experience in the case and control groups were 34.1 ± 4.8 and 10.1 ± 6 years and 31.6 ± 5.5 and 8.8 ± 7 years, respectively. According to the GHQ results, only anxiety and insomnia subscales showed a significant difference between the two groups. The white blood cell and red blood cell counts in the case and control groups were 6715.6 ± 1591 and 7594 ± 2416, 5.3×10⁶±4.6×10⁵ and 5.05×10⁶±5.39×10⁵ per ml, respectively. Analysis of the results showed that the difference between the two groups was significant.

CONCLUSION

The results revealed that blood parameters in the BTS operators showed more changes. Thus, it can be concluded that these health impacts result from occupational exposure to BTS waves.

Measurement of Base Transceiver Station Exposure in the Extra-Village Environment- A Pilot Study

In recent years, communication using electromagnetic (EM) radiation became an integral part of our lives. As a result, there is a large number of base transceiver stations (BTSs) which act as a source of high EM exposure for inhabitants mainly in the “hot-spot” areas. They employ higher values of radiation, thus, providing potentially harmful effects on living or working environment. The aim of this pilot study was to study a distribution of hot-spots and EMF power in a vicinity of BTSs. BTS was located in an extra-village area at least 500 m away from the nearest city or surrounded villages in the district of Martin. The targeted area of EM radiation from the BTS was divided into two smaller zones, the right and the left. For a better visualization, topographic maps were created. Using spectral analyzer Aaronia Spectran HF-6085, intensities of EMF within the frequency range from 880 – 960 MHz (GSM900) were recorded. Maximum values of EMF power flux density were 146.827 μW/m2 in horizontal and 96.448 μW/m2 in vertical plane. Minimal va lues were 0.052 μW/m2 in horizontal and 0.179 μW/m2 vertical plane respectively. The maps revealed two hotspots in the left zone and also two (smaller and larger) hotspots in the right zone. Our values were below the actual limits given by the Slovak Republic and the International Commission for Non-Ionizing Radiation Protection (ICNIRP) safety guidelines. However, the values from the hotspots were above the limits suggested by the BioInitiative Report. Our results indicate an elevation of EMF values in the hot-spots even in the extra-village areas. Further studies are needed to analyze in detail EMF parameters in the hot-spots, and their effects on living and working environments.

Spatial variability of outdoor exposure to radiofrequency radiation from mobile phone base stations, in Khartoum, Sudan

The wide-spread exposure to constantly evolving wireless technologies believed to pose a serious health threat. Human beings are persistently exposed to RF radiation from mobile phones and their base stations. The current study aimed at classifying and characterizing the exposure to RF radiation from the mobile phone base stations. Spatial distribution measurements were carried out in Khartoum city during two time periods, first in 2012 (pilot survey) and again during Sept. 2019-Jan. 2020, to cover a total of 282 antennas operating with GSM900, GSM1800, and UMTS2100. The tested antennas belong to three mobile communication companies namely Sudani, Zain, and MTN companies, that randomly coded into company A, company B, and company C for security purposes. Measurements were performed using frequency-selective RF analyzer at fixed distances from the antennas/towers. Data were subjected to advanced repeated measures ANOVA, linear discriminant analysis (LDA), and spatial interpolation with ArcGIS. The averages of GSM900, GSM1800, and UMTS measurements were 0.01933 W/m², 0.0067 W/m², and 0.0046 W/m². The high levels of power densities for each single antenna were recorded at 90 m, 110 m, 130 m, and at 150 m distances, for the majority (70%) of the measured antennas and the peak/highest values reported mainly at 110 m distance. Conversely, the discriminant loadings as part of LDA, suggested that, much of variance among measurements is attributed to measurements at 150 m, 170 m, and 190 m distances, while visual illustration of group centroids implied that, the RF signals of the different companies were measured separately which support accuracy of frequency-selective measurements. The LDA has confirmed the ANOVA results that, the overall difference between the three companies was statistically significant for UMTS, and GSM900 measurements but not significant for GSM1800 measurements. Kriging interpolation using ArcGIS provided a strong evidence of great spatial distribution of exposure across the study area, with market places and typical urban residential quarters showing highest levels of RF. Few extreme values exceeding ICNIRP limits are reported but excluded from the calculations because of an issue of normality of data that is considered a prerequisite for parametric data analysis. Existence of extreme levels of RF indicates a need for further investigation and some antennas of Company B are mounted on towers belongs to Company C, implying multi exposure. Unexpected pattern of RF levels continued to increase up to 190 m distance and possibly beyond 190 m is reported for UMTS measurements of Company C.

The Effect of Continuous Low-Intensity Exposure to Electromagnetic Fields from Radio Base Stations to Cancer Mortality in Brazil

Background: this study aims to estimate the rate of death by cancer as a result of Radio Base Station (RBS) radiofrequency exposure, especially for breast, cervix, lung, and esophagus cancers. Methods: we collected information on the number of deaths by cancer, gender, age group, gross domestic product per capita, death year, and the amount of exposure over a lifetime. We investigated all cancer types and some specific types (breast, cervix, lung, and esophagus cancers). Results: in capitals where RBS radiofrequency exposure was higher than 2000/antennas-year, the average mortality rate was 112/100,000 for all cancers. The adjusted analysis showed that, the higher the exposure to RBS radiofrequency, the higher cancer mortality was. The highest adjusted risk was observed for cervix cancer (rate ratio = 2.18). The spatial analysis showed that the highest RBS radiofrequency exposure was observed in a city in southern Brazil that also showed the highest mortality rate for all types of cancer and specifically for lung and breast cancer. Conclusion: the balance of our results indicates that exposure to radiofrequency electromagnetic fields from RBS increases the rate of death for all types of cancer.

Introduction to Environmental Harmful Factors

In this Chapter, we systematically and comprehensively described various environmental harmful factors. They were classified into four aspects: physical factors, chemical factors, biological factors, and physiological and psychological stress factors. Their classification, modes of presence, toxicity and carcinogenicity, routes of exposure to human and toxic effects on the female reproductive health were introduced. It is expected that the exposure routes could be controlled and eliminated, and the pathogenic mechanism of environmental harmful factors should be investigated and explained to protect female reproductive health.

Health risks from radiofrequency radiation, including 5G, should be assessed by experts with no conflicts of interest

The fifth generation, 5G, of radiofrequency (RF) radiation is about to be implemented globally without investigating the risks to human health and the environment. This has created debate among concerned individuals in numerous countries. In an appeal to the European Union (EU) in September 2017, currently endorsed by >390 scientists and medical doctors, a moratorium on 5G deployment was requested until proper scientific evaluation of potential negative consequences has been conducted. This request has not been acknowledged by the EU. The evaluation of RF radiation health risks from 5G technology is ignored in a report by a government expert group in Switzerland and a recent publication from The International Commission on Non-Ionizing Radiation Protection. Conflicts of interest and ties to the industry seem to have contributed to the biased reports. The lack of proper unbiased risk evaluation of the 5G technology places populations at risk. Furthermore, there seems to be a cartel of individuals monopolizing evaluation committees, thus reinforcing the no-risk paradigm. We believe that this activity should qualify as scientific misconduct.

Correlation of Blood Oxidative Stress Parameters to Indoor Radiofrequency Radiation: A Cross Sectional Study in Jordan

Background: Electromagnetic pollution is a general health concern worldwide, as cell phone towers are ubiquitous and are located adjacent to or on the roof of schools, and hospitals. However, the health risks are still inconclusive. This cross-sectional study evaluated the potential effect of electromagnetic radiation generated from various resources including cell phone towers on blood glutathione S transferase activity (e-GST) and total antioxidant activity of the Jordanian population. Methods: The power density of three districts in the city of Irbid, Jordan was mapped to generate “outside the houses” and “inside the houses” maps. The effect of categorical variables (gender, using a cell phone, presence of Wi-Fi modem, previous exposure to medical imaging) and continuous variables (distance from the base station, the elevation of the house, the duration of stay in the house, power density outside houses, power density inside houses) on e-GST and total antioxidant activity were investigated. Results: The EMR generated outside the houses—including cell phone towers—did not reach inside the houses at the same power and had no significant influence on e-GST activity. The EMR inside the house, which primarily came from internal resources, has a significant effect on e-GST activity. The duration of stay inside the house, the use of cell phones, and the presence of a Wi-Fi modem had a proportional effect on e-GST activity. The total antioxidant activity was statistically equal between the tested and control groups. Conclusions: Several factors such as building materials restricted the penetration of EMR reaching inside the houses. EMR generated inside rather than outside the houses had a proportional effect on e-GST. The differences in e-GST were compensated successfully by other antioxidant mechanisms. Further research is needed to identify other possible sources of antioxidants, and to evaluate long-term effects and genetic polymorphism.