Comprehensive radiofrequency electromagnetic field measurements and assessments: a city center example

Kurnaz, Çetin; Mutlu, Mustafa

doi:10.1007/s10661-020-08312-3

Cited by 14 publications

(9 citation statements)

References 22 publications

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“…Average values of electric strength (V/m) of two studies conducted in Europe were close to our results, where the first study was conducted in Belgium reported average values of 0.64 V/m, 0.53 V/m, and 0.33 V/m for GMS900, GSM1800, and UMTS, respectively, with these values being also far below the ICNIRP recommended values (Iyare et al 2019). The second European study was an interesting nationwide investigation in Turkey using frequency-selective analyzer, with results far below ICNIRP limits and resembled our findings particularly the averages of GSM1800 and UMTS measurements (Kurnaz and Mutlu 2020). Similarly, two studies conducted in Asia, in Republic of Korea (Kim andPark 2010), andin Iran (Nayyeri et al 2013), showed conformity with ICNIRP recommended limits.…”

Section: Compliance With Icnirp Guidelines and General Safetysupporting

confidence: 82%

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

Mohammed

Elzaki

Babiker

et al. 2021

Environ Sci Pollut Res

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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/m 2 , 0.0067 W/m 2 , and 0.0046 W/m 2 . 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.

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Section: Compliance With Icnirp Guidelines and General Safetysupporting

confidence: 82%

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

Mohammed

Elzaki

Babiker

et al. 2021

Environ Sci Pollut Res

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“…In another updated study from Turkey, RF-EMF measurements were conducted between 2016 and 2018; measurement results were compared with the limit values by Turkey's Information and Communication Technologies Authority (ICTA) and International Commission on Non-Ionizing Radiation Protection (ICNIRP). They found that overall, RF-EMF values did not exceed these limits and they were below levels that threaten public health (Kurnaz C and Mutlu M 2020).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Electromagnetic Radiation level in the Outdoor from Wireless Transmission Systems in Northern West Bank – Palestine

Khatib¹

2021

PTUKRJ

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Exposure to electromagnetic fields is a major public concern due to the possible hazardous effects on health caused by exposure to these radiations. The aims of this study were to measure radiofrequency radiation levels from different wireless systems and to compare the results with the recommended limits by the World Health Organization (WHO) which should be less that 450 µw/cm2. The study was a cross-sectional study that was conducted in different cities and villages from the northern governorates in the West Bank; Jenin, Tulkarm, Nablus, Qalqilya, Tubas and Salfit. To measure the radiation levels ranging from 80 MHz to 2 GHz, a Portable Field Strength Meter with the suitable set of antennas was used. This frequency range covers the radiations from different sources as mobile phone base stations, local radio and television stations, mobile phones, Wireless Local Area Network, Bluetooth, wireless computer periphery and even microwave ovens. Points close to mobile phone base stations and local radio and television stations in addition to randomly selected points were included. The scanning process for each point was done twice and the higher reading was included in the analysis. The results were compared to the recommended limits of radiations by WHO. Also, a questionnaire was distributed to evaluate people knowledge and perception regarding radiations. A total of 2000 readings were obtained with a mean ± SD = 0.25 ± 0.214 µw/cm2. The highest reading was 1.91 µw/cm2. All the readings were below the limits of radiations recommended by the WHO. Most of the readings in the tested area 1184 (59.2%) were between 0.1 -0.45 µw/cm2 which are 1000- 4500 time less than the maximum allowed level (450 µw/cm2), 527 (26.35%) readings were <0.1 µw/cm2, 271 (13.55%) reading were between 0.45-1 µw/cm2 and the rest 17 (0.85%) were >1 µw/cm2 which were still 236-450 times less than the allowed WHO level. Regarding the questionnaire part, most of the participants use mobile phones frequently, they think radiations are dangerous and can cause cancer, they prefer not to have mobile base stations close to their living places. In this study, a scanning for all electromagnetic radiation sources caused by different sources of wireless communication systems showed theoretically no dangerous radiation levels and all the recorded levels were much lower than the recommended levels by the WHO. Most people feel afraid from radiations and think they are dangerous.

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“…The exposure limits in the research by [6] are more up-to-date and can be adapted by regulators at the national level. The study in [7] provides the exposure level limit for base station operation at 800 MHz, 900 MHz, 1800 MHz, 2100 MHz, and 2600 MHz are 29.1 V/m, 28.8 V/m, 40.72 V/m, and 49.23 V/m respectively, which are recommended by ICTA [8].…”

Section: A Emf Standards and Limitationsmentioning

confidence: 99%

Deep Learning Models for Time-Series Forecasting of RF-EMF in Wireless Networks

Nguyen,

Cheema,

Kurnaz

et al. 2024

IEEE Open J. Commun. Soc.

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Radio-frequency electromagnetic field (RF-EMF) forecasting plays an important role in the evaluation of regulatory compliance, network planning and system optimization. The knowledge of RF-EMF levels is essential to ensure compliance with standards and avoid public health concerns, especially with the arrival of new frequencies and scenarios in fifth-generation (5G) and sixth generation (6G) wireless networks. This work provides a comprehensive study on time series forecasting for RF-EMF measured in frequency from 100 kHz -3 GHz. The state-of-the-art deep learning model architectures consist of deep neural network (DNN), convolutional neural network (CNN), long-short term memory (LSTM), and transformer are applied for time series forecasting. The prediction performance is evaluated under three different scenarios -namely single-step input single-step output (SISO), multi-step input single-step output (MISO), and multi-step input multi-step output (MIMO). The findings from the simulation demonstrate that SISO forecasting is inadequate in predicting long-term radio-frequency electromagnetic fields (RF-EMF) data as it lacks accuracy while MISO and MIMO forecasting scenarios offer more precise predictions. Specifically, in these two scenarios where the input width and label width are both set to 20 steps, the LSTM and CNN models exhibit superior performance compared to other models. Nonetheless, as the input width and label width in a MIMO scenario increase, the accuracy of both CNN and LSTM models decline considerably, whereas the transformer model consistently maintains good performance. Additionally, the transformer model continues to deliver accurate predictions as the label width and shift length increase, which is not the case for DNN, CNN, and LSTM models.

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Comprehensive radiofrequency electromagnetic field measurements and assessments: a city center example

Cited by 14 publications

References 22 publications

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

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

Evaluation of Electromagnetic Radiation level in the Outdoor from Wireless Transmission Systems in Northern West Bank – Palestine

Deep Learning Models for Time-Series Forecasting of RF-EMF in Wireless Networks

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