Vol. 2, 2017

Radiation Protection


Çetin Kurnaz, Begüm Korunur Engiz, Ahmet Turgut

Pages: 85-89

DOI: 10.21175/RadProc.2017.18

In parallel with technological developments, cellular systems and therefore base stations have begun to take up more space in our daily lives. Since each base station behaves like a radiofrequency electromagnetic field (RF-EMF) source, this increase in base stations leads to an increase in the value of RF-EMF. Therefore, it is very important to measure and evaluate the RF-EMF emitted from the base stations regarding its influence on human health. In this study, RF-EMF measurements were taken in ten different locations (schools, hospitals, homes, shopping malls, etc.) during 24 hours to investigate the time/location dependent changes in RF-EMF. For the measurement, the PMM8053 EMF meter measuring the total RF-EMF in the frequency range of 100 kHz-3 GHz was used, the highest electric field strengths (Emax) in the environment and the average electric field strengths (Eavg) were recorded. The measurement results show that the electric field strengths (E) originating from the base stations change significantly depending on the measurement location and time (usage intensity). The changes in E measured during the daytime in the home environment are softer, while for workplaces they are sharper due to opening/closing time. It is seen from the measurements that the highest Emax is 7.88V/m and the highest Eavg is 2.95V/m. In order to analyze the 24-hour measurements more precisely, four specific time intervals such as morning (6am-12am), afternoon (12am-6pm), evening (6pm-12pm) and night (12pm-6am) were selected. The mean E value for morning is 1.55V/m, while they are 1.94V/m, 1.48V/m and 1.16V/m for afternoon, evening and night respectively. E level at night increases by 67.2% compared to in the afternoon. At the end of the study, daily variations of E values were examined and empirical models were proposed using curve fitting methods. With the use of these models, the E in the environment can be predicted with an accuracy of up to 95%.
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