Several devices have been developed to assess exposure to radiofrequency electromagnetic field (RF-EMF). Since the existing solutions to measure the personal exposure induced by emerging 5G New Radio (NR) are expensive, complex, and bulky, a new cost efficient and low-complexity sensor is developed, that aims to measure RF-EMF exposure in different scenarios of data transmission within different areas. With this novel sensor, activity-based microenvironmental surveys were conducted across seven European countries: Belgium, Hungary, Italy, Poland, Switzerland, the Netherlands, and the United Kingdom. The device is attached to a smartphone to quantify the auto-induced uplink (a-UL) transmission component of the total exposure for a broadband frequency range from 100 MHz to 6000 MHz and is thus denoted as add-on sensor. In-situ measurements were performed for three usage scenarios, namely non-user (i.e., environmental exposure), maximum downlink (max DL), and maximum uplink (max UL) scenarios, in a large city, a secondary city, and three rural villages a priori selected within each country. Power levels were lowest in non-user scenarios (median: −2.64 dBm or 0.54 mW), increasing by a factor of 5.00 dB in maximum downlink scenarios and by a factor of 14.15 dB in maximum uplink scenarios. In the maximum uplink scenarios, the highest median a-UL power of 18.68 dBm (= 73.79 mW) was recorded in The Netherlands, while the lowest median a-UL power of 4.77 dBm (= 3 mW) was observed in the UK. The analysis of the measured data showed a prominent trend of a 2.72 dB lower power in the cities compared to the villages. Further comparisons were made based on microenvironment groups, where the lowest a-UL power levels (median: 12.35 dBm) were measured in outdoor areas, with an increase of 1.78 dB and 1.91 dB in power was measured compared to public transport and public places, respectively. This study compares RF-EMF power levels between different countries, urbanization settings, and usage scenarios, which is important for future epidemiological studies.

Python, 3.11.4