Abstract
Mobile communication devices have become a staple of our everyday lives. From smartphones and tablets to fitness trackers and health sensors, all of these devices use radiofrequency electromagnetic fields (RF-EMF) for communication. Extensive mobile communication networks, ranging from second generation to the recently introduced fifth generation cellular networks, as well
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as WiFi, Bluetooth and many others communication technologies are in use to support these devices. Consequently, RF-EMF exposure has become nearly continuous. With the rise in use of RF-EMF came concern about related potential adverse health effects. To address these concerns an accurate and biologically relevant exposure assessment is required. This is no easy task, as there are many factors influencing RF-EMF exposure levels. RF-EMF sources can be divided into those nearby an individual (near-field) and further away (far-field). Beginning with the near-field, ideally all devices using RF-EMF are included in the assessment. For each device we would need to know a) the frequency and duration of use, b) functions used (e.g., calling, streaming), c) amount of data transfer needed for each function, d) where it is located relative to the body, and e) which communication network is used. For far-field sources, such as mobile phone base stations, radio broadcasting towers, and WiFi networks, we would like to the strength of the source and the location of the individual relative to the source. In addition, there are personal characteristics determining exposure levels: age, sex, BMI, amount of adipose tissue all influence levels at an anatomical site of interest (e.g., the brain). The above information can be collected by asking individuals about their mobile device use via questionnaires or interviews, by using exposure measurement devices, or by modelling exposure levels. With each method having its own strengths and limitations. Once the information is available, the next step is integrating the exposure levels of all individual sources into a single dose estimate. The aim of this thesis was to highlight the challenges of RF-EMF exposure assessment by exploring some of the above-mentioned data collection methods. Following this, a novel method of integrative individual exposure assessment was designed. With the rapid pace at which mobile communication technology is advancing, novel devices and improved communication technologies are being released regularly. These developments are challenging to integrating exposures. RF-EMF exposure assessment will therefore remain a highly dynamic subject as long as the current trend of technological innovation continues.
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