No abstract

The increasing deployment of mobile communication base stations led to an increasing demand for epidemiological studies on possible health effects of radio frequency emissions. The methodological challenges of such studies have been critically evaluated by a panel of scientists in the fields of radiofrequency engineering/dosimetry and epidemiology. Strengths and weaknesses of previous studies have been identified. Dosimetric concepts and crucial aspects in exposure assessment were evaluated in terms of epidemiological studies on different types of outcomes. We conclude that in principle base station epidemiological studies are feasible. However, the exposure contributions from all relevant radio frequency sources have to be taken into account. The applied exposure assessment method should be piloted and validated. Short to medium term effects on physiology or health related quality of life are best investigated by cohort studies. For long term effects, groups with a potential for high exposure need to first be identified; for immediate effect, human laboratory studies are the preferred approach.

BackgroundThe development of new wireless communication technologies that emit radio frequency electromagnetic fields (RF-EMF) is ongoing, but little is known about the RF-EMF exposure distribution in the general population. Previous attempts to measure personal exposure to RF-EMF have used different measurement protocols and analysis methods making comparisons between exposure situations across different study populations very difficult. As a result, observed differences in exposure levels between study populations may not reflect real exposure differences but may be in part, or wholly due to methodological differences.MethodsThe aim of this paper is to develop a study protocol for future personal RF-EMF exposure studies based on experience drawn from previous research. Using the current knowledge base, we propose procedures for the measurement of personal exposure to RF-EMF, data collection, data management and analysis, and methods for the selection and instruction of study participants.ResultsWe have identified two basic types of personal RF-EMF measurement studies: population surveys and microenvironmental measurements. In the case of a population survey, the unit of observation is the individual and a randomly selected representative sample of the population is needed to obtain reliable results. For microenvironmental measurements, study participants are selected in order to represent typical behaviours in different microenvironments. These two study types require different methods and procedures.ConclusionApplying our proposed common core procedures in future personal measurement studies will allow direct comparisons of personal RF-EMF exposures in different populations and study areas.

The selection of an adequate exposure assessment approach is imperative for the quality of epidemiological studies. The use of personal exposimeters turned out to be a reasonable approach to determine exposure profiles, however, certain limitations regarding the absolute values delivered by the devices have to be considered. Apart from the limited dynamic range, it has to be taken into account that these devices give only an approximation of the exposure due to the influence of the body of the person carrying the exposimeter, the receiver characteristics of the exposimeter, as well as the dependence of the measured value on frequency band, channel, slot configuration, and communication traffic. In this study, the relationship between the field strength measured close to the human body at the location of the exposimeter and the exposure, that is, the field strength at the location of the human body without the human body present, is investigated by numerical means using the Visible Human model as an anatomical phantom. Two different scenarios were chosen: (1) For FM, GSM, and UMTS an urban outdoor scenario was examined that included a transmitting antenna mounted on the roof of one of four buildings at a street crossing, (2) For WLAN an indoor scenario was investigated. For GSM the average degree of underestimation by the exposimeter (relation of the average field levels at the location of the exposimeter to the field level averaged over the volume of the human body without the body present) was 0.76, and for UMTS 0.87; for FM no underestimation was found, the ratio was 1. In the case of WLAN the degree of underestimation was more pronounced, the ratio was 0.64. This study clearly suggests that a careful evaluation of correction factors for different scenarios is needed prior to the definition of the study protocol. It has to be noted that the reference scenario used in this study does not allow for final conclusions on general correction factors.

Exposimeters are increasingly applied in bioelectromagnetic research to determine personal radiofrequency electromagnetic field (RF-EMF) exposure. The main advantages of exposimeter measurements are their convenient handling for study participants and the large amount of personal exposure data, which can be obtained for several RF-EMF sources. However, the large proportion of measurements below the detection limit is a challenge for data analysis. With the robust ROS (regression on order statistics) method, summary statistics can be calculated by fitting an assumed distribution to the observed data. We used a preliminary sample of 109 weekly exposimeter measurements from the QUALIFEX study to compare summary statistics computed by robust ROS with a naïve approach, where values below the detection limit were replaced by the value of the detection limit. For the total RF-EMF exposure, differences between the naïve approach and the robust ROS were moderate for the 90th percentile and the arithmetic mean. However, exposure contributions from minor RF-EMF sources were considerably overestimated with the naïve approach. This results in an underestimation of the exposure range in the population, which may bias the evaluation of potential exposure-response associations. We conclude from our analyses that summary statistics of exposimeter data calculated by robust ROS are more reliable and more informative than estimates based on a naïve approach. Nevertheless, estimates of source-specific medians or even lower percentiles depend on the assumed data distribution and should be considered with caution.