Bürgi A, Frei P, Theis G, Mohler E, Braun-Fahrländer C, Fröhlich J, Neubauer G, Egger M, Röösli M. A model for radiofrequency electromagnetic field predictions at outdoor and indoor locations in the context of epidemiological research. Bioelectromagnetics. Ahead of print. Oct 15, 2009.
To assess possible effects of radiofrequency field (RF) radiation in epidemiological studies, it is necessary to determine levels of exposure for all study participants. While proxies are available for exposures from mobile and cordless phones (e.g. duration of calls), no useful proxies exist for stationary transmitters (mobile phone base stations and broadcast transmitters). Simple proxies, such as distance from base stations, are not appropriate for exposure assessment. Measurements of exposure with personal exposure meters cannot be conducted for large populations. Thus, modeling of exposure is a useful alternative. A model has been developed that adequately predicts RF EMF at outdoor locations. However, indoor RF EMF predictions have not been evaluated.
The aim of this study was to compare model predictions of RF-EMF from stationary transmitters with actual RF-EMF outdoor and indoor measurements.
The model was based on detailed technical specification data available for each transmitter at the Air Quality Agency of Basel (Switzerland). These data were supplemented with the actual operational transmitter parameters from the Swiss Federal Office of Communications. For modeling indoor RF-EMF, its interactions with housing characteristics, such as geometry and type of walls, windows, interior structure and furnishing must be taken into account. Since it is not feasible to obtain such detailed information for large study areas, a simplification was made: a constant average damping coefficient for all building walls and roofs was used. It was then tested whether such a simplification provides sufficiently accurate predictions. The agreement between the model predictions and the measurement results was evaluated by calculating Spearman rank correlations and weighted Cohen’s kappa statistics. Three sets of measurements were used: the indoor measurements in the bedrooms of participants (n=133), the outdoor measurements at street level (n=113) and measurements outside the windows of the bedrooms (n=131).
The Spearman’s rank order correlation coefficient of the predictions with the street level measurements was 0.64, with indoor measurements 0.66 and with window measurements 0.67. The values of Cohen’s kappa coefficients were 0.44 (95% CI: 0.32-0.57) for indoor measurements, 0.48 (95% CI: 0.35-0.61) for street level measurements and 0.53 (95% CI: 0.42-0.65) for window measurements.
Interpretation and conclusion
There was a satisfactory agreement between the modeled values and the measurements. The agreement for the indoor data was only slightly reduced compared to the outdoor data despite the more complex indoor environment. Thus, the assumption of a constant damping coefficient gives meaningful results. The model could still be slightly improved by a different choice of damping coefficients, but the actual value is not important when the aim is to rank exposures for determining high and low exposure homes. The authors conclude that “the model is robust and quite insensitive to the exact choice of parameters and it is well suited to classify exposure levels for application in an epidemiological study.”