Validity of at home model predictions as a proxy for personal exposure to radiofrequency electromagnetic fields from mobile phone base stations

A.L. Martens, J.F.B. Bolte, J. Beekhuizen, H. Kromhout, T. Smid, R.C.H. Vermeulen

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Background: Epidemiological studies on the potential health effects of RF-EMF from mobile phone base stations require efficient and accurate exposure assessment methods. Previous studies have demonstrated that the 3D geospatial model NISMap is able to rank locations by indoor and outdoor RF-EMF exposure levels. This study extends on previous work by evaluating the suitability of using NISMap to estimate indoor RF-EMF exposure levels at home as a proxy for personal exposure to RF-EMF from mobile phone base stations. Methods: For 93 individuals in the Netherlands we measured personal exposure to RF-EMF from mobile phone base stations during a 24h period using an EME-SPY 121 exposimeter. Each individual kept a diary from which we extracted the time spent at home and in the bedroom. We used NISMap to model exposure at the home address of the participant (at bedroom height). We then compared model predictions with measurements for the 24h period, when at home, and in the bedroom by the Spearman correlation coefficient (r<inf>sp</inf>) and by calculating specificity and sensitivity using the 90th percentile of the exposure distribution as a cutpoint for high exposure. Results: We found a low to moderate r<inf>sp</inf> of 0.36 for the 24h period, 0.51 for measurements at home, and 0.41 for measurements in the bedroom. The specificity was high (0.9) but with a low sensitivity (0.3). Discussion: These results indicate that a meaningful ranking of personal RF-EMF can be achieved, even though the correlation between model predictions and 24. h personal RF-EMF measurements is lower than with at home measurements. However, the use of at home RF-EMF field predictions from mobile phone base stations in epidemiological studies leads to significant exposure misclassification that will result in a loss of statistical power to detect health effects.
Original languageEnglish
Pages (from-to)221-226
JournalEnvironmental Research
Volume142
DOIs
Publication statusPublished - 2015

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