Estimating Environmental RF-EMF Exposure From Mobile Networks Using Signal Indicators Collected With the ETAIN 5G Scientist App.

Authors

Nekane Sandoval-Diez, Lea Belácková, Hamed Jalilian, Adriana Fernandes Veludo, Lloyd Roga, Isabelle Deltour, Stuart Henderson, Sarah Loughran, Arno Thielens, Marco Zahner, Jürg Fröhlich, Anke Huss, Martin Röösli

Published in

Bioelectromagnetics. Volume 47. Issue 7. Pages e70071.

Abstract

Smartphones track indicators of a reference signal to maintain network connectivity. These can serve as proxies for a subset of environmental radiofrequency electromagnetic field (RF-EMF) exposure from mobile networks. In this multi-country study, we developed conversion functions linking signal indicators collected with the ETAIN 5G-Scientist app to measured RF-EMF exposure. Parallel measurements were conducted across six European countries and Australia using frequency-selective exposimeters and the app. Associations of signal strength and quality indicators with exposure were analysed using log-linear regression models: (1) serving-cell level, relating exposure in the serving frequency band to indicators of the connected cell (Europe n = 2960; Australia n = 747); and (2) location level, relating indicators aggregated across network providers to environmental exposure from 4G-LTE and 5G-NR networks (Europe n = 61; Australia n = 48). At the serving-cell level, 4G-LTE signal strength was positively associated with exposure, while signal quality was inversely associated. For 5G-NR, only signal strength was associated with exposure, with weaker and less consistent relationships than for 4G-LTE. At location level, aggregation across providers reduced model error, suggesting residual variability operates at short temporal and spatial scales. These findings support using smartphone signal indicators to estimate components of environmental RF-EMF exposure, although further validation is needed, particularly for 5G-NR. Bioelectromagnetics. 00:00-00, 2026. © 2026 Bioelectromagnetics Society.

PMID:
42867131
Bibliographic data and abstract were imported from PubMed on 14 Oct 2026.

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