Citizen science-based mapping of radiofrequency electromagnetic field exposure in Europe : A proof-of-concept...

Characterising population-level radiofrequency electromagnetic field (RF-EMF) exposure from mobile telecommunications networks at fine spatial resolution has been limited by the cost and scale of conventional measurement methods. The ETAIN 5G Scientist app, developed within the ETAIN (Exposure To electromAgnetic fIelds and plaNetary health) project uses citizen science smartphone data to produce calibrated RF-EMF exposure maps for 4G/5G networks at 25 x 25 m ² spatial resolution across Europe. As of 2026, the app had collected over 19 million measurements globally. We conducted a proof-of-concept validation of these exposure maps in two European cities with contrasting urban and network characteristics: Utrecht (Netherlands) and Basel (Switzerland). Walking measurement campaigns used the ETAIN app installed on smartphones and the ExpoM-RF4 reference exposimeter carried simultaneously in a backpack. App-derived exposure estimates were compared with spatiotemporally matched reference measurements at the grid-cell level. Validation was performed separately for 4G Long-Term Evolution (LTE) and combined 4G LTE and 5G New Radio (NR) environmental exposure estimates. 4G LTE exposure estimates correlated strongly with measured exposure in both Utrecht (r_s = 0.87, 95% CI: 0.80-0.91, N = 127) and Basel (r_s = 0.71, 95% CI: 0.63-0.78, N = 214; both p < 0.001). The combined 4G LTE and 5G NR exposure estimates correlated strongly in Utrecht (r_s = 0.88, 95% CI: 0.81-0.92; 19.7% of grid cells with 5G NR detection) but showed moderate correlation in Basel (r_s = 0.41, 95% CI: 0.29-0.53; 45.3% of grid cells with 5G NR detection). Sensitivity analyses showed map reliability stabilised at five or more observations per grid cell and improved when data from two network operators were combined. These results demonstrate that the ETAIN 5G Scientist application can produce validated, spatially continuous RF-EMF exposure maps at scale. The approach is well suited for characterising exposure trends as mobile networks continue to evolve.