Assessing RF EMF exposure in multiple microenvironments across ten European countries with a focus on 5G
Abstract
Category: Epidemiology Tags: radiofrequency electromagnetic fields, 5G, environmental exposure, Europe, public health, uplink exposure, precautionary limits DOI: 10.1016/j.envint.2025.109540 URL: sciencedirect.com Overview This study systematically measures environmental, auto-induced downlink (DL), and uplink (UL) radiofrequency electromagnetic field (RF-EMF) exposure across more than 800 microenvironments in ten European countries. The research focuses on the exposure linked to the implementation of 5G, with measurements taken in outdoor, indoor, and public transport environments across two cities and three villages per country. Methodology - Three user scenarios were assessed: flight mode (non-user), induced maximum DL traffic (max DL), and induced maximum UL traffic (max UL). - RF-EMF exposure was measured using an ExpoM-RF 4, covering 35 frequency bands (87.5 MHz - 6 GHz). - The mobile phone was positioned 30 cm from the measurement device during all scenarios. Findings - In the non-user scenario, mean exposure ranged from 0.33 to 1.72 mW/m2 per country, lower in Switzerland, Belgium, and Italy. - RF-EMF levels were, on average, 80% lower in villages compared to cities. - Downlink (DL) bands contributed most to environmental exposure in non-user scenarios. - During max DL, exposure increased mainly due to the 5G 3.5 GHz band, with mean exposure per country between 2.61 and 11.12 mW/m2. - Max UL scenarios saw the highest exposure, with 50% of mean levels per country above 16 mW/m2, particularly high in the Netherlands, Italy, and Belgium. - Villages experienced 35% higher exposure during max UL compared to cities. - Countries with precautionary limits had lower environmental exposure but higher auto-induced uplink exposure during data upload. Conclusion Environmental RF-EMF exposure in non-user scenarios aligns with previous European research and remains below international guideline values, showing little change since the introduction of 5G. Urban settings, due to a denser network, experience greater exposure, primarily from Mobile DL frequency bands. Countries like Switzerland, Italy, and Belgium, with precautionary limits, demonstrated generally lower environmental exposure. However, inducing traffic can substantially increase personal RF-EMF exposure, highlighting the importance of continued research, especially concerning the health impacts from frequent or high-level exposures, and as 5G deployment progresses. Future research should track the evolution of environmental and auto-induced RF-EMF exposure, paying close attention to these changes as 5G technology expands across Europe.
AI evidence extraction
Main findings
RF-EMF exposure was measured across >800 microenvironments in ten European countries under three scenarios (flight mode/non-user, induced max DL, induced max UL) using an ExpoM-RF 4 (35 bands, 87.5 MHz–6 GHz). Mean non-user exposure ranged 0.33–1.72 mW/m² per country and was ~80% lower in villages than cities, with DL bands contributing most. Induced traffic increased exposure, particularly from the 5G 3.5 GHz band during max DL (mean 2.61–11.12 mW/m²), while max UL produced the highest exposures (50% of country means >16 mW/m²); countries with precautionary limits showed lower environmental exposure but higher auto-induced UL exposure during upload. The authors state non-user environmental exposure remained below international guideline values and showed little change since 5G introduction.
Outcomes measured
- Environmental RF-EMF exposure (mW/m²) in microenvironments
- Auto-induced downlink (DL) RF-EMF exposure (mW/m²)
- Auto-induced uplink (UL) RF-EMF exposure (mW/m²)
- Contributions by frequency bands (including 5G 3.5 GHz)
- Differences by microenvironment (outdoor/indoor/public transport), urban vs village, and by country precautionary limits
Suggested hubs
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5g-policy
(0.66) Focuses on 5G-related exposure (including 3.5 GHz) and compares countries with precautionary limits.
View raw extracted JSON
{
"study_type": "exposure_assessment",
"exposure": {
"band": "RF",
"source": "environmental + mobile phone (auto-induced downlink/uplink)",
"frequency_mhz": null,
"sar_wkg": null,
"duration": null
},
"population": null,
"sample_size": null,
"outcomes": [
"Environmental RF-EMF exposure (mW/m²) in microenvironments",
"Auto-induced downlink (DL) RF-EMF exposure (mW/m²)",
"Auto-induced uplink (UL) RF-EMF exposure (mW/m²)",
"Contributions by frequency bands (including 5G 3.5 GHz)",
"Differences by microenvironment (outdoor/indoor/public transport), urban vs village, and by country precautionary limits"
],
"main_findings": "RF-EMF exposure was measured across >800 microenvironments in ten European countries under three scenarios (flight mode/non-user, induced max DL, induced max UL) using an ExpoM-RF 4 (35 bands, 87.5 MHz–6 GHz). Mean non-user exposure ranged 0.33–1.72 mW/m² per country and was ~80% lower in villages than cities, with DL bands contributing most. Induced traffic increased exposure, particularly from the 5G 3.5 GHz band during max DL (mean 2.61–11.12 mW/m²), while max UL produced the highest exposures (50% of country means >16 mW/m²); countries with precautionary limits showed lower environmental exposure but higher auto-induced UL exposure during upload. The authors state non-user environmental exposure remained below international guideline values and showed little change since 5G introduction.",
"effect_direction": "unclear",
"limitations": [],
"evidence_strength": "moderate",
"confidence": 0.7800000000000000266453525910037569701671600341796875,
"peer_reviewed_likely": "yes",
"keywords": [
"radiofrequency electromagnetic fields",
"RF-EMF",
"5G",
"environmental exposure",
"Europe",
"microenvironments",
"downlink",
"uplink",
"ExpoM-RF 4",
"3.5 GHz",
"precautionary limits",
"public transport",
"urban",
"village"
],
"suggested_hubs": [
{
"slug": "5g-policy",
"weight": 0.66000000000000003108624468950438313186168670654296875,
"reason": "Focuses on 5G-related exposure (including 3.5 GHz) and compares countries with precautionary limits."
}
]
}
AI can be wrong. Always verify against the paper.
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