Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Radio-frequency electromagnetic field exposure and contribution of sources in the general population: an organ-specific integrative exposure assessment

Journal of Exposure Science & Environmental Epidemiology volume 31, pages 999–1007 (2021)Cite this article

Abstract

In order to achieve an integrated radio-frequency electromagnetic fields (RF-EMF) dose assessment, detailed information about source-specific exposure duration and output power is needed. We developed an Integrated Exposure Model (IEM) to combine energy absorbed due to use of and exposure to RF-EMF sources and applied it to a sample of the general population to derive population RF-EMF estimates. The IEM used specific absorption rate transfer algorithms to provide RF-EMF daily dose estimates (mJ/kg/day) using source-specific attributes (e.g. output power, distance), personal characteristics and usage patterns. Information was obtained from an international survey performed in four European countries with 1755 participants. We obtained median whole-body and whole-brain doses of 183.7 and 204.4 mJ/kg/day. Main contributors to whole-brain dose were mobile phone near the head for calling (2G networks) and far-field sources, whereas the latter together with multiple other RF-EMF sources were main contributors for whole-body dose. For other anatomical sites, 2G phone calls, mobile data and far-field exposure were important contributors. The IEM provides insight into main contributors to total RF-EMF dose and, applied to an international survey, provides an estimate of population RF-dose. The IEM can be used in future epidemiological studies, risk assessments and exposure reduction strategies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Relative contribution of sources to total dose of whole-body and whole-brain (HFK hands-free kit, BAN body area network, VR virtual reality headset).
Fig. 2: Relative contribution of mobile phone functions to total dose of whole-body and whole-brain.
Fig. 3: Relative contribution of tablet functions to total dose of whole-body and whole-brain.

Similar content being viewed by others

Code availability

The model is available upon request: R.C.H.Vermeulen@uu.nl. Version 1.2.6 was used to generate the results presented here.

References

  1. World Health Organization. WHO research agenda for radiofrequency fields. WHO Libr. Cat. Data. 2010; p. 42.

  2. Röösli M, Lagorio S, Schoemaker MJ, Schüz J, Feychting M. Brain and salivary gland tumors and mobile phone use: evaluating the evidence from various epidemiological study designs. Annu Rev Public Health. 2019. https://doi.org/10.1146/annurev-publhealth-040218-044037.

  3. Cardis E, Richardson L, Deltour I, Armstrong B, Feychting M, Johansen C, et al. The INTERPHONE study: design, epidemiological methods, and description of the study population. Eur J Epidemiol. 2007. https://doi.org/10.1007/s10654-007-9152-z.

  4. Bürgi A, Frei P, Theis G, Mohler E, Braun-Fahrländer C, Fröhlich J, et al. A model for radiofrequency electromagnetic field predictions at outdoor and indoor locations in the context of epidemiological research. Bioelectromagnetics. 2010;31:226–36.

    PubMed  Google Scholar 

  5. Varsier N, Plets D, Corre Y, Vermeeren G, Joseph W, Aerts S, et al. A novel method to assess human population exposure induced by a wireless cellular network. Bioelectromagnetics. 2015. https://doi.org/10.1002/bem.21928.

  6. Aerts S, Wiart J, Martens L, Joseph W. Assessment of long-term spatio-temporal radiofrequency electromagnetic field exposure. Environ Res. 2018. https://doi.org/10.1016/j.envres.2017.11.003.

  7. Roser K, Schoeni A, Bürgi A, Röösli M. Development of an RF-EMF exposure surrogate for epidemiologic research. Int J Environ Res Public Health. 2015;12:5634–56.

    Article  Google Scholar 

  8. Lauer O, Frei P, Gosselin MC, Joseph W, Röösli M, Fröhlich J. Combining near- and far-field exposure for an organ-specific and whole-body RF-EMF proxy for epidemiological research: a reference case. Bioelectromagnetics. 2013;34:366–74.

    Article  Google Scholar 

  9. Liorni I, Capstick M, van Wel L, Wiart J, Joseph W, Cardis E, et al. Evaluation of specific absorption rate in the far-field, near-to-far field and near-field regions for integrative radiofrequency exposure assessment. Radiat Prot Dosimetry. 2020. https://doi.org/10.1093/rpd/ncaa127.

  10. CREST. Characterization of exposure to radio-frequency (RF) induced by new uses and technologies of mobile communication systems. 2013. http://radiation.isglobal.org/crest.

  11. Joseph W, Pareit D, Vermeeren G, Naudts D, Verloock L, Martens L, et al. Determination of the duty cycle of WLAN for realistic radio frequency electromagnetic field exposure assessment. Prog Biophys Mol Biol. 2013. https://doi.org/10.1016/j.pbiomolbio.2012.10.002.

  12. Slottje P, Yzermans CJ, Korevaar JC, Hooiveld M, Vermeulen RCH. The population-based occupational and environmental health prospective cohort study (AMIGO) in the Netherlands. BMJ Open. 2015. https://doi.org/10.1136/bmjopen-2014-005858.

  13. Eeftens M, Struchen B, Birks LE, Cardis E, Estarlich M, Fernandez MF, et al. Personal exposure to radio-frequency electromagnetic fields in Europe: is there a generation gap? Environ Int. 2018;121:216–26.

    Article  Google Scholar 

  14. CBS Statistics Netherlands. CBS Statistics Netherlands. 2017. https://opendata.cbs.nl/statline/#/CBS/nl/dataset/81565NED/table?ts=1538661827972. Accessed 4 Oct 2018.

  15. Statistik B für. Schweizerische Eidgenossenschaft Eidgenössisches Departement des Inneren EDI. 2012.

  16. Eurobarometer S Health and food. Spec. Barom. 246. 2006.

  17. Goedhart G, van Wel L, Langer CE, de Llobet Viladoms P, Wiart J, Hours M, et al. Recall of mobile phone usage and laterality in young people: the multinational Mobi-Expo study. Environ Res. 2018. https://doi.org/10.1016/j.envres.2018.04.018.

  18. Berman D, Crump K. Final draft: technical support document for a protocol to assess asbestos-related risk. Report no. 9345.4-06. Washington, DC: U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response; 2003.

  19. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2013. http://www.r-project.org/.

  20. Kühn S, Kuster N. Field evaluation of the human exposure from multiband, multisystem mobile phones. IEEE Trans Electromagn Compat. 2013;55:275–87.

    Article  Google Scholar 

Download references

Funding

This project was funded by the European Union’s FP7 programme within the framework of the international project GERoNiMO (‘Generalized EMF Research using Novel Methods—an integrated approach: from research to risk assessment and support to risk management’, GA603794, 01/2014–12/2018) and by ANSES—the French Agence nationale de sécurité sanitaire de l’alimentation, de l’environment et du travail—within the CREST project (‘Characterization of exposure to radio-frequency (RF) induced by new uses and technologies of mobile communication systems’, 2013–2017). This project also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant agreement No. 665501 with the research Foundation Flanders (FWO). Arno Thielens is an FWO [PEGASUS]² Marie Skłodowska-Curie fellow. ISGlobal is a member of the CERCA Programme, Generalitat de Catalunya.

Author information

Authors and Affiliations

  1. Institute for Risk Assessment Sciences (IRAS), Utrecht University, Yalelaan 2, 3584 CM, Utrecht, The Netherlands

    Luuk van Wel, Anke Huss & Roel Vermeulen

  2. Foundation for Research on Information Technologies in Society (IT’IS Foundation), Zeughausstrasse, 43, 8004, Zurich, Switzerland

    Ilaria Liorni & Myles Capstick

  3. Department of Information Technology, Ghent University, IMEC, Waves Research Group, Ghent, 9052, Belgium

    Arno Thielens & Wout Joseph

  4. Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley Wireless Research Center, Berkeley, CA, 94704, USA

    Arno Thielens

  5. Chair C2M, LTCI Télecom Paris, Institut polytechnique de Paris, Paris, France

    Joe Wiart

  6. Swiss Tropical and Public Health Institute, Basel, 4051, Switzerland

    Martin Röösli & Milena Foerster

  7. University of Basel, Basel, Switzerland

    Martin Röösli & Milena Foerster

  8. Université de Lyon, Université Claude Bernard Lyon, Ifsttar, UMRESTTE, UMR T_9405, 8 Avenue Rockefeller, 69373, Lyon, France

    Amelie Massardier-Pilonchery

  9. Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, 69495, Pierre Bénite, France

    Amelie Massardier-Pilonchery

  10. Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain

    Elisabeth Cardis

  11. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Elisabeth Cardis

  12. CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

    Elisabeth Cardis

Authors
  1. Luuk van Wel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilaria Liorni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anke Huss
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arno Thielens
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Joe Wiart
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wout Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Martin Röösli
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Milena Foerster
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Amelie Massardier-Pilonchery
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Myles Capstick
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Elisabeth Cardis
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Roel Vermeulen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roel Vermeulen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

van Wel, L., Liorni, I., Huss, A. et al. Radio-frequency electromagnetic field exposure and contribution of sources in the general population: an organ-specific integrative exposure assessment. J Expo Sci Environ Epidemiol 31, 999–1007 (2021). https://doi.org/10.1038/s41370-021-00287-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41370-021-00287-8

Keywords

This article is cited by

Search

Advanced search

Quick links