Influence of tissue conductivity on foetal exposure to extremely low frequency magnetic fields at 50 Hz using stochastic dosimetry

PLoS One. 2018 Feb 7;13(2):e0192131. doi: 10.1371/journal.pone.0192131. eCollection 2018.

Abstract

Human exposure to extremely low frequency magnetic fields (ELF-MF) at 50 Hz is still a topic of great interest due to the possible correlation with childhood leukaemia. The estimation of induced electric fields in human tissues exposed to electromagnetic fields (EMFs) strictly depends on several variables which include the dielectric properties of the tissues. In this paper, the influence of the conductivity assignment to foetal tissues at different gestational ages on the estimation of the induced electric field due to ELF-MF exposure at 50 Hz has been quantified by means of a stochastic approach using polynomial chaos theory. The range of variation in conductivity values for each foetal tissue at each stage of pregnancy have been defined through three empirical approaches and the induced electric field in each tissue has been modelled through stochastic dosimetry. The main results suggest that both the peak and median induced electric fields in foetal fat vary by more than 8% at all gestational ages. On the contrary, the electric field induced in foetal brain does not seem to be significantly affected by conductivity data changes. The maximum exposure levels, in terms of the induced electric field found in each specific tissue, were found to be significantly below the basic restrictions indicated in the ICNIRP Guidelines, 2010.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Electromagnetic Fields*
  • Female
  • Fetus*
  • Humans
  • Nonlinear Dynamics
  • Pregnancy
  • Stochastic Processes

Grants and funding

The study is funded by the French National Research Program for Environmental and Occupational Health of ANSES (2015/1/202): Project ELFSTAT - In depth evaluation of children’s exposure to ELF (40 – 800 Hz) magnetic fields and implications for health risk of new technologies, 2015-2019.