3D space-dependent models for stochastic dosimetry applied to exposure to low frequency magnetic fields

Bioelectromagnetics. 2019 Apr;40(3):170-179. doi: 10.1002/bem.22179. Epub 2019 Mar 26.

Abstract

In this study, an innovative approach that combines Principal Component Analysis (PCA) and Gaussian process regression (Kriging method), never used before in the assessment of human exposure to electromagnetic fields (EMF), was applied to build space-dependent surrogate models of the 3D spatial distribution of the electric field induced in central nervous system (CNS) of children of different ages exposed to uniform magnetic field at 50 Hz of 200 μT of amplitude with uncertain orientation. The 3D surrogate models showed very low normalized percentage mean square error (MSE) values, always lower than 0.16%, confirming the feasibility and accuracy of the approach in estimating the 3D spatial distribution of E with a low number of components. Results showed that the electric field values induced in CNS tissues of children were within the ICNIRP basic restrictions for general public, with 99th percentiles of the E values obtained for each orientation showing median values in the range 1.9-2.1 mV/m. Similar 3D spatial distributions of the electric fields were found to be induced in CNS tissues of children of different ages. Bioelectromagnetics. 9999:1-10, 2018. © 2019 Bioelectromagnetics Society.

Keywords: 3D surrogate modeling; ELF-MF exposure; Kriging method; children exposure; stochastic dosimetry.

MeSH terms

  • Adolescent
  • Child
  • Environmental Exposure / adverse effects
  • Environmental Exposure / analysis*
  • Female
  • Humans
  • Magnetic Fields / adverse effects*
  • Male
  • Models, Anatomic*
  • Normal Distribution
  • Principal Component Analysis
  • Stochastic Processes