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

E Chiaramello (1) , Laurent Le Brusquet (2) , M. Parazzini (1) , S. Fiocchi (1) , M Bonato (1) , P Ravazzani (1)

1 CNR - Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni IEIIT
2 L2S - Laboratoire des signaux et systèmes

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, was applied to build space-dependent surrogate models of the 3D spatial distribution of the electric field induced in central nervous system of children of different age exposed to uniform magnetic field at 50 Hz with uncertain orientation. The 3D surrogate models showed very low normalized percentage mean square error values, confirming the feasibility and the accuracy of the approach in estimating the 3D spatial distribution of E with a low number of components. The electric field induced in children tissues were within the ICNIRP basic restrictions for general public and that no significant difference was found in the level of the exposure and in the 3D spatial distribution of the electric fields induced in tissues of children of different ages.

Domains: Santé publique et épidémiologie; Applications [stat.AP]; Modeling and Simulation; Statistics [math.ST]

E Chiaramello, Laurent Le Brusquet, M. Parazzini, S. Fiocchi, M Bonato, et al.. 3D space-dependent models for stochastic dosimetry applied to exposure to low frequency magnetic fields. Bioelectromagnetics, 2019, 40, pp.170-179. ⟨10.1002/bem.22179⟩. ⟨hal-01871257⟩