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Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling

Author

Listed:
  • Marta Bonato

    (Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni IEIIT CNR, 20133 Milano, Italy
    Dipartimento di Elettronica, Informazione e Bioingegneria DEIB, Politecnico di Milano, 20133 Milano, Italy)

  • Marta Parazzini

    (Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni IEIIT CNR, 20133 Milano, Italy)

  • Emma Chiaramello

    (Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni IEIIT CNR, 20133 Milano, Italy)

  • Serena Fiocchi

    (Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni IEIIT CNR, 20133 Milano, Italy)

  • Laurent Le Brusquet

    (Laboratoire des Signaux et Systèmes (L2S), CentraleSupélec, CNRS, Univ. Paris-Sud, Université Paris-Saclay, 91192 Gif-sur-Yvette, France)

  • Isabelle Magne

    (Medical Studies Department of EDF (Electricite de France), 92300 Levallois-Perret, France)

  • Martine Souques

    (Medical Studies Department of EDF (Electricite de France), 92300 Levallois-Perret, France)

  • Martin Röösli

    (Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland
    University of Basel, 4001 Basel, Switzerland)

  • Paolo Ravazzani

    (Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni IEIIT CNR, 20133 Milano, Italy)

Abstract

In this study, children’s exposure to extremely low frequency magnetic fields (ELF-MF, 40–800 Hz) is investigated. The interest in this thematic has grown due to a possible correlation between the increased risk of childhood leukemia and a daily average exposure above 0.4 µT, although the causal relationship is still uncertain. The aim of this paper was to present a new method of characterizing the children’s exposure to ELF-MF starting from personal measurements using a stochastic approach based on segmentation (and to apply it to the personal measurements themselves) of two previous projects: the ARIMMORA project and the EXPERS project. The stochastic model consisted in (i) splitting the 24 h recordings into stationary events and (ii) characterizing each event with four parameters that are easily interpretable: the duration of the event, the mean value, the dispersion of the magnetic field over the event, and a final parameter characterizing the variation speed. Afterward, the data from the two databases were divided in subgroups based on a characteristic (i.e., children’s age, number of inhabitants in the area, etc.). For every subgroup, the kernel density estimation (KDE) of each parameter was calculated and the p -value histogram of the parameters together was obtained, in order to compare the subgroups and to extract information about the children’s exposure. In conclusion, this new stochastic approach allows for the identification of the parameters that most affect the level of children’s exposure.

Suggested Citation

  • Marta Bonato & Marta Parazzini & Emma Chiaramello & Serena Fiocchi & Laurent Le Brusquet & Isabelle Magne & Martine Souques & Martin Röösli & Paolo Ravazzani, 2018. "Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling," IJERPH, MDPI, vol. 15(9), pages 1-19, September.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:9:p:1963-:d:168619
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    References listed on IDEAS

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    1. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    2. Davis, Richard A. & Lee, Thomas C.M. & Rodriguez-Yam, Gabriel A., 2006. "Structural Break Estimation for Nonstationary Time Series Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 223-239, March.
    3. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
    4. Ilaria Liorni & Marta Parazzini & Benjamin Struchen & Serena Fiocchi & Martin Röösli & Paolo Ravazzani, 2016. "Children’s Personal Exposure Measurements to Extremely Low Frequency Magnetic Fields in Italy," IJERPH, MDPI, vol. 13(6), pages 1-19, May.
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    Cited by:

    1. Maria Rosaria Scarfì & Mats-Olof Mattsson & Myrtill Simkó & Olga Zeni, 2019. "Special Issue: “Electric, Magnetic, and Electromagnetic Fields in Biology and Medicine: From Mechanisms to Biomedical Applications”," IJERPH, MDPI, vol. 16(22), pages 1-3, November.

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