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Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling
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 rel...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163697/ https://www.ncbi.nlm.nih.gov/pubmed/30205571 http://dx.doi.org/10.3390/ijerph15091963 |
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author | Bonato, Marta Parazzini, Marta Chiaramello, Emma Fiocchi, Serena Le Brusquet, Laurent Magne, Isabelle Souques, Martine Röösli, Martin Ravazzani, Paolo |
author_facet | Bonato, Marta Parazzini, Marta Chiaramello, Emma Fiocchi, Serena Le Brusquet, Laurent Magne, Isabelle Souques, Martine Röösli, Martin Ravazzani, Paolo |
author_sort | Bonato, Marta |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6163697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61636972018-10-12 Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling Bonato, Marta Parazzini, Marta Chiaramello, Emma Fiocchi, Serena Le Brusquet, Laurent Magne, Isabelle Souques, Martine Röösli, Martin Ravazzani, Paolo Int J Environ Res Public Health Article 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. MDPI 2018-09-08 2018-09 /pmc/articles/PMC6163697/ /pubmed/30205571 http://dx.doi.org/10.3390/ijerph15091963 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bonato, Marta Parazzini, Marta Chiaramello, Emma Fiocchi, Serena Le Brusquet, Laurent Magne, Isabelle Souques, Martine Röösli, Martin Ravazzani, Paolo Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling |
title | Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling |
title_full | Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling |
title_fullStr | Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling |
title_full_unstemmed | Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling |
title_short | Characterization of Children’s Exposure to Extremely Low Frequency Magnetic Fields by Stochastic Modeling |
title_sort | characterization of children’s exposure to extremely low frequency magnetic fields by stochastic modeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163697/ https://www.ncbi.nlm.nih.gov/pubmed/30205571 http://dx.doi.org/10.3390/ijerph15091963 |
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