Cluster Analysis of Residential Personal Exposure to ELF Magnetic Field in Children: Effect of Environmental Variables

Personal exposure to Extremely Low Frequency Magnetic Fields (ELF MF) in children is a very timely topic. We applied cluster analysis to 24 h indoor personal exposures of 884 children in France to identify possible common patterns of exposures. We investigated how electric networks near child home a...

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Published inInternational journal of environmental research and public health Vol. 16; no. 22; p. 4363
Main Authors Tognola, Gabriella, Chiaramello, Emma, Bonato, Marta, Magne, Isabelle, Souques, Martine, Fiocchi, Serena, Parazzini, Marta, Ravazzani, Paolo
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 08.11.2019
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Abstract Personal exposure to Extremely Low Frequency Magnetic Fields (ELF MF) in children is a very timely topic. We applied cluster analysis to 24 h indoor personal exposures of 884 children in France to identify possible common patterns of exposures. We investigated how electric networks near child home and other variables potentially affecting residential exposure, such as indoor sources of ELF MF, the age and type of the residence and family size, characterized the magnetic field exposure patterns. We identified three indoor personal exposure patterns: children living near overhead lines of high (63-150 kV), extra-high (225 kV) and ultra-high voltage (400 kV) were characterized by the highest exposures; children living near underground networks of low (400 V) and mid voltage (20 kV) and substations (20 kV/400 V) were characterized by mid exposures; children living far from electric networks had the lowest level of exposure. The harmonic component was not relevant in discriminating the exposure patterns, unlike the 50 Hz or broadband (40-800 Hz) component. Children using electric heating appliances, or living in big buildings or in larger families had generally a higher level of personal indoor exposure. Instead, the age of the residence was not relevant in differentiating the exposure patterns.
AbstractList Differently from the majority of previous studies on the analysis of ELF MF exposure [5,6,7,8,9,10,11,12,13,14,15,16,17,18], we did not perform a parametric modeling by using, e.g., correlation analysis or multivariate analysis but we applied cluster analysis—a machine learning technique—to identify recurrent and common patterns of indoor personal exposures. Differently from parametric analyses such as the well-known correlation, regression, and factor analysis, cluster analysis does not require that the data come from a parametric model (linear or non-linear): cluster analysis aims at partitioning the observed data in groups so that members of a given group share more similarities among them if compared with the members of the other groups. Namely: (i) we aimed to understanding how the patterns of indoor personal exposure found by cluster analysis were affected by the presence of indoor sources of ELF MF such as electric heating and by variables that might indirectly contribute to indoor exposure such as the physical parameters of the residences (the age and type of residence (i.e., individual vs apartment )) and the family size; and (ii) we analyzed not only the 50 Hz MF (as in [19]) but also the broadband (40–800 Hz) and harmonic (100–800 Hz) components to assess their potential contribution in differentiating the patterns of indoor personal exposure. [...]in the present study we also considered the possible effect of uncertainty in geolocalization of electric networks near children homes.
Personal exposure to Extremely Low Frequency Magnetic Fields (ELF MF) in children is a very timely topic. We applied cluster analysis to 24 h indoor personal exposures of 884 children in France to identify possible common patterns of exposures. We investigated how electric networks near child home and other variables potentially affecting residential exposure, such as indoor sources of ELF MF, the age and type of the residence and family size, characterized the magnetic field exposure patterns. We identified three indoor personal exposure patterns: children living near overhead lines of high (63-150 kV), extra-high (225 kV) and ultra-high voltage (400 kV) were characterized by the highest exposures; children living near underground networks of low (400 V) and mid voltage (20 kV) and substations (20 kV/400 V) were characterized by mid exposures; children living far from electric networks had the lowest level of exposure. The harmonic component was not relevant in discriminating the exposure patterns, unlike the 50 Hz or broadband (40-800 Hz) component. Children using electric heating appliances, or living in big buildings or in larger families had generally a higher level of personal indoor exposure. Instead, the age of the residence was not relevant in differentiating the exposure patterns.
Author Magne, Isabelle
Parazzini, Marta
Chiaramello, Emma
Ravazzani, Paolo
Fiocchi, Serena
Souques, Martine
Tognola, Gabriella
Bonato, Marta
AuthorAffiliation 1 CNR IEIIT—Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, 20133 Milan, Italy; emma.chiaramello@ieiit.cnr.it (E.C.); marta.bonato@ieiit.cnr.it (M.B.); serena.fiocchi@ieiit.cnr.it (S.F.); marta.parazzini@ieiit.cnr.it (M.P.); paolo.ravazzani@ieiit.cnr.it (P.R.)
3 Dipartimento di Elettronica, Informazione e Bioingegneria DEIB, Politecnico di Milano, 20133 Milan, Italy
2 EDF Electricité de France, 75017 Paris, France; isabelle.magne@edf.fr (I.M.); martine.souques@edf.fr (M.S.)
AuthorAffiliation_xml – name: 2 EDF Electricité de France, 75017 Paris, France; isabelle.magne@edf.fr (I.M.); martine.souques@edf.fr (M.S.)
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Issue 22
Keywords magnetic field
residential exposure
children
electric heating
ELF MF
family size
residence type
cluster analysis
machine learning
residence age
Language English
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Snippet Personal exposure to Extremely Low Frequency Magnetic Fields (ELF MF) in children is a very timely topic. We applied cluster analysis to 24 h indoor personal...
Differently from the majority of previous studies on the analysis of ELF MF exposure [5,6,7,8,9,10,11,12,13,14,15,16,17,18], we did not perform a parametric...
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StartPage 4363
SubjectTerms Artificial intelligence
Broadband
Child
Children
Children & youth
Cluster Analysis
Correlation analysis
Datasets
Electric heating
Electrical networks
Electricity
Electromagnetic Fields
Environmental effects
Environmental Exposure - analysis
Exposure
Factor analysis
Family
Family Characteristics
Family size
France
Housing
Humans
Hypotheses
Indoor environments
Learning algorithms
Leukemia
Machine learning
Magnetic fields
Multivariate analysis
Nonlinear analysis
Physical properties
Records
Studies
Variables
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Title Cluster Analysis of Residential Personal Exposure to ELF Magnetic Field in Children: Effect of Environmental Variables
URI https://www.ncbi.nlm.nih.gov/pubmed/31717366
https://www.proquest.com/docview/2329658756/abstract/
https://search.proquest.com/docview/2314252126
https://pubmed.ncbi.nlm.nih.gov/PMC6888053
Volume 16
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