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 in | International journal of environmental research and public health Vol. 16; no. 22; p. 4363 |
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Main Authors | , , , , , , , |
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Language | English |
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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. |
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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.) – name: 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.) – name: 3 Dipartimento di Elettronica, Informazione e Bioingegneria DEIB, Politecnico di Milano, 20133 Milan, Italy |
Author_xml | – sequence: 1 givenname: Gabriella orcidid: 0000-0002-2433-449X surname: Tognola fullname: Tognola, Gabriella organization: CNR IEIIT-Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, 20133 Milan, Italy – sequence: 2 givenname: Emma orcidid: 0000-0003-2503-4779 surname: Chiaramello fullname: Chiaramello, Emma organization: CNR IEIIT-Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, 20133 Milan, Italy – sequence: 3 givenname: Marta orcidid: 0000-0002-5941-297X surname: Bonato fullname: Bonato, Marta organization: Dipartimento di Elettronica, Informazione e Bioingegneria DEIB, Politecnico di Milano, 20133 Milan, Italy – sequence: 4 givenname: Isabelle orcidid: 0000-0002-5527-1049 surname: Magne fullname: Magne, Isabelle organization: EDF Electricité de France, 75017 Paris, France – sequence: 5 givenname: Martine surname: Souques fullname: Souques, Martine organization: EDF Electricité de France, 75017 Paris, France – sequence: 6 givenname: Serena orcidid: 0000-0002-6568-8841 surname: Fiocchi fullname: Fiocchi, Serena organization: CNR IEIIT-Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, 20133 Milan, Italy – sequence: 7 givenname: Marta orcidid: 0000-0001-9008-7530 surname: Parazzini fullname: Parazzini, Marta organization: CNR IEIIT-Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, 20133 Milan, Italy – sequence: 8 givenname: Paolo orcidid: 0000-0003-0282-3329 surname: Ravazzani fullname: Ravazzani, Paolo organization: CNR IEIIT-Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, 20133 Milan, Italy |
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Keywords | magnetic field residential exposure children electric heating ELF MF family size residence type cluster analysis machine learning residence age |
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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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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 |
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