Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloUse of Machine Learning in the Analysis of IndoorELF MF Exposure in Children
Anno di pubblicazione2019
FormatoElettronico
Autore/iGabriella Tognola(1), Marta Bonato(1,2), Emma Chiaramello(1), Serena Fiocchi(1), Isabelle Magne(3), Martine Souques(3), Marta Parazzini(1), Paolo Ravazzani(1)
Affiliazioni autori(1) CNR IEIIT--Consiglio Nazionale delle Ricerche, Istituto di Elettronica e di Ingegneria dell'Informazione e delle Telecomunicazioni, Milan, Italy; (2) Dipartimento di Elettronica, Informazione e Bioingegneria DEIB, Politecnico di Milano, Milan, Italy; (3) EDF Electricite de France, Levallois-Perret, France.
Autori CNR e affiliazioni
  • EMMA CHIARAMELLO
  • MARTA BONATO
  • PAOLO GIUSEPPE RAVAZZANI
  • GABRIELLA TOGNOLA
  • MARTA PARAZZINI
  • SERENA FIOCCHI
Lingua/e
  • inglese
AbstractCharacterization of children exposure to extremely low frequency (ELF) magnetic fieldsis an important issue because of the possible correlation of leukemia onset with ELF exposure.Cluster analysis--a Machine Learning approach--was applied on personal exposure measurementsfrom 977 children in France to characterize real-life ELF exposure scenarios. Electric networks nearthe child's home or school were considered as environmental factors characterizing the exposurescenarios. The following clusters were identified: children with the highest exposure living 120-200 mfrom 225 kV/400 kV overhead lines; children with mid-to-high exposure living 70-100 m from63 kV/150 kV overhead lines; children with mid-to-low exposure living 40 m from 400 V/20 kVsubstations and underground networks; children with the lowest exposure and the lowest number ofelectric networks in the vicinity. 63-225 kV underground networks within 20 m and 400 V/20 kVoverhead lines within 40 m played a marginal role in differentiating exposure clusters. Clusteranalysis is a viable approach to discovering variables best characterizing the exposure scenarios andthus it might be potentially useful to better tailor epidemiological studies. The present study did notassess the impact of indoor sources of exposure, which should be addressed in a further study.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
Pagine a-
Pagine totali14
RivistaInternational journal of environmental research and public health (Online)
Attiva dal 2004
Editore: MDPI, - Basel
Lingua: inglese
ISSN: 1660-4601
Titolo chiave: International journal of environmental research and public health (Online)
Titolo proprio: International journal of environmental research and public health. (Online)
Titolo abbreviato: Int. j. environ. res. public health (Online)
Numero volume della rivista16
Fascicolo della rivista7
DOI10.3390/ijerph16071230
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • PubMed (Codice:30959870)
  • Scopus (Codice:2-s2.0-85064541238)
  • ISI Web of Science (WOS) (Codice:000465595800143)
Parole chiavechildren, ELF MF, magnetic field, indoor exposure, cluster analysis, Machine Learning
Link (URL, URI)-
Titolo parallelo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IEIIT — Istituto di elettronica e di ingegneria dell'informazione e delle telecomunicazioni
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
Use of Machine Learning in the Analysis of IndoorELF MF Exposure in Children (documento privato )
Descrizione: Articolo pubblicato
Tipo documento: application/pdf