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A nested machine learning approach to short-term PM2.5 prediction in metropolitan areas using PM2.5 data from different sensor networks.
Li, Jing; Crooks, James; Murdock, Jennifer; de Souza, Priyanka; Hohsfield, Kirk; Obermann, Bill; Stockman, Tehya.
Afiliación
  • Li J; Department of Geography and the Environment, University of Denver, United States of America. Electronic address: Jing.Li145@du.edu.
  • Crooks J; Division of Biostatistics and Bioinformatics, National Jewish Health, United States of America; Department of Epidemiology, Colorado School of Public Health, United States of America.
  • Murdock J; Department of Geography and the Environment, University of Denver, United States of America.
  • de Souza P; Department of Urban and Regional Planning, University of Colorado - Denver, United States of America; CU Population Center, University of Colorado - Boulder, United States of America.
  • Hohsfield K; University of Colorado, School of Medicine, United States of America.
  • Obermann B; Department of Public Health and Environment, City and County of Denver, United States of America.
  • Stockman T; Department of Public Health and Environment, City and County of Denver, United States of America; Civil, Environmental and Architectural Engineering Department, University of Colorado - Boulder, United States of America.
Sci Total Environ ; 873: 162336, 2023 May 15.
Article en En | MEDLINE | ID: mdl-36813194
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Total Environ Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Total Environ Año: 2023 Tipo del documento: Article Pais de publicación: Países Bajos