Prediction of daily mean and one-hour maximum PM concentrations and applications in Central Mexico using satellite-based machine-learning models.

Journal: Journal of exposure science & environmental epidemiology
PMID: 36088418

Abstract

BACKGROUND: Machine-learning algorithms are becoming popular techniques to predict ambient air PM concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM concentrations (mean PM) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM).

Authors

  • Iván Gutiérrez-Avila
    Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. ivan_2c@hotmail.com.
  • Kodi B Arfer
    Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Daniel Carrion
    Monash Imaging, Monash Health, Clayton, Victoria, Australia.
  • Johnathan Rush
    Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Itai Kloog
    Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.
  • Aaron R Naeger
    Earth System Science Center, University of Alabama in Huntsville, Huntsville, AL, USA.
  • Michel Grutter
    Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Ciudad de México, México.
  • Víctor Hugo Páramo-Figueroa
    Comisión Ambiental de la Megalópolis, Ciudad de México, México.
  • Horacio Riojas-Rodríguez
    Dirección de Salud Ambiental, Instituto Nacional de Salud Pública, Cuernavaca Morelos, México.
  • Allan C Just
    Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

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