Cargando…
Prediction of daily mean and one-hour maximum PM(2.5) concentrations and applications in Central Mexico using satellite-based machine-learning models
BACKGROUND: Machine-learning algorithms are becoming popular techniques to predict ambient air PM(2.5) 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(2.5) concentrations (mea...
Autores principales: | Gutiérrez-Avila, Iván, Arfer, Kodi B., Carrión, Daniel, Rush, Johnathan, Kloog, Itai, Naeger, Aaron R., Grutter, Michel, Páramo-Figueroa, Víctor Hugo, Riojas-Rodríguez, Horacio, Just, Allan C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9731899/ https://www.ncbi.nlm.nih.gov/pubmed/36088418 http://dx.doi.org/10.1038/s41370-022-00471-4 |
Ejemplares similares
-
A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003 to 2019
por: Gutiérrez‐Avila, Iván, et al.
Publicado: (2021) -
Daily exposure to PM(2.5) and 1.5 million deaths: A time-stratified case-crossover analysis in the Mexico City Metropolitan Area
por: Gutiérrez-Avila, Iván, et al.
Publicado: (2023) -
Assessing capacity to social distance and neighborhood-level health disparities during the COVID-19 pandemic
por: Carrión, Daniel, et al.
Publicado: (2020) -
Neighborhood-level disparities and subway utilization during the COVID-19 pandemic in New York City
por: Carrión, Daniel, et al.
Publicado: (2021) -
Short-term exposure to PM(2.5) and 1.5 million deaths: a time-stratified case-crossover analysis in the Mexico City Metropolitan Area
por: Gutiérrez-Avila, Iván, et al.
Publicado: (2023)