Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM<sub>2.5</sub> Temporal and Spatial Distributions

Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM<sub>2.5</sub> Temporal and Spatial Distributions

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM<sub>2.5</sub>), mainly in urban areas with low air quality monit...

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Bibliographic Details
Main Authors: Johana M. Carmona, Pawan Gupta, Diego F. Lozano-García, Ana Y. Vanoye, Iván Y. Hernández-Paniagua, Alberto Mendoza
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Remote Sensing
Subjects:
air pollution
fine particulate matter
satellite data
neural networks
ensemble models
Online Access:https://www.mdpi.com/2072-4292/13/16/3102

Internet

https://www.mdpi.com/2072-4292/13/16/3102

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