Prediction of daily mean and one-hour maximum PM_2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models.

ABSTRACT:

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 (mean PM_2.5) 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_2.5).

Objective

Our goal was to develop a machine-learning model to predict mean PM_2.5 and max PM_2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019.

Methods

We present a new modeling approach based on extreme gradient boosting (XGBoos

SUBMITTER: Gutierrez-Avila I 

PROVIDER: S-EPMC9731899 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature