Background
“Short-term exposure to ambient PM2.5 is a leading contributor to the global burden of diseases and mortality. However, few studies have provided the global spatiotemporal variations of daily PM2.5 concentrations over recent decades.”
Methods
"In this modelling study, we implemented deep ensemble machine learning (DEML) to estimate global daily ambient PM2.5 concentrations at 0.1° × 0.1° spatial resolution between Jan 1, 2000, and Dec 31, 2019.
In the DEML framework, ground-based PM2.5 measurements from 5446 monitoring stations in 65 countries worldwide were combined with GEOS-Chem chemical transport model simulations of PM2.5 concentration, meteorological data, and geographical features.
At the global and regional levels, we investigated annual population-weighted PM2.5 concentrations and annual population-weighted exposed days to PM2.5 concentrations higher than 15 μg/m3 (2021 WHO daily limit) to assess spatiotemporal exposure in 2000, 2010, and 2019.
Land area and population exposures to PM2.5 above 5 μg/m3 (2021 WHO annual limit) were also assessed for the year 2019. PM2.5 concentrations for each calendar month were averaged across the 20-year period to investigate global seasonal patterns."