Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies

Lary DJ, Lary T and Sattler

Abstract

There is an increasing awareness of the health impacts of particulate matter and a growing need to quantify the spatial and temporal variations of the global abundance of ground level airborne particulate matter (PM2.5). In March 2014, the World Health Organization (WHO) released a report that in 2012 alone, a staggering 7 million people died as a result of air pollution exposure , one in eight of the total global deaths. A major component of this pollution is airborne particulate matter (e.g. PM2.5 & PM10).