Estimating the Global Abundance of Ground Level Particulate Matter (PM2. 5) Since 1997 (original) (raw)

Abstract

ABSTRACT With the increasing awareness of the health impacts of particulate matter there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground level airborne particulate matter (PM2.5). Here we use a suite of remote sensing and meteorologi-cal data products together with ground based observations of PM2.5 from 8,329 measurement sites in 55 countries taken between 1997-2014 to train a machine learning algorithm to estimate the daily distributions of PM2.5 from 1997-present. In this first paper of a series we present the method-ology and global average results from 1997-2014 and demonstrate that the new PM2.5 data product can reliably represent global observations of PM2.5 for epidemiological studies.

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