摘要：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 with a diameter of 2.5 microns or less (PM2.5).Here we use a suite of remote sensing and meteorological data products together with groundbased observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 to train a machinelearning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present.In this first paper of a series, we present the methodology and global average results from this period and demonstrate that the new PM2.5 data product can reliably represent global observations of PM2.5 for epidemiological studies.
