Estimating the global abundance of ground level presence of particulate matter (PM2.5)
AbstractWith 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.
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Copyright (c) 2014 David J. Lary, Fazlay S. Faruque, Nabin Malakar, Alex Moore, Bryan Roscoe, Zachary L. Adams, York Eggelston
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