@article{Lary_Faruque_Malakar_Moore_Roscoe_Adams_Eggelston_2014, title={Estimating the global abundance of ground level presence of particulate matter (PM2.5)}, volume={8}, url={https://www.geospatialhealth.net/gh/article/view/292}, DOI={10.4081/gh.2014.292}, abstractNote={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.}, number={3}, journal={Geospatial Health}, author={Lary, David J. and Faruque, Fazlay S. and Malakar, Nabin and Moore, Alex and Roscoe, Bryan and Adams, Zachary L. and Eggelston, York}, year={2014}, month={Dec.}, pages={S611-S630} }