title: Estimating the global abundance of ground level presence of particulate matter (PM2.5).
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 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 ground-based observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 to train a machine-learning 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.
authors.affiliation: Hanson Center for Space Science, University of Texas at Dallas, Dallas. david.lary@utdallas.edu.
authors.forename: David J
authors.initials: DJ
authors.lastname: Lary
authors.forename: Fazlay S
authors.initials: FS
authors.lastname: Faruque
authors.forename: Nabin
authors.initials: N
authors.lastname: Malakar
authors.forename: Alex
authors.initials: A
authors.lastname: Moore
authors.forename: Bryan
authors.initials: B
authors.lastname: Roscoe
authors.forename: Zachary L
authors.initials: ZL
authors.lastname: Adams
authors.forename: York
authors.initials: Y
authors.lastname: Eggelston
mesh_terms: D000397:Air Pollution / Q000009:adverse effects; D000465:Algorithms; D004784:Environmental Monitoring / Q000379:methods; D014943:Global Health / Q000706:statistics & numerical data; D018840:HSP70 Heat-Shock Proteins; D006801:Humans; D052638:Particulate Matter / Q000009:adverse effects / Q000032:analysis*; D058998:Remote Sensing Technology; D014887:Weather
chemical_list: D018840:HSP70 Heat-Shock Proteins; D052638:Particulate Matter; C080352:mortalin