Estimating the global abundance of ground level presence of particulate matter (PM2.5)

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 sensi...

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Bibliographic Details
Published in:Geospatial health 2014-12, Vol.8 (3), p.S611-S630
Main Authors: Lary, David J, Faruque, Fazlay S, Malakar, Nabin, Moore, Alex, Roscoe, Bryan, Adams, Zachary L, Eggelston, York
Format: Article
Language:English
Subjects:
Air Pollution - adverse effects
Algorithms
Environmental Monitoring - methods
Global Health - statistics & numerical data
HSP70 Heat-Shock Proteins
Humans
Particulate Matter - adverse effects
Particulate Matter - analysis
PM2.5, machine-learning, remote sensing
Remote Sensing Technology
Weather
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Summary: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.
ISSN:1827-1987
1970-7096
1970-7096
DOI:10.4081/gh.2014.292