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

Submitted: 30 December 2014
Accepted: 30 December 2014
Published: 1 December 2014
Abstract Views: 4863
PDF: 2029
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

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.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

How to Cite

Lary, D. J., Faruque, F. S., Malakar, N., Moore, A., Roscoe, B., Adams, Z. L., & Eggelston, Y. (2014). Estimating the global abundance of ground level presence of particulate matter (PM2.5). Geospatial Health, 8(3), S611-S630. https://doi.org/10.4081/gh.2014.292

List of Cited By :

Crossref logo