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

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Summary

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 are used to train a machine-learning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present.

Type
article
Published
2014-12-01
Cited by
90
References
150
Access
Open access

Keywords

Particulates, Environmental science, Abundance (ecology), Suite, Product (mathematics)

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