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Environmental Health Insights

Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies

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Environmental Health Insights 2015:Suppl. 1 41-52

Review

Published on 12 May 2015

DOI: 10.4137/EHI.S15664


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Abstract

With the increasing awareness of 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 (PM₂.₅). Here we use a suite of remote sensing and meteorological data products together with ground based observations of PM₂.₅ from 8,329 measurement sites in 55 countries taken between 1997 and 2014 to train a machine learning algorithm to estimate the daily distributions of PM₂.₅ from 1997 to the present. We demonstrate that the new PM₂.₅ data product can reliably represent global observations of PM₂.₅ for epidemiological studies. An analysis of Baltimore schizophrenia emergency room admissions is presented in terms of the levels of ambient pollution. PM₂.₅ appears to have an impact on some aspects of mental health.



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