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Global Estimates and Long-term Trends of Fine Particulate Matter Concentrations (1998–2018)

Abstract

Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998–2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R2 = 0.90–0.92; slope = 0.90–0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (?0.28 ± 0.03 ?g/m3/yr), Europe (?0.15 ± 0.03 ?g/m3/yr), India (1.13 ± 0.15 ?g/m3/yr), and globally (0.04 ± 0.02 ?g/m3/yr). The positive trend (2.44 ± 0.44 ?g/m3/yr) for India over 2005–2013 and the negative trend (?3.37 ± 0.38 ?g/m3/yr) for China over 2011–2018 are remarkable, with implications for the health of billions of people.

Article / Publication Data
Active/Online
YES
Volume
54
Available Metadata
Accepted On
June 03, 2020
DOI ↗
Fiscal Year
NOAA IR URL ↗
Peer Reviewed
YES
Publication Name
Environmental Science & Technology
Published On
June 03, 2020
Publisher Name
American Chemical Society
Print Volume
54
Print Number
13
Page Range
7879–7890
Issue
13
Submitted On
March 20, 2020
Project Type
LAB SUPPORTED
URL ↗

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