Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment

Gavin Shaddick, Matthew L. Thomas, Heresh Amini, David Broday, Aaron Cohen, Joseph Frostad, Amelia Green, Sophie Gumy, Yang Liu, Randall V. Martin, Annette Pruss-Ustun, Daniel Simpson, Aaron Van Donkelaar, Michael Brauer

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

Air pollution is a leading global disease risk factor. Tracking progress (e.g., for Sustainable Development Goals) requires accurate, spatially resolved, routinely updated exposure estimates. A Bayesian hierarchical model was developed to estimate annual average fine particle (PM2.5) concentrations at 0.1° × 0.1° spatial resolution globally for 2010-2016. The model incorporated spatially varying relationships between 6003 ground measurements from 117 countries, satellite-based estimates, and other predictors. Model coefficients indicated larger contributions from satellite-based estimates in countries with low monitor density. Within and out-of-sample cross-validation indicated improved predictions of ground measurements compared to previous (Global Burden of Disease 2013) estimates (increased within-sample R2 from 0.64 to 0.91, reduced out-of-sample, global population-weighted root mean squared error from 23 μg/m3 to 12 μg/m3). In 2016, 95% of the world's population lived in areas where ambient PM2.5 levels exceeded the World Health Organization 10 μg/m3 (annual average) guideline; 58% resided in areas above the 35 μg/m3 Interim Target-1. Global population-weighted PM2.5 concentrations were 18% higher in 2016 (51.1 μg/m3) than in 2010 (43.2 μg/m3), reflecting in particular increases in populous South Asian countries and from Saharan dust transported to West Africa. Concentrations in China were high (2016 population-weighted mean: 56.4 μg/m3) but stable during this period.

Original languageEnglish
Pages (from-to)9069-9078
Number of pages10
JournalEnvironmental Science and Technology
Volume52
Issue number16
Early online date29 Jun 2018
DOIs
Publication statusPublished - 21 Aug 2018

ASJC Scopus subject areas

  • Chemistry(all)
  • Environmental Chemistry

Cite this

Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment. / Shaddick, Gavin; Thomas, Matthew L.; Amini, Heresh; Broday, David; Cohen, Aaron; Frostad, Joseph; Green, Amelia; Gumy, Sophie; Liu, Yang; Martin, Randall V.; Pruss-Ustun, Annette; Simpson, Daniel; Van Donkelaar, Aaron; Brauer, Michael.

In: Environmental Science and Technology, Vol. 52, No. 16, 21.08.2018, p. 9069-9078.

Research output: Contribution to journalArticle

Shaddick, G, Thomas, ML, Amini, H, Broday, D, Cohen, A, Frostad, J, Green, A, Gumy, S, Liu, Y, Martin, RV, Pruss-Ustun, A, Simpson, D, Van Donkelaar, A & Brauer, M 2018, 'Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment', Environmental Science and Technology, vol. 52, no. 16, pp. 9069-9078. https://doi.org/10.1021/acs.est.8b02864
Shaddick, Gavin ; Thomas, Matthew L. ; Amini, Heresh ; Broday, David ; Cohen, Aaron ; Frostad, Joseph ; Green, Amelia ; Gumy, Sophie ; Liu, Yang ; Martin, Randall V. ; Pruss-Ustun, Annette ; Simpson, Daniel ; Van Donkelaar, Aaron ; Brauer, Michael. / Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment. In: Environmental Science and Technology. 2018 ; Vol. 52, No. 16. pp. 9069-9078.
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