Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution

• Type of Material: Article
• Authors: Gavin Shaddick, University of Exeter; Matthew L. Thomas, University of Bath; Amelia Green, University of Bath; Michael Brauer, University of British Columbia; Aaron van Donkelaar, Dalhousie University; Rick Burnett, Health Canada; Howard Chang, Emory University; Aaron Cohen, Health Effects Institute; Rita Van Dingenen, European Commiss; Carlos Dora, World Health Organization; Sophie Gumy, World Health Organization; Yang Liu, Emory University; Randall Martin, Dalhousie University; Lance Waller, Emory University; Jason West, University of North Carolina; James V. Zidek, University of British Columbia; Annette Pruss-Ustun, World Health Organization
• Language: English
• Date: 2018-01-01
• Publisher: Wiley: 12 months
• Publication Version: Final Published Version
• Copyright Statement: © 2017 World Health Organization, Journal of the Royal Statistical Society: Series C (Applied Statistics) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1111/rssc.12227
• Title of Journal or Parent Work: Journal of the Royal Statistical Society: Series C
• ISSN: 0035-9254
• Volume: 67
• Issue: 1
• Start Page: 231
• End Page: 253
• Grant/Funding Information: Amelia Green was supported for this work by WHO contracts APW 201255146 and 201255393.; Matthew Lloyd Thomas is supported by a scholarship from the Engineering and Physical Sciences Research Council Centre for Doctoral Training in Statistical Applied Mathematics at Bath, under project EP/L015684/1
• Keywords: Mathematics; Physical Sciences; TROPOSPHERIC CHEMISTRY; DISEASE; Data fusion; Global burden of disease; INFERENCE; Statistics & Probability; Spatial modelling; CHEMISTRY-TRANSPORT MODEL; Science & Technology; Integrated nested Laplace approximations; OUTPUT; Air pollution; Environmental health effects; Bayesian hierarchical modelling
• Research Categories: Health Sciences, Public Health; Biology, Biostatistics

Abstract

Air pollution is a major risk factor for global health, with 3 million deaths annually being attributed to fine particulate matter ambient pollution (PM2.5). The primary source of information for estimating population exposures to air pollution has been measurements from ground monitoring networks but, although coverage is increasing, regions remain in which monitoring is limited. The data integration model for air quality supplements ground monitoring data with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models. Set within a Bayesian hierarchical modelling framework, the model allows spatially varying relationships between ground measurements and other factors that estimate air quality. The model is used to estimate exposures, together with associated measures of uncertainty, on a high resolution grid covering the entire world from which it is estimated that 92% of the world's population reside in areas exceeding the World Health Organization's air quality guidelines.