Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution. Issue 1 (13th June 2017)

• Record Type: Journal Article
• Authors: Shaddick, Gavin; Thomas, Matthew L.; Green, Amelia; Brauer, Michael; van Donkelaar, Aaron; Burnett, Rick; Chang, Howard H.; Cohen, Aaron; Dingenen, Rita Van; Dora, Carlos; Gumy, Sophie; Liu, Yang; Martin, Randall; Waller, Lance A.; West, Jason; Zidek, James V.; Prüss‐Ustün, Annette
• Is Part Of: Journal of the Royal Statistical Society. Volume 67:Issue 1(2018:Jan.)
• Page Start: 231
• Page End: 253
• Publication Date: 2017-06-13
• Subjects: Air pollution -- Bayesian hierarchical modelling -- Data fusion -- Environmental health effects -- Global burden of disease -- Integrated nested Laplace approximations -- Spatial modelling; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12227
• Languages: English
• ISSNs: 0035-9254

Abstract

Summary: 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.