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Title: Global estimation of air quality and the burden of disease associated with ambient air pollution Authors:  Gavin Shaddick - University of Exeter (United Kingdom) [presenting]
Abstract: Ambient (outdoor) air pollution is a major risk factor for global health with an estimated 3 million annual deaths being attributed to fine particulate matter ambient pollution (PM2.5). The primary source of information for estimating exposures has been measurements from ground monitoring networks, however there are regions in which monitoring is limited. Ground monitoring data therefore needs to be supplemented with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models. A hierarchical modelling approach for integrating data from multiple sources is proposed allowing spatially-varying relationships between ground measurements and other factors that estimate air quality. Set within a Bayesian framework, the Data Integration Model for Air Quality (DIMAQ) is used to estimate exposures, together with associated measures of uncertainty, on a high-resolution grid. Bayesian analysis on this scale can be computationally challenging and here Integrated Nested Laplace Approximations (INLA) are used. Estimated exposures from the model, produced on a high-resolution grid (10km x 10km) covering the entire globe and based on summaries of resulting the posterior distributions in each cell, it is estimated that 92\% of the world's population reside in areas exceeding the World Health Organization's Air Quality Guidelines.