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Air pollution is a major risk factor for global health, with both ambient andhousehold air pollution contributing substantial components of the overallglobal disease burden. One of the key drivers of adverse health effects is fineparticulate matter ambient pollution (PM$_{2.5}$) to which an estimated 3million deaths can be attributed annually. The primary source of informationfor estimating exposures has been measurements from ground monitoring networksbut, although coverage is increasing, there remain regions in which monitoringis limited. Ground monitoring data therefore needs to be supplemented withinformation from other sources, such as satellite retrievals of aerosol opticaldepth and chemical transport models. A hierarchical modelling approach forintegrating data from multiple sources is proposed allowing spatially-varyingrelationships between ground measurements and other factors that estimate airquality. Set within a Bayesian framework, the resulting Data Integration Modelfor Air Quality (DIMAQ) is used to estimate exposures, together with associatedmeasures of uncertainty, on a high resolution grid covering the entire world.Bayesian analysis on this scale can be computationally challenging and hereapproximate Bayesian inference is performed using Integrated Nested LaplaceApproximations. Model selection and assessment is performed by cross-validationwith the final model offering substantial increases in predictive accuracy,particularly in regions where there is sparse ground monitoring, when comparedto current approaches: root mean square error (RMSE) reduced from 17.1 to 10.7,and population weighted RMSE from 23.1 to 12.1 $\mu$gm$^{-3}$. Based onsummaries of the posterior distributions for each grid cell, it is estimatedthat 92% of the world's population reside in areas exceeding the World HealthOrganization's Air Quality Guidelines.

Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution