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

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 degrees x 0.1 degrees 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 R-2 from 0.64 to 0.91, reduced out-of-sample, global population-weighted root mean squared error from 23 mu g/m(3) to 12 mu g/m(3)). In 2016, 95% of the world's population lived in areas where ambient PM2.5 levels exceeded the World Health Organization 10 mu g/m(3) (annual average) guideline; 58% resided in areas above the 35 mu g/m(3) Interim Target-1. Global population-weighted PM2.5 concentrations were 18% higher in 2016 (51.1 mu g/m(3)) than in 2010 (43.2 mu g/m(3)), 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 mu g/m(3)) but stable during this period.