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° × 0.1° 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 R2 from 0.64 to 0.91, reduced out-of-sample, global population-weighted root mean squared error from 23 μg/m3 to 12 μg/m3). In 2016, 95% of the world's population lived in areas where ambient PM2.5 levels exceeded the World Health Organization 10 μg/m3 (annual average) guideline; 58% resided in areas above the 35 μg/m3 Interim Target-1. Global population-weighted PM2.5 concentrations were 18% higher in 2016 (51.1 μg/m3) than in 2010 (43.2 μg/m3), 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 μg/m3) but stable during this period.