1. Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment
- Author
-
Aaron van Donkelaar, Annette Prüss-Ustün, Matthew L. Thomas, David M. Broday, Aaron J Cohen, Yang Liu, Sophie Gumy, Joseph Frostad, Gavin Shaddick, Michael Brauer, Daniel Simpson, Heresh Amini, Randall V. Martin, and Amelia Green
- Subjects
Burden of disease ,China ,010504 meteorology & atmospheric sciences ,Mean squared error ,Population ,Air pollution ,010501 environmental sciences ,medicine.disease_cause ,Atmospheric sciences ,01 natural sciences ,Global Burden of Disease ,Bayes' theorem ,Africa, Northern ,Air Pollution ,medicine ,Environmental Chemistry ,Bayesian hierarchical modeling ,education ,0105 earth and related environmental sciences ,Air Pollutants ,education.field_of_study ,Bayes Theorem ,General Chemistry ,Particulates ,Africa, Western ,Environmental science ,Particulate Matter ,Satellite - Abstract
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 lev...
- Published
- 2018
- Full Text
- View/download PDF