TY - JOUR
T1 - Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment
AU - Shaddick, Gavin
AU - Thomas, Matthew L.
AU - Amini, Heresh
AU - Broday, David
AU - Cohen, Aaron
AU - Frostad, Joseph
AU - Green, Amelia
AU - Gumy, Sophie
AU - Liu, Yang
AU - Martin, Randall V.
AU - Pruss-Ustun, Annette
AU - Simpson, Daniel
AU - Van Donkelaar, Aaron
AU - Brauer, Michael
PY - 2018/8/21
Y1 - 2018/8/21
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049336718&partnerID=8YFLogxK
U2 - 10.1021/acs.est.8b02864
DO - 10.1021/acs.est.8b02864
M3 - Article
AN - SCOPUS:85049336718
VL - 52
SP - 9069
EP - 9078
JO - Environmental Science & Technology
JF - Environmental Science & Technology
SN - 0013-936X
IS - 16
ER -