Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment
Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment
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DOI：
10.1021/acs.est.8b02864
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发表时间：
2018-08-21
影响因子：
11.4
通讯作者：
Brauer, Michael
中科院分区：
文献类型：
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作者：
Shaddick, Gavin;Thomas, Matthew L.;Brauer, Michael
空气污染是全球主要的疾病风险因素。跟踪进度（例如，可持续发展目标）需要准确的、空间分辨率的、定期更新的暴露估计。开发了一个贝叶斯分层模型，以估计2010-2016年全球0.1度x0.1度空间分辨率下的年平均细颗粒物（PM2.5）浓度。该模型纳入了来自117个国家的6003个地面测量值、基于卫星的估计值和其他预测值之间的空间变化关系。模型系数表明，在监测器密度低的国家，卫星估算的贡献较大。样本内和样本外交叉验证表明，与以前的估计（2013年全球疾病负担）相比，地面测量的预测有所改善（样本内R-2从0.64增加到0.91，样本外减少，全球人口加权均方根误差从23 μ g/m3减少到12 μ g/m3）。2016年，世界上95%的人口居住在PM2.5水平超过世界卫生组织10 μ g/m（3）（年平均值）指南的地区; 58%的人口居住在高于35 μ g/m（3）的地区。2016年全球人口加权PM2.5浓度（51.1 μ g/m（3））比2010年（43.2 μ g/m（3））高出18%，特别反映了人口众多的南亚国家和撒哈拉沙漠沙尘输送到西非的增加。中国的浓度很高（2016年人口加权平均值：56.4微克/立方米），但在此期间保持稳定。
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.