Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004-2013.
Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004-2013.
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DOI：
10.1289/ehp.1409481
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发表时间：
2016-02
影响因子：
10.4
通讯作者：
Liu Y
中科院分区：
文献类型：
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作者：
Ma Z;Hu X;Sayer AM;Levy R;Zhang Q;Xue Y;Tong S;Bi J;Huang L;Liu Y
30年的经济快速发展导致中国PM2.5（颗粒物≤ 2.5 μm）污染严重且普遍。然而，由于历史PM2.5浓度数据有限，对PM2.5暴露对健康影响的研究受到阻碍。我们使用最新的卫星数据估计了2004年至2013年中国环境PM2.5浓度，分辨率为0.1°，并利用现有的地面观测数据评估了模型性能。我们开发了一个两阶段的空间统计模型，使用中分辨率成像光谱仪（MODIS）收集6气溶胶光学厚度（AOD）和同化气象，土地利用数据，和PM2.5浓度从中国最近建立的地面监测网络。提出了一种将联合收割机MODIS暗目标和深蓝AOD数据相结合的逆方差加权（IVW）方法。我们使用地面观测评估了2004年至2014年初模型预测的PM2.5浓度。总体模型交叉验证R2和相对预测误差分别为0.79和35.6%。模型年（2013年）之后的验证表明，它准确地预测了PM2.5浓度，在月（R2 = 0.73，回归斜率= 0.91）和季节（R2 = 0.79，回归斜率= 0.92）水平上偏差很小。季节变化表明，冬季是污染最严重的季节，夏季是最干净的季节。对PM2.5预测水平的分析显示，2004年至2007年期间，平均每年增加1.97微克/立方米，2008年至2013年期间减少0.46微克/立方米。我们的卫星驱动模型可以提供可靠的历史PM2.5估计值，其分辨率与北美长期PM2.5暴露对健康影响的流行病学研究中使用的分辨率相当。该数据源可能会推动中国PM2.5健康影响的研究。马志，胡新，Sayer AM，Levy R，Zhang Q，Xue Y，Tong S，Bi J，Huang L，Liu Y. 2016.基于卫星的PM2.5浓度时空趋势：中国，2004-2013年。环境健康展望124：184-192; http：dx.doi.org/10.1289/ehp.1409481
Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data. We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations. We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China’s recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations. The overall model cross-validation R2 and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R2 = 0.73, regression slope = 0.91) and seasonal (R2 = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 μg/m3 between 2004 and 2007 and a decrease of 0.46 μg/m3 between 2008 and 2013. Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China. Ma Z, Hu X, Sayer AM, Levy R, Zhang Q, Xue Y, Tong S, Bi J, Huang L, Liu Y. 2016. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013. Environ Health Perspect 124:184–192; http://dx.doi.org/10.1289/ehp.1409481