State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Xianlin Campus , Box 624, 163 Xianlin Avenue, Nanjing 210023, China.
Environ Sci Technol. 2014 Jul 1;48(13):7436-44. doi: 10.1021/es5009399. Epub 2014 Jun 13.
Estimating ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) R(2) is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m(3). The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m(3). Our predicted annual PM2.5 concentrations indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Standard (CNAAQS) Level 2 standard. Our results also confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China.
利用空间统计模型从卫星衍生的气溶胶光学深度 (AOD) 估算地面水平 PM2.5 是评估大地理区域内 PM2.5 暴露的空间和时间特征的一种很有前途的新方法。然而，由于缺乏地面 PM2.5 测量值来校准模型，因此北美以外的研究受到限制。利用新建立的国家监测网络，我们开发了一个全国范围的地理加权回归 (GWR) 模型，该模型利用融合卫星 AOD 作为主要预测因子来估算中国的每日 PM2.5 浓度。结果表明，气象和土地利用信息可以大大提高模型性能。整体交叉验证 (CV) R(2) 为 0.64，均方根预测误差 (RMSE) 为 32.98μg/m(3)。预测的年平均 PM2.5 的平均预测误差 (MPE) 为 8.28μg/m(3)。我们预测的年平均 PM2.5 浓度表明，超过 96%的中国人口生活在超过中国国家环境空气质量标准 (CNAAQS) 二级标准的地区。我们的结果还证实，卫星衍生的 AOD 与气象场和土地利用信息相结合，可以成功应用于扩展中国的地面 PM2.5 监测网络。
