Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China.
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China.
Sci Total Environ. 2015 Feb 1;505:1156-65. doi: 10.1016/j.scitotenv.2014.11.024. Epub 2014 Nov 20.
Satellite measurements have been widely used to estimate particulate matter (PM) on the ground, which can affect human health adversely. However, such estimation from space is susceptible to meteorological conditions and may result in large errors. In this study, we compared the aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) to predict ground-level PM2.5 concentration in Xi'an, Shaanxi province of northwestern China, using an empirical nonlinear model. Meteorological parameters from ground-based measurements and NCEP/NCAR reanalysis data were used as covariates in the model. Both MODIS and MISR AOD values were highly significant predictors of ground-level PM2.5 concentration. The MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration and explained 67% and 72% of the daily PM2.5 concentration variation, respectively. Seasonal analysis showed that the MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration, with the MISR model having a higher correlation coefficient (R) and thus giving a better fit in all seasons. The MISR model had high prediction accuracy in all seasons, with average R(2) and absolute percentage error (APE) of 0.84 and 15.3% in all four seasons, respectively. The prediction of the MODIS model was best during winter (R(2)=0.83) with an APE of 19%, whereas it was relatively poor in spring (R(2)=0.56) with an APE of 21%. Further analysis showed that there was a significant improvement in correlation coefficient when using the nonlinear multiple regression model compared to using a simple linear regression model of AOD and PM2.5. These results are useful for assessing surface PM2.5 concentration and monitoring regional air quality.
卫星测量已被广泛用于估算地面颗粒物（PM），这可能会对人类健康造成不利影响。然而，从太空进行这种估算容易受到气象条件的影响，可能会导致较大的误差。在这项研究中，我们比较了中分辨率成像光谱仪（MODIS）和多角度成像光谱辐射计（MISR）检索的气溶胶光学深度（AOD），以利用经验非线性模型预测中国西北部陕西省西安市的地面 PM2.5 浓度。模型中使用了来自地面测量和 NCEP/NCAR 再分析数据的气象参数作为协变量。MODIS 和 MISR AOD 值均是地面 PM2.5 浓度的高度显著预测因子。MODIS 和 MISR 模型对地面 PM2.5 浓度的预测能力相当，分别解释了每日 PM2.5 浓度变化的 67%和 72%。季节分析表明，MODIS 和 MISR 模型对地面 PM2.5 浓度的预测能力相当，MISR 模型的相关系数（R）更高，因此在所有季节的拟合效果都更好。MISR 模型在所有季节的预测精度都很高，四个季节的平均 R2 和绝对百分比误差（APE）分别为 0.84 和 15.3%。MODIS 模型在冬季的预测效果最好（R2=0.83），APE 为 19%，而在春季的预测效果相对较差（R2=0.56），APE 为 21%。进一步分析表明，与使用 AOD 和 PM2.5 的简单线性回归模型相比，使用非线性多元回归模型可以显著提高相关系数。这些结果对于评估地面 PM2.5 浓度和监测区域空气质量很有用。
