Department of Environmental Engineering, Kyoto University, Kyoto, Japan.
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
PLoS One. 2020 Oct 13;15(10):e0240430. doi: 10.1371/journal.pone.0240430. eCollection 2020.
To establish a new model for estimating ground-level PM2.5 concentration over Beijing, China, the relationship between aerosol optical depth (AOD) and ground-level PM2.5 concentration was derived and analysed firstly. Boundary layer height (BLH) and relative humidity (RH) were shown to be two major factors influencing the relationship between AOD and ground-level PM2.5 concentration. Thus, they are used to correct MODIS AOD to enhance the correlation between MODIS AOD and PM2.5. When using corrected MODIS AOD for modelling, the correlation between MODIS AOD and PM2.5 was improved significantly. Then, normalized difference vegetation index (NDVI), surface temperature (ST) and surface wind speed (SPD) were introduced as auxiliary variables to further improve the performance of the corrected regression model. The seasonal and annual average distribution of PM2.5 concentration over Beijing from 2014 to 2016 were mapped for intuitively analysing. Those can be regarded as important references for monitoring the ground-level PM2.5 concentration distribution. It is obviously that the PM2.5 concentration distribution over Beijing revealed the trend of "southeast high and northwest low", and showed a significant decrease in annual average PM2.5 concentration from 2014 to 2016.
为建立一个新的模型来估算中国北京地区的地面 PM2.5 浓度，首先推导出并分析了气溶胶光学深度（AOD）与地面 PM2.5 浓度之间的关系。边界层高度（BLH）和相对湿度（RH）被证明是影响 AOD 与地面 PM2.5 浓度之间关系的两个主要因素。因此，它们被用来修正 MODIS AOD，以增强 MODIS AOD 与 PM2.5 之间的相关性。当使用修正后的 MODIS AOD 进行建模时，MODIS AOD 与 PM2.5 之间的相关性得到了显著提高。然后，引入归一化差异植被指数（NDVI）、地表温度（ST）和地表风速（SPD）作为辅助变量，进一步提高了修正回归模型的性能。对 2014 年至 2016 年北京地区地面 PM2.5 浓度的季节和年平均分布进行了绘制，以便直观地分析。这些可以作为监测地面 PM2.5 浓度分布的重要参考。很明显，北京地区的 PM2.5 浓度分布呈现出“东南高、西北低”的趋势，并且在 2014 年至 2016 年期间，年平均 PM2.5 浓度呈显著下降趋势。
