Guo Jian-Ping, Wu Ye-Rong, Zhang Xiao-Ye, Li Xiao-Wen
Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China.
Huan Jing Ke Xue. 2013 Mar;34(3):817-25.
With the fast economic development in China in recent years, air pollutions are becoming increasingly serious. It is, therefore, imperative to develop new technology to solve this issue. Due to the wide spatial coverage of satellite remote sensing, along with the relatively lower cost compared to ground-based in situ aerosol measurements, satellite retrieved aerosol optical depth (AOD) is widely recognized as a good surrogate of surface PM2.5 concentrations. In this study, two years (2007-2008) of AOD data from moderate resolution imaging spectroradiometer (MODIS) onboard Terra at five observational sites of China (Benxi, Zhengzhou, Lushan, Nanning, Guilin), combined with five meteorological factors such as wind speed, wind direction, temperature humidity and planetary boundary height, were used as important input to establish the Back Propagation (BP) neural networks model, which was applied to estimate PM2.5. Afterwards, the model estimated PM2.5 was validated by in situ PM2.5 measurements from the five sites. Specially, scatter analysis showed that the linear correlation coefficient (R) between ground PM2.5 observation and model estimated PM2.5 at Lushan was the highest (R = 0.6), whereas the R values at the four other sites were lower, ranging from 0.43 to 0.49. Time series validations were performed as well, indicating that the R value significantly varied from day to day. However, the R value could be significantly improved by fitting the five-day moving average ground observation values against the model estimated PM2.5 data. Also, the R value at Lushan was the highest (R = 0.83), suggesting that MODIS AOD can be used to monitor PM2.5 by the BP networks model developed in this study.
近年来，随着中国经济的快速发展，空气污染日益严重。因此，开发新技术来解决这一问题势在必行。由于卫星遥感的空间覆盖范围广，且与地面原位气溶胶测量相比成本相对较低，卫星反演的气溶胶光学厚度（AOD）被广泛认为是地表PM2.5浓度的良好替代指标。在本研究中，利用来自搭载在Terra卫星上的中分辨率成像光谱仪（MODIS）的两年（2007 - 2008年）AOD数据，该数据来自中国五个观测站点（本溪、郑州、庐山、南宁、桂林），并结合风速、风向、温度、湿度和行星边界层高度等五个气象因素，作为重要输入来建立反向传播（BP）神经网络模型，该模型用于估算PM2.5。之后，通过五个站点的原位PM2.5测量对模型估算的PM2.5进行验证。特别地，散点分析表明，庐山地面PM2.5观测值与模型估算的PM2.5之间的线性相关系数（R）最高（R = 0.6），而其他四个站点的R值较低，范围为0.43至0.49。还进行了时间序列验证，表明R值每天都有显著变化。然而，通过将五天移动平均地面观测值与模型估算的PM2.5数据进行拟合，R值可得到显著提高。此外，庐山的R值最高（R = 0.83），这表明MODIS AOD可通过本研究开发的BP网络模型用于监测PM2.5。
