大气与环境光学学报, 2015, 10 (1): 51, 网络出版: 2015-01-27
基于多源数据的PM2.5反演方法
Methods of PM2.5 Inversion Based on Multi-Source Data
细模态气溶胶光学厚度 多元回归拟合模型 PM2.5 PM2.5 fine mode aerosol optical depth MODIS MODIS multivariate regression models
摘要
利用MODIS L1B 1 km分辨率数据和NASA 的V5.2 气溶胶改良业务反演算法,对南京都市圈2013年9月至2014年1月期 间的气溶胶光学厚度(aerosol optical depth, AOD)进行反演,并运用AERONET地基气溶胶监测网的AOD产品对反 演结果进行验证。然后,将经过验证后的AOD与国家环保部建立的国家空气质量自动监测点实测得到的PM2.5质量 浓度和PM10质量浓度根据经典统计学进行线性回归性分析,并用气溶胶细模态光学厚度和气象因子对PM2.5质量 浓度与MODIS AOD之间的模型进行修正,修正后的模型为y=136.78+9.16x1-0.36x2-3.98x3, R2=0.79, 并应用该模型,证实气溶胶光学厚度可以用来对城市空气质量进行评价。
Abstract
Earth-observing satellites have provided satellite image datasets for urban-scale air quality monitoring. Aerosol optical depths (AOD) at 1 km resolution is retrieved from MODIS on Terra and Aqua Satellite by using the improved algorithm. The 1 km AOD data are validated by using AOD measurements of AERONET station in the period from September 2013 to January 2014. Then the validated 1 km AOD data are compared with ground-based PM2.5 mass concentration and PM10 mass concentration in Nanjing metropolitan area respectively. Then these models have been amended by fine aerosol optical depths and the meteorological factors. The revised model of Nanjing metropolitan area is y=136.78+9.16x1-0.36x2-3.98x3, with R2=0.79. By applying the model, it is confirmed that the aerosol optical depths can be used to evaluate the quality of urban air.
景瑞环, 麻金继, 汪超. 基于多源数据的PM2.5反演方法[J]. 大气与环境光学学报, 2015, 10(1): 51. JING Ruihuan, MA Jinji, WANG Chao. Methods of PM2.5 Inversion Based on Multi-Source Data[J]. Journal of Atmospheric and Environmental Optics, 2015, 10(1): 51.
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