Estimating ground-level PM2.5 in the eastern united states using satellite remote sensing
Estimating ground-level PM2.5 in the eastern united states using satellite remote sensing
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DOI：
10.1021/es049352m
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发表时间：
2005-05-01
影响因子：
11.4
通讯作者：
Koutrakis, P
中科院分区：
文献类型：
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作者：
Liu, Y;Sarnat, JA;Koutrakis, P
基于每日PM2.5（空气动力学直径小于2.5微米的颗粒物）浓度与多角度成像光谱辐射计（MISR）测量的气溶胶光学厚度（AOT）之间的回归，开发了一个经验模型，并使用2001年期间美国东部的数据进行了测试。总体而言，该经验模型解释了PM2.5浓度48%的变异性。该模型的均方根误差为6.2微克/立方米，相应的平均PM2.5浓度为13.8微克/立方米。当去除PM2.5浓度大于40微克/立方米的数据时，模型结果显示是观测值的无偏估计量。发现几个因素，如行星边界层高度、相对湿度、季节以及监测站点的其他地理属性，会影响PM2.5和AOT之间的关联。这项研究的结果表明卫星遥感在区域环境空气质量监测中作为地面网络的延伸具有巨大潜力。随着遥感技术和全球数据同化系统的不断进步，从卫星遥感器获得的AOT测量值可能作为一种确定地面颗粒物浓度的补充信息源，提供一种具有成本效益的方法。
An empirical model based on the regression between daily PM2.5 (particles with aerodynamic diameters of less than 2.5 mu m) concentrations and aerosol optical thickness (AOT) measurements from the multiangle imaging spectroradiometer (MISR) was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM2.5 concentrations. The root-mean-square error of the model was 6.2 mu g/m(3) with a corresponding average PM2.5 concentration of 13.8 mu g/m(3). When PM2.5 concentrations greater than 40 mu g/m(3) were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geographical attributes of monitoring sites, were found to influence the association between PM2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks. With the continual advancement of remote sensing technology and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost-effective approach as a supplemental source of information for determining ground-level particle concentrations.