A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations
A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations
复制标题
DOI：
10.5194/acp-11-7991-2011
复制
发表时间：
2011-01-01
影响因子：
6.3
通讯作者：
Koutrakis, P.
中科院分区：
文献类型：
--
作者：
Lee, H. J.;Liu, Y.;Koutrakis, P.
调查PM2.5对人类健康影响的流行病学研究容易受到暴露测量误差的影响，这是暴露估计中的一种偏差，因为它们依赖于研究区域内有限数量的PM2.5监测器的数据。卫星数据可用于扩大空间覆盖范围，可能增强我们估计特定位置或特定对象暴露于PM2.5的能力，但有些人报告预测能力较差。提出了一种新的中分辨率成像光谱仪（MODIS）气溶胶光学厚度（AOD）定标方法。随后，这种方法被用来预测地面每日PM2.5浓度在新英格兰地区。检索了2003年新英格兰地区的MODIS气溶胶光学厚度数据，并利用美国环境保护署（EPA）26个PM2. 5监测点的PM2. 5浓度数据对气溶胶光学厚度数据进行了校正。一个混合效应模型，允许每日PM（2.）5-AOD关系用于预测特定位置的PM2.5水平。在监测点测得的PM2.5浓度进行了比较，预测相应的网格单元。横截面和纵向的观测和预测的浓度之间的比较表明，提出的新的校准方法，使MODIS AOD数据的PM2.5浓度的一个潜在有用的预测。此外，研究范围内的PM2.5估计水平与空气污染源有关。我们的方法使得有可能调查研究域内PM2.5浓度的空间格局。
Epidemiological studies investigating the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location-or subject-specific exposures to PM2.5, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concentrations measured at 26 US Environmental Protection Agency (EPA) PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-today variability in daily PM(2.)5-AOD relationships was used to predict location-specific PM2.5 levels. PM2.5 concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM2.5 concentrations. Furthermore, the estimated PM2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM2.5 concentrations within the study domain.