Civil and Environmental Engineering, Technion, Haifa, Israel.
Environ Pollut. 2013 Nov;182:417-23. doi: 10.1016/j.envpol.2013.08.002. Epub 2013 Aug 29.
Satellite observations may improve the areal coverage of particulate matter (PM) air quality data that nowadays is based on surface measurements. Three statistical methods for retrieving daily PM2.5 concentrations from satellite products (MODIS-AOD, OMI-AAI) over the San Joaquin Valley (CA) are compared--Linear Regression (LR), Generalized Additive Models (GAM), and Multivariate Adaptive Regression Splines (MARS). Simple LRs show poor correlations in the western USA (R(2) ~/= 0.2). Both GAM and MARS were found to perform better than the simple LRs, with a slight advantage to the MARS over the GAM (R(2) = 0.71 and R(2) = 0.61, respectively). Since MARS is also characterized by a better computational efficiency than GAM, it can be used for improving PM2.5 retrievals from satellite aerosol products. Reliable PM2.5 retrievals can fill in missing surface measurements in areas with sparse ground monitoring coverage and be used for evaluating air quality models and as exposure metrics in epidemiological studies.
卫星观测可以提高目前基于地面测量的颗粒物 (PM) 空气质量数据的面积覆盖范围。本文比较了三种从卫星产品（MODIS-AOD、OMI-AAI）中提取每日 PM2.5 浓度的统计方法——线性回归 (LR)、广义加性模型 (GAM) 和多元自适应回归样条 (MARS)。简单的 LR 在美西地区的相关性较差 (R(2) ~/= 0.2)。结果发现，GAM 和 MARS 均优于简单的 LR，其中 MARS 略优于 GAM（R(2) = 0.71 和 R(2) = 0.61）。由于 MARS 的计算效率也优于 GAM，因此可以用于提高卫星气溶胶产品中 PM2.5 的反演精度。可靠的 PM2.5 反演可以填补地面监测覆盖稀疏地区的缺失地面测量数据，并用于评估空气质量模型以及作为流行病学研究中的暴露指标。
