Improved retrieval of PM2.5 from satellite data products using non-linear methods.

Abstract: 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 PM_2.5 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 PM_2.5 retrievals from satellite aerosol products. Reliable PM_2.5 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. [Copyright &y& Elsevier]