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

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.