High-Resolution Satellite-Derived PM_2.5 from Optimal Estimation and Geographically Weighted Regression over North America

We used a geographically weighted regression (GWR) statistical model to represent bias of fine particulate matter concentrations (PM_2.5) derived from a 1 km optimal estimate (OE) aerosol optical depth (AOD) satellite retrieval that used AOD-to-PM_2.5 relationships from a chemical transport model (CTM) for 2004-2008 over North America. This hybrid approach combined the geophysical understanding and global applicability intrinsic to the CTM relationships with the knowledge provided by observational constraints. Adjusting the OE PM_2.5 estimates according to the GWR-predicted bias yielded significant improvement compared with unadjusted long-term mean values (R² = 0.82 versus R² = 0.62), even when a large fraction (70%) of sites were withheld for cross-validation (R² = 0.78) and developed seasonal skill (R² = 0.62-0.89). The effect of individual GWR predictors on OE PM_2.5 estimates additionally provided insight into the sources of uncertainty for global satellite-derived PM_2.5 estimates. These predictor-driven effects imply that local variability in surface elevation and urban emissions are important sources of uncertainty in geophysical calculations of the AOD-to-PM_2.5 relationship used in satellite-derived PM_2.5 estimates over North America, and potentially worldwide. (Figure Presented).