High-Resolution Satellite-Derived PM_(2.5) from Optimal Estimation and Geographically Weighted Regression over North America

摘要

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 (R~2 = 0.82 versus R~2 = 0.62), even when a large fraction (70%) of sites were withheld for cross-validation (R~2 = 0.78) and developed seasonal skill (R~2 = 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.