High-resolution satellite-derived PM2.5 from optimal estimation and geographically weighted regression over North America

van Donkelaar, A; Martin, RV; Spurr, RJ; Burnett, RT

HERO ID

3008845

Reference Type

Journal Article

Year

2015

Language

English

PMID

26261937

HERO ID 3008845
In Press No
Year 2015
Title High-resolution satellite-derived PM2.5 from optimal estimation and geographically weighted regression over North America
Authors van Donkelaar, A; Martin, RV; Spurr, RJ; Burnett, RT
Journal Environmental Science & Technology
Volume 49
Issue 17
Page Numbers 10482-10491
Abstract We used a geographically weighted regression (GWR) statistical model to represent bias of fine particulate matter concentrations (PM2.5) derived from a 1 km optimal estimate (OE) aerosol optical depth (AOD) satellite retrieval that used AOD-to-PM2.5 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 PM2.5 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 PM2.5 estimates additionally provided insight into the sources of uncertainty for global satellite-derived PM2.5 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-PM2.5 relationship used in satellite-derived PM2.5 estimates over North America, and potentially worldwide.
Doi 10.1021/acs.est.5b02076
Pmid 26261937
Wosid WOS:000360773600026
Is Certified Translation No
Dupe Override No
Is Public Yes
Language Text English