HR: 1340h
AN: A33C-1420    [Abstracts]
TI: Spatio-Temporal Associations of MISR and GOES AOD with Ground-Level PM2.5 Concentrations in Eastern US
AU: Paciorek, C
EM: paciorek@hsph.harvard.edu
AF: Harvard School of Public Health, 655 Huntington Avenue SPH II, Room 407, Boston, MA 02215,
AU: * Liu, Y
EM: yangliu@hsph.harvard.edu
AF: Harvard School of Public Health, 401 Park Drive Room 420 West, Boston, MA 02115,
AU: Macias, H M
EM: hmoreno@hsph.harvard.edu
AF: Harvard School of Public Health, 108 Longwood Ave., Apt. 4, Brookline, MA 02446,
AU: Kondragunta, S
EM: Shobha.Kondragunta@noaa.gov
AF: NOAA/NESDIS/Center for Satellite Applications and Research, 5200 Auth Road, Camp Springs, MD 20746,
AB: As a geostationary satellite, GOES can provide half-hourly AOD measurements during daytime, making available much more dense observations than MISR which is aboard NASA's polar-orbiting satellite. However, limited by instrument design, GOES AOD data have significantly higher uncertainty as compared to MISR observations. We studied the association between aerosol optical depth (AOD) observations from GOES and MISR and daily concentrations of ground-level fine particulate matter (PM2.5) in the eastern United States. Our objective is to integrate GOES and MISR aerosol data into a Bayesian hierarchical modeling system in order to provide spatially and temporally resolved PM2.5 exposure estimates for an on-going large scale health effect study. Our preliminary results show that correlations between AOD and ground-level PM2.5 over time at fixed locations are reasonably high, except in the winter. Correlations over space at fixed times are lower and simple averaging over time actually reduces correlations dramatically. Instead, we propose a calibration approach based on a generalized additive model (GAM) that produces a calibrated AOD value much more highly correlated with PM2.5 and that allows averaging over time to produce stronger correlations. The strength of the association after calibration demonstrates the promise of GOES and MISR AOD for use in supplementing PM2.5 observations and filling in gaps in the sparse monitoring network.
DE: 0345 Pollution: urban and regional (0305, 0478, 4251)
DE: 0365 Troposphere: composition and chemistry
DE: 0368 Troposphere: constituent transport and chemistry
DE: 3315 Data assimilation
DE: 3355 Regional modeling
SC: Atmospheric Sciences [A]
MN: 2007 Fall Meeting