TY - JOUR
T1 - Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES)
AU - Chudnovsky, Alexandra A.
AU - Lee, Hyung Joo
AU - Kostinski, Alex
AU - Kotlov, Tanya
AU - Koutrakis, Petros
N1 - Funding Information: The authors wish to thank the anonymous reviewers for their constructive comments. This research was supported by a postdoctoral fellowship from the Environment Health Fund (EHF), Jerusalem, Israel. This study was funded by the Harvard EPA PM Center (R-832416), Harvard Clean Air Research Center (CLARC) (R-83479801), and the Yale Center for Perinatal, Pediatric and Environmental Epidemiology (NIH-NIEHS R01-ES-016317). Also, support was provided by NIEHS grants (ES009825 and ES00002). A.B.K.’s work was supported by the NSF grant ATM05-5467. The authors greatly appreciate advice from Dr. Joy E. Lawrence and Dr. Mike Wolfson from HSPH. The authors also thank Jeffrey Robel from the Customer Services Branch, National Climatic Data Center for providing the GASP AOD data and technical support. The authors appreciate the assistance of Steven Melley from HSPH for the GIS support and Dr. Yang Liu from Emory University, for guidance and data management in IDL.
PY - 2012/9
Y1 - 2012/9
N2 - Although ground-level PM2.5 (particulate matter with aerodynamic diameter &2.5 μm) monitoring sites provide accurate measurements, their spatial coverage within a given region is limited and thus often insufficient for exposure and epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate location- and/or subject-specific exposures to PM2.5. In this study, the authors apply a mixed-effects model approach to aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) to predict PM2.5 concentrations within the New England area of the United States. With this approach, it is possible to control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles, and ground surface reflectance. The model-predicted PM2.5 mass concentration are highly correlated with the actual observations, R2 = 0.92. Therefore, adjustment for the daily variability in AOD-PM2.5 relationship allows obtaining spatially resolved PM2.5 concentration data that can be of great value to future exposure assessment and epidemiological studies.The authors demonstrated how AOD can be used reliably to predict daily PM2.5 mass concentrations, providing determination of their spatial and temporal variability. Promising results are found by adjusting for daily variability in the AOD-PM2.5 relationship, without the need to account for a wide variety of individual additional parameters. This approach is of a great potential to investigate the associations between subject-specific exposures to PM2.5 and their health effects. Higher 4 × 4-km resolution GOES AOD retrievals comparing with the conventional MODerate resolution Imaging Spectroradiometer (MODIS) 10-km product has the potential to capture PM2.5 variability within the urban domain.
AB - Although ground-level PM2.5 (particulate matter with aerodynamic diameter &2.5 μm) monitoring sites provide accurate measurements, their spatial coverage within a given region is limited and thus often insufficient for exposure and epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate location- and/or subject-specific exposures to PM2.5. In this study, the authors apply a mixed-effects model approach to aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) to predict PM2.5 concentrations within the New England area of the United States. With this approach, it is possible to control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles, and ground surface reflectance. The model-predicted PM2.5 mass concentration are highly correlated with the actual observations, R2 = 0.92. Therefore, adjustment for the daily variability in AOD-PM2.5 relationship allows obtaining spatially resolved PM2.5 concentration data that can be of great value to future exposure assessment and epidemiological studies.The authors demonstrated how AOD can be used reliably to predict daily PM2.5 mass concentrations, providing determination of their spatial and temporal variability. Promising results are found by adjusting for daily variability in the AOD-PM2.5 relationship, without the need to account for a wide variety of individual additional parameters. This approach is of a great potential to investigate the associations between subject-specific exposures to PM2.5 and their health effects. Higher 4 × 4-km resolution GOES AOD retrievals comparing with the conventional MODerate resolution Imaging Spectroradiometer (MODIS) 10-km product has the potential to capture PM2.5 variability within the urban domain.
UR - http://www.scopus.com/inward/record.url?scp=84865281052&partnerID=8YFLogxK
U2 - 10.1080/10962247.2012.695321
DO - 10.1080/10962247.2012.695321
M3 - مقالة
SN - 1096-2247
VL - 62
SP - 1022
EP - 1031
JO - Journal of the Air and Waste Management Association
JF - Journal of the Air and Waste Management Association
IS - 9
ER -
