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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Spatiotemporal Prediction of Fine Particulate Matter Using High-Resolution Satellite Images in the Southeastern US 2003-2011
Journal of exposure science & environmental epidemiology, v 26(4), pp 377-384
17 Jun 2015
PMID: 26082149
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Numerous studies have demonstrated that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM(sub 2.5) to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM(sub 2.5) exposures. In this paper, we used AOD data with other PM(sub 2.5) variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM(sub 2.5) at a 1 sq km resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM(sub 2.5) measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 cu micrograms for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM(sub 2.5) concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM(sub 2.5). Our model results will also extend the existing studies on PM(sub 2.5) which have mostly focused on urban areas because of the paucity of monitors in rural areas.
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Details
- Title
- Spatiotemporal Prediction of Fine Particulate Matter Using High-Resolution Satellite Images in the Southeastern US 2003-2011
- Creators
- Mihye Lee - Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USAItai Kloog - Ben-Gurion University of the NegevAlexandra Chudnovsky - Tel Aviv UniversityAlexei Lyapustin - Goddard Space Flight CenterYujie Wang - University of Maryland, Baltimore CountySteven Melly - Drexel UniversityBrent Coull - Harvard UniversityPetros Koutrakis - Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USAJoel Schwartz - Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA
- Publication Details
- Journal of exposure science & environmental epidemiology, v 26(4), pp 377-384
- Publisher
- Springer Nature
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative
- Web of Science ID
- WOS:000378111800005
- Scopus ID
- 2-s2.0-84931314907
- Other Identifier
- 991019167711004721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Environmental Sciences
- Public, Environmental & Occupational Health
- Toxicology