Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

• Type of Material: Article
• Authors: Yang Liu, Emory University; Christopher J. Paciorek, Harvard University; Petros Koutrakis, Harvard University
• Language: English
• Date: 2009-06-01
• Publisher: National Institute of Environmental Health Sciences (NIEHS)
• Publication Version: Final Published Version
• Copyright Statement: No copyright
• License: Creative Commons Creative Commons CC0 1.0 Universal
• Final Published Version (URL): http://dx.doi.org/10.1289/ehp.0800123
• Title of Journal or Parent Work: Environmental Health Perspectives
• ISSN: 0091-6765
• Volume: 117
• Issue: 6
• Start Page: 886
• End Page: 892
• Grant/Funding Information: The study is supported by the Harvard–U.S. Environmental Protection Agency Center on Particle Health Effects (R-827353 and R-832416) and by the Health Effects Institute (HEI) (4746-RFA05-2/06-7), an organization jointly funded by the U.S. Environmental Protection Agency (EPA) (Assistance Award No. R-82811201) and certain motor vehicle and engine manufacturers.
• Keywords: GOES; MORTALITY; DEPTH; satellite aerosol remote sensing; PM2.5; Public, Environmental & Occupational Health; PARTICULATE AIR-POLLUTION; AEROSOL OPTICAL-THICKNESS; Environmental Sciences; Environmental Sciences & Ecology; spatial synoptic classification; AOD; Science & Technology; Toxicology; LONG-TERM EXPOSURE; Life Sciences & Biomedicine; AREA; GAM; GASP; GROUND-LEVEL PM2.5; RUC
• Research Categories: Health Sciences, Public Health

Abstract

Background: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM2.5 ground networks to cover a much larger area. Objectives: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM2.5 concentrations. Methods: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM2.5 concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. Results: The AOD model has a higher predicting power judged by adjusted R2 (0.79) than does the non-AOD model (0.48). The predicted PM2.5 concentrations by the AOD model are, on average, 0.8 0.9 μg/m3 higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM2.5, meteorologic parameters are major contributors to the better performance of the AOD model. Conclusions: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM2.5 concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM2.5 spatial patterns related to AOD availability.