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

• Authors: Yang Liu, Emory University; Christopher J. Paciorek, Harvard University; Petros Koutrakis, Harvard University
• Journal Title: Environmental Health Perspectives
• Volume: Volume 117, Number 6
• Publisher: National Institute of Environmental Health Sciences (NIEHS) | 2009-06-01, Pages 886-892
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1289/ehp.0800123
• Subjects: Health Sciences, Public Health
• Keywords: Science & Technology; Life Sciences & Biomedicine; Environmental Sciences; Public, Environmental & Occupational Health; Toxicology; Environmental Sciences & Ecology; AOD; GAM; GASP; GOES; PM2.5; RUC; satellite aerosol remote sensing; spatial synoptic classification; PARTICULATE AIR-POLLUTION; GROUND-LEVEL PM2.5; AEROSOL OPTICAL-THICKNESS; LONG-TERM EXPOSURE; MORTALITY; DEPTH; AREA

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.