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

被引:385
作者
Liu, Yang [1 ]
Paciorek, Christopher J. [2 ]
Koutrakis, Petros [1 ]
机构
[1] Harvard Univ, Sch Publ Hlth, Dept Environm Hlth, Boston, MA 02115 USA
[2] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
关键词
AOD; GAM; GASP; GOES; PM2.5; RUC; satellite aerosol remote sensing; spatial synoptic classification; AEROSOL OPTICAL-THICKNESS; GROUND-LEVEL PM2.5; LONG-TERM EXPOSURE; AIR-POLLUTION; MORTALITY; DEPTH;
D O I
10.1289/ehp.0800123
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
BACKGROUND: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters <= 2.5 mu M (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 R-2 (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 mu g/m(3) 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.
引用
收藏
页码:886 / 892
页数:7
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